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JMIR Preprints
A preprint server for pre-publication/pre-peer-review preprints as well as ahead-of-print (accepted) manuscripts
Background: The latest WHO-GLOBOCAN data came out in October 2020. In India, approximately 1,78,361 i.e 13.3% new cases of breast cancer are detected per annum with 90,408 i.e. 10.6% deaths. With the 5-year overall survival, a study reported it to be 95% for stage I patients, 92% for stage II, 70% for stage III and only 21% for stage IV patients but with lifelong complications. There is evidence-based protocol for physiotherapy treatment in breast cancer but the lack of knowledge about the treatment and its benefits forms a barrier between patient and therapist. The lack of knowledge can be fulfilled by the app as it will give easy access to people while giving information about importance of physiotherapy in improving QOL and additional information about home-based exercises and prosthetics. Objective: To evaluate the awareness and knowledge of physiotherapy and evaluate Quality of life of breast cancer survivors after using the BRAVE App as intervention. Methods: 30 females suffering from breast cancer above the age 18 years were chosen. After the collection of baseline data and knowledge and awareness questionnaire the link to BRAVE App was provide. After 8 weeks post intervention data was collected. Results: Statistical analysis was done by using Chi-Square test for signifying the lack of knowledge and awareness about oncological rehabilitation in the breast cancer survivors. The pre and post intervention data was statistically analyzed by Wilcoxon Signed-Rank Test which gave significant improvement in post intervention functional score and quality of life. The overall mean for complete satisfaction usage of the BRAVE APP is near to 4.0 indicating higher satisfaction value. Conclusions: The pilot study is concluded as after the satisfied usage of BRAVE App there was improvement in functional score directly improving quality of life while addressing the lack of knowledge and awareness about physiotherapy with a cost-effective east to use intervention. Clinical Trial: The study was registered in the Clinical Trial Registry India with registration number CTRI/2025/05/086125.
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Background: The latest WHO-GLOBOCAN data came out in October 2020. In India, approximately 1,78,361 i.e 13.3% new cases of breast cancer are detected per annum with 90,408 i.e. 10.6% deaths. With the ...
Background: The latest WHO-GLOBOCAN data came out in October 2020. In India, approximately 1,78,361 i.e 13.3% new cases of breast cancer are detected per annum with 90,408 i.e. 10.6% deaths. With the 5-year overall survival, a study reported it to be 95% for stage I patients, 92% for stage II, 70% for stage III and only 21% for stage IV patients but with lifelong complications. There is evidence-based protocol for physiotherapy treatment in breast cancer but the lack of knowledge about the treatment and its benefits forms a barrier between patient and therapist. The lack of knowledge can be fulfilled by the app as it will give easy access to people while giving information about importance of physiotherapy in improving QOL and additional information about home-based exercises and prosthetics. Objective: To evaluate the awareness and knowledge of physiotherapy and evaluate Quality of life of breast cancer survivors after using the BRAVE App as intervention. Methods: 30 females suffering from breast cancer above the age 18 years were chosen. After the collection of baseline data and knowledge and awareness questionnaire the link to BRAVE App was provide. After 8 weeks post intervention data was collected. Results: Statistical analysis was done by using Chi-Square test for signifying the lack of knowledge and awareness about oncological rehabilitation in the breast cancer survivors. The pre and post intervention data was statistically analyzed by Wilcoxon Signed-Rank Test which gave significant improvement in post intervention functional score and quality of life. The overall mean for complete satisfaction usage of the BRAVE APP is near to 4.0 indicating higher satisfaction value. Conclusions: The pilot study is concluded as after the satisfied usage of BRAVE App there was improvement in functional score directly improving quality of life while addressing the lack of knowledge and awareness about physiotherapy with a cost-effective east to use intervention. Clinical Trial: The study was registered in the Clinical Trial Registry India with registration number CTRI/2025/05/086125.
Background: Nantong City is experiencing deepening aging. Traditional elderly care models fail to meet the increasingly diverse needs of older adults. Smart elderly care integrating technologies such ...
Background: Nantong City is experiencing deepening aging. Traditional elderly care models fail to meet the increasingly diverse needs of older adults. Smart elderly care integrating technologies such as the Internet of Things, big data, and artificial intelligence has emerged as an innovative pathway to improve service efficiency and accessibility. However, little empirical research clarifies the true demand for smart elderly care among older adults in aging cities. Objective: This study aimed to systematically analyze the current status and key factors influencing the demand for smart elderly care services among older adults in a deeply aging city in China. Nantong City, Jiangsu Province, was used as a case study. Methods: This study was a cross-sectional survey. We administered face-to-face questionnaires to older adults aged 60 and above in Nantong City. This yielded 403 valid responses (effective response rate: 92.6%). The univariate chi-square tests, Mann-Whitney U tests, and binary logistic regression were used to identify the independent influencing factors of the demand for smart elderly care. We also performed stratified analyses by gender and living arrangement. In addition, we conducted an interaction analysis between age and educational level. Results: Among surveyed older adults, 74.4% (300/403) reported demand for smart elderly care services. Logistic regression identified age (OR=0.655, 95% CI 0.465-0.921), educational level (OR=1.600, 95% CI 1.092-2.343), self-rated health (OR=3.697, 95% CI 2.579-5.300), and living arrangement (OR=1.638, 95% CI 1.121-2.393) as independent influencing factors (all P<.05). Other important factors included acceptance of smart services (OR=0.618, 95% CI 0.451-0.848), satisfaction with services (OR=0.614, 95% CI 0.473-0.798), awareness of smart elderly care (OR=0.558, 95% CI 0.405-0.768), and willingness to pay (OR=0.575, 95% CI 0.445-0.743). Stratified analyses showed that age significantly inhibited demand only in males (OR=0.62, 95% CI 0.41-0.94). Higher educational level was linked to greater demand among those living alone (OR=2.85, 95% CI 1.34-6.05). The interaction analysis indicated that the positive effect of education level decreased with increasing age (age × education interaction, OR=0.804, 95% CI 0.659-0.981, P=.032). Conclusions: Older adults in rapidly aging eastern Chinese cities exhibit strong demand for smart elderly care. However, this is held back by low awareness, limited digital skills, and a low willingness to pay. This presents a structural contradiction of "high demand - low adaptation". Key influencing factors are health status, living arrangement, awareness, and willingness to pay, with notable population differences. To improve supply-demand matching, we recommend digital skill training, age-friendly technology design, tailored services, and community trust-building.
Background: Medical vision-language models (VLMs) are increasingly used for clinical imaging tasks, but evaluation often emphasizes task accuracy rather than whether models are robust, calibrated, vis...
Background: Medical vision-language models (VLMs) are increasingly used for clinical imaging tasks, but evaluation often emphasizes task accuracy rather than whether models are robust, calibrated, visually grounded, fair, and safe. Existing reviews summarize architectures or broad medical generative AI applications but do not systematically map trustworthiness evaluation practices for medical VLMs. Objective: This scoping review aimed to map which trustworthiness dimensions are evaluated for medical VLMs and multimodal large language models applied to clinical imaging, catalog the datasets, metrics, perturbation protocols, and image-reliance controls used, summarize reported safeguards, and identify reporting gaps to inform a minimum evaluation checklist. Methods: We conducted a PRISMA-ScR scoping review. PubMed/MEDLINE, Embase, Scopus, Web of Science Core Collection, and IEEE Xplore were searched for peer-reviewed English language studies published from January 1, 2022, to March 25, 2026. Eligible studies evaluated at least one trustworthiness dimension of a medical VLM in a clinical imaging context. Eight dimensions were charted: robustness, hallucination, visual grounding, calibration and uncertainty, fairness, interpretability, distribution-shift generalization, and safety. Two reviewers independently screened records and charted study characteristics, evaluation methods, grounding controls, safeguards, and reproducibility indicators. The protocol was not prospectively registered. Results: We screened 516 records, including 506 database records and 10 records from citation chasing and targeted update searches. After title and abstract screening, 80 reports advanced to full-text review; 72 unique reports remained after merging 8 duplicates. Thirty-four peer reviewed studies met inclusion criteria, and 29 preprints were tracked separately. Robustness was the most commonly evaluated dimension (14/34, 41.2%), followed by hallucination (9/34, 26.5%), distribution shift (6/34, 17.6%), and visual grounding (5/34, 14.7%). Interpretability and fairness were each evaluated in 4 studies (11.8%); calibration and safety were each addressed in 2 studies (5.9%). Only 5 studies (14.7%) used any image-reliance control, and 4 (11.8%) reported subgroup fairness analysis. Conclusions: Trustworthiness evaluation for medical VLMs remains uneven and incomplete. The largest gaps are grounding controls, calibration reporting, fairness analysis, and deployment safeguards. We propose the Minimum Trustworthiness Evaluation Checklist (MiTEC), an eight-item framework to help authors, reviewers, and regulators assess whether medical VLMs are evaluated beyond task accuracy.
Background: Extended reality (XR), encompassing virtual, augmented, and mixed reality, is an emerging digital health technology with growing relevance for cardiovascular training and education. Cardio...
Background: Extended reality (XR), encompassing virtual, augmented, and mixed reality, is an emerging digital health technology with growing relevance for cardiovascular training and education. Cardiovascular specialties require advanced visuospatial understanding and procedural competence, yet traditional training pathways are increasingly limited by time, case exposure, and variability in learning opportunities Objective: This review aims to synthesise the current evidence on the use of XR technologies in cardiovascular education. Methods: A structured literature search was performed across PubMed, Scopus, and Web of Science to identify studies published between 2014 and February 2025 evaluating XR-based educational interventions in cardiovascular medicine. Eligible studies included original research reporting educational or training outcomes in medical students, trainees, or clinicians. Evidence was narratively synthesised according to educational domain and XR modality. Results: Across the included studies, XR was most commonly applied using virtual reality for three-dimensional visualisation of cardiac anatomy and pathology, as well as simulation-based procedural training in interventional cardiology, electrophysiology, and cardiac surgery. XR-enabled learning was consistently associated with high learner engagement and satisfaction, with several studies demonstrating improvements in spatial understanding, procedural performance metrics, and learner confidence compared with conventional teaching approaches. Evidence for augmented and mixed reality remains sparse. However, the evidence remains limited by heterogeneity in study design, small sample sizes, short-term outcome assessment, and limited evaluation of knowledge retention. Conclusions: XR represents a promising digital health adjunct to cardiovascular education, with particular value in immersive visualisation and simulation. Robust, longitudinal studies with standardised educational outcomes are required to support evidence-based integration into cardiovascular training programmes.
Background: Clinical educators regularly use simulation-based education to help learners develop clinical reasoning, procedural skills, and communication strategies. However, differences between simul...
Background: Clinical educators regularly use simulation-based education to help learners develop clinical reasoning, procedural skills, and communication strategies. However, differences between simulated and live clinical environments—such as missing case details, technical problems, or incomplete evaluation tools—can reduce realism and divert learners’ attention from the intended learning goals. While published design guidelines recommend pilot testing simulations, they do not explain how to systematically identify usability problems before piloting. Human factors evaluation methods can address this gap by identifying issues early and providing design insights. Objective: This tutorial describes our pragmatic usability testing protocol for evaluating educational simulations before piloting with learners. Using a telemedicine maternal care simulation as an example, we demonstrate how mixed-methods usability techniques can identify technical, workflow, and assessment-related issues in simulation design. Methods: We recruited obstetricians and family practitioners to test our prototype simulation. To measure multiple usability dimensions concurrently, we adapted several published instruments, including an Agile Task Analysis (ATA), the Single Ease Question (SEQ), the System Usability Scale (SUS), the Satisfaction with Simulation Experience Scale (SSES), and the NASA Task Load Index (NASA-TLX). We also developed a bespoke competency assessment rubric to measure tester performance in completing clinical telemedicine tasks. We recorded observations on the ATA while testers used a Think-Aloud protocol. We then debriefed testers and administered the post-test questionnaires. We summarized quantitative data using descriptive statistics and analyzed qualitative data using theory-based deductive coding and published usability heuristics. Results: Twelve testers identified 52 usability issues over two testing cycles; 15 issues required corrections before piloting with learners. Revisions included changes to standardised patient scripts, synthetic patient data, and scoring rubric instructions. Testers assigned a median SEQ score of six or higher to four of the six workflow steps; most struggled with unfamiliar telemedicine tasks or performing a virtual physical exam. The mean SUS score was 77.4, indicating above-average usability. The NASA-TLX subscale scores were highest for mental workload (mean 35.15) but appeared well calibrated for our target learners. The SSES scores were positive, suggesting faculty testers thought the simulation offered valuable learning opportunities.
As a result of usability testing, we successfully piloted the simulation with 23 obstetrics and family medicine residents without encountering any implementation issues. Faculty easily scored residents on eight telehealth competencies. Most residents were entrustable with completing clinical tasks, apart from an unfamiliar virtual care checklist and assisting standardised patients with home blood pressure monitoring. Conclusions: Incorporating usability evaluation methods throughout the simulation development lifecycle can identify and mitigate design problems that might otherwise limit simulation fidelity and instructional effectiveness. Our tutorial showcases a modular mixed-methods approach that can be adapted to study an array of simulation types and research questions related to workflow, technology, and learning objectives. Clinical Trial: This was not a clinical trial. This simulation/tutorial was reviewed by the University of Oklahoma IRB # 13738
Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with persistent impairments in executive functioning. Although pharmacological and behavioral interventions reduce core sympto...
Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with persistent impairments in executive functioning. Although pharmacological and behavioral interventions reduce core symptoms, complementary cognitive approaches are needed to enhance higher-order self-regulation. Episodic future thinking (EFT) is closely linked to executive control but has rarely been integrated as an additive component of executive-function (EF) training in pediatric ADHD. Virtual reality (VR) provides an ecologically valid platform for embedding EFT within immersive, consequence-based environments. Objective: The objective of this study was to evaluate the effectiveness of adding a virtual reality–based episodic future thinking (VR-EFT) module to conventional executive-function training for children with ADHD. Methods: In this randomized controlled trial, 80 children aged 5–12 years with ADHD were assigned to EF-only training (n = 40) or EF training plus a VR-based EFT module (n = 40) delivered over 2 weeks (six sessions per week). Executive functioning was assessed pre- and post-intervention using the Behavior Rating Inventory of Executive Function (BRIEF), the Executive Function Assessment Scale (EFAS), and the Childhood Executive Functioning Inventory (CHEXI). Results: Significant main effects of Assessment Occasion indicated overall improvement following EF training (ps < .01). Significant Assessment Occasion × Training Condition interactions were observed for the BRIEF and EFAS, reflecting greater pre-to-post improvements in the EF + VR-EFT group, particularly in higher-order executive domains. CHEXI outcomes demonstrated a more mixed pattern, with some domains showing significant interaction effects whereas others improved similarly across training conditions. Conclusions: Adding a VR-based episodic future thinking module to conventional EF training resulted in selective improvements in several higher-order executive processes. The findings indicate domain-specific additive effects rather than generalized executive-function enhancement. Clinical Trial: ClinicalTrials.gov NCT07591896
Background: : Polycystic ovary syndrome (PCOS) is a highly prevalent endocrine-metabolic disorder driven by a complex interplay of hyperandrogenism, insulin resistance, and chronic low-grade inflammat...
Background: : Polycystic ovary syndrome (PCOS) is a highly prevalent endocrine-metabolic disorder driven by a complex interplay of hyperandrogenism, insulin resistance, and chronic low-grade inflammation. While Metformin addresses systemic insulin sensitivity, it leaves the upstream trigger of gut dysbiosis and mucosal endotoxemia unmanaged. This study introduces a distinct therapeutic paradigm by combining Metformin with a strain-specific formulation of Lactobacillus crispatus to target the gut-metabolic axis directly. Unlike generic probiotic blends, L. crispatus actively mitigates gastrointestinal inflammation through specialized Surface Layer Proteins (SLPs) that physically reinforce the epithelial barrier against lipopolysaccharide (LPS) translocation, alongside strain-derived hydrogen peroxide production that upregulates host mucosal PPAR- γ to block NF-kB driven cytokine transcription ( TNF-α and IL-6). By dampening this localized inflammatory cascade, this metabolic-inflammatory regimen aims to protect IRS-1 signaling, alleviate hyperinsulinemia, to restore normal ovarian steroidogenesis. Objective: Primary Objective
The PCOME Study is to compare the change in Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) from baseline to 3 months across the three intervention groups—Probiotic, Metformin, and Combination therapy—in women diagnosed with PCOS.
Secondary Objectives
The PCOME Study aims to evaluate the impact of the three intervention regimens (Probiotic, Metformin, and Combination therapy) on the following parameters at 3 months:
1. Inflammatory and Gut Health Markers
• Serum and fecal calprotectin levels
• Serum acylated ghrelin (AGH) levels
• Fecal short-chain fatty acid (SCFA) concentrations
• Serum zonulin levels (as a marker of intestinal permeability)
2. Gut Microbiome Profile
• Alpha diversity (e.g., richness and evenness)
• Beta diversity (community structure)
• Differential abundance of key bacterial taxa, including Lactobacillus, Bifidobacterium, Akkermansia muciniphila, and the Bacteroidetes/Firmicutes ratio
3. Metabolic and Hormonal Markers
• Additional markers of insulin resistance: fasting glucose, fasting insulin, and glycated hemoglobin (HbA1c).
• Markers of hyperandrogenism: total testosterone, free androgen index (FAI), and sex hormone-binding globulin (SHBG)
4. Clinical and Anthropometric Outcomes
• Body mass index (BMI) and waist-hip ratio (WHR)
• Assessment of hirsutism using the modified Ferriman-Gallwey score
• Menstrual regularity Methods: This open-label, randomized, hospital-based trial at AIIMS Bhubaneswar will assess the effects of probiotic and/or metformin therapy in women aged 18–40 years diagnosed with PCOS per Rotterdam criteria. A total of 138 participants will be randomized equally into three groups: probiotics only, metformin only, and combination therapy. The 3-month intervention includes metformin (1000 mg/day) and a specified Lactobacillus crispatus containing probiotic (2 capsules/day). The primary outcome is the change in Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Key secondary outcomes include changes in serum/fecal calprotectin, AGH, total testosterone, and fecal microbiome profile. Results: Not ready yet Conclusions: This trial will provide critical, mechanistic evidence on whether the targeted, strain-specific action of L. crispatus can work synergistically with Metformin to heal the intestinal mucosal barrier, arrest systemic endotoxemia, and rescue disrupted gut-brain-endocrine signaling. By validating this novel metabolic-inflammatory regimen against localized (calprotectin) and systemic (AGH, HOMA-IR) endpoints, this study intends to establish a highly integrated, microbiome-targeted therapeutic strategy capable of optimizing long-term metabolic and reproductive health outcomes in women with PCOS. Clinical Trial: CTRI/2025/09/094113
While joint hypermobility can result from various medical conditions, it is most commonly associated with a group of related genetic conditions that affect connective tissue known as Ehlers–Danlos s...
While joint hypermobility can result from various medical conditions, it is most commonly associated with a group of related genetic conditions that affect connective tissue known as Ehlers–Danlos syndromes (EDSs). As there is currently no specific genetic testing for the diagnosis of Ehlers–Danlos hypermobility syndrome (hEDS), diagnosis is strictly made based on clinical criteria, which include physical features such as pain and family history, in addition to a scoring system known as the Beighton Score—a 9-point scale used to measure joint hypermobility—with a score of >4 considered significant. While hEDS often causes chronic muscle and joint pain, the underlying mechanisms remains poorly understood. Dysautonomia, characterized by common symptoms such as anxiety, vertigo, and increased heart rate when standing (orthostatic intolerance), in addition to multiple gastrointestinal symptoms, is highly prevalent among hEDS patients. We hypothesize that hypermobility due to ligamentous instability of the upper cervical spine, C1 and C2, results in impingement of the carotid sheath, the carotid artery and, more significantly, the vagus nerve, thus explaining the myriad symptoms that accompany hEDS. We also propose the novel use of extracorporeal shock wave therapy (ESWT) to treat this instability.
Patient perspectives on artificial intelligence (AI) use in healthcare have not been well studied. Yet, this understanding is crucial for ensuring these perspectives are addressed in AI development an...
Patient perspectives on artificial intelligence (AI) use in healthcare have not been well studied. Yet, this understanding is crucial for ensuring these perspectives are addressed in AI development and deployment in clinical settings. This review aims to synthesize existing literature and identify key themes regarding patient perspectives on AI. The electronic search strategy sourced 351 studies from five databases: Medline ProQuest, Ovid Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, and Google Scholar. Ultimately, 20 studies were included in this review, along with four additional sources from grey literature. Key themes that emerged across these studies included: 1. the inability of AI to replace physicians, 2. human authority in high-stakes clinical situations, 3. lack of education relating to AI, 4. mistrust of AI, 5. the need for greater AI transparency. Importantly, patient concerns within these categories exhibited remarkable heterogeneity, which reinforces the need for flexible AI tools that address the diverse needs of their patients. Despite these concerns, patient consensus overwhelmingly favoured the inclusion of AI in healthcare as a tool for physician support. Consensus stemmed from patients’ hope for the AI-supported clinician of the future to be the ‘ideal physician’. This paper is intended to serve as a practical guide to aid healthcare policymakers’ and practitioners’ understanding of patient perspectives regarding AI in healthcare. Only once this understanding has been achieved can AI technologies truly reach their full potential.
Background: Chatbot-based interventions have shown promise in common mental health conditions such as depression and anxiety. However, their application in schizophrenia (SZ), particularly for psychia...
Background: Chatbot-based interventions have shown promise in common mental health conditions such as depression and anxiety. However, their application in schizophrenia (SZ), particularly for psychiatric medication counseling, remains extremely limited. Objective: This study aimed to investigate the effects of a rule-based psychiatric medication counseling chatbot on clinical and patient-reported outcomes in patients with SZ. Methods: A total of 31 outpatients with SZ participated in a single-group pre–post study. Participants used a rule-based chatbot via a mobile app for 3 months. The chatbot provided structured guidance on antipsychotic medications, including side effects, management strategies, medication use, expected therapeutic effects and duration of medication. Primary outcomes included medication adherence (Adherence Rating Scale [ARS]), subjective well-being (Subjective Well-being under Neuroleptic Treatment Scale [SWN]), and side effects (Udvalg for Kliniske Undersøgelser Side Effect Rating Scale [UKU]). Secondary outcomes included psychopathology (PANSS), functioning (SOFAS), and insight (SUMD-K). Results: A total of 31 participants (mean age 33.91 years, SD 11.60; 20 males) completed the study. Medication adherence (ARS) showed a trend-level increase (4.77 vs 4.94, t=2.02, p=.057) but did not reach statistical significance. Among SWN subdomains, self-control improved significantly (mean difference 1.48, 95% CI 0.03 to 2.94, p=.045). UKU total severity scores decreased significantly (19.48 vs 14.52, mean difference −4.96, 95% CI −8.53 to −1.40, p=.008), driven by reductions in psychic (mean difference −1.97, 95% CI −3.52 to −0.41, p=.015) and miscellaneous symptom domains (mean difference −1.58, 95% CI −3.06 to −0.10, p=.037). Among secondary outcomes, PANSS positive symptoms decreased significantly (11.39 vs 10.35, mean difference −1.04, 95% CI −1.90 to −0.17, p=.021), whereas functioning (SOFAS) and insight (SUMD-K) did not change significantly. Older age (β=0.157, p=.017) and living with family members (β=4.304, p=.047) were associated with improvements in physical functioning, and greater chatbot use was associated with improvements in social integration (β=0.150, p=.029) and socio-occupational functioning (β=0.082, p=.024). Conclusions: A rule-based psychiatric medication counseling chatbot was associated with modest but significant improvements in subjective well-being and perceived side effect burden in patients with SZ, while its impact on medication adherence and broader clinical outcomes was limited. These findings suggest that chatbot-based interventions may serve as a useful adjunctive tool in SZ care, particularly for addressing medication-related concerns. Clinical Trial: Clinical Research Information Service (CRIS) KCT0011949; https://cris.nih.go.kr (registration number: KCT0011949)
Background: Clinical reasoning competency development is central to veterinary education. Generative artificial intelligence (GenAI) opens new possibilities for supporting students in acquiring these ...
Background: Clinical reasoning competency development is central to veterinary education. Generative artificial intelligence (GenAI) opens new possibilities for supporting students in acquiring these competencies, yet its effectiveness as a reasoning support tool in case-based learning (CBL) remains unclear. Objective: This study examined whether a commercially available GenAI chatbot could support veterinary students in CBL and evaluate its potential for clinical reasoning training. Methods: Following systematic evaluation, Microsoft Copilot was selected for its accessibility, functionality, and data protection compliance, and students were provided with a user-oriented manual including prompt instructions. In an interventional crossover study involving 60 fourth-year veterinary students at a Swiss university, participants alternated between AI-supported and traditional case-based learning (CBL) across four clinical cases. Clinical reasoning outcomes were assessed by a dedicated lecturer per case using 34 scored items, complemented by student surveys and lecturer reflections. Results: Clinical reasoning outcomes showed no meaningful evidence of a difference between AI-supported and traditional CBL groups (W = 6719, p = 0.464), with results varying across cases. Post-class surveys (n = 38) indicated that most students viewed GenAI support positively: 68% agreed the AI provided relevant inputs they had not previously considered, 58% perceived reduced task difficulty, and 61% found the AI-generated starting point effective. However, 45% also reported negative effects on case understanding and dissatisfaction with the overall learning experience. Qualitative feedback highlighted benefits such as information retrieval and stimulation of reflection, alongside limitations related to superficial or inaccurate AI outputs. Conclusions: These findings indicate that AI integration alone is insufficient to enhance clinical reasoning in case-based learning. Without sufficient AI literacy on top of developing clinical competencies, the cognitive demands of verifying AI-generated outputs may offset potential benefits in complex reasoning tasks. Tailoring AI integration to learner experience, scaffolding, and prior AI exposure appear relevant to realizing GenAI's potential for clinical reasoning development in veterinary education.
Background: Preoperative assessment of lymph node metastasis (LNM) risk is needed to support risk stratification and individualized treatment planning in patients with colorectal cancer (CRC). However...
Background: Preoperative assessment of lymph node metastasis (LNM) risk is needed to support risk stratification and individualized treatment planning in patients with colorectal cancer (CRC). However, imaging-based nodal staging may be affected by image quality and readers’ experience, and its ability to detect microscopic metastatic disease remains limited. Machine learning models based on routinely available clinical data may provide an accessible and interpretable approach to support individualized preoperative decision-making. Objective: This study aimed to establish an interpretable machine learning–based model to estimate the preoperative risk of LNM risk in patients with CRC. The model incorporated preoperative variables that are routinely obtained in clinical practice and was further evaluated in an independent external validation set. Methods: We retrospectively analyzed data from 2725 patients diagnosed with CRC at two independent hospitals. The internal cohort was randomly split at a 7:3 ratio for model training and testing. The second-center cohort served as the independent external validation set. The outcome was pathologically confirmed regional LNM. Candidate variables included demographic characteristics, laboratory indicators, tumor markers, and tumor-related clinicopathological features available before surgery. Variables independently associated with LNM were identified using logistic regression analyses. Seven machine learning models were constructed using LightGBM, random forest, support vector machine, logistic regression, decision tree, XGBoost, and naive Bayes. Model performance was checked by discrimination, calibration, clinical utility, and classification metrics. We used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Accuracy, sensitivity, specificity, F1 score, positive predictive value (PPV), and negative predictive value (NPV) described classification performance. Calibration curves compared predicted risks with observed outcomes. Decision curve analysis estimated the model’s net clinical benefit. SHapley Additive exPlanations (SHAP) analysis interpreted the selected model and assessed predictor contributions. Results: The final cohort included 2725 patients. There were 753 patients for model training, 321 for testing, and 1651 for external validation. In multivariable logistic regression, body mass index, preoperative carcinoembryonic antigen level, primary tumor site, clinical T stage, histological type, and tumor differentiation were independently associated with LNM. Among the seven models, random forest showed the most balanced performance. In the test set, this model had an AUC of 0.806. Its accuracy was 0.735, sensitivity was 0.737, and specificity was 0.734. In the external validation set, the AUC was 0.782. Accuracy, sensitivity, and specificity were 0.690, 0.661, and 0.708. Conclusions: An interpretable machine learning model estimated LNM risk in CRC with acceptable performance. Random forest showed stable discrimination in the independent external validation set. It may support individualized preoperative risk stratification, but prospective validation and implementation studies are still needed. Clinical Trial: Not applicable
Background: Digital health technologies (DHTs) for psychosis may help address the substantial gap in access to psychological services, yet prior syntheses are limited by heterogeneous designs and popu...
Background: Digital health technologies (DHTs) for psychosis may help address the substantial gap in access to psychological services, yet prior syntheses are limited by heterogeneous designs and populations. T Objective: This systematic review and meta-analysis aimed to synthesize evidence from randomized controlled trials (RCTs) to estimate the relative effectiveness of DHTs in individuals with confirmed psychotic disorders. Methods: Web of Science, PubMed, Embase, Scopus, PsycINFO, and CENTRAL were searched from inception to January 2026. Eligible studies were RCTs enrolling adults with psychotic disorders that evaluated DHT-delivered psychological interventions targeting psychotic symptoms. Comparators included passive and active controls. Primary outcomes were positive, negative, and overall symptoms. Secondary outcomes included depression, anxiety, functioning, quality of life, dropout, and adverse events. Results: Forty-one RCTs (N = 4139) were included. Compared with passive controls, DHTs showed small to moderate significant reductions in positive (g = -0.18, 95% CI: -0.33 to -0.03; I2= 60%), negative (g = -0.32, 95% CI: -0.56 to -0.07; I2= 63%), and overall symptoms (g = -0.41, 95% CI: -0.71 to -0.10; I2= 78%) at posttreatment, with effects for positive symptoms also at follow-up. No significant effects were observed when compared with active controls. Subgroup analyses indicated significant effects for delusions but not auditory hallucinations, and stronger effects for therapist-supported versus interventions delivered fully automated. Secondary outcomes showed small improvements both posttreatment and follow-up in depression, anxiety, and general functioning, but not for quality of life. Heterogeneity was moderate to high in some of the analyses. Dropout rates were comparable across groups, with no consistent pattern of serious adverse events identified, although safety reporting was inconsistent. Conclusions: DHTs represent a promising approach, with outcomes that appear broadly comparable to face-to-face interventions, while offering potential advantages in accessibility, scalability, and flexibility. Further high-quality RCTs with active comparators and standardized safety monitoring are needed. Clinical Trial: CRD42021251108
Background: Multimodal digital monitoring systems offer a promising avenue for the continuous, unobtrusive assessment of functional health and daily living in older adults, yet the extent to which exi...
Background: Multimodal digital monitoring systems offer a promising avenue for the continuous, unobtrusive assessment of functional health and daily living in older adults, yet the extent to which existing studies leverage the core advantages of multimodality remains unclear. Objective: This scoping review aimed to systematically map the characteristics and implementation practices of multimodal digital monitoring systems used to assess daily living and functional health in older adults. Methods: A systematic search was conducted across four electronic databases (PubMed, EMBASE, CINAHL, and IEEE Xplore; January 2020–November 2025), from which 30 studies were included and data were charted using a standardized extraction form. Results: The predominant deployment profile combined ambient and mobile/wearable sensors in passive sensing configurations within home settings. Functional and physiological monitoring were well-represented; however, nutritional monitoring was entirely absent and social engagement substantially underrepresented, indicating a systematic gap relative to the multidimensional health needs of older adults. Despite increasing hardware sophistication, analytical integration of multimodal data remained limited. Conclusions: These findings show that although multimodal sensing has matured at the hardware level, the field is yet to fully realize its analytical and integrative potential in supporting the complex, person-centered needs of ageing populations.
Vector-borne diseases (VBDs) represent a rapidly growing public health challenge in the United States. Between 2001 and 2023, over one million VBD cases were reported, driven primarily by mosquito and...
Vector-borne diseases (VBDs) represent a rapidly growing public health challenge in the United States. Between 2001 and 2023, over one million VBD cases were reported, driven primarily by mosquito and tick-borne pathogens. Environmental change, including rising temperatures, shifting precipitation patterns, land use change, and suburban expansion into previously inhospitable habitats, has enabled vector populations to establish and thrive across new geographic ranges, accelerating both the incidence and geographic spread of VBDs.
Despite this trajectory, VBD control in the U.S. remains highly fragmented and decentralized, organized primarily at the local or regional level with substantial heterogeneity in funding, technical capacity, and surveillance infrastructure. Current approaches to VBD control are predominantly reactive and triggered by outbreaks rather than driven by proactive, data-informed prevention. Critically, existing epidemiological models and control frameworks have not fully incorporated human behavioral variables (e.g., perception, outdoor activity patterns, mitigation behavior) into their assessments of transmission risk. This represents a fundamental gap, given that human behavior is both a driver of exposure and a modifiable target for intervention.
Progress has been made in several adjacent areas. Environmental and climatic drivers of VBD transmission are increasingly well-characterized. Mathematical models have advanced considerably in their ability to forecast vector abundance, habitat suitability, disease risk under changing conditions while integrating data on human behavior. Public health communication theory has produced evidence-based principles for designing effective behavior change messaging. And tools from mathematical optimization and adaptive decision-making have demonstrated real-world utility in guiding surveillance and intervention allocation under resource constraints. However, these advances have largely developed in parallel rather than in integration.
The 2024 Center for Disease Control and U.S. Department of Human Health Services Vector-Borne Disease National Strategy explicitly call for better understanding of individual exposure factors, improved prevention and control approaches, and more effective dissemination of detection and response tools, situated within a One Health framework to enhance coordination and communication across human, animal, and environmental contexts. This call makes the current moment particularly timely for an integrated framework that bridges biological, environmental, and social science approaches to VBD risk.
This viewpoint responds to that need by proposing a dynamic social-ecological systems risk communication framework that integrates environmental surveillance data, vector and pathogen monitoring, mathematical optimization tools, and human behavioral and risk perception data into a cohesive, adaptive feedback system. The framework is designed to be generalizable across regions and VBD types, and to support evidence-based health policy and targeted public communication that is responsive to dynamic and spatially variable risk conditions. It is intended for an audience spanning epidemiology, public health, human behavior, geography, applied mathematics, and health policy.
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is primarily characterized by deficits in core executive functions (EF)—inhibitory control, attention, and working memory—which lead to ...
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is primarily characterized by deficits in core executive functions (EF)—inhibitory control, attention, and working memory—which lead to decreased academic achievement and social adaptation issues. Objective: This study aimed to systematically evaluate the efficacy of exergame interventions on EF in children(mean age 8.3~11.5) with ADHD and to investigate the moderating effect of medication status to provide a scientific basis for digital therapeutics (DTx). Methods: Following PRISMA guidelines, a meta-analysis was conducted on randomized controlled trials (RCTs) published between 2018 and 2025. Using CMA 2.0 software, pooled effect sizes (Hedges' g) were calculated based on a random-effects model. Results: Significant improvements (p < .05) were observed across all domains: inhibitory control (g = -0.408), attention (g = -0.328), and working memory (g = 0.634). Notably, moderator analysis revealed no significant difference between groups in inhibitory control based on medication status (Q = 0.002, p = .961), demonstrating comparable improvement in drug-naive children. While heterogeneity was low for inhibitory control and attention, working memory showed a moderate level (I2 = 54.451%), suggesting variance due to the sensitivity of measurement tools. Conclusions: These findings demonstrate that exergames are an effective non-pharmacological intervention applicable regardless of clinical background. Specifically, the robust effect observed in working memory (g = 0.927) establishes a scientific foundation for designing evidence-based DTx for children with ADHD.
Medical artificial intelligence (AI) systems are often evaluated through aggregate performance metrics and output-level fairness measures. However, clinically meaningful harms may remain hidden when s...
Medical artificial intelligence (AI) systems are often evaluated through aggregate performance metrics and output-level fairness measures. However, clinically meaningful harms may remain hidden when systems perform well on average while underperforming for data-poor, underrepresented, or structurally marginalized populations. This Viewpoint uses the concept of refined exclusion to synthesize a recurring pattern in medical AI: systems may appear technically successful at the population level while transferring uncertainty, misclassification, delayed recognition, or reduced clinical reliability to groups that are less visible within training data, validation cohorts, proxy definitions, and deployment workflows.
Drawing on representative cases from population health management, chest radiograph AI, dermatology, computational pathology, and foundation model applications, we argue that refined exclusion should not be treated merely as algorithmic bias or a defect of model outputs. Rather, it reflects a data governance failure with direct implications for patient safety. Moving beyond output-centered algorithmic fairness, we propose data justice as a governance foundation for medical AI, organized across distributional, procedural, and substantive dimensions.
We further outline operational checkpoints across the medical AI lifecycle, including subgroup learnability assessment, data provenance documentation, local validation, procurement-stage accountability, explainability-based proxy audits, post-deployment subgroup monitoring, and patient participation. Reframing refined exclusion as a patient safety problem shifts the central governance question from “Is this model accurate on average?” to “For whom is this system safe, reliable, and clinically accountable?”
Background: In many resource-limited mountainous settings, frail older adults face substantial barriers to accessing health services. Although multiple stakeholders play important roles in supporting ...
Background: In many resource-limited mountainous settings, frail older adults face substantial barriers to accessing health services. Although multiple stakeholders play important roles in supporting their nutritional health, they often lack structured and engaging tools to promote evidence-based dietary practices, contributing to persistent nutritional deficits and frailty progression. Previous studies have examined the feasibility of digital nutrition interventions for older adults; however, multi-stakeholder perspectives and context-specific requirements for gamified nutrition education remain insufficiently understood, limiting the development of sustainable and locally adapted interventions. Objective: To explore the nutrition education needs of frail older adults in mountainous areas from a multi-stakeholder perspective, including their expectations regarding system functions and usage patterns, in order to inform the development of a practical and sustainable gamified nutrition education intervention. Methods: Participants were recruited using purposive and snowball sampling strategies. From November to December 2025, semistructured interviews were conducted with 33 participants, including frail older adults in mountainous areas, family caregivers, and primary health care workers, recruited from key counties targeted for national rural revitalization and former poverty-alleviation counties in Guizhou Province. Data were analyzed using directed content analysis guided by the Dynamics-Mechanics-Components gamified design theoretical model. Results: Three interrelated themes were identified. Older adults demonstrated limited nutritional knowledge and unhealthy dietary behaviors. Stakeholders also described unclear caregiving roles and weak communication between families and primary health care workers. Participants further emphasized the importance of simple functions, age-friendly sensory design, and localized interface features in gamified nutrition education. Conclusions: Gamified nutrition education for frail older adults in mountainous areas should incorporate multi-stakeholder collaboration, age-friendly design, and locally adapted content to improve acceptability and engagement.
Background: Kratom (Mitragyna speciosa) and kratom-derived products are increasingly encountered through online vendors, social media, forums, video platforms, public comment spaces, and retail-linked...
Background: Kratom (Mitragyna speciosa) and kratom-derived products are increasingly encountered through online vendors, social media, forums, video platforms, public comment spaces, and retail-linked digital advertising. Newer products containing added or concentrated 7-hydroxymitragynine (7-OH) or mitragynine pseudoindoxyl have intensified public-health concern because they may be marketed under the broad label of kratom while differing from traditional leaf or powder products. Digital environments may shape consumer perceptions by emphasizing natural, therapeutic, functional, or harm-reduction narratives while providing inconsistent information about dependence, withdrawal, product variability, and safety. Objective: This review aimed to map empirical evidence on how kratom, 7-OH, mitragynine pseudoindoxyl, and related kratom-derived products are represented, marketed, and discussed in digital environments, with attention to benefit claims, risk messaging, product-form distinctions, and gaps for future infodemiology research. Methods: A scoping evidence map was conducted using a PRISMA-ScR–informed rapid review workflow. Two retained database exports were audited: a Scopus export and an FIU Libraries discovery-layer RIS export. After deduplication by DOI and title-year, 2920 unique records remained for screening. Sources were eligible if they focused on kratom, 7-OH, mitragynine, mitragynine pseudoindoxyl, or clearly kratom-derived products and empirically analyzed a digital environment, including social media, forums, Reddit, Facebook, YouTube, Erowid, public comments, vendor websites, product pages, online markets, or online product marketing. Narrative reviews, editorials, agency webpages, offline-only clinical or toxicology studies, and online surveys that did not analyze the online information environment itself were excluded. Data were charted by platform, study design, sample or unit of analysis, product type, claims, risk themes, marketing patterns, and limitations, then synthesized narratively. Results: Of 2920 unique records, 2879 were excluded at title, abstract, and gateway screening. Forty-one reports underwent detailed assessment, 21 were excluded with record-level reasons, and 20 empirical digital-environment sources were included. Included sources examined Reddit, Facebook, YouTube, Erowid experience reports, FDA public comments, social-media corpora, online vendor websites, darknet or cryptomarket listings, ready-to-drink product websites, and online pages for 7-OH or mitragynine pseudoindoxyl products. Across sources, kratom was frequently framed as self-treatment, harm reduction, natural wellness, functional enhancement, or a safer alternative to regulated substances. Commonly discussed motivations included pain, anxiety or depression symptoms, opioid withdrawal, fatigue, focus, energy, and mood. Vendor and product-marketing studies documented incomplete consumer health information, limited balanced risk communication, variable disclosure of dependence or withdrawal risks, and emerging concern around concentrated alkaloid products, beverages, gummies, tablets, strips, and other consumer-friendly dosage forms. Conclusions: The digital kratom literature is small but coherent. Online discourse and marketing frequently present kratom and related products as natural, therapeutic, functional, or useful for self-treatment, while risk communication is inconsistent. Emerging evidence on concentrated 7-OH, mitragynine pseudoindoxyl, ready-to-drink products, and youth-appealing dosage forms highlights the need to distinguish botanical kratom leaf or powder from extracts and concentrated alkaloid products. Clinicians and public-health professionals should ask not only whether patients use kratom, but also what product form they use, why they use it, where they learned about it, and whether they understand potential risks related to product variability, dependence, withdrawal, and co-use. Future infodemiology research should directly examine short-form video platforms and performance-oriented marketing. Clinical Trial: Not applicable. This study is a scoping evidence map and did not involve a clinical trial.
Background: GLP-1 receptor agonists (GLP-1 RAs) — semaglutide, liraglutide, and tirzepatide — are among the most widely prescribed medications globally, with disproportionate uptake among women of...
Background: GLP-1 receptor agonists (GLP-1 RAs) — semaglutide, liraglutide, and tirzepatide — are among the most widely prescribed medications globally, with disproportionate uptake among women of reproductive age. The quality of YouTube content on GLP-1 RAs and reproductive health has not been previously characterized. Objective: This study aimed to evaluate the quality, accuracy, and misinformation burden of YouTube videos addressing GLP-1 RAs in a reproductive health context, and to assess the validity of large language model (LLM)-assisted quality scoring as a scalable surveillance tool. Methods: We conducted a PRISMA-compliant cross-sectional analysis of 137 YouTube videos retrieved via YouTube Data API v3 on 8 March 2026. Two physicians independently scored each video using four validated instruments: Global Quality Scale (GQS, 1-5), modified DISCERN (mDISCERN, 0-5), JAMA Benchmark Criteria (0-4), and a 3-point Medical Accuracy Scale. Videos were classified Useful or Misleading by consensus. A large language model (Claude Sonnet; temperature = 0) independently scored all videos blinded to human ratings. Results: Overall quality was below acceptable (GQS 2.60 ± 1.04; 65.7% scored below 3); 22.6% (n = 31) were classified Misleading. Short videos (≤ 180 s; 54.7%) had the lowest mean GQS (1.95) and the highest misinformation rate (29%). ROC analysis identified 212 s as the optimal quality threshold (AUC = 0.892). Off-label content carried the highest misinformation rate (73%); preconception discontinuation protocols were absent from 61% of videos. Engagement metrics were uncorrelated with quality (ρ = −0.141). The LLM showed strong rank-order agreement with human raters (GQS ρ = 0.845; mDISCERN ρ = 0.840) but exhibited systematic upward score inflation. Conclusions: YouTube content on GLP-1 RAs and reproductive health is predominantly poor quality and frequently misleading. Short-format videos pose the greatest patient safety risk, critical preconception information is frequently absent, and viral reach does not select for accuracy. LLM-assisted evaluation is valid and scalable for rank-order surveillance but requires expert calibration before operational deployment.
Background: The landscape of online health information-seeking is shifting from traditional search engines to include conversational interactions with Large Language Models (LLMs). Qualitative studies...
Background: The landscape of online health information-seeking is shifting from traditional search engines to include conversational interactions with Large Language Models (LLMs). Qualitative studies on user goals and strategies for using LLMs for health-related purposes are lacking. Objective: To identify user goals, interaction patterns, and prompting strategies for health-related LLM interactions using a cross-cultural longitudinal approach. Methods: We conducted a 14-day longitudinal diary study with 36 participants (18 in the United States and 18 in India). A total of 277 diary entries recording health-related LLM use were analyzed using the Goals Associated with Health Information Seeking (GAINS) framework, descriptive statistics, and inductive thematic analysis. Our participants used a variety of LLMs including Chat GPT, Google Gemini, Meta AI, and Claude Results: Participants use LLMs for a wide range of health purposes including searching for general health information, symptom diagnosis, analyzing health information, and fitness tracking. Across these varied cases, participants reported high levels of satisfaction using LLMs for health-related purposes (96% US, 97% India). They report high levels of success across GAINS goals including action planning, reassurance, understanding health problems and hope (>70% of entries rated as completely or mostly successful). Participants in the United States were more likely to include personal context in prompts (71/132, 54% US vs 42/144, 29% India; P<.001) and use expressive prompts (29/131, 22% US vs 11/137, 8% IN). Participants in India were more likely to use directive prompts (40/137, 29% IN vs 12/131, 9% US). Indian participants reported a significantly higher rate of positive emotional impact ("felt better" 94% vs 64% US), while US participants were more likely to feel "the same" (34% vs 3% India). Conclusions: User strategies for online health information seeking are shifting from navigational browsing to conversational prompting. Cultural contexts moderate how users interact with and feel using LLMs for health. These differences appear more in prompting styles than in user goals and motivations. Understanding these nuances is critical for designing globally relevant, safe, and effective health AI systems.
Background: Pediatric influenza is a major public health concern. While Chinese patent medicines play a significant role in its treatment, information regarding drug recommendations in existing clinic...
Background: Pediatric influenza is a major public health concern. While Chinese patent medicines play a significant role in its treatment, information regarding drug recommendations in existing clinical guidelines is often fragmented, and the logic of syndrome differentiation-based medication is complex, limiting the efficiency of clinical decision-making. Objective: The objective of this study is to construct a structured Knowledge Graph for the treatment of pediatric influenza with Chinese patent medicines by integrating multiple authoritative guidelines and consensuses, and to develop an intelligent Question-Answering System based on this graph to provide precise clinical decision support. Methods: Using three guidelines and consensuses—including the Clinical Practice Guidelines for the Treatment of Pediatric Influenza with Chinese Patent Medicines (2024)—as core data sources, we manually and systematically extracted and standardized 11 types of entities, including influenza diagnosis, symptoms, Traditional Chinese Medicine (TCM) syndromes, therapeutic drugs, usage methods, and evidence levels. A domain ontology was constructed to define multidimensional semantic relationships between entities, and the Neo4j graph database was utilized for knowledge storage and visualization. Results: The constructed Knowledge Graph consists of 433 nodes and 604 relationships, integrating 22 recommended Chinese patent medicines, 21 symptoms, and 6 types of TCM syndromes. The graph visualizes the dynamic decision-making path of "symptom-syndrome-drug" and embeds GRADE evidence levels and recommendation strengths. It supports intelligent symptom-based queries and provides comprehensive decision support, including drug recommendations, specific usage and dosage, combination medication suggestions, and risk warnings for contraindications. A quantitative evaluation of the Intelligent Question-Answering System, based on 80 clinically representative queries assessed by three independent TCM experts, yielded an overall Response Accuracy of 88.8%, a Precision of 90.2%, a Recall of 87.5%, and an F1-score of 88.8%, with a Hallucination Rate of 3.8%, demonstrating the system's clinical reliability and effectiveness. Conclusions: This study successfully constructed a structured Knowledge Graph for the treatment of pediatric influenza with Chinese patent medicines, effectively addressing the fragmentation of knowledge in traditional guidelines. This graph assists physicians, particularly those in primary care and Western medicine practitioners, in rapidly understanding the logic of syndrome differentiation and making standardized medication decisions, thereby reducing the barrier to diagnosis and treatment of pediatric influenza. This study provides a feasible paradigm for the digital transformation of TCM guideline knowledge and the development of clinical intelligent auxiliary tools.
Background: Behavioral interventions are more effective when co-designed with people who have lived and living experience (PWLLE) of the target issue. However, traditional co-design processes position...
Background: Behavioral interventions are more effective when co-designed with people who have lived and living experience (PWLLE) of the target issue. However, traditional co-design processes position participants as idea generators whose concepts must be translated into functional tools by professional developers, creating a gap between participant vision and final product. The emergence of vibe coding—a practice in which users describe desired software functionality in natural language and artificial intelligence (AI) generates the corresponding code—presents a novel opportunity to close this gap by enabling participants to build their own intervention tools directly. Objective: This paper proposes a methodological framework for integrating vibe coding into the co-design of behavioral interventions, whereby PWLLE are guided by researchers to brainstorm, prototype, and iteratively build digital tools that address their self-identified needs. Methods: We synthesized literature from participatory design, behavioral science, experience-based co-design (EBCD), and AI-assisted software development to construct a 5-phase protocol: (1) Contextual Grounding, (2) Participatory Ideation, (3) Vibe Coding Workshops, (4) Iterative Refinement, and (5) Evaluation and Reflection. Each phase is described in detail with procedural guidance, ethical considerations, and worked examples. We identify key facilitators (researcher scaffolding, prompt literacy training, structured AI interaction protocols) and barriers (technical limitations of AI-generated code, digital literacy disparities, quality assurance requirements) to implementation. Results: Preliminary pilot vibe coding activities were conducted through SmokeFreeConnect (SFC), a community-based smoking cessation program designed to enhance social connection and peer support for individuals attempting to quit combustible cigarette smoking. One pilot session led to the collaborative conceptualization of SmokeFreeQuest, a prototype smoking cessation application, with positive feedback received regarding the development process and prototype concept. As of May 2026, formal participant recruitment and enrollment for the proposed focus groups had not yet commenced. Additional vibe coding sessions are planned for Summer 2026, with initial findings expected in January 2027. Conclusions: Vibe coding offers a transformative extension to participatory co-design methodologies in behavioral science. By enabling PWLLE to directly build the tools they need, this approach strengthens participation beyond consultation, aligns with self-determination theory (SDT), and generates interventions that are more contextually responsive. Future research should include feasibility trials, effectiveness evaluations, and the development of ethical guidelines for AI-mediated participatory research. Clinical Trial: N/A
Background: Medical students are increasingly expected to perform medical practices under supervision during clinical clerkships. In Japan, a 2021 revision to the Medical Practitioners Act formally su...
Background: Medical students are increasingly expected to perform medical practices under supervision during clinical clerkships. In Japan, a 2021 revision to the Medical Practitioners Act formally supported students’ active participation in supervised medical practice. However, in community-based and rural clerkships, students may still have limited opportunities to transition from observing clinical care to performing tasks themselves. Because community-based attending physicians (CAPs) often determine when and how students can participate in care, faculty development (FD) for CAPs may help expand students’ participation. Objective: This study examined whether students placed at clerkship sites with FD-trained CAPs performed a wider range of Model Core Curriculum–recommended medical practices than students placed at sites without FD-trained CAPs. We also analyzed students’ daily reflections to assess how medical practice-related learning appeared in their written accounts. Methods: We conducted an explanatory sequential mixed methods study involving 112 fifth-year medical students who completed 3-week community-based clinical clerkships. Students were placed at sites with FD-trained CAPs (FD group; n=34) or without FD-trained CAPs (non-FD group; n=78). Students recorded daily experiences of 50 Model Core Curriculum–recommended medical practices as performed, observed/simulated, or not performed. Student-performed practices were compared between groups. Daily reflection sheets were divided into textual units and deductively coded according to Model Core Curriculum learning objectives. Generative artificial intelligence (AI) was used as an auxiliary tool to support deductive coding; final coding decisions and interpretations were made by human researchers. Results: A total of 112 students were included (FD group, n=34; non-FD group, n=78). Students in the FD group performed more types of examination and non-invasive practices than those in the non-FD group (median 10 vs 5 of 23 practices; Mann-Whitney U test, P<.001; r=0.49). Treatment and invasive practices were numerically higher in the FD group but did not differ significantly (median 5.5 vs 4 of 27 practices; Mann-Whitney U test, P=.08; r=0.23). Reflection sheet analysis yielded 4097 textual units, of which 822 (20.1%) were coded as medical practice-related content. Medical practice-related reflection codes were most frequent in week 1 (non-FD group, 214/815, 26.3%; FD group, 94/424, 22.2%) and decreased thereafter. The groups differed substantially in placement contexts, including medically underserved placements (FD group, 33/34, 97.1%; non-FD group, 4/78, 5.1%) and home-visit service exposure (FD group, 29/34, 85.3%; non-FD group, 43/78, 55.1%). Conclusions: Students at clinical clerkship sites with FD-trained CAPs reported performing a wider range of examination and non-invasive practices, whereas treatment and invasive practices did not differ significantly. These findings indicate associations rather than causal effects because the groups differed in placement context and analyses relied on self-reported experience data. FD-trained supervision, together with site characteristics such as medically underserved placements and home-visit service exposure, may expand opportunities for lower-risk clinical practices.
Background: Automated clinical coding with large language models has shown promise, but most approaches depend on supervised fine-tuning, static label spaces, or opaque prediction mechanisms that are ...
Background: Automated clinical coding with large language models has shown promise, but most approaches depend on supervised fine-tuning, static label spaces, or opaque prediction mechanisms that are difficult to audit and update. These limitations are particularly relevant in ICD-10-CM coding, where models must navigate complex documentation patterns, ambiguity, and evolving coding rules. Recursive learning architectures may offer an alternative by enabling systems to improve through explicit natural-language memory rather than parameter updates. Objective: This study evaluated whether a recursive learning architecture with an externalized Learning File could improve zero-shot ICD-10-CM coding performance on discharge summaries, while preserving interpretability and enabling analysis of longitudinal learning dynamics. Methods: We developed PANDORA, a zero-shot coding system composed of a Coder, a Reviewer, and a persistent natural-language Learning File derived from prior coding errors. Using discharge summaries from MIMIC-IV and a Top-50 ICD-10-CM benchmark, we compared a no-memory baseline (Phase 1) against a memory-augmented configuration (Phase 4). Performance was assessed across 20 recursive training iterations and on a held-out testing set of 500 cases, using micro-F1, macro-F1, precision, and recall at both exact-code and ICD-3 levels. Error composition, representative memory-guided decisions, and temporal degradation associated with memory growth were also analyzed. Results: In the held-out testing set, the memory-augmented system improved exact-code micro-F1 from 0.307 to 0.527 and precision from 0.203 to 0.515, while recall decreased from 0.630 to 0.540. At the ICD-3 level, micro-F1 improved from 0.372 to 0.560. Across training iterations, the memory-augmented condition achieved an exact-code micro-F1 of 0.605 versus 0.318 in the no-memory baseline. Gains were driven primarily by large reductions in false positives, indicating that the Learning File improved precision more than recall. A qualitative review showed that the system used accumulated rules to suppress unsupported codes and to recover context-sensitive diagnoses. However, performance declined after iteration 10 as the Learning File grew larger and less discriminative, suggesting that memory bloat is an important failure mode of recursive learning. Conclusions: A recursive learning architecture with explicit natural-language memory substantially improved zero-shot ICD-10-CM coding performance, primarily through better precision and more controlled code assignment. The approach offers transparency benefits because improvements can be traced to human-readable learned rules rather than hidden parameter changes. However, recursive systems require active memory governance, as unchecked rule accumulation may degrade performance over time. These findings support memory-based adaptation as a promising direction for interpretable clinical coding systems and other high-stakes clinical NLP tasks.
Background: Basic life support (BLS) significantly improves survival and neurological outcomes of out-of-hospital cardiac arrest (OHCA) victims. However, bystander cardiopulmonary resuscitation (CPR) ...
Background: Basic life support (BLS) significantly improves survival and neurological outcomes of out-of-hospital cardiac arrest (OHCA) victims. However, bystander cardiopulmonary resuscitation (CPR) rates vary widely worldwide, reaching only 40% in Geneva, Switzerland. The European Resuscitation Council’s "Kids Save Lives" statement advocates for integrating BLS education into mandatory school curricula to improve bystander CPR rates and thus OHCA outcomes. Training medical students as BLS instructors could help address the shortage of qualified instructors needed to implement school-based BLS programs in primary schools. Objective: The objectives of this pilot study were to assess medical students' willingness to become BLS instructors for primary school classes and evaluate the impact of their teaching on schoolchildren’s immediate knowledge acquisition and self-confidence. Methods: This pilot implementation study was conducted in Geneva, Switzerland. A team of specialized physicians and senior medical students presented the project to second- and third-year medical students during their emergency skills training curriculum. Students were selected to participate after completing an online questionnaire. Selected students underwent instructor training and certification, enabling them to deliver a BLS course in a primary school class. Participating 7th-grade schoolchildren completed a questionnaire designed to assess immediate knowledge acquisition and confidence.
The primary outcome was the proportion of medical students interested in becoming BLS instructors. Secondary outcomes included children's mean knowledge score and confidence in calling the emergency phone number. The likelihood of becoming an instructor according to demographic characteristics, prior experiences, and personal interests was assessed. The association of age, school socioeconomic status, and prior first aid training with knowledge and confidence outcomes was also examined. Results: Among 325 eligible students, 87 (26.8%) expressed willingness to fully participate in this program. The 16 selected students successfully completed the instructor training and delivered courses in 16 primary school classes. No significant predictors of willingness were identified.
A total of 295/361 (81.7%) children completed the post-course questionnaires. Knowledge acquisition was high, with a mean score of 5.4 ± 1.1 out of 7. High proportions of correct responses were observed for most BLS concepts, except for breathing assessment and recovery position indications. Overall, 78% of children reported confidence in calling emergency services. No significant predictors of knowledge or confidence were found. Conclusions: Junior medical students can effectively deliver BLS training to school children and represent a valuable workforce for school-based BLS programs. However, sustainable scale-up will require involving other instructors. Further studies should assess the feasibility of large-scale implementation.
Background: Most adolescent mental health issues remain undetected and untreated, with insomnia a potent risk factor for psychopathology and suicidality. Digital single-session interventions (SSIs), t...
Background: Most adolescent mental health issues remain undetected and untreated, with insomnia a potent risk factor for psychopathology and suicidality. Digital single-session interventions (SSIs), targeting sleep, offer scalable, accessible opportunities for preventive solutions that may overcome barriers such as stigma and low engagement, particularly among underserved groups. Objective: This study evaluated the feasibility of a mobile-based sleep intervention delivered through educational settings. Methods: This study employed a non-randomised, mixed-methods, longitudinal design to evaluate the feasibility of a multi-level intervention in young people aged 14-18 years. 1,046 participants were recruited from schools and colleges across England. Participants accessed a stepped-care mobile intervention and completed self-report measures at baseline and 6-week follow-up. Outcomes included feasibility (recruitment, retention), engagement (usage analytics, completion rates), and changes in insomnia (Insomnia Severity Index) and mental health symptoms (Revised Children’s Anxiety and Depression Scale). Logistic regression models examined predictors of attrition and engagement Results: 1048 participants completed baseline assessments and 656 (62.6%) completed 6-week follow-up. Among participants with linked usage data (n=603), 57.7% completed the core intervention content. Attrition was higher among males and lower among participants from more deprived areas. Significant improvements were observed in insomnia symptoms (Cohen d=0.29, p<.001), as well as anxiety and depression (d=0.11-0.19, all p<.01). Among participants with baseline insomnia symptoms, 26.9% achieved remission at follow-up. Conclusions: The intervention was well-received across diverse groups, supporting the acceptability and scalability of brief, school-based digital interventions to reduce sleep-related mental health disparities Clinical Trial: Not applicable.
Background: Artificial intelligence (AI) is rapidly transforming healthcare, yet medical students’ perspectives in Eastern European contexts remain underexplored. Understanding their knowledge, atti...
Background: Artificial intelligence (AI) is rapidly transforming healthcare, yet medical students’ perspectives in Eastern European contexts remain underexplored. Understanding their knowledge, attitudes, and practices is essential to guide curriculum reform and ensure that future healthcare professionals are prepared to use AI safely and effectively. Objective: This study assessed the knowledge, attitudes, and practices related to AI integration among students at the Medical University of Plovdiv, Bulgaria. Methods: A cross-sectional survey was conducted in May 2025 among 732 medical students (240 international, 492 Bulgarian) using a 20-item anonymous questionnaire covering AI familiarity, attitudes, ethical concerns, and educational needs. Analyses were performed in R Statistical Software (v4.5.3); P<.05 was considered statistically significant. Results: Approximately 88-90% of students reported AI familiarity with no significant between-group difference. Social media was the dominant information source (55% international, 65% Bulgarian), with university education cited by only 10-15%. International students showed significantly higher AI tool usage for studying (69.6% vs 44.1%), stronger belief in AI’s transformative potential (70.0% vs 56.1%), and greater ethical concern levels (P=.03). Both groups equally supported AI curriculum integration (~51%) and identified loss of the human element (~70%) as the primary ethical concern. Roughly one-third of all students felt unprepared for clinical AI applications. Conclusions: Students demonstrate broad AI awareness but significant gaps persist in formal education and practical readiness. Systematic integration of AI into medical curricula – emphasizing hands-on training, ethical considerations, and critical evaluation skills – is needed to bridge the gap between awareness and clinical competence.
Background: Digital mental health tools (DMHTs) are increasingly used by young people as first points of contact for mental health support, yet evidence on how their design and implementation collecti...
Background: Digital mental health tools (DMHTs) are increasingly used by young people as first points of contact for mental health support, yet evidence on how their design and implementation collectively shape help-seeking outcomes remains fragmented. Objective: This umbrella review aimed to synthesise findings from systematic, scoping, and narrative reviews to identify best practices and minimum standards for promoting help-seeking through DMHTs targeting young people aged 12–25. Methods: Following PRISMA guidelines, a comprehensive search of six electronic databases was conducted covering the period 2015–2025, supplemented by grey literature. Methodological quality of included reviews was assessed using AMSTAR 2. Results: Twenty-six reviews met the eligibility criteria. Findings indicate that DMHTs improve help-seeking intentions, primarily through enhanced mental health literacy and reduced stigma; however, these improvements do not reliably translate into observable help-seeking behaviour. Key facilitators for engagement included anonymity, privacy, and social connectedness. Best practices identified included co-design with young people, personalisation of content, and clinical moderation. DMHTs were most effective when integrated into established referral pathways, such as school systems or primary healthcare. Conclusions: While DMHTs are effective in improving help-seeking intentions, their real-world impact remains limited, highlighting the need for integrated approaches to design, implementation, and behavioural outcomes. These findings provide actionable guidance for policymakers and practitioners integrating DMHTs into youth mental health systems.
Background: AI-driven clinical systems can improve diagnosis, prognosis, and resource allocation, but they may reproduce disparities encoded in historical healthcare data. Existing mitigation methods ...
Background: AI-driven clinical systems can improve diagnosis, prognosis, and resource allocation, but they may reproduce disparities encoded in historical healthcare data. Existing mitigation methods typically target a single source of bias, while clinical datasets often contain interacting representation, proxy, integrity, and temporal biases. Objective: This study develops and evaluates a unified multi-stage framework for detecting and mitigating multiple forms of bias in structured healthcare machine learning data. Methods: We designed a compositional pipeline, D_clean = T_temp(T_int(T_proxy(T_repr(D)))), in which each stage conditions on the corrected output of the previous stage. To address cross-dataset heterogeneity, all datasets were first mapped into a prespecified harmonized clinical-concept space with explicit missing-concept masks. The five anchor features used for alignment were race, sex, age_group, income_proxy, and n_prior_visits. The final harmonized representation contained 37 clinical concepts plus 37 corresponding binary mask indicators, yielding a 74-dimensional model input after categorical expansion and mask concatenation. The primary model was trained on the Diabetes 130-US Hospitals dataset. External validation used CMS SynPUF for readmission prediction and NHANES for stage-level fairness and distributional stress testing rather than unsupported direct outcome transfer. Integrity bias was assessed with distributional tests appropriate to each variable type; Benford-style leading-digit analysis was restricted to unbounded count or charge-like variables and was not applied to bounded physiological laboratory values such as HbA1c. Results: On the primary Diabetes 130-US Hospitals test split, the proposed pipeline improved AUC from 0.798 to 0.812 and reduced Demographic Parity Difference (DPD) from 0.134 to 0.052. The DPD reduction was statistically significant (bootstrap 95% CI -0.094 to -0.069; paired permutation P < .001). On CMS SynPUF after harmonized concept mapping, DPD decreased from 0.141 to 0.066. NHANES stage-level validation showed improved representation balance and proxy attenuation, while HbA1c integrity checks were evaluated with bounded-variable distributional baselines rather than Benford's Law. Mixture-of-experts processing isolated 7,938 of 101,766 records (7.8%) flagged for integrity concerns and improved fairness without discarding records Conclusions: A coordinated data-centric pipeline can improve both fairness and predictive performance when dataset heterogeneity, variable-specific integrity assumptions, and subgroup-specific processing are made explicit. The revised framework resolves the methodological risk of unsupported zero-shot transfer by introducing a harmonized concept layer and reporting outcome validation only where the prediction task and feature space are aligned
Background: Patient-reported outcome measure (PROM) completion is hindered by patient-level barriers—including motor, sensory, cognitive, and motivational constraints—that risk insufficient partic...
Background: Patient-reported outcome measure (PROM) completion is hindered by patient-level barriers—including motor, sensory, cognitive, and motivational constraints—that risk insufficient participation and non-response bias. While technology-enabled approaches such as multimodal speech assistance hold promise for reducing these barriers, assistance is a complex interaction: it can both alleviate and introduce barriers depending on how well it aligns with patients’ routines and needs. Objective: This qualitative study explores how patients perceive the advantages and disadvantages of AI-based speech assistance for PROM collection, focusing on how assistance functionalities interact with individual barriers and completion practices. Methods: We conducted semi-structured qualitative interviews with 96 psychosomatic and neurological rehabilitation outpatients, embedded in a pragmatic cross-randomised controlled trial. Participants completed PROMs with and without an AI-based speech assistance system offering speech output, speech input, and guidance by a socially interactive agent (SIA) that was physically, virtually, or voice-only embodied. The system was iteratively refined during data collection to address usability and performance issues. We included a broad sample to reflect real-world care settings, including patients without reported barriers. Using inductive content analysis (61 codes, grouped into 4 overarching and 9 subthemes), we examined perceived advantages and disadvantages of the three main assistance functionalities and multimodal interaction. Reporting followed the COREQ guideline. Results: The speech output function emerged as the most widely valued assistance feature, with many patients reporting improved concentration, question comprehension, and deeper engagement with item content. The social agent was described as making the interaction more engaging and less monotonous, by at the same time not evoking social pressure. Speech input was perceived as helpful by some, especially for those with motor impairments or a preference for verbal expression. However, each function also introduced challenges: speech output disrupted reading routines for some, the social agent was perceived as distracting or unnecessary by others, and speech input was criticised for recognition errors, inefficiency, and privacy concerns. Conclusions: AI-based speech assistance for PROM collection offers significant potential to reduce barriers and enhance patient engagement, but its effectiveness depends on alignment with individual needs, preferences and routines. While speech output proved broadly beneficial, speech input and socially interactive agents require careful design to avoid introducing new barriers, particularly for marginalised groups. Configurable, modular assistance systems that adapt to diverse user preferences and impairments are essential for equitable implementation. Future research should focus on inclusive co-design and longitudinal studies to refine these technologies for real-world clinical use. Clinical Trial: German Clinical Trail Register-ID: DRKS00035213
Background: Ischemic heart disease (IHD) remains the leading cause of mortality worldwide, with patients with type 2 diabetes mellitus (T2DM) experiencing substantially increased cardiovascular risk. ...
Background: Ischemic heart disease (IHD) remains the leading cause of mortality worldwide, with patients with type 2 diabetes mellitus (T2DM) experiencing substantially increased cardiovascular risk. Objective: This study aimed to develop and validate the MyHEART-DM Score, a prognostic screening tool for predicting IHD risk among Malaysian patients with T2DM using real-world registry data. Methods: A retrospective cohort study was conducted using data from the National Diabetes Registry (NDR) Johor, Malaysia, from 2020 to 2024. Patients were divided into development (2020–2021), internal validation (2022–2023), and external validation (2024) cohorts. Multivariable logistic regression was used to develop the prognostic model, and weighted scores were generated using a shrinkage approach. Model performance was assessed using discrimination and calibration analyses. Results: A total of 11,082, 13,068, and 7,613 patients were included in the development, internal validation, and external validation cohorts, respectively. The final MyHEART-DM Score incorporated 15 prognostic variables and demonstrated moderate but consistent discrimination across all cohorts, with area under the receiver operating characteristic curve values of 0.686, 0.694, and 0.687, respectively. Calibration performance was satisfactory, with calibration intercepts close to 0 and slopes close to 1 across all cohorts. The score stratified patients into low (<20), intermediate (20–40), and high-risk (>40) categories, with progressively higher odds of IHD observed across risk groups. Conclusions: The MyHEART-DM Score may serve as a practical tool for IHD risk stratification among Malaysian patients with T2DM in primary care settings and may contribute toward achieving Sustainable Development Goal 3 by supporting early detection and prevention of cardiovascular complications among high-risk populations. Further prospective validation and integration into clinical decision support systems are recommended. Clinical Trial: Not applicable.
Background: Patients in urban safety-net settings face substantial barriers to accessing community
health resources despite the availability of resource lists embedded in electronic health record (EH...
Background: Patients in urban safety-net settings face substantial barriers to accessing community
health resources despite the availability of resource lists embedded in electronic health record (EHR)
systems. The existing EHR-based resource directories are often difficult to navigate during timeconstrained clinical encounters. Providers report that existing resource lists are often too long,
inadequately filtered, and poorly suited to in-encounter use. No widely available tool allows mental
health providers in safety-net settings to generate curated, county-specific, patient-ready resource
lists at the point of care. Objective: This study aimed to evaluate the provider-level feasibility and acceptability of Bridgedd, a web-based community resource navigation tool, among licensed mental health providers at a single urban safety-net hospital. Specifically, we assessed (1) the usability of the tool, (2) its perceived value relative to existing community resource navigation tools, and (3) provider intent to adopt the tool in routine clinical practice. Methods: We conducted a single-site, cross-sectional feasibility and acceptability pilot of Bridgedd, a web-based provider-facing community resource navigation tool, at Grady Memorial Hospital in Atlanta, Georgia. The survey instrument was investigator-developed and the project was determined to be non-human subjects research by the Emory University IRB. Licensed mental health providers used the tool and completed a brief structured survey assessing ease of use, comparative value relative to existing tools, intent to use the tool with patients, and open-ended feedback. Quantitative responses were summarized with descriptive statistics; open-ended responses were grouped into thematic categories. Results: Eleven licensed mental health providers (therapists, n = 4; licensed professional counselors, n = 3; social workers, n = 2; mental health counselor, n = 1; certified peer specialist–mental health, n = 1) completed the survey. Nine of 11 respondents (82%) rated the tool as very easy to use, and no respondent reported technical problems. Eight of 11 respondents (73%) agreed or strongly agreed that Bridgedd was better than the tools they currently use to help patients navigate community health resources, and 8 of 11 (73%) reported intent to use the tool with patients if made available. Open-ended feedback centered on three themes: expanded geographic coverage, greater depth within categories (particularly mental health), and a mechanism for keeping resource information current. Conclusions: Mental health providers at a large urban safety-net hospital found Bridgedd easy to use, preferable to existing community resource navigation tools, and potentially suitable for integration into routine practice. Findings support continued iterative development, broader provider sampling, and a planned patient-level evaluation.
Background: Osteoarthritis (OA) has traditionally been viewed as a degenerative joint disease characterized by cartilage deterioration. However, growing evidence indicates that OA is better understood...
Background: Osteoarthritis (OA) has traditionally been viewed as a degenerative joint disease characterized by cartilage deterioration. However, growing evidence indicates that OA is better understood as a systemic immune-metabolic disorder driven by the interplay of metabolic dysregulation, chronic low-grade inflammation, and abnormal immune activation. Objective: This review summarizes the bidirectional crosstalk between immunity and metabolism during OA progression. Methods: Immune cells, especially macrophages and T cells, contribute to cartilage degradation and synovial inflammation through the release of pro-inflammatory mediators. At the same time, metabolic abnormalities, including mitochondrial dysfunction, aberrant glycolysis, and lipid dysregulation, disturb chondrocyte homeostasis and further amplify inflammatory responses by modulating immune function, thereby forming a self-perpetuating vicious cycle. Results: Based on this framework, emerging immunometabolic therapeutic strategies are discussed, including interventions targeting AMPK/mTOR signaling and key enzymes involved in glycolysis and lipid metabolism, as well as advanced biological approaches such as gene editing, synthetic biology, and stem cell-based therapies. Conclusions: Integrating multi-omics technologies with personalized medicine may enable precise patient stratification and dynamic monitoring, supporting a shift from symptomatic management to disease modification in OA. Future research should focus on clarifying the dynamic immunometabolic network underlying OA and promoting the clinical translation of innovative strategies through artificial intelligence and interdisciplinary collaboration.
Background: Antimicrobial resistance (AMR) is a global health emergency, disproportionately impacting sub-Saharan Africa where fragmented laboratory information systems (LIS) and inconsistent antimicr...
Background: Antimicrobial resistance (AMR) is a global health emergency, disproportionately impacting sub-Saharan Africa where fragmented laboratory information systems (LIS) and inconsistent antimicrobial susceptibility testing (AST) practices limit both clinical decision-making and surveillance capacity. At the University Teaching Hospital of Butare (CHUB) in Rwanda, a baseline Laboratory Assessment of Antibiotic Resistance Testing Capacity (LAARC) identified systemic gaps, including 0% AST panel standardization, 17% cumulative antibiogram generation capacity, and no enforcement of testing quality rules. A standards-based digital infrastructure integrating CLSI- and EUCAST-compliant AST panels into OpenClinic GA with bidirectional WHONet export was implemented in 2024. The implementing study expressly deferred quantitative key performance indicator (KPI) evaluation to allow sufficient post-implementation observation time. Objective: To quantify the impact of the implemented infrastructure on five KPI domains: (1) AST data capture volume and specimen throughput; (2) data standardization and ease of analysis; (3) laboratory turnaround time (TAT); (4) testing quality, including detection and resolution of specimen-antibiotic incompatibilities, method violations, and intrinsic resistance violations; and (5) antimicrobial resistance (AMR) profiles, including appropriate use of screener discs. Methods: A retrospective pre-post study compared Q1 2024 (pre-implementation; n=505 culture-positive specimens, legacy LIS export) with Q1 2026 (post-implementation; n=3,669 specimens, WHONet-format export) at CHUB. Outcomes included specimen throughput, nomenclature and data structure standardization, TAT (specimen receipt to results release, days), testing quality flags (Nitrofurantoin/Nalidixic acid on non-urine specimens; vancomycin by disc diffusion on staphylococci; ampicillin on intrinsically resistant Enterobacteriaceae), screener disc utilization (Pefloxacin, Cefoxitin), and resistance rates for key pathogens against CLSI 2024 breakpoints. Results: Specimen throughput increased 7.3-fold (+626%). Unique organism names decreased from 76 to 39 (-49%), with 11 non-standard spellings of Escherichia coli alone pre-implementation reduced to one WHO-standard entry. All 7,465 post-implementation records carried CLSI 2024 annotations versus 0% pre-implementation. Critical data-analysis fields, age categories, department, TAT timestamps, WHONET antibiotic codes, absent pre-implementation were fully present post-implementation. Pre-implementation errors included: 6 instances of Nitrofurantoin or Nalidixic acid on non-urine specimens, 4 instances of ampicillin on intrinsically resistant Enterobacteriaceae (including 1 spurious susceptible result), and 44 vancomycin results by disc diffusion on staphylococci and streptococci (16 on Staphylococcus aureus), an unreliable method per CLSI. All resolved to zero post-implementation. Screener disc use was transformed: Pefloxacin (fluoroquinolone surrogate for Enterobacteriaceae) was absent pre-implementation and introduced in 95 tests post-implementation; Cefoxitin (MRSA surrogate) was used without a systematic inferential framework pre-implementation (n=81, no inferential display) and formalised under the screener protocol post-implementation (n=33, MRSA rate 24.2%). Median TAT from specimen receipt to results release was 2.80 days (IQR 2.03-3.77). Among key pathogens, Escherichia coli ciprofloxacin resistance was 54.2% and ceftriaxone resistance 42.2%, while carbapenem susceptibility was preserved (imipenem resistance 1.5%). Conclusions: Implementation of a CLSI/EUCAST-compliant digital AST and surveillance infrastructure at CHUB produced measurable, clinically meaningful improvements across all five KPI domains, yielding CHUB's first institution-wide cumulative antibiogram in GLASS-compatible WHONet format. These results fulfill the deferred quantitative evaluation and provide a replicable evidence base for tertiary institutions across sub-Saharan Africa.
Background: Development social-emotional in children preschool ( ages 3–6 years ) is foundation important for skills interaction social , regulation emotions , empathy , and readiness school . Thera...
Background: Development social-emotional in children preschool ( ages 3–6 years ) is foundation important for skills interaction social , regulation emotions , empathy , and readiness school . Therapy play ( play therapy ) a lot used as approach intervention For support aspects this , however proof existing scientific Still scattered and diverse . Therefore that , is necessary mapping systematic to type therapy play , characteristics intervention , as well as reported results in literature . Objective: map proof scientific about use therapy play For increase development social-emotional in children preschool , including type interventions used , program characteristics , instruments assessment and results main reported Methods: article using Prisma Flow Diagram with the keywords used are effectiveness and play therapy or therapeutic play and development social and emotional and child. Used 10 Articles of the 384 articles that have been selected from 4 databases , namely : Cochrane, PubMed, and ScienceDirect, sage journal Results: The results of this study are a number of study about effectiveness therapy play highlight the benefits can increase aspect emotional , behavioral , and social in various group child . Conclusions: Conclusion : In general overall , conclusion from analysis various journal study about therapy play show that therapy This give impact positive in aspects emotional , behavioral , and social child
Open Peer Review Period: May 22, 2026 - May 7, 2027
Background: Chronic pain affects millions globally and frequently persists despite conventional biomedical treatment. Pain Reprocessing Therapy (PRT), a neuroscience-based intervention, has demonstrat...
Background: Chronic pain affects millions globally and frequently persists despite conventional biomedical treatment. Pain Reprocessing Therapy (PRT), a neuroscience-based intervention, has demonstrated significant efficacy, yet access remains limited to specialized clinical settings. Objective: This paper presents SomaAI, a prototype web application designed to deliver PRT-informed somatic tracking outside the clinic through an integrated combination of AI voice coaching, interactive 3D body mapping, and gamification mechanics. Methods: SomaAI was developed using Next.js 14, Unity WebGL, and ElevenLabs Conversational AI. System evaluation proceeded in two phases: synthetic user testing using five chronic pain profiles to stress-test the prototype, followed by a real-participant pilot study currently being initiated in collaboration with the Pain Psychology Center, recruiting 5-10 adults with chronic pain for 2-4 weeks of in-home use. Results: Synthetic testing informed iterative refinements to the Unity WebGL bridge, ElevenLabs agent prompt architecture, and safety-signal tracking interface. Real-participant pilot results are pending. Conclusions: SomaAI represents a proof-of-concept for embedding PRT-based somatic tracking within a gamified, AI-guided digital platform. If validated, this approach could democratize access to brain-based chronic pain recovery for the millions who currently lack access to specialized therapy.
Background: Generative artificial intelligence (AI) is increasingly embedded within hospital electronic health record (EHR) systems to assist documentation. Most evidence concerns general-purpose chat...
Background: Generative artificial intelligence (AI) is increasingly embedded within hospital electronic health record (EHR) systems to assist documentation. Most evidence concerns general-purpose chatbots or ambient voice scribes used by attending physicians; how EHR-data-integrated tools are taken up across the training continuum, and whether they reduce self-reported documentation time, remain poorly characterized, particularly outside North American settings. Objective: We examined survey-reported use, self-reported documentation-time effects and perceived module-level maturity of an EHR-integrated generative-AI documentation tool (OneRecord, within a HIS 3.0 system) across professional groups, and the factors associated with use and with time improvement. Methods: We conducted a single-centre, cross-sectional electronic survey of clinical trainees and staff, reported per CHERRIES and STROBE, across four professional groups: undergraduate clinical learners (UGY), residents (R), postgraduate-year trainees (PGY) and nurse practitioners (NP). Respondents reported documentation time for admission and progress notes with and without AI assistance using ordinal bands and rated content-module maturity. Paired ordinal changes were assessed with Wilcoxon signed-rank tests, transition matrices and marginal-homogeneity tests (Bowker, Stuart-Maxwell); estimated minute savings (band midpoints, bootstrap CIs) were a secondary metric with sensitivity analyses. Multivariable logistic regression modelled use and admission-time improvement; module maturity (structured-data vs synthesis) was compared with a respondent-clustered (GEE) logistic model. All time outcomes were self-reported and should be read as perceived changes rather than objective EHR-measured efficiency. Results: Of 282 respondents (UGY n=111, R n=88, NP n=49, PGY n=34), 165 (58.5%) had used the tool. PGY trainees were substantially more likely to have used it than UGY (adjusted odds ratio [aOR] 6.37, 95% CI 2.27-17.84). Among 165 tool users, admission-note times shifted toward shorter categories (Stuart-Maxwell P<.001; 53% improved, 95% CI 46%-61%; estimated mean saving 6.0 minutes, 95% CI 4.8-7.3, robust across sensitivity assumptions). Progress-note changes were smaller (P=.002). After adjusting for baseline documentation time, professional group was not independently associated with improvement (baseline aOR 1.95, 95% CI 1.35-2.83), indicating that larger apparent gains among early learners reflected longer baseline times. Structured-data modules were far more likely than synthesis modules to be rated usable without optimisation (synthesis vs structured aOR 0.29, 95% CI 0.21-0.41; 55.5% vs 24.9%). With no invited-user denominator, all use estimates refer to survey-reported use among respondents. Conclusions: Over half of respondents (165/282, 58.5%) reported using the tool, with reductions in documentation-time categories, especially for admission notes. However, early learners remained the longest-duration documenters, and improvement reflected baseline documentation time rather than training stage. Structured-data summarisation was perceived as more mature than clinical synthesis. These findings support reframing AI-assisted documentation as a supervised practice of verification, editing and accountability, and we propose Digital Charting Entrustment as a candidate EPA-aligned framework for future assessment research.
Background: Gambling consumers show low uptake and engagement with tools designed to support safer gambling practices and reduce the risk of experiencing gambling-related harms. We used co-design prin...
Background: Gambling consumers show low uptake and engagement with tools designed to support safer gambling practices and reduce the risk of experiencing gambling-related harms. We used co-design principles to design and develop a digital tool for safer gambling (‘BetWell’) to overcome known barriers to tool uptake, including a focus on gambling problems. BetWell aims to increase awareness of personal gambling expenditure and knowledge of how gambling products function to support informed decision-making about gambling. It was designed based on behavioural change theories and presents a personalised amalgamation of gambling expenditure relative to alternative spend options, and psychoeducational information via a quiz. Objective: This exploratory study assessed the perceived acceptability and usefulness of the newly designed digital tool ‘BetWell’ and gathered end-user feedback to improve future prototypes. The study explored whether usability, gambling severity, financial wellbeing, gambling frequency, spend tracking, activity statement use, and the number of accounts held impacted acceptability and perceived usefulness, and the extent to which these factors independently predicted acceptability and perceived usefulness when controlling for life satisfaction, gambling satisfaction, and demographic variables. Methods: This cross-sectional study recruited 140 gambling consumers (M=41.3, SD=10.9 years) via the market research panel CRNRSTONE. Participants accessed and engaged with BetWell and completed an online survey to share their perceptions towards the tool. Results: The overall acceptability (M=32/40, SD=5.2) and perceived usefulness (M=20/25, SD=3.9) of the tool were considered ‘good’ and ‘useful’, respectively by participants. Individuals with higher gambling severity scores were more likely to perceive the tool as useful than those of the lower risk categories. Individuals with higher financial wellbeing were more likely to perceive the tool as useful and acceptable compared to those in lower financial wellbeing categories. Higher usability scores corresponded with higher tool acceptability. Previous efforts to monitor and track gambling spend was associated with both acceptability and perceived usefulness. Gambling frequency and the number of gambling accounts were not related to acceptability and perceived usefulness. Qualitative feedback included participant identified suggestions for improvements such as a need for interactive elements, a more detailed view of gambling expenditure, automation of activity statement upload functionality, more challenging and positively framed quiz content, and an emphasis on data security. Conclusions: The study provides preliminary support for the acceptability and perceived usefulness of a newly developed tool to increase awareness of gambling expenditure and knowledge on how gambling products function, providing directions for future improvements to the tool.
Background: Objective functional assessment of musculoskeletal (MSK) conditions remains a persistent clinical challenge, with current approaches relying on subjective physical examination and patient-...
Background: Objective functional assessment of musculoskeletal (MSK) conditions remains a persistent clinical challenge, with current approaches relying on subjective physical examination and patient-reported outcome measures that lack consistency for scalable monitoring. Objective: This proof-of-concept study evaluated the feasibility of an extended reality (XR)-based framework for structured inertial measurement unit (IMU) kinematic data collection, automated machine learning (ML) classification of shoulder pathology, and clinically interpretable feature importance analysis. Methods: Six functional tasks derived from the Disabilities of the Arm, Shoulder, and Hand (DASH) instrument were implemented as goal-directed XR simulations. Forty patients with shoulder conditions and 20 healthy controls performed these tasks while 6-degree-of-freedom (6-DoF) kinematic data were captured from the XR headset and controllers at 50 Hz. Three model architectures were evaluated: recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformers, across four clinical classification paradigms of increasing diagnostic specificity: patients versus controls, rotator cuff tears (RCTs) versus controls, other shoulder conditions versus controls, and RCTs versus other conditions. All models were assessed for classification performance and clinical explainability through feature importance analysis. Results: RNNs demonstrated the most consistent performance across tasks and paradigms (mean BA: 0.753, SD: 0.069), with peak performance on Jar Opening for RCTs versus controls (mean BA: 0.86, AUC: 0.82). Jar Opening, Back Washing, and Cutting yielded the highest discriminability across models. Paradigm 4 (RCTs versus other shoulder conditions) yielded the lowest classification performance across all models (BA: 0.49-0.71), with several models returning AUC values below 0.50. Head compensation emerged as the most important feature. Conclusions: Clinically grounded XR tasks produce structured, model-ready IMU data sufficient for shoulder pathology detection, demonstrating framework feasibility for objective MSK assessment. Head compensation was identified as a candidate kinematic marker of shoulder dysfunction. Differential diagnosis between pathology subgroups remains an open challenge, motivating task, and model designs tailored to specific clinical contrasts.
Background: Each year, family caregivers help the 2 million newly-diagnosed patients with cancer manage symptoms; however few interventions have been developed and proven efficacious to enhance their ...
Background: Each year, family caregivers help the 2 million newly-diagnosed patients with cancer manage symptoms; however few interventions have been developed and proven efficacious to enhance their symptom support role. The recent expansion of electronic patient-reported outcomes (ePROs), including remote symptom monitoring, represents a potential opportunity to explore novel complementary caregiver symptom management support that leverages patient ePROs. Objective: The objective of this study was to elicit feedback on a theory-based intervention concept, called FamilyAWARE, consisting of one-on-one coaching on providing effective symptom support to patients and access to a web-based dashboard reporting summaries of their care recipient’s most recent ePRO symptom reports and tailored recommendations. Methods: This was a qualitative formative evaluation study (NIH Stage IA) where family caregivers (n=20), patients with stage I-IV cancer (n=20), and oncology clinicians (n=25) were recruited from a comprehensive cancer center in the U.S. South and participated in semi-structured, one-on-one interviews (January 2025-July 2025). Participants were shown an outline of the FamilyAWARE intervention concept and asked open-ended questions about the proposed content, format, and delivery of the intervention. Professionally transcribed interviews were analyzed using a thematic analysis approach. Results: Analysis yielded four primary themes: 1) First impressions and relevance: Participants’ initial impressions of FamilyAWARE were generally positive, with caregivers viewing it as highly relevant and potentially beneficial. Patients and clinicians expressed more cautious optimism, shaped by considerations of relevance and minimizing burden. 2) Key factors for acceptability and engagement: Caregivers showed strong interest in the intervention’s potential to increase confidence in symptom support; however, engagement was seen as contingent on the perceived value, minimal burden, and flexible delivery of the intervention, particularly given time and technology constraints. 3) Coaching content and delivery: The proposed format was widely viewed as feasible. Participants emphasized content that helps caregivers balance assisting patients with promoting independence, while also supporting them in deciding when to call for help and proactive symptom strategies. Opinions on delivery by phone, in-person, or videoconference varied. 4) Dashboard design and information access: Participants emphasized the importance of a simple, mobile-friendly dashboard designed for low digital literacy, with plain language and visual cues. Caregiver access to patient symptom data was broadly supported if patient permission was obtained. Conclusions: Findings highlight strong interest and insights into designing a caregiver-focused intervention that integrates one-on-one coaching with ePRO patient symptom reports and tailored recommendations. Participant insights from this study will directly inform refinement of the intervention’s content, format, and delivery, with the goal of advancing it to pilot testing. Clinical Trial: N/A
Background: Healthcare-associated infections (HAIs) remain a significant challenge for healthcare facilities worldwide, contributing to patient morbidity, mortality, and substantial economic burden. I...
Background: Healthcare-associated infections (HAIs) remain a significant challenge for healthcare facilities worldwide, contributing to patient morbidity, mortality, and substantial economic burden. In the United States alone, the Centers for Disease Control and Prevention (CDC) estimates approximately 1 in 31 hospitalized patients acquires at least one HAI during their stay, resulting in billions of dollars in preventable healthcare costs annually [1]. Despite longstanding evidence-based guidance on infection prevention, sustained improvements in infection rates have been difficult to achieve across diverse healthcare settings.
Electronic hand hygiene monitoring (EHHM) systems offer a promising approach to enhancing adherence to infection prevention protocols while providing robust data for operational decision-making. By delivering real-time feedback to healthcare workers and tracking performance at the unit and facility level, these systems can identify and correct lapses in hand hygiene behavior, a primary contributor to HAIs [2]. Prior studies demonstrated that automated monitoring has improved compliance and reduced infection rates, though long-term, multi-facility analyses remain limited [3]. Objective: This analysis evaluates HAI performance among 25 client facilities of BioVigil Technologies, an EHHM vendor based in Ann Arbor, Michigan. This study uses publicly reported CMS data, assessing both infection outcomes and operational impact. By comparing pre- and post-implementation periods, we aim to quantify the clinical and economic benefits of EHHM across a variety of hospital types and sizes, providing insight into strategies for infection prevention improvement. Methods: Facilities were included if they met three predefined criteria: BioVigil’s EHHM implementation must have occurred in 2023 or earlier; CMS publicly reported HAI data had to be complete for both the 2024 calendar year and the year immediately preceding implementation; and CMS HAI reporting had to be available at the individual facility level. These criteria yielded a final study population of 25 healthcare facilities.
The facilities included 16 short-term acute care (STAC) hospitals, one long-term acute care (LTAC) hospital, seven critical access hospitals, and one VA hospital, collectively comprising 4,198 beds and 284 clinical units. CMS-reported HAIs for STAC hospitals included CAUTI, CLABSI, MRSA, SSI, and CDI; LTAC, critical access, and VA hospitals reported CAUTI, CLABSI, and CDI. Earliest full EHHM implementation occurred in 2016 (two facilities) and most recent in 2023 (three facilities).
This retrospective, observational analysis evaluated HAI outcomes aggregated at the hospital level. Percent change from baseline to 2024 was calculated for each facility and across the study population. Descriptive statistics, including mean, median, and 95% confidence intervals, characterized sustained changes in infection counts. EHHM operational data from 2024 were also analyzed to quantify hand hygiene opportunities, real-time corrective reminders, and overall compliance rates. All data were derived from publicly available CMS reports and de-identified operational records; no patient-level data was used. Results: In 2024, BioVigil’s EHHM system captured 67,663,570 hand hygiene opportunities across all 25 facilities. During this period, 1,112,148 potential cross-contamination events were corrected in real time through badge-based reminders. This contributed to an average hand hygiene compliance rate of 91.25% across the study population.
Of the 25 facilities, 22 demonstrated reductions in total HAI counts compared to their pre-implementation baseline. Four facilities achieved net-zero HAIs in 2024, representing a 100% reduction. One facility exhibited no change, and two experienced minimal increases of two and four cases, respectively — both from unusually low baseline years that likely underrepresent each facility’s true pre-EHHM infection burden.
Across all 25 facilities, the mean sustained HAI reduction was 46.95% (95% CI 35.5–58.4%), closely aligning with the median of 50%. Among the 22 improving facilities, the mean reduction was 56.25% (95% CI 43.2–69.3%) with a median of 55.13%, collectively representing 300 fewer CMS-reported HAI cases in 2024. Using a conservative average direct cost of $29,412 per HAI case [4], this reduction corresponds to an estimated $8.82 million in avoided costs. This estimate excludes extended length of stay, readmissions, and CMS value-based purchasing penalties, meaning the true economic benefit is likely substantially greater.
Operational efficiencies were also observed: 16 of 25 facilities reported hand hygiene data to The Leapfrog Group. Had these facilities relied exclusively on manual observation, an estimated 136,620 staff hours and approximately $5.74 million in associated labor costs would have been required in 2024 [5]. Together, these findings highlight the combined clinical and operational value of automated hand hygiene monitoring at scale. Conclusions: In conclusion, this analysis highlights that the majority of BioVigil Technologies’ clients experienced noticeable HAI reductions in 2024 compared to pre-implementation performance, regardless of system use duration. These outcomes suggest that electronic hand hygiene monitoring represents an effective, sustainable, and data-driven strategy to support infection prevention programs, improve patient safety, and enhance healthcare system efficiency. Continued adoption of scalable monitoring technologies may play an important role in advancing sustained infection prevention performance across diverse healthcare settings. Clinical Trial: Electronic hand hygiene monitoring; Infection Reduction; Clinical outcomes
Mixed reality and augmented reality technologies are increasingly being explored for intraoperative visualization, navigation, and surgical decision support. However, translation into routine surgical...
Mixed reality and augmented reality technologies are increasingly being explored for intraoperative visualization, navigation, and surgical decision support. However, translation into routine surgical practice will depend not only on technical accuracy, but also on whether these systems are usable, physically tolerable, and robust in the operating-room environment. In this viewpoint, informed by selected peer-reviewed literature, we highlight three human factors domains that require greater attention in the evaluation of mixed reality surgical guidance systems: physical characteristics, dexterity and motor skills, and environmental factors. Across the literature, recurrent challenges include ergonomic burden, limited field of view, headset weight, visual and perceptual constraints, fatigue, interaction burden associated with gesture and voice control, and vulnerability to noise, glare, crowding, and occlusion in real clinical environments. Although mixed reality may improve spatial awareness and reduce attention shifts in some settings, these benefits remain constrained by persistent usability, workflow, and environmental limitations. Viewed through a broader systems human factors lens, current literature appears concentrated mainly on user-device interaction and selected task-level issues, with comparatively less attention to team, organisational, training, and governance factors. We argue that future human factors research should move beyond technical validation alone and adopt a broader systems perspective that explicitly addresses user diversity, sterile and low-burden interaction, environmental resilience, and the wider conditions required for safe clinical integration. Such an approach is essential if mixed reality surgical guidance systems are to become safe, usable, and clinically adoptable.
Background: Cardiometabolic risk factors that emerge during childhood and adolescence can track into adulthood and increase the risk of cardiovascular disease. Although traditional physical activity c...
Background: Cardiometabolic risk factors that emerge during childhood and adolescence can track into adulthood and increase the risk of cardiovascular disease. Although traditional physical activity can improve cardiovascular health, it may lack appeal and fail to promote regular participation among children and adolescents. Active video games combine physical activity with video games enjoyment and may increase engagement in exercise among this population. Objective: To evaluate the effects of active video games on cardiometabolic health in children and adolescents and to explore potential moderators of these effects. Methods: PubMed, Web of Science, the Cochrane Library, and Embase were searched from database inception to March 2026. Eligibility criteria were established according to the PICOS framework. Randomized controlled trials involving children and adolescents aged 4 to 18 years were included if they examined active video games or virtual reality-based exergames compared with no intervention, sedentary video game controls, or habitual physical activity. Outcomes included body mass index (BMI), low-density lipoprotein (LDL), high-density lipoprotein (HDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), heart rate (HR), and glucose. Subgroup analyses were conducted according to gender, intervention type, and weight status to explore potential sources of heterogeneity. A random-effects model was used to pool effect sizes with 95% confidence intervals. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Sensitivity analyses were performed to evaluate the robustness of the findings, and publication bias was examined using funnel plots combined with Egger’s test. A comprehensive assessment of evidence quality will be conducted using the GRADE system. Results: Nine RCTs involving 867 participants were included. active video games produced significant reductions in BMI (Hedges’ g = −0.24, 95% CI [−0.39, −0.09], P=.002) and LDL (Hedges’ g = −0.50, 95% CI [−0.76, −0.23], P<.001). Subgroup analysis shows that the beneficial effects on BMI were primarily observed in the overweight subgroup (Hedges’ g = −0.31), although the between-subgroup difference was not statistically significant. No significant effects were observed for HDL, SBP, DBP, TC, TG, HR, or glucose. Conclusions: Active video games may reduce BMI and LDL in children and adolescents, but no significant effects were observed for HDL, SBP, DBP, TC, TG, HR, or glucose. These findings should be interpreted with caution given the low to very low certainty of the overall evidence. Further high-quality RCTs are needed to confirm these findings. Clinical Trial: PROSPERO CRD420261303390; https://www.crd.york.ac.uk/PROSPERO/myprospero
Background: Clinical trial data pipelines require strict adherence to Clinical Data Interchange Standards Consortium (CDISC) regulatory standards, including the Study Data Tabulation Model (SDTM) and ...
Background: Clinical trial data pipelines require strict adherence to Clinical Data Interchange Standards Consortium (CDISC) regulatory standards, including the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM). General-purpose large language models (LLMs) frequently violate these requirements through terminology hallucinations, noncompliant variable mappings, and fabricated derivation logic. Standard domain adaptation techniques, including retrieval-augmented generation (RAG) and fine-tuning, do not encode the structured workflow dependencies inherent in layered regulatory pipelines. No prior work has systematically evaluated AI-assisted regulatory statistical programming with domain-specific knowledge injection constrained by workflow topology. Objective: This study aimed to evaluate whether graph-constrained skill loading—a mechanism that injects domain-specific regulatory knowledge into an agentic AI system based on workflow position within a directed acyclic graph (DAG)—can improve the regulatory compliance quality of AI-generated clinical data artifacts compared with an unconstrained baseline. Methods: The clinical trial evidence pipeline was modeled as a 45-node DAG spanning 7 procedural layers. An Adaptive Priority Scheduler selectively loads domain-specific skills (59 SKILL.md rule files containing CDISC dictionaries, derivation logic, and validation protocols) based on graph proximity within a fixed token budget. We conducted a controlled evaluation comparing 3 conditions—unbounded baseline, graph-constrained framework, and framework with distilled principles—across 10 regulatory tasks (n=30 runs per condition, N=90 total). Performance was assessed using automated code-density metrics and a blinded LLM-as-a-judge evaluation protocol with 2 independent panels. Results: Graph-constrained skill loading significantly improved overall output quality (+0.47 on a 5-point scale; 95% confidence interval [CI] 0.12–0.80; P=.004), regulatory structure compliance (+0.63; P<.001), and terminology precision (+0.50; P=.01). The LLM-based compliance evaluation corroborated these findings, with a compliance margin of +0.64 (framework: 4.34 vs baseline: 3.70) and substantial interpanel agreement (Cohen κ=0.76; Pearson r=0.95). The constraint mechanism incurred a modest 12% increase in token cost. An exploratory condition adding distilled global principles showed slight performance degradation (−0.13), suggesting attention saturation from redundant constraints. Conclusions: Graph-constrained skill loading produces statistically significant improvements in regulatory compliance quality for clinical trial data generation, with favorable cost-efficiency ratios compared with general-purpose agentic approaches. The consistent improvements across multiple assessment tiers and the identification of boundary limitations (eg, metadata synthesis tasks) provide a foundation for future validation with real-world clinical trial data and practicing domain experts. All code and evaluation materials are publicly available.
Background: Artificial intelligence (AI) is increasingly integrated into prostate cancer diagnostics, with the potential to improve accuracy and efficiency. However, it also raises important questions...
Background: Artificial intelligence (AI) is increasingly integrated into prostate cancer diagnostics, with the potential to improve accuracy and efficiency. However, it also raises important questions about the conditions and barriers that may influence its successful implementation in this clinical context. Objective: This study examined how patients and healthcare professionals perceive the integration of AI in prostate cancer diagnostics, with particular attention to its impact on clinical relationships and the roles of patients and physicians. Methods: A sequential explanatory mixed-methods design was used. Quantitative data were collected through an online questionnaire administered to patients with localized prostate cancer (N=51). Descriptive analyses focused on perceived benefits, willingness to use AI, and associated concerns. Qualitative data were collected through focus groups and semi-structured interviews with patients (n=16) and physicians (n=11). Data were analyzed using iterative, inductive thematic analysis. Results: Quantitative findings showed that despite recognizing the potential benefits of AI, patients remained divided regarding the use of such tools in their own care. Qualitative findings suggest that this hesitation cannot be explained solely in terms of perceived performance or utility. Rather than simply reducing complexity in clinical decision-making, AI appeared to reconfigure the certainties on which trust within the patient–physician relationship is established. This reconfiguration was reflected across epistemic, ethical, and role-related dimensions. Patients emphasized difficulties in understanding AI-generated knowledge, whereas clinicians focused on issues of reliability, validation, and clinical relevance. Ethical concerns centered on responsibility, which was consistently attributed to physicians, while errors made by AI were perceived as less acceptable than those made by clinicians. Role-related uncertainties were reflected in ambivalent patient positions: while some participants sought more information to remain involved in decision-making, others preferred to rely on physicians, reflecting variation in how patients engage with complex clinical information. AI was generally viewed as a supportive tool rather than a replacement for clinical judgement, while its integration was associated with evolving professional roles, including increased demands for interpretation, communication, and oversight. Conclusions: The integration of AI in prostate cancer diagnostics is shaped not only by its technical performance, but by how it reconfigures trust within the patient–physician relationship. Rather than eliminating uncertainty, AI redistributes it across knowledge, responsibility, and social roles. Ensuring that AI contributes positively to clinical practice therefore requires careful attention to clinician oversight, communication, and the relational context in which decisions are made. Clinical Trial: NCT07074405 (ClinicalTrials.gov)
Background: Healthcare workers experienced significant mental health challenges, particularly anxiety, during the COVID-19 pandemic. Although social media became a primary source of information and co...
Background: Healthcare workers experienced significant mental health challenges, particularly anxiety, during the COVID-19 pandemic. Although social media became a primary source of information and connection, it was also a potential source of stress. The influence of social media on healthcare workers’ anxiety is not well-understood. Objective: This study examined associations between social media use and anxiety symptoms among healthcare workers during the COVID-19 pandemic. Methods: This study examined associations between social media use and anxiety symptoms among healthcare workers during the COVID-19 pandemic.
Methods: We conducted a cross-sectional analysis of data from the 2021 UC COVID study (N=427 healthcare workers). Anxiety symptoms were assessed using the Generalized Anxiety Disorder-2 (GAD-2). Social media use across five platforms (Twitter, Facebook, Instagram, other social media platforms, and other media sources) was evaluated using confirmatory factor analysis within a structural equation modeling framework. The confirmatory factor analysis supported a single latent factor representing overall social media use, with all platform indicators loading significantly (p=<0.01) with moderate loadings (0.33–0.59). Model fit was acceptable (χ²(5)=12.49, p=<0.01), indicating that the five observed variables coherently reflected a unified social media use construct. Logistic regression models estimated associations between overall social media use and anxiety symptoms, both unadjusted and adjusted for demographic, occupational, and health-related characteristics. Results: Of the 427 healthcare workers, Facebook was utilized the most with 54% of respondents utilizing the platform at least once a day. A total of 29% reported clinically relevant anxiety symptoms (GAD-2 ≥ 3). Overall, higher social media use was significantly associated with anxiety symptoms (OR=1.77, CI: 1.15-2.73). Older age was significantly associated with lessened anxiety symptoms (aOR=0.97, CI: 0.95–0.99). Healthcare workers with a history of mental health diagnoses reported higher levels of anxiety symptoms (aOR=2.38, CI: 1.38–4.09). Non-Hispanic, non-White healthcare workers reported fewer anxiety symptoms compared to White healthcare workers (aOR=0.49, CI: 0.27–0.91). Participants reporting higher income had significantly lower odds of anxiety symptoms than those in the lower-income group (aOR = 0.38, CI: 0.18–0.81). Conclusions: Social media use during the pandemic was associated with elevated anxiety symptoms among healthcare workers. However, the rapidly evolving digital landscape underscores the need for continued research. Future studies should include emerging social media sources (i.e., TikTok, Reddit, YouTube, etc.) and repeat factor analyses as digital behaviors shift over time. Longitudinal and mixed-methods approaches are necessary to understand patterns and methods of social media use, accounting for misinformation and disinformation, emotional involvement, and content types such as photos and videos. Larger and more diverse healthcare worker samples, stronger mental health measures (e.g., GAD-7, depression, and burnout scales), and analyses stratified by clinical role and work environment will be essential to guide interventions that support healthcare worker well-being in a post-pandemic era with new challenges.
Background: The high prevalence of sedentary lifestyles and non‑communicable diseases in Malaysia calls for scalable physical activity interventions. Hence, in this study, we leverage on the potenti...
Background: The high prevalence of sedentary lifestyles and non‑communicable diseases in Malaysia calls for scalable physical activity interventions. Hence, in this study, we leverage on the potential benefits of social media for exercise promotion, particularly Instagram. Objective: This pilot study examined the acceptability, observed changes, and predictors of improvement associated with an Instagram‑based exercise promotion among sedentary adults in Klang Valley, Malaysia. Methods: A total of 56 sedentary adults (34 females, 22 males) were recruited; 50 completed the 12‑week intervention (mean sedentary behaviour 7.30±2.75 hours/day; retention rate 89.3%). Participants joined a private Instagram page delivering cardiorespiratory‑focused exercise content every two days. Pre‑ and post‑intervention assessments included anthropometry, body composition (InBody 370), 6‑Minute Walk Test (6MWT), and Client Satisfaction Questionnaire‑8 (CSQ‑8). Results: Significant pre‑post changes were observed in body weight (mean change -2.05±2.88 kg, P<.001), BMI (-0.83±1.11 kg/m², P<.001), body fat percentage (-2.23±1.91%, P<.001), and 6MWT distance (67.82±40.81 m, P<.001). The mean total CSQ‑8 score was 27.02±4.91 (out of 32), indicating high satisfaction. Baseline body fat percentage, baseline 6MWT distance, and gender were associated with the degree of functional change (R²=0.71). Conclusions: This pilot study suggests that an Instagram‑based intervention is acceptable and may be associated with positive health changes among sedentary adults. These findings support the need for a definitive randomised controlled trial in the future.
Background: Medical documentation burden remains a significant driver of physician burnout1,2, particularly in high-volume environments like the pediatric emergency department. While ambient artifici...
Background: Medical documentation burden remains a significant driver of physician burnout1,2, particularly in high-volume environments like the pediatric emergency department. While ambient artificial intelligence (AI) scribing has shown promise in adult medicine settings in decreasing mental and documentation burden3,4, pediatric care involves unique triadic interactions between the physician, patient, and often several caregivers. This inherent situation might expose the AI software to an as-of-yet untested accuracy issue when a LLM is challenged to extract information from several patient sources. Objective: We conducted a 3-month pilot study at UCSF Benioff Children’s Hospital Oakland to evaluate the feasibility of this technology and early clinical perceptions by physicians in a dedicated pediatric urgent care setting. This was an exploratory effort meant to inform further hypotheses for a future formal prospective trial. Methods: We conducted a 3-month pilot study at UCSF Benioff Children’s Hospital Oakland to evaluate the feasibility of this technology and early clinical perceptions by physicians in a dedicated pediatric urgent care setting. This was an exploratory effort meant to inform further hypotheses for a future formal prospective trial. Results: Between April and August 2024, eight pediatric emergency physicians voluntarily utilized an AI scribe software (Ambience Healthcare, San Francisco, CA) after an hour training onboarding session. There are 32 providers in the group and 8 volunteered for the project. The AI scribing software generated drafts for the History of Present Illness, Review of Systems, and Physical Exam, which were then edited and accepted by the provider. At the time, Medical Decision Making was excluded from the AI-generated content. The software was used when the participants worked alone without assistance from trainees.
Measured outcomes: physician perceptions of these interactions and their concomitant AI-generated notes were evaluated by four 5-point Likert-scored questions. These questions were asked both just before beginning the use of the software and then after three months of its use. As this was an exploratory feasibility study, results are presented descriptively. In the three months of this trial, there were 320 patient encounters performed by the eight providers. Mean scores showed modest upward trends across all six parameters, with no metrics declining. The most notable shifts occurred in perceived documentation manageability and the ability to complete charting within a shift. (see Table 1)
Table 1: Mean Likert Scores of Physicians’ Perception (n=8)
Question Before Using AI Scribe software After 3-month’s use of AI Scribe software
My documentation is Manageable 2.25 2.
Work Feels Sustainable 2.13 2.25
Time spent on Documentation is easy 1.00 1.26
I complete most of my charting on shift 1.0 1.25
Scoring: 1, strongly disagree 5, strongly agree Conclusions: While limited by small sample size and potential selection bias among early adopters, these findings suggest that ambient AI is deemed by clinicians to be a feasible tool for capturing and notating the complex, multi-party dialogue inherent in pediatric care. These data serve as a foundation for future powered trials building off of these perceptions. We now will perform a larger prospective trial in a study evaluating AI scribing’s utility in a ‘triadic’ clinical encounter environment of Pediatric Emergency Care, using metrics typical of ambient listening technology looked at in recent prospective trials. These will include time in notes, ‘pajama time’, patient throughput, edit frequency and relative value units generated Clinical Trial: https://irb.ucsf.edu/
Background: Whole lung lavage (WLL) remains the standard treatment for patients with pulmonary alveolar proteinosis (PAP). However, WLL procedures are not fully standardized, and there is no global co...
Background: Whole lung lavage (WLL) remains the standard treatment for patients with pulmonary alveolar proteinosis (PAP). However, WLL procedures are not fully standardized, and there is no global consensus on the best approach, such as performing one-session versus two-session WLL. Objective: This study aimed to assess and compare the safety and effectiveness of these two methods in a real-world clinical environment. Methods: In this retrospective cohort study, we included patients with PAP who underwent WLL at a tertiary hospital in China from September 2009 to May 2024. Patients were categorized into one-session and two-session WLL groups. The primary outcome was the incidence of WLL-related complications. Secondary outcomes included improvements in ΔPaO2, procedure duration, the rate of extubation within 24 hours, length of hospital stay, and hospitalization costs. Results: A total of 26 patients with PAP underwent 37 WLL procedures, with 15 procedures in the one-session group (15 patients) and 22 procedures in the two-session group (11 patients). The incidence of WLL-related complications, including acid-base disorders, desaturation, and pleural effusions, did not significantly differ between the groups. However, compared to the two-session WLL group, the one-session group demonstrated significantly greater improvement in ΔPaO2 (14.20 [2.6, 29.2] mmHg vs 2.50 [-3.2, 5.45] mmHg, p=0.015) and shorter hospital stay (10 [7, 14] days vs 18 [10, 26] days, p=0.020). No significant differences were observed in the procedure duration, extubation rate within 24 hours, or hospitalization costs between the groups. Conclusions: For patients with PAP, one-session WLL offers comparable safety to two-session WLL, with significantly greater improvement in ΔPaO2 and shorter hospital stays, without increasing hospitalization costs. These results suggest that adopting one-session WLL may optimize patient outcomes and resource utilization in clinical practice.
Background: Auditory-cognitive training (ACT) combines auditory and cognitive exercises to improve listening and cognition in individuals with hearing difficulties, yet lacks standardized implementati...
Background: Auditory-cognitive training (ACT) combines auditory and cognitive exercises to improve listening and cognition in individuals with hearing difficulties, yet lacks standardized implementation and consistent evidence. Objective: This mixed evidence synthesis aimed to map ACT’s implementation characteristics and summarize its reported short-term and maintenance effects on trained and non-trained cognitive and auditory outcomes, including hearing functioning in daily life. Methods: We carried out a systematic search including Embase, Cochrane Library, PubMed, and APA PsycNet from inception to January 15, 2026. Studies evaluating integrated auditory–cognitive training interventions in older adults were included, covering RCTs, quasi-experimental, and pre–post intervention designs, with 12 articles included. Meta-analysis was restricted to RCTs with sufficient available data. Results: Results showed that ACT yields significant short-term improvements in speech-in-noise (SIN) recognition (SMD = −0.25, 95% CI: −0.47 to −0.04, p = 0.02) and memory (SMD = 1.02, 95% CI: 0.53 to 1.51, p < 0.001). The SIN improvement (0.25 dB) is modest and below clinical meaningfulness. Effects on pure-tone audiometry, subjective hearing function, attention, working memory, executive function, and global cognition were limited and nonsignificant. Current evidence is limited by unstandardized training dosage/duration, limited linguistic generalizability across non-English languages, and insufficient assessment of patient-reported real-life listening outcomes. Conclusions: ACT produces consistent short-term improvements in speech-in-noise recognition and memory-related functions. Integrated auditory-cognitive training. Future research should focus on standardized training protocols, real-life hearing assessments, multilingual tool adaptation, and long-term outcome monitoring to advance the clinical application and generalizability of ACT.
As artificial intelligence (AI) systems approach and sometimes exceed human diagnostic performance on medical imaging tasks, a foundational question for clinical informatics is what sustains the safet...
As artificial intelligence (AI) systems approach and sometimes exceed human diagnostic performance on medical imaging tasks, a foundational question for clinical informatics is what sustains the safety value of human oversight once individual accuracy is no longer the differentiator. We argue that the operative parameter is not accuracy but decorrelation: the degree to which human and AI errors differ on the same cases. The classifier-combination literature establishes that pairwise error correlation (ρ) governs whether additional overseers meaningfully extend the vetting ceiling; when ρ approaches one, the effective number of independent overseers collapses to one regardless of how many systems are nominally deployed. We propose that ρ should be treated as a first-class quantity in the informatics of algorithmic safety. Drawing on published agreement data across 22 modality-task combinations in ophthalmology, radiology, and pathology, we offer illustrative cross-domain patterns suggesting a qualitative association between interpretive complexity (an author-proposed heuristic we call interpretive dimensionality) and concordance, and we observe that concordance appears to rise within fixed tasks over time as AI absorbs the relevant feature space. We call this erosion decorrelation wasting and contrast it with decorrelation injection, in which a novel substrate or task specification transiently lowers ρ. These patterns motivate a multi-lever Strategic Cognitive Reserve combining (1) maintaining clinicians in the diagnostic loop, (2) redesigning human-AI interaction, (3) exploiting the broader clinical context window, and (4) periodically introducing novel substrates. The framework is hypothesis-generating rather than prescriptive; we close with testable predictions requiring paired case-level error data. Multimedia Appendices 1 and 2 provide the task-level source data and the formal dynamics.
Background: The Functional Independence Measure (FIM) is a standard functional assessment tool in rehabilitation medicine; however, in routine clinical practice, formal assessments are often conducted...
Background: The Functional Independence Measure (FIM) is a standard functional assessment tool in rehabilitation medicine; however, in routine clinical practice, formal assessments are often conducted only at limited intervals, making it difficult to capture patients’ functional recovery trajectories during hospitalization at a high temporal resolution. In contrast, free-text clinical notes routinely documented by physical therapists, occupational therapists, and speech-language pathologists contain rich observational information on patients’ functional status, but this information has not been systematically utilized for quantitative assessment. Objective: The aim of this study was to estimate FIM scores at multiple time points from Japanese free-text rehabilitation notes using a large language model (LLM), to evaluate estimation performance by disease and therapy type, and to analyze biases in the distribution of functional information inherent in clinical notes. Methods: We retrospectively analyzed free-text notes written by physical therapists, occupational therapists, and speech-language pathologists for patients hospitalized with cerebral infarction, hip fracture, or vertebral fracture at Saiseikai Moriyama Municipal Hospital between 2019 and 2024. Using zero-shot in-context learning, an LLM was employed to estimate scores for all 18 FIM items at each time point. The estimated scores were aligned with ground-truth FIM assessments recorded on the same day and evaluated using mean absolute error (MAE), root mean squared error (RMSE), and weighted kappa. In addition, stratified analyses based on admission FIM scores and therapy-specific FIM item coverage analyses were conducted. Results: Overall, the FIM scores estimated by the LLM demonstrated the feasibility of estimating FIM scores from rehabilitation notes. Estimations based on occupational therapy notes showed consistently lower MAE and higher weighted kappa across all disease groups. In contrast, speech-language pathology notes contained limited information on motor items, resulting in relatively larger estimation errors. Patients with lower admission FIM scores tended to exhibit larger estimation errors, suggesting that differences in observational context across therapy records influenced estimation performance. Conclusions: Overall, the FIM scores estimated by the LLM demonstrated the feasibility of estimating FIM scores from rehabilitation notes. Estimations based on occupational therapy notes showed consistently lower MAE and higher weighted kappa across all disease groups. In contrast, speech-language pathology notes contained limited information on motor items, resulting in relatively larger estimation errors. Patients with lower admission FIM scores tended to exhibit larger estimation errors, suggesting that differences in observational context across therapy records influenced estimation performance.
This study demonstrates that FIM scores can be estimated from free-text rehabilitation notes using the LLM. The proposed approach has the potential to complement sparsely observed FIM assessments without increasing bedside evaluation burden, enabling higher-frequency monitoring of functional recovery trajectories. Time-series functional assessments estimated by LLMs may further support early prediction of discharge outcomes and the development of personalized rehabilitation planning.
Background: Artificial intelligence (AI) enabled voice electronic medical records (EMRs) are increasingly promoted as tools to reduce clinician documentation burden; however, empirical evidence from m...
Background: Artificial intelligence (AI) enabled voice electronic medical records (EMRs) are increasingly promoted as tools to reduce clinician documentation burden; however, empirical evidence from multilingual, resource-constrained health systems in sub-Saharan Africa remains limited. Objective: This study examined healthcare professionals’ perceptions, anticipated benefits, and concerns regarding AI voice-enabled EMRs in Ethiopia to inform context-sensitive implementation strategies. Methods: We conducted a qualitative, multi-site study using semi-structured written responses from 43 Ethiopian healthcare professionals recruited via purposive and maximum variation sampling between January and February 2025. Data were analyzed in Taguette using a hybrid deductive-inductive approach integrating three complementary frameworks: the Consolidated Framework for Implementation Research (CFIR), the Technology Acceptance Model (TAM), and Normalization Process Theory (NPT). Analytical rigor was strengthened through independent dual coding, structured reconciliation, reflexive memos, and a version-controlled audit trail. Results: Three overarching themes were identified. First, participants anticipated clear clinical benefits including reduced typing burden, improved documentation continuity, and enhanced patient interaction yet expressed substantial concerns about automation errors, accent-related transcription failures, and persistent infrastructural instability. Second, usability barriers including interface complexity, inadequate training, and digital anxiety shaped technology acceptance across cadres. Third, ethical and governance concerns particularly regarding data confidentiality, unclear consent procedures, and fear of surveillance emerged as major determinants of trust. Cross-framework synthesis revealed that adoption readiness was jointly shaped by organizational capacity, usability perceptions, emotional-cognitive responses, and evolving workflow expectations. Conclusions: Successful implementation of AI voice-enabled EMRs in Ethiopia requires coordinated investments in digital infrastructure, locally adapted language models, strengthened data governance, and iterative user onboarding. These findings underscore the urgency of context-sensitive and ethically grounded approaches when deploying speech-based AI in low-resource health systems. Clinical Trial: none
Background: Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of interest, recurrent thoughts of death, and significant physical and cognitive symptoms. Despite effec...
Background: Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of interest, recurrent thoughts of death, and significant physical and cognitive symptoms. Despite effective treatment, up to one-third of patients relapse within months after discharge from depression care, revealing a critical gap in post-discharge surveillance, especially in rural areas with limited access to care and follow-up engagement. The Collaborative Care Model (CoCM), a widely adopted framework for managing depression in primary care, has improved detection and treatment outcomes; however, monitoring symptom recurrence after patients leave structured care still remains challenging. Digital technologies, especially smartphones, offer a promising way to close this gap by capturing passive behavioral and physiological data, including psychomotor activity, sleep, movement, social interaction, and light exposure. Building on prior work showing that passively sensed data can capture depression severity, mood fluctuations, and antidepressant use, we propose developing a clinically integrated digital biomarker to predict depression recurrence. Objective: (1) Test whether patterns in smartphone sensor features predict next-month recurrence of depressive symptoms in post-discharge CoCM patients with an AUC ≥ 0.8; and (2) to evaluate whether Electronic Health Record (EHR) integration of a passive sensing–based prediction model is associated with reduced MDD recurrence over six months compared to usual care. Methods: First, we will recruit up to 120 patients who have been enrolled within the Collaborative Care model to install the HIPAA-compliant MoodTriggers app on their personal smartphones to passively collect multimodal sensor data and complete monthly PHQ-9 assessments for six months. ConvLSTM models will predict next-month PHQ-9 scores, and Shapley values will identify influential features. Acceptability will be assessed through participation metrics and interviews. Second, a randomized trial of up to 200 patients will test effectiveness by evaluating whether EHR-integrated alerts based on the digital biomarker reduce depressive symptom recurrence over six months following CoCM discharge. Results: Funded 2025–2029, this project aims to create a scalable, clinically embedded digital biomarker for post-discharge depression care. Conclusions: N/A Clinical Trial: The study procedures have been registered on clinicaltrials.gov at NCT07174557.
Background: Transition to practice can be challenging for new-to-practice nurse practitioners (NPs) and physician assistants/associates (PAs). Internationally, there has been an increase in the number...
Background: Transition to practice can be challenging for new-to-practice nurse practitioners (NPs) and physician assistants/associates (PAs). Internationally, there has been an increase in the number of postgraduate training programs across health care settings. A scoping review found that outcomes were reported in 60% of 216 articles. However, there is no evaluation and synthesis of associated program outcomes. Objective: By building upon earlier work, the purpose of this systematic review is to assess the evidence quality of the literature specifically reporting on program-level outcomes for NP and PA postgraduate training programs. We will appraise the rigor of outcomes measurement, analysis, and reporting. Methods: This review will follow the JBI approach for systematic reviews. MEDLINE (PubMed), CINAHL (EBSCOhost), Cochrane Library (Wiley) Cochrane Central Register of Controlled Trials, Social Science Database (ProQuest), and Health Source: Nursing/Academic Edition will be searched for articles published in peer-reviewed journals since 1990 and in English. Studies on NP and PA postgraduate training programs (residencies, fellowships, onboarding) reporting program-level outcomes (job placement, turnover and retention, productivity, and costs of programs) will be included. Studies using quantitative and mixed-methods study designs will be included. Conference abstracts, editorials/opinions, dissertations/theses, reviews, and qualitative studies will be excluded. Screening, data extraction, and critical appraisal will be conducted by 2 independent reviewers. Relevant data will be extracted, and the level of rigor of each study will be established using standardized critical appraisal tools. Results: This review began in June 2025, and searches of databases were completed in October 2025. The title and abstract screening stage was completed December 2025. As of May 2026, we have started full text screening. Conclusions: Understanding the quality of outcomes reporting of post-graduate training programs for NPs and PAs will be valuable to researchers, administrators, and clinicians, as findings can inform future program development, implementation, and evaluation. Clinical Trial: PROSPERO 2025 CRD420251091013
Population-based cancer screening programmes are designed to enable early detection and timely treatment of selected cancers, with the ultimate aim of reducing morbidity and mortality; yet participati...
Population-based cancer screening programmes are designed to enable early detection and timely treatment of selected cancers, with the ultimate aim of reducing morbidity and mortality; yet participation remains critically low in the Campania region of southern Italy. Within the MIRIADE project, previous studies identified psychosocial antecedents of cervical, breast and colorectal cancer screening adherence (attitude, subjective norms, perceived behavioural control, self-identity, anticipated regret, action and coping planning). Based on these variables, a profiling study identified data-driven psychosocial subgroups among citizens eligible for colorectal cancer screening (CRCS; three profiles) and female-only cancer screenings (FOCS; four profiles), and one study developed and validated a framework for multiple reasons for screening participation. Building on these empirical foundations, the present paper describes a protocol for a randomized controlled trial (RCT) comparing the efficacy of targeted and tailored persuasive messaging strategies—and a usual-care control—for promoting screening participation.
The T0 baseline assessment was conducted as part of the companion profiling study, during which participants completed psychosocial measures and ranked their personal reasons for screening participation, after which they were profiled via Reduced k-means. At T1, participants are randomly allocated to: (a) a targeted condition, receiving a persuasive message adapted to their profile’s psychosocial characteristics; (b) a tailored condition, receiving a message that additionally integrates the individual’s most personally relevant reason for screening; or (c) a usual-care control. The primary outcome is post-intervention screening intention (T1). A six-month behavioural follow-up (T2) assesses self-reported screening uptake. Message effectiveness and personal relevance are evaluated as process outcomes. Pre-tests with a subsample ensure message adequacy.
This protocol provides a theory-grounded, empirically informed framework for experimental comparison of targeted and tailored interventions for both colorectal and female-only cancer screenings. It employs data-driven profiling, integrates individually relevant reason-based content, and is designed for scalability within regional public health infrastructure.
Protocol version: Version 1.0, 15/05/2026.
Background: Older Chinese American adults with limited English proficiency frequently rely on culturally and linguistically tailored online media platforms for dementia education and health literacy. ...
Background: Older Chinese American adults with limited English proficiency frequently rely on culturally and linguistically tailored online media platforms for dementia education and health literacy. Existing digital health evaluation frameworks primarily assess the internal quality and accuracy of educational materials while overlooking the recommendation environments through which users encounter health information. Commercial recommendation systems may expose users to medically unverified or commercially motivated health content adjacent to evidence-based dementia education resources. Objective: This protocol establishes a reproducible computational framework for auditing recommendation environments surrounding Cantonese-language dementia education videos on YouTube. The study introduces the concept of algorithmic noise adjacency, defined operationally as the concentration of recommendation nodes whose informational utility diverges from evidence-based dementia education objectives within local recommendation neighborhoods. Methods: The protocol uses an automated socio-technical audit framework centered on 2 Cantonese-language dementia education videos previously examined in longitudinal digital outreach research. A Selenium WebDriver pipeline with a headless Chromium browser architecture and fingerprinting mitigations will simulate 1200 independent browsing sessions distributed uniformly across a 90-day window. Sessions will be routed through rotating residential proxy infrastructure localized to Southern California Chinese American communities. During each session, the top 10 sidebar recommendation videos adjacent to the source clinical asset (the anchor video) will be extracted.
Recommendation metadata will undergo structured semantic classification into 5 mutually exclusive categories: (A) verified public health and clinical infrastructure; (B) diaspora culture and entertainment; (C) commercially motivated health content lacking established clinical validation; (D) unverified alternative medicine; and (E) ambiguous or unclassified baseline noise. Double-coding and consensus adjudication of a random 10% sample will be used to establish inter-rater reliability. Generalized linear mixed-effects models (GLMM) with logit link functions, session-level random intercepts, and reciprocal rank slot-position weighting will evaluate recommendation characteristics while accounting for clustering within browsing sessions. Primary outcome measures include the Noise Adjacency Ratio (NAR), slot-weighted NAR, recommendation recurrence density, and neighborhood entropy. Secondary analyses will evaluate temporal recommendation drift and cross-session recommendation variability. Results: Protocol development and pilot automation testing were finalized in May 2026. Automation stress testing, semantic calibration, and proxy validation are scheduled for September 2026. Data collection is projected to occur over a 90-day interval following deployment of the finalized extraction architecture. Conclusions: This protocol proposes a structural informatic auditing framework for minority-language digital health ecosystems. By shifting evaluation from isolated content quality toward surrounding recommendation neighborhoods, the study may provide digital health researchers with a reproducible methodology for characterizing health-information exposure conditions among linguistically isolated populations.
Background: Artificial intelligence (AI) is increasingly influencing health care and dentistry, particularly in education, radiographic interpretation, literature search, treatment planning support, d...
Background: Artificial intelligence (AI) is increasingly influencing health care and dentistry, particularly in education, radiographic interpretation, literature search, treatment planning support, documentation, and patient communication. However, the successful implementation of AI in dental education and clinical practice depends not only on technological development, but also on users’ knowledge, attitudes, readiness, perceived benefits, ethical concerns, and educational needs. Objective: This study aimed to assess knowledge, attitudes, readiness, perceived benefits, perceived barriers, ethical concerns, and educational needs related to AI in dentistry among dental students, recent dental graduates, and young dentists from Kosovo, Albania, and North Macedonia. Methods: A cross-sectional formative survey was conducted using an anonymous online questionnaire. The target population included dental students, recent dental graduates, practicing dentists, and residents or specialists in dentistry. The questionnaire assessed demographic and professional characteristics, previous exposure to AI, knowledge about AI in dentistry, attitudes toward AI, readiness to use AI, perceived benefits, perceived barriers and ethical concerns, and educational needs. Likert-scale items were coded from 1 to 5, and domain scores were calculated as mean values. Descriptive statistics were used to summarize participant characteristics, AI exposure, and domain scores. Results: A total of 235 participants were included in the analysis. Most participants were female (155/235, 66%), and the mean age was 25.2 years. Overall, 206 participants (87.7%) had previously heard of AI, and 164 participants (69.8%) had used AI-based tools such as ChatGPT, Gemini, Copilot, or similar platforms. However, only 31 participants (13.2%) reported receiving formal education or training about AI in dentistry or health care. The highest mean domain score was observed for educational needs (mean 4.31, SD 0.21), followed by barriers and ethical concerns (mean 4.25, SD 0.18), attitudes toward AI (mean 4.17, SD 0.20), and perceived benefits (mean 4.04, SD 0.21). Lower scores were observed for readiness to use AI (mean 3.78, SD 0.24) and knowledge about AI in dentistry (mean 3.55, SD 0.38). Conclusions: Dental students and young dentists from Kosovo, Albania, and North Macedonia demonstrated high awareness of AI and generally positive attitudes toward its future role in dentistry. However, formal AI education was uncommon, and self-reported knowledge and readiness were lower than attitudes, perceived benefits, and educational needs. These findings suggest a gap between interest in AI and structured preparedness for its safe use in dental education and clinical practice. AI implementation in dentistry should be accompanied by formal training, validated tools, ethical guidance, data protection measures, and clear professional guidelines.
Background: Latina pregnant women and mothers of infants in the United States experience elevated risk for perinatal anxiety, stress, and depression alongside persistent barriers to culturally and lin...
Background: Latina pregnant women and mothers of infants in the United States experience elevated risk for perinatal anxiety, stress, and depression alongside persistent barriers to culturally and linguistically responsive care. Artificial intelligence (AI)-powered digital health tools offer a potentially scalable approach to address these gaps, yet evidence on their acceptability and effectiveness for this population remains limited. Objective: To evaluate the feasibility, acceptability, and preliminary efficacy of Rosie, a Spanish-language AI-powered chatbot designed with and for Latina mothers, on maternal mental health and healthcare utilization outcomes. Methods: We conducted a mixed-methods pilot randomized controlled trial with 30 Spanish-speaking Latina participants who were pregnant or parenting an infant younger than six months. Participants were randomized 1:1 to the Rosie intervention or a comparison Book Club condition that provided monthly children’s board books. Surveys administered at baseline and three-month follow-up assessed depression (PHQ-9), anxiety (GAD-7), perceived stress (PSS), and healthcare utilization. Quantitative analyses examined within- and between-group changes, and qualitative feedback assessed acceptability and user experience. Results: Baseline demographic characteristics and mental health scores were generally comparable between groups. Over three months, participants in the Rosie group demonstrated statistically significant within-group reductions in anxiety (mean change -2.00, p = 0.02) and perceived stress (mean change -12.20, p = 0.003). Depression scores declined modestly in both groups, with no significant between-group differences. No significant differences were observed in maternal emergency room utilization or adherence to well-baby visits. Engagement with Rosie was high, and qualitative feedback indicated that participants found the chatbot useful and supportive. Conclusions: This pilot study suggests that a culturally and linguistically tailored AI-powered chatbot is feasible, acceptable, and shows preliminary promise for reducing anxiety and stress among Latina mothers during the perinatal period. Larger, adequately powered trials are needed to determine effectiveness and inform integration of AI-enabled tools into maternal and child health care to advance health equity.
Background: Hospital-based violence intervention programs (HVIPs) reduce the long-term impacts of violence by linking survivors to resources that address social needs. However, HVIP effectiveness is o...
Background: Hospital-based violence intervention programs (HVIPs) reduce the long-term impacts of violence by linking survivors to resources that address social needs. However, HVIP effectiveness is often hindered by disorganized communication between Violence Prevention Professionals (VPPs) and their clients. Mobile health (mHealth) tools have the potential to improve communication, but none have been co-designed with survivors of violence to meet their unique needs. This study extends prior mHealth application (app) development aligned with VPP workflows by applying a Human-Centered Design (HCD) approach that centers the client-facing user experience, ensuring the app reflects clients' lived realities, preferences, and barriers to engagement. Objective: This study aims to design an mHealth app to improve communication and access to resources for survivors of violence using HCD and iterative prototyping methods. Methods: We developed the client-facing component using HCD and integrated three Ideation Phase methods: Participatory Design, Low-Fidelity Prototype Testing, and High-Fidelity Prototype Testing. Participatory Design included semi-structured interviews, co-design activities and card sorting activities with clients, followed by inductive qualitative analysis to inform the initial low-fidelity design. Low-Fidelity Prototype Testing included guided user-testing interviews about static wireframes, followed by inductive qualitative analysis to inform the high-fidelity design. High-Fidelity Prototype Testing utilized user-testing interviews and the Rapid Iterative Testing and Evaluation (RITE) method. An impact ratio was calculated to quantify the proportion of usability issues successfully addressed through iterative design changes. Results: Fifteen unique clients participated across 16 testing sessions. Participatory Design identified six key themes: (1) trust, (2) personal connection, (3) guidance in meaningful decision-making, (4) client empowerment, (5) intuitive and comprehensive design, and (6) dynamic journey and sense of progress. Low-Fidelity Testing reinforced these themes and identified two additional themes: (7) app personalization and (8) tailored resource curation. High-Fidelity Testing reinforced themes 1–8 and uncovered seven more: (9) celebration of client successes, (10) standardization of verbiage and design choices, (11) control over boundaries, (12) legitimization of experience, (13) accessibility of VPPs, (14) gratitude expression, and (15) integration with medical care. RITE identified 98 actionable usability issues, of which 89 were addressed, yielding a 91% impact ratio. The final app expanded from 12 initial low-fidelity wireframes to 32 refined high-fidelity wireframes. Conclusions: This study used HCD and RITE to develop a client-facing mHealth app tailored to survivors of violence receiving HVIP case management services. The 91% impact ratio demonstrates RITE's effectiveness in rapidly prioritizing and resolving usability issues. The final app integrates features that enhance usability, empowerment, connection, decision-making, and support while ensuring accessibility and consistency. This approach may be adaptable to other mHealth tools for specialized populations, though further research with larger samples and in other settings is needed to assess generalizability.
Background: Obesity represents a major global public health challenge. Digital behavior change interventions (DBCIs) have emerged as scalable, technology-enabled strategies for delivering evidence-bas...
Background: Obesity represents a major global public health challenge. Digital behavior change interventions (DBCIs) have emerged as scalable, technology-enabled strategies for delivering evidence-based behavioral interventions using behavior change techniques (BCTs). However, current evidence remains fragmented regarding global research trends and the multi-dimensional distribution of BCTs within DBCIs across populations, intervention types, and health outcomes. Objective: This study aims to explore DBCIs among overweight and obese adults, focusing on temporal trends in research and patterns of BCTs utilization. Methods: A combined bibliometric analysis and scoping review was conducted based on publications from the Web of Science Core collection up to 2025. Publication trends, global collaboration pattern, digital technologies, BCT usage, intervention outcomes, and evidence gaps were systematically analyzed. Results: Research on DBCIs for obesity has grown rapidly since 2007, with leading contributions from high-income countries, accompanied by strengthened international collaboration and a gradual shift toward interdisciplinary and integrated digital health approaches. BCTs are typically applied in combination, with self-monitoring (79.4%) and goal setting (73.7%) as the core techniques, mainly targeting diet and physical activity. Their distribution varies significantly across digital technology types, targeted behaviors, clinical outcomes, and comorbid conditions. Conclusions: Current DBCIs prioritize behavioral self-regulation and cardiometabolic risk improvement. To enhance long-term sustainability and real-world effectiveness, future interventions should adopt a theory-driven framework, integrate psychological and physiological components, and implement personalized adaptive designs. Furthermore, integrating a big data-enabled systems paradigm of behavior will enable more dynamic, mechanism-informed, and proactive DCBIs for obesity management.
Background: Large language models (LLMs) are increasingly being incorporated into clinical practice for tasks such as rapid evidence retrieval, documentation support, and clinical decision-making. How...
Background: Large language models (LLMs) are increasingly being incorporated into clinical practice for tasks such as rapid evidence retrieval, documentation support, and clinical decision-making. However, real-world data on clinician adoption, trust, verification practices, and perceived ethical or security concerns remain limited. Objective: To evaluate real-world use of LLMs among clinicians at a large academic medical center and assess perceptions regarding usefulness, reliability, ethical appropriateness, data security, and verification practices. Methods: We conducted a web-based cross-sectional survey of clinicians within the Department of Medicine at Mayo Clinic (Rochester, Minnesota, USA) between December 2025 and February 2026. Eligible participants included attending physicians, nurse practitioners, physician assistants, residents, and fellows. The survey evaluated awareness and clinical use of LLMs, frequency and context of use, perceived usefulness and ease of use, trust and data security perceptions, verification practices, behavioral intention to use, and comparisons with traditional point-of-care reference tools. Descriptive statistics were used to summarize responses, and associations between years of clinical experience and LLM use were assessed using chi-square tests. Results: A total of 254 clinicians completed the survey (response rate 11.6%). Awareness of LLMs was high (248/254, 97.6%), and 227/246 (92.3%) respondents aware of LLMs reported clinical use. Daily use was reported by 103/222 (46.4%) respondents, while 196/222 (88.3%) reported at least weekly use. OpenEvidence was the most commonly used clinical platform (187/227, 82.4%). LLMs were primarily used for rapid evidence retrieval (174/227, 76.7%), support in complex clinical scenarios (84/227, 37.0%), and guideline summarization (75/227, 33.0%). Additional reported uses included drafting clinical communications, summarizing patient histories, educational activities, and research-related tasks. Most respondents considered LLM use ethically appropriate (202/220, 91.8%) and regarded outputs as generally reliable, although confidence in data security was lower (115/217, 53.0%). Verification practices varied, with 120/217 (55.3%) reporting always or often verifying outputs. Many respondents rated LLMs more favorably than traditional reference tools such as UpToDate and PubMed. Reported use did not differ significantly across years of clinical experience. Conclusions: LLMs were widely used among respondents for both clinical and administrative tasks at a large academic medical center. Clinicians reported frequent use across diverse workflows, particularly for rapid information retrieval and support with complex clinical questions. Although perceptions of ethical appropriateness and usefulness were generally favorable, variability in verification practices and lower confidence in data security highlight the need for institutional guidance, governance frameworks, and education to support safe and consistent use of LLMs in clinical practice.
Background: Autonomic dysfunction is common post-concussion and may be compounded by inactivity. Accordingly, physical activity progression is an important component of concussion rehabilitation. Fitb...
Background: Autonomic dysfunction is common post-concussion and may be compounded by inactivity. Accordingly, physical activity progression is an important component of concussion rehabilitation. Fitbit devices can provide objective longitudinal data on physical activity, physiologic response to activity, and sleep. However, participants must have adequate adherence to device wear to provide accurate data. The threshold for adequate adherence may change based on the research question, making it important to understand how adherence changes with different thresholds. Further, it is unclear how much staff support (i.e., reminders) may be required to maintain an adequate level of adherence. Objective: (1) Evaluate adherence to Fitbit device wear during daytime and nighttime using 3 different thresholds (50%, 75%, and 90% wear time) over 13 weeks in adolescents presenting for specialty concussion care and (2) report on the number of reminders required to achieve the observed level of adherence. Methods: Eighty adolescents were recruited from a specialty concussion clinic. Participants were asked to wear a Fitbit Sense 2 device day and night for 1 year. We analyzed data collected in the first 13 weeks. We used 3 different adherence thresholds: 50%, 75%, and 90%. For each threshold, a day or night was considered valid if the percentage of minutes with recorded heart rate data was greater than or equal to the defined threshold. We fit two robust linear mixed-effects models with number of valid days or valid nights as the outcome, a fixed effect for time (week), a fixed effect for threshold (50%, 75%, and 90%), a time by threshold interaction, and a random slope and intercept for participant. We also calculated the percentage of participants who required a reminder to wear and/or synchronize the device and the average number of reminders sent per participant across the study period. Results: Seventy participants were included in the analyses. We found that over 13 weeks, adolescents had average daily adherence ranging from 3.0-4.4 days/week (42%-63%) and nightly average adherence ranging from 3.6-3.9 nights/week (51%-56%). For all thresholds, adherence was highest during the first week. The number of valid wear days (β=-0.2, p<0.0001) and nights (β=-0.2, p<0.0001) decreased each week, regardless of the adherence threshold used. Fifty participants (71%) required at least 1 reminder. Overall, an average of 3.1 reminders were sent per participant across the study period. Conclusions: We found that adolescents presenting for specialty concussion care had adequate adherence to Fitbit device wear over 13 weeks. Approximately three quarters of the participants required at least 1 reminder from the study team to wear and/or synchronize their Fitbit device. This suggests that collecting longitudinal data with a Fitbit device is feasible in adolescents presenting for specialty concussion care but requires reminders to promote adherence.
Background
Occupational stress and burnout are pervasive among nurses, adversely affecting their well-being, job satisfaction, and the quality of patient care. Mindfulness-Based Resilience Training h...
Background
Occupational stress and burnout are pervasive among nurses, adversely affecting their well-being, job satisfaction, and the quality of patient care. Mindfulness-Based Resilience Training has emerged as a potential intervention to mitigate these challenges.
Objective
The study proposes to evaluate the effectiveness of an 8-week Mindfulness-Based Resilience Training program on occupational burnout, stress, coping strategies and biophysiological parameters among staff nurses at a tertiary care centre.
Methods
This randomized controlled trial will recruit registered nursed from a tertiary care centre with more than 1 year of experience. Eligible participants will be randomly assigned to either the intervention group or control group. The Mindfulness-Based Resilience Training comprises of weekly 90-minute sessions over 8 weeks, integrating mindfulness practices, cognitive restructuring and resilience-building techniques. The outcomes include occupational burnout, stress levels, coping strategies and biophysiological parameters. Data will be collected at baseline, immediately post intervention (post-test- 1) and at 12-weeks (post-test-2). Descriptive statistics, independent t-tests, and repeated measures ANOVA will be used to analyse the data.
Results
The study is currently in the participant recruitment and data collection phase. Baseline assessments have been initiated, and post-intervention evaluations will be conducted as per protocol. The findings are expected to provide evidence on the effectiveness of Mindfulness-Based Resilience Training among staff nurses.
Conclusion
It is hypothesized that the Mindfulness-Based Resilience Training program will significantly reduce occupational burnout and perceived stress, enhances adaptive coping strategies and improves biophysiological parameters among staff nurses. Results may support the integration of Mindfulness-Based Resilience Training into workplaces as a wellness initiative for nurses.
Trail Registration: The study has been registered at Clinical Trials Registry of India (CTRI/2025/03/083718; Registration date: 28/03/2025)
Keywords: Burnout, coping skills, nurses, psychological wellbeing, perceived stress scale
Background: Sedentary behavior (SB) is prevalent among early adolescents and young adults (eAYAs; ages 12–21 years) with acute lymphoblastic leukemia (ALL) and contributes to adverse cardiometabolic...
Background: Sedentary behavior (SB) is prevalent among early adolescents and young adults (eAYAs; ages 12–21 years) with acute lymphoblastic leukemia (ALL) and contributes to adverse cardiometabolic and health-related quality-of-life (HRQoL) outcomes. However, SB-focused interventions during active therapy are lacking. Objective: This trial aimed to evaluate the feasibility and acceptability of a 10-week, multi-component mobile health (mHealth) intervention among eAYAs receiving maintenance therapy for ALL. Methods: This 12-week, single-arm study enrolled 20 eAYAs with ALL receiving maintenance therapy. The 10-week intervention included a wearable activity tracker with inactivity-triggered prompts, individualized coaching sessions, and an app-based peer support group. The intervention was integrated into routine maintenance therapy by aligning the study timeline, visits, and procedures with a single maintenance therapy cycle. Primary endpoints were feasibility (defined by participant retention) and acceptability (via exit surveys and interviews). Secondary endpoints included pre-post intervention changes in device-measured and self-reported SB; exploratory analyses evaluated changes in cardiometabolic biomarkers and HRQoL. Results: Among the 20 participants, there was a 95% retention rate and a high level of intervention acceptability, with 95% reporting the intervention helped reduce SB and 100% recommending it to peers with cancer. Although statistically significant changes in device-measured sedentary time were not detected, there were notable decreases in prolonged sedentary bouts of 1 hour or more (−22.5 min/day, P=.59) and increases in daily steps (+49.5 steps/day, P=.39). Self-reported sitting time significantly decreased (−154 min/day, P=.006). No statistically significant changes were detected in cardiometabolic biomarkers; however, significant improvements were observed in HRQoL domains of physical functioning (+3.1, P=.04) and sleep/rest fatigue (+8.3, P=.05). Conclusions: This novel SB intervention met predetermined feasibility and acceptability criteria and demonstrated potential to reduce SB among eAYAs receiving ALL maintenance therapy, supporting further evaluation in larger trials. Clinical Trial: ClinicalTrials.gov: NCT06182163.
Background: What is a tumor, viz. how to define it? This seemingly naïve question has for years nagged at us as pathologists or other medical professionals who study cancer. This is because tumors ar...
Background: What is a tumor, viz. how to define it? This seemingly naïve question has for years nagged at us as pathologists or other medical professionals who study cancer. This is because tumors are defined differently in the literature from different viewpoints, such as from the slants of evolution, morphology, clinical manifestation, etc., and whether some outgrowths such as keloids are neoplastic or not are still debatable. Objective: This essay aims to present our definitions of outgrowths and cell deaths, and then to propound our idea that more researches should be put on benign neoplasms. Methods: We summarized key pathological features of cellular outgrowths to distinguish neoplastic lesions from non-neoplastic ones and summarized descriptions of cell deaths from the literature to bolster our viewpoint on apoptosis. We also present features of, and differences between, benign and malignant neoplasms, which lead to the inference that studies on tumorigenesis should first be focused on the benign neoplasms. Results: Benign neoplastic cells are immortal and autonomous, and autonomy can be manifested in either cellular replication or cellular function. Malignant cells have additional attributes, including decreased differentiation, more epigenetic and genetic alterations, invasiveness, metastatic potential, therapeutic refractoriness, etc. Of these traits, however, only cellular immortality and autonomy are unique and are indispensable criteria for rating a neoplasm, whereas the others can all be discerned in certain normal cells. the apoptosis originally defined by Kerr et al. is evolutionarily developed to remove obsolete or redundant cells like hyperplastic ones, but some neoplastic cells may also die of apoptosis because they retain certain stalwart to their parental organism. Efforts of the research fraternity have hitherto been put mainly on the malignancies, which have too many tangled alterations for researchers to disentangle. Conclusions: Tumorigenesis should probably be studied in two steps: The first is to Identify the cellular or molecular alterations that are the proximate causes for cellular immortality and autonomy, for which benign neoplasms or cell lines may be better models because they have many fewer alterations than their malignant counterparts. The second step is to study the malignant properties using malignant neoplasms or cell lines as model systems.
Background: Fragmented care across sectors inadequately addresses the complex care needs of children with special healthcare needs (SHCN), increasing the risk of adverse health and developmental outco...
Background: Fragmented care across sectors inadequately addresses the complex care needs of children with special healthcare needs (SHCN), increasing the risk of adverse health and developmental outcomes and placing a burden on caregivers. Although integrated care may reduce unmet needs and improve care quality, its implementation remains constrained by limited evidence on patient journeys, challenges in care delivery, and the costs associated with care practices. Objective: The Pediatric Integrated Care (PICAR) Study aims to (1) examine patient journeys across care sectors and care integration among children with different special healthcare needs (SHCN), compared to those without SHCN, their correlates, and associations with health-related outcomes, (2) identify challenges in care delivery, (3) quantify direct and indirect costs from healthcare system and family perspectives, and (4) develop recommendations to strengthen integrated care for this population. Methods: This study uses an exploratory sequential mixed-methods design comprising (1) semi-structured interviews, (2) claims data analyses, (3) a prospective cohort study, and (4) health economic analyses. We conduct interviews with children with SHCN, their caregivers, and healthcare professionals across disciplines and sectors. Claims data from a statutory health insurance fund cover children aged 3–15 years over six-years, including those with selected index diagnoses (type 1 diabetes, asthma, disorders of psychological development, behavioral and emotional disorders, cerebral palsy), and children without SHCN. A random subsample is invited to participate in the cohort study.
Interviews explore patient journeys, care-related burden, and opportunities to improve care delivery. Claims data capture service utilization and associated costs across sectors. Informed by qualitative findings, cohort surveys assess perceived care provision (e.g., care integration), family-related aspects (e.g., navigational health literacy), and child and caregiver health status. Health economic analyses quantify direct and indirect costs. Interview data are analyzed using qualitative content and thematic analyses. Claims data are examined using state sequence analyses to identify patterns in patient journeys. Cohort data are analyzed using regression models, propensity score matching, and cluster analyses to examine correlates of care integration, associations between care challenges and health outcomes, and subgroups of families with vulnerability profiles. Data integration and development of recommendations occur iteratively. Results: Data collection and analyses of the semi-structured interviews were completed in June 2025, with initial findings published in March 2026. We conducted the first survey of the cohort study between May and October 2025, and plan a second wave for September 2026. Quantitative analyses and mixed-methods integration are ongoing. Conclusions: PICAR is among the first studies to comprehensively investigate patient journeys and associated costs among children with different SHCN. The findings inform improvements in routine care delivery, support development of interventions to strengthen integrated care, and provide guidance for health policy, ultimately improving outcomes for children with SHCN and their caregivers. Clinical Trial: not applicable
Background: Background: Young adults increasingly rely on online resources for mental health information and support. Alongside active information-seeking, they also encounter information passively, e...
Background: Background: Young adults increasingly rely on online resources for mental health information and support. Alongside active information-seeking, they also encounter information passively, e.g. through algorithmically curated content on social media. In spite of the growing literature on information-seeking, less is known about how young adults navigate broader online mental health information ecosystems, including active and passive exposure, verification practices, and the impact on self-diagnosis behaviours. Objective: Objective: This study investigates how young adults interact with online mental health ecosystems by examining (1) passive exposure to mental health content, (2) active information-seeking behaviours, (3) verification practices, (4) evaluations of online information sources, and (5) self-diagnosis experiences and attitudes. Methods: Methods: We conducted an anonymous online survey (n=106) with young adults aged 18–28, living in the United Kingdom and Ireland. The survey examined interactions with online mental health content, verification behaviours, emotional impacts, perceptions of information sources, and self-diagnosis experiences. Quantitative data were analysed using descriptive statistics, and open-ended responses were analysed using inductive content analysis. Results: Results: Passive encounters with mental health content were common, with 97.2% of participants frequently encountering such content. Most participants (72.6%) also actively sought mental health information online. While passive exposure occurred primarily through social media platforms such as Instagram (85.7%) and TikTok (55.2%), active information-seeking was more commonly associated with health-focused websites (81.6%). Official health websites were perceived as the most trustworthy and reliable sources, while social media and influencer content were rated as more engaging. Verification practices were infrequent, particularly for passively encountered content, with 53.3% of participants reporting that they rarely or never verified such information. Self-diagnosis was common, with 76.4% of participants strongly suspecting having a mental health condition. Participants described self-understanding, emotional validation, and barriers to professional care as key motivators for self-diagnosis. Conclusions: Conclusions: This study contributes evidence on how young adults navigate online mental health information. It shows that young people frequently encounter mental health information, both actively and passively, and demonstrates how multiple sources combine to form an overall information ecosystem. Verification practices were uncommon. We found an inverse relationship between trust in different sources and engagement, and identified gaps between self-diagnosis and professional support. Based on our findings, we identify key opportunities and challenges for future research, including the need to support verification practices and the challenge of translating self-diagnosis into more formal help-seeking.
Background: Virtual reality comfort sessions are increasingly used in residential dementia care, but environment selection remains ad hoc — staff have no systematic way to identify which virtual exp...
Background: Virtual reality comfort sessions are increasingly used in residential dementia care, but environment selection remains ad hoc — staff have no systematic way to identify which virtual experience best suits a given resident. Objective: In this feasibility and proof-of-concept study, we present a decision-support pipeline that integrates wearable physiological signals (heart rate variability, electrodermal activity), behavioral observations, and semantic environment characteristics to provide care staff with data-driven VR environment recommendations on a per-resident basis. Methods: Twenty-one residents with major neurocognitive conditions received 420 immersive VR sessions (5 environments, 2 sessions/week, 10 weeks) in a long-term care facility. We defined a hybrid wellbeing target requiring convergence of behavioral calm (PAS-2 vocal intensity = 0) and elevated parasympathetic tone (RMSSD above the participant’s personal median), addressing the ceiling effect of behavioral measures alone (65% of sessions already rated calm). A compact model using 23 features (10 physiological, 3 behavioral, 10 environment) was selected from over 2,100 configurations. Results: Environment features carried a statistically significant signal for predicting hybrid calm (permutation test p = 0.045, Cohen’s d = 1.75); four alternative models using 344–370 features ignored environment entirely (all p > 0.79). Person-level effects dominated prediction variance (partial η² = 0.42, p < 0.001), but the environment signal enabled above-chance environment-level discrimination (pairwise ranking accuracy 57.9% vs. 50% chance). Water presence was the most influential environment characteristic. A physiology-only ablation achieved comparable prediction accuracy but produced undifferentiated recommendations — predicting who is calm rather than which environment calms them. The pipeline generated 60 plain-language reports (30 family, 30 staff) via a large language model with evidence tracing; all passed safety checks (100% forbidden-term compliance), though staff reports showed higher data fidelity while family reports better maintained non-medical framing. Conclusions: The environment-level signal is subtle but real, person-specific, and clinically interpretable — consistent with the expectation that VR environment choice produces modest rather than pharmacological-magnitude effects in this population.
Background: Digital phenotyping uses passively collected digital-sensing data to characterize real-world behavioral patterns. Such data may help identify everyday lifestyles that are relevant to menta...
Background: Digital phenotyping uses passively collected digital-sensing data to characterize real-world behavioral patterns. Such data may help identify everyday lifestyles that are relevant to mental well-being, but most prior approaches have used variable-centered methods that focus on single behaviors rather than person-centered combinations of behaviors across daily life. Objective: This study aimed to examine whether digitally captured behavioral and environmental data can be used to derive meaningful lifestyle profiles and whether these profiles are associated with mental well-being. Methods: The study used a two-week intensive longitudinal design with a German quota sample of 553 adults (Mage = 42.27, SD = 12.89; 44.4% female). Across the study period, participants contributed 7,635 person-days of smartphone-recorded data on social app use, mobility, physical activity, screen use, ambient loudness, and brightness, along with self-reported mental well-being and Big Five personality traits. We used an innovative two-level latent profile analysis to simultaneously identify day-level profiles at Level 1 and person-level profiles at Level 2. We then examined associations between person-level lifestyle profiles and mental well-being, including whether these associations were moderated by Big Five personality traits. Results: The analysis identified eight day-level profiles and seven person-level profiles. One person-level profile characterized by lighter phone usage combined with heavier physical activity reported greater positive functioning, an important aspect of mental well-being, than another profile characterized by extensive mobility combined with intensive social app use. Personality traits did not significantly moderate the associations between lifestyle profiles and mental well-being. Conclusions: These insights advance digital phenotyping by showing that interpretable, person-centered lifestyle profiles could reflect aspects of mental well-being. Potential clinical implications, including transparent monitoring and multi-behavior interventions are discussed.
Background: Purpose
To explore in depth the care experiences and support challenges faced by elderly caregivers when using smart health technologies from the perspective of the digital divide, provid...
Background: Purpose
To explore in depth the care experiences and support challenges faced by elderly caregivers when using smart health technologies from the perspective of the digital divide, providing a basis for developing elderly-friendly health service models.
Methods
Starting in 6/1/2026 and ending in 25/3/2026.
A descriptive qualitative research design was employed. Using purposive sampling, 16 elderly caregivers of hospitalized elderly patients in the tertiary hospital in Guangzhou were recruited from January to March 2026 for semi-structured interviews. Directed content analysis was used to analyze the data.
Results
The experiences and dilemmas of older caregivers in using digital health technologies were summarized into 3 core themes and 6 sub-themes: (1) Barriers at the acquisition and access level (imbalance between physical/mental decline and technology design, lack of intergenerational family support); (2) Dilemmas at the operational and usage level (cognitive overload caused by a lack of knowledge, doubts and concerns about the effectiveness and safety of technology); (3) The divide at the cognitive and trust level (persistence and reliance on traditional caregiving methods, insufficiency of professional support systems and help-seeking channels).
Conclusions
This study reveals that the digital divide faced by elderly caregivers is not merely a barrier to access, but fundamentally a digital disability among the elderly, which significantly undermines the quality of care. Bridging this gap requires a three-dimensional strategy: enhancing individual digital literacy, implementing inclusive age-friendly designs, and building multi-sectoral support networks. Addressing these challenges is essential to strengthening caregiver resilience and ensuring the systemic sustainability and equity of digital health integration. Objective: Purpose
To explore in depth the care experiences and support challenges faced by elderly caregivers when using smart health technologies from the perspective of the digital divide, providing a basis for developing elderly-friendly health service models. Methods: Methods
Starting in 6/1/2026 and ending in 25/3/2026.
A descriptive qualitative research design was employed. Using purposive sampling, 16 elderly caregivers of hospitalized elderly patients in the tertiary hospital in Guangzhou were recruited from January to March 2026 for semi-structured interviews. Directed content analysis was used to analyze the data. Results: Results
The experiences and dilemmas of older caregivers in using digital health technologies were summarized into 3 core themes and 6 sub-themes: (1) Barriers at the acquisition and access level (imbalance between physical/mental decline and technology design, lack of intergenerational family support); (2) Dilemmas at the operational and usage level (cognitive overload caused by a lack of knowledge, doubts and concerns about the effectiveness and safety of technology); (3) The divide at the cognitive and trust level (persistence and reliance on traditional caregiving methods, insufficiency of professional support systems and help-seeking channels). Conclusions: Conclusions
This study reveals that the digital divide faced by elderly caregivers is not merely a barrier to access, but fundamentally a digital disability among the elderly, which significantly undermines the quality of care. Bridging this gap requires a three-dimensional strategy: enhancing individual digital literacy, implementing inclusive age-friendly designs, and building multi-sectoral support networks. Addressing these challenges is essential to strengthening caregiver resilience and ensuring the systemic sustainability and equity of digital health integration. Clinical Trial: This study has undergone ethical approval: Scientific Research Ethics Committee of the General Hospital of the Southern Theater Command,17 July 2025,NZLLKZ2025064
Background: In the era of rapid digital evolution, artificial intelligence (AI) and machine learning (ML) have emerged as valuable tools for organizations seeking to extract insights from large datase...
Background: In the era of rapid digital evolution, artificial intelligence (AI) and machine learning (ML) have emerged as valuable tools for organizations seeking to extract insights from large datasets. However, the adoption of AI and ML, traditionally associated with technical domains like computer science and data science, poses challenges for non-technical professionals such as public health researchers. While existing no-code tools offer potential solutions, they are rarely tailored to the specific research workflows and local contexts of African institutions. Objective: This study aimed to design, develop, evaluate, and deploy the AutoML No-Code platform that supports end-to-end predictive analytics across different domains, with demonstrated applications in public health, such as disease risk prediction, stroke outcome modelling, air pollution–mortality analysis, and maternal and child health research. Methods: The platform was co-developed using an iterative, Agile approach in collaboration with public health researchers at the African Population and Health Research Center (APHRC). Version 1.0.0 was informed by a comprehensive needs assessment involving 7 researchers. Version 1.0.0 was informed by a needs assessment involving seven researchers. Version 2.0.0 introduced significant enhancements, including Observational Medical Outcomes Partnership (OMOP-based) analytics, data anonymization tools, automated research question generation using a large language model, and deep learning workflows for image classification, segmentation, and object detection. Version 2.0.0 was evaluated across two structured workshop settings: the Machine Learning for Health (ML4H) Workshop in Entebbe, Uganda (November 2025), where 21 participants from six African institutions applied the platform to real-world public health datasets using a mixed-methods approach combining structured closed and open-ended questionnaires, and a two-week training at Kampala International University (KIU) under the Data Science Without Borders (DSWB) project, where 26 participants completed an end-of-training survey. The platform supports the entire analytics pipeline, covering data ingestion, preprocessing, model training, evaluation, and interpretation. Results: Version 2.0.0 was well received across both settings, with participants reporting improvements in usability and strong acceptability of the platform. At the ML4H Workshop, the platform was successfully applied to 14 diverse public health use cases, including modelling the relationship between air pollution and mortality, predicting stroke outcomes, and analysing maternal and child health, infectious disease, and mental health outcomes, with several analyses generating manuscript-ready outputs. At KIU, 24 out of 26 (92%) participants agreed or strongly agreed that the training improved their understanding of ML concepts, and 24 out of 26 (92%) reported enhanced skills in data management and visualization, despite 16 out of 26 (62%) having no prior machine learning experience. Users across both settings highlighted the platform's capacity to unify data preprocessing, model development, and evaluation within a single interface, reducing reliance on programming skills. However, evaluation was conducted across a relatively small number of institutions and participants, which may limit the generalizability of findings. Conclusions: The AutoML No-Code platform provides a flexible no-code environment for AI and ML-driven predictive analytics across domains, with particular relevance for public health research in Africa. Deployment across multiple institutions has provided early evidence of its practical utility in supporting researchers with limited programming backgrounds. Further evaluation across diverse user groups and larger-scale deployments is needed to better understand its generalizability and long-term impact on research productivity and decision-making.
Background: Neurological disorders present an increasing health burden worldwide and cause significant impairments which impede physical, social and cognitive function. Wearable technologies show prom...
Background: Neurological disorders present an increasing health burden worldwide and cause significant impairments which impede physical, social and cognitive function. Wearable technologies show promise for bolstering health monitoring in neurological populations due to their ease of use and accessibility, yet their applicability for use in measuring relevant parameters remains unclear. Objective: This scoping review aims to map the current evidence surrounding the use of smart wearable technologies in people with neurological disorders. Methods: Following the Preferred Reporting Items for Systematic Review Extension for Scoping Reviews (PRISMA-ScR), studies were systematically reviewed from five electronic databases: MEDLINE, EBSCOHost, Cochrane Library, IEEE Xplore, and Web of Science. Key features, limitations and potential clinical applications of these devices were identified. Two independent reviewers screened and selected the studies. Two reviewers then summarised the selected studies using an Excel data extraction sheet and used the NIH Risk of Bias tool to critically appraise them. Results: Of seventy-nine studies included in this review, twenty-seven focused on stroke, thirty-four on Parkinson’s Disease (PD), eight on Multiple Sclerosis (MS), seven on dementia, two each on Traumatic Brain Injury (TBI) and Amyotrophic Lateral Sclerosis (ALS), one each on epilepsy and Progressive Supranuclear Palsy (PSP), three on Spinal Cord Injury (SCI), and one on Huntington’s Disease (HD). Conclusions: Smart wearables demonstrate accuracy and feasibility, particularly in stroke and PD, with most studies focusing on physical parameters such as gait patterns. Future research should include psychosocial and physiological outcomes, use larger and more standardised samples, and address underrepresented neurological conditions, to better define the broader applicability of smart wearables. Clinical Trial: 10.17605/OSF.IO/ZHCPE
During the perioperative phase of cardiac surgery, the intricate interplay between pulmonary ischemia-reperfusion injury, protracted mechanical ventilation, and systemic inflammatory responses frequen...
During the perioperative phase of cardiac surgery, the intricate interplay between pulmonary ischemia-reperfusion injury, protracted mechanical ventilation, and systemic inflammatory responses frequently precipitates significant lung injury. To address this, implementing individualized positive end-expiratory pressure (PEEP) aims to bolster respiratory function and effectively mitigate the incidence of postoperative pulmonary complications. The fundamental mechanism of PEEP involves facilitating alveolar recruitment and averting alveolar collapse, thereby restoring functional residual capacity. Given the caveats of potential circulatory suppression, academic focus has increasingly pivoted toward identifying optimal PEEP titration strategies. Although various individualized protocols have been proposed, they often present a trade-off between clinical precision and practical utility. Electrical Impedance Tomography (EIT) offers a dynamic solution by monitoring regional ventilation distribution, providing the real-time feedback essential for precise PEEP titration. For post-cardiac surgery patients burdened by high transport risks, EIT’s inherent non-invasive, radiation-free, and bedside capabilities substantially enhance the safety and operational feasibility of individualized PEEP management.
Background: Type 2 diabetes mellitus (T2D) is associated with an increased risk of mild cognitive impairment (MCI) and dementia. However, there are currently no effective and engaging interventions to...
Background: Type 2 diabetes mellitus (T2D) is associated with an increased risk of mild cognitive impairment (MCI) and dementia. However, there are currently no effective and engaging interventions to delay the onset of dementia or reverse cognitive decline. Objective: To explore feasibility and cognitive outcomes of an immersive virtual reality (IVR) cognitive training program in older adults with T2D and MCI. Methods: Prospective pilot study including adults with T2D aged >65 years and diabetes duration >5 years. After a neuropsychological test battery (NTB) patients were allocated into three groups: normocognitive, MCI without intervention, and MCI receiving VR cognitive training. Assessments were performed at baseline and at 9 months. Cognitive outcomes were derived from were studied both at the individual test level and as domain-level summaries (using percentile-based composite scores for processing speed, attention/executive function, memory, visuo-construction, and language). Between-group differences were assessed using Kruskal–Wallis tests for continuous variables and Fisher’s exact test for categorical. To estimate the intervention effect among participants with MCI, baseline-adjusted ANCOVA models were performed. Results: 30 adults with T2D were included (n=10 each group). Baseline clinical characteristics were broadly comparable across groups. NTB categorical status improved more frequently in the VR group: 7/10 converted from MCI to normocognition versus 1/10 in the MCI non-intervention group (Fisher p=0.020). In key NTB tests, the VR group showed a significant improvement in executive and visuo-construction domains: TMT-B performance median 512.5 to 241.0 seconds (p=0.024) and significantly higher follow-up visuo-construction performance in VR group versus standard care (adjusted β=21.55 percentile points, 95% CI 1.34 to 41.75; p=0.038). Conclusions: In this small pilot, immersive VR cognitive training was associated with a higher rate of cognitive status improvement and better executive and visuo-construction and executive functioning at 9 months. Larger randomized trials are warranted to confirm efficacy and assess long-term sustainability. Clinical Trial: N/A
Background: Mental health needs have increased significantly while access to care remains limited, prompting interest in digital tools that can help bridge treatment gaps. Artificial Intelligent (AI) ...
Background: Mental health needs have increased significantly while access to care remains limited, prompting interest in digital tools that can help bridge treatment gaps. Artificial Intelligent (AI) mental health chatbots are increasingly being used to help address the shortage of mental health providers by offering accessible, scalable support when traditional services are unavailable or overwhelmed. Existing research focuses largely on technical development and ethical issues, with far less attention to user experiences and adoption factors. Objective: This study provides a multifaceted analysis of user perceptions, experiences, and attitudes toward two widely used mental health AI chatbots—Woebot and Wysa. By analyzing user reviews, the study aimed to identify potential benefits, limitations, and areas for improvement that influence consumer adoption. Methods: Linear regression analysis revealed a decline in Woebot’s average star ratings over time, while Wysa ratings remained stable. However, due to the ordinal nature of star ratings and a low R² value, these findings warrant cautious interpretation, and future analyses may benefit from using ordinal regression models. Sentiment analysis—conducted using both AI-based and lexical-based tools—indicated overall positive sentiment, although sarcasm and linguistic ambiguity posed challenges. Thematic analysis, informed by previous work from [1], identified key adoption determinants such as performance expectancy, price value, trust, and perceived anthropomorphism. Results: Concerns about privacy, generic responses, emotional disconnect, and cost emerged as significant barriers. The study highlights the need for more consistent standards in chatbot evaluation, data privacy, and trust-building in AI healthcare tools. Suggestions for improving chatbot usability include integrating feedback loops, developing more nuanced sentiment analysis through models like BERT and LSTM, and expanding linguistic research on user-chatbot interactions. Furthermore, equitable access remains a priority, with policy discussions underway to support insurance reimbursement for AI-based mental health tools. Conclusions: Overall, the findings underscore the promise of mental health chatbots while emphasizing the necessity for continued research, ethical oversight, and interdisciplinary collaboration to ensure these tools are safe, effective, and accessible to all.
Background: Laparoscopic surgical performance relies on accurate visual perception and attention, yet visual misperception is a leading cause of error in laparoscopic cholecystectomy. Eye-tracking res...
Background: Laparoscopic surgical performance relies on accurate visual perception and attention, yet visual misperception is a leading cause of error in laparoscopic cholecystectomy. Eye-tracking research has shown differences in visual strategies across expertise levels. However, there is a need to better understand how surgeons of varying expertise perceive anatomy and how indocyanine green (ICG) fluorescence influences attention and perception. This study uses a mixed-methods approach to examine gaze behavior and perceptual accuracy during surgical viewing. Objective: This study explores differences in visual perception and attention when viewing laparoscopic cholecystectomy procedures under white light and ICG fluorescence imaging conditions across surgeons of varying levels of expertise. Methods: Sixty-one surgeons (20 experts, 16 intermediates, 25 novices) participated in this study conducted at the 2025 Society of American Gastrointestinal and Endoscopic Surgeons Meeting. Surgeons watched a series of laparoscopic cholecystectomy video clips while their eye movements were recorded using a Tobii Pro Fusion device. The videos included both continuous viewing and paused scene conditions which required that they identify anatomical structures. Eye-tracking metrics and anatomical identification accuracy were analyzed across expertise levels and imaging conditions. Identification accuracy for each paused scene was scored as correct, suggestive, or incorrect. Results: Experts demonstrated higher anatomical identification accuracy, particularly in ICG fluorescence scenes with complex or abnormal anatomy. Significant associations between expertise level and anatomical identification accuracy were found for identifying the cystic artery (p=0.018) and duct (p=0.046) in the ICG scene. Level of expertise had a significant effect on average fixation (p=0.034), with experts exhibiting higher saccade counts than novices (p=0.032). Heatmaps visualizing the spatial distribution of fixations showed that differences in the spatial distribution of attention across expertise levels were more pronounced in mixed lighting conditions during dissection or confirmation of the CVS. Across imaging conditions, differences in gaze behavior were more evident in complex scenes, although differences in many eye-tracking metrics did not reach statistical significance. Conclusions: The integration of eye-tracking measures with perceptual accuracy scoring provided insight into how surgeons perceive and attend to visual cues across surgical scenes. Differences in perceptual accuracy and gaze patterns across levels of expertise were more pronounced in perceptually complex scenes under ICG fluorescence.
Background: Telehealth, broadly defined as the use of telecommunication technologies for remote healthcare, was adopted as a necessary strategy to maintain continuity of care and reduce the risk of in...
Background: Telehealth, broadly defined as the use of telecommunication technologies for remote healthcare, was adopted as a necessary strategy to maintain continuity of care and reduce the risk of infection during the COVID-19 pandemic. Although the rapid adoption of telehealth since the pandemic has increased access to these services, older adults still face unique barriers to effective use. Understanding these barriers and digital-readiness factors is crucial to promoting equitable access to healthcare. Objective: This systematic literature review explored the primary barriers to telehealth and digital access among older adults by synthesizing evidence on technological, individual/skills-based, and systemic/structural challenges, as well as facilitators and factors influencing digital readiness. Methods: Following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, a comprehensive search of the EBSCOhost database identified 1,740 records, along with three additional records from other sources. After removing 1,500 duplicates and irrelevant records, 243 titles and abstracts were screened, 30 full texts were assessed, and 16 studies published between 2022 and 2025 were systematically reviewed. The included studies comprised cross-sectional observational studies (n=4, 25.0%), qualitative studies (n=5, 31.3%), mixed-methods studies (n=2, 12.5%), scoping reviews and meta-syntheses (n=3, 18.8%), and other designs (n=2, 12.5%). Studies were included if they examined barriers to access to telehealth, digital readiness, or related outcomes among adults aged 50 years or older. Data were extracted on study characteristics, barriers, facilitators, and outcomes. A thematic synthesis was conducted across the domains of technological, individual, and systemic barriers. Results: The review included 16 studies from various geographic regions, mainly from the United States and other high-income countries. All 16 studies identified barriers in three categories: technological barriers (internet connectivity, device access, platform usability), individual or skill-based barriers (low digital literacy, cognitive and sensory impairments, technology anxiety), and systemic or structural barriers (lack of training and support, inadequate infrastructure, language barriers). Key facilitators included prior technology experience, family support, and hybrid care models. Digital readiness varied across education, socioeconomic status, and prior technology exposure. Conclusions: Older adults encounter interconnected technological, individual, and systemic barriers to telehealth access. Overcoming these challenges requires multilayered strategies, including investing in infrastructure, providing tailored digital literacy training, offering ongoing technical support, and adopting person-centered care models. Future studies should prioritize equity-focused solutions for the most vulnerable older adult groups.
Background: Large language models (LLMs) have demonstrated potential as auxiliary tools in digital health scenarios, such as depression management. However, their effectiveness depends on their abilit...
Background: Large language models (LLMs) have demonstrated potential as auxiliary tools in digital health scenarios, such as depression management. However, their effectiveness depends on their ability to meet both rigorous professional standards and individualised patient needs. Currently, a gap exists in research that systematically evaluates the quality of LLM responses from both medical and patient perspectives, hindering the development of “patient-centred” medical artificial intelligence. Objective: This study aimed to develop a dual-paradigm evaluation framework that integrates professional-safety and experience-practicality perspectives, and to systematically compare how clinicians and patients evaluate LLM-generated responses to common questions about depression, in order to identify communication features that can bridge the cognitive divide. Methods: We selected the 10 most frequently asked questions from patients with depression and generated responses using four mainstream Chinese LLMs (DeepSeek-V3.2, GLM-4.6, Qwen-3-Max, and Kimi-k2-thinking). Ten psychiatrists and 130 clinically diagnosed patients with depression were invited to independently conduct blind scoring from their respective professional or experiential perspectives across six evaluation dimensions. Results: Significant differences were found between healthcare providers and patients across all evaluation dimensions (p < 0.05), with the greatest perceptual gap observed in “safety boundaries and risk awareness” (effect size r = 0.38). Key findings include: (1) The symbiosis of safety and empathy: From the patient’s perspective, perceived “safety” of a response was highly positively correlated with its “linguistic approachability” (ρ > 0.5), in stark contrast to the negative correlation observed in the physician group (ρ = -0.289). This suggests that safety warnings incorporating expressions of empathy are more likely to gain patient acceptance and trust. (2) Structural differences in evaluation logic: Patients tended to evaluate “clarity”, “practicality”, and “approachability” as an integrated whole (strong positive correlations), whereas doctors were able to assess “medical accuracy” as an independent core metric. Conclusions: Based on these findings, this study proposes that “bridging communication” should serve as the core developmental paradigm for future medical AI. This paradigm emphasises that an effective AI response requires a delicate balance between professional rigour and individual relevance, centring on two key transformations: translating standardised medical language into personal narratives that resonate with patients’ lived experiences, and transforming structured knowledge into actionable, personalised guidance. The best-performing models in this study (GLM-4.6 and Kimi-k2-thinking) demonstrated preliminary evidence of this “bridging” characteristic in their responses. This study not only evaluates existing models but, more importantly, provides a crucial theoretical framework and empirical basis for building the next generation of medical AI assistants that possess genuine communicative intelligence, empower patients, and support clinical practice. Clinical Trial: NONE
Open Peer Review Period: May 19, 2026 - May 4, 2027
Digital therapeutics (DTx) represent a transformative opportunity to advance health equity and expand access to evidence-based care. However, Canada’s fragmented regulatory approach risks widening, ...
Digital therapeutics (DTx) represent a transformative opportunity to advance health equity and expand access to evidence-based care. However, Canada’s fragmented regulatory approach risks widening, rather than narrowing, existing health inequities. While Health Canada’s risk-based classification framework appropriately distinguishes Class I from Class II devices, regulatory ambiguity, institutional variation, and limited transparency prevent life-saving innovations from reaching vulnerable populations who need them most. Drawing on community engagement across healthcare and academic institutions, this viewpoint argues that DTx governance is fundamentally a public health issue and proposes three public health-oriented reforms: standardized institutional quality management, Artificial Intelligence (AI) transparency and accountability, and integrated Research Ethics Boards-Health Canada pathways, alongside a stronger lifecycle governance approach for dynamic digital tools.
Background: Clinical practice guidelines are central to evidence-based mental health care, yet their implementation remains inconsistent across clinical settings. Digital health technologies and AI-en...
Background: Clinical practice guidelines are central to evidence-based mental health care, yet their implementation remains inconsistent across clinical settings. Digital health technologies and AI-enabled decision-support systems offer new opportunities to support guideline use, but their translation into routine mental health practice remains limited by clinical, organisational, technical, and governance barriers. Objective: This scoping review aimed to identify and synthesise barriers to implementing clinical guidelines in mental health services and to examine their implications for computable, interoperable, and governable digital decision support. Methods: A structured scoping review was conducted using systematic search, screening, and narrative synthesis. Studies addressing guideline implementation, digital health technologies, computable knowledge, interoperability, AI-enabled decision support, or governance in mental health were included. Barriers were extracted, coded, and synthesised across clinical, organisational, technical, digital, governance, and human-relational domains. Results: Forty-six studies were included. Barriers spanned clinical and organisational constraints, limitations in evidence and knowledge representation, interoperability and data challenges, AI and decision-support issues, governance and ethical concerns, and human-relational factors. These barriers were frequently interconnected, limiting the translation of evidence-based recommendations into consistent, computable, and accountable clinical workflows. The findings indicate that technology alone is insufficient to improve guideline implementation unless it is embedded within service workflows, interoperable data infrastructures, governance mechanisms, and clinician-facing accountability structures. Conclusions: Mental health guideline implementation requires more than the availability of clinical recommendations or digital tools. Current approaches lack an integrated pathway linking evidence, computable guideline logic, interoperable data, decision support, and governance. This review proposes the Guideline-as-Guardrail model, in which computable guidelines function as executable constraints for digital and AI-enabled decision support while preserving clinical judgement, patient values, shared decision-making, and professional accountability.
Background: Perinatal mental health disorders affect approximately 20% of women and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjecti...
Background: Perinatal mental health disorders affect approximately 20% of women and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented. Objective: This study examines the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor. Methods: We conducted a systematic review following PRISMA guidelines (PROSPERO: CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, ACM Digital Library) were searched in January 2026 without time restrictions. Evidence was synthesized narratively, and risk of bias was assessed using PROBAST+AI. Results: From 3,741 records identified across six databases, 10 studies met the inclusion criteria, covering postpartum depression (PPD), prenatal stress, discrete emotions during pregnancy (e.g., happiness, anxiety, sadness), and maternal loneliness. Models showed promising utility, particularly for PPD (multiclass AUC = 0.85; binary AUC = 0.871; F1 = 0.9872). HRV features (RMSSD, SDNN) were the most consistently informative physiological features, while GPS-derived mobility, physical activity, and sleep were the most informative behavioral markers, though their interpretation required perinatal-specific contextualisation. However, the field faces three critical constraints: methodologically, 67% of assessment units were rated at high risk of bias; in outcome scope, research has concentrated on PPD, while anxiety, stress, and other prevalent conditions remain largely unaddressed; and in technological breadth, sensing modalities and analytical approaches represent only a narrow subset of those now available. Conclusions: Wearable and smartphone sensing show early promise for predicting perinatal mental health outcomes, with autonomic and behavioral features emerging as complementary digital biomarkers whose interpretation requires perinatal-specific contextualisation. Advancing toward clinical utility will require broader coverage of mental health outcomes, larger longitudinal cohorts, standardised analytical pipelines and reporting practices, adoption of modelling approaches better suited to perinatal trajectories, and human-centred monitoring designs.
Background: The global HIV/AIDS epidemic persists, and women living with HIV/AIDS (WLWHA) experience substantial disease burden, social stigma, and discrimination, creating an urgent need for mental h...
Background: The global HIV/AIDS epidemic persists, and women living with HIV/AIDS (WLWHA) experience substantial disease burden, social stigma, and discrimination, creating an urgent need for mental health support. Objective: This study evaluated the acceptability, usability, and real-world experience of the HOPES+ AI Empathy Agent among WLWHA, identified core intervention needs, and provided empirical evidence for AI empathy tool development in HIV mental health. Methods: A parallel convergent mixed-methods pilot study enrolled 41 WLWHA.. Quantitative assessments were conducted using records of task completion times and the Post-Study System Usability Questionnaire (PSSUQ); qualitative analysis was performed through semi-structured interviews and Colaizzi content analysis; and a comprehensive evaluation was conducted based on the ISO 9241-11 usability standards across three dimensions: effectiveness, efficiency, and subjective satisfaction. Results: Participants reported high acceptability and usability of the HOPES+ AI Empathy Agent, noting it effectively reduced perceived stigma. The primary barriers to use included cost concerns, privacy and data security worries, limited digital literacy, and a current lack of need for additional mental health support. Participants explicitly expressed a need for mental health services that are easily accessible, highly private, convenient, and available in minority languages. Conclusions: WLWHA have urgent mental health needs related to perceived stigma. The HOPES+ AI Empathy Agent is an acceptable, usable low‑barrier tool for stigma intervention, supporting broader implementation and optimization.
Background: Simulated patients (SPs) are essential in nursing education but face persistent challenges: trained-actor scarcity, instructor burden, cost, and disrupted continuity during pandemics or ar...
Background: Simulated patients (SPs) are essential in nursing education but face persistent challenges: trained-actor scarcity, instructor burden, cost, and disrupted continuity during pandemics or armed-conflict environments where in-person simulation cannot be sustained. Recent LLM-based simulated patient systems address some of these issues, yet existing studies focus exclusively on the patient role, leaving the dialogic partner—the AI nurse—as a static prompt or unspecialized model. This asymmetry compromises both data-collection quality and pedagogical applicability. Objective: We report (a) the construction of two specialized fine-tuned models, NURSE-ONE (AI nurse) and PATIENT-ONE (AI simulated patient), through a five-step bidirectional fine-tuning pipeline; (b) an evaluation of the resulting models using Berelson content analysis; and (c) the design and analysis of a coexistence simulation ecosystem in which the two models operate jointly to support three pedagogical operating modes. We additionally examine the system's applicability to disaster-preparedness nursing education. Methods: On a single consumer-grade GPU (NVIDIA RTX 4090, 24 GB VRAM), the multimodal base model gemma4-E4B-it was fine-tuned via Low-Rank Adaptation (LoRA) under a five-step pipeline: (1) construction of a 251-scenario base from 29 institutionally-authored standard nursing care plans and 20 situational items from the 115th Japanese National Nursing Examination; (2) provisional dialogue system using AutoGen v18; (3) NURSE-ONE fine-tuning with three training-volume conditions; (4) four-condition Berelson comparison; (5) PATIENT-ONE fine-tuning using NURSE-ONE-generated dialogue data. All adapters were merged using PEFT, converted to GGUF q4_k_m (5.07 GB per role), and served via Ollama. Three coexistence operating modes were operationalized in a working web UI. Results: Four-condition Berelson analysis (κ=0.742, χ²=41.991, df=15, p=0.0002) showed that the 735-case nurse-side condition achieved the highest open-question rate (OQ=21.5%) and was adopted as NURSE-ONE. PATIENT-ONE A/B/C Berelson analysis (κ=0.718, χ²=11.818, p=0.297) showed no significant category-distribution shift; the 735-case condition achieved the lowest response-length variability and highest information-collection rate (47.9%). Across both experiments, nurse-side LoRA modulated qualitative questioning style while patient-side LoRA controlled quantitative response stability—a functional asymmetry operating on orthogonal axes. All three coexistence modes were instantiated within the pre-registered latency threshold (≤3 s per turn). Conclusions: A five-step bidirectional fine-tuning pipeline produced two specialized models whose joint deployment constitutes a novel design pattern for nursing-education simulation. The functional asymmetry between qualitative nurse-side variation and quantitative patient-side stabilization underwrites the dyadic system's coherence; the resulting ecosystem supports three pedagogically distinct operating modes on a single shared substrate, runs entirely on consumer-grade hardware in Japanese, and is deployable in offline disaster-preparedness contexts where cloud-dependent alternatives are unusable.
Background: Generative artificial intelligence (AI) tools, including large language model (LLM)-based agents, are increasingly explored for medical and nursing education. However, longitudinal empiric...
Background: Generative artificial intelligence (AI) tools, including large language model (LLM)-based agents, are increasingly explored for medical and nursing education. However, longitudinal empirical studies examining how individual educators collaborate with AI agents to develop digital teaching materials remain scarce. Most prior work has focused on short-term usability evaluations or performance benchmarks of completed systems, leaving the iterative development process itself under-examined. Objective: This study aimed to (1) characterize the collaborative workflow between a single nurse educator and an AI agent (Claude Code) across 19 months of continuous digital material development, (2) quantify changes in the educator's prompt-engineering behavior over time, and (3) derive reproducible conditions and a phase model that may guide other educators seeking to adopt AI agents for curriculum development. Methods: A hybrid Design-Based Research (DBR) and Design Science Research (DSR) methodology was employed. The practitioner-researcher (a nurse educator) collaborated with Claude AI (web/desktop, November 2024-May 2025) and Claude Code (terminal-based AI agent, May 2025 onward) over 19 months (November 2024-May 2026), accumulating approximately 1,000 hours, 41,914 messages across 609 sessions, and producing 14 or more digital artifacts spanning six technical layers. Dialogue data (n=8,286 utterances from 20 sampled sessions) were classified into six speech-act categories using Berelson content analysis; inter-rater reliability was assessed by two independent coders (Cohen kappa=0.565, moderate agreement). Specificity density, a novel metric defined as the ratio of domain-specific directives to total educator utterances, was tracked longitudinally across nine iterative design cycles. Results: A five-phase collaborative workflow model emerged: (1) natural-language trial-and-error, (2) structured instruction via CLAUDE.md norm files, (3) domain-specific prompt maturation, (4) multi-agent orchestration through Model Context Protocol (MCP) connectivity, and (5) autonomous agent delegation. Specificity density rose from 0.21 (Phase 1) to 0.91 (Phase 5), indicating progressive externalization of the educator's tacit clinical and pedagogical knowledge. Domain success rates varied substantially (web/HTML artifacts: 57%; 3D lip-sync systems: 10%), reflecting differential technical complexity. Five reproduction conditions were identified: (C1) a norm-file ecosystem, (C2) iterative prompt maturation, (C3) failure-log accumulation, (C4) MCP connectivity, and (C5) sustained engagement exceeding 500 hours. Conclusions: This 19-month longitudinal case study demonstrates that sustained educator-AI agent collaboration can yield a rich portfolio of digital nursing education materials when supported by structured norm files, iterative prompt refinement, and failure-log-driven learning. The five-phase model and five reproduction conditions offer an empirically grounded framework for educators and institutions considering the integration of AI coding agents into health professions education. Limitations include the single-practitioner design (n=1), reliance on one AI platform family, and the absence of direct learner-outcome evaluation. Future multi-site studies with controlled comparisons and student performance metrics are warranted.
Background: Quality indicators in primary care remain predominantly disease-specific and professionally defined, with limited incorporation of what matters most to people living with multiple long-ter...
Background: Quality indicators in primary care remain predominantly disease-specific and professionally defined, with limited incorporation of what matters most to people living with multiple long-term conditions (MLTC) and their caregivers. Existing frameworks and quality standards provide important conceptual direction, but few produce a pragmatic set of ready-to-use indicators. Objective: To co-develop a set of disease-agnostic quality-of-care indicators with people living and caring for those with MLTC that can pragmatically inform service improvement in primary care. Methods: This protocol describes a three-round modified RAND/UCLA Appropriateness Method (RAM) study. The first round will evaluate the importance of candidate indicators through an online questionnaire to patients, caregivers and expert healthcare professionals. Indicators without agreement or requiring revision will proceed to a structured consensus meeting (round 2) with subsequent rerating rounds (round 3) until consensus is reached. Results: This RAM study is currently underway, with planned completion in late 2026. Fifty-four candidate indicators have been generated through a scoping review of published quality indicator development studies and a qualitative interview study with 21 patients and caregivers living with MLTC. Ongoing patient and public involvement and engagement with four community partners has further informed the study design, interpretation of multi-stage findings, and refinement of candidate indicators. The indicators have been mapped to a structure-process-outcome quality framework and will be taken forward into a modified RAND/UCLA Appropriateness Method process. Conclusions: This study will generate a set of co-designed and implementation-oriented primary care quality indicators for MLTC. The final indicator set is intended to support future measurement, quality improvement, and later field testing in real-world primary care systems.
Background: The increasing shortage of skilled nursing staff, growing documentation demands, and rising complexity of care processes in long-term residential care highlight the need for digital soluti...
Background: The increasing shortage of skilled nursing staff, growing documentation demands, and rising complexity of care processes in long-term residential care highlight the need for digital solutions that support nursing workflows while remaining adaptable to real-world care environments. Augmented reality (AR) technologies have the potential to improve access to context-sensitive information and support documentation processes directly at the point of care. In particular, AR-supported clinical dashboards may enable the real-time visualization of relevant patient information during nursing activities without interrupting ongoing care activities or requiring additional device interactions. Although previous studies have explored AR applications in nursing education and documentation, little is known about the usability, practical applicability, and implementation requirements of AR-supported dashboard systems in long-term residential care settings. Furthermore, the participatory development of such technologies together with nursing professionals remains underexplored. Objective: This study aims to explore nursing professionals’ expectations, perceived potentials, barriers, and practical requirements regarding the participatory development and potential use of an AR-supported clinical dashboard in long-term residential care. The project seeks to identify user-centered design principles and implementation requirements for the iterative development of an AR-supported information and documentation system tailored to nursing practice. Methods: The study follows an exploratory qualitative design embedded within a participatory and agile development process. The theoretical framework is informed by the updated Consolidated Framework for Implementation Research (CFIR). Data collection will take place in a long-term residential care facility in Saxony-Anhalt, Germany, which serves as a real-world laboratory for the iterative development and evaluation of the AR-supported clinical dashboard. Data collection and iterative development processes are conducted between 2025 and 2027. Initial focus groups will explore general expectations, barriers, and potential use scenarios without a technical prototype. In subsequent development cycles, progressively refined prototypes will be integrated into the discussions to obtain structured user feedback for iterative system adaptation. Focus groups will be audio recorded, transcribed verbatim, and analyzed using the Gioia methodology to identify first-order concepts, second-order themes, and aggregate dimensions. MAXQDA software will support qualitative data analysis. Results: The project started in April 2025 with an initial setup and development phase. The study is currently in the early development stage, with data collection and iterative prototype development ongoing and scheduled to continue until March 2027. Conclusions: The study is expected to generate practice-oriented insights into the participatory development and implementation of AR-supported clinical dashboards in long-term residential care. The findings may contribute to the development of context-sensitive and workflow-oriented digital solutions that support workflow integration and facilitate context-sensitive information access within routine nursing care. Clinical Trial: The study will be registered in the German Clinical Trials Register (DRKS).
Background: Background: Hypoglycemia, defined as blood glucose below 70 mg/dL, can lead to seizures, coma, and death. Timely prediction using continuous glucose monitoring (CGM) data is clinically imp...
Background: Background: Hypoglycemia, defined as blood glucose below 70 mg/dL, can lead to seizures, coma, and death. Timely prediction using continuous glucose monitoring (CGM) data is clinically important, but deep learning models often require data from many patients, while CGM data are sensitive and difficult to share. Objective: Objective: This study aimed to develop and evaluate a privacy-preserving federated node-selection framework (Fed-node-selection) for 30-minute-ahead hypoglycemia prediction that improves predictive performance and reduces communication and computational overhead. Methods: Methods: Each patient was treated as a federated node that trained a local neural-network predictor using the previous 2 hours of CGM data (24 samples) to predict hypoglycemia 30 minutes ahead, sharing only model parameters with a central server. During training, nodes ranked locally trained models on temporally partitioned validation data, and consistently top-ranked influential nodes were selected for downstream aggregation or ensemble inference. We evaluated two implementations: FedAvg-node-selection, which aggregates selected node updates, and FedEns-node-selection, which ensembles selected local models. Performance was assessed using temporal validation in an 89-patient type 1 diabetes cohort, a patient-disjoint holdout cohort of 22 unseen patients, and an external AZT1D cohort. Results: Results: In temporal validation, FedEns-node-selection achieved balanced accuracy of 85.98% (SD 8.21%), outperforming FedEnsemble with all nodes (83.42%, SD 7.48%) and baseline FedAvg (82.57%, SD 7.84%). FedEns-node-selection also reduced missed hypoglycemia events, with a false negative rate of 14.26% (SD 11.43%) compared with 21.4% (SD 10.6%) for FedEnsemble. In zero-shot evaluation on 22 unseen patients, FedEns-node-selection achieved balanced accuracy of 90.77% (SD 12.23%) and reduced the false negative rate to 11.01% (SD 20.46%). On the external AZT1D cohort, it maintained balanced accuracy of 88.37% (SD 4.36%) without retraining. Conclusions: Conclusions: Selectively using influential federated nodes improved the accuracy, efficiency, and portability of privacy-preserving hypoglycemia prediction. FedEns-node-selection was particularly effective for reducing missed hypoglycemia events while avoiding raw CGM data sharing.
Background: Clinical skills training is central to medical education, yet traditional teaching methods face persistent challenges including inconsistent patient exposure, subjective feedback, and limi...
Background: Clinical skills training is central to medical education, yet traditional teaching methods face persistent challenges including inconsistent patient exposure, subjective feedback, and limited faculty and resource availability. Artificial intelligence (AI), encompassing machine learning, deep learning, and expert systems, offers emerging opportunities to address these gaps through adaptive, data-driven educational tools. Despite rapid AI adoption in clinical practice, structured integration into medical curricula remains limited. Objective: This structured narrative review synthesizes current evidence on the role of AI in clinical skills training across undergraduate and postgraduate medical education, with a focus on efficacy across skill domains, curricular integration requirements, and ethical considerations for responsible implementation. Methods: A structured literature search was conducted across PubMed, Scopus, and Google Scholar for studies published between January 2019 and June 2025. A total of 42 studies were included in the final synthesis: 27 empirical investigations encompassing randomized controlled trials, systematic reviews, scoping reviews, observational studies, and surveys, and 15 non-empirical sources including policy frameworks, governance perspectives, and protocols. Findings were synthesized thematically. Results: Evidence across three converging domains was identified. First, AI demonstrates meaningful efficacy signals in procedural and surgical skills training, with one randomized controlled trial demonstrating AI tutoring to be non-inferior to expert instruction, while diagnostic reasoning and non-technical skills show early but more exploratory evidence. Second, a persistent disconnect exists between AI adoption in clinical practice and curricular scaffolding, with over 75% of surveyed students reporting no formal AI training despite high motivation among both students and faculty. Third, algorithmic bias, data privacy, deskilling through over-reliance, and infrastructure disparities represent structural equity and ethics concerns requiring deliberate governance frameworks. Conclusions: AI holds meaningful potential for clinical skills training, but it requires system-level investment in pedagogically grounded curricular integration, standardized competency frameworks, and equity-centered ethical governance. Future research should prioritize multi-institutional, longitudinal studies that link AI-enhanced educational outcomes to real-world clinical performance.
Background: Although urticaria is a common inflammatory skin disorder that affects up to 20% of the world population at some point during their life, there is a lack of systematic investigation into t...
Background: Although urticaria is a common inflammatory skin disorder that affects up to 20% of the world population at some point during their life, there is a lack of systematic investigation into the impact of both exogenous and endogenous factors on its prevalence. Objective: To gain a better understanding of the reasons behind the increasing prevalence of urticaria in Shenzhen. Methods: We investigated the prevalence and risk factors of urticaria among residents of Shenzhen using a multistage stratified random sampling method within each monitoring area in 2023. Results: The prevalence of urticaria among adults in Shenzhen was 3.18%, with a higher prevalence among females (3.54%) compared to males (2.76%). Additionally, we observed an increasing trend in prevalence with higher levels of education. Age, living district, occupation, and marital status were also found to significantly influence the prevalence of urticaria. Furthermore, a personal medical history and a family history of urticaria were identified as significant risk factors. Interestingly, our study also found that the consumption of certain beverages was associated with the prevalence of urticaria, with females being more affected by the consumption of beverages in general, while males were more affected by the consumption of milk and non-carbonated sugary beverages. Additionally, our study found that the frequency of use of cosmetic and sunscreen products had a significant impact on the prevalence of urticaria among female residents. Conclusions: our study provides important and comprehensive information that can guide prevention efforts and further exploration into the pathogenesis of urticaria. Clinical Trial: NA.
Background: Mosquito-borne diseases remain a major global health burden and are increasingly influenced by climatic and environmental changes. Agent-based models (ABMs) allow detailed representation o...
Background: Mosquito-borne diseases remain a major global health burden and are increasingly influenced by climatic and environmental changes. Agent-based models (ABMs) allow detailed representation of host–vector interactions and environmental heterogeneity; however, modelling approaches vary substantially across studies. Objective: This scoping review aimed to identify and characterize ABMs developed to simulate mosquito-borne disease transmission in relation to climatic and weather-related drivers, focusing on model structure, environmental integration, and validation approaches. Methods: Following PRISMA-ScR guidelines, literature searches were conducted in PubMed and IEEE Xplore up to 10 December 2025. Studies were included if they described agent-based or individual-based models simulating transmission between humans and mosquitoes and incorporated weather- or climate-dependent processes. Data were extracted using a structured framework covering methodological and environmental characteristics. Results: Sixteen studies met the inclusion criteria, primarily addressing dengue (n=7) and malaria (n=6) in tropical and subtropical regions. Most models reused existing frameworks and represented humans as individual agents, while mosquito populations were sometimes aggregated. Temperature and rainfall were the most frequently included environmental drivers, but their implementation ranged from simple empirical functions to mechanistic process-based implementations and varied considerably in temporal resolution. Calibration to empirical data was common, whereas validation procedures were often limited or insufficiently reported. No studies simulated European settings. Conclusions: ABMs provide valuable tools for investigating climate-sensitive transmission dynamics of mosquito-borne diseases. However, substantial heterogeneity in model design, environmental integration, and validation limits comparability. Improved reporting standards, better validation data, and increased focus on temperate regions and future climate scenarios are essential to improve comparability, reproducibility, and policy relevance of ABMs in climate-sensitive disease modelling.
Background: Work-related psychosocial hazards are increasingly recognised as modifiable determinants of mental health and wellbeing. In Great Britain, the Health and Safety Executive (HSE) Management ...
Background: Work-related psychosocial hazards are increasingly recognised as modifiable determinants of mental health and wellbeing. In Great Britain, the Health and Safety Executive (HSE) Management Standards identify six areas of work design associated with work-related stress: Demands, Control, Support, Relationships, Role and Change. Construction presents a distinctive surveillance challenge because work is often temporary, project-based, commercially pressured and delivered through fragmented supply chains. It remains unclear whether existing generic psychosocial risk assessment frameworks capture the hazards that need to be monitored in UK and Republic of Ireland construction. Objective: To identify work-related psychosocial hazards reported in peer-reviewed studies of construction workers in the UK and Republic of Ireland, map them against the HSE Management Standards, and assess their implications for construction-specific psychosocial risk identification and assessment. Methods: This rapid scoping review followed a published protocol and was reported in line with PRISMA-ScR, with adaptations for rapid review methodology. Web of Science, PsycINFO and MEDLINE were searched for English-language peer-reviewed studies published between 2000 and 2025. Eligible studies included UK or Republic of Ireland construction workers and reported work-related psychosocial risk factors linked to mental health or wellbeing. Data were extracted on study characteristics, participant demographics, psychosocial hazards and equity-related variables. Hazards were mapped to HSE Management Standards domains and assessed as captured, partially captured or not captured by HSE Stress Indicator Tool wording. Hazards that did not fit a single domain were synthesised thematically with hazards not captured by the tool to identify surveillance-relevant gaps. Results: Twelve studies were included: seven qualitative, two quantitative and three mixed-methods studies. Samples were predominantly male, and reporting of equity-related characteristics was limited, particularly for ethnicity, migrant status and employment type. Approximately 90 coded hazard entries were mapped across the six HSE domains. Around 28 aligned clearly with Stress Indicator Tool wording, including long hours, high workload, time pressure, limited decision authority, poor support, interpersonal conflict, role conflict and poor communication around change. However, approximately 60 hazards were not captured and two were partially captured. A further 22 hazards could not be mapped to a single HSE domain. These unmapped and not-captured hazards clustered into three surveillance-relevant categories: project-based commercial, programme, planning and delivery pressures; fragmented workforce structure, supply-chain position, financial insecurity and unequal access to protection; and delivery-first culture, weak worker voice and constrained recovery or help-seeking. Conclusions: The HSE Management Standards provide a useful foundation for psychosocial risk assessment in UK and Republic of Ireland construction, but they do not fully capture hazards generated by construction’s project-based commercial model, fragmented employment arrangements and delivery-first culture. Construction-specific surveillance and risk assessment should retain the HSE domains while adding indicators of upstream procurement, programme, planning, payment, employment and supply-chain conditions.
Background: Other infectious diarrhea (OID) remains an important public health concern in China because of its high incidence, marked seasonality, and substantial burden, particularly among children. ...
Background: Other infectious diarrhea (OID) remains an important public health concern in China because of its high incidence, marked seasonality, and substantial burden, particularly among children. Accurate short-term forecasting and early warning are important for timely public health response. However, previous OID forecasting studies have mainly relied on reported case data, and the added value of multisource indicators remains insufficiently evaluated. Objective: This study aimed to develop and evaluate a multisource CNN-BiLSTM-SE Attention model for short-term forecasting and early warning of reported other infectious diarrhea cases in Chongqing, China. Methods: Daily OID case counts in Chongqing from January 2015 to June 2025 were collected, together with meteorological variables and Baidu search indices related to infectious diarrhea. After data normalization, Pearson correlation analysis and random forest variable-importance analysis were used for predictor selection. A CNN-BiLSTM-SE Attention hybrid model was developed to integrate multisource data, extract local temporal patterns, model temporal dependencies, and recalibrate informative feature channels. Forecasting performance was evaluated using RMSE, MAE, MAPE, and R², and compared across different input settings and benchmark models. In addition, 5-day-ahead predictions were converted into binary warning signals using training-set 75th and 90th percentile thresholds, and compared with a persistence baseline. Results: Under the full-input setting, the CNN-BiLSTM-SE Attention model achieved the best predictive performance, with an R² of 0.7828, RMSE of 35.418, MAE of 25.411, and MAPE of 17.27%. Compared with the case-only model, R² increased by 0.0326, while RMSE and MAE decreased by 2.560 and 1.643, respectively. The proposed model also outperformed random forest, XGBoost, CNN, and LSTM. In the threshold-based early-warning evaluation, the full-input model showed better overall warning performance than the persistence baseline at both the 75th and 90th percentile thresholds. Conclusions: The CNN-BiLSTM-SE Attention hybrid model improved short-term forecasting of reported OID case counts in Chongqing. Integrating epidemiological, meteorological, and internet search data provided complementary information, suggesting potential utility for OID surveillance, forecasting, and early warning.
Background: Occupational burnout among healthcare and knowledge workers is associated with absenteeism, staff turnover, reduced well-being, and patient-safety risks. Existing burnout interventions are...
Background: Occupational burnout among healthcare and knowledge workers is associated with absenteeism, staff turnover, reduced well-being, and patient-safety risks. Existing burnout interventions are often reactive and depend heavily on self-reported symptoms. Wearable biosensors may provide continuous physiological and behavioral signals that allow earlier burnout-risk detection and more timely intervention delivery. Objective: This study aimed to evaluate whether a wearable biosensor–based machine learning system could predict 48-hour burnout risk and whether algorithmically triggered just-in-time adaptive interventions could reduce burnout symptoms over 16 weeks compared with wearable-only monitoring and passive control. Methods: A pre-registered, 16-week, three-arm parallel randomized controlled trial was conducted among 218 full-time healthcare and knowledge workers in the United Kingdom. Participants were assigned to a just-in-time intervention arm, a wearable-only arm, or a passive control arm. Wearable devices collected heart rate variability, electrodermal activity, skin temperature, actigraphy, and sleep data. Sixty-four physiological, sleep, activity, and ecological momentary assessment features were extracted. A stacked ensemble model combining XGBoost, bidirectional LSTM, and Random Forest classifiers predicted 48-hour burnout-risk onset. The primary outcome was the Maslach Burnout Inventory–General Survey score at 16 weeks. Linear mixed models assessed intervention effects, and structural equation modeling tested mediation through prediction accuracy and intervention engagement. Results: The ensemble model achieved moderate predictive performance, with AUROC = 0.78, sensitivity = 0.74, and specificity = 0.80, outperforming an HRV-only baseline. SHAP analysis identified RMSSD, sleep efficiency, and LF/HF ratio as leading predictors. At 16 weeks, the just-in-time intervention arm showed a significantly greater reduction in burnout than passive control, with an adjusted mean difference of -0.48 and a medium effect size. However, the just-in-time intervention did not significantly outperform the wearable-only arm on the primary outcome. Sequential mediation analysis indicated that prediction accuracy and intervention engagement jointly mediated the effect on burnout outcomes. Conclusions: A wearable biosensor–driven machine learning system can predict short-term burnout risk with moderate accuracy and may support clinically meaningful burnout reduction when linked to just-in-time adaptive interventions. However, the absence of clear superiority over wearable-only monitoring, the moderate false-positive burden, and limited follow-up duration suggest that larger, longer, and more diverse confirmatory trials are needed before large-scale implementation. Clinical Trial: ISRCTN14832991
Background: With the rapid digitalization of oncology care, digital health literacy has become a critical determinant of self-management and quality of life (QoL) among gynecologic cancer patients. Ho...
Background: With the rapid digitalization of oncology care, digital health literacy has become a critical determinant of self-management and quality of life (QoL) among gynecologic cancer patients. However, most existing studies treat digital health literacy as a homogeneous construct, potentially overlooking distinct skill patterns among patients. Furthermore, the rising financial burden of cancer treatment often forces patients to adopt various medical cost-coping behaviors, yet the underlying mechanism of how different digital health literacy profiles influence these behaviors and subsequent well-being remains poorly understood. Objective: The study aimed to identify heterogeneous patterns of digital health literacy via Latent Profile Analysis and to elucidate how medical cost-coping behaviors mediate the relationship between these digital health literacy profiles and QoL among gynecologic cancer patients. Methods: A cross-sectional study was conducted and convenience sample of gynecologic cancer patients was recruited between January and April 2024. Participants were assessed for digital health literacy, medical cost-coping behaviors and QoL. Latent profile analysis was performed using Mplus 8.3 to identify digital health literacy subgroups. Mediation analysis was conducted using the lavaan package in R 4.3.3 software, adjusting for key sociodemographic and clinical covariates. Results: A total of 378 questionnaires were analyzed. Three distinct digital health literacy profiles emerged: “Digital health novices” (n=86, 22.75%), “Competent navigators” (n= 217, 57.41%) and “Critical but hesitant users” (n=75, 19.84%). Mediation analysis revealed that higher digital health literacy proficiency was positively associated with QoL. Medical cost-coping behaviors significantly mediated this relationship. Compared to the “Digital health novices”, the total effects of on QoL were 13.23 for “Competent navigators” and 20.32 for “Critical but hesitant users” (both P <0.001). The indirect effects via fewer medical cost-coping behaviors were 2.78 (95% CI: 1.47–4.39) and 4.34 (95% CI: 2.69–6.23), respectively. Conclusions: This study highlights the heterogeneity of digital health literacy among gynecologic cancer patients. Findings suggest that proficient digital health literacy is not only directly associated with higher QoL but also linked indirectly to better health outcomes through its association with lower levels of medical cost-coping behaviors. Future interventions could focus on empowering patients to seek, evaluate, and apply digital health information, which may be associated with reduced maladaptive medical cost coping behaviors and improve overall well-being. Clinical Trial: NA
Background: Comprehensive geriatric assessment (CGA) is central to the care of older adults with complex and multidimensional needs, but its delivery in routine practice is often limited by substantia...
Background: Comprehensive geriatric assessment (CGA) is central to the care of older adults with complex and multidimensional needs, but its delivery in routine practice is often limited by substantial time, workforce, and coordination demands. Digital and intelligent technologies are increasingly being applied to CGA delivery across clinical and community settings; however, evidence on their assessment coverage, technological formats, functional features, and architectural patterns remains fragmented and has not been comprehensively synthesized. Objective: This scoping review aimed to synthesize existing evidence on the assessment domain coverage, technological formats, functional features, and architectural patterns of digital and intelligent technologies used in CGA for older adults., thereby providing a reference for future research and Clinical practice. Methods: A systematic retrieval of relevant research was conducted using databases including PubMed, EMBASE, Web of Science, Scopus and the Cochrane Library, The time frame for the retrieval spans from database inception to March 2026. Relevant studies were screened and analyzed systematically. Results: A total of 34 studies published between 2008 and 2025 were included. Digital and intelligent CGA tools were used across inpatient, outpatient, home-based, community, and long-term care settings, with 14 studies (41.2%) involving more than one care setting. The most frequently assessed domains were functional status (30, 88.2%), cognition (28, 82.4%), and nutrition and psychological well-being (24, 70.6%). Web-based systems (21, 61.8%) and applications (16, 47.1%) were the main technological formats. The most common functions were electronic data capture and management (34, 100%), rule-driven clinical decision support (33, 97.1%), and automated scoring (30, 88.2%). Technological formats showed different domain-coverage patterns. Web-based systems were mainly used for functional status, cognition, nutrition, and psychological well-being. EHR/EMR/HIS-integrated systems were more concentrated in medication, comorbidities, and pressure injury, whereas audio-video telecommunication systems covered few. Technologies for continuous objective monitoring, performance-based assessment, and specialised interpretation remained less commonly applied. Overall, existing tools mainly digitised scale-based CGA domains and routinely recorded clinical information, relied on rule-based rather than adaptive decision support, and extended assessment across care settings. Conclusions: Digital and intelligent CGA shows potential to make multidomain assessment more structured, efficient, and continuous. However, current systems still have incomplete domain coverage, limited integration of emerging technologies, and insufficient evidence on measurement validation, adaptive intelligence, and downstream care implementation. Future research should use more rigorous designs to evaluate effectiveness and develop age-friendly, interoperable, and care pathway–embedded CGA systems that support earlier risk identification, personalised care planning, and continuous health management for older adults.
Background: Current risk stratification in acute myocardial infarction complicated by cardiogenic shock (AMI-CS) relies on static admission-time scores that cannot capture a patient's dynamic response...
Background: Current risk stratification in acute myocardial infarction complicated by cardiogenic shock (AMI-CS) relies on static admission-time scores that cannot capture a patient's dynamic response to initial resuscitation. We developed and externally validated a parsimonious, interpretable machine learning model—V20-lite—that predicts hemodynamic deterioration during the critical 24- to 48-hour window using only the physiological trajectory from the first 24 hours of intensive care.
Methods: We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database for model development and internal validation, with the eICU Collaborative Research Database (eICU-CRD, version 2.0) serving as an independent, geographically diverse external validation cohort. Adult patients with AMI-CS were identified using ICD-9/10 codes. The primary outcome was a composite of hemodynamic deterioration occurring between 24 and 48 hours after ICU admission, defined by sustained hypotension, worsening hyperlactatemia, progressive oliguria, acute kidney injury, vasopressor escalation, or death. We engineered 70 candidate features from the 0–24-hour window and trained seven machine learning algorithms. SHapley Additive exPlanations (SHAP) were used to distill an 8-feature composite model (V20-lite) from the best-performing XGBoost algorithm. We assessed discrimination, calibration (Integrated Calibration Index [ICI], Brier score), and clinical utility (decision curve analysis). Logistic recalibration was pre-specified to address calibration drift in external validation. The model was deployed as an open-access Shiny application.
Results: The derivation cohort included 1,633 patients (mean age 70.5 years, 61.8% male) from MIMIC-IV; 653 patients (mean age 68.1 years, 61.9% male) from eICU-CRD comprised the external validation cohort. The primary outcome occurred in 482 (29.5%) and 246 (37.7%) patients, respectively. In the internal test set (n=327, event rate 30.6%), V20-lite achieved an area under the receiver operating characteristic curve (AUC) of 0.815 (95% CI, 0.763–0.867), significantly outperforming the admission Sequential Organ Failure Assessment (SOFA) score (AUC 0.639; DeLong P<0.001). Sensitivity analysis confirmed that removing trajectory data reduced AUC to 0.588. In external validation, the original model maintained robust discrimination (AUC 0.756; 95% CI, 0.716–0.795). Observed calibration drift (intercept −0.324; ICI 0.144) was fully corrected by logistic recalibration (intercept 0.000; ICI 0.032; Brier score 0.186). SHAP analysis identified the 24-hour creatinine ratio, 24-hour lactate, and lactate-to-MAP ratio as the dominant prognostic drivers. Subgroup analysis revealed reduced performance in patients with initially normal lactate (AUC 0.671; P for interaction <0.001).
Conclusions: V20-lite provides accurate, interpretable, and dynamically informed risk stratification for early hemodynamic deterioration in AMI-CS using eight routine bedside parameters. The model's calibration is transportable across heterogeneous healthcare systems through simple logistic recalibration, and its open-access deployment facilitates immediate clinical application.
Background: Child and adolescent mental health services (CAMHS) and child development clinics (CDCs) in the UK are facing unprecedented demand, resulting in prolonged waiting times for assessment of n...
Background: Child and adolescent mental health services (CAMHS) and child development clinics (CDCs) in the UK are facing unprecedented demand, resulting in prolonged waiting times for assessment of neurodevelopmental conditions, including autism spectrum disorder and attention deficit hyperactivity disorder. Delayed diagnosis and intervention are associated with poorer outcomes, with administrative burden being a key contributor to limited assessment capacity. Emerging digital health technologies like ambient voice technology assisted documentation (AVT-A) can reduce this burden and improve service efficiency. This second phase study builds upon the authors’ previous AVT proof-of-concept study. Objective: 1. To assess the performance of AVT-A across a broad range of use cases in CAMHS outpatient clinics and a CDC
2. To assess the impact of AVT-A on quantitative and qualitative clinical outcomes, including administrative burden
3. To explore clinician, patient and carer perceptions towards AVT-A in clinical settings Methods: A mixed-methods pre-post service development pilot was conducted from December 2024 to March 2025, comparing AVT-A with manual documentation. The study was conducted across three sites: two CAMHS outpatient clinics and one CDC. Clinicians spanning mental health and neurodevelopmental roles were invited to participate, testing 12 different use cases. The primary outcome measure was self-reported time taken to complete administrative tasks per clinical encounter. Secondary outcome measures included qualitative clinician experience and patient/carer perception of AVT-A. Results: 37 clinicians provided baseline and intervention timesheet data. Most were full-time working mental health nurses, aged 25-34 and female. A total of 1,085 clinical appointments were recorded, with 50% (n=543) using AVT-A. Across all use cases, the mean administration time was 28% less with AVT-A compared to manual documentation (p<0.001). However, satisfaction with documentation accuracy and confidence in documentation quality were higher with manual documentation compared with AVT-A (85% vs 69%, p=0.11, and 81% vs 73%, p=0.46, respectively), with no statistically significant differences observed. No significant differences were found in self-reported efficiency of seeing patients or completing administrative work. Data from patient/carer surveys revealed no significant differences between AVT and manual documentation across any measures. Conclusions: In the face of significant demand, AVT-A presents an opportunity to improve service capacity, clinician wellbeing and patient experience. AVT significantly reduces documentation burden in neurodevelopmental assessments and is acceptable for patients and carers. However, challenges including documentation accuracy and quality must be addressed.
Background: Home-based exercise programs for congenital muscular torticollis (CMT) depend heavily on caregivers, yet adherence is often hindered by cognitive and emotional burdens. Objective: This stu...
Background: Home-based exercise programs for congenital muscular torticollis (CMT) depend heavily on caregivers, yet adherence is often hindered by cognitive and emotional burdens. Objective: This study conducted a formative usability evaluation of an AI-based CMT rehabilitation system comprising a clinician web interface and a caregiver mobile application to identify user requirements and design improvements during early development. Methods: A formative evaluation was performed involving eight clinical experts (rehabilitation physicians, nurses, and physical therapists). Participants engaged in focus group interviews (FGIs) following a product demonstration and completed the System Usability Scale (SUS). Qualitative data were analyzed thematically using QualCoder, while quantitative usability was assessed through SUS scoring. Results: The thematic analysis identified two core themes: "Usability Barriers of the CMT Rehabilitation System" and "Improvement Requirements," encompassing 10 subthemes and 21 concepts. Key findings highlighted the necessity of enhancing AI feedback reliability and simplifying data visualization to reduce the cognitive workload of caregivers. The mean SUS score was 73.44 (SD = 18.01), indicating "Good" and "Acceptable" (Grade B-) usability. Conclusions: While the AI-based CMT rehabilitation system demonstrated acceptable usability, the experts emphasized that it must function as a therapeutic bridge between clinicians and caregivers. The findings of this study provide immediate design specifications for the manufacturing of AI-based rehabilitation devices and offer actionable operational strategies for enhancing service delivery in home-based clinical environments. Future iterations should incorporate the identified improvements and undergo summative evaluation in actual home environments to verify clinical effectiveness.
Background: Extensive research consistently reveals significant health disparities among Black communities in Canada, highlighting a gap in the publicly funded universal healthcare model in providing ...
Background: Extensive research consistently reveals significant health disparities among Black communities in Canada, highlighting a gap in the publicly funded universal healthcare model in providing equitable care. Such a system should ensure that healthcare needs are adequately addressed for all residents. However, anti-Black racism across all sectors of Canadian society and deficient access to health services have been identified as contributors to poor health outcomes among Black people living in Canada. Black communities are also challenged with over-exposure to adverse social determinants of health, such as poverty, inadequate housing, and high rates of racialized systemic violence, which increase susceptibility to both physical and mental health conditions. The ongoing systemic health inequities experienced by Black communities in Canada highlight the lack of understanding of these communities’ health needs. Currently, there is no comprehensive picture of the relationship between the social determinants of health and health outcomes of Black communities in Canada. Objective: This paper outlines a protocol for a scoping review that aims to explore the breadth of the literature that examines the impact of social determinants of health on physical and mental health outcomes among Black communities in Canada, as well as the gaps in this literature. Methods: The scoping review adopts the approach outlined by Arksey and O’Malley. In addition, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework will be utilised to ensure comprehensive reporting of the review’s methods and findings. This review aims to investigate the scope and breadth of research gaps on the impact of social determinants of health on physical and mental health outcomes among Black communities in Canada. The databases searched to identify studies that meet the selection criteria were MEDLINE, Embase, APA PsycINFO, CINAHL Plus, Sociological Abstracts, PAIS Index, and Web of Science Core Collection. A two-stage screening process identified relevant articles and the information extracted from the articles will be iteratively refined. The review uses thematic and descriptive analyses to meet scoping aims. Results: The title, abstract, and full-text screening was concluded in November 2025. Fifty-six articles were selected following a two-stage screening process. Data collection and analysis are in progress and are expected to be completed by May 2026. Drafting of the manuscript will be done thereafter with the findings from the scoping review expected to be provided for peer review between July and September 2026. Conclusions: The scoping review will help inform policy, practice, and research on the impact of the social determinants of health on Black people in Canada. The findings will also provide important information on the systemic causes of poor health outcomes for Black people in Canada.
Background: Drowning is a leading cause of unintentional injury-related mortality worldwide, particularly in swimming pool environments where rapid detection is critical to prevent fatalities. Traditi...
Background: Drowning is a leading cause of unintentional injury-related mortality worldwide, particularly in swimming pool environments where rapid detection is critical to prevent fatalities. Traditional monitoring approaches rely on lifeguards and rule-based systems, which are often limited by human error, delayed response, and environmental challenges such as occlusion, glare, and multiple swimmers. Machine learning and computer vision methods have emerged as promising alternatives for automated drowning detection; however, their methodological rigor, real-world applicability, and reliability remain unclear. Objective: This study aimed to systematically review advancements in machine learning–based drowning detection systems over the past 15 years. The review focused on detection methods, sensing modalities, evaluation metrics, and validation practices. It also examined how studies addressed real-world challenges such as occlusion, lighting variability, and multi-swimmer environments, and identified key gaps related to generalizability, reliability, and privacy. Methods: A systematic literature review was conducted using major electronic databases, including IEEE Xplore, Scopus, PubMed, Web of Science, ACM Digital Library, and Google Scholar. A total of 5,904 records were initially identified and screened by two independent reviewers using predefined inclusion and exclusion criteria. A final set of 30 studies was included for data extraction and synthesis. The methodological quality and risk of bias were assessed using the PROBAST tool, while r eporting transparency was evaluated using the TRIPOD guidelines. Data analysis was conducted in R. Results: The findings indicate that drowning detection research is dominated by supervised, vision-based approaches (80% of studies), with convolutional neural networks (38%) and YOLO-based architectures (24%) being the most commonly used methods. While reported performance is generally high, with mean accuracy exceeding 92.9%, evaluation practices are heavily centered on accuracy, with limited reporting of precision, recall, false alarm rate, and latency. Only 5 out of 30 studies (16.7%) reported real-world validation, and no studies conducted cross-dataset evaluation. In addition, critical real-world challenges such as occlusion, glare, and crowding were rarely addressed (<10% of studies), and all studies relied on private datasets. The PROBAST assessment further indicated a high risk of bias across all included studies, primarily due to limited validation and non-representative data sources. Conclusions: Machine learning–based drowning detection systems have demonstrated substantial progress in algorithm development, particularly in vision-based models. However, this progress is not matched by equivalent advances in validation, robustness, and real-world applicability. The field is characterized by a significant gap between experimental performance and deployment readiness. Future research should prioritize standardized evaluation frameworks, external validation, comprehensive performance reporting, and the integration of multimodal and privacy-preserving approaches to enable reliable real-world implementation.
Background: The PreOperative Score to Predict PostOperative Mortality (POSPOM) is a validated tool developed in European cohorts, but its performance in diverse U.S. populations and across socioeconom...
Background: The PreOperative Score to Predict PostOperative Mortality (POSPOM) is a validated tool developed in European cohorts, but its performance in diverse U.S. populations and across socioeconomic subgroups remains unclear. Objective: To evaluate the performance of POSPOM in a diverse U.S. cohort and evaluate its performance in high-risk surgical patients and across sociodemographic subgroups. Methods: We performed a retrospective cohort study using the All of Us Research Program. Adults (≥18 years) undergoing POSPOM-eligible surgeries were included. POSPOM scores were calculated using age, procedure type, and comorbidities. Outcomes included 30-day and 1-year postoperative mortality. Logistic regression was used to assess the association between POSPOM score and mortality. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), with subgroup analyses by race, ethnicity, income, and education. A high-risk subgroup (procedure points ≥14) was analyzed separately. Results: The study analyzed a total of 17,262 patients, observing a 30-day mortality rate of 0.07% and a 1-year mortality rate of 0.26%. For 30-day mortality, the POSPOM score demonstrated an AUC of 0.764 and an odds ratio (OR) of 1.112 per point (95% CI 1.040–1.190), while for 1-year mortality, the AUC was 0.638 with an OR of 1.054 (95% CI 1.017–1.092). Within the high-risk subgroup (n=536), the AUC for 30-day mortality was 0.583. For 1-year mortality in this same subgroup, the AUC was 0.692, and the OR was 0.882 (95% CI 0.781–0.998), reflecting an inverse association between the score and mortality. Finally, race-stratified analyses indicated that Black patients had higher observed mortality and higher AUC values when compared to White patients. Conclusions: POSPOM performs well for short-term mortality in a diverse U.S. cohort but shows reduced and inconsistent performance in high-risk populations and across demographic subgroups, highlighting the need for recalibration and more equitable risk prediction tools.
Background: Documentation burden contributes to physician burnout, with trainees spending up to 50% of clinical time on electronic health record (EHR) tasks. Ambient artificial intelligence (AI) scrib...
Background: Documentation burden contributes to physician burnout, with trainees spending up to 50% of clinical time on electronic health record (EHR) tasks. Ambient artificial intelligence (AI) scribe technology uses speech recognition and large language models to generate clinical notes from patient-clinician conversations, yet evidence of its impact on trainee workload in dermatology remains limited. Objective: This study evaluated the impact of an ambient AI scribe (Abridge) on subjective workload and documentation burden among dermatology residents at an academic medical center. Methods: We conducted a single-arm, pre-post study at Yale School of Medicine. Pre- and post-implementation surveys were administered via Qualtrics and matched by participant identity, yielding 13 paired respondents (from 18 pre- and 14 post-survey completions; 1 non-user excluded). The primary outcome was the NASA Task Load Index (Raw TLX, 6-item composite, 0–100 scale; lower = less workload). Secondary outcomes included the AMIA TrendBurden instrument (5 items, 0–100 visual analog scale [VAS]), self-reported documentation time at 3 clinical timepoints, and 9 post-implementation experience items. Paired analyses used Shapiro-Wilk normality testing to select between paired t-tests and Wilcoxon signed-rank tests. Bonferroni correction was applied to TrendBurden comparisons (α = .010). Effect sizes were calculated as Cohen's d. Results: Participants were predominantly female (62%), aged 25–34 years (85%), and distributed across PGY-2 (31%), PGY-3 (23%), and PGY-4 (46%) training levels. The NASA-TLX composite score decreased significantly from 67.3 (SD 13.2) at baseline to 44.0 (SD 16.6) post-implementation (P = .003; Cohen's d = 1.01), representing a 34.7% reduction. Five of 6 individual workload dimensions improved: Mental Demand (P = .006), Physical Demand (P = .004), Temporal Demand (P < .001), Effort (P = .011), and Frustration (P = .010). Performance self-assessment was unchanged (P = .636). Two of 5 TrendBurden items were significant with Bonferroni correction: "Documentation impedes quality care delivery" decreased from 78.6 to 36.6 (P = .002), and "Ease of EHR documentation" improved from 34.7 to 61.5 (P = .006). All 5 positive post-implementation experience items exceeded the scale midpoint (means 59.6–68.2 on a 0–100 VAS), and trainees endorsed continued use of the AI scribe (mean 75.9). Concerns about AI hallucinations (mean 41.2) and missed information (mean 33.2) remained below the scale midpoint. Conclusions: Implementation of an ambient AI scribe significantly reduced subjective workload and documentation burden among dermatology residents, with a large effect size replicating prior multi-site findings. Two documentation burden items survived Bonferroni correction, and trainee concerns about AI accuracy remained below the scale midpoint. These findings support integration of ambient AI scribes into graduate medical education in dermatology. Clinical Trial: Not Applicable.
Open Peer Review Period: May 15, 2026 - Apr 30, 2027
Background: High-fidelity medical simulation is currently synonymous with expensive, hardware-intensive robotic mannequins. This paradigm creates a “hardware trap,” where quality clinical training...
Background: High-fidelity medical simulation is currently synonymous with expensive, hardware-intensive robotic mannequins. This paradigm creates a “hardware trap,” where quality clinical training is restricted to wealthy urban centers, leaving rural and low-resource settings—particularly in the Global South—behind. Objective: This viewpoint proposes a shift from hardware-centric simulation to “Lightweight Simulation” (LWS) powered by web-based technologies such as JavaScript and Progressive Web Apps (PWAs). It argues that prioritizing cognitive fidelity over physical realism is essential for democratizing medical education globally. Methods: Economic, logistical, and educational barriers associated with conventional simulation models were analyzed alongside evidence from digital health education literature. A policy-oriented framework for decentralized simulation was developed based on curricular integration, reimbursement strategies, and open-source implementation principles. Results: Lightweight, browser-based simulations substantially reduce infrastructure dependency and cost per learner-hour while enabling asynchronous, offline-capable training on low-cost mobile devices. Existing evidence suggests that screen-based simulations and virtual patient systems can effectively support diagnostic reasoning and clinical decision-making competencies comparable to traditional methods. Integrating these systems into digital health ecosystems additionally creates opportunities for scalable patient education and remote competency development. Conclusions: Achieving global health equity requires decoupling clinical competency from expensive physical simulation infrastructure. Decentralized, software-driven simulation models represent not merely a technological innovation but a structural strategy for expanding equitable access to medical training. By prioritizing accessibility, cognitive fidelity, and interoperability, lightweight simulation platforms may enable scalable competency-based education in underserved settings worldwide.
Nineteen dyads of rural-dwelling African Americans with MCI and their care partners were recruited through multifaceted outreach strategies to academic, clinical, community, and government organizatio...
Nineteen dyads of rural-dwelling African Americans with MCI and their care partners were recruited through multifaceted outreach strategies to academic, clinical, community, and government organizations, as well as to the community-at-large.
Background: Background: Extended reality (XR) technologies including virtual reality (VR), augmented reality (AR), and mixed reality (MR) are progressively evolving from peripheral innovations to esse...
Background: Background: Extended reality (XR) technologies including virtual reality (VR), augmented reality (AR), and mixed reality (MR) are progressively evolving from peripheral innovations to essential elements of modern medical education. While their effectiveness in knowledge acquisition is well-documented, a significant gap remains regarding their impact on the psychological well-being and subjective experiences of health professions students. This systematic review and meta-analysis synthesize empirical evidence on the psychological effects of XR, specifically evaluating engagement, motivation, cognitive load, stress, and technology acceptance. Objective: This systematic review and meta-analysis aimed to identify and synthesize empirical evidence on the psychological effects of XR modalities (VR, AR, and MR) in medical education, compare their relative effectiveness across different learner populations and educational contexts, and provide evidence-based recommendations for optimal implementation. Methods: Method: A comprehensive search of seven electronic databases (PubMed/MEDLINE, Scopus, Web of Science, Embase, PsycINFO, CINAHL, and ERIC) was conducted in accordance with PRISMA 2020 guidelines. Of the 2,890 records identified, 84 studies were included in the narrative synthesis, with 42 meeting methodological homogeneity requirements for quantitative meta-analysis using a random-effects model. Results: Results: Quantitative synthesis revealed significant positive impacts on student engagement (pooled SMD = 0.68, 95% CI: 0.54–0.82) and usability (SMD = 0.63, 95% CI: 0.49–0.77) across all XR modalities. XR interventions were associated with beneficial reductions in performance anxiety (SMD = -0.45, 95% CI: -0.65 to -0.25) and extraneous cognitive load (SMD = -0.53, 95% CI: -0.72 to -0.34). While VR showed the strongest effects for presence and immersion (SMD = 0.78), it also produced the highest incidence of cybersickness (28–42%) compared to AR (≤8%). Conclusions: Conclusion: XR technologies offer broad psychological benefits by enhancing immersion and motivation while simultaneously reducing cognitive load and emotional burdens. However, these benefits depend on the situation and the quality of the instructional design, hardware specifications, and learner expertise. The findings provide a strategic framework for educators to implement psychologically supportive immersive learning experiences, emphasizing that pedagogical planning must precede technology purchase.
Background: Workplace violence (WPV)—including physical assaults, threats, and verbal/psychological aggression—disproportionately affects healthcare workers, particularly in high-acuity and behavi...
Background: Workplace violence (WPV)—including physical assaults, threats, and verbal/psychological aggression—disproportionately affects healthcare workers, particularly in high-acuity and behavioral health settings. Although many organizations provide WPV prevention education, training is often delivered through scalable didactic or online modules that may be limited in preparing staff for high-stress, interpersonal crisis encounters. Immersive virtual reality (VR) offers a safe-to-fail, experiential modality that can evoke realistic emotional responses and support repeated practice of de-escalation skills. However, evidence is limited regarding how debriefing format influences learning from VR-based WPV training. Objective: To evaluate the usability of DEFUSE, an immersive VR-based WPV prevention and behavioral management training, and to compare debriefing formats (individualized, in-headset VR debrief vs educator-led, in-person group debrief) with respect to changes in aggression attribution beliefs. Methods: Frontline clinical and nonclinical staff were recruited from three major healthcare institutions in Central Ohio. Using a quasi-experimental design with convenience allocation, participants completed a single DEFUSE training session consisting of two VR modules and one of two debriefing formats: (1) individualized, in-headset VR debriefing following each module (n = 44), or (2) educator-led group debriefing following completion of both modules (n = 47). Usability was assessed immediately after post-exposure. Beliefs toward causes of patient aggression and management were assessed using the Management of Aggression and Violence Attitude Scale (MAVAS) at pre-post exposure, and 4–6-week follow-up. Participants were also invited to semi-structured focus groups; interviews were audio-recorded and thematically analyzed. Quantitative outcomes were examined using ANCOVA and multilevel modeling. Results: Ninety-one participants completed the study, with 44 (52%) completing follow-up at 4–6 weeks. Overall VR usability was high (SUS-equivalent M = 78.44) and did not differ between debriefing conditions (p = .457). Baseline-adjusted ANCOVA indicated a significant advantage for the VR-based debrief on MAVAS environmental attributions at immediate post-exposure (p < .05), whereas other between-condition differences were nonsignificant. Multilevel models showed significant time effects for situational (B = 0.08, SE = 0.022, p = .001) and environmental (B = 0.10, SE = 0.029, p = .001) attributions across three assessments, while biological attributions showed no significant change over time. Condition effects were not retained in final longitudinal models. Qualitative findings indicated that emotional realism and behavioral fidelity supported experiential learning, and that individualized, emotionally paced debriefing with timely, action-linked feedback facilitated reflection and skill consolidation. Participants emphasized that scalability depends on minimizing interface friction for novice users and embedding VR training within onboarding and shift-based workflows. Conclusions: DEFUSE demonstrated strong usability and supported changes in staff beliefs toward more contextual understandings of patient aggression over time. The VR-based, in-headset debrief produced outcomes largely comparable to educator-led debriefing, with indications of added benefit for environmental attributions.
Background: Radiation Dermatitis (RD) is one of the most common adverse reactions of Radiation Therapy (RT), with the incidence of moderate-to-severe radiation dermatitis exceeding 30%. This not only ...
Background: Radiation Dermatitis (RD) is one of the most common adverse reactions of Radiation Therapy (RT), with the incidence of moderate-to-severe radiation dermatitis exceeding 30%. This not only prolongs the treatment course and increases the burden on the healthcare system, but also significantly reduces patients' quality of life. Short-video platforms are increasingly used in health communication, providing a channel for the popularization of medical science knowledge. However, the quality and reliability of health-related content, especially research on diseases such as radiation dermatitis, remain to be further explored. Objective: The main purpose of this study is to evaluate the quality of videos about Radiation Therapy (RT) on TikTok and Bilibili, and the secondary purpose is to study the related factors of video quality. Methods: We searched for the keyword “Radiation Dermatitis”on both TikTok and Bilibili platforms separately, identifying 400 videos and ultimately including 190 short videos related to radiation dermatitis. Videos were categorized based on the uploader’s background (professional healthcare providers, non-professional healthcare providers, individual users, and institutions). Two independent reviewers compared key metrics, including the number of likes, comments, video duration, and quality scores (GQS, JAMA, and Modified DISCERN). Results: The analysis revealed that TikTok videos were significantly shorter in duration than those on Bilibili (p < 0.05), yet they demonstrated higher audience engagement and received higher ratings. The median Modified DISCERN and GQS scores were 3 (IQR: 2–3) and 3 (IQR: 2–3), respectively, which were higher than those on Bilibili. Videos created by professional healthcare providers demonstrated higher reliability and quality, with Modified DISCERN scores of 3 and GQS scores of 3. Correlation analysis revealed a significant positive correlation between video shares and saves, as well as notable positive correlations between engagement metrics and GQS, JAMA, and Modified DISCERN scores (e.g., Modified DISCERN score and number of likes, r = 0.86). Conclusions: Social media platforms provide partial support for the dissemination of health information about RD, but the overall video quality remains suboptimal. We recommend that professional creators pursue platform certification to enhance the dissemination of high-quality RD-related videos.
Background: The TCF7L2 gene is a crucial genetic risk element for prevalent, polygenic Type 2 Diabetes (T2D). Although many research efforts emphasize intronic variants, infrequent non-synonymous sing...
Background: The TCF7L2 gene is a crucial genetic risk element for prevalent, polygenic Type 2 Diabetes (T2D). Although many research efforts emphasize intronic variants, infrequent non-synonymous single-nucleotide polymorphisms (nsSNPs) found in the coding region, especially within the HMG-box domain, are still mostly uninvestigated.
This research seeks to explore the structural and functional impacts of these mutations on the Wnt/β-catenin signaling pathway and in the regulation of GLP-1. Objective: This study aims to understand how different changes in the TCF7L2 gene affect its structure and function and how these changes might lead to T2D.
To do this, we will find new gene changes (nsSNPs) from a specific part of the genome using data from dbSNP and ClinVar. We will then look at how harmful these changes might be and how stable the proteins made by these genes are. We will create 3D models of the normal and changed versions of TCF7L2 and check if they work properly. We will also use computer simulations to see how well these proteins connect with β-catenin and the glucagon gene. We will run long simulations to check how stable these connections are over time. Finally, we will map these changes onto the Wnt signaling pathway to understand how they affect the process that leads to T2D. Methods: We examined sequence data from the NC_000010.11 region of human chromosome 10 (113151270..113151374) through computational genomics. We obtained missense mutations (such as rs176632 and rs183524814) from the dbSNP and ClinVar databases. Pathogenicity was estimated using SIFT and PolyPhen-2 through the Galaxy platform. We constructed the 3D models of both wild-type and mutated TCF7L2 proteins with PyMOL, assessed binding affinities with AutoDock Vina, and conducted 100 ns Molecular Dynamics (MD) simulations through GROMACS. Downstream signaling effects were charted in relation to the KEGG Wnt signaling pathway map map04310 Results: Mutations like W480L caused notable steric interference in the HMG-box domain, diminishing thermodynamic stability (ΔΔG shift).
Molecular docking revealed that the altered TCF7L2 protein exhibits a reduced binding affinity for β-catenin (ΔG = -8.2 kcal/mol) in contrast to the wild-type (ΔG = -10.5 kcal/mol).
Additionally, the engagement with the proglucagon promoter DNA was weakened Conclusions: The missense mutations in the HMG-box domain interfere with TCF7L2's binding function, hindering Wnt-dependent transcription and GLP-1 secretion, thereby offering a molecular explanation for pancreatic β-cell dysfunction in T2D Clinical Trial: Not Applicable
Background: Background
AI-driven tools have shown promise in assisting patients with healthcare navigation and improving e-health literacy, but research systematically examining their acceptance from...
Background: Background
AI-driven tools have shown promise in assisting patients with healthcare navigation and improving e-health literacy, but research systematically examining their acceptance from the perspective of patients remains limited. Objective: Objective
Assess patients’ barriers to healthcare navigation, e-health literacy, and willingness to use AI-assisted nursing services, and use a mixed-methods approach to develop evidence-based nursing policy recommendations. Methods: Methods
The study employed an explanatory mixed-methods design. The first phase consisted of quantitative analysis, using hierarchical multiple regression to identify independent predictors of use intention. In the second phase, purposeful sampling was conducted based on the results of the first phase, followed by semi-structured interviews, which were analyzed using Braun and Clarke’s thematic analysis method. Data were integrated using connecting and merging strategies, and the results were presented in a joint presentation matrix. Results: Results
Perceived usefulness and e-health literacy were positive predictors of AI use intention, while perceived risk was a negative predictor. Barriers to accessing medical care did not reach statistical significance in the final model. The qualitative analysis identified five dimensions and 15 subthemes. Conclusions: Conclusion
Perceived usefulness and e-health literacy are important factors driving patient’s use intention of AI-based healthcare services, while multidimensional perceived risks—including distrust of technology, privacy concerns, and emotional detachment—constitute the primary barriers. The digital divide places older adults with chronic conditions, who have the most urgent healthcare needs, at a disadvantage when it comes to AI applications. Healthcare policies should promote a service model that combines AI systems with human triage and establish a nurse-led process for reviewing AI outputs. Clinical Trial: Not applicable
Background: Medication nonadherence remains a major global health challenge, contributing to preventable disease, hospitalizations, and healthcare costs. Mobile health (mHealth) applications incorpora...
Background: Medication nonadherence remains a major global health challenge, contributing to preventable disease, hospitalizations, and healthcare costs. Mobile health (mHealth) applications incorporating gamification and financial incentives have shown potential to improve adherence; however, most research has focused on patient perspectives, with limited understanding of how non-patient stakeholders perceive their feasibility, risks, and implementation. Understanding non-patient stakeholder perspectives in relation to patient viewpoints is essential for informing future policy development and establishing practical, industry-supported safeguards that protect consumers while enabling innovation. Objective: This study aimed to explore non-patient stakeholder perspectives on the use of gamification and financial incentives in mHealth apps for medication adherence and to integrate these with previously reported patient perspectives to inform consensus-based design and policy considerations. Methods: A mixed-methods study was conducted using a modified virtual Nominal Group Technique (vNGT). Non-patient stakeholders across healthcare, industry, and policy sectors in Australia were recruited. Data collection involved a pre-session survey followed by online focus groups. Qualitative responses were analyzed using thematic analysis supported by AI-assisted coding. Consensus statements derived from themes were rated during the focus groups. Additional prompts were used to elicit further discussion where consensus was not immediately achieved. Results: A total of 20 participants were included in the study. Six key themes were identified: tailored gamification for adherence, financial incentives as a contested motivator, designing for diversity and inclusion, usability barriers to engagement, trust through data governance, and validated and sustainable innovation. These informed 24 consensus statements, of which 54% (13/24) achieved unanimous agreement. Stakeholders strongly endorsed personalization, simplicity, and transparent data practices, while expressing nuanced concerns regarding the ethical use, sustainability, and potential unintended consequences of financial incentives. Compared with prior patient findings, the participants demonstrated substantial alignment on core design principles but contributed additional system-level considerations related to feasibility, scalability, and regulation. Conclusions: Non-patient stakeholders largely reinforce patient priorities while extending them with critical perspectives on implementation, governance, and sustainability. Gamification and financial incentives are viewed as potentially effective but require careful, ethically grounded design to balance engagement with long-term motivation and trust. These findings support the development of stakeholder-informed guidelines for responsible mHealth innovation and highlight the importance of integrating patient and system-level perspectives in digital health design. Future research should prioritize co-designed longitudinal studies utilizing apps with gamification and a range of incentive offers with clear redemption processes to evaluate the long-term impact on medication adherence across diverse patient populations.
Background: High-performing perioperative prediction models have not consistently translated into clinical benefit, in part because model outputs must be delivered through clinical decision support sy...
Background: High-performing perioperative prediction models have not consistently translated into clinical benefit, in part because model outputs must be delivered through clinical decision support systems (CDSS) that align with anesthesia workflows and end-user needs. Objective: To identify anesthesia professionals’ requirements for perioperative CDSS and use these findings to inform the design specification of a user-centered perioperative CDSS. Methods: This user-centered study was conducted in four sequential phases: translation of a previously validated explainable machine-learning model into candidate CDSS functions; three rounds of focus group–based iterative prototyping; a nationwide cross-sectional questionnaire survey; and CDSS finalization based on iterative prototyping and survey findings. The survey assessed requirements for information display, alerting, explainability, intervention support, and workflow integration among anesthesia-related professionals in China. Results: Three rounds of focus group discussion and iterative prototyping generated a preliminary prototype comprising candidate modules for information display, alerting, explainability, intervention support, and workflow integration. A total of 2401 valid questionnaires were analyzed. Respondents generally preferred direct risk presentation, probability-based alerting, interpretable displays of modifiable risk factors, actionable intervention support, and integration within existing clinical platforms. These findings informed the final specification of an integrated CDSS within the anesthesia information system, including dynamic risk prediction, threshold-based alerting, explainable risk attribution, and evidence-informed intervention recommendations. Conclusions: In this user-centered design study, anesthesia professionals identified key requirements for perioperative CDSS, including direct information display, clinically meaningful alerts, explainable risk-factor presentation, actionable recommendations, and workflow integration. These findings may inform the translation of perioperative prediction models into decision support tools that are more usable and acceptable in routine anesthesia practice.
Background: The platform-based economy has expanded rapidly through the integration of digital platforms into sectors such as transportation, delivery, and freelance work. Platform labor combines feat...
Background: The platform-based economy has expanded rapidly through the integration of digital platforms into sectors such as transportation, delivery, and freelance work. Platform labor combines features of precarious employment and digitalized work organization, encompassing both location-based and web-based work. However, the occupational health implications of platform work remain insufficiently understood, particularly regarding how risks differ across platform worker groups. Objective: This study aimed to explore how platform workers experience their working conditions and how platform work affects their health, wellbeing, and safety. Methods: A participatory photovoice study was conducted with platform-based taxi drivers, delivery couriers, and freelancers living in Stockholm. Between September and November 2022, 16 participants were recruited into three groups (5–6 participants per group). Across five sessions, participants documented their working lives through photographs and discussed them collectively, generating 105 photographs in total. Data were analyzed collaboratively to identify key themes and recommendations related to working conditions, health, and wellbeing. Results: Participants identified 14 themes representing major determinants of health, wellbeing, and safety at work, as well as 23 recommendations for improving working conditions. Workers reported exposure to both platform-specific risks, including algorithmic management and digital surveillance, and traditional occupational risks such as psychosocial strain, ergonomic challenges, and traffic-related hazards. Experiences differed substantially across platform work types. Delivery and taxi drivers reported greater exposure to physical and traffic-related risks, whereas freelancers emphasized psychosocial demands and digital work intensification. Economic insecurity and costs associated with maintaining work equipment emerged as common challenges across all groups. Attitudes toward flexibility, autonomy, and algorithmic management also varied between worker categories. Conclusions: This study highlights important similarities and differences in working conditions and health risks across platform work types. The findings suggest that research and occupational health interventions targeting platform workers should differentiate between specific forms of platform labor to better capture the diversity of workers’ experiences and exposures.
Background: Respiratory Syncytial Virus (RSV) is a leading cause of hospitalization among infants in the U.S., yet uptake of maternal RSV vaccination and infant RSV monoclonal antibody products remain...
Background: Respiratory Syncytial Virus (RSV) is a leading cause of hospitalization among infants in the U.S., yet uptake of maternal RSV vaccination and infant RSV monoclonal antibody products remains low. Objective: This qualitative study examined behavioral and social drivers shaping RSV immunization decisions. Methods: Twelve interviews were conducted with pregnant and postpartum adults across the U.S. Participants were purposively sampled to reflect diverse RSV immunization intentions. The WHO’s Behavioral and Social Drivers of Vaccination Framework guided data collection and analysis. Interviews were analyzed using framework analysis and quantitative screening data were descriptively summarized to stratify intention. Results: RSV immunizations decisions were shaped by interacting emotional, social, and practical considerations. Positive-intention individuals reported greater trust in medical expertise, experiences with RSV illness, and confidence in vaccination. Unsure or negative-intention individuals emphasized concerns about vaccine novelty, side effects, and eligibility timing. Participants expressed preference for maternal vaccination due to perceived immediacy of protection, though practical barriers like vaccine availability created uncertainty. Trusted clinician communication was cited as essential for decision-making. Conclusions: RSV immunization decisions reflect intertwined motivational, social, and structural factors. Integrating maternal RSV vaccination into prenatal care and providing early, patient-centered education may strengthen confidence, reduce barriers, and improve timely uptake.
Background: Co-creation is increasingly used in health research, public health, and participatory initiatives to support inclusive, collaborative, and evidence-informed problem-solving. However, the i...
Background: Co-creation is increasingly used in health research, public health, and participatory initiatives to support inclusive, collaborative, and evidence-informed problem-solving. However, the integration of digital technologies into co-creation processes remains fragmented and largely ad hoc, with limited frameworks available to guide technology selection, evaluation, and development. Objective: This study aimed to develop the Co-Tech Taxonomy, an empirically grounded evaluative framework for assessing digital technologies used in co-creation and participatory digital health ecosystems. Methods: Using the Nickerson–Varshney–Muntermann (NVM) taxonomy-building method, the taxonomy was developed through the analysis of six foundational conceptual and empirical frameworks related to co-creation, participatory processes, and digital technologies. The taxonomy was subsequently refined through iterative empirical classification of 84 technologies used in co-creation contexts. Results: The final taxonomy consists of seven functional dimensions: governance, inclusivity, methodology, collaboration, engagement, data management, and cognitive support. Each dimension is operationalised across three progressive levels of co-creation alignment. The empirical mapping revealed that current digital ecosystems remain insufficiently aligned with participatory collaboration requirements, particularly regarding governance, inclusivity, and AI-supported cognitive facilitation. While communication and data-management functionalities were comparatively mature, participatory governance, collaborative decision-making, and AI explainability remained underdeveloped across most evaluated technologies. The taxonomy also enabled the development of a three-tier indicative certification model to support technology assessment and implementation. Conclusions: The Co-Tech Taxonomy provides a structured evaluative framework for assessing existing technologies, identifying implementation and innovation gaps, and guiding the development of more inclusive, transparent, interoperable, and AI-ready participatory digital infrastructures. The framework offers a practical foundation for strengthening digitally supported co-creation and participatory collaboration within health-related contexts.
Background: Generative artificial intelligence (GenAI) is increasingly used to produce patient-friendly clinical documentation, yet evaluation of these outputs remains inconsistent and difficult to sc...
Background: Generative artificial intelligence (GenAI) is increasingly used to produce patient-friendly clinical documentation, yet evaluation of these outputs remains inconsistent and difficult to scale. Patient-friendliness is commonly reduced to narrow readability metrics, such as Flesch-Kincaid grade level, without accounting for clinical accuracy, completeness, or the patient perspective. No standardized framework exists to evaluate the quality and safety of AI-generated patient-friendly documentation across document types or the full documentation lifecycle. Objective: To develop and preliminarily validate CLEAR (Clinical Language Evaluation and AI Documentation Review), a theoretically grounded evaluation framework for AI-generated patient-friendly clinical documentation across the generation, review, and monitoring stages of the AI documentation lifecycle. Methods: CLEAR was developed using Messick's validity framework across four stages: content validation, response process, internal structure, and consequences. Domains were identified through a targeted literature review and reviewed by a panel of six clinical and operational experts. An iterative, consensus-based process involving four board-certified internists across 10 rounds refined domain definitions and scoring instructions. Inter-rater reliability was assessed on 50 AI-generated patient-friendly discharge summaries using Cohen's kappa and Gwet's AC1 for binary domains and intraclass correlation coefficients (ICC) and Gwet's AC2 for continuous domains. Additionally, 19 semi-structured stakeholder interviews with clinicians, informaticists, institutional leaders, and patient education experts explored operational needs and implementation contexts. Results: CLEAR comprises five domains for evaluating patient-friendly AI documentation: readability, understandability, patient-centeredness, accuracy, and completeness. Inter-rater reliability was good to almost perfect across all subjectively scored domains per Gwet's agreement coefficients. Stakeholder interviews independently identified three operational gaps aligned with the CLEAR lifecycle: lack of structured guidance for prompt engineering, subjectivity in human review, and absence of scalable monitoring infrastructure, directly validating the framework's real-world relevance. CLEAR was applied across three illustrative implementation contexts: prompt engineering for patient-friendly echocardiogram reports, structured human review of discharge summaries, and development of LLM-as-judge automated monitoring tools. Conclusions: CLEAR provides a preliminarily validated evaluation framework designed to span the full AI documentation lifecycle, from prompt engineering through human review to automated monitoring. By conceptualizing patient-friendliness as a multidimensional construct that integrates communication quality with patient safety, CLEAR offers practical infrastructure for consistent and scalable governance of patient-facing AI documentation in healthcare systems.
Background: Acute kidney injury (AKI) is a common and serious complication following cardiothoracic surgery, occurring in up to 30% of patients. Cardiac surgery-associated AKI (CSA-AKI) is associated ...
Background: Acute kidney injury (AKI) is a common and serious complication following cardiothoracic surgery, occurring in up to 30% of patients. Cardiac surgery-associated AKI (CSA-AKI) is associated with increased morbidity, mortality, and progression to chronic kidney disease. Currently, no pharmacological interventions have been approved for clinical use to reduce the incidence or severity of CSA-AKI. It is hypothesized that early modulation of the inflammatory response, triggered by the release of damage-associated molecular patterns during surgery, may improve renal outcomes. Ilofotase alfa, a recombinant human alkaline phosphatase, has demonstrated potential to attenuate renal injury through its immunomodulatory effects in animal studies. Objective: This manuscript presents the protocol for a Phase 2 clinical trial evaluating the safety and efficacy of ilofotase alfa in preventing renal damage following cardiac surgery. Methods: This is a Phase 2, multi-centre, randomized, double-blinded, placebo-controlled trial employing a two-arm, parallel-group design. Adult patients at risk for CSA-AKI undergoing complex open-heart surgery will be randomized to receive two intravenous doses (2x128 mg) of ilofotase alfa or placebo, just before and after surgery.
Ethics and Dissemination
The study has been approved by all relevant institutional review boards and independent ethics committees. It will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines, and all applicable regulatory requirements. The results of this trial will inform the potential role of ilofotase alfa in preventing cardiac surgery associated renal injury and improving longer-term clinical outcomes and will be published in a peer-reviewed scientific journal. Results: The primary endpoint is the serum creatinine ratio, defined as the highest serum creatinine level within five days postoperatively relative to the preoperative baseline, with the occurrence of major adverse kidney events up to day 60 as the secondary endpoint. In addition, safety assessments and the AKI as defined by the KDIGO creatinine-criterion will be assessed. Patients will be followed for a total of 60 days. Conclusions: The results of this study will will enable us to assess safety as well as the efficacy of ilofotase alfa in attenuating renal injury and improving long-term renal outcomes. Clinical Trial: Trial registration numbers
EUCT Number:2023-505859-45
US IND Number:117 605
ClinicalTrials.gov ID:NCT06168799
Background: The severe shortage of dermatologists in Ethiopia creates significant barriers to specialized skincare, particularly for rural populations. Teledermatology (TD) offers a promising solution...
Background: The severe shortage of dermatologists in Ethiopia creates significant barriers to specialized skincare, particularly for rural populations. Teledermatology (TD) offers a promising solution to bridge this gap. Objective: This study aimed to explore the pre-implementation perceptions of healthcare professionals in Hawassa city, Ethiopia, to identify the key facilitators and critical barriers influencing the potential adoption of a TD service. Methods: A qualitative study was conducted via in-depth and key informant interviews with 22 participants, including physicians, health officers, facility administrators, and allied health professionals from nine public health centers in Hawassa city. A convenience sampling approach was used. Data were collected through semi-structured interviews, transcribed, and analyzed via a rigorous thematic analysis approach to identify recurring themes related to facilitators, barriers, and implementation strategies. Results: Healthcare professionals demonstrated positive attitudes and recognized the potential of TD to improve patient access and enhance their clinical knowledge. Key facilitators included the widespread availability of personal smartphones and high intrinsic motivation among staff. However, significant barriers were also identified, with subthemes including technological infrastructure gaps, systemic and policy vacuums, patient-related concerns about privacy and trust, and provider-related issues such as the need for targeted training. Conclusions: Healthcare professionals expressed willingness to adopt TD, identifying key facilitators (e.g., smartphone access, personal motivation) and notable barriers (e.g., infrastructure challenges, unclear policies, privacy concerns). Successful implementation will require targeted investments, clear guidelines, staff training, and community buy-in.
Radiologists routinely interpret volumetric imaging studies in clinical picture archiving and communication systems (PACS), but few have direct experience with the raw data formats, programming librar...
Radiologists routinely interpret volumetric imaging studies in clinical picture archiving and communication systems (PACS), but few have direct experience with the raw data formats, programming libraries, and code-based tools that underlie the artificial intelligence (AI) systems they are increasingly asked to evaluate and deploy. Recent multi-institutional surveys demonstrate that radiology residents favor inclusion of formal AI and machine learning instruction in residency curricula and prefer hands-on learning over lectures alone, yet most existing curricula remain didactic, and open-source tools that lower the technical barrier to volumetric image manipulation are needed to support this shift toward applied informatics literacy. This tutorial describes the design, implementation, and educational application of a lightweight, browser-executable Python-based magnetic resonance imaging (MRI) viewer intended to introduce radiology trainees, medical students, and AI researchers to volumetric data structures and code-based image manipulation, with the goal of lowering the technical and infrastructural barrier to imaging informatics literacy through a reproducible, open-source, openly licensed tool that requires no local software installation. The viewer was implemented in Python 3.13 using NiBabel for Neuroimaging Informatics Technology Initiative (NIfTI) parsing, NumPy for array manipulation, Plotly for interactive grayscale slice rendering, and scikit-image for marching cubes surface extraction, and comprises two modules: a scroll-based 2D slice viewer with axial, sagittal, and coronal navigation, and a 3D mesh viewer for volumetric surface visualization. The publicly available Stanford Artificial Intelligence in Medicine and Imaging (AIMI) Brain Metastasis dataset was used as the volumetric data source, and the complete tool was deployed in a Google Colab cloud notebook to remove local installation requirements. The viewer loads NIfTI-formatted MRI volumes (256 × 256 × 150 voxels) and renders interactive multiplanar grayscale slices with mouse-scroll and button-based navigation, replicating the slice-traversal experience of clinical PACS without requiring DICOM infrastructure or proprietary software, while the 3D mesh module uses an intensity-thresholded marching cubes isosurface to produce a rotatable volumetric rendering. The complete codebase, including the data loading workflow, viewer modules, and design rationale, is available as an interactive Colab notebook accessible through any modern web browser. By using openly licensed libraries and a publicly available dataset, the viewer provides radiologists, trainees, and imaging researchers with a code-level entry point into volumetric data manipulation and supports reproducible educational deployment, prototyping for downstream AI workflows, and interdisciplinary collaboration between radiologists and developers; limitations include the absence of DICOM workflow support, formal usability evaluation, and segmentation or registration capability.
Background: Depression, anxiety, and stress-related difficulties represent a major global health burden, while access to timely psychological treatment remains limited for many individuals. Online psy...
Background: Depression, anxiety, and stress-related difficulties represent a major global health burden, while access to timely psychological treatment remains limited for many individuals. Online psychological therapy has emerged as a promising approach to increasing access to care, yet evidence from routine, real-world clinical settings – particularly for services delivered without standardized treatment protocols – remains limited. Objective: This study aimed to evaluate clinical outcomes and patient-reported satisfaction associated with routine online psychological therapy delivered within an insurance-based care setting. Methods: A retrospective observational service evaluation was conducted using de-identified routine care data. Clients aged 15 years and older who initiated online psychological therapy and completed baseline and end-of-treatment assessments on all three outcome measures were included. Symptoms of depression, anxiety, and perceived stress were measured using the Patient Health Questionnaire--9 (PHQ-9), Generalized Anxiety Disorder--7 (GAD-7), and Perceived Stress Scale--10 (PSS-10), respectively. Changes in symptoms were examined using paired sample t-tests and within-sample effect sizes (Cohen's d with 95% confidence intervals), and reliable change indices. Clinically meaningful improvement was defined as a ≥50% reduction in symptom scores. Patient satisfaction was assessed using single-item ratings collected at treatment completion. Results: A total of 1,221 clients were included. Clients completed a mean of 4.7 therapy sessions over an average treatment duration of 62.7 days (SD=40.4). Significant reductions were observed across all outcome measures, with large within-sample effect sizes for depression (d=1.25, 95% CI [1.17, 1.32]), anxiety (d=1.49, 95% CI [1.41, 1.57]), and perceived stress (d=1.46, 95% CI [1.38, 1.54]). Clinically meaningful improvement was observed in 60.7% of clients for depression, 67.5% for anxiety, and 30.8% for perceived stress. At treatment completion, 80.7% of clients scored below the clinical cut-off for depression (PHQ-9 < 10) and 79.2% scored below the cut-off for anxiety (GAD-7 < 8). Reliable change analysis indicated that 56-65% of clients achieved statistically reliable improvement, with deterioration rates below 2% across all measures. Mean patient-reported satisfaction scores ranged from 8.55 to 8.96 on a 10-point scale, with a mean recommendation-likelihood score of 8.61. Conclusions: Routine online psychological therapy delivered within an insurance-based care setting was associated with substantial pre–post symptom reductions, high rates of clinically meaningful and reliable change, and high patient-reported satisfaction under real-world conditions. Because the evaluation used a completer-based pre–post design without a control group, observed changes cannot be attributed causally to treatment. The findings are consistent with the feasibility and acceptability of flexible online therapy models in routine mental health care; inferences about comparative effectiveness require randomized or otherwise controlled designs.
Background: Digital health interventions offer a scalable approach to modern weight management but their specific efficacy in modifying psychological eating behavior traits remains underexplored. Synt...
Background: Digital health interventions offer a scalable approach to modern weight management but their specific efficacy in modifying psychological eating behavior traits remains underexplored. Synthesizing this evidence is critical for optimizing future digital therapeutics. Objective: To evaluate the impact of digital health interventions on specific eating behavior traits among adults with overweight or obesity compared to standard care and to determine the influence of intervention duration and theoretical frameworks on these outcomes. Methods: A comprehensive literature search was conducted across six major electronic databases to identify relevant randomized controlled trials. Eligible studies included adults with elevated body mass indices and measured psychological constructs of eating behavior. Standardized mean differences were calculated using a random effects model and evidence certainty was evaluated utilizing the GRADE framework. Results: Fifteen unique trials comprising 1518 participants met the inclusion criteria. The pooled synthesis demonstrated that digital interventions yielded robust reductions in both emotional eating and uncontrolled binge eating supported by a moderate certainty of evidence. Subgroup analysis indicated that sustained digital engagement exceeding eight weeks is essential to achieve definitive improvements in emotional eating. The overall impact on cognitive restraint was highly variable. However this statistical inconsistency was systematically driven by underlying therapeutic frameworks where traditional cognitive behavioral therapies significantly increased restrictive behaviors while mindfulness and acceptance based approaches distinctly reduced them. Conclusions: Digital health platforms effectively mitigate maladaptive eating patterns particularly when user engagement is sustained beyond eight weeks. The theoretical divergence observed in cognitive restraint outcomes highlights the necessity for a precision medicine approach in digital behavioral care. Clinicians should evaluate dominant eating phenotypes prior to prescribing digital tools to ensure therapeutic algorithms match individual psychological profiles. Clinical Trial: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261285265
Background: Oropharyngeal exercises have been used as a therapeutic option for treating obstructive sleep apnea (OSA) after stroke clinically and in research. They target the underlying mechanism of O...
Background: Oropharyngeal exercises have been used as a therapeutic option for treating obstructive sleep apnea (OSA) after stroke clinically and in research. They target the underlying mechanism of OSA by improving the sensorimotor function of the upper airway muscles. Objective: We assessed the acceptance of a new smartphone-based application titled OPEX, which aimed to deliver oropharyngeal exercises to individuals with OSA remotely via digital technology. Methods: This study included 30 participants from a clinical trial that assessed the feasibility and efficacy of oropharyngeal exercises for OSA after stroke compared to a sham intervention. The OPEX application was used to deliver exercises to the groups at home. After completing the exercise program, all participants were asked to complete a 13-item questionnaire and a semi-structured interview. The relationship between cognitive skills and the level of acceptance of OPEX was also explored. Results: Interviews with 30 participants revealed high engagement across three themes: ease of use, convenience, and motivation. Most (≈75%) described the OPEX app as intuitive and accessible, supporting independent exercise completion and consistent adherence. Participants valued its practicality in removing logistical barriers and integrating therapy into daily routines, while a few preferred occasional in-person contact for motivation. Those with higher MoCA scores (≥24) were more independent, though no significant correlation was found between cognition and app acceptance (r (28) = –.05, p = .80). Conclusions: This is the first study to provide evidence regarding the acceptance of the new app, OPEX, among stroke patients with OSA. Our findings provide insights into the adoption of oropharyngeal exercise apps by stroke survivors with OSA in a clinical trial context.
Background: Health misinformation on social media remains a public health concern because false or misleading content can spread rapidly during crises and may undermine adherence to evidence-based gui...
Background: Health misinformation on social media remains a public health concern because false or misleading content can spread rapidly during crises and may undermine adherence to evidence-based guidance. Prior work suggests that emotionally salient, interesting, and cognitively accessible content is more likely to attract engagement; however, less is known about whether interpretable linguistic features can distinguish COVID-19 fake news from official public health communication and from non-fake news. Objective: This study examined whether affective, lexical, and discourse-level linguistic features can classify COVID-19 fake news on X/Twitter and identify which features provide the strongest single-feature discrimination. Methods: This observational computational study combined COVID-19 tweets from official public health and institutional accounts with publicly available fake news and non-fake news datasets. Official communications were collected from 12 English-language accounts between December 2019 and December 2022 and filtered using COVID-19 keyword criteria. Fake news and non-fake news tweets were drawn from the COVID Rumor, CONSTRAINT/AAAI, CoAID, and TruthSeeker datasets. The final analytic corpus included 25,181 official communication tweets, 22,424 fake news tweets, and 5,415 non-fake news tweets. Features included sentiment, valence, arousal, dominance, word frequency ranks, and GisPy-derived discourse features such as referential cohesion, semantic and WordNet verb overlap, imageability, concreteness, and hypernymy. Two binary classification tasks were conducted: fake news versus official communication and fake news versus non-fake news. Random oversampling was used to balance classes. Single-feature decision stumps were used for interpretable feature ranking, and decision tree, AdaBoost, and Light Gradient Boosting Machine classifiers were fitted using all features. Results: For fake news versus official communication, the strongest single feature was transformer-based sentiment (accuracy=0.7699), followed by number of paragraphs (accuracy=0.7481), referential cohesion (accuracy=0.7251), and number of sentences (accuracy=0.6915). Using all features, testing accuracy was 0.8703 for the decision tree, 0.9186 for AdaBoost, and 0.9583 for Light Gradient Boosting Machine. For fake news versus non-fake news, single-feature accuracies were lower; the strongest features were median content-word rank (accuracy=0.6722) and arousal (accuracy=0.6432). Using all features, testing accuracy was 0.7379 for the decision tree, 0.8258 for AdaBoost, and 0.9655 for Light Gradient Boosting Machine. Conclusions: Interpretable linguistic features distinguished COVID-19 fake news from official public health communication and, to a lesser extent at the single-feature level, from non-fake news. Sentiment, text segmentation, referential cohesion, word rank, and arousal appear particularly relevant. The findings support further evaluation of transparent linguistic markers for infodemiology and public health communication research, but external validation, temporal evaluation, and models that include account-level and engagement-level features are needed before operational deployment.
Background: The spread of online health misinformation has been on the rise for years but was greatly accelerated during the COVID-19 pandemic, during which novel conspiracy theories and misleading me...
Background: The spread of online health misinformation has been on the rise for years but was greatly accelerated during the COVID-19 pandemic, during which novel conspiracy theories and misleading messages about vaccines emerged and long-standing arguments against vaccines gained momentum in public narratives. Health misinformation was especially prolific on social media, with strategies like content moderation implemented far less extensively for Spanish-language content. While some studies have identified common persuasive tactics used to propagate health misinformation or conspiratorial thinking, few have explored use of these tactics in Spanish-language content on social media for U.S. audiences. Objective: This study examined persuasive tactics used to promote COVID-19 and vaccine misinformation and conspiracies in Spanish-language comments on Brigada Digital de Salud’s public Facebook page. Methods: Informed by an integration of Elaboration Likelihood Model and theories in motivated reasoning, confirmation bias and selective exposure, and social identity, a qualitative content analysis was conducted on a data set of 902 comments in response to 13 posts on the Brigada Digital de Salud Facebook page that were published between June 15, 2022 to January 1, 2023. Persuasive tactics were conceptualized as being either low elaboration tactics, reliant on heuristics and superficial cues to seed beliefs (peripheral route exposure), or high elaboration tactics that sought to entrench misinformation and conspiracy beliefs (central route reinforcement). Comments applying these tactics were then analyzed to identify thematic commonalities. Results: Results demonstrated that six low elaboration persuasive tactics in three overarching categories were used to propagate COVID-19 and vaccine misinformation and conspiracies, including making value-based appeals and amplifying uncertainty (emotional arousal), making identity-based appeals and social norm creation (in-group identity framing), and questioning transparency of health officials or pharmaceutical companies or trustworthiness of healthcare providers (distrust priming). Results also showed that five high elaboration persuasive tactics in four overarching categories were used, including using arguments that vaccines were ineffective and unnecessary (pseudo-evidence), reinforcing distrust with conspiratorial arguments (entrenchment of distrust), questioning the accountability of healthcare providers and also leveraging the authority of healthcare professionals to promote vaccine misinformation (authority reversal), and dismissing official data (counterarguing). Conclusions: Recent policy shifts have scaled back content moderation on social media platforms and there is growing divisiveness of public health discourse in the U.S. These findings increase our understanding of persuasive tactics commonly used among Spanish-speaking audiences to promote health misinformation and conspiratorial thinking on digital platforms. Future strategies should use trusted, in-group messengers and pair trust-building peripheral cues with gradually introduced central route, identity-affirming framed messaging to reduce resistance to the accurate health information. Strategies should also seek to improve communities’ digital health literacy to mitigate misinformation vulnerability and support informed health decision-making. Clinical Trial: n/a
Background: Forecasting emergency department (ED) arrivals is foundational to operational decision-making, including clinician scheduling, bed management, throughput initiatives, and revenue forecasti...
Background: Forecasting emergency department (ED) arrivals is foundational to operational decision-making, including clinician scheduling, bed management, throughput initiatives, and revenue forecasting. The historical baseline for many health systems has been expert consensus combined with prior-year volume—a process that is often opaque, variably calibrated, and limited in its ability to accommodate seasonality and structural disruption. Objective: To compare the accuracy of forecasts produced by commonly available generative AI interfaces with a Holt–Winters (HW) exponential smoothing baseline, and to determine whether interactive updating (“proximal forecasting”) identifies a pragmatic recency window for fiscal-year planning. Methods: We conducted a two-part observational forecasting study using monthly ED visit volumes from four hospitals. 12-month forecasts were generated using 12- and 24-month historical windows and compared across HW and five large language model (LLM) interfaces (ChatGPT, Claude, Copilot, Perplexity, Gemini) using standardized prompts and adjusting for seasonality. We then generated FY2025 forecasts using a fixed training window and systemically varying the end month of available data to simulate a budget-cycle lag. Forecast accuracy was measured using mean absolute percentage error (MAPE) and root mean square error (RMSE). Results: Forecasts produced by one LLM interface (ChatGPT 4.0) demonstrated accuracy comparable to HW (MAPE 3.63±1.64 vs 3.26±0.92), whereas other interfaces showed higher error and greater variability (Perplexity 5.39±1.88; Copilot 5.15±1.77; Claude 8.14±3.19; Gemini 4.27±1.31). No approach met a predefined threshold in the pediatric ED. In adult EDs with typical seasonality, forecasts generated up to four months from the fiscal-year start were comparable to fiscal-year–proximal forecasts (mean MAPE difference 0–0.4%). Pediatric and highly seasonal sites demonstrated higher baseline error and recency bias susceptibility. Conclusions: With adequate historical context, an LLM interface can produce monthly ED volume forecasts with accuracy similar to Holt-Winters exponential smoothing and enable “proximal forecasting” workflows for fiscal-year planning. Strongly seasonal and pediatric settings may require tailored approaches.
Background: Since the introduction of the term public health informatics in the published literature in 1995, the term rapidly increased in usage within that literature up to 2001 with the peak in 200...
Background: Since the introduction of the term public health informatics in the published literature in 1995, the term rapidly increased in usage within that literature up to 2001 with the peak in 2002 and a rapid decline after that. Similarly, this MeSH term was created in 2003 and showed a similar but offset trend in usage with a steady decline since 2005. Since that time, most reviews within the field have focused on specific informatics concepts or their applications within disease domains or practice areas such as disaster response. Objective: To characterize the current practice of public health informatics within the United States, specifically within governmental health departments. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews guidelines and recommendations were followed to search three databases, PubMed which is the National Library of Medicine’s biomedical literature database which includes MEDLINE among others, ProQuest’s Public Health Database, and the Cumulative Index to Nursing & Allied Health (CINAHL) database for a variety of public health informatics terms. Articles then were reviewed by two individuals in a two-tiered process first to identify compatibility with inclusion criteria and then to identify the informatics implementation and jurisdiction. Results: A total of 4,581 publications were identified, 141 duplicate articles across the three data sources and 25 that were published prior to 2010 were removed. Based on initial title and meta-data screening, 3,249 articles were removed. The remaining 1,166 received abstract and full-text review and resulted in 229 articles that met all eligibility criteria. Of the themes represented, 30.80% of articles included surveillance systems or disease registries and 24.05% included some aspects of health information exchange (HIE) including connecting to an HIE entity, performing direct exchange between clinical systems, or developing system interoperability. Published articles represented state health department work 32.07% of the time, followed by Federal or Tribal implementations 30.38% of the time. Implementations at local health departments were the least frequent at 16.03% of the time. Conclusions: There is a growing body of published literature and grey materials around public health informatics. This scoping review buttresses other published scoping reviews by providing a cross-sectional view of the ecosystem of public health informatics applied work within public health authorities in the US. This cross-sectional view can be seen as a foundation from which to further characterize practice within these authorities. The broad distribution of publications across the variety of public health jurisdictions echoes the idea that most authorities are involved in informatics work in one way of another. The variation in themes across these implementation locations can be used to refine technical assistance and training offerings by professional societies. Clinical Trial: This project was reviewed by the Institutional Review Board at East Tennessee State University and determined to not constitute human subjects research.
Background: Painful diabetic peripheral neuropathy (P-DPN) is a common and disabling complication of diabetes affecting a substantial proportion of patients and significantly impairing quality of life...
Background: Painful diabetic peripheral neuropathy (P-DPN) is a common and disabling complication of diabetes affecting a substantial proportion of patients and significantly impairing quality of life. Current pharmacological treatments provide only modest pain relief and are often associated with side effects that limit long-term adherence. Consequently, there is a critical need for novel, mechanism-based treatment approaches that target the underlying neurobiological processes of chronic pain. Objective: The aim of this study is to investigate the efficacy and underlying neurophysiological mechanisms of source-localized EEG neurofeedback (EEG-NF) in individuals with P-DPN compared with a sham neurofeedback control condition. Methods: The study is a randomized, multi-center, blinded, and sham-controlled clinical trial investigating the treatment efficacy of source localised EEG-NF in individuals with P-DPN. Fifty-five subjects with either type 1 or type 2 diabetes, diabetic neuropathy (DPN) and an average pain ≥4 on a Numeric Rating Scale (NRS) of 0–10 will be randomized 1:1 to receive either active EEG-NF or sham NF across 10 sessions. The primary outcome is the change in mean 7-day average pain intensity (NRS) from baseline (T0) to post-intervention (T1) measured by an electronic pain diary. Secondary outcomes include changes in different aspects of neuropathic pain (Neuropathic Pain Scale (NPS), Pain Catastrophizing Scale (PCS), sleep interference (PROMIS SF Sleep and Fatigue), mental health (PROMIS SF depression), and health-related quality of life (WHOQL-Brief). Neurophysiological outcomes will also be assessed using quantitative EEG and standardized low-resolution electromagnetic tomography (swLORETA) using predefined metrics of abnormal cortical activity, connectivity as well as the achieved degree of EEG normalisation. This trial will evaluate both the efficacy and potential mechanisms of EEG-NF in the treatment of P-DPN. Results: Ethical approval for the study was obtained by the Regional Committees on Health Research Ethics in Region of Southern Denmark (Projekt-ID S-20220074). Ethical approval date 17 May 2024, first patient first visit 14 August 2024. The data collection is expected to be done in December 2026. The trial is registered at ClinicalTrials.gov (NCT06603792). Conclusions: This trial will provide important evidence regarding the efficacy and neurophysiological mechanisms of EEG-NF as a non-pharmacological treatment for P-DPN. If effective, EEG-NF may represent a novel mechanism-based intervention targeting central pain processing abnormalities in P-DPN and contribute to the development of more personalized treatment approaches for chronic neuropathic pain. Clinical Trial: ClinicalTrials.gov (NCT06603792)
Background: CrossFit is a popular high intensity training modality combining weightlifting, gymnastics, and cardiovascular conditioning. Despite its benefits, concerns remain regarding musculoskeletal...
Background: CrossFit is a popular high intensity training modality combining weightlifting, gymnastics, and cardiovascular conditioning. Despite its benefits, concerns remain regarding musculoskeletal injury risk. Movement screening tests such as the Functional Movement Screen (FMS), Selective Functional Movement Assessment (SFMA), Y Balance Test (YBT), and Biering Sorensen Test have been proposed to evaluate mobility, stability, and motor control. However, their predictive validity in CrossFit athletes is not well established. Objective: This protocol describes a prospective cohort study designed to investigate the screening (predictive) value of movement and motor control screening test scores and their relationship with musculoskeletal injuries in CrossFit athletes Methods: A minimum of 142 male and female CrossFit athletes aged 18–45 years with at least six months of training experience will be recruited from Tehran training centers based on sample size calculation (G*Power, effect size = 0.25, power = 0.80, alpha = 0.05, plus 20% attrition). Baseline assessments will include standardized movement screening tests (FMS, SFMA, YBT, Biering Sorensen). The primary outcome is the occurrence of any new CrossFit related musculoskeletal injury during six months of prospective follow up, defined as any complaint leading to training modification, medical consultation, or absence from training for ≥24 hours. Injuries will be verified through bi weekly follow up calls and coach/medical staff communication. Statistical analysis: Logistic regression, Cox proportional hazards models, sensitivity, specificity, predictive values, and ROC curve analysis will be used to evaluate the predictive utility of screening scores. Results: This study will clarify whether movement screening scores can serve as reliable predictors of injury risk in CrossFit athletes Conclusions: This protocol will provide evidence on the role of movement screening tests in predicting musculoskeletal injuries among CrossFit athletes, with implications for injury prevention and athlete safety. Clinical Trial: NA
Background: Chest X-ray (CXR) based screening is critical for improving Tuberculosis (TB) case detection in high-burden settings, where a substantial proportion of cases are subclinical or asymptomati...
Background: Chest X-ray (CXR) based screening is critical for improving Tuberculosis (TB) case detection in high-burden settings, where a substantial proportion of cases are subclinical or asymptomatic with only radiographic evidence of TB. Although the WHO recommends the use of CXR for active TB screening among high-risk populations, access to conventional digital radiography remains limited in peripheral and resource-constrained settings. Recent advances in ultraportable, handheld X-ray devices offer a promising alternative to radiographic evaluation generating interest in their indigenization and ease of programmatic deployment. However, handheld X-ray devices require systematic validation against standard digital radiography for safer and efficient deployment Objective: This validation protocol describes the process for systematic evaluation of adequacy of image quality and diagnostic accuracy of CXR images for TB screening. The study aims to provide a standard comparison for evaluation of the quality of CXR images produced by ultraportable handheld X-ray against conventional digital X-ray systems. Methods: This protocol describes a cross-sectional paired comparison study to evaluate the image quality and diagnostic adequacy of CXRs acquired using handheld devices relative to standard digital radiography. Adults aged ≥18 years indicated for CXR examination will be consecutively enrolled until a target enrollment (sample size) of 100 participants is reached. Each participant will undergo two standard postero-anterior CXR examinations, one using a handheld X-ray device and one using a standard digital system. Anonymized images will be independently assessed by two senior radiologists using image quality criteria, including anatomical coverage, spatial resolution, exposure, penetration, positioning, and overall interpretability, that would standardize the CXR for reading and interpretation of Chest X-ray including detection of lung abnormalities consistent with TB. The primary outcome will be the limits of agreement between handheld and standard devices based on overall image quality scores. Agreement in abnormality detection and inter-reader reliability will be assessed using Cohen’s kappa statistics, with standard digital radiography as the reference. Results: The study has been initiated at ICMR- Rajendra Memorial Research Institute of Medical Sciences (RMRIMS), Patna. The study protocol underwent ethical review and was approved by the Institutional Ethics Committee. Conclusions: The proposed protocol helps to create a common pathway for normalizing and standardizing portable hand-held Xray by providing a broad overview on the parameters to be assessed, the methodology to be used for comparison for successful deployment in remote areas without compromising on accuracy of detection.
Background: Digital interventions for addiction have demonstrated effectiveness and scalability, yet their implementation remains uneven, particularly within social services, where responsibility for ...
Background: Digital interventions for addiction have demonstrated effectiveness and scalability, yet their implementation remains uneven, particularly within social services, where responsibility for non-emergency addiction care often resides. In addition, limited research has examined how social workers interpret and engage with these technologies in practice. Objective: This study aimed to examine how social workers’ technological frames shape their evaluations of digital interventions, with particular attention to domain-level incongruence and contextual inconsistency across practice situations. Methods: An embedded mixed-methods design was used, combining survey data (N=169) with qualitative open-ended responses and 10 semi-structured interviews. Participants completed a validated questionnaire assessing attitudes toward digital interventions and evaluated internet-based interventions across three case vignettes and intervention scenarios. Quantitative analyses included typology construction, repeated-measures ANOVA, and gap analysis (value–use discrepancy). Qualitative data were analyzed using deductive thematic analysis. The study was guided by Technological Frames theory. Results: Practitioners reported moderately positive attitudes (mean 3.81/5) and rated both value (mean 6.18/10) and appropriateness (mean 6.07/10) above the scale midpoints. Four practitioner typologies emerged: Holistic Adopters (37.9%), System Skeptics (31.4%), Client-Centric Advocates (16.6%), and Efficiency Supporters (14.2%). A consistent value–use gap indicated that digital interventions were perceived as more valuable in principle than appropriate in practice (mean difference 0.12, P<.001), with no significant variation across typologies. Appropriateness ratings varied significantly across intervention scenarios, indicating frame inconsistency, with greater acceptance in later-stage scenarios. Qualitative findings suggested that digital interventions were viewed as valuable in principle but context-dependent in practice and were therefore typically positioned as complements rather than substitutes for face-to-face care. Conclusions: Social workers’ evaluations of digital interventions are shaped by both structural misalignment across technological frame domains and situational variation across contexts. The consistent gap between perceived strategic value and practical appropriateness highlights the importance of implementation conditions and contextual fit, rather than attitudinal resistance. These findings suggest that successful integration of digital interventions in social services requires alignment with professional practices, relational care values, and context-sensitive implementation.
Background: There has been an increasing emphasis on community-engaged research in rural settings. Community advisory groups (CAGs) are one common way to include community perspective and voice in hea...
Background: There has been an increasing emphasis on community-engaged research in rural settings. Community advisory groups (CAGs) are one common way to include community perspective and voice in health-related research. However, understanding is still limited on how rural CAGs are operated and reported on. Objective: This scoping review will explore the current status of rural CAG operation and reporting and describe the attributes of CAGs in rural, health-related research contexts including form, function, methods, impact, and influence. Methods: This scoping review follows the methodological framework outlined by Levac et al., with reporting guided by the PRISMA Extension for Scoping Reviews. We searched three bibliographic databases in September 2025 to identify relevant peer-reviewed literature: PubMed via the National Library of Medicine interface, Scopus via the Elsevier interface, and CINAHL via the EBSCOhost interface. We utilized a three-stage, fully human screening process to identify articles for inclusion. We extracted structured information from each included article using a standardized data charting matrix in Excel and used the Guidance for Reporting Involvement of Patients and the Public Long Form reporting checklist as an analytic framework. Results: The database searches produced 1,135 papers. There were 21 papers selected for full-text screening, and nine were included in the review. The included articles used a variety of terms to describe CAG work and varied in their member composition and topic areas. The form, function, methods, and impact by which CAGs were engaged and evaluated were well reported in the included articles while cost, benefit, and theoretical rationale or development were mostly absent. No significant patterns related to study design, research topic, member type, or level of engagement emerged from the data. Instead, the CAG composition, activities, and evaluation appeared to be study and context specific. Conclusions: The nine studies included in this review provide important insight into rural CAG operations. CAG implementation appeared to be more focused on a pragmatic approach as opposed to theory development. Gaps and variation in reporting identify a need for more consistent and in-depth reporting of CAG implementation to allow for better understanding and comparison of CAG approaches. Clinical Trial: 10.17605/OSF.IO/V23KC
Background: Multidisciplinary science programs tackling grand societal challenges often lack effective innovation strategies, particularly towards an integrated application idea. Objective: This study...
Background: Multidisciplinary science programs tackling grand societal challenges often lack effective innovation strategies, particularly towards an integrated application idea. Objective: This study describes how to move from monodisciplinary research lines in early-stage biotech research consortia towards an interdisciplinary convergence research strategy. Methods: We conceptualise the research process as an innovation process and explore how different researchers can integrate their ideas through generative co-design (GCD) tools and the Business Model Canvas. Empirical data were gathered from three workshops, a consortium discussion, and two surveys within a large multidisciplinary biotech consortium. Our analysis focused on social interaction, drawing on both individuals' tacit and explicit knowledge. Results: Minimum feasible products, as opposed to minimum viable products, developed in GCD workshops facilitated the collaboration across disciplines and integrated knowledge across topics. Conclusions: The study offers theoretical contributions for researchers and practical insights for funding bodies aiming to enhance the impact of early-phase biotech research.
Background: Digital mental health interventions (DMHIs) can reduce geographical constraints, stigma, and offer cross-sectoral care for patients with severe mental illness. Despite the obvious advantag...
Background: Digital mental health interventions (DMHIs) can reduce geographical constraints, stigma, and offer cross-sectoral care for patients with severe mental illness. Despite the obvious advantages associated with DMHIs, utilizing its potential in clinical practice can be challenging. User involvement plays a particularly crucial role in ensuring the successful development and implementation of DMHIs. However, involving users and particularly clinicians during the development process of DMHIs can be challenging and time-consuming, hampering the success and utilization of DMHIs in clinical practice. Objective: The aim of this study is to apply heuristics for evaluating Telma-PSYK, a cross-sectoral telemonitoring application, targeting patients with severe mental illness, and to inform future development iterations. Methods: A Participatory Heuristic Evaluation (PHE) consisting of 15 heuristics was applied to evaluate Telma-PSYK. A 5-point severity scale to rate the severity of the heuristic violation was applied. Work-domain experts, i.e., clinicians working in mental health services (n=5) as well as usability experts (n=4), were recruited as usability inspectors. Results: Inspectors who participated in the PHE identified nine heuristic violations. The most frequently violated heuristic was Aesthetic and Minimalist Design, accounting for 44.44 % (4/9) of the total violations. Severity ratings ranged from 1 to 5, with the most critical violations related to the Aesthetic and Minimalist Design heuristic, where usability problems in monitoring individual patient mental health status were identified. Conclusions: Usability problems reported by clinicians and usability experts can guide and inform the next development iteration, ensuring that it is grounded in clinical needs and requirements, thereby increasing the potential for successful uptake and implementation of Telma-PSYK in clinical practice. By applying the PHE method, this study shows how clinicians can be meaningfully involved as decision makers, shaping and guiding the development of DMHIs. The findings further highlight that usability challenges are closely linked to the presentation and interpretation of clinical data, which may be critical for successful implementation in practice. Clinical Trial: N.A
School bullying affects roughly one in three students worldwide and produces well-documented academic and psychiatric harm in victims, perpetrators, and witnesses. Conventional school responses (zero-...
School bullying affects roughly one in three students worldwide and produces well-documented academic and psychiatric harm in victims, perpetrators, and witnesses. Conventional school responses (zero-tolerance policy, teacher-mediated reporting, individual counseling) are reactive by design and ill-equipped for cyberbullying or for under-resourced settings. Intelligent interactive virtual environments (IIVEs), built as 3D educational games, take a different approach: students enter repeatable bullying scenarios in which rule-driven feedback trains the cognitive and behavioral responses that classroom instruction cannot.
This viewpoint develops a theoretical account of how IIVEs work and derives design principles from it. Drawing on Cognitive-Behavioral Intervention for Trauma in Schools (CBITS), social and emotional learning (SEL), situated learning theory, and self-regulated learning (SRL), we propose a Three-Phase Mechanism Model: empathy and awareness cultivation, cognitive-behavioral skill rehearsal, and generalization with sustained self-regulation. Five design principles follow from the mechanism: scenario authenticity and ecological validity, multi-role perspective-taking, adaptive cognitive-behavioral feedback, SEL integration, and safety-by-design. We illustrate each principle through published IIVE-based anti-bullying games and consider what equitable deployment requires in rural and under-resourced settings, where the gap between conventional intervention and student need is widest. The result is mechanism-grounded design guidance for educators, school mental health practitioners, and developers of digital adolescent interventions.
Background: Diabetes-related depression refers to a depressive state triggered or exacerbated by factors such as diabetes diagnosis, the burden of treatment, and the risk of complications. The differe...
Background: Diabetes-related depression refers to a depressive state triggered or exacerbated by factors such as diabetes diagnosis, the burden of treatment, and the risk of complications. The differences in efficacy among various telemedicine intervention models remain unclear, and the lack of direct and indirect comparisons across multiple methods makes it impossible to establish a clear hierarchy of relative merits. Objective: This study aims to conduct a systematic review and network meta-analysis of randomized controlled trials evaluating telemedicine interventions for diabetes-related depression. The study will synthesize efficacy data from various telemedicine intervention models, compare the effectiveness and acceptability of different intervention strategies, and identify the optimal intervention approach. Methods: We conducted a literature search in the PubMed and Web of Science (WOS) databases, covering the period from the inception of each database through January 17, 2026. We included peer-reviewed English-language studies examining the association between telemedicine interventions and psychological outcomes in patients with diabetes. The risk of bias and methodological quality of the studies was assessed using the Cochrane Risk of Bias Assessment Tool version 2.0. Data analysis was performed using the meta and metafor packages in R version 4.1.0 (R Statistical Computing Project). The standardized mean difference (SMD) was used to pool the effect sizes. We conducted a series of sensitivity analyses to assess the robustness of the results. Results: A total of 16 RCT studies involving 246 studies were included. The overall pooled effect size, estimated using a random-effects model, was SMD = -0.33 (95% CI: -0.43, -0.24), indicating a small-to-moderate beneficial effect of interventions compared to control conditions (p < 0.001). The overall pooled effect size, estimated using a random-effects model, was OR = 1.07 (95% CI: 0.71, 1.63). Moderate heterogeneity was observed across studies (I² = 49.4%, τ2 = 0.3292, p = 0.0133)。Subgroup analyses were conducted based on intervention type. For telephone-based interventions, the pooled odds ratio was 1.38 (95% CI: 0.98, 1.94; I² = 0.3%). For app-based interventions, the pooled odds ratio (OR) was 1.07 (95% CI: 0.49, 2.34; I² = 0.7%). For online training interventions, the pooled odds ratio (OR) was 0.33 (95% CI: 0.07, 1.64; I² = 0.0%). For video interventions, the odds ratio (OR) was 0.50 (95% CI: 0.09, 2.73). Overall, the odds ratio derived from the random-effects model was 1.07 (95% CI: 0.71, 1.63; I² = 0.5%), indicating no significant difference in all-cause dropout rates between the intervention and control groups. Conclusions: Digital psychological interventions are an effective means of alleviating depressive symptoms, with structured online training proving particularly effective. Clinical Trial: The study protocol has been registered in the PROSPERO database (registration number: CRD420251233353).
Background: The natural sources contribute about 82 percent of the total radiation dose to human beings. These levels are usually low; however, chronic exposure to even low-level radiation is a social...
Background: The natural sources contribute about 82 percent of the total radiation dose to human beings. These levels are usually low; however, chronic exposure to even low-level radiation is a social health concern as it may cause such biological effects as DNA damage or cancer. Environmental radioactivity is a common occurrence, as it is caused by radionuclides present in the crust of the Earth, the atmosphere and the cosmic environment, which are classified as primordial, cosmogenic or anthropogenic. Radionuclides such as Uranium-238, Thorium-232, and Potassium-40 dates back to the formation of the Earth and those such as Carbon-14 are produced by the cosmic ray interactions. Although there is high importance of monitoring these levels, there is a clear deficiency of documented data of baseline in rural settlements in Central India, that is in the Wardha district. To verify adherence to the safety limits of 1 mSv/year (to the population, above the natural background) of the safety of the radioactivity introduced by the Atomic Energy Regulatory Board (AERB) and the International Commission of Radiological Protection (ICRP) it is therefore necessary to establish a local radiation profile of Village Sawangi. Objective: The objective of this study is to assess natural background radiation levels in Village Sawangi by measuring radiation exposure at strategic residential, agricultural, water-source, and construction locations using calibrated survey instruments. The study further aims to evaluate spatial variation in radiation distribution, estimate the annual effective dose using UNSCEAR conversion coefficients, identify potential radiation hotspots through spatial analysis, and compare the measured levels with national and international radiation safety standards established by the AERB and ICRP. Methods: The proposed observational research will be carried out in the developing rural-to-semi-urban village of Village Sawangi in the Deoli tehsil where land use and construction trends are rapidly evolving. A systematic survey of 15-20 strategic locations will be used in the research which will include: residential houses, agriculture farms, water sources, and construction sites. Sites will be chosen according to certain inclusion criteria with the consideration of commonly occurring village habitats without known initial radiation contamination that can be measured repeatedly. On the other hand, any sites that contain known radioactive waste industrial or medical or personal property on which consent is not given will be omitted. Calibrated portable gamma survey meters, ionization-based survey meters and TLD badge will be the data collection instruments. These quantifiable exposure rates will then be translated into an annual effective dose by use of the standard conversion coefficients that are offered by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Results: the study is to measure radiation levels at strategic points using calibrated survey meters, to assess spatial variation across residential and agricultural zones, to calculate the annual effective dose using UNSCEAR conversion coefficients, and to identify radiation hotspots through spatial analysis. Conclusions: This study will provide the first systematic assessment of natural background radiation levels in Village Sawangi, establishing a vital radiological baseline for a developing rural-to-semi-urban area. By identifying potential radiation "hotspots" and calculating the annual effective dose for residents, the research will determine whether the local environment complies with the safety limits of 1 mSv per year set by the AERB and ICRP. Ultimately, these findings will contribute to broader regional radiation mapping and serve as a critical reference for future public health surveillance and epidemiological research in the Wardha district. Clinical Trial: NA
Background: The Ending the HIV Epidemic (EHE) initiative remains a national priority in the United States (U.S.), aiming to reduce new HIV infections by 90% by 2030. As we cross the initiative’s mid...
Background: The Ending the HIV Epidemic (EHE) initiative remains a national priority in the United States (U.S.), aiming to reduce new HIV infections by 90% by 2030. As we cross the initiative’s midpoint, there has been a renewed commitment to strengthening the HIV workforce’s capacity to plan, implement, and sustain effective HIV prevention, treatment, and care interventions. Despite substantial improvements in HIV outcomes, uneven implementation of evidence-based interventions reflects persistent gaps between available evidence and its translation into locally actionable practice. Achieving EHE goals requires tailoring implementation to the diverse epidemiological, social, and structural conditions shaping HIV outcomes across jurisdictions. Research increasingly highlights the value of integrated, contextual data to strengthen public health decision-making. Linking indicators spanning multiple conceptual domains across regional, local and individual levels can support a more robust understanding of the distinct drivers of HIV outcomes, yet existing data systems remain fragmented across domains and scales. A harmonized, multisource, multilevel database is therefore essential to support targeted, needs-based and data-driven implementation under the EHE initiative. Objective: This project has two objectives: (1) to build a high-quality contextual database integrating multiple sources of public data using transparent, replicable, and updateable methods, and (2) to develop and document systematic workflows for ongoing database updates, quality assurance, and to support future use aligned with open-science frameworks and standard data practices. Methods: This project will follow best practices in data architecture, acquisition, standardization, and quality assurance. For Objective 1, we will integrate data across multiple geographic levels (e.g., ZIP code, county) for the years 2020-2025, with measures categorized into conceptual domains (e.g., epidemiologic, sociodemographic) guided by established theoretical frameworks to facilitate future analyses. For Objective 2, we will develop a tiered data structure to enable transparent and reproducible data management, using a GitHub repository to store all documentation, processing scripts, and quality assurance logs to align with open science practices. Database construction and quality assurance methods were informed by targeted literature reviews in PubMed. Data sources will be identified from three inputs: existing data repositories, datasets identified through targeted literature reviews, and reports or grey-literature with consistent formatting and permissive terms of use suitable for web scraping. Stakeholder engagement will be integrated through all phases of database development, informing variable selection, usability, and validation to enable iterative refinement and revision. Results: Literature reviews were conducted from October to November of 2025, to inform database construction methods, source identification, and protocol development. Data acquisition will begin in May 2026. Conclusions: This contextual database will provide a reproducible and scalable data resource to support public health planning and advance implementation science by enabling more context-responsive decision-making under the EHE initiative.
Background: Background:Psychological distress is common among cancer patients and negatively impacts treatment adherence and quality of life. Radiotherapy, with its unique procedures such as daily ses...
Background: Background:Psychological distress is common among cancer patients and negatively impacts treatment adherence and quality of life. Radiotherapy, with its unique procedures such as daily sessions and physical immobilization, may induce distress distinct from general cancer anxiety. However, existing screening tools cannot differentiate these distress sources. This study leverages online patient narratives and natural language processing to distinguish radiotherapy-specific distress from general cancer distress. Objective: Objective: This study aims to systematically identify, differentiate, and compare the composition, structure, and emotional characteristics of general cancer distress versus radiotherapy-specific distress through the analysis of large-scale online patient narratives. Methods: Methods: Employing a retrospective observational design, we screened 52,831 relevant posts published between 2015 and 2025 on the online health community Reddit, ultimately including 9,860 first-person patient narratives meeting inclusion criteria. We employed Latent Dirichlet Allocation (LDA) thematic modeling for topic identification, supplemented by manual qualitative coding for thematic classification. Structural relationships between topics were analyzed via correlation heatmaps, while emotional polarity and discrete sentiments for both distress categories were quantitatively compared using VADER and RoBERTa models. Results: Results: Radiotherapy-specific distress formed an independent and substantial domain, accounting for 50.4% (n=4,969) of all narratives, comparable to the 49.0% (n=4,831) attributed to general cancer distress. Thematic correlation analysis confirmed that both categories exhibited high internal cohesion but weak inter-category associations, indicating structural independence. Sentiment analysis further revealed that radiotherapy-specific distress carried stronger negative emotional intensity (p < 0.001), with core emotions dominated by “fear” (55.8%) and “anger/frustration” (25.4%); whereas general cancer distress was more frequently expressed as ‘anxiety’ (45.2%) and “sadness” (33.1%). Conclusions: Conclusion: This study demonstrates that radiotherapy-specific distress is not a subtype of general cancer anxiety but constitutes an independent domain with distinct compositional and emotional characteristics. Developing targeted assessment and care strategies addressing radiotherapy-specific challenges is essential for achieving truly patient-centered, individualized psychosocial support in oncology. Clinical Trial: Not applicable.
Background: Adverse events (AEs) after hospitalization are common and disproportionately affect adults with multiple chronic conditions (MCC). Patient-reported symptoms and self-assessed health may en...
Background: Adverse events (AEs) after hospitalization are common and disproportionately affect adults with multiple chronic conditions (MCC). Patient-reported symptoms and self-assessed health may enable earlier detection of post-discharge AEs, but scalable, workflow-integrated approaches are limited. Objective: To identify user requirements for, and field test, an automated remote monitoring system to enhance AE surveillance during transitions. Methods: We conducted a mixed-methods study using an iterative, user-centered design approach. Semi-structured interviews with patients and clinicians informed system requirements, followed by real-world field testing. The prototype leveraged interoperable electronic health record data services, delivered automated post-discharge check-ins using symptom questionnaires and patient-reported outcomes (PROs), provided risk-stratified health advice, and escalated high-risk symptoms to clinicians. Descriptive statistics assessed feasibility and utilization; conventional content analysis identified user needs and implementation considerations. Results: Thirty-seven patients with MCC and 23 clinicians participated. Key requirements included clear communication of personalized risk based on red-flag symptoms, actionable guidance aligned with discharge instructions, explicit delineation of responsibility between inpatient and outpatient clinicians, and selective escalation to minimize burden. In field testing with 20 patients, 60% of automated questionnaires were completed. Seven patients received risk-stratified advice for new or worsening symptoms; among those with moderate- or high-risk alerts, emergency department visits occurred within one week of discharge. Patients found the system understandable and helpful, while clinicians noted challenges interpreting PRO trends. Conclusions: A user-informed, automated remote monitoring system was feasible and acceptable for AE surveillance during transitions but should prioritize clear risk communication, role clarity, and interpretable patient-reported data to support safer transitions in this population.
Background: House dust mite (HDM) sensitization commonly begins in early life and contributes to persistent allergic airway inflammation and asthma chronicity. Primary prevention via early-life enviro...
Background: House dust mite (HDM) sensitization commonly begins in early life and contributes to persistent allergic airway inflammation and asthma chronicity. Primary prevention via early-life environmental control is a key pathway to reduce HDM sensitization and asthma risk. Objective: To characterize child caregivers’ knowledge, attitudes, and practices (KAP) regarding pediatric HDM control using a hybrid literature/expert-driven and social media-driven approach, and examine associations between KAP levels, child age and caregiver social media activity. Methods: This cross-sectional study comprised two interconnected components: (1) mining of content published between August 2023 and July 2025 from five major Chinese social media platforms, analyzed via Latent Dirichlet Allocation (LDA); and (2) a social media-enhanced web-based KAP survey administered in November 2025 to child caregivers in Chongqing, a warm-humid region where HDMs dominate indoor allergens, with participants recruited via local child health facilities. In total, 132,341 social media documents and 2,275 caregivers of children <18 years were included in the analysis. The main outcomes included social media discourse patterns and domain-specific KAP levels across five dimensions: foundational knowledge (K1), recommended control knowledge (K2), attitude toward social media topics (A1), attitude toward recommended methods (A2), and control practices (P). Stratified analysis was conducted by two exposure variables: child age (≤3 years vs >3 years) and caregiver social media activity (active vs. inactive). Results: LDA topic modeling identified five distinct topic clusters in the social media content. Commercial, emotional, and misleading content collectively dominated the information landscape, accounting for 83.3% of included documents, with commercial content often systematically conflating the concepts of “disinfection” and “mite elimination”. Only 16.7% was classified as health educational content focusing on HDM allergy prevention. The average KAP levels of K1, K2, A1, A2, and P domains were 62.9%, 84.7%, 57.0%, 37.8%, and 25.8%, respectively. Social media emerged as the primary knowledge source (80.7%), with methodological knowledge gaps (47.5%) being the top implementation barrier. Caregivers of children ≤3 years had significantly lower self-rated knowledge (23.5% vs. 28.3%, P=.01), stronger endorsement of recommended methods, but also greater information overload (OR 1.39, 95% CI 1.15-1.67, P<.001) and decision difficulties (OR 1.23, 95% CI 1.01-1.52, P<.001). Socially active caregivers showed better performance across multiple items in five domains, but also increased non-recommended practices (ultraviolet irradiation: OR 1.85, 95% CI 1.35-2.53, P<.001) and misconception acceptance (allergy impact exaggeration: OR 1.39, 95% CI 1.04-1.87, P=.03). Conclusions: Complex and suboptimal KAP levels exist, particularly among caregivers of young children (≤3 years). Social media activity associates with both enhanced implementation of control practices and elevated misconception endorsement. These findings reveal critical educational gaps and the necessity of social media intervention. Clinical Trial: Not applicable.
Background: Computer vision (CV) technologies are increasingly applied to gait and movement analysis to assess cognitive decline and mental health conditions. By extracting physical biomarkers such as...
Background: Computer vision (CV) technologies are increasingly applied to gait and movement analysis to assess cognitive decline and mental health conditions. By extracting physical biomarkers such as gait and posture, CV offers a non-invasive, scalable approach for early identification of cognitive and psychosocial changes. Objective: This scoping review aims to explore how CV-based methods use physical biomarkers to predict cognitive and mental health outcomes. Methods: A scoping review was conducted in accordance with the Arksey and O’Malley framework and reported using PRISMA-ScR guidelines. Searches were performed across Medline, Embase, Web of Science, IEEE Xplore, and PubMed. Eligible studies were randomised controlled trials, cohort, or longitudinal studies involving human participants, both healthy and those with underlying pathology. Included studies used CV to analyse physical biomarkers for predicting cognitive or mental health outcomes, with objective comparators. Data were extracted on study characteristics, populations, CV methodologies, motion tasks, biomarkers, and outcomes. Results: Following study selection, 41 studies were selected. Most used markerless motion capture, pose estimation, or deep learning. Temporospatial features were the most commonly analysed biomarkers. Cognitive decline, depression, anxiety, and psychosocial well-being were frequently targeted outcomes. Many studies found significant links between these biomarkers and cognitive or mental health outcomes. They also proposed predictive models, most commonly classification or regression frameworks, compared to validated screening tools. These models aim to support early identification. Conclusions: Computer vision offers a promising approach to predicting cognitive and mental health outcomes. Future work should emphasise standardisation, clinical validation, and broader population applications.
AI-ECG models trained on 12-lead–derived Lead I demonstrated comparable endpoint discrimination on smartwatch-derived Lead I but substantially divergent training trajectories, revealing cross-modali...
AI-ECG models trained on 12-lead–derived Lead I demonstrated comparable endpoint discrimination on smartwatch-derived Lead I but substantially divergent training trajectories, revealing cross-modality generalization gaps not detected by endpoint metrics alone.
Background: Muslims have demonstrated poorer engagement and outcomes relative to other faith groups, barriers to care in part due to cultural incongruence, stigma and perceived limited compatibility b...
Background: Muslims have demonstrated poorer engagement and outcomes relative to other faith groups, barriers to care in part due to cultural incongruence, stigma and perceived limited compatibility between Islamic values and mainstream mental health services. Yet formal mental health services in the UK have under-representation from Muslims in the UK. Beyond that digital modalities offer scalable delivery but there is a scarcity of culturally adapted interventions for Muslim populations. Objective: To assess the usability, feasibility, acceptability, and preliminary effects of a mobile faith-adapted cognitive behavioral therapy intervention designed to reduce depressive symptoms among Muslim users. Methods: A single-arm feasibility study was conducted with assessments at baseline and 8-weeks. Eligible UK-based Muslim adults with depressive symptoms self-enrolled and completed an eight-session self-guided intervention integrating behavioral activation, cognitive restructuring, and coping consolidation with Islamic concepts (e.g. tawakkul, sabr, Qur’anic affirmations, prophetic exemplars). Engagement indices, app usability (Mobile App Rating Scale), depressive symptoms (PHQ-9) were assessed quantitatively, and experiences of app use were assessed using qualitative questions. Results: Of 89 individuals screened, 51 were eligible; 28 downloaded the app and 19 provided complete post-intervention data. App quality was rated favorably (overall Mobile App Rating Scale= 4.09/5), with particularly high scores for functionality and aesthetics. Participants endorsed strong behavioral-impact perceptions, including increased knowledge, attitude change, help-seeking intention, and stigma reduction (84–95%). Mean PHQ-9 scores decreased from 10.89 to 6.00 (Δ = −4.89; Cohen’s d = 1.34); 47.4% achieved ≥50% reduction and 26.3% met the remission threshold. Qualitative feedback consistently attributed increased relevance, comfort, and engagement to the faith-integration. Conclusions: A faith-congruent mobile CBT intervention appears feasible and acceptable, although drop-out rates were high introducing bias. It was perceived as psychologically and culturally legitimate among Muslims who may be otherwise disengaged from mainstream care. Progression to a fully powered controlled evaluation would be the next step. Faith-aligned interventions may represent a necessary approach for reducing structural underutilization of psychological care in Muslim populations. Clinical Trial: Ethical approval for this study was obtained from the King’s College London Research Ethics Committee REC reference number MRSU-24/25-45173.
Open Peer Review Period: May 8, 2026 - Apr 23, 2027
Background: Reliable deployment of machine learning systems in healthcare requires mechanisms for determining whether individual predictions can be trusted. Conventional confidence-based approaches of...
Background: Reliable deployment of machine learning systems in healthcare requires mechanisms for determining whether individual predictions can be trusted. Conventional confidence-based approaches often fail to capture underlying uncertainty, particularly in high-capacity models where predictions may remain highly confident despite unstable reasoning. Objective: This study proposes a Decision-Calibrated Explainable AI (DC-XAI) framework for evaluating prediction reliability using stability-based signals derived from both model outputs and feature attribution explanations. Methods: The proposed DC-XAI framework integrates two complementary reliability signals: prediction stability under stochastic perturbations and explanation stability measured by feature-attribution consistency. These signals are combined into a three-tier decision system consisting of ACCEPT, ACCEPT WITH CAUTION, and DEFER categories to support reliability-aware clinical decision-making. The framework was evaluated using the MIMIC-IV critical care dataset for in-hospital mortality prediction. Evaluation was conducted using a two-level strategy, comprising a global performance assessment on the full test set (n = 13,074) and a perturbation-based stability analysis on a representative subset (n = 1,000). Logistic Regression, XGBoost, and Multi-Layer Perceptron (MLP) architectures were compared. Results: The results revealed a significant Stability–Accuracy Gap across model architectures, demonstrating that predictive performance alone does not reliably reflect prediction trustworthiness. Logistic Regression exhibited a strong monotonic relationship between stability and accuracy, whereas XGBoost demonstrated brittle stability, maintaining stable predictions despite incorrect outputs. The MLP exhibited non-monotonic stability behaviour, where instability in feature attribution did not consistently correspond to prediction failure. These findings indicate that the relationship between stability and reliability is architecture-dependent and that explanation stability alone is insufficient as a universal trust signal. Conclusions: The proposed DC-XAI framework provides a practical mechanism for reliability-aware clinical AI deployment by integrating prediction stability and explanation consistency into a triage-based decision process. The findings challenge the assumption that stability is a universal proxy for reliability and highlight the need for architecture-aware trust calibration in safety-critical healthcare AI systems.
Open Peer Review Period: May 8, 2026 - Apr 23, 2027
Ambient scribe tools are increasingly being used to generate draft clinical documentation in an effort to reduce documentation burden, enhance workflow, and improve clinician and patient experience. T...
Ambient scribe tools are increasingly being used to generate draft clinical documentation in an effort to reduce documentation burden, enhance workflow, and improve clinician and patient experience. Their optimal use, however, depends on a clear understanding of how sound generated during clinical encounters is transformed into draft notes and where characteristic failure modes may arise. This narrative review explains the processing pipeline underlying ambient scribe technology and translates that account into a practical framework for clinician use. Literature was reviewed between November 2025 and March 2026 using PubMed, IEEE Xplore, IsisCB Explore, ACM Digital Library, arXiv, Google Scholar, and citation tracking. Priority was given to peer-reviewed sources, with selective inclusion of foundational technical and conceptual works when appropriate. The review is organized around three processing stages: acoustic capture and digitization; speech recognition and transcript generation; and draft progress note generation. Ambient scribes are understood here as multistage, machine-mediated systems in which predictable failure modes may arise, including omission of clinically relevant information, speaker misattribution, contextual distortion, unsupported insertion, reversal of clinical meaning, and operational inefficiency from output that is verbose or factually incorrect. This review links these mechanisms to a practical clinician framework centered on judgment
before the encounter, deliberate communication and acoustic capture practices during the encounter, and targeted review of predictable artifact classes after note generation. By focusing on mechanism, predictable failure modes, and clinician response, this review provides a practical foundation for informed clinical use. That foundation is intended to support use that is both more effective and more cautious.
Background: Frailty affects approximately 10–15% of community-dwelling older adults globally and is associated with increased risk of falls, hospitalization, and mortality. Conventional frailty asse...
Background: Frailty affects approximately 10–15% of community-dwelling older adults globally and is associated with increased risk of falls, hospitalization, and mortality. Conventional frailty assessment relies on clinician-administered instruments such as the Fried Frailty Phenotype, which are time-intensive and show only moderate inter-rater reliability. Skeleton-based and sensor-based gait analysis—encompassing computer-vision pose estimation, depth-camera skeleton tracking, and inertial measurement unit (IMU)-based motion capture—has emerged as a candidate technology for objective, scalable frailty screening. However, the methodological quality, diagnostic performance, and clinical applicability of these approaches have not been comprehensively evaluated using a structured risk-of-bias framework. Objective: This systematic review synthesizes evidence on the diagnostic accuracy, methodological quality, and clinical applicability of technology-based gait analysis systems for frailty prediction in community-dwelling and institutionalized older adults. Methods: Following the PRISMA 2020 statement, we searched PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science, and the Cochrane Central Register of Controlled Trials for studies published between January 2018 and April 2026. The lower bound was set to coincide with the publication of two enabling technologies (ST-GCN and YOLOv3, both 2018). Two reviewers independently screened records against predefined PICOS criteria. Risk of bias and applicability concerns were assessed using the QUADAS-2 tool across four domains: Patient Selection, Index Test, Reference Standard, and Flow and Timing. Because of clinical and methodological heterogeneity across the small number of eligible studies, a narrative synthesis was performed rather than quantitative meta-analysis Results: Of 371 records retrieved (after de-duplication), 7 studies (total N = 2,226 older adults) met inclusion criteria. Sensing modalities comprised computer-vision skeleton extraction (n = 2), IMU-based motion capture (n = 4), and marker-based 3D motion capture (n = 1). Reported overall classification accuracy ranged from 79% (in a study with dual-dataset external validation) to approximately 97.5% (in a study that excluded the frail class from training due to small sample size). Frail-class sensitivity varied from 22.1% to 95.6% and tracked methodological choices—class imbalance handling, participant- vs sample-level data partitioning, and external validation—rather than algorithmic differences. Only 1 study was judged at low risk of bias across all 4 QUADAS-2 domains; 4 studies were judged at overall high risk of bias and 2 raised some concerns. Convergent biomechanical signatures of frailty included reduced gait speed, reduced ankle plantar flexion and range of motion, reduced knee heel-strike angle, and increased hip toe-off angle. Conclusions: Skeleton-based and sensor-based gait analysis show promise as adjunctive tools for frailty screening but do not yet meet the methodological threshold for first-line clinical deployment. The evidence base is limited by reliance on internal validation, small frail-class sample sizes, cross-sectional designs, and absence of cross-population testing. Future research should treat participant-based stratified data partitioning, external dataset validation, and explicit reporting of frail-class sensitivity as minimum methodological standards before clinical translation. Digital health stakeholders deploying such systems in geriatric care should plan for privacy-preserving inference, demographic generalizability testing, and longitudinal validation as prerequisites for routine use. Clinical Trial: CRD420261373600
Background: Chronic pain significantly impacts physical function and quality of life. Current assessments rely on patient-reported measures or clinic-based tests, which may not accurately reflect real...
Background: Chronic pain significantly impacts physical function and quality of life. Current assessments rely on patient-reported measures or clinic-based tests, which may not accurately reflect real-world activity. Objective: To provide a preliminary evaluation of quantitative methods using wrist accelerometry data to quantify physical performance in a pilot study on individuals with chronic pain. Methods: Thirteen participants wore wrist-mounted accelerometers for seven days. The mean absolute deviation (MAD) metric was computed from raw acceleration data to classify sedentary and active behaviors. The durations and accumulation patterns of time spent in sedentary and active bouts were investigated. Activity intensity levels were also analyzed using wrist-based actigraphy cut-points previously developed for the general population. Additionally, total step counts were estimated from wrist actigraphy data. Correlations between actigraphy metrics and pain scores, age, and performance on a 6-minute walk test were examined. Results: The average daily percentage of time spent in sedentary and active states (light intensity and higher), based on the MAD metric, was 62% and 38%, respectively. However, the wrist-based actigraphy cut-points, previously established for the general population, tended to classify a greater proportion of time as sedentary (70%) and a smaller proportion as active (30%). Sedentary time was primarily accumulated through longer sedentary bouts. The average (SD) total daily step count was 9,344 (2,403) steps per day. Only average daily activity bout duration was associated with 6MWT (ρ = 0.56, p = 0.046). Conclusions: The MAD-based metrics demonstrated feasibility for capturing light-intensity activities in individuals with chronic pain, suggesting an improved method for tracking functional outcomes. These findings should be interpreted as preliminary and hypothesis-generating, informing future studies on the integration of personalized, objective metrics into chronic pain management to better assess treatment efficacy and promote meaningful activity changes.
Background: Objective Structured Clinical Examinations (OSCEs) are widely used to assess clinical competence in postgraduate medical training, but their design, delivery, and evaluation require substa...
Background: Objective Structured Clinical Examinations (OSCEs) are widely used to assess clinical competence in postgraduate medical training, but their design, delivery, and evaluation require substantial faculty time, standardized patients, and institutional resources. Generative artificial intelligence (AI) may offer a scalable approach to creating, administering, and scoring OSCE stations, although its validity compared with human faculty assessment remains unclear, particularly in postgraduate surgical education. Objective: This study evaluated the feasibility and validity of creating a custom generative pre-trained transformer (GPT) to generate and deliver OSCE stations for postgraduate urology residents and compared AI-based scoring with human faculty grading. Methods: We conducted a prospective validation study. ChatGPT-4 generated and administered two OSCE stations for postgraduate year 3–5 urology residents. Stations simulated common urologic scenarios and were reviewed by faculty to ensure clinical accuracy. Performances were scored by ChatGPT using structured rubrics and independently graded by three blinded faculty examiners. Agreement between AI and human grading was assessed using correlation coefficients, intraclass correlation coefficients (ICC) and Bland–Altman analysis. Scores were also compared with other OSCE stations to assess construct validity. Results: Nine residents completed both stations. Mean human-graded and AI-graded scores were 51 ± 19% vs 65 ± 16% for Case 1 and 38 ± 11% vs 36 ± 12% for Case 2, respectively. Strong correlations were observed between AI and human graders (Case 1: r = 0.95, p < 0.001; Case 2: r = 0.83, p = 0.011), with moderate-to-high agreement (ICC = 0.70 and 0.83). Bland–Altman analysis demonstrated minimal bias. Over 80% of participants agreed the stations reflected appropriate realism and educational relevance. Conclusions: AI-assisted OSCE generation and evaluation using ChatGPT is feasible and demonstrates close alignment with faculty grading in postgraduate urology training. This approach may serve as a scalable adjunct to competency-based assessment, reducing examiner burden while maintaining validity, provided appropriate human oversight is maintained.
Background: Background: The assessment of the risk of bias (RoB) of each study is a necessary but labor-intensive step in conducting a systematic review (SR). There is a potential for artificial intel...
Background: Background: The assessment of the risk of bias (RoB) of each study is a necessary but labor-intensive step in conducting a systematic review (SR). There is a potential for artificial intelligence (AI) to reduce the time and effort needed in RoB assessment and improve consistency between reviewers, but little is known about the accuracy of AI RoB assessments. Objective: Objective: In this study, we aim to evaluate the performance of a large language model (LLM), when given the questions from the RoB tool and the relevant passages from the full-text articles, in assessing RoB in observational studies of environmental exposures and health outcomes. Methods: Methods: We evaluated the performance of two LLMs (GPT-5-mini and Google Gemini-3) on 128 observational studies from a SR of per- and polyfluoroalkyl substances (PFAS) and health outcomes conducted by the National Academies of Sciences. The Navigation Guide (NavGuide), a domain-based RoB tool designed for environment studies, was used. For each article-domain pair, the LLM was provided with the questions and guidance from the Navigation Guide and the human-identified text passages addressing that domain. The LLM returned a structured RoB rating, which were compared to the human-adjudicated ratings to quantify agreement and identify patterns of discrepancy. The protocol was prospectively registered through the Open Science Framework. Results: Results: The LLMs demonstrated moderate agreement with human consensus RoB assessments for exact matches (51% to 65%), but this remained lower than the agreement among humans (88% to 91%). Performance improved substantially for partial matches, reflecting agreement in direction but not magnitude (e.g., "low" vs. "probably low"), with percent agreement of 92% and 96% for the two LLMs (98% to 99% for humans). Performance varied across the NavGuide domains, with the worst performance in domain 1 (selection bias) and the best in domain 8 (conflict of interest). The models tended to be more conservative than humans, often assessing higher risk of bias ratings (e.g., “probably low” when humans assigned “low”). Conclusions: Conclusions: Our results suggest that, although agreement was moderate and inconsistent across domains, LLM-based RoB assessment may be sufficiently accurate for use as a second reviewer with human oversight. Clinical Trial: The protocol was prospectively registered through the Open Science Framework: https://osf.io/srtqd/overview?view_only=068261b912fe40308e4f199520d6c16f
Background: The rapid integration of artificial intelligence (AI) in higher education has generated increasing interest in AI chatbots as virtual tutors in nursing education. These tools have the pote...
Background: The rapid integration of artificial intelligence (AI) in higher education has generated increasing interest in AI chatbots as virtual tutors in nursing education. These tools have the potential to provide personalized, on-demand support in asynchronous learning environments, where students often experience limited interaction and delayed feedback. Despite growing adoption, there is a lack of empirically grounded research on effective instructional design approaches and student learning experiences associated with AI chatbot integration in nursing education. Objective: This study aimed to examine undergraduate nursing students’ learning experiences with, and perceptions of, an AI chatbot designed as a virtual tutor in an asynchronous online course. A secondary objective was to explore the effectiveness of the PROSE (Persona, Rubric, Objective, Steps, Example) model as a framework for aligning chatbot interactions with course learning objectives. Methods: An institutional review board–approved mixed-methods pilot study was conducted with 66 undergraduate nursing students enrolled in an asynchronous online course at a large public university in the United States. The chatbot was integrated into course activities to provide formative feedback on writing assignments and support quiz preparation. Data collection included pre- and post-intervention surveys measuring perceptions, attitudes, and expectations of AI (30 Likert-scale items), as well as a post-activity survey with quantitative and open-ended qualitative responses. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using thematic analysis to identify patterns in student experiences. Results: Findings indicated consistent increases across all measured domains, including perceptions of AI in professional contexts, attitudes toward AI technology, and expectations for AI-supported feedback and mentorship. Students reported that the chatbot enhanced interactivity, reduced feelings of isolation, and supported self-directed learning and understanding of complex content. Qualitative findings highlighted key benefits such as immediate access to assistance, alternative explanations, and support for studying and assessment preparation. However, students also identified challenges related to accuracy, response specificity, technical performance, and concerns about reduced human interaction and potential overreliance on AI tools. Conclusions: AI chatbots designed with pedagogical alignment can serve as valuable supplemental tools in online nursing education by enhancing engagement and perceived learning support. However, effective implementation requires careful attention to accuracy, transparency, and the preservation of human instructor presence. These findings suggest that AI chatbots are most effective when positioned as complements to, rather than replacements for, human teaching, and when integrated through structured instructional design frameworks such as the PROSE model.
Background: Perinatal depression is a common and impactful condition affecting maternal and neonatal outcomes worldwide. Digital mental health interventions, particularly chatbots and conversational a...
Background: Perinatal depression is a common and impactful condition affecting maternal and neonatal outcomes worldwide. Digital mental health interventions, particularly chatbots and conversational agents, have emerged as scalable and accessible tools to expand support across the perinatal continuum. However, the extent to which these technologies effectively support perinatal depression remains unclear. Objective: To map and synthesize the evidence on the use of chatbots and conversational agents for supporting perinatal depression, focusing on intervention characteristics, outcomes, safety mechanisms, and equity considerations. Methods: We conducted a scoping review following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. Searches were performed in PubMed/MEDLINE, Scopus, Web of Science, CINAHL, Embase, LILACS, and Google Scholar up to April 2026. Eligible studies evaluated chatbot-based or conversational AI interventions providing psychological or emotional support to pregnant or postpartum women. Data were extracted on study design, intervention features, clinical and implementation outcomes, safety and screening mechanisms, and accessibility dimensions. Results: Nine studies were included, comprising one randomized controlled trial, multiple pilot and feasibility studies, one qualitative study, one real-world observational study, and one protocol. Most interventions demonstrated high feasibility, acceptability, and user engagement. The only randomized trial reported modest short-term reductions in depressive symptoms, with no consistent effects on anxiety or perinatal-specific measures. Substantial heterogeneity was observed in intervention design, theoretical frameworks, delivery platforms, and outcome metrics. Safety mechanisms, including automated risk detection and escalation, were limited or absent in most studies. Equity varied by delivery modality, with SMS-based interventions showing greater accessibility in low-resource settings, while app-based approaches required higher levels of digital access and literacy. Conclusions: Chatbots represent a promising and scalable approach to support perinatal mental health, particularly for expanding access to low-intensity psychological support. However, current evidence is limited by methodological heterogeneity, short follow-up, and insufficient safety integration. These tools should complement, rather than replace, professional care. Future research should prioritize rigorous trials, standardized outcomes, embedded safety protocols, and equity-oriented design to support integration into digital nursing care and health systems.
Background: Cerebral palsy (CP) is a common motor disability in children, and it refers to a group of disorders that affect a child’s ability to move, maintain balance and posture. Children with CP-...
Background: Cerebral palsy (CP) is a common motor disability in children, and it refers to a group of disorders that affect a child’s ability to move, maintain balance and posture. Children with CP-related motor disorders experience disturbances of sensation, perception, cognition, communication, and behaviour, that are due to epilepsy. They may also have secondary musculoskeletal problems. Interprofessional team is needed to manage CP. However, in KwaZulu-Natal, rehabilitation services are fragmented, with departments working in isolation. Thus, there is a need to develop an interprofessional model of care responsive to the needs of those involved in the care of children with cerebral palsy (CWCP). Objective: To propose an evidence-based interprofessional model of care responsive to the needs of children with cerebral palsy in KwaZulu-Natal. Methods: This study will employ a phased sequential multi-method approach, with Phases 1, 2 and 3 having two, three and three objectives, respectively. Phase one (Objectives 1-2) will be conducted through a systematic scoping review to map the current evidence on the interprofessional rehabilitation practices utilized in low- and middle-income countries (LMICs) to manage CP, complemented with in-depth interviews (IDIs) on how Health Care Practitioners (HCPs) in KwaZulu-Natal manage children with CP. Using IDIs, FGDs and survey questionnaires, Phase two (Objectives 3- 5) will focus on exploring caregivers’ perspectives on the rehabilitation of children with CP, their lived caregiving experiences, and determine the level of caregiver burden. Phase three will use phases one and two results to propose and validate a model of care through a Delphi technique. The study has obtained ethics approval and gatekeeper permission from the University of KwaZulu-Natal’s Biomedical Research Ethics Committee and the Provincial Department of Health, respectively. Results: Results of this study will be disseminated through publications in peer-reviewed journals, conference presentations and the thesis archived in the University’s library repository. Conclusions: This study is anticipated to provide an evidence-based interprofessional model of care responsive to the needs of children with CP, given that models used in high-income countries may not be appropriate for Low- and Middle- Income Countries’ contexts.
Background: Continuous glucose monitoring (CGM) offers clinical and behavioural benefits for people with type 2 diabetes (T2D), including improved glycaemic control and enhanced self-management. Howev...
Background: Continuous glucose monitoring (CGM) offers clinical and behavioural benefits for people with type 2 diabetes (T2D), including improved glycaemic control and enhanced self-management. However, important evidence gaps remain regarding whether CGM use is equitably distributed across patient groups and whether Objective: To examine the relationship between CGM use among individuals with type 2 diabetes (T2D) and a range of patient characteristics, including socio-demographic factors linked to health inequities, digital health literacy, clinical characteristics, and service utilisation. Methods: A cross-sectional online survey was conducted in November 2024 among adults in the UK with self-reported type 2 diabetes (T2D), recruited via the YouGov panel. The primary outcome was self-reported CGM use. Predictor variables included PROGRESS-Plus characteristics (age, gender, ethnicity, religion, education, occupation, household income, disability, and social engagement), digital health literacy (eHEALS scale), clinical characteristics (disease duration, current treatment, and complications), overall health status (number of long-term conditions), and healthcare utilisation (frequency of visits). Descriptive statistics and multivariable logistic regression were used to examine associations between CGM use and patient characteristics. Results: Among 403 participants, 12.7% reported CGM use. Nearly half of participants were aged 65 years or older, and 56.80% were male. Most participants were White 83.90% and lived in urban areas. Higher odds of CGM use were observed among insulin users (OR=3.80, 95% CI: 1.6–9.22, p<0.001). No other demographic, clinical, or service utilisation variables were statistically significantly associated with CGM use. Conclusions: CGM use was primarily driven by insulin therapy, consistent with established clinical pathways within the National Health Service that prioritise access for this group. No significant variation was observed across demographic, socioeconomic, or health literacy-related characteristics, suggesting no clear evidence of inequalities in this sample. These findings indicate potentially equitable access, although further research in larger and more diverse populations is needed to confirm these patterns.
Background: Background: Artificial intelligence–enabled clinical decision support systems (AI-CDSSs) are increasingly deployed for venous thromboembolism (VTE) prevention. However, healthcare profes...
Background: Background: Artificial intelligence–enabled clinical decision support systems (AI-CDSSs) are increasingly deployed for venous thromboembolism (VTE) prevention. However, healthcare professionals’ perceptions and experiences of these systems across diverse regional, occupational, and specialty contexts remain poorly understood, with limited evidence on how AI integration influences clinical workflows, responsibility allocation, and professional trust within multi‑tiered healthcare systems. Objective: Objective: This study aimed to systematically investigate healthcare professionals’ perceptions and experiences of using AI-CDSS for VTE prevention across different institutional levels and clinical roles in China. Methods: Methods: A nationwide qualitative study was conducted using semi‑structured interviews with 23 healthcare professionals from diverse institutional levels and clinical roles. Data collection proceeded until thematic saturation was reached. All interviews were transcribed verbatim and analyzed using inductive thematic analysis. Results: Five core themes were identified: (1) AI reduces workload but complicates clinical responsibility; (2) patient involvement is perceived as beneficial yet problematic; (3) digital readiness shapes implementation feasibility; (4) trust in AI varies by professional role; and (5) responsibility and risk remain ambiguous after AI introduction. Facilitating factors included clearly defined responsibility assignment, comprehensive training, incentive mechanisms, and institutional oversight. Key barriers comprised economic costs, additional workload burden, and complex hospital approval processes. Conclusions: Our findings reveal structural tensions arising from the interaction between professional roles, institutional readiness, and responsibility distribution during AI integration. These results underscore the need for tiered, role‑specific implementation strategies and provide practical insights for the sustainable deployment of AI in VTE prevention.
Background: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase s...
Background: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase survival, and reduce health resource use and costs. Such systems can help oncology patients self-manage chemotherapy symptoms, reduce workloads for clinicians through automated decision support, and resolve problems earlier. However, there is a need for more research on the cost-effectiveness of such interventions. Objective: This review aims to (1) summarize and evaluate the quantitative and qualitative evidence related to the cost-effectiveness and economic evaluation methods of ePROM-integrated interventions, and (2) extract data and validate assumptions useful for health economic modelling of ePROM-based treatment strategies. Methods: We searched for original English-language papers published on or before March 2025 on Ovid (including MEDLINE and Embase), Scopus, and the International Health Technology Assessment Database (INAHTA) using search strings that combined terms related to ePROMs, health economics, and cancer/oncology. We included papers reporting health economic-related outcomes for ePROM interventions designed for adult cancer populations and excluded screening tools and conference abstracts. Results: We included 34 publications from 27 unique studies, and identified and analyzed 26 ePROM-integrated interventions within these. Most (23/26) of the included interventions explicitly described some form of alert handling and automated decision support based on remote ePROM monitoring. 5/34 publications presented full cost-utility analysis results, of which 3 were characterized by high uncertainty and a lack of clear differences in costs and health outcomes between ePROMs and standard care, while 2 presented strong evidence of cost-effectiveness due to quality-of-life improvements, reduced hospitalizations, and potentially more autonomy in health-related travel (e.g., ePROM-monitored patients can drive or walk to the hospital instead of using taxis or ambulances). A further 5/34 publications reported partial health economic results (e.g., cost-consequence, budget impact), of which 1 detected no difference in strategies, while 4 reported lower health resource use and costs of ePROMs, mainly due to hospitalization reductions. 12/27 studies included a qualitative component but mostly focused on user experience and design-related themes; only 2/12 of these addressed economic-specific themes (e.g., changes in workflow and resource use due to ePROM implementation and integration), indicating some potential for time saving due to ePROM monitoring. Conclusions: There is some evidence that ePROM-integrated interventions can be cost-effective in cancer care, but the evidence base remains limited. Where evidence does exist, cost-effectiveness appears driven by reduced hospitalization and improved quality of life. Qualitative research within the included studies rarely addressed economic questions. We provide a detailed parameter extraction for use in future economic modelling and recommend research priorities, including quantitative mapping of ePROM symptom data onto health resource use patterns, and qualitative work exploring how ePROM implementation affects clinical workloads and patient-perspective costs.
Background: Recent advances in artificial intelligence (AI), particularly large language models, have generated growing interest in their application to medical education and examination preparation. ...
Background: Recent advances in artificial intelligence (AI), particularly large language models, have generated growing interest in their application to medical education and examination preparation. However, the accuracy, reasoning quality, and adherence to clinical guidelines of these tools in postgraduate urology assessments remain unclear. Objective: To evaluate the performance of three AI tools ChatGPT (GPT-4.0), Claude-4.5, and AMBOSS on European Board of Urology (EBU)-style multiple-choice questions, with particular focus on accuracy, insight, concordance, and adherence to European Association of Urology (EAU) guidelines. Methods: A total of 200 single-best-answer questions from the EBU In-Service Assessment workbook (2021–2022) were input into each AI model. Models were prompted to select an answer and provide an explanation. Two post-FRCS urologists independently assessed outputs. Accuracy was defined as correct answer selection. Insight was evaluated across three domains: non-obvious deduction, discriminative reasoning, and clinical validity, graded as low, moderate, or high. Concordance was defined as logical alignment between the answer and its explanation. Results: ChatGPT demonstrated the highest accuracy (85.5%), compared to Claude and AMBOSS (both 79.5%). Concordance was also highest for ChatGPT (95%), followed by Claude (88%) and AMBOSS (76%). Non-obvious deduction was predominantly low-to-moderate across all models, reflecting the recall-based nature of many questions. ChatGPT and Claude showed stronger discriminative reasoning, while AMBOSS demonstrated limited exclusion of alternative options. Clinical validity was high overall, with ChatGPT showing the greatest consistency with EAU guidelines. Conclusions: AI tools can achieve high accuracy on EBU-style assessments; however, differences in reasoning quality and guideline adherence are evident. ChatGPT demonstrated superior performance across all evaluated domains, supporting its role as a potential adjunct in postgraduate urology education.
This preliminary study reports that Chain-of-Thought prompting may mitigate central tendency bias in generative AI rubric-based assessment and modestly improve agreement with human ratings of physical...
This preliminary study reports that Chain-of-Thought prompting may mitigate central tendency bias in generative AI rubric-based assessment and modestly improve agreement with human ratings of physical therapy case reports, though these findings vary across large language models.
Background: Chronic kidney disease (CKD) affects more than 850 million people worldwide, yet the emotional and psychosocial burdens remain underrecognized in nephrology practice. This review explores ...
Background: Chronic kidney disease (CKD) affects more than 850 million people worldwide, yet the emotional and psychosocial burdens remain underrecognized in nephrology practice. This review explores the overlooked dimensions of loneliness, uncertainty, and emotional suffering in CKD, drawing attention to their implications for both patients and caregivers. Objective: The objective of the current review article is to elucidate the mechanisms of the distresses experienced by the hemodialysis patients and their carers Methods: We conducted a narrative synthesis of qualitative and quantitative studies across pediatric, adult, and elderly populations, with additional perspectives from culturally and linguistically diverse groups and caregivers Results: Loneliness and social isolation affect more than 40% of dialysis patients and are consistently linked with depression, reduced adherence, hospitalization, and increased mortality. Uncertainty about disease trajectory and treatment outcomes generates chronic psychological strain, particularly in older adults. Caregivers report high emotional burden, social withdrawal, and diminished well-being. Despite growing recognition of these issues, systematic psychosocial screening and targeted interventions remain rare in routine nephrology. Conclusions: This review highlights loneliness and uncertainty as silent but modifiable risk factors in CKD, on par with traditional biomedical markers. We argue that nephrology urgently requires a shift toward a biopsychosocial model of care that includes structured emotional screening, culturally responsive interventions, and caregiver support. By reframing psychosocial suffering as a clinical priority rather than a secondary concern, nephrology can move toward more person-centered and equitable care.
Background: At present, patients, caregivers, and doctors lack information to choose the most effective assistive technologies (ATs) for people with chronic conditions. This is also true for epilepsy ...
Background: At present, patients, caregivers, and doctors lack information to choose the most effective assistive technologies (ATs) for people with chronic conditions. This is also true for epilepsy where studies have shown that lack of information about these tools is a major barrier to wider adoption. Objective: The aim of the present study is to describe the development and evaluation of EpiRate, a feedback system for ATs for people with epilepsy. Methods: In the development phase, qualitative methods were used to determine user requirements and iteratively improve prototypes of the feedback system. In the evaluation phase, a quantitative usability study with 146 participants with epilepsy was conducted. Usability was assessed using established questionnaires measuring first impression, perceived usability (PWU-G), and visual aesthetics (VisAWI-S), and results were compared across devices (smartphone, laptop/desktop, tablet). Results: Formative feedback from potential users and domain experts indicated the need for a multidimensional feedback system incorporating diverging perspectives — of users, caregivers and clinicians — with the possibility of open-ended comments per rating category. The summative evaluation showed good usability across all measures (PWU-G: M=5.40; VisAWI-S: M=5.37; First impression: M=5.32, all on a 7-point scale), with an average task completion time of approximately two minutes. Usability ratings were consistently positive across devices, with only minor differences between smartphone, laptop and tablet users. The four-category rating structure (usability, functionality, reliability and overall impression) was rated as ideal by 86% of participants. Conclusions: EpiRate is an easy-to-use, cross-device web platform for collecting verified AT feedback from people with epilepsy. The software is published under an open-source licence and can be adapted for use in other clinical populations.
Depression is a common comorbidity among cancer patients that significantly worsens mortality, quality of life, and treatment adherence. Financial, geographic, and cultural barriers may limit access t...
Depression is a common comorbidity among cancer patients that significantly worsens mortality, quality of life, and treatment adherence. Financial, geographic, and cultural barriers may limit access to conventional mental health care, and in response, many patients may self-prescribe digital mental health tools (DMHTs) without clinical guidance. Surveys indicate that roughly one in four Americans already uses large language models (LLMs) for mental health support, and this pattern almost certainly extends to cancer patients. LLM-based mental health chatbots fall under the broader category of therapeutic conversational agents (TCAs), which is the focus of this paper.
This viewpoint argues that clinicians are in an analogous position to that of providers faced with unregulated dietary supplements: patients are using these tools regardless of physician endorsement, and they need informed clinical guidance. We first characterize the biological, psychological, and social dimensions of depression that are specific to cancer patients—including tumor type, disease stage, comorbidities, psychiatric history, personality factors, social support, and financial burden—and explain why these dimensions create heterogeneous risk profiles that must inform TCA deployment decisions.
We then review current evidence on TCA capabilities and limitations. TCAs demonstrate efficacy for mild-to-moderate depression in short-course trials, and users form therapeutic bonds with them comparable to those formed with human therapists. However, critical limitations remain. TCAs fail to respond appropriately to simulated suicidality; are limited in cultural competence for non-Western and non-English-speaking populations; and may cause sycophancy-driven "delusional spiraling” among users.
Drawing on this analysis, we offer ten clinical recommendations organized around technology assessment, depression severity, unhealthy use patterns, and future FDA-approved deployment. We recommend that TCAs serve only as adjuncts—never replacements—for patients with moderate-to-severe depression, high self-harm risk, or problematic technology use patterns. We also recommend that clinicians never delegate crisis monitoring to these tools.
Finally, we argue that purely outcome-based frameworks for evaluating TCA integration risk undervaluing the intrinsic goods of human therapeutic relationships, particularly for cancer patients confronting isolation, existential distress, and mortality. Human-centered care ought to remain grounded in genuine vulnerability and reciprocity, and this approach should serve as the normative foundation guiding TCA adoption.
Open Peer Review Period: May 6, 2026 - Apr 21, 2027
Background: Procedural pain and distress in children aged 1–5 years are common in emergency medicine but remain underaddressed. Non-pharmacological distraction is a well-evidenced first-line adjunct...
Background: Procedural pain and distress in children aged 1–5 years are common in emergency medicine but remain underaddressed. Non-pharmacological distraction is a well-evidenced first-line adjunct, yet practical digital tools designed for emergency department (ED) workflows in low-resource settings are scarce. No validated digital distraction application exists for the Thai ED context. Objective: To describe the iterative, evidence-informed design and development of KidCalm ER, a single-file progressive web application intended to reduce procedural distress in children aged 1–5 years during ED procedures. Methods: KidCalm ER was designed iteratively by a board-certified emergency physician drawing on five systematic reviews and meta-analyses (Cochrane and high-quality non-Cochrane) examining distraction efficacy, interactivity benefits, and pain memory reframing. The tool was built as a single HTML5 file without external dependencies to meet the practical constraints of the Thai ED environment. No human subjects research was conducted. Results: KidCalm ER delivers five sequential interactive game levels using tap, sustained-hold, and drag mechanics, with bilingual narration (Thai/English), age-adaptive difficulty for children aged 1–2 versus 3–5 years, character selection, and mission-based narrative framing. The tool requires no installation or internet connection after initial load and runs on standard tablets or smartphones. Conclusions: KidCalm ER is a low-cost, evidence-informed, immediately deployable tool for procedural distraction in pediatric emergency care. It has not yet been validated in a clinical trial. Planned next steps include a structured usability evaluation with child-caregiver dyads and ED nursing staff, followed by a prospective pilot study within the Bangkok Hospital ED. The tool is available at https://kidcalm-er.netlify.app.
Background: Depressive disorders are one of the most prevalent psychiatric disorders globally and impose considerable individual and societal burdens. Psychotherapy, including cognitive behavioral the...
Background: Depressive disorders are one of the most prevalent psychiatric disorders globally and impose considerable individual and societal burdens. Psychotherapy, including cognitive behavioral therapy, is recommended as a first-line treatment especially for mild to moderate depressive disorders. However, face-to-face psychotherapy is often limited by issues of accessibility and cost. Digital therapeutics (DTx) have gained increasing attention as alternatives for overcoming these hurdles. With advances in digital technology, digital placebos have been increasingly adopted as comparators in the clinical trials for DTx. However, the characteristics of the clinical trials, the magnitude of digital placebos and their moderators remain poorly understood. Objective: The objectives of this study were to investigate the characteristics of clinical trials using digital placebos as comparators, and to assess the magnitude of the digital placebo effects and their moderators on depressive symptoms measured by Patient Health Questionnaire-9 (PHQ-9). Methods: The blind randomized clinical trials (RCTs) evaluating PHQ-9 by setting digital placebos as comparators were identified by searching MEDLINE, Scopus, Web of Science, PsycINFO, CINAHL, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, ISRCTN in November 2025. The characteristics of the RCTs and of the digital placebos were reviewed systematically. The meta-analysis including sub-group analyses and meta-regressions were conducted to investigate the magnitude and the moderators of the digital placebos. Results: 29 articles and 30 studies with 5680 participants were included in this systematic review and meta-analysis. The most common trial design was 2-arm, parallel-group study conducted in a single country, adopting “Replaced” and “Mobile” as the placebo approach and delivery type, respectively. The pooled effect size for all the included studies was Hedges’ g = 0.44 (95% CI 0.29 to 0.59) with an overall I2 = 93.2 %. Subgroup analyses showed moderate-to-large and statistically significant placebo effect in the group of primary psychiatric disorders (Hedges’ g = 0.69; 95% CI 0.40 to 0.99). Meta-regressions indicated that the group of primary psychiatric disorders and baseline PHQ-9 score were the independent moderators of the digital placebo effects and the major contributing factors of the high heterogeneity (R2 = 51.5%). Conclusions: Statistically significant digital placebo effects were observed on depressive symptoms, and target population and baseline PHQ-9 score were identified as the independent moderators. These findings would have implications for the planning of future DTx clinical trials using digital placebos for depressive symptoms.
Background: Hypertension is a leading cardiovascular risk factor, yet only about half of affected individuals achieve blood pressure (BP) control, with persistent disparities among low-income and raci...
Background: Hypertension is a leading cardiovascular risk factor, yet only about half of affected individuals achieve blood pressure (BP) control, with persistent disparities among low-income and racial/ethnic minority populations. Wireless home blood pressure monitoring (HBPM) integrated with remote patient monitoring shows promise for improving BP management, but evidence is limited in medically underserved, predominantly Hispanic populations served by Federally Qualified Health Centers (FQHCs). Objective: This pilot study aimed to evaluate the feasibility and acceptability of wireless HBPM among predominantly Hispanic adults with hypertension receiving care at FQHCs and to explore its effect on BP control compared with conventional HBPM using paper logs. Methods: We conducted a 6-month prospective, two-arm randomized controlled pilot trial nested within the San Diego: Heart Attack and Stroke Free Zone cardiovascular risk-reduction program. Adults aged ≥18 years with uncontrolled or newly diagnosed hypertension (BP ≥140/90 mmHg) were enrolled across three FQHC networks and randomized to a wireless HBPM arm (Qualcomm Life 2NET hub with an A&D UA-651 BLE monitor; weekly transmitted summaries reviewed by health coaches) or a standard arm (calibrated home BP monitor with paper log). Both arms received standardized health coaching. Feasibility was defined as the proportion of expected BP readings successfully transmitted; acceptability was assessed via a post-intervention satisfaction survey in the wireless arm. Exploratory analyses compared BP outcomes between arms using t-tests, chi-square/Fisher exact tests, and multivariable logistic regression. Results: Of 200 participants (73 wireless; 127 standard), mean age was 61 years, 57% were women, and 51% self-identified as Hispanic, with no significant baseline differences between arms. In the wireless arm, 86% (63/73) successfully transmitted multiple readings, with a median of 159 readings (mean 160.6, SD 129.1) over 6 months, corresponding to 76.2% adherence to the prescribed schedule; only 5% of standard-arm participants returned their paper logs. On the satisfaction survey (response rate 82.2%), 95% agreed or strongly agreed that the device was easy to set up and use, and 75% reported they would more consistently use the wireless than the conventional cuff. Both arms showed significant reductions in systolic and diastolic BP at 6 months (P<.001). At 6 months, 63% of the wireless arm and 74% of the standard arm achieved systolic BP <140 mmHg (P=.079); adjusted analyses showed no significant between-group difference in BP control (adjusted odds ratio 1.6, 95% CI 0.78-3.4; P=.19). Conclusions: Wireless HBPM was feasible and highly acceptable in a medically underserved, predominantly Hispanic FQHC population, with strong data-transmission adherence and high user satisfaction. Although this pilot was not powered for definitive efficacy comparisons, the findings support wireless HBPM as a viable platform for hypertension management in underserved settings and offer foundational evidence to inform contemporary digital health and remote patient monitoring program design.
Background: Hypertension remains a major contributor to cardiovascular morbidity and mortality worldwide, and long-term blood pressure control depends greatly on patients’ ability to engage in daily...
Background: Hypertension remains a major contributor to cardiovascular morbidity and mortality worldwide, and long-term blood pressure control depends greatly on patients’ ability to engage in daily self-management. Interventions such as mobile health, web-based education, SMS reminders, telemonitoring, nurse-led support, and structured patient education are increasingly used to improve hypertension self-management. However, the evidence is diverse in terms of intervention type, setting, delivery mode, and outcomes measured. Objective: This scoping review aimed to map the range of interventions used to improve self-management among adults with hypertension and summarize the outcomes targeted by these interventions. Methods: A scoping review was conducted using evidence identified from PubMed, CINAHL, Cochrane Library, and Web of Science. Eligible studies included adults with hypertension and evaluated interventions designed to improve self-management or related outcomes, including medication adherence, blood pressure monitoring, lifestyle modification, self-efficacy, health literacy, quality of life, and blood pressure control. Data were charted according to author, year, country, study design, population, intervention, outcomes, and key findings. A descriptive numerical summary and thematic synthesis were used to map the evidence. Results: A total of 76 studies were included. Most studies were published between 2022 and 2025, indicating growing interest in hypertension self-management interventions. Randomized trials accounted for the largest proportion of included studies. Mobile app and SMS-based interventions were the most common intervention category, followed by education or self-management training, family/community support, digital monitoring or telehealth, web-based programs, and nurse- or clinician-led support. Blood pressure control was the most frequently assessed outcome, followed by self-management behaviors, medication adherence, self-efficacy, health literacy, knowledge, and quality of life. Many interventions improved systolic blood pressure, medication adherence, self-monitoring, and self-management behaviors, although some studies reported stronger behavioral than clinical effects. Thematic analysis showed that effective interventions commonly combined structured education, digital delivery, self-monitoring, feedback, reminders, and human support. Conclusions: The evidence shows that hypertension self-management interventions are expanding rapidly, particularly through digital and mobile health approaches. Interventions appear most promising when they combine education, monitoring, feedback, reminders, and professional or family support rather than relying on technology alone. More context-sensitive research is needed in low-resource settings, especially in Africa, to determine which intervention components are most feasible, scalable, and effective for improving blood pressure control and long-term self-management.
Background: Background: The integration of mental health services into primary health care is a pivotal strategy for reducing the treatment gap in low- and middle-income countrie. Despite strong globa...
Background: Background: The integration of mental health services into primary health care is a pivotal strategy for reducing the treatment gap in low- and middle-income countrie. Despite strong global policy endorsement, implementation remains inconsistent and is shaped by a complex interplay of health system, sociocultural, and contextual factors. Qualitative evidence capturing stakeholder perspectives on these factors has not been synthesised in a theory-informed manner, limiting the depth and policy relevance of existing reviews. Objective: Objective: This review aims to synthesise qualitative evidence on individual, interpersonal, organisational, and policy-level factors that influence the integration of MHS into PHC in LMICs. Methods: Methods: This protocol adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and the Joanna Briggs Institute (JBI) methodology for qualitative systematic reviews. A comprehensive search will be conducted in PubMed, Embase, Web of Science, Scopus, CINAHL, and the Cochrane Library for studies published from January 2000 to December 2025. Eligible studies will include qualitative and mixed-methods research involving key stakeholders in primary mental health care in LMICs. Two independent reviewers will screen records, extract data using JBI SUMARI, and appraise study quality using the JBI Critical Appraisal Checklist for Qualitative Research. Data synthesis will employ the JBI meta-aggregation approach, with findings organised using a socio-ecological framework. Confidence in synthesised findings will be assessed using the ConQual approach. Results: Results: The review is currently in progress. The protocol was registered in PROSPERO (CRD42024533735) before commencement of formal screening or data extraction. Preliminary scoping searches have been conducted to inform the search strategy. Database searching, title and abstract screening, full-text assessment, and data extraction are planned for completion by the end of 2026. Synthesised findings are expected to be published in 2026. Conclusions: Conclusions: This review will generate contextually grounded, policy-relevant evidence to support the design and scale-up of integrated MHS in resource-constrained settings. Findings will identify modifiable factors affecting integration across multiple health system levels and inform the development of evidence-based implementation strategies in LMICs. Clinical Trial: PROSPERO CRD42024533735
Diabetes has become a significant global chronic non-communicable disease with continuously rising prevalence, imposing a heavy long-term burden on public health systems. Its management is inherently ...
Diabetes has become a significant global chronic non-communicable disease with continuously rising prevalence, imposing a heavy long-term burden on public health systems. Its management is inherently long-term and complex, relying on continuous lifestyle adjustments and treatment adherence, including blood glucose monitoring, medication, dietary control, and exercise. However, the traditional medical model, which depends mainly on regular outpatient follow-ups, struggles to provide continuous intervention in patients’ daily behaviors, limiting long-term management effectiveness. Diabetes management therefore depends heavily on patients’ daily self-management, yet studies show widespread issues of insufficient compliance and difficulty sustaining behavioral changes, leading to unsatisfactory blood sugar control. With the increasing popularity of smartphones and wearable devices, mobile health (mHealth) has emerged as an important tool for chronic disease management by offering continuous and individualized support. In diabetes care, mHealth interventions enable real-time monitoring and promote self-management improvement through data feedback, reminders, and remote support, while also breaking through time and space limitations to improve healthcare accessibility. Although previous systematic reviews and meta-analyses indicate that mHealth interventions typically reduce HbA1c by approximately 0.3% to 0.5%, significant differences in effectiveness across studies suggest that outcomes may be influenced by functional design and implementation methods. Crucially, most existing reviews classify interventions by technology type rather than by functional modules, making it difficult to reveal the specific mechanisms of action of different functions. Furthermore, the prevailing “holistic assessment” approach limits in-depth understanding of intervention mechanisms and hinders optimization of intervention design. This review therefore systematically examines mHealth diabetes management interventions from a functional module perspective to address these gaps. This review aims to systematically evaluate the progress of mHealth interventions in diabetes management through the lens of functional modules. Specifically, it seeks to: (1) construct a classification framework for mHealth diabetes management interventions based on five core functional modules—self-monitoring and data collection, feedback and reminders, provider-patient interaction, health education and behavioral support, and personalized intervention; (2) analyze the influence of different functional modules on glycemic control, self-management behaviors, and treatment adherence; (3) explore the characteristics and advantages of integrated multi-functional interventions compared to single-module approaches; and (4) provide a systematic theoretical framework and practical reference for optimizing mHealth intervention design in diabetes care. This review draws on evidence from randomized controlled trials and systematic reviews to systematically evaluate mHealth interventions in diabetes management. The study employs a functional module-based analytical approach, categorizing mHealth interventions into five core modules: self-monitoring and data collection, feedback and reminders, provider-patient interaction, health education and behavioral support, and personalized intervention. Through this lens, the review examines the mechanisms linking functional modules to behavioral change and clinical outcomes, and compares the stability and efficacy of single-module versus integrated multi-functional interventions. The findings indicate that mHealth interventions generally yield a small-to-moderate reduction in HbA1c levels, alongside consistent improvements in self-management behaviors and treatment adherence. The underlying mechanism operates through a "functional module – behavioral change – clinical outcome" pathway, rather than through the isolated impact of individual modules. Further analysis reveals that integrated multi-functional interventions offer superior stability and efficacy compared to single-module approaches, attributable to cross-module synergy and continuous behavioral reinforcement mechanisms. Despite the demonstrated benefits of mHealth interventions in diabetes management, significant challenges remain, including declining user engagement, inconsistent data quality, limited integration with traditional clinical workflows, and concerns regarding health equity and data security. Future research should prioritize mechanism-based intervention design, advance personalized and intelligent solutions, and facilitate the deep integration of mHealth within conventional healthcare systems to enhance the sustainability and clinical utility of these interventions. This review provides a systematic theoretical framework and practical reference for optimizing mHealth intervention design in diabetes care.
Background: Vital sign measurement is essential to assess a patient’s physiological status to guide clinical decisions on triage and resuscitation in the emergency department (ED). Manual measuremen...
Background: Vital sign measurement is essential to assess a patient’s physiological status to guide clinical decisions on triage and resuscitation in the emergency department (ED). Manual measurement contributes to bottlenecks and delays in the triage and care processes when a surge in service demand or manpower shortage occurs. Remote photoplethysmography (rPPG) enables contactless vital sign estimation by analyzing changes in light reflected from the skin in smartphone-captured facial videos caused by pulsatile changes in tissue blood volume and light absorption. rPPG has the potential to automate vital sign measurement. However, its accuracy in the ED remains unclear. Objective: We aimed to compare the accuracy of rPPG–based contactless heart rate (HR) estimation with manual measurement in the ED and assess patient satisfaction and comfort with different measurement methods. Methods: This was a prospective cross-sectional study on a convenience sample of ambulatory adult patients in a tertiary ED in Hong Kong from October to November 2024. The reference standards were manual HR measurements by a research nurse using a standard hospital device. Simultaneously, 25-second facial videos were recorded with an iPhone for contactless HR estimation using a proprietary convolutional neural network algorithm. The accuracy of the contactless method was assessed using the intraclass correlation coefficient (ICC), mean absolute error (MAE), root-mean-square error (RMSE), and Bland–Altman plot, with ± 5 beats per minute (bpm) predefined as clinically acceptable levels of agreement. Patient satisfaction and comfort, rated on a 0-100 mm visual analog scale (VAS), were compared between the two methods using the Wilcoxon signed-rank test. Results: We analyzed 478 videos from 161 patients, including 97 women and 64 men. The mean patient age was 55.0 (SD 16.6) years, and 73.9% were Chinese (119/161) with Fitzpatrick skin types of 3 (61.5%, 99/161) and 4 (34.8%, 56/161). The contactless method had an ICC of 0.991 (95% CI 0.987-0.993), a MAE of 1.38 beats per minute (bpm), and a RMSE of 1.82 bpm. The Bland–Altman plot showed a bias of 0.54 bpm (95% CI –2.89 to 3.97 bpm), which falls within the predefined clinically acceptable levels of agreement. Patient satisfaction (contactless VAS 95.4 mm vs. manual VAS 90.3 mm, P<.001) and comfort (contactless VAS 98.0 mm vs. manual VAS 87.0 mm, P<.001) were significantly higher with the contactless method compared to manual measurement. Conclusions: rPPG–based HR estimation from smartphone-captured facial videos is accurate in the ED. Further validation studies involving broader patient populations with a wider range of vital signs and skin colors are warranted. Future research should focus on extending the application of rPPG technology to other vital signs to fully realize its potential in automating vital sign measurements. Clinical Trial: ClinicalTrials.gov NCT06536647; https://clinicaltrials.gov/study/NCT06536647
Background: Discriminating mild cognitive impairment (MCI) from subjective cognitive decline (SCD) and healthy controls (HC) remains challenging. Digital tools can overcome classic paper-and-pencil te...
Background: Discriminating mild cognitive impairment (MCI) from subjective cognitive decline (SCD) and healthy controls (HC) remains challenging. Digital tools can overcome classic paper-and-pencil test limitations, offering sensitive and engaging screening for early cognitive impairment. Objective: To evaluate the diagnostic accuracy of CogniPaz, an ecologically oriented, tablet-based, gamified cognitive assessment tool composed of nine tasks designed to evaluate memory and executive functions, in discriminating MCI from SCD and HC. Methods: This prospective observational study recruited participants with cognitive complaints and HC at La Paz University Hospital Cognitive Impairment Unit (April 2025–January 2026). Following clinical and Montreal Cognitive Assessment (MoCA) evaluations, participants were classified into HC (no cognitive complaints, MoCA≥26), SCD (cognitive complaints, MoCA≥26), and MCI (cognitive complaints without functional impairment, MoCA<26; subdivided into amnestic [aMCI] and non-amnestic phenotypes). An evaluator blinded to MoCA results oversaw the automated, self-administered CogniPaz assessment (including automatic timing). Receiver operating characteristic (ROC) analyses (with age-adjusted models compared via DeLong's test) evaluated diagnostic accuracy, Spearman assessed CogniPaz-MoCA correlation, and a 0-9 scored questionnaire measured CogniPaz usability. A subset of participants with available biomarker data underwent an exploratory analysis to assess the tool's capacity to distinguish biologically confirmed neurodegeneration suggestive of Alzheimer Disease (AD). Results: Participants (N=84; median age: 72.0 years) included HC (n=27; 74.0), SCD (n=17; 59.0), and MCI (n=40; 74.0) groups, the latter subclassified into aMCI (n=22; 76.5) and non-amnestic MCI (n=18; 71.5). The aMCI group was significantly older than the SCD group (P=.003). The median administration time for CogniPaz was 9.39 minutes (IQR 7.53–11.38). CogniPaz scores correlated strongly with MoCA (r=.81, 95% CI .75-.89, P<.001). CogniPaz achieved high diagnostic accuracy, independently of age: MCI vs HC (AUC=0.880, 95% CI 0.798-0.961), MCI vs SCD (AUC=0.915, 95% CI 0.841-0.990), aMCI vs HC (AUC=0.910, 95% CI 0.830-0.990), and aMCI vs SCD (AUC=0.943, 95% CI 0.878-1.000). It showed capacity for detecting biologically confirmed neurodegeneration (n=39, AUC=0.810, 95% CI 0.661-0.958). At the<28.5 points cutoff, sensitivity ranged from 85.0% to 90.9% and specificity from 74.1% to 82.4% across comparisons. Particularly, it was accurate in differentiating aMCI vs SCD (90.9% sensitivity, 82.4% specificity). User satisfaction was high (median 8.4/9). Conclusions: CogniPaz showed good capacity to differentiate MCI/aMCI from SCD and HC, and appears promising for identifying AD-related neurodegeneration, with a strong correlation with MoCA score and good patient acceptance. These results support its use as a digital screening tool for early-stage cognitive impairment. External, multi-centre longitudinal validation is essential before considering its implementation in primary care settings.
Background: The COVID-19 pandemic accelerated telehealth adoption, particularly for older adults, yet end-user non-acceptance of digital health tools remains a persistent barrier. This evaluation enga...
Background: The COVID-19 pandemic accelerated telehealth adoption, particularly for older adults, yet end-user non-acceptance of digital health tools remains a persistent barrier. This evaluation engaged the Mobile Health and Wellness Program (MHWP), a nurse-led mobile clinic serving urban-dwelling older adults with chronic conditions, to implement Health Recovery Solutions (HRS) Remote Patient Monitoring (RPM) devices to extend its reach and boost health equity. Objective: This pre-implementation research project aimed to identify the strongest predictors of the intention to use technology among MHWP care delivery providers and participants. Using the constructs from the Technology Acceptance Model (TAM) to inform implementation of HRS RPM devices within MHWP. Methods: Two cross-sectional surveys were administered to MHWP providers (n = 31) and participants (n = 18). Reliability of the constructs was assessed via Cronbach’s α, and multiple linear and ridge regression models were fit to predict Behavioral Intention (IN). Analyses were exploratory given the small sample sizes and should be interpreted as hypothesis-generating. Results: For MHWP providers, the model explain 70% of variance in intention to use telemonitoring (R2 = .697, P < .001), with Perceived Usefulness (PU) and Perceived Ease of Use (PEU) as the strongest predictors of IN. For MHWP participants, the model was not statistically significant (R2 = .527, P = .24), with Availability emerging as the most influential construct in exploratory ridge regression. Conclusions: Provider-focused implementation should prioritize demonstrating practical benefits and ensuring user-friendly operation. For participants, unobstructed access to telehealth technology appears most critical for fostering adoption intent. These cross-stakeholder findings offer preliminary exploratory insights to guide equitable RPM implementation in community-based care. Clinical Trial: N/A
Background: Acromial stress fractures represent a clinically important complication in reverse shoulder arthroplasty (RSA) with reported incidences up to 11%. Although modern implants improve the curr...
Background: Acromial stress fractures represent a clinically important complication in reverse shoulder arthroplasty (RSA) with reported incidences up to 11%. Although modern implants improve the current situation, the precise role that load transfer plays in acromial fracture risk in RSA is not completely understood. To date, there is no preoperative method for risk stratification using individualised patient-specific factors. Objective: We present a prospective, imaging-based, radiologist-assisted finite element framework to estimate acromial stress risk after RSA. This protocol outlines its implementation and a first feasibility analysis. Methods: Ten to fifteen consecutive patients scheduled for elective primary RSA will undergo standardised high-resolution CT and 1.5T/3T MRI. Rotator cuff integrity will be graded using the Goutallier classification; the ordinal scores will be aggregated into a patient-specific Cuff Deficiency Index (CDI) using a preliminary weighting scheme (supraspinatus 0.40, infraspinatus 0.30, subscapularis 0.20, teres minor 0.10) subject to validation on the pilot cohort. This index will serve as a direct scalar multiplier for deltoid loading parameters in the finite element model. Patient-specific geometries will be created from HU-mapped cortical bone (CT) and MRI-derived deltoid anatomy. Physiological and RSA loading scenarios will be simulated in FEBio v3.x under quasi-static conditions. Acromial von Mises stresses will be extracted in anatomically defined Levy zones. Morphometric and stress-based parameters will be correlated with six-month postoperative radiographs. This study is registered on ClinicalTrials.gov (NCT07545707). Results: Patient recruitment and finite element simulations have not yet commenced. Data collection is scheduled to begin in the second quarter of 2026. This protocol paper reports the planned workflow and analysis strategy only. Conclusions: By prospectively integrating radiological grading of rotator cuff integrity into a patient-specific finite element workflow, this study aims to explore whether preoperative imaging and biomechanical modelling can identify anatomical patterns associated with increased acromial stress in RSA. Clinical Trial: ClinicalTrials.gov NCT07545707
Background: Radiology is an important branch of healthcare as it allows one to diagnose and plan treatment correctly. The quality of the image and patient safety should be ensured by following standar...
Background: Radiology is an important branch of healthcare as it allows one to diagnose and plan treatment correctly. The quality of the image and patient safety should be ensured by following standardized Quality Assurance (QA) schemes. In this study we will compare the QA guidelines as stipulated by the AERB (Atomic Energy Regulatory Board), IAEA (International Atomic Energy Agency) and AAPM (American Association of Physicists in Medicine) to determine their applicability and challenges encountered in their implementation in Indian environments. The main QA (Quality Assurance) parameters are examined in order to determine the gaps and constraints. On this basis, a combined QA (Quality Assurance) protocol is established to enhance compliance, equipment operation and patient safety. Objective: To compare national and international QA (Quality Assurance) protocols (AERB (Atomic Energy Regulatory Board), AAPM (American Association of Physicists in Medicine), IAEA (International Atomic Energy Agency)). Identify the gaps, variations, and limitations in the current QA (Quality Assurance) practices, develop a comprehensive standardized QA (Quality Assurance) protocol. Methods: This descriptive research will be done in DMIHER, Sawangi Meghe, India, to methodologically examine what is currently being practiced in terms of Quality Assurance (QA) measures of radiation departments against both national and international standards. A step-by-step methodology will be adopted. AERB (Atomic Energy Regulatory Board) will first review its procurement and QA (Quality Assurance) policies to know the present radiation safety practice and equipment performance standards in India. It will be preceded by a closer inspection of QA (Quality Assurance) systems and audit programs by IAEA (International Atomic Energy Agency) and AAPM (American Association of Physicists in Medicine) with regards to the frequency of testing, the level of performance, and quality control measures. A comparative study will help to find differences, gaps, and limitations in AERB (Atomic Energy Regulatory Board) guidelines. Recommendations will be offered and a single QA (Quality Assurance) system will be worked out based on the findings. Results: The study has received ethical approval and will begin in November 2025. Data collection from selected radiation units will be completed by the end of 2025 through review of existing QA (Quality Assurance) protocols. Comparative analysis with AERB (Atomic Energy Regulatory Board), IAEA (International Atomic Energy Agency), and AAPM (American Association of Physicists in Medicine) standards will identify gaps with final analysis and recommendations expected by November 2026. Conclusions: The study will present a comparison of QA (Quality Assurance) protocols with AERB (Atomic Energy Regulatory Board), IAEA (International Atomic Energy Agency), and AAPM (American Association of Physicists in Medicine) standards, identifying gaps and inconsistencies in current practices. It will support the development of standardized QA (Quality Assurance) guidelines for routine use, improving regulatory compliance, enhancing radiation safety for patients and staff, and ensuring greater accuracy in diagnostic imaging.
Background: Artificial intelligence (AI) is increasingly embedded in healthcare, yet its benefits remain unevenly distributed due to persistent concerns regarding bias, inequity, and socio-cultural mi...
Background: Artificial intelligence (AI) is increasingly embedded in healthcare, yet its benefits remain unevenly distributed due to persistent concerns regarding bias, inequity, and socio-cultural misalignment. Although existing Ethical AI frameworks typically emphasize universal principles, they often insufficiently address the socio-cultural contexts in which AI systems are developed, implemented, and used. Objective: This systematic review aimed to examine how socio-cultural factors shape ethical challenges in healthcare AI, influence the interpretation of ethical principles, and inform context-sensitive design and governance strategies. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of PubMed, IEEE Xplore, and Web of Science for studies published between 2018 and 2025. Eligible studies addressed ethical issues related to AI in healthcare through a socio-cultural lens. A thematic synthesis combining inductive and deductive coding was used to analyze reported challenges, context-dependent ethical interpretations, and proposed mitigation approaches. Results: A total of 49 studies were included. The findings show that ethical challenges in healthcare AI are deeply embedded in structural inequalities, data collection, curation, and documentation practices, institutional conditions, and cultural norms rather than being purely technical problems. Key challenges included algorithmic bias, underrepresentation of minorities in datasets, cultural and linguistic mismatches, limited transparency and trust, and systemic disparities in access to AI technologies. The reviewed literature proposed a broad range of technical, design-related, and governance-oriented strategies, but these remained fragmented and were rarely integrated systematically across the AI lifecycle. Based on this synthesis, the study proposes the Inclusive Ethical AI Framework (IEAF), a socio-technical framework that systematically translates socio-cultural context into context-sensitive ethical interpretations and actionable design and governance decisions across the AI lifecycle. Conclusions: The findings highlight that ethical challenges in healthcare AI are fundamentally shaped by socio-cultural context and cannot be addressed through technical solutions or universal ethical principles alone. Instead, effective and equitable AI systems require the systematic integration of socio-cultural considerations into data practices, system design, and governance across the AI lifecycle. Clinical Trial: PROSPERO CRD420251058607; prospectively registered.
Background: Endometriosis affects 10 % of reproductive-age women globally, with diagnostic delays of 7–10 years in Brazil’s SUS. Digital platforms offer promise, but few are ethically designed wit...
Background: Endometriosis affects 10 % of reproductive-age women globally, with diagnostic delays of 7–10 years in Brazil’s SUS. Digital platforms offer promise, but few are ethically designed with AI for low-resource settings. Objective: To report real-world deployment of EndoConnect Alpha in SUS primary care, evaluate usability/acceptability, and describe its evolution into the NAM-Endora ethical AI framework. Methods: Applied methodological study. EndoConnect Alpha (React.js + Firebase) deployed in 10 SUS units (Ceará). n = 60 (45 patients, 15 professionals). Instruments: SUS, TAM, engagement metrics, clinical-psychosocial outcomes. Ethics CAAE 82094924.8.0000.5049. INPI BR5120250005556-0. Results: SUS 88.9 ± 9.8 (excellent); TAM 91.4.57/5. Trail completion 79 %. Pain reduction 23 % (VAS p=0.02), therapy adherence +17 %, anxiety −14 %. Strong SUS-TAM correlation (ρ=0.76, p<0.001). Conclusions: EndoConnect Alpha is feasible and impactful in SUS. NAM-Endora provides scalable ethical AI governance for LMICs. Multicenter validation planned. Clinical Trial: Not applicable (formative research)
Background: The healthcare polycrisis in Canada comprises several concurrent crises namely workforce shortages; food insecurity; housing stress; and health inequities experienced by indigenous peoples...
Background: The healthcare polycrisis in Canada comprises several concurrent crises namely workforce shortages; food insecurity; housing stress; and health inequities experienced by indigenous peoples, low-income families, and racialized communities. There is a need for a scalable digital surveillance system to monitor these related but distinct health system crises concurrently using Natural Language Processing (NLP), as well as statistical forecasting techniques. Objective: The objective of this study will be to utilize a multi-method NLP pipeline to understand discourse dynamics, causal interdependencies between crisis themes, and how responsive policy-making process are to both the discourse and causal interdependencies between crisis theme in Canada’s healthcare system from 2020-2025. Methods: We used an NLP pipeline to examine the language and sentiment of 6757 online news headlines regarding Canada’s health care system over the course of approximately 5 years (2020-2025). This included utilizing two different methods for analyzing sentiment: a pre-trained BERT model for fine-tuning with a test accuracy of 0.97, and VADER (a valence-aware dictionary and sEntiment Reasoner) for analyzing valence and subjectivity. Further, we utilized latent dirichlet allocation (LDA) to identify topics or themes within the online news headlines. Then, we utilized time series analysis (tsa) to predict trends in each topic/theme utilizing both ARIMA (AutoRegressive Integrated Moving Average) and ets (error, trend, and seasonality) methods. Finally, we used granger causality testing to determine if past values of one theme could be used to predict future values of another theme. Results: Our results indicated that the BERT model achieved a test accuracy of 0.97 when applied to the task of identifying sentiment in the online news headline data set. Our results also indicated that seven major discourse topics (themes) emerged from our analysis. Of those seven themes, The Food Insecurity theme was dominant (accounted for 17.8% of all articles written about it). Furthermore, our results showed strong statistical evidence (f = 17.361; p-value adjusted for multiple comparisons using the FDR = 0.002) that changes in the healthcare system crises were related to changes in discussions regarding housing. Also, our results showed no statistically significant change in sentiment after any policy intervention was implemented based upon our sentiment analysis results (Cohen's d < 0.2). Our forecasting results did indicate that all five themes would continue to grow through 2026. Conclusions: These findings illustrate the complexity of issues currently facing Canada and suggest that policymakers should consider implementing digital surveillance systems based upon NLP to monitor and analyze complex relationships between these multiple crises. Further, our findings suggest that there is a persistent policy perception gap with significant implications for designing health informatics surveillance systems.
Background: Loneliness is increasingly recognized as a major public health concern in higher education with nearly one in four students feeling lonely most or all of the time, with 80% reporting moder...
Background: Loneliness is increasingly recognized as a major public health concern in higher education with nearly one in four students feeling lonely most or all of the time, with 80% reporting moderate-to-severe loneliness. Compassion from others, defined as receiving warmth, care, and understanding, may buffer loneliness. Yet, many students may struggle with receiving compassion due to shame, self criticism, and fear of compassion, which hinder their capacity to engage in supportive relationships. Objective: The proposed scoping review (ScR) aims to map the existing literature on compassion from others and its relationship to loneliness among university students, clarify conceptual boundaries, identify psychological and institutional influences, and highlight gaps to inform future research and intervention development. Methods: This protocol for the scoping review follows PRISMA ScR guidance. Eligibility criteria are structured using the PI(E)COS framework. Searches in the final scoping review (ScR) will be conducted in PsycINFO, PubMed, Scopus, Web of Science, ERIC, ProQuest, CINAHL and Google Scholar. This protocol proposes the methodological framework of Arksey and O’Malley with enhancements from the Joanna Briggs Institute (JBI) for use in the anticipated scoping review. Jointly and consistent with Joanne Briggs Institute (JBI) guidance for ScR, we will aim to map the breadth and nature of existing research; therefore, no critical appraisal or risk of bias assessment will be undertaken, as these are not required for scoping reviews unless justified by specific objectives Results: The ScR protocol will document the predicted tools for mapping definitions and measures of compassion from others; the prevalence and correlates of loneliness; psychological barriers, such as shame and threat sensitivity; and institutional contributors, such as academic culture and supervisory relationships. Conclusions: This ScR protocol provides the initial outline of the future ScR methods of compassion from others research in higher education. Findings will support the development of compassionate academic environments that foster students’ sense of belonging and reduce their loneliness. Clinical Trial: OSF portal with access from the link https://osf.io/4tnqc/overview
Background: Metabolic Syndrome (MetS), associated with hyperinsulinemia and insulin resistance, represents a significant global health concern Objective: This study aimed to explore the epidemiology o...
Background: Metabolic Syndrome (MetS), associated with hyperinsulinemia and insulin resistance, represents a significant global health concern Objective: This study aimed to explore the epidemiology of MetS in Western Sudan Methods: This study is a cross-sectional clinic-based investigation conducted at Prof. Medical Complex in Prof. Medical Research Consultancy Center (Prof. MRCC) located in El-Obeid city, the capital of North Kordofan State, Sudan, covering the period from January 1, 2025, to April 3, 2026. We applied the International Diabetes Federation (IDF) criteria for diagnosing MetS. Results: Men made up 56% of the total, whereas women accounted for 44%. The analysis of waist circumference (WC) among participants, alongside high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) levels, revealed that 5.7% exhibited abnormalities indicative of metabolic syndrome. Additionally, 28.6% had low HDL-C levels coupled with high TG levels, while 12.4% presented with low HDL levels and elevated WC. Individuals exhibiting elevated TG levels and high WC constituted 7.5% of individuals. The relationship between male sex and the risk of low HDL-C is demonstrated by a relative risk (RR) of 1.385, with a 95% confidence interval (95% CI) spanning from 1.178 to 1.628 and a P-value of <.001. Conclusions: The prevalence of MetS in Western Sudan was 5.7%, with a notably higher rate observed in males. The prevalence of low HDL-C levels surpassed global averages, although it was marginally lower than findings from sub-Saharan Africa. Hypertriglyceridemia levels aligned with sub-Saharan data and were slightly elevated compared to the global range. The findings underscore the necessity for public health interventions, particularly in promoting regular physical activity and encouraging healthy dietary habits.
Background: Large language models (LLMs) are increasingly used in orthopaedic education, but their performance on the 2025 Israeli Orthopaedic In-Training Examination (OITE) remains poorly defined. Ob...
Background: Large language models (LLMs) are increasingly used in orthopaedic education, but their performance on the 2025 Israeli Orthopaedic In-Training Examination (OITE) remains poorly defined. Objective: To compare the performance of two contemporary reasoning-oriented LLMs on the 2025 Israeli OITE and to identify item-level factors associated with correctness. Methods: All 95 multiple-choice questions (MCQs) from the 2025 OITE were analyzed. ChatGPT 5.1 thinking and Gemini Pro 3 thinking each answered all questions once using a standardized prompt. The primary outcome was question-level correctness according to the official answer key. Questions were annotated for question type, question stem word count, image presence, and model-reported certainty. Model-specific factors associated with correctness were assessed using logistic regression, and head-to-head comparisons were performed using the exact McNemar test and paired generalized estimating equations (GEE). Results: Both models achieved identical overall accuracy (68/95, 71.6%), with no measurable difference on paired comparison (odds ratio [OR], 1.00; 95% CI, 0.58-1.73; P=1.000). Accuracy was substantially higher on questions without images than on image-containing questions (90.5% vs 56.6%). In model-specific multivariable analyses, image presence was independently associated with lower correctness for both ChatGPT (adjusted OR [aOR], 0.12; 95% CI, 0.04-0.40; P<.001) and Gemini (aOR, 0.08; 95% CI, 0.02-0.28; P<.001). For Gemini, longer question stem word count was independently associated with greater odds of correctness (aOR per 10 words, 1.47; 95% CI, 1.01-2.13; P=.042), and Application questions were associated with lower odds of correctness than Knowledge questions (aOR, 0.26; 95% CI, 0.08-0.85; P=.026). In the primary paired GEE model, no measurable difference in correctness was observed between Gemini and ChatGPT (OR, 1.00; 95% CI, 0.58-1.73; P=1.000), whereas Application questions (OR, 0.34; 95% CI, 0.14-0.84; P=.019) and image-containing questions (OR, 0.09; 95% CI, 0.03-0.24; P<.001) were associated with lower odds of correctness. Conclusions: ChatGPT 5.1 thinking and Gemini Pro 3 thinking demonstrated similar overall performance on the 2025 Israeli OITE, with identical crude accuracy and no measurable difference in paired comparative analyses. Both models were substantially less accurate on image-containing questions, and Application questions were also associated with lower correctness, particularly in Gemini-specific and paired analyses. These findings suggest that current LLMs may have value as adjunctive educational tools in orthopaedic knowledge domains, but they remain insufficiently reliable for unsupervised use, particularly in image-dependent and application-based settings. Clinical Trial: not applicable
Background: Early warning systems are widely used to detect acute clinical deterioration, which may be defined as a significant worsening in health over a few hours that may lead to adverse outcomes s...
Background: Early warning systems are widely used to detect acute clinical deterioration, which may be defined as a significant worsening in health over a few hours that may lead to adverse outcomes such as code blue activation, unplanned intensive care unit admission, or death. These systems rely on regular measurement of physiological parameters, such as heart rate and blood pressure, which are converted into warning scores using deterioration prediction algorithms (DPAs). A range of DPAs are currently in use, most commonly simple track-and-trigger tools or summative scoring systems. More complex machine learning approaches have been proposed that may improve prediction accuracy. However, heterogeneity in outcome definitions and reported model performance metrics hinders evidence synthesis needed to support deployment of proposed models in clinical contexts. Objective: This scoping review aims to identify the range of DPAs developed for use in pediatric inpatient early warning systems, as well as operational definitions of deterioration and reported performance metrics. Methods: The review will follow the Joanna Briggs Institute methodology for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. The population of interest is hospitalized children. The concept under review is deterioration prediction algorithms, defined as decision-support tools that use routinely monitored physiological parameters to alert clinicians to worsening clinical status. The context will be inpatient ward settings, excluding emergency departments, neonatal units, and intensive care environments. Studies will be identified from searches on the MEDLINE (Ovid), Scopus, Web of Science, Cochrane, and ACM DL databases. Studies will be screened by two independent reviewers against the inclusion and exclusion criteria. A broad range of study types, including prospective and retrospective analyses, will be eligible for inclusion. Data on the choice of algorithmic approach, definition of deterioration, and reported performance metrics will be collated and analyzed. The results will be presented descriptively in tabular and narrative formats. Results: At the time of submission, the protocol has been registered and the search strategy finalized. A formal database search has been carried out in April 2026. Screening and data extraction are expected to occur over the following 6 months, after which the findings will be published. Conclusions: This protocol describes the planned scoping review of deterioration prediction algorithms for pediatric inpatient care. The completed review will summarize the types of algorithms evaluated, the outcomes used to define deterioration, and the performance metrics reported. These findings will support further evidence synthesis in this emerging field. Clinical Trial: Registered on Open Science Framework at https://osf.io/eg9cs(DOI: 10.17605/OSF.IO/EG9CS)
This research letter describes how the opt-in requirement federal regulations for text messaging influenced the study population of those who opted-in or opted-out of a clinical trial using text messa...
This research letter describes how the opt-in requirement federal regulations for text messaging influenced the study population of those who opted-in or opted-out of a clinical trial using text messages to improve cardiovascular (CV) health.
Background: Patients with rare cancers experience clinical vulnerability owing to limited expert clinicians, equivocal treatment options, and a lack of accessible, reliable information. Medullary thyr...
Background: Patients with rare cancers experience clinical vulnerability owing to limited expert clinicians, equivocal treatment options, and a lack of accessible, reliable information. Medullary thyroid cancer (MTC) is a rare cancer characterized by an advanced, progressive, chronic disease state that often has clinical equipoise in treatment decisions. Objective: In a previous needs assessment study, an MTC stakeholder group of patients, caregivers, and clinicians identified needs for patient and caregiver education regarding each disease stage and for effective strategies for patient-clinician communication. Here, we report the patient-centered development of a novel online educational resource and patient decision aid, MTCEducate.org, as well as its online dissemination and usability and acceptability analysis. Methods: Working with the same stakeholder group as in the needs assessment and additional patient input, we used the identified needs to guide the development and refinement of MTCEducate.org, e.g., by using storyboarding with iterative feedback. The finalized site was strategically promoted through patient support organizations, social media platforms, national conferences, the American Thyroid Association, and a robust MTC Registry listserv. Site metrics were assessed via Google Analytics for one year following site publication, and site usability and acceptability were evaluated via a confidential survey embedded within the website. Results: MTCEducate.org underwent six rounds of stakeholder feedback before its online dissemination in April 2023. Over the first year after publication, the site had 2079 unique visitors and 376 return users. To access the site, 63.6% of visitors used a mobile device, and 36.5% used a desktop computer or tablet. The site had visitors from 69 countries; most visitors (69.3%) were from the United States. Visitors accessed the site most often via direct links found in emails, newsletters, or X posts (51.6%), followed by Facebook (44.8%). Seventy-six visitors fully completed the site usability and acceptability survey. Most respondents identified as women (73%), non-Hispanic (92%) and White (96%). Most respondents were ≥45 years (87%), had an associate’s degree or higher (92%), and were MTC patients or survivors (92%). Most respondents correctly answered MTC knowledge–based questions about tumor markers (96%), lymph node dissection (93%), and Food and Drug Administration–approved medications (73%). Most respondents found the site easy to use (88%) and well-integrated (84%) and indicated that they could learn to use it quickly (86%) and confidently (81%). Conclusions: Using a patient-centered approach, we created MTCEducate.org, an online patient decision aid, whose multipronged deployment reached an international audience. MTCEducate.org’s format and content were well received and had high usability and acceptability. The site visitors’ lack of racial and ethnic diversity and their advanced educational status underscore the need to understand the preferred access and communication styles and educational resources desired by underrepresented populations.
Background: Does belief in negative thoughts cause depression and other negative feelings? This question has been hotly debated for 2,000 years but never resolved. Part of the problem is that the caus...
Background: Does belief in negative thoughts cause depression and other negative feelings? This question has been hotly debated for 2,000 years but never resolved. Part of the problem is that the causal linkages between negative thoughts and feelings are likely to be high-speed, and conceivably almost immediate, so current assessment tools like the Beck Depression Inventory or the PHQ-9 cannot be used because they look at changes in moods occurring over long periods of time. We address this important question using new scales and non-recursive structural equation modeling techniques in two large cohorts. Objective: To measure the simultaneous causal effects of changes in negative thoughts on changes in negative feelings while controlling for the simultaneous causal effect of changes in negative feelings on negative thoughts. Methods: The bidirectional causal effects were assessed in a 2023 pilot study of a digital cognitive therapy app during a three-day intensive experience, involving 290 beta testers. These findings were replicated in a 2025 pilot study of the Feeling Great app, involving 6,690 commercial users.
Changes in thoughts and feelings were assessed with highly reliable scales that assessed how people were thinking and feeling in the here-and-now. Results: The 2023 study was consistent with a large, rapid, highly significant causal effect of negative thoughts on seven negative feelings: depression, anxiety, guilt, inadequacy, loneliness, hopelessness, and anger. The findings were also consistent with a small causal effect of negative feelings on negative thoughts, but these findings did not achieve statistical significance.
There were no significant differences in the parameter estimates in the 2025 study. However, the causal effect of negative feelings on negative thoughts did achieve statistical significance due to the massive increase in the power of this study. Conclusions: 2,000 years ago, the Greek philosopher Epictetus claimed that thoughts, and not events, trigger negative feelings. To the best of our knowledge, this is the first study using SEM that has addressed this problem in large databases. The findings strongly supported the claim of Epictetus, but provided only weak support for a causal effect in the opposite direction.
The findings are also consistent with the key premise of all of the cognitive therapies, namely that belief in negative thoughts causes depression and other negative feelings, and that a change in belief in negative thoughts triggers rapid change in negative feelings. Clinical Trial: N/A
Background: Borderline personality disorder (BPD) is associated with substantial suffering, clinical complexity, and limited access to evidence-based treatment such as Dialectical Behavior Therapy (DB...
Background: Borderline personality disorder (BPD) is associated with substantial suffering, clinical complexity, and limited access to evidence-based treatment such as Dialectical Behavior Therapy (DBT). Artificial intelligence conversational agents (AI-CA) are increasingly discussed as scalable tools for mental health support, but little is known about how DBT clinicians understand AI-CA possible role in treatment. Objective: This study described clinicians’ perspectives on integrating AI conversational agents into DBT for BPD in the future. Methods: Seventeen psychologists in Sweden, each with at least one year of clinical DBT experience (average 6.4 years), participated in semi-structured interviews as part of this qualitative study. Interviews were conducted in Swedish, transcribed verbatim, and analyzed using reflexive thematic analysis within a constructivist framework. Results: Three main themes and two subthemes were developed from the data. The first main theme, "Who are we in therapy?" with subthemes “The search for boundaries” and “AI and human therapists are unique and alike”, explored how participants defined AI relationally, positioning it variously as a tool, team member, or supervisor, and sometimes harmful competitor. How these positionings were configured shaped what AI was seen as allowed to do. The second main theme, "The stoic helper", captured how AI was constructed as an extension of the ideal therapist: calm, adaptable, and emotionally composed, able to provide support in moments when human therapists could not or preferred not to be present. What participants reportedly hoped AI could be often mirrored qualities they found difficult to sustain in their own clinical work. The third main theme, “The well-intended accommodator,” captured concerns that AI may reinforce dependency and function as a safety behavior by supporting reassurance-seeking rather than autonomy. A central concern was not whether AI could generate validating responses, but whether it could know when validation supports change and when it becomes maladaptive accommodation (functional ambiguity). Conclusions: Perceived benefits mainly centered on accessibility and support for DBT skills generalization, whereas key concerns involved alliance disruption, overaccommodation/reinforcement of behaviours that would ideally be targeted for change, dependency, and questions regarding responsibility in high-risk situations. Integrating AI-CA into DBT is not only a technical question but a relational and ethical one. How AI is positioned in relation to the therapist, person in treatment, and team shapes which tasks are considered acceptable and what form integration can take. The findings highlight the need for implementation frameworks that account for relational dynamics, treatment-specific considerations and functional ambiguity that may arise when AI operates in complex therapeutic contexts.
Background: Virtual reality (VR)-based interventions have been recognised as promising nonpharmacological therapies for improving the ability to perform daily activities, as well as for reducing depre...
Background: Virtual reality (VR)-based interventions have been recognised as promising nonpharmacological therapies for improving the ability to perform daily activities, as well as for reducing depression and anxiety, among stroke survivors. However, the comparative effectiveness of different types of VR interventions remains unclear. Therefore, this study aimed to compare and rank the efficacy of various VR interventions for improving the ability to perform daily activities, as well as for treating depression and anxiety, among stroke survivors. Objective: A frequentist network meta-analysis was conducted to compare and rank the relative effects of various VR interventions on improving the ability to perform daily activities, as well as for treating depression and anxiety, among stroke survivors. Methods: We systematically searched six English-language and three Chinese-language databases from their inception to December 2025. Randomised controlled trials (RCTs) investigating the effectiveness of VR interventions in stroke survivors were included. A random-effects model was used for pairwise meta-analyses to directly assess the effects of individual VR interventions. A consistency model was employed for the network meta-analysis to evaluate the relative effects and probability of being the best model for the different VR interventions. Results: This analysis included 21 RCTs involving four different nonpharmacological interventions. The preliminary evidence from this study suggests that VR comprehensive cognitive gaming (VR-CCG) has the potential to be the most effective intervention for improving the ability of stroke survivors to perform daily activities. Furthermore, VR team therapy (VR-TM) and VR sport training (VR-SPT) may have positive effects in reducing depression and anxiety, respectively. However, these findings should be interpreted with caution, as they are based on a limited number of studies (with one study focusing on VR-CCG for ADL and VR-TM for depression, and two studies focusing on VR-SPT for anxiety), along with wide confidence and prediction intervals. Further meta-regression analyses revealed no significant associations between the predefined covariates (including age, country, session duration, intervention frequency, intervention period, and level of VR immersion) and any of the outcomes. Conclusions: This study provides evidence supporting the effectiveness of VR-based interventions in improving the ability to perform daily activities, as well as treating depression and anxiety, in poststroke patients. However, given the limitations of this study and the small number of included studies, these findings should be interpreted with caution. Further high-quality research is needed to validate the optimal application scenarios and mechanisms of action for different VR interventions. This will provide more robust, direct evidence regarding their comparative effectiveness.
Background: Myopia has escalated into a critical public health crisis among children and adolescents in China. While numerous studies have explored risk factors using traditional statistical methods, ...
Background: Myopia has escalated into a critical public health crisis among children and adolescents in China. While numerous studies have explored risk factors using traditional statistical methods, there remains a challenge in handling high-dimensional behavioral data and accurately identifying the most predictive variables for precision prevention. Objective: This study aimed to investigate the prevalence of myopia and identify key influencing factors among primary and secondary school students in Shangrao, China, using machine learning (ML) models. Methods: A school-based cross-sectional study was conducted in October 2024, involving 22,359 students from grades 4 to 12. Data on demographics, visual acuity, non-cycloplegic autorefraction, and behavioral factors were collected. A multi-stage feature selection process, integrating univariate logistic regression with four ML algorithms (LASSO, RF, XGBoost, and LightGBM), was employed to identify the most predictive variables. The optimal logistic regression model was used to construct a clinical nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, model interpretability was enhanced using Shapley Additive Explanations (SHAP) to quantify the impact of each feature. Results: The overall prevalence of myopia was 58.51%. Prevalence increased significantly with grade level, ranging from 38.15% in upper primary school to 77.74% in senior high school. Females (63.40%) and students with a parental history of myopia (62.07% for one parent, 68.32% for both) exhibited a significantly higher prevalence (all P < 0.001). Among the ML models, the logistic regression model demonstrated the best predictive performance (AUC = 0.726, 95% CI: 0.718–0.734) and was visualized as a nomogram incorporating nine key predictors. The nomogram showed robust discriminative ability (AUC = 0.725), good calibration, and provided net clinical benefit across a wide threshold probability range (25–90%). SHAP analysis revealed that grade level, parental history of myopia, and gender were the most critical predictors. Key modifiable protective factors included maintaining a proper reading distance and spending recess outdoors. Conclusions: The prevalence of myopia is high among students in Shangrao. The ML-derived nomogram serves as a practical tool for risk assessment, effectively identifying both established and modifiable risk factors. Our findings support a precision prevention strategy focusing on high-risk groups while promoting behavioral interventions, such as ensuring adequate reading distance and encouraging outdoor recess.
Background: : In Erbil City, Iraq, youth increasingly face difficulties expressing emotions in both offline and online contexts, influenced by cultural norms, personal inhibition, and digital engageme...
Background: : In Erbil City, Iraq, youth increasingly face difficulties expressing emotions in both offline and online contexts, influenced by cultural norms, personal inhibition, and digital engagement. Objective: This study aimed to identify the personal, social, and digital determinants associated with barriers to emotional expression among youth in Erbil, with particular attention to platform-specific digital behaviors. Methods: This cross-sectional study was conducted from June 4th to September 25th, 2025, across multiple public and educational settings in Erbil using a convenience sampling method. The questionnaire included demographic information and the Barriers to Emotional Expression in the Digital Age Scale (BEEDA v1.0), which measured personal, social, and digital barriers to emotional expression. Statistical analysis was performed using SPSS version 26.0 and JASP version 0.18.0. Results: A total of 385 participants were included in the study. The main personal barriers were staying strong and silent (M = 2.89, SD 1.16) and guilt for burdening others (M = 2.77, SD 1.14). Key social barriers involved fear of emotional misuse (M = 2.82, SD 1.19) and pressure to appear strong (M = 2.63, SD 1.18), while digital barriers included fear of misunderstanding posts (M = 2.68, SD 1.19) and deleting messages before sending (M = 2.15, SD 1.17). Low family support and extended online engagement (>6 hours/day; aOR 8.92, 95% CI 3.78-21.05) were most strongly associated with high emotional expression barriers. Conclusions: A total of 385 participants were included in the study. The main personal barriers were staying strong and silent (M = 2.89, SD 1.16) and guilt for burdening others (M = 2.77, SD 1.14). Key social barriers involved fear of emotional misuse (M = 2.82, SD 1.19) and pressure to appear strong (M = 2.63, SD 1.18), while digital barriers included fear of misunderstanding posts (M = 2.68, SD 1.19) and deleting messages before sending (M = 2.15, SD 1.17). Low family support and extended online engagement (>6 hours/day; aOR 8.92, 95% CI 3.78-21.05) were most strongly associated with high emotional expression barriers. Clinical Trial: Not Applicable
Background: Digital phenotyping offers opportunities to capture real-time behavioral and physiological markers associated with mood disorders. For example, sleep and physical activity are two key beha...
Background: Digital phenotyping offers opportunities to capture real-time behavioral and physiological markers associated with mood disorders. For example, sleep and physical activity are two key behavioral exposures consistently shown to reduce depression and anxiety symptoms. However, feasibility and acceptability challenges, particularly relating to device burden and data reliability, remain barriers to upscaling sample sizes. Objective: This mixed-methods feasibility study evaluated the acceptability, usability, and data completeness of passive monitoring of sleep and physical activity via a smart ring and active ecological momentary assessment (EMA) of mood and contextual factors via a smartwatch in adults from the general population. Methods: Participants wore a consumer-grade Oura smart ring (Generation 3) to passively track sleep and physical activity for two weeks while concurrently completing brief mood EMAs (depression, anxiety and wellbeing) on a study-issued smartwatch. Feasibility metrics included device adherence, EMA completion rate, and patterns of missingness. Acceptability was assessed through semi-structured interviews. Exploratory analyses examined associations between daily mood, sleep and physical activity. Results: Adherence to both devices was high, with participants wearing the ring consistently (mean wear time = 97.3%, range = 91.8% to 98.9%) and completing most EMA prompts (mean compliance = 90%, range = 75.0%-100%). The smartwatch occasionally delivered a higher number of prompts than originally scheduled and showed some variability in timing of prompts. However, qualitative interviews indicated overall acceptability of both devices, with a slight preference for the smart ring, described as comfortable and unobtrusive. Some participants experienced the watch as bulky, and reported technical difficulties, overall indicating lower willingness to wear the smart watch again in future studies. There was no difference in the patterns of data missingness by time of day or day of the week. Mood ratings demonstrated reasonable variability, but exploratory associations between mood and either physical activity or sleep were not detected, likely due to limited statistical power. Conclusions: Passive monitoring via a smart ring and smartwatch-based EMA were both feasible, with high wear time and completion rates. Acceptability was higher for the smart ring, particularly in the context of concurrent device use. Smart rings appear well suited for scalable passive data collection, while future studies should ensure robust EMA scheduling mechanisms. Scaling this protocol up into larger samples is likely required to evaluate mood–behavior associations.
Background: Leprosy remains a significant global health challenge, further complicated by increasing antibiotic resistance in Mycobacterium leprae. Traditional medicinal plants offer promising sources...
Background: Leprosy remains a significant global health challenge, further complicated by increasing antibiotic resistance in Mycobacterium leprae. Traditional medicinal plants offer promising sources of novel anti-leprotic compounds, especially in regions with limited access to advanced drug discovery infrastructure. Objective: This study aimed to investigate Centella asiatica, a traditional Indonesian medicinal plant, as a potential source of new anti-leprosy agents using an AI-driven molecular docking approach. Methods: A five-step methodology was employed, beginning with an ethnomedicine review to identify relevant bioactive compounds. Target proteins and ligands were selected from the RCSB Protein Data Bank and PubChem database. Pharmacological activity screening was conducted using PASS Online to predict the potential anti-leprotic effects. The MARA AI platform facilitated prompt-driven protein preparation and cloud-based molecular docking against the 2NTV target protein. The pharmacokinetic properties of the compounds were assessed using SwissADME to evaluate drug likeness and absorption potential. Results: Flavonoids Quercetin, Kaempferol, and Apigenin demonstrated strong binding affinities ranging from -9.04 to -9.32 kcal/mol against the 2NTV protein and complied with Lipinski’s Rule of Five, indicating favorable pharmacokinetic profiles suitable for oral administration. Rutin exhibited the highest binding affinity(-11.21 kcal/mol); however, it violated key pharmacokinetic parameters, suggesting its suitability for topical rather than systemic use. Conclusions: The prompt-driven platform provides an efficient, accessible, and scalable workflow for in silico screening of anti-leprotic compounds without requiring high-performance computing resources. While the docking results are promising, further validation through Molecular Dynamics simulations is necessary to confirm compound stability and efficacy. This AI-assisted approach supports accelerated drug discovery efforts in resource-limited endemic regions and aligns with the World Health Organization’s “Zero Leprosy 2030” initiative by facilitating the identification of novel therapeutic candidates. Clinical Trial: Ethical approval was not required for this study as it was conducted entirely through in silico computer simulations. The research did not involve any human participants, animal subjects, or the use of biological samples
Background: Patient-facing cancer portals are increasingly used to provide education, support interpretation of results, navigate services, and guide self-management across the cancer journey. However...
Background: Patient-facing cancer portals are increasingly used to provide education, support interpretation of results, navigate services, and guide self-management across the cancer journey. However, variation in content quality, transparency, readability, accessibility, and governance can undermine equity, safety, and trust. Objective: To develop and present EU-CiP20 as a first-phase, evidence-informed, operational, and auditable framework of quality criteria for cancer patient portal content. Methods: We synthesised established instruments and authoritative guidance on online health information quality, health literacy and plain-language communication, transparency and conflicts of interest, patient engagement, privacy and data protection, digital governance, accessibility, and AI-related safety. Candidate criteria were harmonised from a broader evidence-mapped set (EU-CiP30) into a streamlined taxonomy (EU-CiP20) using explicit consolidation rules and an auditable mapping trail. Each category was operationalised into four observable sub-criteria and scored using a pragmatic 0-2 scale. EU-CiP20 is presented as an initial comprehensive framework to be refined in the next phase through stakeholder focus groups, an online survey with affected cancer patients, expert inquiry, and a Delphi expert panel, with the aim of reducing the 20 criteria to a final operational core of approximately 10 criteria. Results: EU-CiP20 comprises five domains and 20 categories spanning accessibility and comprehensibility; evidence and content governance; relevance and personalisation; human-centred design and empowerment; and ethics, safety, and trust. In the pilot, adjusted EU-CiP20 totals ranged from 19.5% to 40.6%. The most consistent gaps were governance signals required for portal readiness, including named clinical ownership, explicit review cycles, evidence traceability, and accessibility auditability. Comparator tools characterised content-level strengths but did not fully capture these governance risks. Conclusions: EU-CiP20 offers a practical and auditable first-phase approach to strengthen governance of patient-facing cancer portal content. It complements existing information-quality instruments by linking readability, evidence governance, relevance, empowerment, transparency, safety, and digital trust within a single operational taxonomy. The work is not yet complete: the current 20-criteria framework will be refined through stakeholder focus groups, an online survey with affected cancer patients, expert inquiry, and Delphi expert panel consensus to produce a shorter final set of approximately 10 criteria, followed by assessment of inter-rater reliability, feasibility, sensitivity to change, and real-world implementation impact.
Background: Vitiligo is a chronic, acquired depigmenting disorder with heterogeneous clinical patterns, clinically important autoimmune associations, and a substantial psychosocial burden. Describing ...
Background: Vitiligo is a chronic, acquired depigmenting disorder with heterogeneous clinical patterns, clinically important autoimmune associations, and a substantial psychosocial burden. Describing phenotype distribution and associated factors at a country level can support pragmatic, locally relevant screening and counseling strategies. Objective: To describe demographic and clinical characteristics of vitiligo patients in Jordan and explore associations by sex and vitiligo subtype. Methods: We performed a retrospective cohort study using medical records from (1) King Abdullah University Hospital (KAUH), Jordan University of Science and Technology (JUST), and (2) Dr. Huda Alqudah Dermatology Private Clinic (Amman). Patients from all regions of Jordan attend these clinics; KAUH predominantly represents northern Jordan, whereas the Amman clinic predominantly represents central Jordan and, to some extent, southern Jordan. Records from December 1, 2024 through December 31, 2025were reviewed. Extracted variables included demographics, vitiligo subtype and activity, sites involved, associated clinical features (Koebner phenomenon, leukotrichia, halo nevus), comorbidities (thyroid disease, diabetes mellitus,pernicious anemia), symptoms and psychosocial impact, and available investigations (thyroid function tests, fasting glucose, vitamin B12, vitamin D, ferritin, zinc, copper). Continuous variables were summarized as median [IQR] and compared using Mann–Whitney U tests; categorical variables were compared using chi-square or Fisher’s exact tests. Two-sided p < 0.05 was considered statistically significant. Results: A total of 715 patients were included.). Median age was 28 [19–40] years; 61.1% were female (431/705 with sex recorded). Median age at onset was 20 [11–33] years for nonsegmental vitiligo and 17 [10-32] for SV. Among those with recorded subtype (n = 679), non-segmental vitiligo predominated (77.9%), followed by segmental vitiligo (15.6%) and unclassified disease (6.5%). Among analyzable disease activity entries (n = 481), disease was progressive in 61.3%, stable in 32.6%, and regressive in 6.0%. The most frequently involved sites were face (48.4%), lower limbs (42.8%), and hands (42.1%). Thyroid disease was documented in 10.3% (72/697). Females had higher thyroid disease prevalence and reported more itching and social isolation (p < 0.05). Compared with non-segmental vitiligo, segmental vitiligo was associated with younger age, shorter disease duration, higher stability, lower Koebner positivity, and more localized involvement (all p < 0.01). Among tested patients, low vitamin D was common (~52.8% among those tested). Ferritin was low in 17% of patients tested 79/459. Vitamin B12 was low in 17% of patients tested n 321/659. NSV are more likely to have koebner phenomenon 30% vs 12% for NSV, Halo naevus is also more common in NSV (12% vs 5% for SV, which was statistically significant ( p < 0.05).NSV is more likely to be progressive and less stable than SV. Itching is more frequent in NSV. Family history was reported in 30% of NSV and in 26 % of SV,with consanguinity marriage was reported in 27 % of patients Social isolation was reported in 26 % of patients ,school absence reported in 7% of paitients. Conclusions: In this large Jordanian cohort, non-segmental vitiligo was the predominant subtype, with frequent facial and acral involvement, meaningful comorbidity and psychosocial burden, and clear subtype-specific clinical differences. These findings support phenotype-informed counseling and targeted screening in routine practice.
Though exergames attract considerable research interest as tools for preserving cognitive function in older adults, a decade of meta-analytic evidence reveals a persistent gap between theoretical prom...
Though exergames attract considerable research interest as tools for preserving cognitive function in older adults, a decade of meta-analytic evidence reveals a persistent gap between theoretical promise and empirical demonstration: exergames rarely outperform conventional motor-cognitive training and do not generate the sustained engagement that preventive benefit requires. These results reflect limitations that are structural: the field focused predominantly on clinical populations rather than primary prevention; training concepts borrowed from rehabilitation protocols rather than grounded in the specific neuroplastic mechanisms of the exergaming medium; and virtual environments that are digitally sophisticated but culturally and aesthetically neutral, systematically excluding the stimulation that converging evidence from neuro-aesthetics, cultural epidemiology, and narrative neuroscience identifies as a core active ingredient of brain health promotion.
To address these limitations simultaneously, this Viewpoint introduces Motor-cognitive Active Gamified Immersive Cultural (MAGIC) exergames, defined as “virtual reality solutions in which motor-cognitive interactions are embedded in artistic content, cultural heritage, or historically situated narrative to exploit optimal neurobiological substrates of the intervention”. Drawing on an integrated theoretical framework, we develop mutually reinforcing mechanistic pillars: multimodal sensorimotor engagement within ecologically valid immersive environments; semantic and narrative activation of memory systems; emotional and autobiographical resonance as a dopaminergic driver of learning consolidation and sustained participation; and the re-engineering of physical effort evaluation from aversive to appetitive through aesthetic pleasure and narrative absorption. A fundamental implication is that in MAGIC exergames, motivation is not engineered through gamification mechanics — it is released by the intrinsic value of inhabiting a culturally meaningful world. We further argue MAGIC exergames correspond to a specific use that is, Cultural Snacking, which consists of the serialization of MAGIC content into brief, narratively open daily episodes of 5 to 10 minutes, designed for 3 to 5 daily consumptions across varied contexts. This dosing architecture is compatible with the motivational profile of healthy older adults who do not identify as patients or trainees.
The paper opens perspectives with a design and research agenda structured around three priorities: characterizing MAGIC exergames by the type of motor-cognitive interactions they deliver rather than by the technology platform; conducting mechanistic neuroimaging investigations targeting the specific biomarkers of neuroplastic benefit that creative cultural engagement is expected to produce; and building the intersectoral innovation ecosystem that brings cultural institutions, digital health teams, and older adults together as co-designers. Without waiting for MAGIC-specific trials, we invite the digital medicine, public health, and silver economy communities to recognize that museums, heritage sites, and cultural institutions are potentially the most ecologically valid, intrinsically motivating, and cost-effective substrates for a new generation of brain health exergames that are as meaningful as they are effective. The scientific, institutional, and economic conditions for this convergence already exist.
Background: Diffusion of innovations theory posits that inequalities arising from the early adoption of new technologies, such as telemedicine, are likely to decrease over time. However, evidence is s...
Background: Diffusion of innovations theory posits that inequalities arising from the early adoption of new technologies, such as telemedicine, are likely to decrease over time. However, evidence is scarce on the evolution of inequalities related to individual telemedicine adoption over time. Objective: This study aims to assess changes in age and socioeconomic inequalities in telemedicine adoption in Japan from 2020 to 2024. Methods: We used data from a nationwide, internet-based panel survey of the general population in Japan. Participants aged 18–75 years who completed both the 2020 baseline and 2024 follow-up surveys were included. The primary outcome was self-reported telemedicine adoption (ever use at each survey). Using multivariable logistic regression models, we regressed telemedicine adoption on (1) indicators of age and socioeconomic status at baseline, (2) survey year, and (3) their interaction, adjusting for other demographic, socioeconomic, and health-related characteristics. We then estimated the adjusted prevalence of telemedicine adoption in 2020 and 2024 for each age and socioeconomic group. Results: We included 10,818 participants (mean [SD] age, 49.7 [16.8] years; 50.7% women). In 2020, 271 participants (2.5%) reported telemedicine adoption; by the 2024 follow-up survey, this increased to 840 participants (7.8%). The prevalence of telemedicine adoption was lower among older individuals, those with lower educational attainment, those with medium income (vs high income), and unemployed individuals (vs upper non-manual workers) in 2020. While the prevalence increased across groups from 2020 to 2024, the increases were smaller among older age groups (70–75 years: +1.0 percentage points [pp] vs 18–29 years: +13.2 pp; difference-in-differences, −12.1 pp; 95% CI, −18.3 to −6.0 pp). Similarly, increases were smaller among unemployed individuals than among upper non-manual workers (+2.8 vs +5.8 pp; difference-in-differences, −3.0 pp; 95% CI, −4.7 to −1.2 pp). Changes in the prevalence of telemedicine adoption did not vary significantly by educational attainment, urban vs rural residence, or income level. Conclusions: Despite growth in telemedicine adoption from 2020 to 2024, age-related and occupational inequalities widened, and educational inequalities persisted, underscoring the need for strategies to reduce age-related and socioeconomic barriers to telemedicine adoption.
Background: Contemporary cardiac surgical risk assessment is largely based on static regression-derived models that do not account for evolving perioperative data or integration within clinical workfl...
Background: Contemporary cardiac surgical risk assessment is largely based on static regression-derived models that do not account for evolving perioperative data or integration within clinical workflows. Although machine learning approaches offer improved capacity to model complex relationships, most existing applications remain retrospective, outcome-specific, and disconnected from real-world clinical implementation. Objective: To describe the development of the Artificial Intelligence for dynamic Risk assessment and Outcome optimization (AIRO) platform, a clinician-led, governance-driven framework designed to support dynamic, explainable risk assessment and clinical decision support across the perioperative continuum. Methods: AIRO was developed using a formative research approach integrating retrospective model development with prospective implementation design within a learning health system framework. Baseline predictive modelling (AIRO 1.0) was conducted using a prospectively maintained cardiac surgery registry comprising approximately 30,000 patients and over 13 million data elements. A standardized modelling pipeline was established to support development of multiple outcome-specific models. The AIRO conceptual framework was structured around three layers: population-level risk stratification, individual-level risk explanation, and dynamic outcome optimization. For clinical deployment (AIRO 2.0), a system architecture was designed to enable real-time model inference within the electronic health record using interoperable data standards (FHIR), supported by a dedicated integration layer and governance framework. An implementation and evaluation strategy was defined to assess feasibility, usability, and clinician adoption. Results: The AIRO platform was developed as an integrated system combining standardized model development, a three-layer conceptual framework, and a scalable architecture for electronic health record integration. Baseline models have been developed for multiple clinically relevant outcomes, including surgical site infection, postoperative delirium, and discharge disposition. A workflow-integrated deployment strategy was defined, intended to enabling near–real-time risk estimation and delivery of explainable outputs within the clinical environment. Governance structures were established supporting data security, auditability, and model oversight. The platform is designed to support prospective deployment and evaluation within routine perioperative care. Conclusions: AIRO represents a conceptual and implementation-focused approach to integrating explainable machine learning into perioperative clinical care. By combining standardized modelling, EHR-integrated deployment, and a governance-driven framework, the platform is designed to support clinically meaningful risk assessment and decision support across the care continuum. Further work is required to evaluate its impact on clinical practice, clinician adoption, and patient outcomes.
This cross-sectional analysis of 20 high-ranking melanoma education websites and 50 Google Image results reveals a significant "digital color gap," characterized by only 25% (5/20) representation of d...
This cross-sectional analysis of 20 high-ranking melanoma education websites and 50 Google Image results reveals a significant "digital color gap," characterized by only 25% (5/20) representation of darker skin tones and a persistent visual-textual mismatch for acral melanoma. Readability analysis showed that 80% (16/20) of resources exceeded the recommended 6th-grade reading level, and the first relevant image of melanoma in darker skin appeared at rank 28. These findings suggest that current online resources may hinder early self-recognition in patients with skin of color, necessitating more representative imagery and accessible language to mitigate diagnostic disparities.
Background: Digital health use among older adults is increasing, yet engagement varies by health status and socioeconomic factors. Comparative analyses across nationally representative surveys can cla...
Background: Digital health use among older adults is increasing, yet engagement varies by health status and socioeconomic factors. Comparative analyses across nationally representative surveys can clarify these differences and inform strategies to improve engagement. Objective: To compare digital health engagement among older adults in the Health Information National Trends Survey and the National Health Interview Survey, determine important predictors related to information seeking, communication with a healthcare provider, and access to test results, and evaluate how variations in survey design and participant composition affect observed associations. Methods: We analyzed 2024 data from NHIS (N=8,818) and HINTS (N=2,120) using weighted descriptive statistics and multivariable logistic regression. Outcomes included (1) seeking health information online, (2) communicating with providers digitally, and (3) viewing test results. Models adjusted for sociodemographic characteristics, health conditions, physical activity, and living arrangements. Multimorbidity-stratified analyses were conducted by estimating models separately for participants with 0–1 conditions and those with 2 or more conditions. To examine how differences in participant composition and survey design influence findings, we conducted an additional subset analysis restricted to comparable demographic groups across surveys. Results: HINTS consistently had higher levels of digital health engagement than NHIS across all outcomes (e.g., seeking health information: 78.5% vs. 57.8%; contacting providers: 60.5% vs. 42.2%). In NHIS, lower self-rated health was associated with greater information seeking and provider communication, whereas these correlations were smaller and less consistent in HINTS. Depression was a consistent predictor of increased digital health use in both surveys. Higher education, income, and female sex were all associated with increased use, whereas living alone was associated with reduced participation.
Multimorbidity-stratified analyses showed stronger associations between poorer perceived health and digital engagement among individuals with multiple chronic conditions in NHIS, while patterns in HINTS were less consistent and more variable across outcomes. Subset analyses showed weaker relationships in HINTS and more consistent results in NHIS, suggesting that differences in survey design and measurement influence observed relationships. Conclusions: Older adults' digital health engagement reflects their health needs and inequality, rather than equitable access. Its association with depression suggests ways to incorporate behavioral health and digital navigation support into patient portals. Disparities in education, income, and living alone emphasize the importance of targeted digital literacy and social support. Differences between NHIS and HINTS show that survey design influences observed relationships, and combining evidence from multiple surveys can help inform equitable digital health policy.
Background: Breast cancer remains a major public health problem globally and in Indonesia. Beyond diagnosis and treatment, increasing attention has been directed toward survivorship outcomes, quality ...
Background: Breast cancer remains a major public health problem globally and in Indonesia. Beyond diagnosis and treatment, increasing attention has been directed toward survivorship outcomes, quality of life (QoL), and patient-centered supportive oncology care. Self-efficacy may play an important role in how patients manage symptoms, cope with treatment-related challenges, communicate care needs, and maintain well-being during survivorship. Objective: This study aimed to identify demographic, clinical, and psychosocial factors associated with QoL among patients with breast cancer at Faisal Islamic Hospital, Makassar, Indonesia, with emphasis on implications for patient-centered supportive oncology care and self-efficacy–based survivorship support. Methods: This cross-sectional observational study included 38 adult women with breast cancer who were receiving treatment or follow-up care at Faisal Islamic Hospital, Makassar. Participants were recruited using purposive sampling. Demographic and clinical data were collected using structured questionnaires and medical record review. QoL was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30. Self-efficacy was assessed using a structured questionnaire evaluating patients’ confidence in managing treatment-related challenges, symptom control, emotional adaptation, and treatment adherence. Data were analyzed using descriptive statistics, bivariate analysis, and multivariable logistic regression. Results: The mean global health status/QoL score was 65.53 (SD 18.57), indicating moderate overall QoL. Emotional functioning had the highest mean functional-domain score, whereas physical functioning had the lowest. Fatigue and pain were the most prominent symptom burdens. In bivariate analysis, QoL was significantly associated with age (P=.027), cancer stage (P=.028), duration of illness (P=.026), and self-efficacy (P=.011). In the multivariable logistic regression model, self-efficacy remained independently associated with QoL (odds ratio 8.709; P=.024), whereas age and treatment type were not statistically significant. Conclusions: Self-efficacy was independently associated with better QoL among patients with breast cancer in this Indonesian hospital-based oncology setting. These findings suggest that survivorship outcomes are shaped not only by clinical characteristics but also by patients perceived ability to manage illness-related challenges. Integrating self-efficacy assessment, psychosocial counseling, patient education, and symptom self-management support into routine oncology services may strengthen patient-centered supportive care and survivorship support for patients with breast cancer in Indonesia.
Background: In Australian residential aged care, infection prevention and control (IPC) lead nurses are a key part of the IPC program. IPC Leads are often the main IPC contact and provide support to s...
Background: In Australian residential aged care, infection prevention and control (IPC) lead nurses are a key part of the IPC program. IPC Leads are often the main IPC contact and provide support to staff to improve IPC and resident outcomes. To enhance their competence and self-efficacy, IPC Leads need to engage in ongoing role development. Connecting with others IPC Leads, learning together and sharing ideas and resources could also be beneficial. Communities of practice (CoPs) have been shown to improve self-efficacy and job satisfaction by fostering belonging, peer learning and problem-solving. Drawing on social learning theory, CoPs encourage learning with and from one another to explore and iteratively develop practice. This study draws on the CoP literature and builds on our previous pilot of a CoP for IPC lead nurses in one state of Australia, which found promising impacts on confidence and practice change. Objective: This study will evaluate CoPs for IPC Leads working in Australian residential aged care. Primarily the study will assess for change in IPC Leads’ self-efficacy. It will also explore job satisfaction, IPC practice improvement and assess the acceptability and feasibility of CoPs. Methods: A before-and-after study design using multi-methods. We will recruit IPC Leads from across Australia to participate in the CoPs. The CoPs will be implemented over 12 months, with online sessions held monthly for 30-60 minutes each. Baseline and 12-month follow-up surveys of IPC Leads’ self-efficacy and job satisfaction, and audits of IPC practice will be conducted. At 12-months follow-up, surveys and interviews with IPC Leads will be assess acceptability and feasibility of the CoPs and explore case examples of IPC Leads facilitating practice change. Researcher notes and costings data collected will be used to further evaluate the feasibility and fidelity of the CoPs. Results: This project was funded in October 2024 and commenced in February 2025. Recruitment of IPC Leads began in March 2026. The CoPs are expected to commence in June-July 2026. Data collection will commence in June 2026 and is expected to be completed by January 2028. Conclusions: Findings will create new research knowledge in the field of CoPs and knowledge translation in the aged care sector and will inform the sustainability and potential for future scale-up of this collaborative social learning approach. Clinical Trial: Not applicable
Background: During cardiovascular surgery, surgeons frequently extend their necks to view monitors. This frequent and repetitive motion can lead to cervical spine discomfort, fatigue or injury. Object...
Background: During cardiovascular surgery, surgeons frequently extend their necks to view monitors. This frequent and repetitive motion can lead to cervical spine discomfort, fatigue or injury. Objective: We designed a study first to examine the significance of cervical ergonomics and then proposed a practical solution to address it effectively. Methods: Five different techniques were tested for accurately and specifically counting the number of neck extensions during surgery. the parameters above. The light sensor technique proved to be ideal method and was chosen for this purpose in 10 different surgical cases. Next, a device was designed to minimize the need for neck extensions by projecting the image of the monitor to a micro-monitor mounted on the surgical loupes that mirror the hemodynamic data monitor directly in front of surgeon’s eye. Results: During routine heart surgery, a surgeon extended the neck to look at the monitor in a range of 7-19 times in a coronary surgery and 12-47 times in a valve surgery in our series. Duration of the extensions are often as short as 2-3 seconds, during which the monitor data is read and then the neck is flexed to lo look back at the field. Conclusions: During heart surgery, especially valve surgery, the number of neck extensions can reach a high value. The large number of these movements may potentially cause fatigue, injury, or health issues in heart surgeons. Using a micro-monitor with data transfer option can eliminate the need for neck extension during heart surgery.
Background: Large language models (LLMs) have been widely used for mental health support. However, current safety evaluations in this field are mostly limited to detecting whether LLMs output prohibit...
Background: Large language models (LLMs) have been widely used for mental health support. However, current safety evaluations in this field are mostly limited to detecting whether LLMs output prohibited words in single-turn conversations, neglecting the gradual erosion of safety boundaries in long dialogues. Objective: This study aims to characterize how safety boundaries erode during multi-turn mental health conversations and to compare different pressure mechanisms that accelerate boundary violations. Methods: We developed a multi-turn stress-testing framework and conducted long-dialogue safety tests on three cutting-edge LLMs using two pressure methods: static progression and adaptive probing. We generated 50 virtual patient profiles and stress-tested each model through up to 20 rounds of virtual psychiatric dialogues. Results: Violations were common across all models, with both pressure modes producing similar violation rates. However, adaptive probing significantly advanced the time-to-breach, reducing the average number of turns from 9.21 in static progression to 4.64. Under both mechanisms, making definitive or zero-risk promises was the primary way in which boundaries were breached. Certainty reassurance accounted for 56.5% of violations in static progression and 48.5% in adaptive probing. Conclusions: These findings suggest that the robustness of LLM safety boundaries cannot be inferred solely through single-turn tests; it is necessary to fully consider the wear and tear on safety boundaries caused by different interaction pressures and characteristics in extended dialogues. Clinical implications include the need for multi-turn safety evaluation protocols and awareness that empathetic responses may gradually drift into boundary violations.
Background: Transcutaneous auricular vagus nerve stimulation (taVNS) is an emerging neuromodulation technique in rehabilitation research. While implanted vagus nerve stimulation is used clinically for...
Background: Transcutaneous auricular vagus nerve stimulation (taVNS) is an emerging neuromodulation technique in rehabilitation research. While implanted vagus nerve stimulation is used clinically for epilepsy and treatment-resistant depression, its non-invasive form has mainly been explored in experimental settings, including physiotherapy and neuropsychiatry. Very few studies have investigated taVNS in speech-language pathology. However, given the vagus nerve's role in motor and sensory swallowing control, taVNS may offer a promising approach for dysphagia management - a frequent and severe complication in elderly stroke patients. Therefore, the development of an innovative protocol integrating taVNS appears pertinent in the context of swallowing rehabilitation. Objective: This protocol aims to evaluate the efficacy of taVNS combined with standard speech-language therapy for improving pharyngolaryngeal swallowing function and quality of life in elderly patients (≥70 years) with acute post-stroke dysphagia. Methods: This single-center, two-arm, randomized controlled clinical trial is conducted in a single-blind design. A total of 20 participants are expected to be enrolled. Eligible patients will be randomly allocated to receive either standard speech-language therapy combined with an inactive tVNS-E device, or standard speech-language therapy combined with non-invasive auricular vagus nerve stimulation via an active tVNS-E device. Both groups will undergo four rehabilitation sessions per week over three weeks. Clinical assessments (GUSS, SWAL-QoL, food trial) will be conducted at baseline T0 (inclusion) and at the end of the protocol at T3 (Week 3 – Day 4). Results: The study is currently in the participant recruitment phase. Recruitment began in April 2026. Baseline and post-test data collection is expected to continue until February 2028. Data analysis is planned for March 2028, and study results are expected to be published in April 2028. Conclusions: This will be the first randomized controlled trial evaluating taVNS for post-stroke dysphagia rehabilitation in patients aged ≥70 years. If effective, taVNS could provide a non-invasive adjunct to conventional speech-language therapy. Clinical Trial: ClinicalTrials.gov (NCT07428590)
Background: Cancer is a significant public health challenge in India, contributing to 8.3% of deaths and 5.0% of DALYs in 2016—nearly double its burden in 1990. Fragmented service delivery, limited ...
Background: Cancer is a significant public health challenge in India, contributing to 8.3% of deaths and 5.0% of DALYs in 2016—nearly double its burden in 1990. Fragmented service delivery, limited specialist availability, late presentation, and significant financial hardship continue to hinder access to timely and effective cancer care, particularly in rural and underserved regions. National initiatives such as the NP-NCD and Ayushman Bharat have expanded diagnostic and treatment coverage; however, critical gaps remain in infrastructure, human resources, and quality of care. A comprehensive, system-level assessment is essential to inform evidence-based planning and strengthen cancer services nationwide. Objective: This study aims to evaluate the availability, readiness, and geographic distribution of cancer care services in India and to identify disparities across rural–urban settings and healthcare sectors. A secondary objective is to develop a framework to strengthen cancer service delivery across the continuum of care. Methods: A cross-sectional, descriptive study will be conducted across 32 States/UTs over three years. Using proportionate sampling, districts will be selected based on rural and urban population distribution. Nodal hospitals—preferably those hosting Hospital-Based Cancer Registries—will coordinate data collection from primary, secondary, and tertiary cancer care facilities. A structured electronic pro forma will capture data across six quality-of-care domains: equitable, effective, patient-centred, safe, efficient, and timely. Data will be entered through an online portal and monitored centrally. Descriptive statistics will summarise service availability, while Chi-square tests will assess differences across facility types, sectors, and geographic strata. Results: Ethical approval was obtained from the ICMR-NCDIR Institutional Ethics Committee (NCDIR/IEC/3058/2022); no individual patient data will be collected, all responses will be anonymised, and participation will be voluntary Conclusions: This situational analysis will generate critical evidence on India’s cancer care landscape, highlighting disparities and system gaps. Findings will support policymakers and programme managers in strengthening infrastructure, workforce deployment, and service delivery to advance equitable and comprehensive cancer care nationwide.
Background: Mental health care is undergoing rapid digital transformation, including video-based teletherapy, digitally delivered interventions, and increasingly, AI-mediated support. Despite growing ...
Background: Mental health care is undergoing rapid digital transformation, including video-based teletherapy, digitally delivered interventions, and increasingly, AI-mediated support. Despite growing evidence for digital mental health interventions, implementation in routine practice has often lagged technological advances. Although teletherapy is becoming more widely accepted, especially since the COVID-19 pandemic, little is known about the psychological processes through which clinicians adapt to disruptive changes in treatment delivery and how those experiences influence attitudes toward subsequent technologies. Objective: This study explored how therapists who had not previously adopted teletherapy interpreted, resisted, and ultimately accepted it when required during the COVID-19 pandemic. It also considered the wider implications of this adaptation process for evaluating future digital mental health technologies. Methods: Six accredited clinical psychologists from the UK, none of whom had previously practised video-based teletherapy, participated in semi-structured interviews following the rapid shift towards remote delivery. Data were analysed using reflexive thematic analysis to explore changes in beliefs, attribution processes, and perceptions of the therapeutic alliance. Results: Four themes were identified: (1) revision of anticipatory beliefs, (2) context-medium misattribution, (3) medium affordances, and (4) relational viability and selective integration. Therapists initially held strong negative expectations about teletherapy that were often not supported by later experience. Participants frequently attributed early difficulties to the digital medium but later realised they arose from broader contextual factors, including home working and pandemic conditions. With sustained practice, clinicians revised their assumptions, adapted their techniques, and came to view teletherapy as a viable mode of care. These outcomes imply a broader process of professional adaptation involving speculative concern, misattribution, and belief revision. Conclusions: Adaptation to teletherapy involved more than acceptance of a new tool. It required therapists to reconsider assumptions about therapeutic presence, safety, and alliance. While this process supported teletherapy uptake, it may also create a risk of overgeneralising lessons learned from one form of digital transition to qualitatively different technologies, such as AI-mediated therapy. Successful digitisation of the therapeutic medium should not be assumed to imply that automating the therapeutic agent is equivalent or safe. Implementation and training frameworks ought therefore to support clinicians in distinguishing changes in the communication medium from changes in the treatment process or therapeutic agent when evaluating emerging digital mental health tools.
Background: Medical record review (MRR) is a common research method that uses information recorded in patient health records to answer health-related questions. MRRs help researchers learn about disea...
Background: Medical record review (MRR) is a common research method that uses information recorded in patient health records to answer health-related questions. MRRs help researchers learn about disease prevalence and practice patterns, treatment variation, outcomes, and healthcare quality and safety. It is relatively inexpensive and can be conducted efficiently. However, challenges such as missing data, inaccurate terminology, and inconsistent documentation are potential sources of bias, undermining the validity of MRRs. Prior MRR checklists are limited due to being outdated, incomplete, or not designed for newer approaches such as those involving electronic health records (EHRs). Objective: To develop expert-approved recommendations and a checklist for the conduct and reporting of MRRs addressing both traditional chart reviews and contemporary EHR-based research. Methods: We will conduct an expert consensus study using a modified Delphi approach following the ACCORD guideline for consensus methods in biomedicine. The process will be informed by a systematic review of the literature on the conduct, reporting, and quality assessment of MRR studies, registered on the Open Science Framework website (https://osf.io/9yj8r/overview). A multidisciplinary panel of 27 experts, primarily drawn from but not restricted to the Pediatric Emergency Research Canada (PERC) network and was selected purposefully to ensure diversity in expertise, background, and experience. Candidate checklist items identified through the literature review and an initial meeting will be evaluated over up to three rounds of anonymous online Delphi surveys using 5-point Likert scales. Items not reaching consensus (<75% approval) will be revised iteratively. A steering committee will oversee the process, and an in-person consensus meeting will be held to refine draft guidelines. Approval of the final version of the recommendations by at least 90% of the experts will be required. The final version will be tested with a broader group of patient partners, international researchers and stakeholders to ensure it is practical and useful. Based on institutional policies, this project was considered outside the mandate of the Research Ethics Board and therefore did not require ethics approval. Results: This project received financial support from the Canadian Institutes of Health Research (Planning and Dissemination Grant# 204652) in November 2025. The first three rounds of Delphi surveys were conducted in December 2025 to March 2026. The recommendations are expected to be completed in the Spring of 2026 and be externally evaluated in the summer of 2026. Conclusions: This project will lead to more rigorously designed MRR studies, thereby improving the quality of healthcare research to inform patient care.
Obesity has become one of the main public health problems worldwide, due to its association with an increased risk of cardiovascular diseases and type 2 diabetes mellitus. In this context, incretin-ba...
Obesity has become one of the main public health problems worldwide, due to its association with an increased risk of cardiovascular diseases and type 2 diabetes mellitus. In this context, incretin-based therapies, particularly those that are GLP-1 receptor agonists, are revolutionizing and transforming metabolic management. This is due to their multiple effects, including glycemic regulation, appetite reduction, and assistance in body weight loss, in addition to lowering cardiovascular risk. However, their use presents a series of challenges, both clinical and practical. Among these are the reduction of lean body mass, the need for long-term use, and the potential for weight regain after discontinuation of the medication. Furthermore, their high cost limits their widespread availability globally, making access difficult for countries with limited resources.
Background: Rheumatoid arthritis (RA) disease activity during disease-modifying antirheumatic drug (DMARD) tapering is commonly monitored using in-person clinical assessment and the 28-joint Disease A...
Background: Rheumatoid arthritis (RA) disease activity during disease-modifying antirheumatic drug (DMARD) tapering is commonly monitored using in-person clinical assessment and the 28-joint Disease Activity Score with C-reactive protein (DAS28-CRP). Although effective, this approach is resource intensive and may be inconvenient for patients. Remote monitoring using patient-reported outcome measures and wearable sensors may offer a practical way to detect flare earlier and support safer tapering pathways. Prior pilot work suggests that accelerometery-derived physical activity, mobility, and sleep metrics are associated with RA disease activity and are acceptable to patients. Objective: This protocol aims to evaluate the feasibility and diagnostic accuracy of remote monitoring for detecting RA flare during DMARD tapering. The study will (1) continuously measure physical activity using wrist-worn accelerometers, (2) collect weekly Rheumatoid Arthritis Flare Questionnaire (RA-FQ) scores, (3) develop a joint modelling framework to estimate flare risk from longitudinal activity data, and (4) retrospectively assess prediction accuracy against patient- and clinician-defined flare onset. Methods: This is a prospective observational cohort study embedded within the ROADMAP DMARD tapering clinic at the Freeman Hospital, Newcastle upon Tyne, United Kingdom. Adults with clinician-confirmed RA in remission (DAS28-CRP <2.4) who are undergoing or about to start DMARD tapering and can walk independently will be recruited. The target sample size is 100 patients. Study visits are aligned with routine ROADMAP appointments and include baseline and week 12 assessments, with an optional 12-week extension (week 24) and ad hoc visits for suspected flare. Patients will wear wrist-worn Axivity AX6 devices continuously for 12 weeks (three devices worn sequentially for 28 days each), with an optional further 12 weeks in the extension phase. Weekly RA-FQ data will be collected via REDCap or paper questionnaires if needed. Clinical assessments include tender and swollen joint counts, patient and physician visual analogue scales, C-reactive protein, DAS28-CRP, and Health Assessment Questionnaire Disability Index scores. Flare status will be defined using clinician assessment supported by DAS28-CRP and/or swollen joint count criteria. A joint modelling framework combining longitudinal accelerometery-derived metrics and time-to-event analysis will generate daily flare-risk predictions. Model performance will be evaluated using area under the receiver operating characteristic curve, sensitivity, specificity, predictive values, and lead time to flare detection. Results: As of April 2026, 16 patients have been recruited. This protocol reports the study design, procedures, and planned analyses; outcome analyses will be reported after follow-up completion. Conclusions: This study will provide pilot evidence on the feasibility and accuracy of multimodal remote monitoring for RA flare detection during DMARD tapering in routine care. Findings will inform model refinement, external validation, and future larger multicentre studies evaluating clinical utility and service impact.
Background: Insufficient physical activity (PA) is a global pandemic associated with the development of noncommunicable diseases. Objective: To examine whether an mHealth PA intervention with financia...
Background: Insufficient physical activity (PA) is a global pandemic associated with the development of noncommunicable diseases. Objective: To examine whether an mHealth PA intervention with financial incentives (FI) is improved with the incorporation of a machine learning (ML)-driven goal setting algorithm. Methods: A 17-week cohort analytic study was conducted among users of the Telus Wellbeing corporate wellness app, an mHealth PA intervention with FI targeting North American employees (March-June 2022). A five-week baseline period was followed by a 12-week intervention, during which users were randomized (1:2) into either (a) static goal (control), or (b) adaptive, ML-driven goal (intervention) groups. A linear mixed model (LMM) analyses compared baseline to Week 12 and was conducted to examine change in primary and secondary outcomes over the intervention period (p<0.05). Estimated marginal means (EMM) were reported across four time points (baseline, Week 4, Week 8, and Week 12). Results: A total of 1,249 participants (control: n=447; intervention: n=802) were included (59.6% 30-to-50 years old; 48.8% women; baseline steps: 6,313/day). LMM analyses suggest the overall weekly mean daily step count trend increased from baseline to Week 12 for the entire sample (i.e., mean difference [95% CI]: 607 [96-1118] steps/day; d=0.07; p=0.022). Regarding the primary study objective, groups did not differ significantly on weekly mean daily step count change from baseline to Week 12 (i.e., mean difference [95% CI]: 19 [-768-806] steps/day; d=0.001; p=0.960). Conclusions: The Telus Wellbeing app increased mean daily step count over a 12-week period. A supervised ML-driven goal setting algorithm did not boost PA or app engagement, compared to static goal setting over 12 weeks. Future research should test refined ML-driven approaches with more diverse study samples that may better boost PA with mHealth intervention. Clinical Trial: This study was pre-registered at ClinicalTrials (NCT06388317), received institutional ethical approval and was conducted following the STROBE guidelines for cohort studies.
Background: Poor medication adherence is a critical impediment to effective COPD management and remains challenging to track in routine care. Digital therapeutic interventions may improve chronic dise...
Background: Poor medication adherence is a critical impediment to effective COPD management and remains challenging to track in routine care. Digital therapeutic interventions may improve chronic disease management through behavioral support, remote monitoring, and patient education. Objective: We developed a multi-component digital therapeutic program combining electronic inhaler monitoring with behavioral support and evaluated its effect on adherence and clinical outcomes in COPD patients. Methods: In this prospective, open-label randomized controlled trial, 158 COPD patients using pressurized metered-dose inhalers were randomized 2:1 and provided with electronic monitoring devices. Patients in the intervention group (n=106) received medication reminders, adherence feedback, and educational content through the device and a mobile application. Adherence and clinical outcomes were assessed over 24 weeks. Results: Mean electronically monitored adherence was significantly higher in the intervention group than in controls (59.5% vs. 33.1%, p<0.01). The intervention group experienced numerically fewer moderate-to-severe exacerbations (32% reduction) and severe exacerbations (46% reduction), though these differences did not reach statistical significance. At 24 weeks, the between-group difference in SGRQ-C total score was 3.10 points (p=0.080), approaching the minimal clinically important difference. No significant differences were observed in CAT score, lung function, or mental health outcomes. Conclusions: The multi-component digital therapeutic program markedly improved objective medication adherence in COPD patients over 24 weeks. Its effect on key clinical outcomes, including exacerbations and health status, remains suggestive but not conclusive, highlighting the need for larger trials and implementation research. Clinical Trial: ClinicalTrials.gov (identifier: NCT05667363). Registered on December 19, 2022.
Background: Extended reality physical activity engages adolescents and young adults through immersive exercise, yet the differential health effects of its highly variable intensity remain unclear. Thi...
Background: Extended reality physical activity engages adolescents and young adults through immersive exercise, yet the differential health effects of its highly variable intensity remain unclear. This review aimed to systematically evaluate the intensity-specific impacts of XR physical activity on physical and mental health outcomes in individuals aged 10--24 years. Objective: The aim is to systematically assess the specific intensity impact of immersive reality-based sports activities on the physical and mental health of individuals aged 10 to 24. Methods: A systematic search of six electronic databases was conducted for randomized controlled trials published between January 2005 and January 2025. Eligible studies reported XR physical activity intensity and assessed physiological, psychological, cognitive, or behavioral outcomes in healthy adolescents and young adults. A narrative synthesis was performed stratified by a prespecified intensity classification standard. The risk of bias was assessed via the Cochrane RoB-2 tool, and evidence certainty was evaluated via the GRADE approach. The protocol was prospectively registered. Results: Eighteen randomized controlled trials involving 1,081 participants were included. The intensity ranged from low to high. Moderate-to-high-intensity extended reality activities yielded significant improvements in body composition, cardiorespiratory fitness, muscular strength, and specific cognitive functions, including working memory and inhibitory control. In contrast, mental health and mood improvements were more pronounced with low-to-moderate intensity, highly immersive activities, with no additional emotional benefits observed at high intensity. While XR activities generally enhance exercise enjoyment, evidence for long-term adherence is inconclusive. Most outcomes demonstrated low or very low certainty of evidence due to risk of bias and imprecision. Conclusions: The health benefits of extended reality physical activity demonstrate a domain-specific intensity‒effect relationship. Effective interventions require tailoring exercise intensity to targeted outcomes and integrating behavioral theory. Current evidence is predominantly limited to young adults and of low certainty, underscoring the need for rigorous, long-term trials to establish precise dose‒response guidelines. Clinical Trial: PROSPERO CRD420261285966.
Background: Despite being a major burden in low- and middle-income countries in India, the understanding of cancer and its treatment remains limited. Adverse drug reactions (ADRs) from chemotherapy, p...
Background: Despite being a major burden in low- and middle-income countries in India, the understanding of cancer and its treatment remains limited. Adverse drug reactions (ADRs) from chemotherapy, polypharmacy, drug interactions and the cost of care also pose challenges for patient safety and health economics. Objective: This protocol describes the design of a prospective, observational, cross-sectional study that will evaluate chemotherapy drugs based on their usage, ADRs, and drug interactions, as well as undertake pharmacoeconomic analyses, including cost-effectiveness analysis, cost-utility analysis, and budget impact analysis to guide safe and cost-effective cancer treatment. Methods: The research will involve 67 adult cancer patients on chemotherapy in the oncology department of a tertiary care hospital in India for eight months. The Case Record Form will be used for data collection. Pharmacovigilance estimates will be done using standardised instruments (WHO-UMC scale, Naranjo algorithm, Hartwig and Siegel scale, Schumock and Thornton criteria). Economic analyses will include cost analysis, cost-effectiveness analysis (ICER), cost-utility analysis (ICUR) and budget impact analysis. Polypharmacy (use of >=5 drugs) and drug-drug interactions will also be assessed. Results: The study will confirm a high burden of ADRs, polypharmacy, and financial toxicity with chemotherapy. Results will demonstrate the link between prescription complexity, safety and cost. Conclusions: Linking pharmacovigilance with pharmacoeconomics will support rational prescribing, encourage generic and biosimilar uptake, and ultimately improve safety, affordability, and access to cancer treatment. Clinical Trial: CTRI/2025/10/096700 (Registered on: 31/10/2025)
Background: In a changing world of work, integrative prevention at work represents a promising avenue for addressing contemporary health, safety and well-being issues. However, for organizations to de...
Background: In a changing world of work, integrative prevention at work represents a promising avenue for addressing contemporary health, safety and well-being issues. However, for organizations to deploy and benefit from this approach, developing an assessment tool for integrative prevention appears to be the first required step. No existing assessment tool can assess all its key characteristics on a unified scale, limiting the operationalization of this approach in organizations. Objective: The general objective of this project is to develop an assessment tool for integrative prevention at work intended for organizations in the health and social services sector. Specifically, this project aims to: 1) to generate items, rating scales and instructions; 2) to validate the content of the tool; 3) to pre-test the tool in organizational settings and 4) to evaluate its psychometric properties. Methods: A four-phase methodological study will be carried out for each research objective. The assessment tool will be developed and tested in the health and social services organizations in Quebec, Canada. Results: Phase 1 is complete: A total of 96 items were created and distributed relatively evenly across five subscales, each reflecting one attribute of integrative prevention at work. Phase 2 will be finalized by the end of 2026. Phases 3 and 4 will be completed by the end of 2028 to provide a validated tool to assess the key characteristics of integrative prevention at work. Conclusions: From an organizational perspective, this tool will provide health and social services organizations with a validated, context-adapted measure of integrative prevention at work, enabling baseline assessment, targeted improvements, and longitudinal monitoring, thereby strengthening prevention practices. From a research perspective, this project will deepen the understanding of integrative prevention at work through its empirical validation.
Background: Outdoor free play is associated with benefits for children’s health, yet opportunities have declined and intervention approaches remain heterogeneous. A clearer understanding is needed o...
Background: Outdoor free play is associated with benefits for children’s health, yet opportunities have declined and intervention approaches remain heterogeneous. A clearer understanding is needed of which interventions are effective and under what implementation conditions. Objective: This systematic review aims to synthesize interventions related to outdoor free play in children aged 0–12 years, evaluate their effects on child health and developmental outcomes, and characterize intervention components, delivery strategies, and implementation features. Methods: We will search MEDLINE, Embase, CINAHL, Scopus, CENTRAL, and Web of Science. Eligible studies will use experimental or quasi-experimental designs to examine interventions related to outdoor free play, defined as child-directed play outdoors that is not structured by adults. Interventions will include but not be limited to modifications to environments, policies, supervision, or resources. The primary outcome will be any quantitative child health or developmental outcome. Studies must provide sufficient detail to identify core components, delivery format, target population, and setting. Two reviewers will independently screen studies, extract data, and assess risk of bias using Cochrane Risk of Bias 2 tool and ROBINS-I. Intervention characteristics and implementation factors will be synthesized descriptively using TIDieR and RE-AIM. Where appropriate, meta-analyses will be conducted using random-effects models, with subgroup analyses by age and setting, and sensitivity analyses restricted to studies at low risk of bias. Results: Funded in Decembre 2025, search strategy ran in April 2026 and title and abstracts screening started as of April 2026. Conclusions: This review will identify which outdoor free play interventions are effective and feasible. By clarifying key components and implementation features, it will support decisions on selecting, adapting, and scaling interventions within local contexts. Clinical Trial: PROSPERO no CRD420261376414; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261376414
Background: Digital interventions provide a scalable, resource-saving approach to promote well-being and health and prevent lifestyle-related chronic health conditions, but their ability to engage and...
Background: Digital interventions provide a scalable, resource-saving approach to promote well-being and health and prevent lifestyle-related chronic health conditions, but their ability to engage and benefit diverse audiences remains a challenge. Objective: This study aims to evaluate a 6-month web-based wellness coaching program among working-age adults. The program is theory- and evidence-based, co-designed, and targets three behavioral domains (physical activity, diet, sleep) with an overarching focus on stress–recovery balance. Methods: The study follows a 2-arm parallel cluster randomized controlled design and lasts 12 months. Participants with age 18–65, proficiency in Finnish, and access to Internet were recruited from diverse worksites (n=13) in Central and Southwestern Finland. The sites were allocated 1:1 to intervention (coaching program) or waitlist control arm (general information on well-being). Data collection comprises fitness tests (handgrip strength, heart rate variability, body composition, waist circumference, BMI) and questionnaires conducted at months 0, 6, and 12, together with continuous monitoring of implementation costs, study uptake, dropout, and engagement (eg, visits to and time spent on the coaching platform). The primary outcome is the participant-level change in self-reported well-being (WHO-5 Well-Being Index) from baseline to 12 months. Secondary outcomes include changes in measures reflecting physical fitness, anthropometrics, lifestyle behaviors, health, and functional capacity. Further evaluation domains include health economic impact (eg, changes in well-being-, productivity-, and quality-adjusted life years), feasibility (study uptake, dropout, and engagement), and user experiences (acceptability, overall evaluation, and readiness to recommend the coaching program). Planned analyses will be conducted on the intention-to-treat principle and include linear mixed-effects models and health economic modelling. Results: The study received ethical approval in May 2025. Participant registration was open in September–October 2025, informed consents were collected in October–November 2025, and baseline assessments were conducted in October–December 2025. Consents were obtained from 294 and complete baseline data from 268 participants. Data collection will be completed within 2026, data analysis is planned for 2026–2027, and the dissemination of results will begin in 2027. The study is conducted as a part of the European Union’s Joint Action on Cardiovascular Diseases and Diabetes (JACARDI) that has received funding from the EU4Health Programme 2021–2027. Conclusions: The study contributes to evidence on the potential of fully automated digital tools to enhance workforce well-being and save societal costs. Clinical Trial: ISRCTN Registry ISRCTN12097902 https://doi.org/10.1186/ISRCTN12097902 (date of registration: 06/08/2025)
Background: Early and accurate identification of diabetic complications is crucial for reducing the associated high rates of morbidity and mortality. In particular, microvascular and macrovascular com...
Background: Early and accurate identification of diabetic complications is crucial for reducing the associated high rates of morbidity and mortality. In particular, microvascular and macrovascular complications represent the most prevalent and critical conditions that determine patient prognosis. Although machine learning methods show promise, several limitations hinder their successful translation into clinical practice. Many methods rely on hard to acquire clinical data and overlook the pathological relationships among complications. Additionally, the propensity of purely data driven models to capture spurious correlations hinders application in clinical practice. Objective: This study aims to address these limitations. The goal is to develop a low-cost, clinically applicable framework the screening of major diabetic complications including microvascular and macrovascular conditions. In this way, the research seeks to improve clinical plausibility and facilitate the practical application of classification models in resource limited settings. Methods: This study proposes a novel diagnostic framework: the Attention-based Neuro-Symbolic Regularization Framework (ANSR). First, the framework constructs an effective feature set by integrating accessible immune-inflammatory biomarkers. Subsequently, an Attention-based Neural Perception Module (ABNP) is employed to extract non-linear risk features from these data. To bridge the gap between data patterns and clinical logic, a Bi-level Symbolic Logic Regularization Mechanism (BSLR) is introduced. This mechanism enforces constraints through two components: a pairwise co-occurrence regularizer that encodes explicit correlation patterns, and a high-order global pattern regularizer that mines global association rules. Results: Experimental results demonstrate that ANSR outperforms all mainstream baseline models in key metrics such as accuracy and Precision-Recall curve. Moreover, the McNemar test confirms that ANSR achieves a statistically significant advantage over all baseline models across all five complications . Conclusions: These findings suggest that the proposed ANSR framework has substantial potential for early and accurate screening of major diabetic complications and for supporting reliable clinical decision-making.
Background: Navigation remains a major challenge for people with visual impairment (PVI) as many assistive technologies (AT) fail due to limited contextual relevance and insufficient involvement of en...
Background: Navigation remains a major challenge for people with visual impairment (PVI) as many assistive technologies (AT) fail due to limited contextual relevance and insufficient involvement of end-users during development. Traditional AT evaluation tools often miss the real-time, situational needs experienced during dynamic, everyday tasks. Objective: This study evaluated a mixed-methods framework combining task-based walking interviews with environmental scene metrics to capture real-time, context-dependent navigation needs of PVI. Findings were used to inform the design of our indoor navigation prototype, Edge A-Eye. Methods: Thirteen adults with visual impairments completed eight sequential indoor navigation tasks during a simulated optometry visit. Participants wore Pupil Labs Neon glasses while performing tasks and provided immediate verbal feedback after completing each task. Luminance, contrast, edge density and spatial entropy were extracted from video recordings to objectively characterize the scene. Multiple-choice responses were analyzed using descriptive statistics and correlations, while open-ended responses underwent thematic analysis. Results: Navigation needs varied substantially by context. Participants preferred early notifications (6–8 m) for entrance doors, but on-command cues for stairs, elevators, and exits. Across tasks, users consistently requested concise, actionable information such as door type, handrail position, or chair location rather than continuous descriptive narration. While human assistance remained preferred in complex areas, many participants expressed willingness to use AI-based guidance if reliability could be ensured. Descriptive comparisons across tasks indicated that higher environmental luminance and stronger object contrast were observed in scenarios where participants with low vision demonstrated higher navigation success. Conclusions: Task-based walking interviews combined with scene analysis provide ecologically valid insights into navigation behavior and user preferences. This methodology supports the development of adaptive, personalized assistive technologies and offers a scalable framework for future AT evaluation.
Background: Artificial intelligence early warning systems are increasingly embedded in inpatient nursing workflows, yet the psychometric performance of generic trust-in-automation scales in this setti...
Background: Artificial intelligence early warning systems are increasingly embedded in inpatient nursing workflows, yet the psychometric performance of generic trust-in-automation scales in this setting remains uncertain. Objective: This study examined whether the classic Jian automation trust scale could function as a meaningful measurement tool in nurses using workflow-embedded artificial intelligence early warning systems under mandatory deployment conditions. Methods: We conducted a multicenter cross-sectional survey among 712 nurses from 8 tertiary hospitals in Shaanxi Province, China. Reporting was guided primarily by CROSS, with supplementary reference to STROBE and DECIDE-AI; TRIPOD+AI was used only to clarify reporting boundaries. Trust_Score and Distrust_Score were evaluated using internal consistency indices, item distributions, and multigroup confirmatory factor analysis diagnostics. Latent profile analysis based on 4 interaction indicators was followed by BCH 3-step distal comparisons with 1000 bootstrap draws. Separate ordinary least squares models with HC3 robust standard errors were then fitted using a single-item global trust rating as the exploratory outcome, and 10-fold cross-validated R² was used to evaluate out-of-sample performance. Results: Trust (α=-0.100; ω=0.120) and Distrust (α=0.082; ω=0.108) showed extremely poor internal consistency, indicating that the instrument did not form an interpretable latent construct in this setting. Trust items were heavily compressed at the high end of the response scale. Multigroup confirmatory factor analysis for the AI-training grouping did not support measurement invariance (configural CFI=0.682; metric CFI=0.285). Latent profile analysis identified 4 interaction profiles, but BCH comparisons showed no between-profile differences in Trust_Score or Distrust_Score after Holm correction. Regression models had very limited explanatory value (adjusted R²=0.011 and 0.016), and all cross-validated R² values were negative (-0.065 to -0.019), indicating worse performance than a naive mean-prediction baseline. Conclusions: A generic trust-in-automation scale showed marked measurement misfit in a strongly workflow-embedded clinical artificial intelligence early warning system context. The present cross-sectional self-report data do not support strong claims about institutional decoupling or ceremonial adoption. The inverse association between better perceived experience and lower single-item global trust in high-risk departments should be treated as an exploratory signal requiring confirmation with system logs and system-level performance data.
Background: Electronic nicotine delivery systems (ENDS) are at the center of global public health debate. China is the largest producer of e-cigarettes while the U.S. has the largest consumer market, ...
Background: Electronic nicotine delivery systems (ENDS) are at the center of global public health debate. China is the largest producer of e-cigarettes while the U.S. has the largest consumer market, yet analyses of news coverage of ENDS comparing China and the United States (U.S.) remain limited. Objective: The primary objective of this study is to identify and compare dominant themes in ENDS-related news coverage across leading broadcast-branded digital outlets in China and the United States, and to assess how these themes and coverage volume changed over time. Methods: We conducted a thematic analysis of 470 ENDS-related stories from January 1, 2020, to July 30, 2025, from four leading broadcast news digital media platforms: CNN.com and FoxNews.com in the U.S.; CCTV.com and ifeng.com in China. Using a single theme approach, coders identified core themes for each article based on prespecified rules and a hierarchical decision structure. Frequencies and proportion of each core theme were summarized for the overall sample and stratified by country. Pearson chi-square tests and binary logistic regression models were conducted to examine cross-national differences with false discovery rate (FDR) adjusted p-values. Temporal changes in themes were examined and visualized. Results: In U.S. coverage, the most prevalent themes were policy and regulatory governance (32.1%), youth appeal, flavors, and school responses (22.4%), and health risks, harms, symptoms, and dependence (13.9%). In Chinese coverage, the most prevalent themes were commercial practices and market dynamics of ENDS (26.0%), policy and regulatory governance (23.4%), and enforcement and compliance (15.7%). Cross-national differences in themes were consistently observed between the two countries. Between 2020 and 2025, coverage in China transitioned away from commercial and market themes toward greater focus on illicit substances and enforcement, while U.S. coverage showed relatively stable focus on commercial market with a gradual increase in enforcement-related reporting. Conclusions: Broadcast news in China and the U.S. may actively shape how ENDS are defined as a public issue and what policy responses appear legitimate. Chinese coverage tends to stress commercial activity and enforcement, whereas U.S. coverage more often foregrounds youth risks and regulatory debates. These distinct thematic patterns may influence risk perceptions and policies in each country and are important to consider in comparative media and public health research.
Background: Vestibular hypofunction results in dizziness, gaze instability, imbalance, and an increased risk of falls. Vestibular exercises are effective in reducing symptoms of dizziness and improvin...
Background: Vestibular hypofunction results in dizziness, gaze instability, imbalance, and an increased risk of falls. Vestibular exercises are effective in reducing symptoms of dizziness and improving gaze stability, balance, and mobility. Unfortunately, adherence to vestibular exercises is low, resulting in persisting symptoms that can have devastating outcomes. Objective: To determine the feasibility, acceptability, and preliminary efficacy of at-home vestibular rehabilitation exercises delivered via an interactive tablet-based app, and to identify predictors of adherence to the app intervention. Methods: Four individuals (mean age 64.5 ± 4.01 years; 2 males and 2 females) with a diagnosed vestibular hypofunction participated in the intervention. Vestibular exercises for gaze stability and balance were performed using the VestRx™ App; additional exercises included habituation and functional mobility training. Exercises were prescribed 5 times a day for 6 weeks following the clinical practice guidelines for vestibular hypofunction. Participants attended weekly in-lab sessions to progress the exercises based on individual recovery patterns and symptom intensity. A combination of quantitative (e.g., dynamic balance, gait speed, gaze stability, symptoms, and survey-based measures of usability) and qualitative (e.g., semi-structured interviews) measures was employed at baseline, 3 weeks, and after 6 weeks of training to evaluate the feasibility, acceptability, and early signal of app-based intervention efficacy. Results: Participants were motivated, and they achieved high adherence to the exercises (between 53% to 92% completion rates). Participants highly endorsed the intervention and said they would be likely to use the app and complete a program like this in the future. They made significant improvements in dynamic balance, gait speed, and confidence with daily activity. Individual factors that promoted adherence to exercises included the engaging nature of the app exercises, the ability to get feedback from the app during exercises, inherent motivation to improve, and comfort with technology. Family support with technology and reminders by family members to do the exercises were identified as facilitators for adherence. Barriers to consistent exercise performance included illness, flaring symptoms, and family events. From a structural and environmental perspective, the responsiveness of the program during exercises, and technical issues with the web portal (unrelated to patient access to the internet) were barriers that influenced adherence; while the ability to exercise based on individual schedules, ability to take the tablet on vacation, and having ample space in the home to leave the tablet undisturbed were identified as facilitators for adherence to exercises. Conclusions: In this early pilot study, the app-intervention was easy to use in the home setting, feasibly implemented, and shows promise for improving vestibular outcomes. Future work should focus on addressing study limitations, including sample size, showing the efficacy of the intervention in a controlled experimental trial, and focus on understanding barriers and facilitators to implementing tablet-based interventions for vestibular hypofunction at larger scale in clinical settings. Clinical Trial: No.
Background: Traumatic knee injuries (TKI) are common, associated with a 4-6 times increased risk of post-traumatic knee osteoarthritis (PTOAK) over the subsequent 15–20 year period. There is clear e...
Background: Traumatic knee injuries (TKI) are common, associated with a 4-6 times increased risk of post-traumatic knee osteoarthritis (PTOAK) over the subsequent 15–20 year period. There is clear evidence that risk can be reduced, but long-term care availability is limited, prompting the development of DHIs (digital health interventions) such as wearable devices, telehealth innovations and mobile apps. Objective: To evaluate existing DHIs against the OPTIKNEE consensus guidelines for PTOAK prevention and investigate adoption into practice. Methods: A search of 7 online databases and the grey literature was completed from inception to 03/06/2025, complemented by hand searching government, charity and university websites for reports and technical prototype papers concerning DHIs to support care after TKI. DHI features were mapped to the OPTIKNEE recommendations, evaluated against the health-technology pathway to identify development stage, and implementation analysed using NPT (Normalisation Process Theory). Results: 81 reports, 53 peer-reviewed and 28 other, concerning 49 distinct DHIs were found. They were designed for injuries of the anterior cruciate ligament (ACL, n=12); ACL meniscus (n=15); meniscus (n=3); ACL or meniscus (n=2), bone (n=2), patella dislocation (n=1), and 14 were non-specific. No DHIs addressed all OTPIKNEE recommendations, however the eight most complete reported 4/7 components, including exercise, information provision, patient reported outcome measures, goal setting and overall patient outcome. A remote, self-assessed strength evaluation was not reported in any DHI. NPT analysis typically demonstrated low DHI adoption levels, and no clear correlation with health technology pathway stage. The DHI with the highest adoption into routine practice, according to NPT, was ‘getUbetter’ with 56% positive scores. Conclusions: There are many available, or developing, DHIs but none include the content recommended by OPTIKNEE to reduce the risk of PTOAK. Further, there is negligible evidence of DHIs being adopted into usual care. There is a clear need to develop guideline-compliant DHIs to support effective prevention.
Background: Frailty prevalence among older adults is rising globally, with significant implications for quality of life, healthcare costs, and economic productivity. While multicomponent lifestyle int...
Background: Frailty prevalence among older adults is rising globally, with significant implications for quality of life, healthcare costs, and economic productivity. While multicomponent lifestyle interventions can reverse frailty, existing programmes such as Vivifrail and the Otago Exercise Programme are limited by poor long-term adherence. Digital technologies combined with behavioural science principles offer a potential solution, yet few interventions have been developed with meaningful input from older adults themselves. Objective: To co-design a digitally-enabled intervention and to evaluate its feasibility, acceptability, and preliminary effects on health behaviours and frailty status in community-dwelling older adults. Methods: A mixed-methods feasibility study was conducted with 30 community-dwelling adults aged 65 and over (mean age 72.6 years; 45% men, 55% women) scoring 0-4 on the Fried Frailty Scale at enrolment. The "Healthy Habits" intervention was iteratively developed through four co-design cycles, incorporating wearable sensors (smartwatch, smart scale, sleep analyser), and a custom mobile application providing personalised feedback on seven habit domains. Participants engaged for a minimum of six weeks, with optional extended participation. Outcomes included retention, engagement metrics, changes in daily step count and sedentary time, grip strength, and frailty scores (Fried and Edmonton scales). Results: Retention was 97% (29/30) at six weeks, with 83% of completers electing to continue participation up to a maximum of 54 weeks. Smartwatch adherence was high (80% wear time). Among participants, 79% (22/28) demonstrated greater than 10% improvement in either daily step count or sedentary time. In the subgroup receiving the digital application (n=7), 71% increased daily steps by more than 10% within six weeks. Mean grip strength improved by 8% (p=0.013) and Edmonton Frailty Scale scores decreased by 48% (p=0.015). Conclusions: A co-designed, digitally enabled intervention based on habit formation principles is feasible and acceptable to older adults, with high retention and preliminary evidence of improvements in physical activity, sedentary behaviour, and frailty markers. These findings support progression to a randomised controlled trial to evaluate efficacy. Clinical Trial: N/A
Background: Socially assistive robots that provide emotional support have been proven effective in dementia care. However, it remains unclear how the characteristics of healthcare and long-term care p...
Background: Socially assistive robots that provide emotional support have been proven effective in dementia care. However, it remains unclear how the characteristics of healthcare and long-term care providers, as well as differences across clinical settings, influence perceptions of effectiveness and the real-world implementation. Objective: To examine, from an exploratory perspective, the factors influencing the perceived effectiveness of the seal-like robot “Paro”—such as the attributes of healthcare and long-term care professionals and the facility environment—and to identify practical insights for more effectively utilizing Paro. Methods: A survey using a convergent parallel mixed-methods study design targeting 162 healthcare and long-term care professionals employed at Japanese medical institutions and long-term care facilities. A questionnaire comprised 20 items on core symptoms, Behavioral and Psychological Symptoms of Dementia (BPSD), caregiver burden, and the purpose of Paro introduction. We analyzed the quantitative data using multiple regression analysis, correspondence analysis, and other methods. Qualitative data from open-ended responses were analyzed using reflexive thematic analysis with researcher triangulation. Results: The results of the multiple regression analysis suggested that, even after adjusting for participants' attributes (differences in perspective based on gender and occupation) and facility type, “continuous use for three years or more” remains a significant factor in reducing caregiving burden (B=0.598, P=.043). Correspondence analysis revealed a dichotomy in Paro's role, depending on the environment and professional role: the “medical/acute care model,” which focuses on managing delirium and sedation in hospitals, and the “daily living model,” which addresses wandering and the desire to return home in long-term care facilities. This polarization was already evident from the initial stages of implementation, with medical staff clearly focused on “treatment” and care staff on “recreation.” The thematic analysis identified four themes, including “Support for self-regulation of emotions and behavior” and “Regaining social roles and interpersonal interactions.” While many professionals recognized that Paro interventions have a positive impact on core symptoms, BPSD, and caregivers, a small number of reports indicated negative effects. Matching the characteristics of people with dementia to their environment and ensuring appropriate professional intervention are essential. Conclusions: This survey confirmed that professionals recognize that Paro’s activities have a positive effect on people with dementia. This perception is influenced by factors such as the participants' expertise and gender. Continuous use for three years or more may be a key factor in reducing the burden of care, regardless of perspectives or attributes. To maximize the benefits of robot use, it is important to understand how staff members’ attributes influence their perception of these benefits and to adopt specific implementation strategies tailored to the unique characteristics of each setting, such as medical institutions and long-term care facilities. Clinical Trial: N/A
Background: Information and communication technology (ICT) has the potential to bridge the post-acute stroke care gap by facilitating home-based rehabilitation. However, evidence on usability and adop...
Background: Information and communication technology (ICT) has the potential to bridge the post-acute stroke care gap by facilitating home-based rehabilitation. However, evidence on usability and adoption of ICT-based rehabilitation programs in Asian health care settings is limited. Objective: This study aimed to evaluate the usability and satisfaction of a tablet-based comprehensive management program for post-acute stroke patients within the Korean Model for Post-Acute Comprehensive Rehabilitation (KOMPACT) project and to identify patient characteristics associated with acceptable usability. Methods: This multicenter prospective cohort study enrolled post-acute stroke patients discharged home within 30 days of onset from 3 university hospitals. Participants used a tablet-based program encompassing medication guidance, rehabilitation content (physical, occupational, swallowing, and language therapy), clinic scheduling, and social welfare information. The System Usability Scale (SUS), satisfaction survey (5-point Likert scale), usage patterns, and narrative feedback were assessed at 1 month post-discharge and 3 months post-onset. Mann-Whitney U tests, Cohen d effect sizes, and Firth’s penalized logistic regression were used to identify factors associated with acceptable usability (SUS ≥68). Results: Of 24 enrolled patients (mean age 62.5, SD 9.1 years; 17 male), 22 completed the 1-month evaluation and 8 completed the 3-month evaluation. The mean SUS score at 1 month was 60.34 (SD 17.48), below the acceptable threshold of 68. Patients younger than 65 years scored significantly higher than older patients (68.3 vs 50.8; P=.019). Only 6 of 22 participants (27%) achieved SUS ≥68. The SUS ≥68 group was younger (56.2 vs 64.2 years), had higher cognitive function (Korean Mini-Mental State Examination 27.5 vs 24.2), lower depressive symptoms (Patient Health Questionnaire-9 1.3 vs 6.0), and greater daily usage time (3.0 vs 1.8 hours; P=.03). Satisfaction scores ranged from 3.68 to 4.23 out of 5, with patients with lower ambulatory function (Functional Ambulatory Category 3) reporting significantly higher satisfaction (P=.019). Conclusions: This multi-domain ICT-based program demonstrated moderate but suboptimal usability and generally acceptable satisfaction. No clinical predictor of acceptable usability reached statistical significance, attributable to the critically limited sample (n=6 events; EPV=0.86); however, descriptive analyses revealed large-to-medium effect sizes for age, depressive symptoms, cognitive function and ambulatory function as clinically plausible, hypothesis-generating signals. Higher satisfaction among patients with lower ambulatory function suggests the program was most beneficial for those with greater unmet rehabilitation needs. These findings underscore the need to tailor interface design and content delivery to patients' age, cognitive, and functional levels to optimize ICT adoption in post-acute stroke rehabilitation.
Background: Demand for rehabilitation services and associated technologies is increasing globally due to population ageing and rising chronic disease burden. Despite their potential to enhance access,...
Background: Demand for rehabilitation services and associated technologies is increasing globally due to population ageing and rising chronic disease burden. Despite their potential to enhance access, efficiency, and outcomes, adoption of rehabilitation technologies in clinical practice remains uneven. Barriers include limited workforce capability and confidence, resource constraints, fragmented governance, and challenges integrating technologies into existing systems. Collaboration among health and care professionals, educators, technology developers, and individuals with lived experience is essential to identify training needs and strengthen capability for technology enabled rehabilitation. Objective: This study aimed to develop an in-depth understanding of education and training needs relating to rehabilitation technologies across 3 stakeholder groups - rehabilitation professionals, technology professionals, and individuals with lived experience to inform future cross stakeholder training initiatives. Methods: A UK-wide qualitative study was conducted using purposive sampling to recruit rehabilitation professionals, technology professionals, individuals with lived experience of injury, illness, or disability. Data were collected through online focus groups and individual interviews and analyzed using reflexive thematic analysis. Themes were subsequently mapped to the Capability, Opportunity, Motivation–Behavior (COM-B) model to identify behavioral determinants with selected domains from the Theoretical Domains Framework (TDF) used to clarify behavioral influences. The Consolidated Framework for Implementation Research (CFIR) was then applied to situate behavioral determinants within organizational and system-level contexts. Results: A total of 50 participants contributed to 7 focus groups and 11 individual interviews. Three overarching themes were identified: (1) perceived capability and confidence in technology engagement; (2) system-level resource constraints; and (3) fragmented governance structures and organizational decision making. COM-B mapping indicated that capability-related influences were most prominent among individuals with lived experience, whereas opportunity related and structural constraints were more influential in accounts from rehabilitation and technology professionals. CFIR analysis highlighted that these behavioral determinants were embedded within, and often constrained by, infrastructure, procurement processes, and wider NHS policy environments. Conclusions: Individuals with lived experience require accessible, ongoing support to build confidence and skills in using rehabilitation technologies. Rehabilitation professionals and technology developers emphasized the need for structured education on available technologies and practical training on integrating them into clinical workflows, particularly for telerehabilitation and emerging digital and AI enabled tools. Findings indicate that training initiatives must extend beyond individual skill development to address organizational resources, infrastructure, and governance processes. Coordinated, cross-stakeholder education and training strategies are essential to support equitable and sustainable adoption of rehabilitation technologies within the NHS. Training programs should be co designed across stakeholder groups, embed behavioral and implementation considerations, and align with NHS infrastructure and procurement pathways. Addressing both capability and system level constraints will be critical for achieving widespread, confident, and sustained use of rehabilitation technologies.
Background: Post-stroke seizures and post-stroke epilepsy are important complications that affect recovery and long-term quality of life after stroke. Predicting which patients are at higher risk rema...
Background: Post-stroke seizures and post-stroke epilepsy are important complications that affect recovery and long-term quality of life after stroke. Predicting which patients are at higher risk remains challenging, and recent studies have explored the use of artificial intelligence to improve prediction. Objective: This scoping review aimed to map how artificial intelligence and related computational methods have been applied to predict post-stroke seizures and epilepsy, to describe the role of neuroimaging in these models, and to assess representation of African settings in the existing literature. Methods: A systematic search identified studies that used statistical, machine learning, radiomics, or deep learning approaches to predict seizure-related outcomes after stroke. Traditional statistical models and clinical risk scores were the most commonly used methods. A smaller number of studies applied machine learning techniques, while radiomics and deep learning approaches were reported in only a few studies. Neuroimaging was mainly used to predict long-term epilepsy rather than early seizures. Most models were evaluated using internal validation only, with limited external validation. Results: Studies from African settings were underrepresented and primarily focused on describing seizure frequency and associated factors using regression analysis. No studies were identified that developed or validated artificial intelligence-based prediction models using African datasets. Conclusions: Overall, the findings show that while interest in AI-based prediction is growing, advanced methods remain limited, and major gaps exist in model validation and geographic representation. These findings highlight the need for context-specific, well-validated prediction models, particularly in African healthcare settings.
Continuous ambulatory HF-HRV monitoring using a chest-worn ECG device is feasible for approximately two to three weeks in adolescents at high risk for suicide, including during school days and sleep....
Continuous ambulatory HF-HRV monitoring using a chest-worn ECG device is feasible for approximately two to three weeks in adolescents at high risk for suicide, including during school days and sleep.
Background: The growing influence of medical influencers on social media is transforming how medical students shape their professional identity, acquire knowledge, and handle mental health. Although c...
Background: The growing influence of medical influencers on social media is transforming how medical students shape their professional identity, acquire knowledge, and handle mental health. Although current research points to both benefits and drawbacks, the long-term effects and the underlying reasons remain unclear. Objective: This study aimed to investigate how medical influencers impact medical students’ mental health via context-specific psychological processes and to analyze how these effects might change with varying levels of institutional response through scenario analysis. Methods: A qualitative foresight method was used, integrating exploratory environmental scanning with inductive thematic analysis of 45 questionnaire responses from medical students, educators, and medical influencers. The data analysis involved open coding, thematic synthesis, and identifying key driving forces and uncertainties. Using these insights, a scenario analysis was performed to outline four potential futures along two axes: the impact on mental health (positive vs. negative) and the level of institutional response (low vs. high). Results: The analysis revealed that key mechanisms such as relatability, visibility, and peer communication can result in supportive or harmful outcomes depending on the interpretive context. Without institutional involvement, students are more vulnerable to psychological stress caused by unstructured interpretation and heightened social comparison. Conversely, guided integration promotes normalization, a sense of belonging, and the development of an adaptive professional identity. Conclusions: The findings indicate that mental health outcomes are influenced more by how individuals interpret and contextualize experiences than by mere exposure. The way institutions respond is vital in shaping whether influencer content encourages support and help-seeking or, conversely, contributes to overwork, unrealistic expectations, and psychological stress.Medical influencers serve as informal agents of professional socialization. Their influence hinges on whether institutions proactively engage with these dynamics or merely observe them. Active involvement can improve mental health support, whereas ignoring these interactions may risk reinforcing damaging norms. Clinical Trial: -
Background: With the rapid development and widespread application of artificial intelligence (AI) technology, AI has demonstrated high accuracy and reliability in medical practice, and patients' trust...
Background: With the rapid development and widespread application of artificial intelligence (AI) technology, AI has demonstrated high accuracy and reliability in medical practice, and patients' trust in algorithmic has gradually increased. However, in clinical practice, disagreements may still arise between algorithmic recommendations and clinical expert experience, and such disagreements can affect patients' trust. To date, however, the impact of these disagreements on patients’ medical trust and the strategies for addressing them have not been systematically reviewed. Objective: To systematically map the impact of disagreements between AI recommendations and clinical expert judgment on patients’ medical trust, identify influencing factors based on Mayer’s integrative model of organizational trust, and summarize strategies to enhance trust. Methods: Following Joanna Briggs Institute (JBI) scoping review methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline, we systematically searched Web of Science, PubMed, Embase, Scopus, and EBSCO up to March 2026, limited to English-language literature. Studies focusing on patients' trust in the context of disagreements between AI and expert opinions were included. Data were charted using the Population, Concept, Context (PCC) framework. Guided by Mayer’s integrative model of organizational trust, influencing factors were analyzed through a framework synthesis approach across the dimensions of ability, benevolence, integrity, and trustor propensity. The protocol was pre-registered on OSF (Registration DOI: 10.17605/OSF.IO/AHSGD). Results: A total of 2,630 records were identified, and 26 studies were ultimately included after screening, including six qualitative studies, seven quantitative studies, three mixed-methods studies, five theoretical studies, and five review articles. These studies were conducted across 10 countries and were published mainly between 2022 and 2026. Disagreements were concentrated in clinical diagnosis and risk assessment, treatment planning and medication decision-making, clinician–patient communication and intelligent interaction, as well as emerging application scenarios. In situations of disagreement, patients commonly expressed skepticism toward both algorithms and experts; overall, however, patients tended to trust experts more than algorithms. Data security and privacy risks, insufficient communication, AI accuracy and reliability, demographic and socioeconomic characteristics, and patients’ disease and health status were identified as high-frequency factors influencing patients’ medical trust. Six trust-enhancing strategies were extracted: transparency and explainability, patient participation and shared decision-making, clinician–patient communication and role positioning, institutional regulation and governance, education and capacity building, and privacy protection and data security. Conclusions: In situations of disagreement between AI and clinical experts, patients’ medical trust is dynamically shaped by ability, benevolence, integrity, and individual-contextual multiple interacting factors. Strengthening transparency, communication, and governance is essential for fostering trust in human–AI collaborative healthcare.
Background: With the implementation of the sports big unit module teaching, senior high school basketball elective students still struggle to proficiently apply motor skills despite systematic learnin...
Background: With the implementation of the sports big unit module teaching, senior high school basketball elective students still struggle to proficiently apply motor skills despite systematic learning, due to fragmented teaching content, monotonous practice scenarios and inadequate cognitive training in existing teaching. Objective: Based on open motor skill learning principles and Gentile’s Two-Dimensional Classification System, this study explores the efficacy of the varied practice method in improving students’ basketball skill application ability, and provides theoretical and practical references for optimizing big unit basketball teaching. Methods: A mixed-methods approach was adopted, including literature review, expert interview, mathematical statistics and experimental method. Sixty homogeneous Grade 11 basketball elective students from the High School Affiliated to Beijing Jiaotong University were randomly divided into an experimental group (varied practice) and a control group (traditional practice). Pre- and post-intervention assessments (basic skills, game performance, cognitive decision-making) and SPSS 27.0 statistical analysis were conducted over 15 weeks. Results: Significant between-group differences were observed in simple/medium-difficult decision reaction time (P<0.05), and extremely significant differences in game passing scores and difficult decision scores (P<0.01). The experimental group showed significant improvements in game decision-making and skill application, while the control group only improved in basic technical tests. Conclusions: The varied practice method effectively enhances students’ basketball skill application and cognitive decision-making abilities, alleviates drawbacks of existing big unit teaching, provides a theoretical framework for PE teachers’ module practice design, and its multi-dimensional evaluation system is worthy of promotion in open motor skill teaching.
Background: Impaired motor function and balance deficits place People with Stroke (PwS) at increased risk of falls, which may lead to injury and reduced physical activity. Perturbation-based balance t...
Background: Impaired motor function and balance deficits place People with Stroke (PwS) at increased risk of falls, which may lead to injury and reduced physical activity. Perturbation-based balance training (PBT), which exposes individuals to repeated balance disturbances, has demonstrated strong potential for fall prevention but is typically limited to specialized rehabilitation clinics. Extending this approach to home environments could improve accessibility and long-term adherence. Action Observation combined with Motor Simulation (AOMS) has been shown to enhance reactive stepping responses and may provide a safe and feasible pathway to translate key element of PBT to the home environment. When integrated with exergames, which combine exercise with gaming elements, it can further enhance motivation and engagement in neurological rehabilitation. Objective: To design a home-based exergame that integrates AOMS principles to train reactive stepping responses and support perturbation-based balance training for PwS. In addition, to evaluate the user experience in terms of game experience, usability and safety perception. Methods: Using a user-centered design methodology, we iteratively developed the HEROES exergame to support home based rehabilitation by promoting practice of reactive stepping responses. The exergame combines physical exercise with cognitive tasks in a gamified virtual environment where the player needs to imagine and mimic the stepping responses of an avatar in response to virtual perturbations. The prototype was evaluated by PwS (n=8) with a single play-test session conducted in a simulated home environment. Game experience was assessed by the Game Experience Questionnaire (GEQ), usability with the eHealth Usability Benchmarking Instrument (HUBBI) and safety perception by a custom questionnaire, all using 5-point Likert scales. In addition, observations and comments from participants and physiotherapists were collected using a think aloud approach to gain deeper insights. Results: Eight PwS participated in the play-test. GEQ scores showed high median ratings for Competence (4.2), Flow (3.9), and Positive Affect (4.7), Tension (1.0), Negative Affect (1.6), and Challenge (2.3) were low. HUBBI results revealed strong Task–Technology Fit (4.25), Interface Design (4.25), and Navigation (4.0), but lower Basic System Performance (2.12), indicating technical issues, such as error in the tracking recognition, that could hinder usability. Safety perception ratings were high: Physical Safety medians were 5.0 (PwS) and 5.0 (physiotherapist), Trust medians were 4.37 and 4.8, while Psychological Safety (the feeling of falling while playing the game) was lower (3.6 for both groups). Conclusions: The HEROES exergame demonstrates that integrating Action Observation and Motor Simulation principles into a home-based perturbation-based balance training approach is feasible. PwS perceived the system as engaging, usable, and physically safe, though improvements in technical robustness and task challenge may be required to sustain long-term engagement. Future work should explore longer-term use and clinical effectiveness to determine whether it can improve reactive stepping performance and reducing falls.
Background: Non-pharmacological interventions play a critical role in dementia care, yet maintaining engagement and adherence remains challenging. Immersive virtual reality (VR) provides a promising p...
Background: Non-pharmacological interventions play a critical role in dementia care, yet maintaining engagement and adherence remains challenging. Immersive virtual reality (VR) provides a promising platform for cognitive training, particularly when integrated with personally meaningful leisure activities that may enhance therapeutic resonance. Objective: This study aimed to examine the feasibility, usability, and preliminary cognitive effects of a leisure-based immersive VR cognitive training program in older adults with dementia. Methods: A single-arm pre–post pilot study was conducted with 16 older adults with mild to moderate dementia recruited from a hospital and a dementia day care center. Participants completed a 15-session VR intervention over 5 weeks, involving gardening-based activities targeting multiple cognitive domains. Feasibility outcomes included VR acceptance (QAVREE), simulator sickness (SSQ), usability (SUS), and emotional responses (OERS). Cognitive, functional, and psychosocial outcomes were assessed using the Montreal Cognitive Assessment (MoCA), Stroop test, Color Trails Test (CTT), Frontal Assessment Battery (FAB), Barthel Index, Instrumental Activities of Daily Living (IADL), Cornell Scale for Depression in Dementia (CSDD), measures of Behavioral and Psychological Symptoms of Dementia (BPSD), Dementia Quality of Life (DQoL), and the Short Warwick–Edinburgh Mental Well-being Scale (SWEMWBS). Paired t-tests were used to examine pre–post differences. Results: Sixteen participants completed feasibility assessments, and fifteen completed the intervention. VR acceptance significantly increased after the first session (p < .001), while simulator sickness significantly decreased (p = .009). Emotional responses indicated high levels of pleasure and alertness with minimal negative affect. Usability was rated as acceptable (SUS = 70.5 ± 12.17). A significant improvement in global cognitive function was observed (MoCA, p = .001, d = 1.03); however, no significant changes were found in domain-specific cognitive measures, activities of daily living, quality of life, or psychosocial outcomes. Conclusions: Leisure-based immersive VR cognitive training appears to be feasible, acceptable, and well tolerated in older adults with dementia. These preliminary findings suggest that aligning technology with meaningful life experiences may enhance global cognitive function and engagement. Further randomized controlled trials with larger samples are warranted to confirm the long-term effectiveness and functional impact of this approach. Clinical Trial: ClinicalTrials.gov Identifier NCT07033468. (08/01/2024)
Background: Maintaining the benefits of pulmonary rehabilitation over time is challenging, and digital follow-up interventions often fail to sustain engagement in everyday use. For nursing-led pulmona...
Background: Maintaining the benefits of pulmonary rehabilitation over time is challenging, and digital follow-up interventions often fail to sustain engagement in everyday use. For nursing-led pulmonary telerehabilitation, mechanism-based design requirements remain insufficiently specified. Objective: This study aimed to characterize the behavioral and implementation mechanisms identified by adults with chronic obstructive pulmonary disease and rehabilitation nurse specialists to sustain engagement with pulmonary telerehabilitation maintenance after center-based pulmonary rehabilitation and to translate these mechanisms into design principles for a minimum viable digital intervention. Methods: A descriptive-exploratory qualitative study was conducted using online focus groups. Four focus groups were held with rehabilitation nurse specialists (n=8) and adults with chronic obstructive pulmonary disease who had completed pulmonary rehabilitation within the previous 12 months (n=8). Sessions were audio-recorded and transcribed verbatim. Data were analysed using directed content analysis based on a priori codebook; two researchers coded the transcripts independently and resolved discrepancies through discussion. Results: Across 121 coded excerpts and 455 code applications, participants converged on a self-regulatory behavioural loop linking structured exercise prescription and content delivery, reminders, task and symptom logging, performance feedback and reinforcement. Professionals additionally emphasised implementation mechanisms required for safe clinical integration, including personalised pathways, simplified navigation, safety alerts and adaptive recommendations. Conclusions: Sustained engagement in pulmonary telerehabilitation maintenance appears to depend on operationalizing a behavioral regulation loop supported by a clinical integration layer rather than on isolated app features. The findings provide nurse-relevant design principles for a minimum viable telerehabilitation intervention to support postrehabilitation maintenance in chronic obstructive pulmonary disease.
Background: Intraoperative hypotension (IOH) is associated with myocardial injury, acute kidney injury, perioperative stroke, and 30-day mortality, yet conventional blood pressure monitoring remains r...
Background: Intraoperative hypotension (IOH) is associated with myocardial injury, acute kidney injury, perioperative stroke, and 30-day mortality, yet conventional blood pressure monitoring remains reactive rather than anticipatory. Deep learning (DL) algorithms applied to continuous physiological waveforms represent a rapidly expanding paradigm for early IOH prediction, but the comparative performance of distinct DL architectures and the influence of prediction-window length, input data modality, IOH reference standard, and analysis unit on diagnostic accuracy have not been systematically synthesised. Objective: To quantify the pooled diagnostic accuracy of DL-based IOH prediction models and to identify methodological and clinical factors that modify their performance. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched through March 2026. Methodological quality was appraised with the PROBAST+AI tool and overall certainty of evidence with the GRADE framework. A bivariate random-effects model generated pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve, with heterogeneity quantified by τ²(Se), τ²(Sp), and the inter-study correlation ρ. Threshold effect was tested with Spearman’s correlation, publication bias with Deeks’ test, and clinical utility with Fagan’s nomogram. Prespecified subgroup analyses (prediction window, DL architecture, input modality, IOH reference standard, analysis unit) and Bayesian random-effects meta-regression explored heterogeneity sources. Results: Twelve studies were included; nine contributed 22 validation datasets to the quantitative synthesis. The pooled sensitivity was 0.78 (95% CI 0.73–0.81), specificity 0.88 (0.82–0.92), and SROC-AUC 0.87 (0.83–0.90); the diagnostic odds ratio was 24.7 (16.1–37.9), positive likelihood ratio 6.31, and negative likelihood ratio 0.26. Heterogeneity was τ²(Se) = 0.25, τ²(Sp) = 1.04, and ρ = −0.28; no significant threshold effect was detected (Spearman ρ = 0.29, P = 0.20). The 5-minute window achieved the highest performance (sensitivity 0.81, 95% CI 0.77–0.85; specificity 0.91, 0.84–0.95). Meta-regression identified DL architecture as the only significant moderator of specificity (P = 0.02), with hybrid CNN-RNN exceeding pure CNN (β = 1.77, 95% CI 0.45–3.09); no covariate significantly moderated sensitivity. Deeks’ test showed no statistically significant publication bias (P = 0.06). At a 10% pre-test probability, post-test probabilities were 41% (positive) and 3% (negative). GRADE certainty was Low. Conclusions: Deep learning models for IOH prediction achieve moderate diagnostic accuracy, with hybrid CNN-RNN architectures and 5-minute prediction windows showing the most favourable performance. The universal absence of formal calibration assessment, scarce external validation, and geographic concentration of the evidence base constrain immediate clinical translation. Prospective multinational validation with mandatory calibration reporting and patient-level evaluation is required before DL-based IOH alerts can be safely integrated into perioperative decision support. Clinical Trial: PROSPERO CRD420261377604.
Background: Polypharmacy is highly prevalent among older adults and is associated with adverse drug events, functional decline, and increased healthcare utilisation. Digital medicines optimisation int...
Background: Polypharmacy is highly prevalent among older adults and is associated with adverse drug events, functional decline, and increased healthcare utilisation. Digital medicines optimisation interventions including clinical decision support systems (CDSS), EHR-integrated tools, and emerging AI-enabled systems have been developed to support medication optimisation, yet their real-world effectiveness, adoption, and sustainability remain highly variable across healthcare settings. Objective: This review aimed to develop and refine explanatory programme theories that clarify how, why, and under what circumstances digital medicines optimisation interventions succeed or fail in optimising medication use among older adults with polypharmacy. Methods: A realist synthesis was conducted in accordance with RAMESES guidelines. Comprehensive searches of MEDLINE, Embase, CINAHL, PsycINFO, Scopus, Web of Science, and the Cochrane Library identified empirical and grey literature on digital medicines optimisation interventions. Databases were searched from inception to December 2025 to capture conceptually rich studies informing theory development across the evolution of digital decision support systems. Studies were included based on relevance and rigour for theory building rather than design hierarchy. CMO configurations were extracted and synthesised using abductive and retroductive reasoning, and findings were mapped onto a multi-level GEAR-up conceptual framework. Results: Twenty-three studies were included, spanning primary care, hospital, community, and emergency settings. Seven programme theories were identified, explaining how workflow-aligned integration, alert relevance, human mediation, patient-centred alignment, organisational readiness, limits of automation, and transparency influence adoption, fidelity, sustainability, and conditional use of digital medicines optimisation tools. Most mechanisms driving uptake such as reduced cognitive burden, trust, and professional legitimacy operated at individual and care-team levels, while organisational and system-level contexts determined sustainability and scale-up. Conclusions: Digital medicines optimisation interventions are effective when aligned with clinical workflows, supported by interprofessional mediation, and reinforced by organisational and ethical governance structures. This realist synthesis provides theory-informed guidance to support the design, implementation, and scale-up of digital medicines optimisation strategies to enhance medication optimisation and patient safety in older adults with polypharmacy
Background: Providing care to a family member or friend with a serious illness like cancer increases risk for poor physical, psychological, and functional health outcomes. Despite their critical role,...
Background: Providing care to a family member or friend with a serious illness like cancer increases risk for poor physical, psychological, and functional health outcomes. Despite their critical role, family caregivers (FCGs) are rarely screened in clinical settings for the wide range of factors that may put them and the person they care for at risk for poor outcomes. Mobile health (mHealth) applications can efficiently facilitate access to high-quality health information for FCGs; however, few are clinically integrated. Objective: This study aimed to evaluate the usability of CareCheck, an mHealth-based digital risk screening tool designed to enable family caregivers' self-awareness of potential caregiving-related risks for adverse health and psychosocial outcomes and to support health care professionals in personalizing interventions that address FCGs' specific risk factors. Methods: We conducted a usability testing study of CareCheck using two evaluation methods: quantitative measurement with a modified 5-item Mobile Health App Usability Questionnaire (MAUQ) and exploratory qualitative thematic analysis based on feedback from FCGs and trained staff. FCGs of individuals with gynecologic cancer were recruited through the inpatient unit and the outpatient gynecologic oncology clinic of a Comprehensive Cancer Center. Participants completed CareCheck and the usability questionnaire via the mHealth app installed on tablets. Staff observed the assessment process and provided feedback. Results: A total of 56 CGs and 2 trained staff participated in the usability study. The mean MAUQ score was 6.49 (SD = 1.06) out of 7, indicating high usability. Qualitative analysis identified recommendations in three categories: 1) Improvements to CareCheck ; 2) Perceptions of CareCheck’s Usability and Functionality, and 3) Clinical Implementation Considerations for CareCheck. Conclusions: FCGs and staff found CareCheck to be user-friendly and easy to navigate. While further iterations are needed to refine content and optimize integration with clinical workflows, CareCheck demonstrated potential as a clinically integrated tool for identifying and addressing FCG risk for poor social, psychological, or health outcomes in gynecologic oncology care settings.
Background: Background: Hypertension remains a predominant global risk factor for cardiovascular disease. Conventional follow-up models frequently fail to address the requirements for real-time monito...
Background: Background: Hypertension remains a predominant global risk factor for cardiovascular disease. Conventional follow-up models frequently fail to address the requirements for real-time monitoring and sustained intervention, whereas mobile health (mHealth) offers a transformative trajectory for chronic disease management. Despite a surge in relevant literature, the diversity of intervention modalities and the fragmented nature of existing evidence necessitate a systematic synthesis. Objective: Objective: This study aimed to comprehensively evaluate the efficacy of mHealth in hypertension management through a systematic review combined with evidence mapping, identifying research gaps to provide evidence-based insights for precision nursing and future research directions. Methods: Methods: A systematic search was conducted across PubMed, Web of Science, Cochrane Library, and Embase for randomized controlled trials (RCTs) involving mHealth interventions for hypertension, with the search period extending through February 2026. Literature was screened according to PICOS criteria, and methodological quality was appraised using the Cochrane Risk of Bias tool (RoB 1.0). Visual analytics, including Sankey diagrams and bubble plots, were employed to characterize the associations between intervention modalities and clinical outcomes. The study protocol was prospectively registered on the Open Science Framework (URL: https://osf.io/2vkwu). Results: Results: A total of 106 publications (comprising 108 RCTs) were included. Publication volume has increased significantly since 2018, with the United States (31 papers) and China (19 papers) being the primary contributors. The intervention paradigm has evolved from rudimentary SMS reminders to a "closed-loop" management model centered on "App + Remote Monitoring," which demonstrates the most robust and consistent positive evidence for blood pressure (SBP/DBP) control and goal attainment rates. Blood pressure parameters occupied the "core evidence layer," while therapeutic adherence and disease knowledge formed the "behavioral evidence layer". Conversely, BMI, mental health, and quality of life remained in the "peripheral evidence layer," characterized by a notably higher proportion of non-significant results. Methodological quality was generally moderate-to-high with robust randomization; however, the implementation of blinding faced prevalent high risks due to the inherent nature of the interventions. Conclusions: Conclusion: mHealth significantly enhances hypertension management efficacy through a digital "monitoring-feedback-adjustment" loop, yet it encounters bottlenecks in achieving profound lifestyle modifications (e.g., weight management) and psychological interventions. Clinical decision-making should prioritize multicomponent interventions featuring real-time interaction. Future research should focus on long-term (>1 year) follow-up and cost-effectiveness transformation in resource-limited settings.
Background: Caregivers play an important role in the management of type 1 diabetes (T1DM) for children and adolescents. Controlling blood glucose levels is an essential part of diabetes management.
...
Background: Caregivers play an important role in the management of type 1 diabetes (T1DM) for children and adolescents. Controlling blood glucose levels is an essential part of diabetes management.
Previous studies have examined access to glucose monitoring devices and health system constraints in low- and middle-income countries and found that in many low- and middle-income countries (LMIC) tools for measuring blood glucose are unavailable and unaffordable. However, very little is known on the perception of caregivers in LMICs with regards to the important element of Self-Management of Blood Glucose (SMBG) and the emerging technologies and their suitability for these contexts. Objective: The aim of this article will be to explore caregivers’ perceptions on how SMBG impacts to everyday life. For the purpose of this study the term caregiver is used referring to either a parent or an adult responsible for assisting with the management T1D with the child/adolescent Methods: This qualitative descriptive study involved 24 interviews with a caregiver (either a parent or adults responsible for assisting with the management T1D with the child/adolescent) and a child or an adolescent living with T1DM identified through a mix of purposive and snowball sampling. Interviews were carried out based on common themes from a couple interview guide. This study was part of a larger study conducted in Kyrgyzstan, Mali, Peru and Tanzania. Interviews were analyzed using thematic analysis. Results: The 24 couple interviews included 18 caregivers of minors living with T1D and 6 adolescents living with T1D. Across settings, caregivers described substantial psychological burden linked to repeated finger pricks, financial strain, and challenges managing diabetes during school and nighttime periods. The tools used raised issues for Blood Glucose Meters (BGM) of finger pricks, follow-up and affordability. Whereas for Continuous Glucose Monitors (CGM) issues of acceptability, accessibility and affordability of these devices was raised by the interviewees. Interviewees also highlighted different disruptions to daily life due to T1DM such as nutrition, school, and the need to access to medical care. Conclusions: Caregivers’ perspectives on blood glucose monitoring highlighted the psychological and financial burden that weighs on them. Technological solutions, such as CGM, make it possible to alleviate certain obstacles of using a BGM and strips, but given their high cost can notably increase the financial burden. Clinical Trial: N/A
Background: Long-term care (LTC) pharmacies deliver medications to skilled nursing, assisted living, and rehabilitation facilities, yet their courier logistics infrastructure remains largely paper-bas...
Background: Long-term care (LTC) pharmacies deliver medications to skilled nursing, assisted living, and rehabilitation facilities, yet their courier logistics infrastructure remains largely paper-based, manually tracked, and fragmented across multiple vendors. Existing workflows lack real-time delivery visibility, digital proof-of-delivery documentation, and structured compliance recordkeeping, creating operational inefficiencies and regulatory exposure under HIPAA, DEA, CMS, and 21 CFR Part 11 requirements. Objective: To describe the design, implementation, and operational outcomes of a centralized Pharmacy Courier Integration Layer that connects LTC pharmacy management systems to multiple courier providers, enabling real-time delivery tracking, standardized milestone event processing, digital proof-of-delivery capture, and multi-agency regulatory compliance. Methods: A centralized integration layer was developed using REST API and event-driven architecture. Six standardized delivery lifecycle milestone events were defined (Pharmacy Arrived, Tote Release, Tote Release Completed, Pharmacy Completed, Delivery Arrived, Delivered) to provide vendor-agnostic tracking across all integrated couriers. A digital proof-of-delivery workflow was implemented capturing recipient ID verification, GPS-timestamped electronic signatures, and four-stream compliance routing to HIPAA, DEA, 21 CFR Part 11, and CMS repositories. A forecast-based early routing mechanism transmitted pre-shipment delivery signals to couriers before package preparation. An integrated courier marketplace interface enabled pharmacy staff to compare courier fees, estimated delivery times, and service types at point of dispatch. The platform was evaluated through systematic observational comparison of pre- and post-implementation workflows across approximately 500 LTC pharmacy settings over 6–12 months. Results: Platform deployment resulted in a 30–50% reduction in administrative effort per delivery, 15–25% improvement in routing efficiency, and 60–70% reduction in audit retrieval time. Paper-based delivery tracking was fully eliminated in favor of event-driven status updates. Digital proof-of-delivery replaced paper workflows and ensured complete chain-of-custody traceability for controlled and uncontrolled substances. The integrated courier marketplace interface allowed pharmacy staff to compare courier options by cost, service type, and estimated delivery time directly within their primary workflow, removing reliance on separate vendor portals. Conclusions: A standards-based digital logistics platform significantly reduces administrative burden, improves regulatory compliance readiness, and increases delivery visibility in LTC pharmacy settings. The architecture is additive, operating between existing systems without displacing current pharmacy management workflows. The platform provides a framework for future extensions including predictive routing, AI-assisted anomaly detection for undelivered prescriptions, and bidirectional EHR integration. For LTC patients who depend entirely on institutional pharmacy delivery, improvements to logistics infrastructure have direct implications for medication safety and continuity of care. Clinical Trial: Not applicable. This study is a quality improvement and operational evaluation and does not involve a clinical trial.
Background: Narcotic drugs require strict lifecycle management; however, traditional manual systems suffer from significant issues such as recording errors, gaps in traceability, and delays in the rec...
Background: Narcotic drugs require strict lifecycle management; however, traditional manual systems suffer from significant issues such as recording errors, gaps in traceability, and delays in the recovery of empty ampoules. Objective: This study aimed to develop and validate a three-dimensional closed-loop framework for controlled substance safety in a HIMSS EMRAM Stage 7 hospital, to address the deficiencies of traditional manual management (e.g., recording errors, traceability gaps, delayed empty ampoule recovery) and improve the quality and efficiency of anesthetic drug lifecycle management. Methods: A single-center, pre-post study was conducted at a HIMSS EMRAM Stage 7 hospital. The pre-intervention period (September–November 2024) utilized traditional manual management; the post-intervention period (December 2024–February 2025) implemented the three-dimensional framework.Key reproducible parameters of the framework were prospectively recorded (barcode encoding rules, prescription review rules, interface logic, and exception handling procedures). Outcome measures included prescription compliance rate, batch number management non-compliance rate, and empty ampoule non-recovery rate. Categorical variables were compared using the chi square test; significance was set at P < 0.05. Results: A total of 3,264 prescriptions were included before the intervention and 3,311 prescriptions after the intervention. The prescription compliance rate increased from 94.83% to 98.71% (χ² = 95.79, P < 0.001); the batch number management non-compliance rate decreased from 2.4% to 0.6% (χ² = 10.96, P = 0.001);the empty ampoule non-recovery rate decreased from 5.6% to 0.79%, a relative reduction of 85.89% (χ² = 47.5, P < 0.001). The framework demonstrated high implementation fidelity (system uptime of 99.92%, and scan compliance >99.6%). The end-to-end batch number traceability mechanism within the framework was directly associated with the decrease in the unrecovered rate. Conclusions: The three-dimensional closed-loop framework proposed in this study enables refined control of anesthetic drugs, with end-to-end batch number traceability serving as the key mechanism driving the significant improvement in empty ampoule recovery rates (a relative decrease of 85.89%). This framework and the published implementation parametersis generalizable and replicable across hospitals with different levels of informatics infrastructure.
Background: The importance of preserving and sharing medical knowledge in the shape of evidence-based medical guidelines is well recognized in the medical community. However, their use to improve pati...
Background: The importance of preserving and sharing medical knowledge in the shape of evidence-based medical guidelines is well recognized in the medical community. However, their use to improve patient care remains a challenge for physicians, especially during patient consultations. For epilepsy, there are several guidelines that epileptologists can use to diagnose and treat epileptic patients, but it is estimated that up to 20% of the patients are still misdiagnosed. This can have severe consequences for patients, which could have been avoided with the right diagnosis and therefore the right treatment, since around 70% of epilepsy patients who are treated can live without unprovoked seizures. Objective: To present the strategy to create a computer interpretable guideline (CIG) from the guidelines of the International League Against Epilepsy (ILAE) and Deutsche Gesellschaft für Neurologie e.V. (DGN). This CIG will be used in the clinical decision support system (CDSS) for diagnosis and therapy recommendation for patients with epilepsy to improve the guideline adherence in the clinical context, the EDiTh project. As this application is considered an investigation device under the European Medical Device Regulation (MDR 2017/745), we also want to demonstrate our modular architecture approach and implementation process to follow the regulatory framework. Methods: An interdisciplinary team created a model from the clinical guidelines. This model consists of three consecutive chained models, where the first one is the transformation of the ILAE seizure classification into a decision tree, the second is a decision table of proposed diagnosis of the DGN guidelines, and the third is a decision table of recommended drug treatments. Then, the software was developed under the regulatory requirements of the MDR and in particular the DIN EN 62304 as an investigation device, dividing the software architecture into different modules depending on the medical risk classification. Results: The CDSS was created using the CIG that the experts elaborated, and we transformed into a tabular structure that can be used with simple parsing directly in the software. The application can be accessed from any computer in the hospital facility through a web browser, and the results can be saved in a PDF file as a copy of the system information. The EDiTh-App is currently being evaluated in a clinical investigation. Conclusions: The creation of a CDSS as medical device software (MDSW) under the MDR can be achieved by modularizing the software architecture into units of different medical risk classification and minimizing the use of unnecessary third-party libraries. Dividing the CIG into several models makes it easier for medical professionals to understand. This can help in their creation and evaluation and speeds up the preparation of a clinical investigation device, thereby improving the patient’s diagnosis and treatment.
Background: Rheumatoid Arthritis (RA) affects over 18 million people worldwide, commonly leading to flares, debilitating symptoms, reduced function, and increased cardiovascular disease risk. Regular ...
Background: Rheumatoid Arthritis (RA) affects over 18 million people worldwide, commonly leading to flares, debilitating symptoms, reduced function, and increased cardiovascular disease risk. Regular physical activity is internationally recommended to address these challenges. However, people with RA face barriers to physical activity and lack tailored physical activity support. Digital physical activity interventions offer a potential resource-efficient strategy for increasing physical activity in RA.
To improve the effectiveness and implementation of complex interventions, the UK Medical Research Council emphasizes the need for stronger end-user engagement and behavioral science integration. Together with physical activity experts, rheumatology providers, and people with RA, we completed an environmental scan, umbrella review, and priority setting exercise to inform the co-development of an RA-specific digital physical activity intervention: JustOneMove.ca. Objective: To describe the co-design process, present the Just One Move intervention, and summarize mixed methods findings from our evaluation survey. Methods: The study was informed by principles of patient-oriented research, user-centered design, and behavioral science (BCW: behavior change wheel, TDF: theoretical domains framework). After mapping physical activity barriers to the BCW and TDF, we completed a 4-step user-centered process to co-design Just One Move (Phase 1). We collaborated with people with RA and web design experts to review, design, refine, and develop the intervention using design sprint methodology. Data on resources required and partner involvement during Phase 1 were summarized descriptively.
A mixed methods evaluation survey (Phase 2) gathered perspectives on usability and usefulness. The evaluation survey included demographics, physical activity self-report, the mobile app rating scale (MARS), and open-ended questions about Just One Move. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using content analysis. Results: Co-design was completed in 7 months, with 30 meetings averaging 60 minutes each. The co-design team included 20 people with RA, 5 research team members, including a rheumatologist, physiotherapist, and physical activity behavior change expert, and 2 external collaborators specializing in healthcare design and web development. Co-design blended research evidence and user preferences through design thinking. Challenges included balancing creativity with feasibility and defining clear expectations.
The Just One Move intervention features 4 elements: a library of RA-friendly movements, a personalized action planner, a collection of inspiring stories from people with RA, and an expert toolkit with practical tips for moving well with RA. Across 37 survey respondents with a median age of 62.0, average intervention quality was 4.0/5 and perceived impact was 3.7/5. Just One Move was easy to use and inspired confidence to move more. However, users wanted clearer instructions and more customizable content to improve the platform. Conclusions: Evidence-informed co-design can produce high-quality, impactful digital physical activity interventions for people with RA. Future work will examine the effectiveness of JustOneMove.ca to increase physical activity.
Background: Preschool children suffering from oral diseases have high levels of visible plaque is found on tooth surfaces signify the importance of oral hygiene maintenance for good oral health. Lite...
Background: Preschool children suffering from oral diseases have high levels of visible plaque is found on tooth surfaces signify the importance of oral hygiene maintenance for good oral health. Literature indicates that preschool children, specifically those aged 3 to 6 years, lack the fine motor skills and cognitive coordination required to perform effective plaque removal independently. Supervised Toothbrushing (STB), have demonstrated superior efficacy in reducing plaque scores and improving health. Older children will perform the task independently, But still they need assistance whereas younger children may need more support and supervision during brushing. Every month, children will participate in supervised toothbrushing programs where they brush their teeth at school using fluoridated toothpaste while being watched over by staff and peers. Despite the availability of various school health initiatives, the implementation of supervised brushing programmes in preschool settings is often limited. This paper presents the study protocol for newly introduced BrushYen Supervised Tooth Brushing Programme aims to promote ideal brushing technique, enhance children's motivation and establish consistent oral hygiene habits. However, there is limited evidence regarding its effectiveness among preschool children. Objective: To evaluate the reduction in plaque scores among preschool children at 1, 3, and 6 months following the implementation of the "Brushyen" program. And to assess the improvement in gingival health status at the same intervals. Another objective of the study is to determine the improvement in toothbrushing skills (dexterity) and parental awareness regarding oral hygiene. Methods: This study is a Quasi-experimental study. A total of 120 preschoolers studying in montessari of The Yenepoya school. The Monthly supervised toothbrushing sessions will be conducted post-lunch, where the BrushYen Champions will assist the teachers in monitoring the preschoolers. Clinical parameters will be recorded using the identical set of indices Visible Plaque Index, Modified Gingival Index, and Modified OHI-S to allow for a direct statistical comparison against baseline values, Participants will be followed up at 1, 3, and 6 months. Results: Enrollment started in June 2026. It is estimated that the enrollment period will be 12 months. Data collection is planned to be completed in 2027. Conclusions: The BrushYen Supervised Tooth Brushing Programme aims to promote ideal brushing technique, enhance children's motivation and establish consistent oral hygiene habits. However, there is limited evidence regarding its effectiveness among preschool children Clinical Trial: Trial Acknowledgement Number is: CTRI/2026/03/107144
Background: Conventional oncology faces a significant challenge in achieving high selectivity, with standard chemotherapy selectivity indices (SI) often limited to 2–5×, resulting in severe off-tar...
Background: Conventional oncology faces a significant challenge in achieving high selectivity, with standard chemotherapy selectivity indices (SI) often limited to 2–5×, resulting in severe off-target toxicity. There is an urgent need for "smart" therapeutic platforms that can verify cellular identity before activating a cytotoxic payload. Objective: The objective of this study was to verify the mechanistic coherence and selectivity of a proposed 2-Deoxy-D-Glucose (2-DG)-conjugated nanobot framework. Specifically, the research aimed to demonstrate how a six-stage "AND-gate" logic—exploiting GLUT1/3 overexpression, intracellular lactate (Warburg effect), and catalase depletion—could control cancer growth while ensuring 100% healthy cell survival. Methods: An agent-based simulation was implemented using the PhysiCell 1.10.4 framework. The model simulated a 2 mm × 2 mm tumor microenvironment containing a heterogeneous population of 456 agents (253 cancer cells and 203 healthy cells). The simulation modeled the biochemical kinetics of the 2-DG-boronate-VitC conjugate, including GLUT-mediated absorption, hexokinase-triggered bond cleavage, and lactate-dependent identity verification. A 20% drug-resistant subpopulation with elevated catalase levels was included to reflect clinical reality. Results: In silico validation demonstrated a 73.5% reduction in the cancer cell population within 24 simulation hours. Critically, the model showed a 0% mortality rate for healthy cells across all timepoints, resulting in a theoretical selectivity index that is effectively infinite. The simulation confirmed that the "AND-gate" logic successfully prevented Vitamin C release in environments with low lactate (1–2 mM), while the intracellular catalase in healthy cells provided a secondary biological safety barrier against peroxide generation. Conclusions: This computational framework provides a validated roadmap for a novel class of programmable therapeutics. By applying rigorous Quality Assurance (QA) principles to biological modeling, the study demonstrates that selective cancer elimination is achievable by combining metabolic sensing with autonomous agent logic. The results justify moving to Phase 2: molecular synthesis and in vitro kinetic testing.
Background: The COVID-19 pandemic disrupted conventional educational practices, prompting the need for innovative teaching methods. Universities and educators turned to technologies such as virtual si...
Background: The COVID-19 pandemic disrupted conventional educational practices, prompting the need for innovative teaching methods. Universities and educators turned to technologies such as virtual simulation (VS) programmes, to promote distance healthcare simulation. This interactive and engaging approach to medical education is generally “well received” by students, and often preferred to traditional lectures or textbook-based learning, yet its educational value in healthcare still has to be demonstrated. Objective: This study evaluated the impact of a VS programme on medical students' education, knowledge and performance in managing critical clinical cases. Methods: To substitute in-person simulation workshops in critical care, we implemented a three-week distance educational programme using an online VS software. This programme spanned three weeks, featuring 25 simulated scenarios based on 10 clinical situations for medical students. We evaluated the impact of the VS on students’ simulated clinical performance, theoretical knowledge, and self-efficacy. We also assessed the users’ experiences. Results: Among the 142 enrolled students, 125 were included in the analysis. They performed 4006 runs over a 3 week periods. Each student ran a median of 18 [12-25] scenarios during the training period and 11 [9-13] during the examination week. VS performance scores improved with training duration. VS significantly enhanced students’ theoretical knowledge and self-efficacy scores, with sustained effects over time. Median MCQs score significantly increased from 17 to 29 (p<0.001), and remained significantly higher at one month. Sum of median SES scores significantly improved from 525 to 670 (p<0.001), remaining significantly higher at one month. Students reported enhanced ability to analyse paraclinical exams and high satisfaction with the VS software. Conclusions: A three-week VS programme effectively enhanced students' knowledge, self-efficacy, and performance on the VS software. VS represents a promising, immersive, and flexible educational tool, proving beneficial even in the absence of traditional training opportunities. Further research should explore its impact on clinical reasoning, actual patient care, and optimal integration into pre-and postgraduate curricula.
Background: Depression and anxiety among youth have increased significantly, highlighting the urgent need to improve their mental health literacy. Gaming platforms, such as Twitch with nearly 100,000 ...
Background: Depression and anxiety among youth have increased significantly, highlighting the urgent need to improve their mental health literacy. Gaming platforms, such as Twitch with nearly 100,000 weekly Minecraft streamers, offer significant potential as innovative venues for mental health promotion. Objective: To co-design, implement, and evaluate a Minecraft streaming-based intervention promoting mental health literacy among adolescents and young adults aged 15-25 years. Methods: Through participatory action research, we conducted ten virtual workshops (October 2023-June 2024) with four streamers and a clinical psychologist to co-develop "Walk Your Therapist"—live streams combining Minecraft gameplay with mental health education. We evaluated the intervention using a quasi-experimental design with the Mental Health Literacy Questionnaire (MHLq), observations, chat analysis, and interviews. Results: Three sessions reached 613 unique viewers. Of these, 97 participants completed baseline assessments (mean age 19±4 years, 86.6% male, 9.3% NEET) and 43 completed follow-up. Participants showed significant improvements in MHLq scores (94±19 to 104±7, p<.001, d=0.70), with gains in knowledge of mental health problems (38.2±9.3 to 44.2±4.9, p<.001), first aid skills (20.9±6.2 to 23.0±4.1, p=.04), and self-help strategies (16.1±3.2 to 17.2±2.2, p=.03). Participants spontaneously discussed gaming addiction and stigma. Streamers reported increased personal mental health awareness and sustained positive community engagement. Conclusions: This co-designed gaming intervention demonstrates promising potential for youth mental health promotion through existing streaming communities. Clinical Trial: ClinicalTrials.gov NCT06473857
Background: Indian National Family Health Survey Data from 2021 suggests that 9.4% of women of reproductive age had an unmet need for spacing or limiting future pregnancies (1). Randomised controlled ...
Background: Indian National Family Health Survey Data from 2021 suggests that 9.4% of women of reproductive age had an unmet need for spacing or limiting future pregnancies (1). Randomised controlled trials evaluating mHealth interventions (programmes delivered via mobile phones) provide evidence of their potential benefit to addressing the determinants of misconceptions and lack of knowledge regarding postpartum family planning (PPFP) methods. The mMitra programme has been implemented since 2014 and uses automated voice-messaging to deliver pregnancy and postpartum care information to socioeconomically vulnerable women in the Mumbai metropolitan area. However, the mMitra programme has not been adequately evaluated to understand its impact on postpartum family planning knowledge and outcomes (2, 3). Objective: To assess whether, how, why, for whom, and in what contexts the mMitra programme affects postpartum family planning knowledge by analysing cohort follow-up survey data, call duration and attendance to measure the impact of listenership, and by refining context–mechanism–outcome configurations through testing effect modification in logistic regression and structural equation models. Methods: This study presents the quantitative findings from the realist evaluation of the mMitra programme. Moderator analysis (through testing for effect modification in a logistic regression model) and structural equation models (SEMs) were used to test context-mechanism-outcome configurations (CMOCs) from an accompanying realist review. Results: After adjusting for religion as a potential confounder and including phone access as an effect modifier in the logistic regression model, mMitra call duration, a proxy measure for programme engagement, did not have an effect on knowledge of one or more highly effect contraceptive (HEC) methods (OR=1.012, p=0.02, 95%CI 1.002-1.021). SEMs show some evidence of an association between shared phone ownership and mMitra programme engagement, potentially via the mechanism of women’s ability to negotiate phone access. Conclusions: This study adds to the growing body of evidence on the usefulness of SEM to test CMOCs in realist evaluations. Issues with data collection and management and the absence of survey data from locations outside of Mumbai limit the generalisability of the conclusions from this study.
Background: Chemotherapy-induced nausea and vomiting (CINV) is one of the most common and distressing symptoms experienced by chemotherapy patients. The digital symptom management model has demonstrat...
Background: Chemotherapy-induced nausea and vomiting (CINV) is one of the most common and distressing symptoms experienced by chemotherapy patients. The digital symptom management model has demonstrated positive effects in high-income countries. However, its application remains in the exploratory phase in the western region of China. Objective: This study aims to explore the willingness of both cancer patients and healthcare providers on digital management of CINV in the western region of China. Methods: In this cross - sectional study, questionnaires were distributed to 271 cancer patients who had undergone chemotherapy and 116 healthcare providers from tertiary hospitals in Western China. The survey aimed to assess the willingness of both chemotherapy patients and healthcare providers on digital management of CINV. The survey evaluated the willingness of both chemotherapy patients and healthcare providers regarding digital management of CINV, including preferences for follow - up methods, challenges faced, and attitudes toward digital tools. Results: Nearly half of the patients(48%) reported that CINV was the most uncomfortable symptom they experienced during chemotherapy. Among discharged patients, 41% continued to experienced CINV, and 37.8% of them did not take any measures to manage their CINV. A majority (62.9%) of patients preferred weekly follow - up, with manual telephone calls and WeChat mini - programs being the most preferred modes of follow - up (85.6% and 59.1%, respectively). Both physicians and nurses agreed that follow - up monitoring and intervention during in-hospital and post - discharge periods were critical for CINV management. Time constraints were identified as a major challenge in managing CINV, with 50.7% of nurses willing to spend 15 to 30 minutes daily, and 66.7% of physicians willing to allocate 0 to 15 minutes daily for CINV management. Conclusions: Both patients and healthcare providers expressed strong support for improved management of CINV, and digital management tools may offer a promising solution to enhance the effectiveness of CINV managemen. However, overcoming time constraints and aligning digital solutions with clinical workflows are crucial for successful implementation. Clinical Trial: No. SCCHEC-02-2024-242
Background: Early detection of clinically relevant changes remains a major challenge in mental health, as deterioration typically unfolds gradually through subtle alterations in daily functioning befo...
Background: Early detection of clinically relevant changes remains a major challenge in mental health, as deterioration typically unfolds gradually through subtle alterations in daily functioning before becoming clinically manifest. Advances in digital phenotyping and smartphone-based monitoring offer new opportunities to capture these changes in real-world settings. Objective: This study aimed to develop and evaluate an algorithm capable of detecting behavioral changes associated with clinically relevant deterioration using passively collected smartphone data, and to examine whether instability in daily behavioral patterns can serve as an early marker of relapse or clinical transition. Methods: We conducted a prospective, observational, multicenter study including 92 participants with various mental health conditions. Passive behavioral data were continuously collected using a mobile application over periods ranging from 1 month to 1 year. Behavioral profiles were generated using heterogeneous mixture models, and changes in behavioral stability were detected using a Bayesian online change-point detection approach. Model performance was evaluated by comparing detected change points with clinically recorded relapse events, using receiver operating characteristic (ROC) analysis and sensitivity-specificity metrics. Results: A total of 40 relapse events with available passive data were included in the analysis. The best-performing model (ADSVWZ configuration) achieved a mean AUC of 82.99% (SD = 2.49). At a false positive rate (FPR) of 5%, the model reached an average true positive rate (TPR) of 40.12% (CI95%: 36.61–43.63), increasing to 61.10% (CI95%: 56.42–65.79) at 10% FPR. Model performance was strongly influenced by the temporal aggregation parameter, with optimal results obtained using minimal accumulation of past observations. Multimodal combinations integrating sleep, activity, and routine structure achieved the highest overall performance. Conclusions: Behavioral instability derived from passively collected smartphone data shows potential as a marker for the early detection of behavioral changes associated with clinically relevant deterioration. This approach enables continuous, individualized monitoring and may support more timely and personalized interventions in mental health care.
Background: Smartphone applications (apps) can support ophthalmic care by enabling patient-led, remote self-assessment, monitoring and management of visual function and eye conditions. However, real-...
Background: Smartphone applications (apps) can support ophthalmic care by enabling patient-led, remote self-assessment, monitoring and management of visual function and eye conditions. However, real-world use depends on their feasibility and acceptability among people with visual impairment and eye disease. Objective: This scoping review aimed to identify and map smartphone apps used by patients or caregivers to assess, monitor, or support the management of visual impairment or eye disease, and to summarise evidence regarding feasibility and acceptability. Methods: A structured search of the following databases, MEDLINE, EMBASE, CINAHL, AMED, PsycINFO, and PsycARTICLES, was conducted for studies published between March 2014 and May 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Studies were included if they reported findings on the feasibility or acceptability of an app for use by patients with visual impairment or eye disease, or by caregivers, to assess, monitor, or support self-management. Studies evaluating apps intended primarily for use by clinicians or third parties for screening (for example, in schools or outreach programmes) were excluded. Results: From 9,133 records, 21 eligible studies were included. Studies evaluated apps measuring visual acuity (VA) or related visual functions (n=6); risk assessment, early detection, or home monitoring of eye conditions (n=6); therapeutic management or patient support (n=6); and patient interest, uptake, and engagement in app use (n=3). Feasibility findings indicated that VA apps showed good agreement with clinical reference measures, although some performed worse in users with poorer baseline vision or under less controlled conditions. Other apps supported risk assessment, home monitoring, treatment adherence, patient education, telemedicine support, and parent- or caregiver-mediated management. Across categories, barriers to feasibility and acceptability included limited digital confidence, device incompatibility, variable access to smartphones, and user training or supervision needs for reliable use, with relatively limited evidence from routine unsupervised settings. Conclusions: Smartphone apps could support patient-led remote monitoring and management in ophthalmic care, but the evidence remains mixed across app types and use contexts. Future work should prioritise user-centred design, validation across a wider range of disease severity, and approaches that support users with lower digital confidence and less access to digital technology to avoid extending existing inequalities in access and use.
Background: Health care professional students represent as important target group for tobacco control interventions, as the behaviour established during their training years may influence both their f...
Background: Health care professional students represent as important target group for tobacco control interventions, as the behaviour established during their training years may influence both their future personal health practice and also their role in cessation counselling to patients. In recent years, mobile health (mHealth) has emerged as promising tool in tobacco cessation by providing accessible, personalized support, progress tracking and real time interventions. However, there is limited evidence on the specific design, personalized support. The Yenquit Application was developed based on 5As and 5Rs to address this gap by integrating user engagement feature aim to facilitate tobacco cessation. Objective: The objective of this study is to develop the YenQuit mobile/web-based application and aims to evaluate its features, effectiveness in promoting smoking cessation, and user engagement and experience among healthcare professional students. Methods: This study is a cross-sectional study. A total of 97 health care professional students that meet the inclusion criteria will have access to application, where they will fill the baseline questionnaire followed by Fagerström Test for Nicotine Dependence (FTND), Usage of application is monitored and end of 30 days of on boarding, the participants will fill the feedback on the effectiveness of the application. Results: : Enrollment started in March 2026. It is estimated that the enrollment period will be 12 months. Data collection is planned to be completed in 2027 Conclusions: The Yenquit mobile application is been developed as a user -friendly, evidence-based digital tool to support smoking cessation among health care professional students. By integrating behavioral counselling approach such as the 5As and 5Rs models, the application has the potential to enhance accessibility to cessation support in academic health settings. Further evaluation will provide insights into its effectiveness, user engagement, and acceptability as a mobile-based cessation intervention Clinical Trial: Trial Acknowledgement Number is: REF/2026/03/127126
Open Peer Review Period: Apr 22, 2026 - Jun 22, 2026
The integration of large language models and agentic artificial intelligence (AI) into pharmacovigilance (PV) workflows introduces adversarial vulnerabilities that are not well addressed by convention...
The integration of large language models and agentic artificial intelligence (AI) into pharmacovigilance (PV) workflows introduces adversarial vulnerabilities that are not well addressed by conventional IT security or standard GxP validation frameworks. Unlike infrastructure breaches, attacks on model analytical judgment can alter seriousness assessments, suppress safety signals, or distort aggregate reports while case records remain intact and operational metrics appear normal. This paper uses structured threat modelling and war-gaming to map nine adversarial attack classes onto PV intake, signal detection, and regulatory submission workflows: prompt injection, data poisoning, adversarial text and multimodal manipulation, supply-chain compromise, model extraction, context-window manipulation, model inversion and membership inference, jailbreaking, and denial of service. Scenarios are grounded in adversarial machine learning research and assessed for structural plausibility rather than presented as confirmed PV incidents. The paper argues that PV faces distinctive systemic amplifiers: mandatory Individual Case Safety Reports submission and cross-MAH data exchange may propagate manipulated content across organizations; GxP validation of version-locked systems can delay remediation; and agentic architectures can convert local model failures into executed actions across safety databases, follow-up communication, and submission workflows. A three-tier defense architecture is proposed, spanning procedural controls, adversarial testing integrated into validation, deterministic enforcement approaches for agentic workflows, and frontier measures such as AI red teaming and federated anomaly detection.
Background: Smartphone-based data collection and ecological momentary assessment (EMA) offer advantages over traditional retrospective questionnaires but require flexible technical solutions that many...
Background: Smartphone-based data collection and ecological momentary assessment (EMA) offer advantages over traditional retrospective questionnaires but require flexible technical solutions that many research groups cannot develop in-house. Existing EMA tools differ in platform coverage, notification options, and support for innovative mobile interaction formats, limiting their suitability for diverse study designs. Objective: This study aimed to (1) describe the development and core features of SMAAT (Sensor-based Mobile Application for Assessment and Tracking), a smartphone-based research platform for designing and deploying mobile surveys and EMA studies, and (2) report initial usability findings from a cross-sectional study using a novel swiping response format, as well as the protocol of an ongoing longitudinal EMA feasibility study implemented with the platform. Methods: SMAAT consists of a web-based dashboard for researchers and companion iOS and Android apps for participants, providing a visual survey builder, multiple notification schedules (including fixed, random, interval, event-based, and geofenced prompts), gamification mechanics, and tools to monitor participant compliance. To evaluate the platform in use, we conducted a between-subjects cross-sectional study in which 97 university students and 132 online panel participants completed blocks of binary questions on smartphones using either a swiping or tapping response format, followed by usability and user-experience questionnaires. We additionally specify the design of an ongoing 8-week EMA feasibility study (80 participants, two prompts per day combining random daytime and fixed evening notifications, plus geofenced prompts for a subsample) that uses SMAAT’s advanced scheduling features. Results: In the cross-sectional study, SMAAT supported successful study setup, enrollment, and survey completion across both iOS and Android devices without major technical problems, and participants in both samples completed the full protocol in a single session. Performance on the binary tasks was generally high, with swiping and tapping showing broadly comparable response-time and accuracy patterns and no clear disadvantages for swiping or consistent effects of response orientation. Usability and pragmatic user-experience ratings were high across conditions, with no meaningful differences between swiping and tapping, while Prolific participants reported higher usability and pragmatic quality than students. Hedonic ratings descriptively favored swiping, although this difference did not reach conventional statistical significance. The EMA feasibility study remains ongoing, so no longitudinal feasibility outcomes are yet available. Conclusions: SMAAT is a flexible smartphone-based research platform for configuring and deploying mobile surveys and EMA studies with diverse item types and notification logics. Initial findings show that SMAAT can support reliable cross-sectional data collection across heterogeneous devices and samples, and that a swipe-based response format can be implemented without compromising task performance, usability, or pragmatic user experience relative to tapping. The detailed EMA protocol further illustrates the platform’s potential for future longitudinal and mixed-trigger mobile research, although feasibility outcomes from that study are not yet available.
Background: Knee osteoarthritis is a prevalent chronic condition associated with pain, functional limitations, and reduced quality of life. Exercise is the cornerstone of nonpharmacological management...
Background: Knee osteoarthritis is a prevalent chronic condition associated with pain, functional limitations, and reduced quality of life. Exercise is the cornerstone of nonpharmacological management; however, long-term adherence to exercise programs remains challenging. Telerehabilitation has emerged as a promising strategy to improve access to supervised exercise, yet evidence comparing synchronous and asynchronous delivery formats, particularly in developing countries, is limited. This study protocol describes a randomized clinical trial designed to compare the effects of synchronous and asynchronous telerehabilitation exercise programs on pain, physical function, quality of life, and exercise adherence in individuals with knee osteoarthritis. Objective: Therefore, this study protocol describes a randomized clinical trial designed to evaluate the effects of two telerehabilitation exercise programs—synchronous (online) and asynchronous—on pain, functional capacity, and quality of life in individuals with knee osteoarthritis over a six-week intervention period. Methods: This single-center randomized clinical trial will include individuals aged 40–75 years with clinical and/or radiographic knee osteoarthritis. Participants will be randomly allocated to a synchronous or asynchronous telerehabilitation exercise program delivered over six weeks. Both groups will follow an identical, structured exercise protocol, differing only in delivery mode. Outcomes will be assessed at baseline and post-intervention. The primary outcome is pain and physical function. Secondary outcomes include quality of life, functional performance, and exercise adherence. The study protocol was adjusted following a preliminary feasibility study to optimize intervention delivery and monitoring procedures. Results: This study did not receive specific funding. The authors received individual scholarships and research support from CAPES and FUNDECT. Data collection began in August 2024, and by October 2025, a total of 30 participants had been recruited. Data analysis is currently ongoing. Manuscript preparation is expected to begin in May 2026, with submission planned for December 2026. Conclusions: This trial will provide evidence regarding the feasibility, adherence, and potential effectiveness of different telerehabilitation delivery formats for individuals with knee osteoarthritis. Findings may inform the development of accessible, low-cost rehabilitation strategies in settings with limited access to in-person care. Clinical Trial: Brazilian Registry of Clinical Trials (REBEC): RBR-3kzr42p; https://ensaiosclinicos.gov.br/rg/RBR-3kzr42p
Background: Online encyclopedic platforms are a common source of health information, but cross-language evaluations of urolithiasis content rated by both clinically informed and lay users remain limit...
Background: Online encyclopedic platforms are a common source of health information, but cross-language evaluations of urolithiasis content rated by both clinically informed and lay users remain limited. Objective: This study compared the quality of urolithiasis information on Wikipedia and Baidu Baike using ratings from urology nurses and patients. Methods: We conducted a cross-sectional comparison of 30 urolithiasis webpages (15 Wikipedia and 15 Baidu Baike). Pages were identified using clinically relevant English and Chinese search terms and were matched by exact topic or the nearest clinically equivalent concept. Two urology nurses and 2 patients independently rated each page using DISCERN and QUEST. For the main analysis, the 2 ratings within each evaluator group were averaged for each webpage. Primary outcomes were DISCERN and QUEST total scores. Platform differences were tested using the Mann-Whitney U test, interrater reliability was assessed with ICC(2,k), and mixed-effects models were used as sensitivity analyses. Results: Interrater agreement was good to excellent. Wikipedia scored higher than Baidu Baike on DISCERN total in nurse ratings (median 59.50 vs 50.50; P=.002) and patient ratings (61.50 vs 56.00; P=.020). Wikipedia also scored higher on QUEST total in nurses (18.50 vs 15.50; P=.038) and patients (19.00 vs 17.00; P=.016). After false discovery rate adjustment, only DISCERN reliability remained higher for Wikipedia in both evaluator groups, whereas no individual QUEST domain remained significant. Mixed-effects analyses were consistent with the primary findings. Conclusions: In this sampled cross-language comparison, Wikipedia received higher overall DISCERN and QUEST scores than Baidu Baike in both nurse and patient ratings. The clearest domain-level difference was in DISCERN reliability. Other domain-specific differences, especially within QUEST, were less consistent after adjustment for multiple testing.
Background: Preterm infants (<37 weeks gestational age) remain at increased risk of developmental challenges, potentially influenced by parent-child interactions and parental competencies. Objective: ...
Background: Preterm infants (<37 weeks gestational age) remain at increased risk of developmental challenges, potentially influenced by parent-child interactions and parental competencies. Objective: To systematically evaluate the evidence on post-discharge nursing or midwifery care or sociomedical aftercare and their effects on developmental outcomes in preterm infants and parent-child interactions compared with preterm infants receiving standard of care. Methods: Five databases were searched for randomised controlled trials (RCTs) and non-randomised studies of intervention (NRSIs) from inception until December 11, 2024. Studies including preterm infants (<37 weeks gestational age) receiving post-discharge medical aftercare were eligible. Children born full-term (≥37 weeks gestational age) and early intervention programmes with a therapeutic approach were excluded. Two reviewers independently screened records and extracted data from studies that examined following predefined outcomes: participation, quality of life, rehospitalisation, behaviour, and parent-child interaction. Risk of bias was assessed using Cochrane RoB2 for RCTs and ROBINS-I for NRSIs. Clinically and methodologically comparable studies were combined in random-effects meta-analyses. Certainty of evidence was evaluated using the Grading of Recommendations, Assessments, Development and Evaluation (GRADE) approach. Results: Sixteen studies investigating 3,047 preterm children (gestational age 24-37 weeks) aged three months to five years were included. Data on rehospitalisation, regulatory disorders, risk of maltreatment, parent-child enrichment and attachment were reported with very low to low certainty evidence. Medical aftercare may slightly improve the parent-child enrichment, while effects on other outcomes remain uncertain. No studies reported data on participation, quality of life nor emotional or conduct problems. Conclusions: Findings reveal a substantial evidence gap and underline the need for further research, particularly focusing on midwifery care and sociomedical aftercare.
(Funding: DLR No. MEDLL1_2022-025.) Clinical Trial: PROSPERO: CRD420251023174
Background: Electronic health records (EHRs) contain extensive unstructured free text that is difficult to incorporate into qualitative research at scale. Existing NLP approaches in health research pr...
Background: Electronic health records (EHRs) contain extensive unstructured free text that is difficult to incorporate into qualitative research at scale. Existing NLP approaches in health research primarily focus on structured data extraction or predictive modeling, leaving qualitative applications underdeveloped. Objective: To develop and evaluate an interpretable, rule based NLP pipeline to curate large EHR text corpora into analytically tractable sub corpora suitable for qualitative research. Methods: We applied a deterministic NLP algorithm (pyTAKES) to 161,111 free text EHR notes from 335 participants diagnosed with dementia in the Adult Changes in Thought (ACT) study. Using a concept dictionary informed by prior qualitative research and clinical expertise, we tagged notes with semantically meaningful concepts and applied filters (eg, concept density, note type, temporal proximity to diagnosis) to distill three focused sub corpora. We evaluated concept performance through manual review and assessed corpus relevance before and after filtration. Results: Sixty two percent of notes contained at least one concept match. Concept review demonstrated acceptable agreement between retrieved text and target phenomena. Filtering reduced the corpus by over 95% while increasing the proportion of caregiving relevant notes from 23.5% to 84.5%. Each sub corpus supported distinct qualitative research questions. Conclusions: NLP methods can efficiently curate large EHR text corpora for qualitative analysis. This approach offers a reproducible and resource efficient alternative to black box machine learning models, enabling qualitative researchers to leverage EHR data at scale.
Background: Proxy-rated questionnaires remain the standard for assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate ...
Background: Proxy-rated questionnaires remain the standard for assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate continuous data that provides quantitative insights into daily activities and behavioral and psychological symptoms, such as sleep disturbance. This study explores the utility of sensing technologies in the detection of changes in physical activity levels and sleep behaviors over time. Objective: This study aims to explore the long-term capabilities of multi-modal sensing technologies for assessing physical activity levels and sleep quality using selected digital biomarkers for nursing home residents with dementia. Objectives were for observation to be aligned with real-world conditions in which such sensing technologies would be applied within a nursing home environment, and to assess whether distinct differences in selected digital biomarkers can be observed accurately and reliably longitudinally. Methods: This study included eleven participants (79–93 years) recruited from two dementia care units in Norway. A smartwatch (Garmin Vivoactive5/Garmin Venu3) and radar-based system (Vital Things, Somnofy) were used to collect 7-days and 6-nights of data on physical activity levels and sleep quality at baseline, 6 months, and 1-year. The Personal Self-Maintenance Score and Neuropsychiatric Inventory–Nursing Home Version (nighttime behaviors section K), were also administered. Digital biomarkers included Euclidean Norm Minus One (ENMO), Sleep Efficiency (SE), Wake After Sleep Onset (WASO), Sleep Regulatory Index (SRI), Sleep Fragmentation Index (SFI), Total Sleep Time (TST), and time out of bed (no presence). Results: Nine total participants were included in the final analysis. Differences were found in Nighttime ENMO (P= .01) and in four of the sleep biomarkers: TST (P=.02), SE (P= .02), WASO (P= .01), and SRI (P=.01). Long-term reliability of the group ENMO was poor (ICC: 0.00-0.02), however, between individuals at each timepoint was moderate - strong (0.58-0.79). Adherence and acceptability of the technologies was high (88-96%), and application of the devices was well tolerated by the participants with no adverse events. Conclusions: The use of sensing technologies could enable more objective, data-driven future care models for people with dementia residing in nursing homes, however, the results emphasized in this study require the recommendation for cautious, well-designed use of digital biomarkers for clinical decision-making. Clinical Trial: The DIPH.DEM study was approved by the Regional Committee for Medical and Health Research Ethics (REK) in Norway in October 2023: approval number 634938.
Background: Eating disorders (EDs) and attention-deficit/hyperactivity disorder (ADHD) co-occur at rates substantially exceeding chance, potentially due to shared neurodevelopmental, genetic, and rewa...
Background: Eating disorders (EDs) and attention-deficit/hyperactivity disorder (ADHD) co-occur at rates substantially exceeding chance, potentially due to shared neurodevelopmental, genetic, and reward-processing mechanisms. Despite growing clinical recognition of this overlap, no comprehensive synthesis of the effects of ADHD pharmacotherapy on ED outcomes in individuals with co-occurring ADHD and EDs currently exists. Clinicians prescribing ADHD medications to this population must balance potential risks, including appetite suppression, weight loss, and misuse, against possible benefits, including improved impulse control, improved engagement in ED treatment, and reduced binge-eating frequency, all in the absence of astructured evidence base to draw upon. Objective: This scoping review aims to: (1) map existing evidence on the effects of ADHD pharmacotherapy on ED symptom outcomes in individuals with co-occurring ADHD and EDs; (2) characterise the safety
and tolerability profile of ADHD medications in this population; (3) describe the range of study designs, populations, medication types, and outcome measures re- ported in the literature; and (4) identify key methodological gaps to inform future research priorities. Methods: The review will follow the PRISMA Extension for Scoping Reviews methodology. Eligible studies will include individuals with a formal diagnosis of ADHD or clinically significant ADHD symptoms, alongside a diagnosed ED or clinically significant ED pathology, across all ED diagnoses and age groups. Any pharmacological agent approved or used off-label for ADHD, including stimulants and non-stimulants, will be eligible as the primary intervention. All study designs will be included, from randomised controlled trials to case reports, consistent with the anticipated sparsity of controlled trial data in this population. Seven electronic databases will be searched (MEDLINE, Embase, PsycINFO, PubMed, CENTRAL, Web of Science, SCOPUS), alongside trial registries and manual searches. Title/abstract and full-text screening will be conducted independently by two reviewers, with discrepancies resolved by a senior reviewer. Findings will be synthesised narratively, structured by population group, medication class, and outcome domain. Results: Ethics & Dissemination Ethical approval is not required as this review involves secondary analysis of publicly available data. Findings will be disseminated via peer-reviewed publication and are intended to inform prescribing practice, highlight evidence gaps, and provide a foundation for future controlled research at the intersection of ADHD and EDs. Conclusions: Ethics & Dissemination: Ethical approval is not required as this review involves secondary analysis of publicly available data. Findings will be disseminated via peer-reviewed publication and are intended to inform prescribing practice, highlight evidence gaps, and provide a foundation for future controlled research at the intersection of ADHD and EDs.
Background: Workforce aging is accelerating, particularly among women aged 50 and over. In the healthcare sector, older nurses face increased exposure to occupational injuries, including musculoskelet...
Background: Workforce aging is accelerating, particularly among women aged 50 and over. In the healthcare sector, older nurses face increased exposure to occupational injuries, including musculoskeletal disorders and burnout, which may result in work disability. Following an occupational injury, nurses must navigate complex decisions involving return to work or retirement. These decisions occur within interacting personal, organizational, healthcare, and compensation systems. The determinants of these post-injury employment trajectories remain insufficiently documented, which limits the development of targeted interventions to support reintegration in employment or retirement transitions. Objective: This study aims to identify the factors that influence employment trajectories of nurses aged 50 and over following an occupational injury. Methods: This study uses a two-stage qualitative design. Stage 1 consists of a life‑story study based on semi‑structured interviews with approximately 20 nurses aged 50 and over who have experienced an occupational injury. Interviews will explore factors related to the worker, the work environment, the healthcare system, and the compensation system. Data will be analyzed using thematic analysis. This stage will generate preliminary recommendations to support return to work or transition to retirement. Stage 2 consists of a nominal group process to validate and refine these recommendations. Four nominal groups will be formed, each including four participants representing nurses, healthcare professionals, employer representatives, and insurer representatives. Participants will assess the relevance, clarity, and completeness of the recommendations. Data will be analyzed using descriptive statistics and qualitative content analysis. Results: Participant recruitment and data collection began in September 2025 and are expected to continue until October 2026. Conclusions: This study will identify key facilitators and barriers shaping post-injury employment trajectories of nurses aged 50 and over. The findings will inform actionable recommendations to support sustainable return to work or structured transition to retirement in this growing segment of the healthcare workforce.
Background: Despite significant advancements in HIV care, all components of the care continuum from diagnosis to antiretroviral treatment (ART) uptake and sustained virologic suppression (VS) are wors...
Background: Despite significant advancements in HIV care, all components of the care continuum from diagnosis to antiretroviral treatment (ART) uptake and sustained virologic suppression (VS) are worse for adolescents and young adults with HIV (AHIV) (ages 12-30). ART adherence remains elusive for ≈60% of AHIV, impeding the goal of the Ending the HIV Epidemic in the United States Initiative (EHE). Even when AHIV are suppressed, medication fatigue and other factors threaten sustained virologic control. Long-acting injectable ART (LAI-ART) has the potential to improve the care continuum for AHIV. Objective: The Strategies to Achieve Viral Suppression for Youth with HIV Study (SAVVY) aims to evaluate the impact and implementation of an informed choice-counseling intervention on ART options, including its impact on intervention acceptability, participants’ ART selection and later facilitated access, and the clinical outcome of VS rates among AHIV. We also aim to assess determinants influencing LAI-ART implementation outcomes, guided by the Consolidated Framework for Implementation Research. Methods: SAVVY is a preference-guided, observational, type-1 effectiveness implementation study, investigating the efficacy of precision engagement approaches to patient counselling on options for ART that are approved for patients with viral load (VL) <50 copies/mL. Patients eligible for enrollment are AHIV (ages 12-30) engaged in clinical care and prescribed ART (N=288). All participants undergo CHOICE counseling (CC), where they are presented with and decide on their preferred ART options (oral ART [oART] or LAI-ART) using a computer-assisted precision engagement tool (HIV-ASSIST). The SAVVY LAI-ART Access Team facilitates [EW1.1][EO1.2]access and logistics for those who qualify for and choose LAI-ART.
At entry, enrollees are divided into two cohorts: those with HIV RNA PCR VL ≥50 copies/mL (Cohort 1a) and those with VL <50 copies/mL (Cohort 1b). Cohort 1a participants are informed of VL requirements to qualify for LAI-ART, receive supportive messages and biweekly VL measurements [EW2.1][EW2.2]for three months, and are re-offered CC upon achieving VL<50 copies/mL. Enrollees with persistent VL ≥50 copies/mL at 3 months continue standard oART with the option of referral for LAI-ART once VL is <50 copies/mL. Cohort 1b participants undergo CC and can proceed to LAI-ART or maintain oART. The primary outcome is VS (VL<20 copies/mL), comparing the oART and LAI-ART groups. SAVVY is JHU IRB-approved and registered on clinicaltrials.gov (NCT06886971). The first participant was enrolled in November 2024 with full accrual anticipated in 36 months. Results: n/a Conclusions: SAVVY represents a pragmatic, feasible implementation strategy towards informed, personalized ART decision-making among AHIV, which could expand access to beneficial novel technologies. It aims to use the proven approach of youth-centered care, where treatment choices are self-driven by youth, to improve LAI-ART uptake and persistence[EW3.1], increase VS, and improve overall outcomes for AHIV.
Background: The Surgical Apgar Score (SAS) is a simple intraoperative tool designed to predict postoperative complications following major surgery. Although validated in certain high-income settings, ...
Background: The Surgical Apgar Score (SAS) is a simple intraoperative tool designed to predict postoperative complications following major surgery. Although validated in certain high-income settings, evidence regarding its accuracy and correlation with complication severity remains limited in low-resource settings, where the burden of postoperative complications remains high. Objective: To predict emergency laparotomy adverse outcomes using the Surgical APGAR Score based stratification system. Methods: This was a prospective observational study that recruited 146 adult patients for emergency laparotomy in three (3) hospitals. Intraoperative data, including the lowest heart rate, lowest MAP, and estimated blood loss postoperatively, were calculated. Outcomes were followed up for 30 days. The severity of complications was assessed using the Clavien-Dindo Classification (CDC) grading scheme and the Comprehensive Complication Index (CCI). The accuracy of SAS was evaluated by determining its discriminatory capacity on the Receiver Operating Characteristics (ROC) curve. Results: The largest age group was 40–60 years (43.2%), and males predominated (71.9%). Small bowel obstruction was the leading indication (26%), and the majority were ASA class II (81.5%). Most patients (85.5%) had SAS between 5 and 7, while 7.6% (n=11) had low SAS (≤4). Overall mortality was 4.8% (n =7), all corresponding to CDC grade V and CCI scores of 100%. SAS demonstrated strong prognostic performance. It correlated negatively with CCI (ρ = –0.388, p < 0.001) and inversely predicted CDC grades (β = –0.930, p < 0.001). ROC analysis showed good discrimination for major complications (AUC 0.780, 95% CI 0.656–0.904, p < 0.001) and excellent discrimination for mortality (AUC 0.852, 95% CI 0.704–0.999, p = 0.003). The optimal Youden’s Index cut-off was SAS ≥5, yielding high sensitivity for major complications (96.9%) and mortality (89.7%) with modest specificity. Conclusions: The SAS demonstrates significant predictive value for postoperative complication severity in this setting, its ease of use makes it a valuable tool for perioperative risk assessment in resource limited settings. Clinical Trial: PACTR202511513563586.
Pan African Clinical Trial Registry (pactr.samrc.ac.za) database,
Background: Adults may experience subjective cognitive decline (SCD). However, it is unclear whether SCD is related to measurable cognitive impairment, particularly women ages 40 to 60 and early deme...
Background: Adults may experience subjective cognitive decline (SCD). However, it is unclear whether SCD is related to measurable cognitive impairment, particularly women ages 40 to 60 and early dementia. Further, Medicare has mandated assessment of cognitive and memory function in individuals over 65 as part of the Medicare Annual Wellness Visit. In order to assess possible impairment and change over time, efficient, objective measures of SCD are needed. Objective: To assess the relationship between performance on an online continuous recognition task (CRT, MemTrax) and age, sex, and memory concern. Methods: This study evaluated CRT performance in participants aged 21-99 who enrolled in an online program (HAPPYneuron) to measure mental functions, including those who reported concerns about them. This program asked participants if they had complaints about their memory, and then the program offered them the opportunity to assess cognition using the CRT. This CRT instructs individuals to attend to visual stimuli (50 images) and respond as quickly as possible to repeated images (25 images). The CRT components were used to measure learning and memory (as related to HITs, response to a repeated image), executive function (as related to CRs, correctly not responding to an initial image presentation), and processing speed (HIT-RTs, average response time to HITs). Results: Analysis of 18,178 (5,795 males, 32%; 12,383 females, 68%) only included those who answered the sex, age, and memory questions. There were 11,786 (65%) between 40 and 70 years of age. Females outnumbered males by over two-fold, beginning about 35 years of age, peaking at 55 years of age at over three-fold, and falling below two-fold at about 65 years of age. Approximately 30% more men complained of memory problems than those who did not, primarily 30 – 60 years old. About 80% more women complained of memory problems, over two-fold more than women who did not, 30-50 years old. The number of HITs, number of CRs, and HIT-RTs varied little between men and women. While those without memory complaints generally performed better than those with memory complaints, there was little difference in performance levels for each group between males and females. For all groups, there was a gradual reduction of performance over age for HITs and CRs and a slowing of HIT-RTs. Conclusions: Most subjects were 40-65, more than twice as many females, suggesting that these demographics have a relationship to concern about SCD. However, there was little difference between males and females for the various CRT components, though SCD was associated with impairment. Age-related declines were progressive, the largest being in slower processing speed, presumably to compensate for age-related changes in cognitive function. Present results suggest clinicians may use these metrics to quantify patient concerns expressed in the primary care setting. Clinical Trial: none
Background: Mental health encompasses not only chronic conditions such as depression or anxiety, but also acute fluctuations in mood that unfold over minutes to hours and can disrupt daily functioning...
Background: Mental health encompasses not only chronic conditions such as depression or anxiety, but also acute fluctuations in mood that unfold over minutes to hours and can disrupt daily functioning. These transient states, such as sudden fatigue, irritability, or low energy, remain largely invisible to current digital health approaches, which typically aggregate behavioral and physiological data over days or weeks to detect trait-level conditions. The ability to detect momentary mood shifts in real time carries significant clinical promise: continuous affective monitoring could enable early detection of mental health crisis, support clinical decisions and clinical trials with continuous mood measurements, and improve occupational safety with detection fo states like fatigue or confusion. However, affective computing research has demonstrated that while physiological signals carry information relevant to mood, most prior work relies on controlled laboratory settings where performance degrades substantially in naturalistic environments, or employs research-grade devices with proprietary sensors unavailable on consumer hardware. Bridging this gap between laboratory-validated sensing and real-world momentary mood detection is essential for translating these clinical possibilities into practice through just-in-time adaptive interventions. Objective: This study investigates whether continuous sensing from a low-cost, opensource smartwatch can support detection of multi-dimensional momentary mood states in naturalistic settings, using personalized models with on-device computation. Methods: We conducted a 7-day field study in which participants (N=10) wore Bangle.js 2 smartwatches that continuously collected physiological and contextual data, including heart rate, accelerometry, barometric pressure, temperature, and GPS, while prompting hourly mood self-reports using the Brunel Mood Scale (BRUMS) across six mood dimensions (tension, depression, anger, vigor, fatigue, confusion) and additional affective and physical states. All feature extraction was performed on-device. We developed personalized mood detection models using best-subset regression across multiple feature combinations. Results: Personalized models decoded momentary states with mean R2 values ranging from 0.09 (pain) to 0.31 (vigor). Fatigue, happiness, vigor, and depression were the most reliably decoded dimensions (mean R2 = 0.26–0.31). Cross-subject decoding was substantially lower, confirming that personalization is essential for accurate mood inference. Including privacy-preserving location features did not significantly improve prediction accuracy beyond physiological and contextual sensors alone. Conclusions: This work demonstrates that a broad range of momentary mood states can be decoded from low-cost, open-source wearable sensors as people go about their daily lives, bridging the gap between controlled laboratory studies and real-world momentary assessment. The finding that personalized models substantially outperform generalized approaches underscores the need for individual calibration in affective computing systems. The on-device, privacy-preserving architecture establishes a foundation for future closed-loop adaptive interventions in clinical and occupational contexts, including continuous monitoring of high-risk psychiatric populations, early warning systems for substance use relapse, and real-time assessment of cognitive and emotional fitness in safety-critical work environments. Clinical Trial: N/A
Background: Dementia and mild cognitive impairment (MCI) are progressive neurocognitive disorders associated with a decline in cognitive abilities, subsequently affecting daily functioning and quality...
Background: Dementia and mild cognitive impairment (MCI) are progressive neurocognitive disorders associated with a decline in cognitive abilities, subsequently affecting daily functioning and quality of life. Pharmacological treatments have limited efficacy, highlighting the need for effective non-pharmacological interventions. Advances in digital technologies, including virtual reality (VR), extended/mixed reality (XR/MR), web-based platforms, and AI-assisted tools, offer promising opportunities for personalized, engaging, and scalable cognitive interventions. Objective: This systematic review aimed to (1) identify the characteristics of technology-assisted cognitive interventions for people with dementia (PwD) and MCI, (2) evaluate their effectiveness on cognitive, functional, and psychological outcomes, such as mood, depressive and anxiety symptoms, motivation, engagement, and quality of life, and (3) explore factors that may influence intervention outcomes, including intervention modality, cognitive stage, personalization, and integration of cognitive-physical components. Methods: A systematic search was conducted across five digital libraries (ACM, PubMed, IEEE Xplore, Sage, ScienceDirect), selected to ensure broad coverage across computer science, engineering, and health-related disciplines, for studies published between 2018 and 2025 , a period chosen to capture recent advances in rapidly evolving digital technologies and ensure the relevance of findings to current research and practice. Nineteen studies meeting the inclusion criteria were analyzed using a mixed quantitative and qualitative approach. Data were extracted on study characteristics, participant demographics, intervention modalities, cognitive domains targeted, duration, personalization/adaptive features, and outcomes. Quantitative synthesis examined effect sizes and statistical trends, while qualitative synthesis addressed non-cognitive outcomes, ecological validity, feasibility, and user experience. Cross-study comparisons identified patterns and potential moderators of intervention efficacy. Results: Among the 19 included studies, interventions employed VR (n=8), XR/MR (n=3), web-based or tablet-based platforms (n=5), and other innovative technologies (n=3). Most studies targeted memory (n=10), attention (n=7), and executive functions (n=6), with several addressing activities of daily living (n=4) and emotional well-being (n=3). Sixteen studies reported significant improvements in at least one cognitive or functional outcome, with immersive VR and combined cognitive-physical interventions showing the strongest evidence. Non-cognitive benefits included enhanced mood, motivation, engagement, and quality of life. Personalized and adaptive features were associated with greater user adherence and efficacy. Trends suggested that cognitive stage, intervention modality, and multimodal integration may influence outcomes, although systematic quantitative analyses of moderators were limited. Conclusions: Technology-assisted interventions, particularly immersive and personalized approaches, are effective in improving cognitive, functional, and psychological outcomes in people with dementia and MCI. VR-based and multimodal cognitive-physical programs appear especially promising. Future research should focus on larger, rigorously-designed trials with standardized outcome measures, longitudinal follow-ups, and formal evaluation of moderators to optimize intervention personalization and scalability. These findings support the integration of digital interventions as viable components of cognitive rehabilitation and healthy aging strategies.
Background: The Strong Families (SF) Program, developed by the United Nations Office on Drugs and Crime, is an evidence-informed family skills intervention designed to support caregivers and children ...
Background: The Strong Families (SF) Program, developed by the United Nations Office on Drugs and Crime, is an evidence-informed family skills intervention designed to support caregivers and children in low-resourced, high-stress circumstances. Following a pilot phase, we revised the program materials according to the expert trainers’ suggestions to best reflect the needs of the target population. Objective: To evaluate the feasibility, effectiveness, and cultural adaptability of the second phase of the Strong Families (SF) program expansion in Northern Thailand. Methods: This mixed methods, pre-post feasibility study involved 68 families (caregivers and children as 1:1) from Chiang Mai, Lamphun, and Chiang Rai – three providences in Northern Thailand. The applied intervention consisted of a three-week program with both separate and joint sessions for caregivers and children. Quantitative data were collected using the Parent and Family Adjustment Scale (PAFAS), Strengths and Difficulties Questionnaire (SDQ), and Child and Youth Resilience Measure-Revised (PMK-CYRM-R). Data were analyzed using linear mixed-effects regression. Qualitative data were gathered through semi-structured interviews with 19 families and analyzed using content analysis to identify key themes. Results: Quantitative analysis using linear mixed-effects regression revealed significant improvements in Coercive Parenting (β -0.10, p = 0.003), child Conduct Problems (β -0.04, p = 0.002), and Peer relationship (β -0.04, p = 0.048) for parents, and Emotional behavior (β -0.05, p = 0.011), and Conduct behavior (β -0.04, p = 0.023) for children. However, scores for Positive Encouragement and Parent-Child Relationships significantly declined, a discrepancy that may be due to response shift bias where participants adopted stricter self-evaluation standards post-intervention. Qualitative findings, showed five themes that highlighted improved family atmosphere, better emotional regulation, and successful translation of knowledge into practice to better develop protective factors for young people. Participants viewed the program as feasible and culturally appropriate but recommended extending the duration and increasing paternal involvement for more significant, sustainable outcomes. Conclusions: The SF program is a culturally adaptable and effective intervention for improving family dynamics in low-resource settings.
Background: Sepsis affects nearly 20 million individuals annually, with hemodynamic resuscitation central to its management. While guidelines recommend a MAP target of 65 mmHg, the optimal threshold r...
Background: Sepsis affects nearly 20 million individuals annually, with hemodynamic resuscitation central to its management. While guidelines recommend a MAP target of 65 mmHg, the optimal threshold remains debated. Objective: This systematic review evaluates the effect of elevated MAP targets on mortality, splanchnic perfusion, renal outcomes, and vasoactive agent toxicity in septic resuscitation. Methods: Following PRISMA 2020 guidelines, we systematically searched PubMed, Embase, and Cochrane databases for studies comparing higher versus standard MAP targets in septic shock patients. Ten studies were included encompassing randomized controlled trials and observational cohorts. Data were synthesized narratively, with a random-effects meta-analysis performed for 28-day mortality. Study quality was assessed using RoB 2 and Newcastle-Ottawa Scale. Results: Ten studies enrolling 2,089 patients were included. No randomized trial demonstrated a statistically significant mortality benefit from elevated MAP targets, with pooled meta-analysis confirming no significant difference between higher and standard MAP groups (OR 1.16; 95% CI 0.92–1.46; P = 0.21; I² = 0%). Direct physiological evidence consistently demonstrated that MAP augmentation above 65 mmHg improves microcirculatory parameters without improving splanchnic perfusion, lactate clearance, or microcirculatory flow. Observational evidence identified a renal-protective MAP zone of 72–75 mmHg specifically in patients with chronic hypertension, chronic kidney disease, and early AKI. Vasopressor load emerged as the strongest independent predictor of multiorgan complications and mortality, surpassing MAP level itself. Elevated MAP targets were consistently associated with higher rates of atrial fibrillation and overall adverse events across multiple studies. Conclusions: Elevated MAP targets above 65 mmHg confer no mortality or perfusion benefit in unselected septic shock populations. Individualized MAP targeting in high-risk subgroups and vasopressor-sparing strategies warrant prioritization in future research.
Background: Mycoplasma pneumoniae (M. pneumoniae) is a primary cause of pediatric respiratory infections worldwide. In China, macrolide resistance rates exceeding 80% make timely diagnostic testing es...
Background: Mycoplasma pneumoniae (M. pneumoniae) is a primary cause of pediatric respiratory infections worldwide. In China, macrolide resistance rates exceeding 80% make timely diagnostic testing essential to replace empirical treatment with targeted therapy. However, most symptomatic children are not tested during clinic visits, representing a missed opportunity for effective infection management. Objective: This study evaluated whether a pay-it-forward (PIF) pro-social intervention could increase M. pneumoniae testing uptake among children compared to standard-of-care (SOC). Methods: This two-arm, pragmatic superiority cluster randomized controlled trial was conducted in December 2023 at two pediatric clinics in Wuxi, China. We enrolled 320 symptomatic children under 14 years, randomly assigned to either the PIF or SOC arm (n=160 each). Clusters, each comprising 10 consecutively eligible children and their caregivers, were randomized to either the pay-it-forward arm or the standard-of-care arm (16 clusters per arm, total 32 clusters). In the PIF arm, participants received a donated test and could voluntarily contribute to a fund for future participants. The primary outcome was M. pneumoniae testing uptake documented in administrative records. Results: A total of 320 participants were enrolled and evenly randomized. Caregivers were predominantly mothers (66·9%) with a mean age of 37·5 years, who largely resided in urban areas (65·6%), were employed (88·8%), and held an undergraduate degree or higher (63·5%). Children had a mean age of 7·3 years (SD 3·4) and were 55·0% male. M. pneumoniae testing uptake was significantly higher in the PIF arm (72·5%) compared to the SOC arm (34·4%), with an adjusted proportion difference of 39·6% (95% CI, 23·6%-55·7%). Subgroup analyses revealed stronger intervention effects among caregivers with higher education (P= 0·004) and income (P= 0·039). In the PIF arm, 9·5% of participants voluntarily contributed to the fund. Approximately 12% of tested participants in both groups were positive for M. pneumoniae. No adverse events were reported. Conclusions: A pay-it-forward intervention significantly increased the uptake of M.pneumoniae testing among symptomatic children, demonstrating the feasibility and effectiveness of this innovative approach to improve access to diagnostic testing for respiratory pathogens. Clinical Trial: This trial is registered with Chinese Clinical Trial Registry, ChiCTR 2300078623.
Background: While automation is recognised for saving time in conventional systematic reviews, a trust gap and steep learning curve persist regarding its accuracy and ease of use. It remains unknown i...
Background: While automation is recognised for saving time in conventional systematic reviews, a trust gap and steep learning curve persist regarding its accuracy and ease of use. It remains unknown if these adoption barriers apply to the more complex process of systematic grey literature reviews, especially given the rapid evolution of large language models. Objective: This study seeks to identify the preferred features of systematic grey literature reviews (SGLRs) generated using an automation tool (automated SGLRs). Methods: Participants aged 18 years or older with experience using evidence from systematic reviews were recruited. A cross-sectional online Best-Worst Scaling questionnaire was distributed through the authors’ contacts. Descriptive, multinomial, and mixed logit analyses were used to estimate respondents' preferred features of automated SGLRs. Results: A total of 168 respondents, comprising researchers, academicians, healthcare professionals, health policymakers, students, and postgraduates, completed the study. Multinomial logit analysis found that the top three preferred features for automated SGLRs were i) high precision and sensitivity comparable to manual methods, ii) acknowledgement and detailed explanation on the AI algorithms, and iii) involvement of ≥ 2 independent human reviewers in the process. The mixed logit model revealed significant heterogeneity in respondents' preferences for automated SGLR features. Further subgroup analysis indicated that respondents aged 18-29 and 40-49 considered detailed explanations and acceptance by everyone to be more important features of automated SGLRs. In contrast, academicians significantly preferred the detailed explanations feature of automated SGLRs. Conclusions: The study concludes that automation tool developers should ensure the accuracy of automated SGLRs, ensure algorithmic transparency, and integrate human-in-the-loop processes to build user trust and drive adoption of automated SGLRs.
Background: Developmental delay is a disorder in which a child does not grasp developmental milestones in areas like: motor skills, cognition, Speech, and societal interaction at an anticipated age. T...
Background: Developmental delay is a disorder in which a child does not grasp developmental milestones in areas like: motor skills, cognition, Speech, and societal interaction at an anticipated age. These delays include cognitive, speech and language, motor, and societal and emotional delays commonly caused by aspects like genetic circumstances, premature birth or low birth weight, neurological conditions, and environmental reasons. Objective: In this study, we used the Meta Quest Pro headset to generate an immersive virtual reality environment through task-based interactions. Including microexpressions, muscle activities, and eye-tracking data, were collected as time series data, apprehending dynamic facial gesture fluctuations. Methods: Subsequent data preprocessing, the primary analysis path leveraged statistical methods, which include: Kruskal–Wallis test, ReliefF, ANOVA, and minimum redundancy maximum relevance (MRMR), to extract and select the furthermost relevant features. The secondary path employed a transformer for automated feature extraction, then both feature sets were afterward classified using machine learning classifiers to evaluate their effectiveness. Results: Using the MRMR method, we achieved 86.4% accuracy with the Coarse Tree classifier with a ratio of 80:20, and 95.6% accuracy with quadratic support vector machine (SVM) with a ratio of 60:40. Whereas the transformer-based approach achieved an accuracy of 86.4% with Quadratic SVM and 95.5% with the Fine KNN classifier at the 60:40 split. Conclusions: This study contributes to the advancement of affordable and accessible screening tools for children worldwide.
Background: Adults with Attention Deficit Hyperactivity Disorder (ADHD) often experience elevated stress levels and reduced quality of life (QoL), which can be partly linked to challenges such as alex...
Background: Adults with Attention Deficit Hyperactivity Disorder (ADHD) often experience elevated stress levels and reduced quality of life (QoL), which can be partly linked to challenges such as alexithymia and rejection sensitivity dysphoria (RSD). Stress Autism Mate (SAM), a free self-guided mobile app originally co-created for autistic adults, may support stress management. The app aims to reduce daily stress by promoting stress recognition and coping through daily stress questionnaires, psychoeducational content, stress reduction tips, peer stories, podcasts, and in-app exercises. Objective: This mixed-methods pilot study explored the feasibility, usability, and preliminary outcomes of the SAM app in adults with ADHD. Methods: A quantitative study informed by single-case experimental design (SCED) principles was complemented by semi-structured qualitative interviews. Questionnaire data were collected from 22 adults with ADHD (77.3% female; Mage = 34.3 years) at four time points spaced four weeks apart: baseline, pretest, posttest, and follow-up. Linear mixed-effects models were used to evaluate changes in perceived stress, QoL, coping self-efficacy, stress recognition, alexithymia, and RSD. Qualitative data were analyzed using deductive thematic analysis guided by the User Experience Technology Acceptance Model (UX-TAM). Results: At posttest, significant increases were observed in stress recognition (d = 0.80) and coping self-efficacy (d = 0.34). At follow-up, only the effect on stress recognition remained (d = 0.82), while delayed improvements emerged in QoL (d = 0.38), alexithymia (d = –0.49), and RSD (d = –1.27). Perceived stress did not change at either time point. Participants reported high adherence and ease of use of the app but described difficulties translating increased stress awareness into adaptive coping behaviors. Most expressed a need for more stimulating, motivating, and personalized features to facilitate coping and sustain engagement. Conclusions: The SAM app appears feasible and usable for adults with ADHD, showing robust improvements in stress recognition and potential benefits for wellbeing over time. However, the app currently seems insufficient to support the implementation of effective coping strategies. Differences from previous SAM trials in autistic adults may reflect distinct executive functioning challenges. Future adaptations incorporating more personalized, just-in-time support (e.g., AI-driven) and more engagement-focused design features (e.g., gamification) may enhance effectiveness in this population.
Background: Large language models (LLMs) are increasingly used to generate differential diagnoses from clinical narratives. However, LLM-based diagnostic clinical decision support systems still lack q...
Background: Large language models (LLMs) are increasingly used to generate differential diagnoses from clinical narratives. However, LLM-based diagnostic clinical decision support systems still lack quantitative measure of how strongly a diagnosis is supported by the available case description. In natural language processing, conditional perplexity score quantifies how predictable a target text is given in a preceding context, with lower scores indicating greater predictability. We hypothesized that this concept can be adapted to diagnostic reasoning by treating the pre-diagnostic case description as the context and a diagnosis as the target text. Objective: To evaluate whether an LLM-based conditional perplexity score can quantify clinical compatibility between a case description and differential diagnoses. Specifically, we hypothesized that the correct LLM-generated diagnosis verified by physicians would have lower conditional perplexity scores than incorrect LLM-generated differential diagnoses. A secondary outcome was to compare this scoring behavior across differential diagnosis lists generated by different LLMs. Methods: We performed a preliminary computational analysis of 392 peer-reviewed diagnostic case reports published in The American Journal of Case Reports in 2022. For each case, the pre-diagnostic clinical description was used as the conditioning context, and the case report-defined final diagnoses were treated as the gold standard. Conditional perplexity scores for differential diagnosis lists previously generated by LLaMA2, Bard, and GPT-4 were computed using an independent longer-context LLM, Qwen2.5-1.5B. We compared case report-defined final diagnoses, correct LLM-generated diagnoses verified by physicians, and incorrect generated diagnoses using nonparametric comparisons and receiver operating characteristic analyses. Results: All 392 cases had complete case descriptions, and case report-defined final diagnoses. Across the top-10 differential diagnosis lists generated by LLaMA2, Bard, and GPT-4, 823 correct LLM-generated diagnoses verified by physicians, and 10,875 incorrect generated diagnoses were analyzed. Case report-defined final diagnoses had lower conditional perplexity scores than incorrect generated diagnoses (median 39.9 [IQR 17.7-119.9] vs 133.3 [37.5-672.1]; P<.001). Correct LLM-generated diagnoses also had lower conditional perplexity scores than incorrect LLM-generated diagnoses (43.3 [16.6-147.5] vs 133.3 [37.6-672.1]; P<.001). Candidate-level discrimination was moderate overall (AUC 0.666, 95% CI 0.647-0.685) and was highest for GPT-4 generated differential diagnosis lists (AUC 0.678, 95% CI 0.647-0.707), followed by LLaMA2 (AUC 0.662, 95% CI 0.626-0.696) and Bard (AUC 0.648, 95% CI 0.616-0.682). In paired within-case analyses, the correct LLM-generated diagnosis had a lower conditional perplexity score than the average incorrect diagnosis in 91.1% of evaluable LLaMA2 cases, 88.9% of GPT-4 cases, and 88.1% of Bard cases (all P<.001). Conclusions: A conditional perplexity score derived from an independent LLM provided a quantitative signal that distinguished case report-defined and correct LLM-generated diagnoses verified by physicians from incorrect LLM-generated diagnoses. These findings support conditional perplexity score as a promising adjunct for studying LLM-generated differential diagnoses. Clinical Trial: Not applicable.
This study finds that rising LLM usage is consistently associated with reduced predictive value of Google symptom searches for forecasting subsequent respiratory hospitalizations across U.S. states....
This study finds that rising LLM usage is consistently associated with reduced predictive value of Google symptom searches for forecasting subsequent respiratory hospitalizations across U.S. states.
Background: Innovation in teaching methods is essential for advancing medical education, particularly for trainees developing crisis management skills. Virtual reality (VR) offers access to immersive,...
Background: Innovation in teaching methods is essential for advancing medical education, particularly for trainees developing crisis management skills. Virtual reality (VR) offers access to immersive, scalable, and accessible learning environments, but its effectiveness compared to traditional mannequin-based simulation remains underexplored. Objective: This prospective randomized controlled trial evaluates the efficacy of VR-based simulation versus traditional gold-standard mannequin-based training in enhancing medical trainees’ knowledge acquisition and application of decision-making concepts for airway trauma management. Methods: Forty medical students were randomized to either the VR (intervention) group or the Mannequin (control) group. Participants engaged in airway trauma management training using their assigned modality. Both groups completed a pre-and post-intervention test to evaluate knowledge acquisition, and undertook a mannequin-based crisis scenario one week after training to evaluate knowledge application. Results: Both groups demonstrated significant knowledge acquisition (VR: mean improvement +2.0/15, P=0.006; Mannequin: mean improvement +3.2/15, P<0.001), though no statistically significant differences were observed between groups (P=0.15). The VR group achieved self-assessed readiness and knowledge saturation faster, on average, than the Mannequin group. Both groups, on average, were successful in the post-training knowledge application test, however, the Mannequin group outperformed the VR group (mean difference: 1.58/15, P=0.021), and recognized a potential airway injury more quickly (P=0.004). Nevertheless, students in the VR group reported greater engagement and satisfaction, expressing a preference for VR as a future learning modality. Conclusions: Overall, VR-based simulation is a promising and engaging method for teaching airway trauma management and demonstrates comparable knowledge acquisition to traditional mannequin-based training. However, mannequin-based simulation still confers advantages for applied performance. Further studies using larger samples, multiple scenarios, and VR-based assessments are needed. Clinical Trial: ClinicalTrials.gov NCT04451590; https://clinicaltrials.gov/study/NCT04451590
Background: Reading performance is closely associated with cognitive function, and eye-tracking metrics have emerged as sensitive, non-invasive indicators of cognitive processes. Recent advances in we...
Background: Reading performance is closely associated with cognitive function, and eye-tracking metrics have emerged as sensitive, non-invasive indicators of cognitive processes. Recent advances in wearable technologies, such as smart glasses, enable continuous and scalable measurement of eye movements in real-world settings. However, rapid, accessible, and objective tools for cognitive screening remain limited. Integrating wearable eye-tracking with multidomain cognitive assessment may provide a scalable digital approach for early detection of cognitive impairment. Objective: To evaluate the association between wearable eye-tracking metrics and cognitive performance and to assess the feasibility of a smart glasses–based reading task as a rapid digital screening tool. Methods: In this prospective observational study, Mandarin-literate adults were recruited from Taipei Veterans General Hospital between May to August 2025. Participants completed a standardized reading task while wearing J7EF Gaze smart glasses. Eight eye-tracking metrics were recorded, followed by the six-domain cognitive assessment using gaze-based interaction. Associations were analyzed via multivariable regression adjusted for age and sex. Results: A total of 134 participants were enrolled (mean age 68.2 ± 13.4 years). Age correlated with all six cognitive domains and the total score, while sex exhibited smaller, domain-specific effects. In unadjusted analyses, total reading time showed the strongest associations with all cognitive domains (p < 0.001), while fixation duration, fixation frequency, and long or ultra-long fixations showed selective associations with orientation. After adjusting for age and sex, total reading time, total fixation time and average fixation time remained significant predictors. Conclusions: Total reading time emerged as a robust, age-independent eye-tracking marker of cognitive performance. Fixation-related metrics showed domain-specific associations, particularly with the puzzle game hobbies domain of the cognitive assessment. Wearable smart glasses with integrated eye tracking may provide a rapid, non-invasive, and scalable approach for digital cognitive screening in clinical and real-world settings.
Background: As the global population ages, technology-enabled apparel has emerged as a potential solution to support aging-in-place for individuals living with dementia. Objective: This scoping review...
Background: As the global population ages, technology-enabled apparel has emerged as a potential solution to support aging-in-place for individuals living with dementia. Objective: This scoping review maps the landscape of wearable, apparel-integrated technologies designed to assist people with dementia in community-based settings. ulation ages, technology-enabled apparel has emerged as a potential solution to support aging-in-place for individuals living with dementia. Methods: Following Arksey and O’Malley’s methodological framework, five stages were implemented: identifying the research questions, selecting relevant studies, charting data, and synthesizing results. A multi-database search strategy retrieved 270 records, from which 21 articles were included after rigorous screening and consensus-based full-text review. Data were extracted across multiple domains, including apparel functionality, technological features, target populations, and caregiver involvement. Results: Thematic analysis revealed five primary functional roles of technology-enabled apparel: dressing assistance, physical monitoring, cognitive monitoring, therapeutic support, and care coordination. While most studies emphasized safety and monitoring, few addressed user dignity, ethical considerations, or co-design with people living with dementia. Findings suggest that although wearable apparel technologies are evolving, significant gaps remain in user-centered design, real-world application, and ethical implementation. Conclusions: This review highlights the importance of involving people with dementia early in the development process, aligning innovations with their values, and shifting the focus beyond caregiver and technology-centric priorities. Future research should prioritize interdisciplinary collaboration and longitudinal evaluation to ensure these technologies enhance, rather than compromise, autonomy and quality of life.
Background: Hypertension is a leading contributor to cardiovascular morbidity in India, with burden in rural populations due to limited access to care and poor adherence to pharmacological management....
Background: Hypertension is a leading contributor to cardiovascular morbidity in India, with burden in rural populations due to limited access to care and poor adherence to pharmacological management. Mobile health (mHealth) interventions offer scalable solutions for self-management in resource-constrained settings. Objective: This study aimed to develop and pilot-evaluate “My Heart,” a citizen-centric Android-based mobile application with integrated clinical decision support to support hypertension self-management in rural Assam, India. Methods: “My Heart” was developed using a Goal-Directed Design framework. The system comprises an Android app for participants and a secure web-based dashboard for investigators, enabling self-reporting of clinical and lifestyle data, longitudinal monitoring, and automated decision support aligned with ESC/ESH and ADA guidelines. Key features include offline data capture, AES-256 encryption, REST API synchronization, and personalized behavioral messaging. Results: The application integrated self-monitoring, decision support, and digital behavior modification. Participants reported improved awareness and engagement, with preliminary improvements in physical activity, dietary salt reduction, substance use, and medication adherence. Users valued reminders and educational content. Barriers included age-related usability challenges, English-only interface, limited distribution pathways, and manual synchronization in low-connectivity areas. Conclusions: “My Heart” demonstrates feasibility of a user-centered mHealth intervention for hypertension self-management in rural India. With multilingual support, cross-platform expansion, and enhanced offline synchronization, the app shows promise as a scalable digital behavior change tool aligned with national NCD programs. Clinical Trial: Not applicable
Background: Background: Digital data streams, including social media and mobility tracking, offer new opportunities for real-time public health surveillance during rapidly evolving crises such as the ...
Background: Background: Digital data streams, including social media and mobility tracking, offer new opportunities for real-time public health surveillance during rapidly evolving crises such as the COVID-19 pandemic. Public emotional responses, captured through social media platforms, may influence compliance with public health interventions, yet their interaction with policy measures remains insufficiently understood. Objective: Objective: This study examines the relationship between population-level stress measured by negative emotions, public health policy implementation, and mobility behavior during the early stages of the COVID-19 pandemic in the United States, with a focus on how emotional responses interact with stay-at-home orders to shape behavioral outcomes. Methods: Methods: We conducted a county-level longitudinal ecological analysis using publicly available data from February to April 2020. Public stress levels were measured using geolocated Twitter data on negative emotions toward COVID-19, while mobility behavior was assessed using SafeGraph social distancing metrics as a proxy for staying at home. Public health policy exposure was operationalized as the number of days stay-at-home orders were in effect. Random-effects regression models were used to evaluate associations between emotional signals, policy duration, and mobility, including interaction effects. Models controlled for demographic, socioeconomic, epidemiological, and political factors. Results: Results: Higher levels of stress, characterized by negative emotions, were significantly linked to greater reductions in mobility (β=18.83, p<0.001). The duration of stay-at-home orders also positively correlated with decreased mobility and notably moderated the relationship between stress levels and mobility (β=0.82, p<0.001), suggesting that emotional responses intensified the impact of policy measures. Other interventions, such as school and business closures, demonstrated less consistent associations with mobility. Conclusions: Conclusions: This study demonstrates the value of integrating social media-derived emotional signals with mobility data for public health surveillance. Emotional responses appear to play a critical role in shaping behavioral compliance, particularly when reinforced by clear policy interventions. These findings suggest that incorporating real-time emotional monitoring into public health strategy may improve the effectiveness of policy communication and implementation during future health emergencies.
Background: Negative symptoms (NS) in schizophrenia spectrum disorders (SSD) are strongly associated with poor functional and clinical outcomes and remain an unmet treatment challenge. Virtual reality...
Background: Negative symptoms (NS) in schizophrenia spectrum disorders (SSD) are strongly associated with poor functional and clinical outcomes and remain an unmet treatment challenge. Virtual reality (VR)-supported psychotherapy may offer a promising approach by enabling immersive, real-time activation and engagement. Additionally, interventions targeting social reward processing deficits in SSD warrant further examination. A VR-supported psychotherapy used in the ENGAGE trial was developed to target NS and social reward deficits in SSD. Objective: The aim of this qualitative study was to explore perspectives and experiences of participants receiv-ing VR-supported psychotherapy in the ENGAGE trial to generate in-depth, real-world insights of this novel treatment approach. Methods: Semi-structured, individual interviews were conducted with 11 of the 16 participants in the inter-vention group and analyzed descriptively using abductive qualitative content analysis. Results: The analysis yielded five categories: Learning about and knowledge of NS, Varying appraisals of VR, Continuation of therapeutic work outside sessions (subcategories: Components supporting conti-nuity and An unexpected contribution), Perceived mechanisms of change (subcategories: Identifica-tion and management of negative thought patterns, Shifting attention toward accomplishments and positive stimuli, Reframing self-criticism and self-compassion, and Reappraisal as a technique to deal with behavioral barriers), and Feasibility and opportunities for refinement. Investigator triangulation and reflexivity ensured trustworthiness. Conclusions: Participants expressed good acceptability and feasibility of the VR-supported psychotherapy, with positive cognitive, emotional, and behavioral responses. However, experiences varied considerably, underscoring the importance of personal relevance and tailored, symptom-specific approaches. Mi-nor practical and technical barriers were identified. Findings provided insight into acceptability, mechanisms, and avenues for tailoring VR-supported psychotherapy for NS.
Time constraints and fragmented pre-consultation information are persistent challenges in general practice. This viewpoint describes the development, deployment, and discontinuation of Sokrates AI, a ...
Time constraints and fragmented pre-consultation information are persistent challenges in general practice. This viewpoint describes the development, deployment, and discontinuation of Sokrates AI, a physician-developed, open-source chatbot designed to conduct Socratic pre-consultation history-taking in Norwegian primary care. Built almost entirely through AI-assisted code-generation through natural language prompting (vibe-coding) using OpenAI Codex/GPT-4 and Cursor.ai, the tool was deployed on a GDPR-compliant server and used informally with approximately 20 patients over one month in autumn 2025. Clinical utility was most apparent for asynchronous e-consultations and for patients disclosing psychosocial concerns; the tool was ultimately discontinued due to implementation burden and the availability of simpler alternatives for routine use.
The central argument of this viewpoint is that AI-assisted development has meaningfully lowered the threshold for clinician-led digital innovation, enabling a practicing physician with no formal software engineering training to build and deploy a functional clinical application. This shift has implications for how the medical profession engages with the AI systems entering clinical practice, and for how medical education should respond. Three further observations from the project—concerning clinical adoption barriers, the unsuitability of LLMs for triage, and the risk of digital inequity—are noted as hypotheses warranting dedicated investigation rather than conclusions this single experience can support.
Background: Large real-world data sources offer a unique opportunity to study the health of diverse ethnic groups. High-quality and accessible ethnicity data is needed to maximise this potential. Obje...
Background: Large real-world data sources offer a unique opportunity to study the health of diverse ethnic groups. High-quality and accessible ethnicity data is needed to maximise this potential. Objective: To validate a newly developed ethnicity phenotype in the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). Methods: Retrospective cross-sectional study of individuals registered at a practice within the Oxford-RCGP RSC on 4th December 2024. An updated ethnicity phenotype was implemented and validated. Ethnicity data quality was assessed by evaluating completeness, distribution, and accuracy through external validation against estimates from the 2021 UK Census. Results: Of 21,902,852 individuals, 88.63% (19,412,154) had a recorded ethnicity following the implementation of the updated ethnicity phenotype. There was a marked improvement in the recording of granular (19-point) ethnicity data, with completeness increasing from 69.06% (15,126,835) to 88.63% (19,412,154) with the updated phenotype. There was significant variation in the completeness of ethnicity data according to demographic subgroups. The proportion of individuals in each ethnicity group was within 3.56 percentage points of the 2021 Census estimates for the same ethnicity group across England. Larger relative differences were observed for non-White ethnic groups. Conclusions: The updated ethnicity phenotype provides high-quality and granular ethnicity data based on official classifications for almost 90% of individuals. The overall ethnicity breakdown in the Oxford-RCGP RSC population was broadly similar to 2021 UK Census estimates. The updated ethnicity phenotype supports secondary uses of primary care CMRs, providing high-quality and accessible ethnicity data to study the health of diverse ethnic groups.
Medical textbook translation remains a bottleneck in global health knowledge dissemination, typically requiring 4 to 8 weeks by physician-translators who possess both clinical expertise and bilingual ...
Medical textbook translation remains a bottleneck in global health knowledge dissemination, typically requiring 4 to 8 weeks by physician-translators who possess both clinical expertise and bilingual fluency. We present an orchestrated multi-model AI translation pipeline that completed the localization of a 158-page Japanese dermatology textbook (conversational genre, A5 format) into Korean in two calendar days of active pipeline execution — following a separate multi-day preparation phase for terminology extraction and database registration. The pipeline is built on three architectural principles: (1) a cross-model validation constraint, in which the AI model that produces the translation is never the model that validates it — an operationalization of the well-established separation-of-duties principle from AI safety; (2) a seven-layer progressive quality assurance system that filters inexpensive-to-detect errors upstream before engaging costly validation downstream; and (3) cumulative terminology databases shared across five sequential book projects, accelerating each successive translation. During the two-day execution, the physician-translator typed zero characters of translation text but made over 400 quality judgment decisions, including confirming 325 terminology entries, evaluating 28 translator note proposals, and approving 447 individual text corrections identified by cross-model review. We argue that the cross-model validation constraint — where the producer and evaluator of medical AI content must be different systems — should become a standard design requirement for AI-generated content in healthcare, and that the physician-translator's role is shifting from text production to quality judgment.
Background: Lumbar puncture in geriatrics can be challenging, requiring technical skill and operator confidence. Simulation provides a valuable opportunity for medical students to practice in a safe e...
Background: Lumbar puncture in geriatrics can be challenging, requiring technical skill and operator confidence. Simulation provides a valuable opportunity for medical students to practice in a safe environment. Despite the potential of augmented reality simulators, their impact on user experience (UX) across different levels of clinical expertise remains insufficiently investigated. Objective: The aim of this study was to evaluate the user experience of two lumbar puncture simulators: M43E (Kyoto Kagaku), a static anatomical model commonly used in French simulation centers, and the Sim&Care 2 (InSimo), an augmented reality simulator with haptic feedback that is less widely implemented in routine training curricula. Methods: This single-center UX study, conducted at the University Hospital of Angers, used a cross-sectional within-subject design among 30 participants (7 Graduated physicians, 16 postgraduate medical students, 7 sixth-year second-cycle medical students), involved 15-minutes use of each simulator. Participants completed a user experience questionnaire (realism, ease of use, perceived educational value), followed by a comparative questionnaire. Non-parametric paired and group comparisons were performed. Results: The Sim&Care 2 was considered more realistic, providing better sensations (P=.003) and helped participants learn something new (P=.005). The M43E was considered simpler to use (P=.013). No significant difference was observed in overall satisfaction. Concerning overall preference, graduated physicians favored the Sim&Care 2 model for its anatomical realism and haptic feedback, while sixth-year second cycle medical students preferred the M43E model for its realistic procedure execution. Conclusions: These findings highlight the complementarity of the simulators and the need to select the appropriate simulator for training according to the learners’ level of expertise: traditional mannequin-based simulation for initial procedural familiarization and augmented reality simulation to enhance three-dimensional anatomical understanding and sensory feedback.
Background: Despite the increasing global availability of robotic exoskeleton walking (REW), its clinical application remains highly inconsistent. This variability can be attributed to a lack of clini...
Background: Despite the increasing global availability of robotic exoskeleton walking (REW), its clinical application remains highly inconsistent. This variability can be attributed to a lack of clinical guidelines and the significant time burden required for clinicians to synthesize emerging evidence. Consequently, there is a need for expert-driven consensus on how to optimize the integration of REW into neurological rehabilitation. Objective: To generate an international, expert-led consensus on the optimal clinical use of robotic exoskeletons in practice using a Delphi methodology. Methods: A three-round classic Delphi process was conducted. An international panel of twenty-six expert physiotherapists with significant clinical experience in REW was recruited. Panelists evaluated the use of robotic exoskeletons in terms of patient selection, treatment effects, dosage (frequency, duration, weeks of treatment), and integration with conventional physiotherapy. Consensus was predefined as 70% agreement, while median and interquartile range measured the central tendency of the results. Results: The panel reached a high level of consensus on several key areas necessary to support the use of REW in practice. Experts provided very strong consensus for specific conditions, notably incomplete spinal cord injury and stroke. Recommendations such as 2 -3 sessions per week for 30-45 minutes in sub-acute incomplete spinal cord injury, were established. Furthermore, the panel reached consensus on the necessity of "combination therapy," viewing REW as superior or complementary to conventional therapy in a variety of clinical circumstances. Conclusions: This study provides robust, expert-validated recommendations on the implementation of REW. REW should be prioritized in the rehabilitation of gait and motor control for incomplete SCI and stroke due to its high levels of consensus for benefit in these conditions. This contrasts with its role in non-ambulant neurological conditions (e.g., complete SCI where health benefits and symptom management are the expected therapeutic effects of REW). These consensus-based recommendations may help to reduce clinical uncertainty, bridging the evidence-practice gap in robotic-assisted rehabilitation, but should not act as a replacement for clinical reasoning and patient-centered decision making.
Background: Wearable health technologies are increasingly adopted by consumers and are emerging as tools for digital health monitoring and behavior change. However, their integration into physical the...
Background: Wearable health technologies are increasingly adopted by consumers and are emerging as tools for digital health monitoring and behavior change. However, their integration into physical therapy practice remains unclear. Understanding clinician-level adoption patterns is necessary to guide implementation and education efforts. Objective: To determine the prevalence, patterns, and clinician characteristics associated with wearable use in US PT practice. Methods: A national cross-sectional online survey was distributed to licensed physical therapists (PTs) and physical therapist assistants (PTAs) from six US state board listservs. Descriptive statistics and bivariate analyses examined associations with wearable use. A multivariable logistic regression model identified independent predictors of adoption. Open responses were examined for additional insights into wearable use. Results: Among respondents, 34.4% reported using wearables with at least one patient. Users incorporated them with 34.5% of their caseload, yielding 11.9% of all patients being exposed to wearables at some point during rehabilitative care. 6.5% are estimated to receive regular (weekly or near-every-session) integration. Wrist-worn devices and smartphones were most common types used, with heart rate, exercise minutes, and step counts as the primary data used by clinicians, with in-session monitoring and patient education as the most cited purposes. In multivariable analyses, personal wearable use (OR 0.22 for non-users, 95% CI 0.16–0.31), frequent neurologic caseload (OR 1.62, 95% CI 1.27–2.06), APTA membership (OR 1.49, 95% CI 1.18–1.87), and primary practice setting were independently associated with adoption. Age, years of practice, rurality, and regional income were not associated. Conclusions: Wearable use in US PT practice is present but selectively applied, with routine integration occurring in a small fraction of patients. Adoption appears driven by personal familiarity and professional engagement rather than demographic factors, suggesting wearable implementation in rehabilitation remains in an early diffusion phase.
Background: Large Language Models (LLMs) are rapidly being adopted across the medical field. Emergency medicine is characterized by the need for critical decision-making under high uncertainty, incomp...
Background: Large Language Models (LLMs) are rapidly being adopted across the medical field. Emergency medicine is characterized by the need for critical decision-making under high uncertainty, incomplete information, and severe time constraints, facing challenges in LLM implementation distinct from other departments. While surveys targeting general physicians or other specialties exist, large-scale surveys specifically targeting emergency physicians are scarce. Objective: This study aimed to assess the usage patterns and perceived issues of LLMs among emergency physicians through a web-based survey conducted by the Japanese Association for Acute Medicine. This study provides foundational knowledge on the current status of LLM use among emergency physicians to promote its safe and effective implementation. Methods: An anonymous, cross-sectional, web-based survey was conducted among participants of the Japanese Association for Acute Medicine between June and August 2025. The analysis included 362 emergency physicians. Survey items comprised respondent attributes, LLM usage experience, frequency, purposes of use, service names, and perceived issues (free text). Free-text responses (n=208) were classified using a deterministic rule-based workflow into 6 themes (multi-label). Ordered logistic regression and logistic regression were performed to evaluate the association between sex, age, and years of clinical experience and multiple LLM-related outcomes, calculating odds ratios (ORs). Results: The mean age of the 362 participants was 49.2 years. 290 physicians (80.1%) had experience using LLMs. Of these, 46.2% used them "daily" and 33.8% "weekly," meaning 80.0% used them at least once a week. Usage rates were higher among younger generations: 95.3% for those ≤39 years vs 69.0% for those ≥60 years. Purposes included personal use (71.7%), academic activities (65.2%), education (57.2%), operational efficiency/administrative tasks (49.3%), and clinical decision support (43.1%). The regression analyses showed that for each one-step increase in age category, the odds of use frequency significantly decreased (OR 0.97, 95% Confidence Intervals (CI) 0.95-0.98; P<0.001), as did the use for clinical decision support (OR 0.96, 95% CI 0.94-0.99; P<0.001). The rule-based thematic classification identified "Accuracy/Reliability" (60.6%) as the most frequent concern. Conclusions: LLM usage has already widely spread among emergency physicians, with younger physicians showing notably higher frequency especially for clinical decision support. This study helps to understand the current status of LLM usage in emergency settings and to discuss future directions for the development of LLM models and usage guidelines tailored to emergency medicine.
Background: Rehabilitation technologies are increasingly positioned as key enablers of accessible, scalable rehabilitation in the United Kingdom (UK), particularly in the context of population ageing ...
Background: Rehabilitation technologies are increasingly positioned as key enablers of accessible, scalable rehabilitation in the United Kingdom (UK), particularly in the context of population ageing and rising long‑term conditions. However, adoption in practice is often constrained by insufficient training for health and care professionals (HCPs). A systematic understanding of training needs is essential to support safe, effective, and sustainable implementation, yet no review has synthesized these needs across the breadth of rehabilitation technologies within the UK. Objective: This scoping review aimed to identify and map the training and education needs of UK HCPs related to rehabilitation technologies and to analyze these needs using the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation–Behavior (COM‑B) model to inform the design of future training interventions. Methods: Following JBI methodology and PRISMA‑ScR guidelines, nine databases were searched from inception to November 2025. Data were extracted using a modified JBI tool and synthesized descriptively. Training needs were coded to the TDF and mapped to COM‑B. Methodological quality was appraised using JBI critical appraisal tools. Results: Sixteen studies (2012–2025) involving 1,032 HCPs met the inclusion criteria. Technologies were grouped into remote rehabilitation, device‑based, and immersive modalities, most commonly applied in neurorehabilitation. Training provision was limited and inconsistent. Identified needs predominantly aligned with psychological and physical capability domains, including knowledge of safety and evidence, remote assessment and intervention skills, device operation, and patient coaching. Fewer needs related to reflective motivation. Contextual factors influencing training uptake included protected time, access to resources, and flexible delivery formats. Conclusions: Training for rehabilitation technologies in the UK remains limited and disproportionately focused on capability‑related needs. Addressing motivational and contextual determinants may enhance adoption and integration into routine practice. These findings informed the development of the RehabTech‑PPC approach, offering a structured, theory‑informed framework to guide the design of future training programs in rehabilitation and assistive technologies. Clinical Trial: The review was registered with Open Science Framework on 31 October 2025: https://doi.org/10.17605/OSF.IO/47UFM
Background: Skilled nursing facilities (SNFs) operate under data-intensive regulatory environments requiring HIPAA-compliant, continuously deployable predictive analytics pipelines. Existing DataOps approaches address individual pipeline components in isolation but lack an integrated clinical informatics architecture tailored to the long-term care setting. Objective: To design, implement, and evaluate an end-to-end DataOps framework for automated predictive analytics in regulated SNF environments, integrating infrastructure-as-code, CI/CD automation, and automated MLOps into a unified architecture. Methods: We developed a five-layer, cloud-native DataOps framework unifying Azure Synapse Analytics, Terraform infrastructure-as-code, and GitHub Actions CI/CD. The framework was deployed across five SNF sites serving over 3,500 patients monthly and evaluated over a three-month pilot with a matched three-month pre-implementation baseline. Results: The framework reduced manual data engineering effort by 30%, improved 30-day readmission prediction ROC-AUC from 0.82 to 0.89, and was associated with a 12.2% reduction in 30-day unplanned readmissions (148 to 130 events). Dashboard latency was maintained below 15 minutes (mean: 11.4 min) and infrastructure provisioning was repeatable within 30 minutes. Conclusions: The proposed DataOps framework provides a reproducible, audit-ready clinical informatics architecture for SNF environments. The integration of CI/CD, infrastructure-as-code, and automated MLOps
addresses a gap in the health informatics literature and offers a practical blueprint for operationalizing predictive analytics under regulatory constraints.
Background: Risk stratification using longitudinal electronic health records (EHRs) remains challenging due to the complex nature of disease trajectories. While deep sequential models have shown promi...
Background: Risk stratification using longitudinal electronic health records (EHRs) remains challenging due to the complex nature of disease trajectories. While deep sequential models have shown promising performance, the inherent complexity of these classifiers often limits their interpretability and clinical trustworthinessin in high-stakes medical decision-making. Objective: This study proposes a representation learning framework to capture clinically meaningful disease progression within a geometric trajectory space, thereby improving model transparency and reducing reliance on complex, non-interpretable classifiers. Methods: We developed a framework that integrates comorbidity structures and clinical deviation vectors to map clinically meaningful disease progression at the representation level. The learned representations were evaluated using both linear (LR) and non-linear (XGBoost) classifiers under rigorous out-of-fold validation. Systematic ablation analyses were performed to disentangle the contributions of the geometric representation design from classifier choice. Results: Across multiple experimental settings, simple linear classifiers achieved performance comparable to gradient-boosted tree models, yielding a peak AUPRC of .651 (overall) and .825 (high-risk subgroup). These findings indicate that the proposed representations effectively internalize non-linear disease interactions, resulting in a linearly separable risk space. Visualization of disease trajectories further demonstrates clear clinical face validity, with high-risk and low-risk patient groups exhibiting distinct geometric patterns consistent with domain knowledge. Conclusions: Our results suggest that clinically meaningful disease trajectories can be effectively captured through geometric representation design, significantly reducing the dependence on complex classifiers for risk stratification. This representation-driven approach offers a transparent, stable, and interpretable framework for longitudinal EHR analysis, with high potential for deployment in clinical risk stratification systems. Clinical Trial: N/A
Background: Air pollution is a major environmental risk to human health, yet access to timely and territorially useful air quality information remains uneven in Brazil. The national monitoring network...
Background: Air pollution is a major environmental risk to human health, yet access to timely and territorially useful air quality information remains uneven in Brazil. The national monitoring network is concentrated in a limited number of metropolitan areas, while many municipalities lack continuous observational data. This constrains the routine use of air pollution information in environmental public health surveillance. Objective: The objective of this paper was to describe AlertAr Saúde, an open online system that provides municipal-level air pollution forecasts and historical estimates for all municipalities in Brazil to support public health surveillance, risk communication, and environmental health analysis. Methods: AlertAr Saúde integrates operational forecast products and global reanalysis products from the Copernicus Atmosphere Monitoring Service (CAMS). Gridded atmospheric fields for PM2.5, PM10, O3, NO2, SO2, and CO, as well as selected meteorological variables, are acquired automatically, standardized, and spatially aggregated to Brazilian municipalities by means of area-weighted zonal statistics based on official municipal boundaries. The outputs are disseminated through a web platform implemented in R/Shiny that provides interactive maps, municipal time series, and data downloads. The evaluation strategy summarized in this paper is based on published CAMS validation reports, with emphasis on aerosol validation relevant to particulate matter-related outputs. Results: The system provides daily updated municipal-level forecasts with a 120-hour horizon and a historical daily municipal database beginning in January 2003. The platform is publicly accessible without authentication and allows users to explore maps, inspect municipal time series, and download the displayed data. By translating global atmospheric products into administrative units aligned with the Brazilian Unified Health System, AlertAr Saúde reduces territorial barriers to the use of air quality information. Published CAMS validation reports indicate that the underlying atmospheric service reproduces major spatial and temporal aerosol patterns at regional and global scales, supporting its use as an indicative input for surveillance and early warning rather than as a substitute for local reference monitoring. Conclusions: AlertAr Saúde demonstrates how atmospheric modeling, spatial aggregation, and web dissemination can be combined into a public health-oriented environmental intelligence tool in Brazil. The system broadens access to air pollution information in underserved territories and provides a practical foundation for surveillance, risk communication, research, and future alerting applications.
Background: Modifiable health risks in pregnancy, including smoking, alcohol consumption, gestational weight gain outside recommended ranges, poor dietary intake and physical inactivity, are well esta...
Background: Modifiable health risks in pregnancy, including smoking, alcohol consumption, gestational weight gain outside recommended ranges, poor dietary intake and physical inactivity, are well established contributors to adverse obstetric outcomes. Although antenatal care guidelines recommend addressing these risks throughout pregnancy, antenatal providers often find it difficult to deliver preventive care within time limited appointments. Hybrid models that combine in person care with digital platforms offer a promising solution to address these barriers, yet little research has explored consumer and provider views on the preferred content and features of such platforms. Objective: To explore consumer and provider perspectives on the ideal content and functionality of a digital platform to deliver preventive care within a hybrid antenatal model. Methods: A descriptive qualitative study was conducted in May and June 2024 across two regional maternity services in New South Wales, Australia. Two workshops were held with recently pregnant consumers and two with antenatal providers. Purposive sampling was used, with targeted recruitment of Aboriginal and Torres Strait Islander people and people from culturally and linguistically diverse backgrounds. Data from structured group activities, observation notes, and audio recordings were thematically analysed using a deductive approach guided by an existing framework that examined what content the platform should include, how it should be delivered, and why it was needed. Results: Twenty-three consumers and twelve antenatal providers participated. Participants identified the need for digital risk assessments and for evidence-based, personalised information and practical resources. They emphasised a requirement for clear support options and extending content into labour, birth, and the postnatal period. Early access to the platform, reminders linked to appointments, and integration with routine antenatal care were viewed as important. Tracking, personalisation, multiple content formats, filtering functions, and sharing options were highlighted as desirable features. Participants described gaps in information early in pregnancy and noted that modifiable health risks were often not addressed in routine antenatal care. They also reported that digital assessments could reduce stigma and support more honest disclosure, and that a hybrid model of care could enhance communication between consumers and providers. Conclusions: Consumers and providers identified key content and functionality for a digital platform to deliver preventive care within a hybrid model of antenatal care. Prioritising credible and culturally tailored content, early access to information that helps address modifiable health risks, and integration with routine antenatal care will be important for developing acceptable and sustainable digital solutions. As these insights were generated during the development stage, future research should examine real world implementation, including uptake, engagement, and how well the platform fits into the workflows and processes of antenatal care.
Background: Physical inactivity among young adults has become a growing public health concern, increasing the risk of chronic diseases and diminishing quality of life. Wearable hip-assist exoskeletons...
Background: Physical inactivity among young adults has become a growing public health concern, increasing the risk of chronic diseases and diminishing quality of life. Wearable hip-assist exoskeletons have emerged as promising interventions that can augment the exercise load during ordinary walking; however, their effects on exercise motivation and perceived benefits remain largely unexplored. Objective: To compare the effects of exoskeleton-assisted walking and conventional walking on exercise continuation intentions and perceived exercise benefits. We also explored whether baseline physical activity levels moderated these effects. Methods: Using a randomized crossover design, 60 young adults (age: 23.45 ± 2.17 years; 26 men, 34 women) performed two walking sessions on the same approximately 1.9-km campus course under two conditions: (1) wearing a wearable hip exoskeleton (Bot Fit Pro, Samsung Electronics) in interval mode and (2) conventional walking without the device. The order of the conditions was randomized, with a minimum 24-h washout period. Immediately after each session, participants completed questionnaires assessing exercise continuation intention (7 items, Cronbach’s α = 0.929) and perceived exercise benefits (10 items, Cronbach’s α = 0.914) on a 5-point Likert scale. The exercise duration and perceived course difficulty were also recorded. Data were analyzed using the Wilcoxon signed-rank test for within-subject comparisons, the Mann–Whitney U test for between-group comparisons, and effect sizes (r = |z| / √N). Bonferroni correction was applied to the two primary outcomes (adjusted α = 0.025). Results: Compared with conventional walking, exoskeleton-assisted walking produced significantly higher scores for exercise continuation intention (z = −2.397, P = 0.017, r = 0.309) and perceived exercise benefits (z = −4.768, P < 0.001, r = 0.615), with no significant differences in exercise duration (P = 0.468), perceived course difficulty (P = 0.063), or order effects (all P > 0.05). Exploratory subgroup analysis revealed that the high-frequency exercise group (≥ 3 times/week, n = 39) showed significant improvements in both outcomes, whereas the low-frequency group (≤ 2 times/week, n = 21) showed no significant differences. The high-frequency group demonstrated a significantly greater improvement in perceived exercise benefits than the low-frequency group (U = 571.0, P = 0.012). Conclusions: Walking with a wearable hip exoskeleton significantly enhanced exercise continuation intention and perceived exercise benefits without increasing the exercise duration or perceived burden. Individuals with a higher baseline physical activity level showed greater sensitivity to the benefits of the exoskeleton, suggesting that wearable exoskeleton technology may serve as an effective motivational tool for promoting walking exercises across varying levels of physical activity. Clinical Trial: Clinical Research Information Service (CRIS) KCT0011848; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=33084&search_page=M&search_lang=&class_yn=
Drug addiction is a growing global public health crisis that affects not only individuals but entire family systems. While effective therapeutic approaches exist, many families struggle to access qual...
Drug addiction is a growing global public health crisis that affects not only individuals but entire family systems. While effective therapeutic approaches exist, many families struggle to access qualified addiction therapists or receive timely, practical guidance on how to support a loved one with substance use disorder. Moreover, families often face a painful emotional dilemma: enabling addictive behavior versus enforcing boundaries that may lead to homelessness, incarceration, or further harm. This paper proposes a novel, complementary approach: the development of a language model fine-tuned on recovery-oriented data, including guidance from addiction therapists and lived experience narratives from recovering addicts. The goal is not to replace professional treatment, but to provide accessible, empathetic, yet emotionally neutral and experience-informed advisory support for parents and loved ones. We argue that recent advances in LLMs, fine-tuning techniques, and alignment methods make this approach both feasible and timely. We outline the motivation, conceptual foundations, technical feasibility, and potential societal impact of such a system.
Background: Generative Artificial Intelligence (AI) tools are emerging as a promising solution to streamline and improve clinical documentation. Discharge instructions are a crucial part of Emergency ...
Background: Generative Artificial Intelligence (AI) tools are emerging as a promising solution to streamline and improve clinical documentation. Discharge instructions are a crucial part of Emergency Department (ED) care, but due to time constraints, they may lack key elements like anticipatory guidance or return precautions. AI may expedite and improve discharge instruction writing by providing comprehensive drafts for clinicians’ review. Objective: Evaluate the quality of AI-generated vs. handwritten discharge instructions, as well as a commercial AI tool’s ease of use and providers’ readiness to adopt similar technologies in clinical workflows. Methods: Surveys were administered via Qualtrics. Participants were asked about their opinions of AI, to rate three discharge summaries, one of which was AI-generated, and to draft discharge instructions with ChatGPT 4.0 using a one-shot method. Additive Likert scores were used to assess enthusiasm and familiarity with AI and the quality of all discharge instructions. Quantitative data was analyzed in GraphPad Prism 10.2.
Two investigators performed narrative synthesis to identify common themes in short-answer responses and ChatGPT prompts/outputs. Ngram analysis examined free-text elements for keyword frequency. Results: 76 clinicians initiated and completed at least one part of the survey. Participants’ familiarity with and enthusiasm for generative AI did not correlate with their provider role or years in practice, and familiarity with AI did not covary with enthusiasm (p>0.05). The AI-generated discharge summary received higher quality scores than either form of handwritten discharge instructions (p<0.01), and fared especially well compared to the brief handwritten discharge summary (p<0.0001). The differences in quality score between discharge instructions were most pronounced amongst residents. The AI-generated discharge summary was consistently attributed to AI (≥88%). Conversely, The brief handwritten discharge summary was consistently identified as not AI (≥86%).
All participants who completed part 3 were satisfied with their AI-generated discharge instructions, and only 26% re-prompted ChatGPT. Quality scores did not differ between providers who prompted once or twice (p>0.05). NGram analysis showed that ChatGPT outputs consistently used the same keywords and provided section headings. On the other hand, initial prompts usually contained common sense keywords like “discharge instructions” and “chest pain,” but re-prompts were highly variable. Conclusions: Generative AI has the potential to help providers write discharge instructions more efficiently and thoroughly. An AI-generated discharge summary was rated higher in quality than two forms of handwritten discharge instructions by ED clinicians, and ED clinicians indicated satisfaction when generating their own discharge instructions using a commercially available generative AI tool. ED clinicians were also able to reliably differentiate between handwritten and AI-generated discharge instructions, underscoring the need for further research of all key stakeholders’ perceptions on quality and safety of an AI-assisted documentation workflow.
Background: Virtual reality (VR) and three-dimensional (3D) visualization technologies are increasingly recognized as valuable tools in surgical fields, especially where spatial understanding is criti...
Background: Virtual reality (VR) and three-dimensional (3D) visualization technologies are increasingly recognized as valuable tools in surgical fields, especially where spatial understanding is critical. In pediatric neuro-oncology, these tools may address specific challenges related to anatomical complexity, child-specific anatomy and the need for precision in tumor resection. Objective: To evaluate the use of VR, 3D and immersive visualization technologies in pediatric brain tumor surgery, focusing on their role in surgical planning and clinical relevance. Methods: A systematic review was conducted following PRISMA 2020 guidelines and registered with PROSPERO (CRD420261279242). Literature searches were performed across PubMed, Embase, Scopus, and Web of Science up to October 25, 2025. Studies were included if they reported original clinical or translational data on the use of VR, 3D, or immersive visualization technologies in pediatric brain tumor surgery or planning. Results: Seven studies met the inclusion criteria. VR/3D tools were mainly used for preoperative planning in high-complexity tumor cases. Reported benefits included improved spatial understanding in 86% of studies, increased planning confidence in 57%, and strong educational value in 86%. However, no study reported objective outcome measures or long-term patient data. Implementation was heterogeneous, with substantial variability in platforms and workflows. Conclusions: VR and 3D technologies show promise in improving planning and spatial understanding in pediatric neuro-oncology. However, evidence is limited by heterogeneity and lack of objective and long-term outcome data, highlighting the need for standardized and robust studies.
Background: Chronic or persistent pain can limit an individual’s ability to work or be productive at work, creating substantial societal and economic burden. Despite this, evidence-based work‑rela...
Background: Chronic or persistent pain can limit an individual’s ability to work or be productive at work, creating substantial societal and economic burden. Despite this, evidence-based work‑related advice and support for people with chronic pain is inconsistent. The Pain‑at‑Work Toolkit was co‑created with people living with pain, health care professionals, and employers to increase knowledge of employee rights, improve access to workplace support, and provide guidance on lifestyle behaviors that facilitate pain self‑management. Objective: This study aimed to establish the feasibility of conducting a definitive cluster randomized controlled trial comparing access to the Pain‑at‑Work Toolkit plus optional occupational therapist telephone support (intervention) with support-as-usual (SAU) from the employer (control). Primary outcomes were feasibility, acceptability, usability, and safety of the digital intervention. We also assessed the feasibility of candidate primary and secondary outcomes and tested research processes required for a definitive trial. Methods: We conducted an open‑label, parallel, two‑arm pragmatic feasibility cluster randomized controlled trial with exploratory health‑economics analysis and a nested qualitative study. Eligible organizations were based in England, had ≥10 employees, and were recruited through professional networks and direct approach. Individual participants were working adults aged ≥18 years, with internet access and self‑reported chronic pain interfering with their ability to undertake or enjoy productive work. A restricted 1:1 cluster‑level randomization allocated organizations to the intervention or control arms. After organizational and individual consent, participants completed a web‑based baseline survey (T0) assessing work capacity, health and wellbeing, and health‑care resource use. Follow‑up occurred at 3 months (T1) and 6 months (T2). Feasibility outcomes included recruitment, intervention fidelity (delivery, reach, uptake, engagement), retention, and follow‑up completion. Qualitative interviews with employees and stakeholders at T2 explored acceptability and contextual factors influencing delivery and uptake. Results: A total of 380 employees from 18 organizations participated. Recruitment exceeded targets at both organizational and individual levels, demonstrating strong feasibility and engagement. Follow‑up completion met predefined feasibility criteria but showed variability, largely due to employee turnover, providing realistic attrition estimates for a future trial. Outcome measures showed acceptable completion rates and variability, supporting their suitability for use in a future definitive trial. Employees and stakeholders reported high acceptability of the Pain‑at‑Work Toolkit, and qualitative findings highlighted improved knowledge, confidence, and self‑management among employees. Stakeholders endorsed the Toolkit’s relevance and practicality within workplace settings. Conclusions: The feasibility trial demonstrated that the Pain‑at‑Work Toolkit and trial procedures are acceptable, scalable, and deliverable across diverse workplaces. Findings identify responsive outcome measures, emphasize the need for strengthened retention strategies, and support the Toolkit’s use as a standalone intervention. Overall, the study provides a strong foundation for progressing to a fully powered definitive trial. Clinical Trial: ClinicalTrials.gov NCT05838677; https://clinicaltrials.gov/study/NCT05838677
International Registered Report Identifier (IRRID): DERR1-10.2196/51474
Background: Bladder cancer (BC) is among the most common cancers in the Netherlands and imposes a substantial clinical and economic burden. Evidence from epidemiological studies, indicates that dietar...
Background: Bladder cancer (BC) is among the most common cancers in the Netherlands and imposes a substantial clinical and economic burden. Evidence from epidemiological studies, indicates that dietary factors are related to BC risk. However, little is known about the potential population-level impact of implementing dietary advice through public campaigns. Objective: This study aimed to estimate the potential effect of a national food campaign, dedicated to BC, on dietary behavior, BC incidence, and related healthcare costs using Structured Expert Elicitation (SEE). The maximum impact was explored in a hypothetical scenario under optimistic and idealized assumptions. Methods: Relevant food groups were identified based on the 2015 Dutch Dietary Guidelines and BLEND study findings. SEE was conducted among Dutch experts in the field of BC, diet, or public health using a bins-and-chips survey to quantify expected dietary changes following a mass media campaign. A maximum-impact scenario analysis translated estimated dietary changes into hypothetical reductions in BC incidence and associated healthcare costs. Results: Fifteen experts (mean experience 21.2 years) participated in the survey. The average expected dietary change across food groups was 5.69%. Relative to the estimated case–non-case dietary intake contrast (15.05%) this corresponds to a potential 12.60% reduction in BC incidence and associated costs. This equates to estimated savings of approximately €14.75 million per year under these hypothetical assumptions. Conclusions: This study demonstrates that a public food campaign has the potential to improve dietary habits in the Netherlands. Using a hypothetical maximum-impact scenario, we estimated that dietary changes induced by a public food campaign could translate into substantial reductions in BC–related healthcare costs, while acknowledging that these estimates represent an upper bound rather than a causal effect. Clinical Trial: This study involved structured expert elicitation through a survey. According to the Dutch Medical Research Involving Human Subjects Act (WMO), the study did not fall under the scope of medical scientific research with human subjects as there was no infringement of the physical and/or psychological integrity of the participant and therefore did not require formal ethical approval.
All participants provided informed consent prior to participation. Responses were collected and processed anonymously. Participants were informed that they could withdraw from the study at any time without consequences.
Background: Pregnant refugee women face intersecting barriers to antenatal care (ANC), including language, legal-entitlement, and access constraints. Digital health interventions, such as mobile remin...
Background: Pregnant refugee women face intersecting barriers to antenatal care (ANC), including language, legal-entitlement, and access constraints. Digital health interventions, such as mobile reminder apps, may help improve continuity of ANC, but rigorous evaluations among refugees are rare and often complicated by implementation challenges. Objective: To evaluate the association between a mobile antenatal reminder application (HERA) and ANC visit attendance among Syrian refugee women in Türkiye, and to document discrepancies between the planned randomized controlled trial (RCT) protocol and real-world implementation. Methods: We conducted a quasi-experimental study at two migrant health centers in Istanbul, comparing pregnant Syrian refugee women with confirmed exposure to the HERA mobile reminder app (n = 48) to a retrospective baseline cohort identified from routine clinic records over the same period (n = 949). The intervention consisted of installing the HERA smartphone application, which provided push-notification reminders for upcoming antenatal visits and brief pregnancy-related information, alongside routine care; the study was originally planned as a three-arm randomized controlled trial, but incomplete recording of allocation and identifiers precluded an intention-to-treat analysis. Results: Pregnant women who received reminders through HERA attended more ANC visits than those in the baseline cohort (mean 2.5 vs 1.9 visits), with consistent evidence of higher probabilities of achieving ≥2 visits across frequentist and Bayesian analyses. Conclusions: A simple mobile reminder intervention was associated with increased ANC attendance among Syrian refugee women, despite substantial implementation barriers that transformed a planned RCT into a quasi-experimental evaluation. Our study highlights both the potential of digital health tools in humanitarian settings and the importance of robust identification and data-linkage strategies when designing digital health trials with refugees. Clinical Trial: NCT05094518 - https://clinicaltrials.gov/ct2/show/NCT05094518
Background: Epidermal growth factor receptor inhibitors (EGFRIs) are widely used in targeted therapy for various malignant tumors. However, these agents frequently cause adverse effects on the skin an...
Background: Epidermal growth factor receptor inhibitors (EGFRIs) are widely used in targeted therapy for various malignant tumors. However, these agents frequently cause adverse effects on the skin and its appendages, with skin toxicity being the most common, occurring in 79%–88% of patients. This severely reduces patients’ quality of life and adherence to anticancer treatment. Currently, there is a lack of well-evidenced preventive interventions. Xiaozhen Formula (XZF), composed of seven Chinese herbal medicines, has the effects of clearing heat and detoxifying, eliminating dampness and resolving nodules, cooling blood and promoting eruption. Preliminary clinical practice shows promising efficacy in preventing EGFRI-related skin toxicity, but high-quality clinical trial evidence remains insufficient. Objective: This study aims to evaluate the efficacy and safety of XZF in preventing EGFRI-induced skin toxicity through a multicenter, randomized, double-blind, placebo-controlled clinical trial, with a focus on its effect on the incidence of rash. Methods: This is a prospective, multicenter, randomized, double-blind, placebo-controlled clinical trial. Eligible participants are patients with malignant tumors aged 18–75 years, scheduled to receive or initially receiving EGFRI therapy with a planned treatment duration of at least 8 weeks. Patients will be randomized 1:1 into the treatment group and the control group. Both groups will receive standard EGFRI therapy. The treatment group will additionally receive oral XZF, while the control group will receive a placebo identical to XZF in appearance, formulation, and administration. The intervention will last 8 weeks. The primary outcome is the incidence of rash during EGFRI treatment, graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE). Secondary outcomes include the incidence of grade ≥2 rash, time to first onset of rash, rash severity, dermatology-related quality of life, adherence to EGFRI therapy, and adverse events. Both intention-to-treat (ITT) and per-protocol (PP) analyses will be conducted, with a two-sided significance level of α=0.05. Results: Compared with placebo, XZF is expected to significantly reduce the incidence of EGFRI-related rash and the proportion of moderate-to-severe rash, delay the onset of rash, and demonstrate a favorable safety profile. Conclusions: This study will provide high-quality evidence for the role of traditional Chinese medicine in preventing EGFRI-induced skin toxicity, improve the management system for adverse reactions to targeted cancer therapy, and offer a new treatment option for comprehensive cancer care, thereby enhancing patient adherence and quality of life. Clinical Trial: This study has been registered with the Chinese Clinical Trial Registry (registration number: ChiCTR2500095160) and has received ethical approval from the Ethics Committee of the Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine (approval number: 2022-LWKY-027) and the ethics committees of other participating hospitals.
Background: University students experience disproportionately high rates of anxiety and depression, which threatens their academic success. Digital mental health interventions (DMHIs) offer a scalable...
Background: University students experience disproportionately high rates of anxiety and depression, which threatens their academic success. Digital mental health interventions (DMHIs) offer a scalable, cost-effective approach to universal prevention. StriveWeekly is a skills-based DMHI designed to prevent anxiety and depression among university students. A prior randomized controlled trial (RCT) demonstrated its effectiveness and acceptability. Objective: This study aimed to test replication of StriveWeekly’s effectiveness in reducing or preventing anxiety, depression, and stress symptoms, when delivered in a next context: with a different student population (undergraduate students at midsized private college), during a global pandemic, and with new engagement strategies. Secondary aims included examining mediators of symptom change, and user engagement and acceptability. Methods: We conducted a cluster-randomized pragmatic RCT with 519 full-time undergraduates (7.2% of student body). Participants were randomized to the intervention (n = 244) or waitlist control (n = 275). StriveWeekly consisted of seven weekly modules teaching cognitive and behavioral skills. Surveys were administered online at baseline, posttest, 2-month follow-up, and 4-month follow-up. Results: Intervention effects replicated prior findings. The group × time interaction was significant (t = −2.62, p = .009, power = 81%) for Depression Anxiety Stress Scale (DASS-21) scores, favoring StriveWeekly with a small between-group effect over the waitlist (d = 0.24). Waitlist participants worsened (d = 0.19), while intervention participants remained stable (d = −0.04). Among asymptomatic students at baseline (n = 202), symptom onset was lower in the intervention group (15.7%) versus waitlist (28.7%; χ² = 4.26, p = .047). Effects were maintained at 2-month follow-up and converged for students who received delayed access to the intervention. Mediation analyses showed direct effects of higher module completion and specific modules (behavioral activation, cognitive reappraisal) on symptom improvement, but no indirect effects via self-reported habit change. Engagement was high: 93% of immediate intervention participants initiated StriveWeekly, and average completion was 41% of modules. Acceptability ratings indicated most participants found the program easy to use and satisfactory overall. Conclusions: StriveWeekly demonstrated small but significant preventive effects on anxiety and depression symptoms, even during pandemic-related stressors. We successfully replicated prior trial results. Findings support the generalizability of StriveWeekly as a scalable, effective DMHI for campus-wide mental health promotion. We discuss considerations for guiding ethical and evidence-based implementation of DMHIs on campuses, especially given the dearth of commercially available DMHIs and the rise of student artificial intelligence (AI) tool use. Clinical Trial: ClinicalTrials.gov: NCT049278450
Background: The Emergency Intensive Care Unit (EICU) is the core setting for the treatment of critically ill patients, where the diagnostic error rate is more than twice that of general inpatient ward...
Background: The Emergency Intensive Care Unit (EICU) is the core setting for the treatment of critically ill patients, where the diagnostic error rate is more than twice that of general inpatient wards, which seriously affects patient prognosis. Large Language Models (LLMs) have shown application potential in clinical diagnosis, but there is still very limited evidence comparing the diagnostic efficacy of critical care-specific LLMs and general-purpose LLMs in the complex diagnostic scenarios of the EICU. Objective: This study aimed to evaluate and compare the diagnostic accuracy of a critical care-specific LLM (Qiyuan 3.0.1) and three mainstream general-purpose LLMs (GPT5.1, DeepSeek V3.1, Qwen3-32B) in EICU diseases, and to provide evidence-based basis for the selection of intelligent auxiliary diagnostic tools in the EICU. Methods: This was a single-center retrospective paired diagnostic accuracy study, which consecutively enrolled 184 critically ill patients admitted to the EICU of Peking University Shenzhen Hospital from April 2025 to March 2026. Standardized datasets were constructed based on the patients' clinical data, including an initial diagnosis dataset (clinical data within 24 hours after admission) and a final diagnosis dataset (complete course data from admission to discharge). A unified zero-shot learning prompt strategy was adopted, and four LLMs independently generated corresponding diagnoses in a double-blind manner. The consensus diagnosis reached by three senior intensive care physicians with more than 10 years of EICU working experience, who were blinded to the model results, was used as the gold standard. The primary endpoint was the Top-1 accuracy in the final diagnosis stage, defined as the proportion of cases where the first primary diagnosis output by the model completely matched the gold standard. Secondary endpoints included the Top-1 accuracy in the initial diagnosis stage and the number of correct diagnoses in the Top-3 outputs in the final diagnosis stage. Cochran's Q test was used for the overall comparison of accuracy among multiple groups, and post hoc pairwise comparisons were performed using the paired McNemar test with Bonferroni correction for type I error. The Friedman non-parametric rank sum test was used for the intergroup comparison of the number of correct Top-3 diagnoses. Results: In the final diagnosis stage, the overall difference in Top-1 accuracy among the four models was statistically significant (Cochran's Q=20.32, df=3, P=4.57×10⁻⁵). The Top-1 accuracy of Qiyuan 3.0.1 was the highest (64.13%, 95%CI 56.83%-71.00%), followed by GPT5.1 (59.24%, 95%CI 51.83%-66.35%), DeepSeek V3.1 (57.07%, 95%CI 49.64%-64.28%), and Qwen3-32B had the lowest accuracy (51.63%, 95%CI 44.26%-58.98%). Post hoc pairwise comparisons showed that the Top-1 accuracy of Qiyuan 3.0.1, GPT5.1, and DeepSeek V3.1 was significantly higher than that of Qwen3-32B (all adjusted P<0.0083), while no significant difference was found in other pairwise comparisons (all adjusted P>0.0083). A similar trend was observed in the initial diagnosis stage, where only Qiyuan 3.0.1 was significantly superior to Qwen3-32B (adjusted P=0.008). The median number of correct Top-3 diagnoses for all four models was 2.0 (IQR 1.0-2.0), with no significant intergroup difference (Friedman χ²=3.34, df=3, P=0.339). Conclusions: The critical care-specific LLM Qiyuan 3.0.1 has superior Top-1 diagnostic accuracy in EICU diseases compared with some general-purpose LLMs, but the absolute diagnostic accuracy of all included models still has considerable room for improvement. LLMs have potential application value as auxiliary diagnostic tools in the EICU, but their clinical application still requires further optimization and multi-center prospective clinical trial validation.
Background: Due to the high incidence of sarcopenia in the elderly and the serious adverse consequences, the existing risk prediction tools often lack systematic variable screening, and the generaliza...
Background: Due to the high incidence of sarcopenia in the elderly and the serious adverse consequences, the existing risk prediction tools often lack systematic variable screening, and the generalizability of the model is also limited. Therefore, it is necessary to develop a more reliable risk prediction model. Objective: To develop and validate sarcopenia risk prediction models in older adults by integrating evidence-driven variable selection with machine learning for early screening and risk stratification. Methods: Extract the candidate risk factors identified through systematic meta-analysis from the the China Health and Retirement Longitudinal Study. Participants (N=2530; prevalence 15.5%) were divided into training sets and test sets in a ratio of 7:3. Use the least absolute contraction and selection olator (LASSO) regression selection predictor to train 10 machine learning models. Use cross-validation, area under the curve (AUC), Brier score, calibration degree and decision curve analysis to evaluate the performance of the model. External verification uses an independent cohort (n=191; incidence rate 16.2%). Shapley Additive Interpretation (SHAP) analyses the contribution of quantitative variables. Results: Elastic network, logical regression and ridge regression all showed a strong degree of differentiation in the test set, and no significant differences were observed. The calibration error at baseline is improved through model adjustment. External verification shows that under different thresholds, the model performance is stable and the net benefit is positive. Shapley’s plus interpretation analysis shows that age and body mass index are the most influential factors, while weakness, cognitive function and depressive symptoms also play an independent role. Conclusions: Elastic Net, Logistic Regression and Ridge Regression showed strong discrimination, calibration and clinical utility, supporting noninvasive, cost-effective early sarcopenia detection and risk stratification. Clinical Trial: PROSPERO (CRD420251083240), https://www.crd.york.ac.uk/prospero/
Background: Limited data utilization in low-resource settings poses a major barrier to the vaccine delivery ecosystem, undermining efforts to achieve equitable immunization coverage. In nomadic popula...
Background: Limited data utilization in low-resource settings poses a major barrier to the vaccine delivery ecosystem, undermining efforts to achieve equitable immunization coverage. In nomadic populations, where reliable data are hard to obtain, individuals face an increased risk of missing crucial vaccination doses as children. One such population is the Maasai in Narok County, Kenya, where the absence of high-volume, high-quality data hampers accurate coverage estimates, impedes efficient resource allocation, and weakens the ability to design and deliver timely interventions. Additionally, data privacy concerns are heightened in groups with limited sensitive data. Objective: First, we aim to identify children at risk of missing key vaccines across a large population to provide timely, evidence-based interventions that support increased vaccination coverage. Second, we aim to better protect the privacy of sensitive health data within a vulnerable population. Methods: We digitized 8 years of child vaccination records from the MOH 510 registry (n=6,913) and applied machine learning models (i.e., Logistic Regression and XGBoost) to identify children at risk. Additionally, we utilize a novel approach to tabular diffusion-based synthetic data generation ("TabSyn") to protect patient privacy within the models. Results: Our findings show that classification techniques can reliably and successfully predict children at risk of missing a vaccine, with recall, precision, and F1-scores exceeding 90% for some vaccines modeled. Additionally, training these models with synthetic data rather than real data—preserving the privacy of individuals within the original dataset—does not lead to a loss in predictive performance. Conclusions: These results support the use of synthetic data implementation in health informatics strategies for clinics with limited digital infrastructure, enabling privacy-preserving, scalable forecasting for childhood immunization coverage.
Open Peer Review Period: Apr 13, 2026 - Mar 29, 2027
Background: Misinformation during pandemics undermines public health interventions, reduces compliance with preventive measures, and exacerbates disease spread. Evidence suggests that communication st...
Background: Misinformation during pandemics undermines public health interventions, reduces compliance with preventive measures, and exacerbates disease spread. Evidence suggests that communication strategies, socio-demographic factors, policy frameworks, and technology-mediated interventions critically influence the management of misinformation. Understanding these determinants is essential for enhancing pandemic preparedness and mitigating infodemics. Objective: This systematic review aimed to (1) assess the impact of public health communication on misinformation management, (2) evaluate socio-demographic, cultural, and technological determinants influencing communication effectiveness, (3) examine policy frameworks and crisis communication models, and (4) identify the role of technology-mediated interventions in countering misinformation during pandemics. Methods: Comprehensive searches were conducted in PubMed, Scopus, Web of Science, CINAHL, PsycINFO, Google Scholar, WHO repositories, and preprint servers for studies published between 2000 and 2025. Eligibility included peer-reviewed studies or reports addressing pandemic communication and misinformation. Data extraction captured study characteristics, interventions, outcomes, and key findings. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). A narrative synthesis was performed due to heterogeneity. Results: Of 1,300 records screened, 114 studies met the inclusion criteria. Public health communication interventions (n=52) reduced misinformation, with timely government campaigns improving trust by 25-40% and social media monitoring reducing false content by 30-45%. Socio-demographic factors (n=47) significantly affected outcomes: higher education and urban residence increased message adherence by 20-35%, while low digital literacy reduced intervention reach by 15-25%. Policy frameworks (n=32) implementing CERC and RCCE improved public compliance by 18-28%, and multi-agency coordination reduced confusion. Technology-mediated interventions (n=42), including AI monitoring, tele-epidemiology, and youth-led campaigns, improved misinformation detection and engagement, enhancing adherence by 22-38%. Conclusions: Integrated, multi-level, culturally tailored, and technologically adaptive strategies are essential for effective misinformation management. Policymakers should implement multi-platform communication strategies, strengthen community engagement, and deploy AI-based monitoring for real-time misinformation management. Thus, strategic communication interventions enhance public trust, improve compliance, and reduce misinformation-related health risks during pandemics.
Background: As the integration of artificial intelligence (AI)-enabled tools expands within clinical practice, understanding contributors to trust and adoption is critical for successful implementatio...
Background: As the integration of artificial intelligence (AI)-enabled tools expands within clinical practice, understanding contributors to trust and adoption is critical for successful implementation. While transparency mechanisms such as confidence scores and explanatory text are often proposed to promote trust in AI applications, the role of clinician’s baseline attitudes toward AI as a determinant of trust has not been well characterized. Objective: To examine the relationship between baseline attitudes toward AI and clinician trust in a simulated AI-enabled diagnostic assistance application, and to assess whether transparency mechanisms modify this relationship. Methods: In a randomized experiment, clinicians (including students and trainees) completed the Attitude Toward Artificial Intelligence (ATAI) scale. They were then presented with 6 case vignettes, followed by exposure to AI assistance in one of three application conditions: control (no transparency), numeric confidence score, or narrative explanation with citation. Participants provided updated diagnoses after assistance across all scenarios. After completion, trust was measured using the Trust and Acceptance of Artificial Intelligence Technology (TrAAIT) scale, including both a total score and application-specific core score. Associations between attitudes and trust were assessed using correlation and linear regression analysis, with transparency condition included as a covariate. Results: The study enrolled 220 participants, excluding 3 with missing trust data. Among analyzed participants, baseline attitudes toward AI were heterogeneous and only slightly positive on average. Attitudes were moderately associated with overall trust in AI (Pearson r = 0.45, P<.001) and remained significantly associated when trust was restricted to application-specific domains (Pearson r = 0.33, P<.001). In multivariable linear regression, higher ATAI scores were independently associated with application-specific trust (β = 0.13 (CI 0.08-0.18)per 1-point increase in ATAI, P< .001). Transparency condition was not independently associated with trust and did not meaningfully modify the relationship between attitudes and trust. Attitudes toward AI were only modestly correlated with self-reported technology adoption orientation. Conclusions: Baseline attitudes toward AI represent a meaningful antecedent to clinician trust in AI-enabled diagnostic tools, and this association persists at the application level. Transparency mechanisms alone were insufficient to overcome the influence of pre-existing attitudes. These findings suggest that efforts to promote trust and adoption of clinical AI may benefit from addressing clinician attitudes directly, rather than relying solely on interface-level transparency features. Clinical Trial: N/A, This is not a clinical trial.
The rapid expansion of artificial intelligence in health care, driven by advanced data and analytics tools, has been accompanied by growing calls for collaboration among technology developers, health ...
The rapid expansion of artificial intelligence in health care, driven by advanced data and analytics tools, has been accompanied by growing calls for collaboration among technology developers, health systems, and communities. Existing iterations of these relationships often performative or legacy fixtures that fail to address persistent underlying asymmetries in power, access, authority, and value. Current data-driven innovations also often reproduce legacy extractive practices across divested populations while providing whole-of-life clinical data from entire communities that remain excluded from the insights and benefits of these tools.
The Extractive-Collaborative Technology Continuum draws on principles of ethical leadership, community-engaged research, and health equity to map how relationships between developers and communities must evolve from extractive models to collaborative learning systems. This transition toward a more ethical posture of change leadership and the implementation of innovation necessitates a shift toward a culture of shared governance and collaborative stewardship with a commitment to shared and mutual benefit.
The ideal state for collaboration considers and appropriately integrates local knowledge, enacts and delivers reciprocal benefits, and links incentives to the maturity of these practices. Moving from a paradigm of data as property to that of data as a promise ensures that innovation serves communities as the fullest expression of the science of health and the art of caring. The legitimacy of change management and implementation of innovation in care environments depends on the integrity of the relationships that develop and sustain trust and collaboration to the benefit of all.
Background: AI is increasingly being explored as a tool to enhance efficiency, access, and diagnostic accuracy in mental health care. However, the perspectives on the use of AI from clinicians, who ar...
Background: AI is increasingly being explored as a tool to enhance efficiency, access, and diagnostic accuracy in mental health care. However, the perspectives on the use of AI from clinicians, who are central to the delivery and oversight of care, remain underexamined. Objective: This study aimed to explore clinicians' perceptions of AI in clinical care, including perceived benefits, risks, barriers to implementation, and training needs. Methods: A cross-sectional mixed-methods survey was distributed from August to November 2024 to mental health professionals in the US. Quantitative data were analyzed using descriptive statistics, while open-ended responses were analyzed thematically to identify key insights. Results: Respondents (n=62) were mostly not currently using AI in practice. The most frequently endorsed benefits included reductions in clinical workload, improved efficiency, and enhanced data analysis. However, a majority expressed discomfort using AI in patient care, and concerns were raised about inaccurate outputs, algorithmic bias, privacy, and weakened therapeutic rapport. Barriers to adoption included clinician resistance, lack of validation, and challenges with technical integration. Most respondents believed that specialized training in AI ethics and applications is important for clinicians. Qualitative findings reinforced concerns about dehumanization, cultural insensitivity, ethical accountability, and insufficient technological literacy. Conclusions: Mental health professionals view AI as a potentially useful adjunct, but not a replacement, in care. Ethical concerns, limited trust, and a strong emphasis on the human dimensions of therapy suggest that implementation must proceed with caution. Clinician-informed strategies, ethical frameworks, and targeted training are essential to support the responsible and effective integration of AI into mental health practice.
Background: Digital health interventions (DHIs) are identified as a potential means of improving dietary health at scale. However, engagement and retention rates are often low, reducing their impact a...
Background: Digital health interventions (DHIs) are identified as a potential means of improving dietary health at scale. However, engagement and retention rates are often low, reducing their impact and effectiveness. In dietary DHIs, a range of application (app) features have been implemented to improve engagement. However, it remains unclear what the key, early predictors of engagement and retention are at scale, in real-world deployment. This is particularly important given that retention is often overestimated in controlled research studies, and most attrition occurs soon after initial engagement. Objective: This study aimed to assess predictors of 7- and 14-day retention with a free-to-use, personalized, dietary DHI, using large-scale, real-world data from a commercial launch. It also aimed to assess predictors of day-to-day retention across the first 14-days of use, given that factors affecting initial engagement may differ from those affecting ongoing use. Methods: Users of a free-to-use dietary DHI that analysed food characteristics for dietary health based on food photographs and packaging barcodes were given unique identifiers (n = 60,987). Demographic information, use of app features, and app usage patterns were included in logistic regression models to assess predictors of retention 7- and 14-days after enrolment in the app. Multilevel modelling was used to assess predictors of engagement on each of the first 14-days of app enrolment. Results: Specific app-related activities and user referral sources consistently predicted a greater likelihood of retention at both 7- and 14-days. While photographing and scanning food were key features, the strongest predictor of engagement was the subsequent logging of consumption, which yielded higher predictive value than the capture events alone, and enabling app notifications to allow engagement reminders. User referral sources were also among the strongest predictors of retention: Users who reported hearing about the app via related podcasts or via “friends and family” had higher odds of retention at 7- and 14 days. Those who heard about the app via television or radio were less likely to engage with the app at 7- and 14-days. Conclusions: This study suggests that immediate interaction with dietary DHI activities linked to real-life behaviors (e.g., logging food eaten) increases the likelihood of continued engagement. It also suggests that engagement with personal contacts who may also be engaged in app-related activities can enhance retention, as can engagement with related media such as podcasts. However, some sub-groups of users may engage with free-to-use dietary DHIs more casually, with potentially less committed motivation to continue. In these situations, greater initial personalization for groups identified as at risk of attrition could enhance engagement.
Background: China has the world’s largest diabetic population, with suboptimal glycemic control and fragmented care posing severe challenges to primary health care (PHC) institutions. Barriers inclu...
Background: China has the world’s largest diabetic population, with suboptimal glycemic control and fragmented care posing severe challenges to primary health care (PHC) institutions. Barriers including uneven practitioner competence, clinical inertia, and disjointed clinical-public health data limit effective diabetes management, while clinical decision support systems (CDSS) show potential for improving chronic disease care but lack real-world evidence for comprehensive integration in Chinese PHC. Objective: To evaluate the effectiveness of a Clinical Decision Support System (CDSS) intervention for patients with poorly controlled type 2 diabetes in primary care settings and to establish a standardized model for intelligent diabetes management. Methods: A retrospective cohort study was conducted among diabetic patients with baseline glycated hemoglobin (HbA1c) ≥7.0 mmol/L who visited 16 community health service centers in Kunshan from January to September 2024. Patients were divided into a CDSS intervention group and a control group (routine diagnosis and treatment). 1:1 nearest neighbor propensity score matching (PSM) was used to balance baseline characteristics. After a 1-year follow-up, the glycemic achievement rates, changes in metabolic indicators and hospitalization incidence were compared. Multivariate regression and subgroup analyses were performed to determine the effect of the CDSS intervention and the subgroup heterogeneity. Results: A total of 27,494 patients were included, resulting in 6,136 matched pairs after PSM (all standardized mean differences <0.1). The average reduction in HbA1c in the intervention group was 0.90±1.67 mmol/L, significantly greater than the 0.20±1.97 mmol/L reduction in the control group (difference=-0.70 mmol/L, 95%CI: -0.76~-0.63, P<0.001). The glycemic control rate was significantly higher in the intervention group (63.9% vs 47.4%, difference=17.0 percentage points, 95%CI: 15.0~18.0, P<0.001). The intervention group also had better metabolic indicators and lower hospitalization rate. Multivariate regression identified CDSS as a significant predictor of HbA1c improvement (regression coefficient=-0.68, OR=2.40, both P<0.001), with greater benefits observed in older patients, those with longer disease duration, baseline HbA1c 8~9 mmol/L and existing complications. Conclusions: CDSS intervention can significantly improve glycemic control, optimize metabolic indicators and reduce hospitalization risk in primary care diabetic patients. which is suitable for grassroots management and can be used as a promotable and replicable standardized plan.
Background: Family planning program has been globally shown to reduce maternal mortality by reducing both total and high-risk pregnancies. Despite the national implementation of this program since the...
Background: Family planning program has been globally shown to reduce maternal mortality by reducing both total and high-risk pregnancies. Despite the national implementation of this program since the 1970s, Indonesia still faces many challenges in achieving family planning goals. Low modern contraceptive prevalence rate (mCPR) remains a problem that impacts public health, population growth, economy, and welfare issues. It should be tackled, especially in rural areas, with multifactorial causes and diverse needs. Various programs have been developed globally to overcome this problem; however, each region has different characteristics and demands that should be understood. Objective: This study aims to develop a theory of change by understanding rural women’s needs and actively collaborating with multiple participant groups to increase modern contraceptive uptake. The theory of change will also be informed by the views of four distinct stakeholders who is responsible for providing contraceptive services (i.e. policy makers at regency and provincial level) in order to make informed recommendations. Methods: This feminist qualitative study embedding participatory action research principles adapts the first three steps of the six essential steps for quality intervention development. The target location is West Sumba Regency, one of Indonesia’s 100 lowest mCPR regencies and located in East Nusa Tenggara, which has the highest total fertility rate in Indonesia. Consisting of two rounds of data collection, this study includes different participant groups (i.e. rural women and men, mothers-in-law, religious figures, cultural leaders, midwives, family planning educators, and policymakers) with different strategies. To ensure data saturation and trustworthiness, we aim to recruit up to 45 participants through purposeful sampling, selecting participants based on the criteria for each group. The data collection methods are focus groups and semi-structured interviews. We will analyze the data using reflexive thematic analysis. Results: The theory of change development focuses on women’s voices and incorporates various perspectives from rural communities, including the service providers and policymakers. Ethics approval has been obtained by the College of Medical, Veterinary, and Life Sciences (MVLS) Research Ethics Committee, University of Glasgow, UK, and the Public Health Faculty, University of Nusa Cendana, Indonesia. We anticipate that we will complete all data collections and analysis by December 2026. Conclusions: The ultimate goal of this study is to develop a theory of change to create a meaningful change in contraceptive services in rural areas. This study will contribute to encourage rural communities to collaborate and empower rural women to overcome their reproductive health problems. By understanding the diverse contexts and specific needs of the rural population, the results will be essential to transforming family planning programs. In doing so, it will significantly enhance women’s reproductive health while also addressing and reducing health inequalities in rural areas.
Background: Virtual Communities of Practice (vCoPs) provide collaborative spaces where healthcare professionals and patients can exchange knowledge, experiences, and support. Gamification—the applic...
Background: Virtual Communities of Practice (vCoPs) provide collaborative spaces where healthcare professionals and patients can exchange knowledge, experiences, and support. Gamification—the application of game design elements in non-game contexts—is increasingly being integrated into digital health platforms to improve engagement. However, the effectiveness of gamification within healthcare-focused virtual communities of practice (vCoPs) remains unclear. Objective: To evaluate how gamification has been implemented within healthcare vCoPs. Methods: A systematic search of eight databases (Medline, CINAHL, Embase, Scopus, Web of Science, PsycInfo, ERIC, Global Health) identified studies published from 2000 onwards. Eligible studies reported empirical evaluations of healthcare-related vCoPs incorporating at least one gamification feature. Titles, abstracts, and full texts were screened independently by four reviewers . Data extraction and risk-of-bias assessment followed PRISMA 2020 guidance . Due to heterogeneity in study designs and outcomes, results were synthesised narratively. Results: Of 890 records identified, 269 duplicates were moved, and 621 unique studies were screened and four studies met inclusion criteria. Designs included one quasi-experimental study, two mixed-methods studies, and one cluster randomised controlled trial. Gamification elements across studies included badges, levels, points, challenge-based tasks, and interactive simulations. Educational and professional-development vCoPs reported improvements in engagement, participation, and self-reported learning. The patient-facing intervention demonstrated improved adherence to the Mediterranean diet but no changes in activation, mental health, quality of life, physical activity, or medication adherence. Conclusions: Gamification within healthcare vCoPs shows potential to enhance engagement and perceived value, particularly in educational and professional settings. Evidence for broader behaviour change or clinical impact remains limited. Further rigorous trials with standardised gamification components and longer follow-up are required to assess effectiveness and scalability. Clinical Trial: This review was registered on PROSPERO, an international prospective register of systematic review (February 2025, reference CRD420251242967).
Background: Virtual reality (VR) platforms have demonstrated small-to-moderate effect sizes for motor, balance, and dual-task outcomes across neurological conditions, yet fewer than one-third of physi...
Background: Virtual reality (VR) platforms have demonstrated small-to-moderate effect sizes for motor, balance, and dual-task outcomes across neurological conditions, yet fewer than one-third of physical therapists regularly integrate VR into practice, citing limited space, inadequate training, and uncertainty about clinical utility. The Instrumented Multitask Assessment System (IMAS) is a compact VR platform combining a 360° omnidirectional walking surface, an immersive headset, and integrated biophysical sensors for real-time adaptive cognitive-motor dual-task training. Preliminary work demonstrated feasibility for dual-task assessment in military service members with mild traumatic brain injury, but clinician perspectives on acceptability and implementation for routine neurorehabilitation remain unexplored. Objective: This study explored practicing neurorehabilitation clinicians’ perceptions of the feasibility, acceptability, and implementation requirements of the IMAS platform. Guided by the Theoretical Framework of Acceptability (TFA), we sought to understand how clinicians perceive the coherence, burden, and potential effectiveness of the IMAS to inform iterative design modifications for subsequent patient-focused pilot testing. Methods: A qualitative descriptive design using semi-structured interviews was employed. Eighteen licensed neurorehabilitation clinicians (physical therapists, occupational therapists, and speech-language pathologists) with a minimum of 2 years’ experience treating adults with neurological disorders were recruited via purposive sampling across diverse US clinical settings. Interviews were conducted via videoconference. Data were analyzed using reflexive thematic analysis with 2 investigators independently coding transcripts and resolving discrepancies through structured consensus meetings. Reporting followed the Standards for Reporting Qualitative Research guidelines. Results: Participants were predominantly female (11/18, 61%), physical therapists (12/18, 67%), held professional doctorates (12/18, 67%), and worked in outpatient settings (11/18, 61%), with a mean of 11.11 (SD 9.05) years of neurorehabilitation experience. Saturation was reached by the sixteenth interview. Analysis yielded four themes: (1) System Design and Customization Needs, reflecting emphasis on principles of neuroplasticity and environmental customization as essential features; (2) Implementation Challenges and Barriers, encompassing learning curves, safety concerns, resource constraints, workflow considerations, and technical limitations as burden dimensions; (3) Perceived Benefits and Advantages, including enhanced therapeutic outcomes, patient safety, engagement through gamification, and immediate feedback; and (4) Training and Support Requirements, highlighting comprehensive hands-on training and tiered technical support as requisites for clinician self-efficacy. Conclusions: Clinicians perceived the IMAS as coherent with established neurorehabilitation principles and therapeutically promising; however, substantial barriers related to cost, space, workflow disruption, and technical reliability were identified as limiting factors for adoption. The TFA revealed that high coherence and perceived effectiveness alone cannot overcome high burden when organizational capacity, financial resources, and technical support remain inadequate. Successful implementation requires coordinated efforts to address safety, reduce costs, validate effectiveness through patient outcome studies, and embed comprehensive training within clinical settings.
Background: Preterm birth (PTB) remains a major global health challenge and a leading cause of neonatal morbidity and mortality. Increasing evidence suggests that gut microbiota–derived metabolites ...
Background: Preterm birth (PTB) remains a major global health challenge and a leading cause of neonatal morbidity and mortality. Increasing evidence suggests that gut microbiota–derived metabolites play a crucial role in maternal–fetal health. Over the past two decades, research in this field has evolved from taxonomic descriptions toward functional and metabolic mechanisms, yet a systematic understanding of this transition remains limited. Objective: This study aimed to systematically characterize the global research landscape on gut microbiota and preterm birth from 2006 to 2025, with a focus on identifying the shift from taxonomic associations to metabolite-centered functional mechanisms and highlighting emerging translational directions. Methods: A bibliometric analysis was conducted using data retrieved from Web of Science Core Collection, Scopus, and PubMed. Publications between 2006 and 2025 were analyzed using the bibliometrix package in R. Analytical approaches included annual publication trend analysis, country and collaboration analysis, keyword co-occurrence networks, thematic evolution analysis, and burst keyword detection. Cross-database validation was performed to ensure the robustness of emerging research themes. Results: A total of 479 core publications were identified, showing a non-linear growth trend with a marked increase after 2018. Early studies primarily focused on taxonomic descriptions of microbial composition, whereas more recent research emphasized functional and metabolic pathways. Keyword and thematic analyses revealed that microbiota-derived metabolites, immune regulation, and probiotic interventions have become central research themes. Cross-database validation confirmed consistent trends, highlighting a transition toward metabolite-centered mechanisms in the field. Conclusions: Research on gut microbiota and preterm birth has gradually shifted from descriptive taxonomic approaches to function-oriented and metabolite-driven perspectives. Microbiome-derived metabolites, such as short-chain fatty acids and tryptophan metabolites, emerge as potential mediators linking microbial activity to host outcomes. These findings provide a conceptual foundation for developing digital biomarkers and data-driven risk prediction models in maternal–fetal health, supporting future integration with mHealth platforms for early monitoring and intervention. Clinical Trial: Not applicable
Background: Aging is commonly associated with declines in physical performance, while physical activity interventions have been shown to benefit older adults. Traditional face‑to‑face intervention...
Background: Aging is commonly associated with declines in physical performance, while physical activity interventions have been shown to benefit older adults. Traditional face‑to‑face interventions often limit participation due to mobility, scheduling, and accessibility challenges. mHealth offers a promising alternative; however, older adults may still face difficulties related to digital literacy, motivation, and perceived ease of use. Involving family members in mHealth‑based physical activity interventions may help enhance engagement and ultimately improve physical performance among community‑dwelling older adults. Objective: This study aimed to examine both the effects of mHealth-based PA interventions involving family members on the physical performance of community-dwelling older adults and the role of family members in these interventions. Methods: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the Synthesis Without Meta-analysis (SWiM) guidelines. A comprehensive search was conducted across Web of Science, CINAHL, Scopus, PsycINFO, PubMed, and Embase from database inception to 30 June 2025. The inclusion criteria focused on involving family support for mHealth-based physical activity interventions targeting community-dwelling older adults aged 60 years or above. Studies involving caregivers without a clearly defined family role were excluded. A narrative synthesis was used to analyze data relevant to the review aims. Results: Seven studies (n = 285) met the inclusion criteria. None of the identified studies explicitly required a family member to participate in the interventions, and only two studies specifically recruited family members. Adherence was assessed using various strategies, with reported rates ranging from 15% to 100%. Most interventions used mHealth technologies for communication and delivery of educational materials. Across studies, family members most commonly served as supervisors, technology-use supporters, and safety monitors. Physical performance outcomes were measured through both physical function and physical activity. Six studies assessed physical function using indicators such as mobility, strength, endurance, and balance; however, the effectiveness of interventions in improving physical function remained inconclusive. Two studies evaluated physical activity levels and reported positive effects. Conclusions: This review provides insights into the roles of family members in mHealth‑based PA interventions for community‑dwelling older adults. Involving family members may enhance physical performance, particularly in physical activity. Closer relationships between family members and older participants were associated with higher adherence. However, the combined effect of family support and mHealth technology on improving overall physical performance remains unclear. Clinical Trial: The protocol for this review was registered with the PROSPERO international prospective register of systematic reviews (registration number CRD42024575953).
Background: Transgender men and transmasculine people who have sex with men (TMSM) are at elevated risk for HIV acquisition, have unmet HIV prevention needs, and have low uptake of antiretroviral pre-...
Background: Transgender men and transmasculine people who have sex with men (TMSM) are at elevated risk for HIV acquisition, have unmet HIV prevention needs, and have low uptake of antiretroviral pre-exposure prophylaxis (PrEP). To our knowledge, there are no published efficacious behavioral interventions to decrease HIV risk specifically for TMSM; this includes peer-delivered strategies that demonstrate high acceptability in this population. Objective: This paper describes the development, refinement, and optimization of theory-informed, peer-delivered digital interventions—which were feasible to implement and highly acceptable among the participant population—designed to increase PrEP uptake and adherence among adult transmasculine people who have sex with individuals assigned male at birth within the context of a full-scale, randomized factorial trial. Methods: Between March 2023-March 2025, we used an iterative community-engaged approach that included: (1) community and key stakeholder input, (2) theory- and evidence-informed manualized content with iterative refinement, (3) interventionist training and preparation, and (4) process evaluation and fidelity monitoring. Two theoretical frameworks and one theory of behavior change guided content development, structure, format and evaluation: The Healthcare Accessibility Framework, the Gender Affirmation Framework, and the Information, Motivation, and Behavioral Skills Model of behavior change. Results: A 4-member Community Advisory Board of transmasculine individuals partnered with the research team to co-design interventions and study procedures. Additional input was obtained through one-on-one key stakeholder consultations with nine topic experts, clinicians, and community experts at partner organizations. Iterative refinement incorporated evidence synthesis, manual drafting, mock sessions, and structured feedback loops resulting in two refined virtually delivered interventions: (1) PrEP4T, a 6-session one-on-one peer navigation intervention (60-90 minutes/session) emphasizing goal setting, harm reduction, and supportive referrals; and (2) LS4TM, a 6-session peer-facilitated group-based behavioral intervention (2 hours/session), focusing on sexual health knowledge, gender affirmation, communication, and social support. A digital standard of care (SOC) resource guide of curated, gender-affirming sexual health, HIV prevention, and community resources was also developed. Interventionist training included approximately 15 hours of knowledge/skill building (asynchronous and live didactic sessions) and at least 12 hours of applied practice (mock sessions with structured feedback). Process evaluation and fidelity relied on participant- and interventionist-completed case reporting forms, reflecting community prioritization of interventionist and participant comfort during sessions. Key lessons learned included the importance of flexible, manualized intervention content structures that support fidelity while allowing personalized adaptation; using gender-affirming language through mirroring and dual phrasing; centering “voice” and “choice” in HIV prevention decision-making; incorporating behavior change scaffolding (e.g., goal setting, elicit-provide-elicit techniques); offering hands-on peer navigation and curated SOC resources to address structural access barriers; and leveraging digital tools to enhance engagement, shared learning, and community connection. Conclusions: Co-designing with transmasculine communities through an iterative, community-engaged development and refinement processes was essential for producing culturally responsive, theoretically-grounded, and gender-affirming HIV prevention interventions. Our findings can inform future peer-delivered and digital HIV prevention strategies tailored to the needs of specific populations, grounded in community partnerships and lived experience. Clinical Trial: ClinicalTrials.gov NCT06182280; https://clinicaltrials.gov/ct2/show/NCT06182280
Background: Person-centered, collaborative and preventive care involving primary care providers and community care providers is considered essential in response to increasingly complex care needs. The...
Background: Person-centered, collaborative and preventive care involving primary care providers and community care providers is considered essential in response to increasingly complex care needs. The implementation of such care, summarised as ‘intersectoral collaboration for health,’ is hindered by limited knowledge of other disciplines, fragmented communication, and unclear expectations. Although education plays an important role in equipping care providers with the necessary knowledge, skills, and values required for intersectoral collaboration for health, intersectoral approaches in education for primary care providers and community care providers remain scarce. Informal forms of education such as serious gaming hold promise to not only improve knowledge and skills, but also stimulate interaction and collaboration. Serious gaming could thus provide a suitable educational format to support intersectoral collaboration for health by simultaneously educating and connecting primary care providers and community care providers. Objective: This study describes the systematic and iterative development of Game2Connect, a serious game designed to support primary care providers and community care providers in implementing intersectoral collaboration for health through the framework of Positive Health. Methods: Game2Connect was developed using a combination of Intervention Mapping and Developmental Evaluation. First, a needs assessment was conducted through interviews with six care recipients and eighteen primary care providers and community care providers. Then, behavioural outcomes for the serious game were formulated. Next, a game concept was drafted in collaboration with 25 representatives from educational institutions, health organisations and serious game developers, and a prototype was created. Finally, the prototype was iteratively refined through nine pilot sessions with 57 primary care providers and community care providers and 22 other professionals based on observations and user experiences. Results: The final version of Game2Connect consists of two facilitated serious gaming sessions that incorporate video cases, theoretical questions, practical challenges, peer feedback, and goal setting in a team-based board game. Iterative refinements resulted in improvements to game design (e.g. a tailored game board and game cards with scannable QR-codes so that questions can be continuously adapted), content (e.g. clearer and more diverse cases and questions), and user experience (e.g. a simplified goal setting assignment). Participants across pilot sessions reported positive experiences, describing the game as engaging, relevant, and insightful. Interactions between disciplines and subsequent insights into each others’ perspectives were identified as key strengths in support of intersectoral collaboration for health. Conclusions: This study shows how the combination of Intervention Mapping and Developmental Evaluation contributed to the development of education that is both scientifically sound and contextually fitting. Furthermore, it highlights the potential for serious gaming as an engaging and interactive educational approach for care professionals. Future research is needed to evaluate the impact of Game2Connect on behaviour and collaboration in practice, and to explore its implementation in more diverse settings.
Background: The aging population has heightened the public health concern posed by emotion-driven health rumors (such as fear and hope). Older adults are particularly vulnerable to such rumors, due to...
Background: The aging population has heightened the public health concern posed by emotion-driven health rumors (such as fear and hope). Older adults are particularly vulnerable to such rumors, due to factors including limited knowledge of e-health literacy (eHL) services and increased sensitivity to health issues. Objective: In this study, we looked at how physical health status and psychological health status differently affect Chinese older adults' willingness to share dread rumors or wish rumors. We also explored whether eHL plays a moderating role in these relationships. Methods: The study used a cross-sectional survey and recruited participants aged 65 and older. The sample was evenly distributed by gender, consisting primarily of individuals with secondary educational attainment, and most participants had no medical background. Results: Results revealed that the willingness to share dread rumors exhibited a significant negative association with physical and psychological health status; an association strengthened by higher levels of eHL. For wish rumors, a significant positive association was identified between health status and the willingness to share rumors; this association was likewise amplified by higher eHL. Conclusions: These findings suggest that physical and psychological health status both influence rumor-sharing willingness in a consistent direction. Meanwhile, eHL acts as a more complex moderator—its effect depends on the type of emotional response that a rumor triggers. These insights add to health communication theory and also point toward more targeted eHL interventions.
Background: Unplanned extubation (UEX) in the intensive care unit (ICU) is a serious adverse event that threatens patient safety. Current prevention strategies rely primarily on nurse surveillance and...
Background: Unplanned extubation (UEX) in the intensive care unit (ICU) is a serious adverse event that threatens patient safety. Current prevention strategies rely primarily on nurse surveillance and physical restraints—both of which have inherent limitations, including the inability to provide continuous monitoring and potential conflicts with patient-centered care principles. Computer vision technology may offer a noncontact monitoring approach to support nursing practice. Objective: This study aimed to develop and preliminarily evaluate a computer vision–based monitoring prototype designed to assist ICU nurses in preventing UEX by detecting patient hand proximity to preset high-risk zones, with particular emphasis on a guard mode that detects protective gear removal. Methods: Following a design science research approach, we built a monitoring system using the open-source MediaPipe library. The system comprised four components: (1) patient tracking with a three-level detection mechanism, (2) hand detection and risk assessment with tiered alerting, (3) dynamic risk zone management, and (4) a PyQt5-based user interface. A guard mode was implemented to specifically monitor for protective gear removal—a critical feature supporting stepwise physical restraint reduction. Laboratory simulations were conducted under various occlusion scenarios. Five ICU nurses participated in usability assessments using a 5-point Likert scale. Performance metrics included tracking accuracy, hand detection coverage, risk judgment accuracy, false alarm rate, and alert response time. Results: Across 120 simulated test scenarios, target localization accuracy reached 92.3%, and dynamic risk zone adaptation accuracy was 90.0%. Hand recognition coverage was 95.8% across 800 frames. Two-level risk judgment accuracy was 93.3%, with the false alarm rate reduced to 2.8%. The average alert response time was 0.4 seconds. In guard mode, the system successfully distinguished between gloved hands (protective gear) and bare hands, triggering dedicated alerts upon detection of gear removal. Nurses rated the system’s usability at 4.2 out of 5. Figures are provided to illustrate the system interface and the visual differences in hand monitoring between normal and guard modes. Conclusions: This study describes the development and preliminary evaluation of a computer vision–based system for UEX prevention. Our findings suggest the system is technically feasible for supporting nursing surveillance. The guard mode, in particular, offers a novel approach to supporting gradual restraint reduction—a key priority in patient-centered care. Further clinical validation is needed to assess its effectiveness in practice settings. Clinical Trial: None
Background: Taiwan has the world’s highest prevalence of hemodialysis (HD), with arteriovenous fistula (AVF) stenosis representing a critical complication affecting dialysis adequacy and patient out...
Background: Taiwan has the world’s highest prevalence of hemodialysis (HD), with arteriovenous fistula (AVF) stenosis representing a critical complication affecting dialysis adequacy and patient outcomes. Traditional AVF monitoring relies on clinical expertise and subjective assessment, potentially delaying stenosis detection. Objective: This study aimed to evaluate electronic stethoscope monitoring for early AVF stenosis detection compared to traditional nursing assessment, and identify risk factors associated with AVF stenosis in HD patients. Methods: We conducted a 3-month prospective study at National Taiwan University Hospital Yunlin Branch Hemodialysis Center from March to June 2023. Thirty adult HD patients with AVF (>3 months dialysis vintage) were enrolled. AVF sounds were recorded 2-6 times weekly at two locations: 3cm proximal to the anastomosis and the arterial needle insertion site. Sound signals were converted to decibel (dB) measurements using short-time Fourier transform. Clinical and demographic data were collected including monthly albumin levels, blood pressure (BP), dialysis parameters, and medical history. Chi-square tests, t-tests, and logistic regression analysis identified stenosis risk factors. Results: Among 30 patients generating 1,462 AVF sound recordings, abnormal AVF sound was defined as <-28.59 dB using three-standard-deviation analysis. One patient developed clinically apparent stenosis during the study period. Retrospective analysis revealed electronic stethoscope detected stenosis one month before clinical diagnosis. Logistic regression identified significant risk factors for stenosis (percutaneous transluminal angioplasties (PTA) within 3 years): diabetes mellitus (OR=18.67, p=0.005), hypertension (p=0.019), body mass index ≤25 (p=0.006), higher mean systolic BP (OR=1.82 per 10mmHg, p=0.018), FX dialyzer type (OR=5.25, p=0.048), and greater number of prior PTAs (OR=2.47, p=0.003). Conclusions: Electronic stethoscope monitoring demonstrated potential for earlier AVF stenosis detection compared to traditional clinical assessment. The identified risk factors—including a novel association with FX dialyzer use—can guide targeted surveillance protocols. Implementing objective acoustic monitoring may improve timely intervention and AVF longevity in high-risk HD patients. Clinical Trial: The study protocol was approved by the Institutional Review Board of NTUH (IRB No. 202302099RINA) and registered at ClinicalTrials.gov (NCT07436559).
Background: Healthcare organizations increasingly rely on complex digital systems, but software onboarding often depends on manuals and classroom-based training that do not fit well with fast-paced cl...
Background: Healthcare organizations increasingly rely on complex digital systems, but software onboarding often depends on manuals and classroom-based training that do not fit well with fast-paced clinical workflows. Interactive in-app guidance may better support learning during real work, although healthcare-specific evidence is still limited. Objective: To synthesize evidence on effective onboarding mechanisms for healthcare software and to explore how interactive in-app guidance compares with traditional onboarding in terms of perceived learning support, cognitive burden, and adoption-related outcomes. Methods: The study used a sequential design with two components:
(1) a systematic literature review following Kitchenham’s procedures; and
(2) a mixed-methods survey administered via Qualtrics to healthcare professionals (n = 44), complemented by a small screened subsample of IT professionals with healthcare DAP implementation experience (n = 5).
Quantitative data were analysed descriptively, and qualitative responses were examined through thematic analysis to explain and contextualize the observed patterns. Results: The findings from both the literature review and the survey showed a consistent pattern: workflow-embedded onboarding approaches, including hands-on practice, stepwise contextual guidance, and searchable in-app support, were perceived to reduce learning friction and cognitive effort while improving confidence. Among healthcare respondents, 61% reported greater willingness to use the software after onboarding. Continued use was mainly associated with remembering how to use features, interface usability, workflow efficiency, and perceived impact on patient care. IT respondents highlighted implementation constraints related to integration, analytics, and compliance, but also perceived reductions in support burden. Conclusions: Interactive, context-sensitive onboarding appears to be a practical strategy to support healthcare software adoption, especially because it aligns learning with real workflows. The findings support the use of workflow-embedded guidance to improve usability in context and user confidence during onboarding, while also indicating the need for stronger healthcare-specific, outcome-based evaluations of DAP-enabled approaches.
Background: Sepsis is a major global cause of morbidity and mortality, and early, accurate severity assessment is essential to improve outcomes among older adults. Traditional diagnostic tools often f...
Background: Sepsis is a major global cause of morbidity and mortality, and early, accurate severity assessment is essential to improve outcomes among older adults. Traditional diagnostic tools often fail to capture the syndrome’s complexity, while machine learning (ML) can exploit routinely collected clinical and laboratory data for more precise classification. Objective: This study aims to develop and validate an explainable machine learning pipeline for early detection and severity stratification of sepsis in geriatric emergency care using routinely available admission laboratory and clinical data. Methods: The analysis was conducted on a large Italian hospital dataset including patients with and without sepsis and six ML algorithms using nested cross-validation were compared. Recursive Feature Elimination was applied for feature selection, and model interpretability was examined through SHAP values. Results: Ensemble methods, particularly XGBoost and Random Forest, showed the best performance in distinguishing sepsis from non-sepsis cases and provided solid severity stratification using only 10–20 features with an accuracy of 76.7%. Key predictors involved hepatic, renal, and immune markers, with Random Forest uniquely identifying absolute neutrophil count and total bilirubin as strong indicators of severe sepsis Conclusions: These findings highlight the potential of tree-based ML models for clinically interpretable, real-time sepsis risk stratification. Clinical Trial: Data for this study were collected as part of the ReportAGE project at IRCCS INRCA (Ancona, Italy), approved by the IRCCS INRCA Ethics Committee (reference CEINRCA-20008) and registered on ClinicalTrials.gov (reference NCT04348396).
Background: The proliferation of AI-driven voice chatbots necessitates updated methods for usability assessment. While the Speech User Interface Service Quality (SUISQ) questionnaire is a comprehensiv...
Background: The proliferation of AI-driven voice chatbots necessitates updated methods for usability assessment. While the Speech User Interface Service Quality (SUISQ) questionnaire is a comprehensive tool, its original structure may not capture the nuances of modern speech bots. Objective: This pilot study details an exploratory evaluation of the SUISQ and the subsequent development of a reduced version of SUISQ for Voice Interfaces (SUISQ-RVI). Methods: Using a dataset (N=48) from a simulated speech bot interaction, we conducted descriptive and exploratory data analyses on the full SUISQ and its previously reduced versions (SUISQ-R and SUISQ-MR). Results: Preliminary results indicate that the original four-factor structure may not be optimal for conversational AI contexts. Guided by our exploratory factor analysis, we reduced the original 25-item instrument to a 17-item scale demonstrating a five-factor structure. In this pilot sample, the SUISQ-RVI outperformed previous reduced versions by explaining more variance with better model fit and reliability. Conclusions: These initial findings suggest the SUISQ-RVI is a promising, streamlined tool for evaluating modern voice interfaces, though further confirmatory validation with larger cohorts is warranted. Clinical Trial: NOT APPLICABLE
Background: The relationship between female physical performance and menstrual cycle (MC) phases is widely discussed in applied sports science and clinical kinesiology. However, current evidence remai...
Background: The relationship between female physical performance and menstrual cycle (MC) phases is widely discussed in applied sports science and clinical kinesiology. However, current evidence remains inconsistent, particularly regarding muscle strength and biomechanical risk factors for injury. Objective: This study aims to examine the relationship between MC phases and lower limb injury risk factors, with a particular focus on the hamstrings-to-quadriceps strength ratio (H/Q ratio). Methods: Following an initial session, participants will monitor their MC over 3 months using a mobile application, urinary ovulation tests, and self-reported symptoms, while also recording perceived readiness for physical activity. Each participant will then complete two laboratory assessments: the early follicular phase (days 1–3 of menstrual bleeding) and the peri-ovulatory phase. Testing will include isokinetic strength assessment, evaluation of muscle–tendon mechanical properties, and body composition analysis. Results: Recruitment will begin in June 2026. A total of 32 participants will be enrolled in two waves (June–July 2026 and June–July 2027). Preliminary results are expected by 06/2028. Conclusions: This study may improve understanding of MC–related changes in neuromuscular function and support individualized, non-invasive approaches to training and injury prevention in women. Associating objectively estimated MC phases with a mobile application, urinary ovulation tests and subjective perceptions of performance will provide insights into the agreement between perceived and physiological indicators of performance readiness. Clinical Trial: NCT07462286
Background: Asymptomatic bacteriuria (ASB) has been associated with preterm birth and pyelonephritis, and until recently, screening for ASB was recommended for all women in first trimester of pregnanc...
Background: Asymptomatic bacteriuria (ASB) has been associated with preterm birth and pyelonephritis, and until recently, screening for ASB was recommended for all women in first trimester of pregnancy. Recent divergence between NICE guidelines (which do not recommend routine screening) and NHS initiatives such as the Saving Babies’ Lives Care Bundle (which recommends screening in high-risk women) may have led to inconsistencies in UK clinical practice. Objective: To describe the protocol for a national multicentre study evaluating variation in ASB screening practices across UK maternity units and examining associated maternal and neonatal outcomes. Methods: The TU(L)IPS study is a national, multicentre observational study conducted through the UK Audit and Research Collaborative in Obstetrics and Gynaecology (UKARCOG) network. The study comprises two components: (1) a structured survey of UK maternity units to characterise local ASB screening guidelines and practices, and (2) a retrospective cohort study using routinely collected clinical data to assess adherence to local and national guidance and to describe maternal and neonatal outcomes. Participating sites contribute data via a standardised REDCap-based data collection platform using predefined variables and harmonised outcome definitions. Analyses will be descriptive, including frequencies and proportions, with comparisons stratified by screening status, urine culture result categories, and preterm birth risk groups. Results: The study was launched nationally in January 2025. Recruitment closed with 28 NHS Trusts participating across the United Kingdom. Stage 1 data collection was completed in December 2025. Stage 2 data collection is scheduled to conclude in March 2026, with the database lock planned for March 2026. Data cleaning and analysis will commence following the database lock. The anticipated study completion date is May 2026. Conclusions: This study will provide a comprehensive national evaluation of variation in ASB screening practices and associated outcomes in pregnancy. By using a standardised, protocolised approach across multiple sites, the TU(L)IPS study aims to generate contemporary evidence to inform clinical practice and national policy.
Background: Renal fibrosis is the final common pathway of chronic kidney disease progression and a critical histological predictor of renal function decline and allograft failure. Routine clinical mon...
Background: Renal fibrosis is the final common pathway of chronic kidney disease progression and a critical histological predictor of renal function decline and allograft failure. Routine clinical monitoring with estimated glomerular filtration rate (eGFR) and albuminuria is insensitive to the development of fibrosis. Renal biopsy is invasive, which limits repeated assessment and suffers from sampling bias. Consequently, non-invasive imaging biomarkers that can accurately quantify fibrosis, monitor disease progression, and predict outcomes are highly desirable.
Multiparametric magnetic resonance imaging (mpMRI) offers extensive characterization of renal structural and functional properties. Previous work has indicated that MRI measures are associated with fibrosis development and with declining renal function. However, there remains a sparsity of longitudinal data and comprehensive validation of MRI measures against histology and measured glomerular filtration rate (mGFR). Objective: Our ongoing longitudinal study aims to validate mpMRI against reference standard kidney biopsy in kidney transplant recipients (KTR) and to compare the time-dependent trajectories of imaging and functional markers across living kidney donors (LKD), KTR and healthy control (HC) cohorts to assess their prognostic value for mGFR decline. Methods: Participants: 32 living kidney donors (LKD), 32 KTR, and 32 healthy controls (HC).
Inclusion criteria: LKD and KTR who have been approved for transplant. HC must show no evidence of renal disease and have normal blood pressure.
Exclusion criteria: Contraindications to MRI or severe claustrophobia.
Time points: Baseline investigations prior to transplant surgery, with follow-up assessments at 3-, 12-, and 24-months post-transplant.
Data collection:
mpMRI is performed at 3T. The MRI protocol includes structural T1-weighted and T2-weighted imaging, T1 mapping, T2 mapping, T2* mapping, DWI, pseudo-continuous ASL, non-contrast-enhanced angiography, and phase-contrast measurement of renal artery flow.
Glomerular filtration rate will be measured by [99mTc]Tc-DTPA clearance.
A baseline allograft biopsy is performed in all KTR during the transplantation surgery. Subsequent protocol biopsies are planned at 3, 12, and 24 months in KTR. Extent of fibrosis is quantified using quantitative stereology.
Primary outcome:
Longitudinal association between quantitative MRI measures and histologically determined renal fibrosis.
Secondary outcomes:
Longitudinal divergence of MRI and functional markers across cohorts;
diagnostic performance for fibrosis; and predictive value for mGFR decline in KTR and LKD.
Linear mixed models will be used to study longitudinal associations. Receiver operating characteristic curve analysis will assess diagnostic performance. Results: Recruitment for the MPRENAL study commenced in November 2024. As of March 2026, enrolment is ongoing with 42 participants recruited. Full data analysis and results are projected for December 2029. Conclusions: Successful completion of this study is expected to provide robust histological validation of mpMRI and establish its utility as a non-invasive tool for monitoring renal health. Clinical Trial: ClinicalTrials.gov NCT06210555;
https://clinicaltrials.gov/ct2/show/NCT06210555
Background: Socially assistive robots are increasingly being explored in health care, but evidence from real pediatric outpatient settings remains limited. In particular, little is known about how con...
Background: Socially assistive robots are increasingly being explored in health care, but evidence from real pediatric outpatient settings remains limited. In particular, little is known about how contextual support, human facilitation, and organizational conditions shape everyday human-robot interaction and practical acceptance in hospital environments Objective: This study aimed to examine how visitor engagement with a social robot differed across 3 implementation conditions in a pediatric outpatient hospital, how engagement differed between children and adults, and which socio-technical factors staff identified as enabling or constraining implementation. Methods: This convergent mixed methods pilot study was conducted at Tallinn Children’s Hospital. The quantitative strand consisted of structured behavioral observation across 3 field conditions, baseline, poster support, and staff facilitation, in which 675 visitors were recorded. The qualitative strand consisted of 4 small staff-group interviews with 12 health care professionals focused on use opportunities, barriers, and implementation needs. Quantitative data were analyzed descriptively, and qualitative data were analyzed using qualitative content analysis with deductive and inductive coding. Results: Observable engagement increased across the 3 conditions, from 3.1% in the baseline phase to 22.6% with poster support and 38.1% with staff facilitation. Children engaged more often than adults across all phases. Staff reported that acceptance depended less on novelty alone than on role clarity, visible usefulness, multilingual guidance, workflow fit, troubleshooting support, and clear organizational ownership. The robot was perceived as most useful for wayfinding, reducing uncertainty, engaging children during waiting, and supporting a calmer outpatient atmosphere. Conclusions: The findings support a socio-technical view of robot acceptance in pediatric health care. Meaningful uptake emerged through the interaction of robot affordances, local mediation, and organizational embedding rather than through technology alone. Public-facing, narrowly scoped functions, such as wayfinding, waiting-time support, parent guidance, and simple procedural information, appear to be the most feasible early use cases for social robots in pediatric hospitals.
Background: Incentivisation is increasingly used to maintain engagement and support behaviour change within communities of practice (CoPs), yet its effectiveness across chronic disease contexts remain...
Background: Incentivisation is increasingly used to maintain engagement and support behaviour change within communities of practice (CoPs), yet its effectiveness across chronic disease contexts remains uncertain. Objective: To examine how incentives are integrated into CoPs and related peer-support models, and to assess their impact on participant activation, engagement, and health-related outcomes. Methods: PubMed/MEDLINE, Embase, Scopus, and CENTRAL were searched from inception to June 2025 using predefined terms relating to CoPs, incentivisation, and patient-centred outcomes. Peer-reviewed empirical studies involving incentivised CoPs or analogous peer-support interventions for adults with chronic conditions were eligible. Four reviewers independently screened studies, extracted data, and assessed risk of bias in line with PRISMA 2020 guidance. Heterogeneity in design and outcomes required narrative synthesis. Results: From 667 records, four randomised controlled trials met inclusion criteria. Financial incentives produced the greatest short-term gains in physical activity, while non-financial approaches such as gamification, points, badges, and structured peer support yielded modest improvements in step count, treatment adherence, or diet quality. No consistent effects were observed for patient activation, self-efficacy, mental health, or quality of life. Engagement moderated effectiveness, although attrition was common. Conclusions: Incentivisation can enhance short-term behavioural outcomes within CoPs, but evidence for sustained psychosocial benefit is limited. Larger, longer-term studies are needed to clarify which incentive strategies deliver durable improvements in engagement and self-management. Clinical Trial: This review was registered on PROSPERO, an international prospective register of systematic review (January 2026, reference CRD420251244276).
Open Peer Review Period: Apr 4, 2026 - Mar 20, 2027
Background: The Democratic Republic of Congo (DRC) faces a severe mental health crisis, exacerbated by decades of conflict, poverty, and a fragile healthcare system. This challenge is directly relevan...
Background: The Democratic Republic of Congo (DRC) faces a severe mental health crisis, exacerbated by decades of conflict, poverty, and a fragile healthcare system. This challenge is directly relevant to achieving Sustainable Development Goal 3 (SDG 3): Good Health and Well-being, particularly Target 3.4, which calls for promoting mental health and well-being by 2030. Objective: The primary research question of this study examines the dual role of cultural belief systems in shaping mental well-being within the complex environment of the DRC, specifically investigating how these systems both contribute to and challenge mental well-being. Methods: This study employed a qualitative research design based on a systematic literature review and synthesis of existing research. Data were drawn from academic databases (PubMed, PsycINFO, Google Scholar), grey literature from NGOs (WHO, UNHCR), and peer-reviewed journals published between 2000 and 2024. A thematic analysis approach was used to analyze the data, involving familiarization, coding, and theme generation. Results: The findings reveal that cultural beliefs function as a double-edged sword. Positive contributions include traditional healing practices, strong community and family support networks, and religious faith (with approximately 80% of the population identifying as Christian), which provide crucial psychological resilience. Conversely, the widespread attribution of mental illness to supernatural causes fosters stigma and leads to harmful practices. Epidemiological data indicates high prevalence rates of mental health disorders in conflict-affected areas, with PTSD estimated between 17% and 50%, and depression rates ranging from 27.8% to 62% in Ebola-affected areas. The mental health system is severely under-resourced, with only 5% of the population having access to services and a workforce of 0.08 psychiatrists per 100,000 people. Conclusions: Cultural belief systems in the DRC have a profound and dual impact on mental well-being. Addressing the mental health crisis requires an integrated approach that leverages the protective aspects of culture while mitigating harmful practices. Developing culturally competent mental health services and fostering collaboration between traditional and modern healthcare providers are essential steps toward achieving SDG 3 in the DRC.
Open Peer Review Period: Apr 2, 2026 - Mar 18, 2027
Background: Post-stroke seizures and post-stroke epilepsy are important complications that affect recovery and long-term quality of life after stroke. Predicting which patients are at higher risk rema...
Background: Post-stroke seizures and post-stroke epilepsy are important complications that affect recovery and long-term quality of life after stroke. Predicting which patients are at higher risk remains challenging, and recent studies have explored the use of artificial intelligence to improve prediction. Objective: This scoping review aimed to map how artificial intelligence and related computational methods have been applied to predict post-stroke seizures and epilepsy, to describe the role of neuroimaging in these models, and to assess representation of African settings in the existing literature. Methods: A systematic search identified studies that used statistical, machine learning, radiomics, or deep learning approaches to predict seizure-related outcomes after stroke. Traditional statistical models and clinical risk scores were the most commonly used methods. A smaller number of studies applied machine learning techniques, while radiomics and deep learning approaches were reported in only a few studies. Neuroimaging was mainly used to predict long-term epilepsy rather than early seizures. Most models were evaluated using internal validation only, with limited external validation. Results: Studies from African settings were underrepresented and primarily focused on describing seizure frequency and associated factors using regression analysis. No studies were identified that developed or validated artificial intelligence-based prediction models using African datasets. Conclusions: Overall, the findings show that while interest in AI-based prediction is growing, advanced methods remain limited, and major gaps exist in model validation and geographic representation. These findings highlight the need for context-specific, well-validated prediction models, particularly in African healthcare settings.
Open Peer Review Period: Mar 28, 2026 - Mar 13, 2027
A brown seaweed, sea tangle (ST) (Laminaria japonica), is a dietary supplement with potential biological activities. If fermented using Lactobacillus brevis, it is converted to -aminobutyric acid (...
A brown seaweed, sea tangle (ST) (Laminaria japonica), is a dietary supplement with potential biological activities. If fermented using Lactobacillus brevis, it is converted to -aminobutyric acid (GABA)-rich fermented sea tangle (FST). The traditional Korean rice wine has been brewed using a variety of natural materials, and its physiological or functional properties have been well documented. But there is a paucity of data suggesting its beneficial effects when supplemented with GABA-rich FST. We therefore examined the biological properties of the Korean rice wine supplemented with GABA-rich FST. For the current experiment, the white rice was fermented using Aspergillus kawachii (AK), Aspergillus oryzae (AO), Rhizopus oryzae (RO) and Monascus purpureus (MP). Thus, the sample was prepared and then divided into the four groups: (1) Group 1: The first brew supplemented with 0.5% (w/v) GABA-rich FST (AK-1, AO-1, RO-1 and MP-1); (2) Group 2: The first and second brews supplemented with 0.25% (w/v) GABA-rich FST each (AK-2, AO-2, RO-2 and MP-2); (3) Group 3: The second brew supplemented with 0.5% (w/v) GABA-rich FST (AK-3, AO-3, RO-3 and MP-3); and (4) Group 4: No supplementation attempted (AK-4, AO-4, RO-4 and MP-4). Then, we performed assays of general chemicals, organic acid, free sugar, volatile flavor compounds, GABA, phenolic compounds and kojic acid. Moreover, we also performed assays of antioxidant, radical scavenging activity and fibrinolytic one. Furthermore, we also assessed alcohol dehydrogenase (ADH) zymogram and band pattern following yeast culture. The degree of antioxidant, radical scavenging activity was the highest in the AK-2, and it was approximately 11% higher as compared with the AK-4. The degree of antioxidant, radical scavenging activity was higher in the AK-2 or the AK-3 as compared with the AK-1 or the AK-4. The degree of fibrinolytic activity reached the highest level in the RO-2. Moreover, it was approximately 2 times higher in the RO-2 as compared with the RO-4. Furthermore, it was 2-3 fold higher in the RO-4 as compared with the AK-4, AO-4 and MP-4.
In conclusion, our results indicate that the Korean rice wine would have beneficial effects, such as antioxidant and fibrinolytic activities, if supplemented with GABA-rich FST.
Open Peer Review Period: Mar 28, 2026 - Mar 13, 2027
Background: We conducted this experimental study to examine whether fermented Angelica gigas (FAG) is effective in preventing oxidative stress in an Sprague-Dawley (SD) rat model of carbon tetrachlori...
Background: We conducted this experimental study to examine whether fermented Angelica gigas (FAG) is effective in preventing oxidative stress in an Sprague-Dawley (SD) rat model of carbon tetrachloride (CTC)-induced acute hepatic injury (AHI).
Methods: Male SD rats (n=36) were used to establish an animal model of CTC-induced AHI was established. They were given an intraperitoneal injection of CTC and then divided into the 6 experimental groups: NC group (n=6, olive oil), NCDG group (n=6, olive oil with 5% [w/w] AG), NFCDG group (n=6, olive oil with 5% [w/w] FAG), CC group (n=6, CTC), CCDG group (n=6, CTC with 5% [w/w] AG) and CFCDG group (n=6, CTC with 5% [w/w] FAG). Then, we compared serum alanine aminotransferase (ALT)/aspartate aminotransferase (AST) levels, serum and hepatic lipid levels and hepatic levels of thiobarbituric acid-reactive-substances (TBARS) and glutathione.
Results: Our results showed that the FAG was effective in significantly protecting the liver from CTC-induced hepatotoxicity and significantly inhibiting CTC-induced accumulation of lipid in the liver.
Conclusions: In conclusion, our results indicate that the FAG might be effective in preventing the hepatic injury due to oxidative stress in an animal model of CTC-induced hepatotoxicity.
Open Peer Review Period: Mar 27, 2026 - Mar 12, 2027
Background: The literature on value-based healthcare (VBHC) implementation has expanded rapidly but remains heterogeneous, making synthesis difficult. This study aimed to map the thematic landscape of...
Background: The literature on value-based healthcare (VBHC) implementation has expanded rapidly but remains heterogeneous, making synthesis difficult. This study aimed to map the thematic landscape of VBHC implementation and identify recurring domains, cross-cutting patterns, and gaps in the evidence. Objective: To map and synthesize the thematic landscape of the value-based healthcare (VBHC) implementation literature using computational thematic synthesis, identifying recurring domains, cross-cutting patterns, and evidence gaps. Methods: PubMed was searched without date restrictions and limited to English-language publications. Screening and full-text eligibility assessment were conducted manually in two stages. Eligible full texts were embedded using a text-embedding model (text-embedding-3-large, 3,072 dimensions), projected with UMAP, and clustered with HDBSCAN using consensus across 10 random seeds. Cluster stability was examined using an alternative embedding model, direct-space clustering, and negative controls. Results: A total of 8,957 records were screened, and 351 full-text articles were included. Computational synthesis identified 18 themes that grouped into four broader domains: condition-specific VBHC applications, measurement and costing approaches, organisational and workforce models, and health system policy and governance. Commentaries and framework papers were the most common design category (19.4%), while empirical studies were comparatively fewer across most themes. The United States and the Netherlands contributed the largest share of publications. Themes supported by stronger measurement infrastructure, including PROMs, ICHOM outcome sets, and TDABC, were more frequently associated with reported value improvements. Conclusions: This evidence-mapping study provides a broad overview of the VBHC implementation literature. Despite wide diffusion of the concept, much of the field remains centred on conceptual discussion rather than empirical evaluation. Future work should prioritise interventional designs and context-sensitive implementation frameworks, particularly in under-represented regions.
Open Peer Review Period: Mar 26, 2026 - Mar 11, 2027
Background: Code-switching between Egyptian Arabic (ARZ) and English is ubiquitous in clinical settings across the Arab world, yet no dedicated benchmark exists for evaluating Automatic Speech Recogni...
Background: Code-switching between Egyptian Arabic (ARZ) and English is ubiquitous in clinical settings across the Arab world, yet no dedicated benchmark exists for evaluating Automatic Speech Recognition (ASR) systems under these conditions. Existing Arabic ASR benchmarks evaluate models on Modern Standard Arabic or single-dialect speech without medical vocabulary or code-switching. Objective: To introduce the Clinera ASR Benchmark, the first benchmark targeting medical ARZ-EN code-switched speech, and to evaluate commercial and open-source ASR systems on accurate recognition of medical terminology using both standard and novel medical-term-aware metrics Methods: We curated 683 utterances of Egyptian Arabic-English medical speech with dense medical-term annotations. We evaluated six ASR systems: the proprietary ElevenLabs Scribe v1, domain-adapted ArAZN-Whisper-S (244M parameters), Whisper-Large-V3, SeamlessM4T v2, Wav2Vec2 Large AR, and Whisper-Medium. We computed standard ASR metrics (WER, CER, MER) and proposed novel medical-term metrics (MT-Precision, MT-Recall, MT-F1, MT-wF1 weighted by inverse document frequency). Statistical comparisons used bootstrap confidence intervals and Wilcoxon signed-rank tests with Bonferroni correction. Results: ElevenLabs Scribe v1 achieved the lowest WER (27.8%) and highest MT-wF1 (60.5%), outperforming all open models by more than six times its size on medical-term recognition. General-purpose multilingual models achieved higher WER while almost entirely failing to recognize medical terms. Among open models, ArAZN-Whisper-S dominated on medical-term efficiency (26.0 MT-wF1 per 100M parameters). Even the best commercial system produced 11 fatal or high-risk medical errors affecting 1.6% of the corpus. Conclusions: Current ASR systems exhibit a critical gap between general transcription accuracy and medical-term recognition in code-switched Arabic-English speech. Our benchmark and novel MT-metrics provide the first standardized evaluation framework for this clinically important setting, revealing that even top-performing systems pose patient safety risks through medical terminology errors.
Open Peer Review Period: Mar 26, 2026 - Mar 11, 2027
Background: Emergency Medical Services (EMS) activations related to influenza-like illness (ILI) increased during the COVID-19 pandemic. Information about the characteristics of EMS activations relate...
Background: Emergency Medical Services (EMS) activations related to influenza-like illness (ILI) increased during the COVID-19 pandemic. Information about the characteristics of EMS activations related to ILI during the COVID-19 pandemic may inform prehospital planning for future pandemics. Objective: The objective of our study was to compare demographic, geographic, and other characteristics of EMS activations related to ILI using data from 2019 (before the pandemic) and 2020 (during the pandemic). Methods: We conducted a cross-sectional analysis of 2019 and 2020 EMS activation data from the National Emergency Medical Services Information System (NEMSIS). The outcome of interest was the number of EMS activations related to ILI. Rates (the number of EMS activations related to ILI divided by the total number of EMS activations initiated with a 9-1-1 call that resulted in patient contact multiplied by 1,000), confidence intervals, and P-values were calculated by year for age, sex, race / ethnicity, census region, census division, urbanicity, primary method of payment, patient disposition, barriers to patient care, and patient destination to assess whether there were significant differences in the rates between years. The percentage change per 1,000 EMS activations from 2019 to 2020 was calculated in addition to rates by quarter for each year to compare trends. Results: There were 21,278,998 total EMS activations in 2019 and 26,923,354 in 2020. The rate of EMS activations related to ILI was 153.4/1,000 (n=3,263,713) in 2019 and 174.6/1,000 (n=4,701,617) in 2020. Rates of EMS activations related to ILI in 2020 were statistically different (P<.001) from 2019 for all demographic, geographic, and other characteristics. The overall rate of EMS activations related to ILI increased by 13.8% from 2019 to 2020. Some of the largest percentage increases in rates between years were for EMS activations that included Hispanic or Latino patients (33.1%), EMS activations in the Middle Atlantic census division (42.0%), and EMS activations with language barriers between the patient and the EMS clinician (39.6%). Quarterly rates of EMS activations related to ILI for EMS activations that included Hispanic or Latino patients and for EMS activations with language barriers between the patient and the EMS clinician were larger during 2020 than during 2019 for all quarters. Conclusions: Compared to 2019, rates of EMS activations related to ILI during 2020 increased for EMS activations that included Hispanic or Latino patients, occurred in the Middle Atlantic census division, and demonstrated language barriers between the patient and the EMS clinician. Further studies are needed to investigate the health impacts of language barriers between the patient and EMS clinician during prehospital care. Prehospital planning efforts that include testing and implementing strategies to address potential language barriers between the patient and the EMS clinician during EMS activations are warranted.
Open Peer Review Period: Mar 24, 2026 - Mar 9, 2027
Background: Clinical trials are essential for evaluating medical interventions, yet approximately 80% fail to meet enrollment targets on time. The challenge of matching patients to suitable trials inv...
Background: Clinical trials are essential for evaluating medical interventions, yet approximately 80% fail to meet enrollment targets on time. The challenge of matching patients to suitable trials involves reviewing unstructured clinical notes against complex eligibility criteria containing dozens of inclusion and exclusion conditions. AI methods promise to automate this matching at scale, but each approach involves fundamental tradeoffs between speed, accuracy, interpretability, and auditability. Objective: This technical narrative review surveys the landscape of AI approaches to patient-trial matching, tracing the evolution from rule-based systems through sparse retrieval, dense retrieval, cross-encoder reranking, knowledge graphs, and large language model approaches. We identify critical gaps and provide an integrative framework for understanding tradeoffs across methods. Methods: We searched PubMed, IEEE Xplore, ACL Anthology, and arXiv using terms including "clinical trial matching," "patient recruitment AI," "eligibility criteria NLP," and "trial-patient retrieval" for literature published 2015 to 2026. We synthesized algorithmic approaches, commercial platforms, evaluation methodologies, and explainability requirements through a technical narrative review. Results: We identified seven algorithmic paradigms: rule-based systems, machine learning classifiers, BM25 sparse retrieval, BERT-based dense retrieval, cross-encoder reranking, large language models (including TrialGPT achieving 87.3% criterion-level accuracy), and knowledge graph approaches. We catalogued sixteen commercial platforms and documented the TREC Clinical Trials Track and n2c2 2018 benchmarks. Each method resolves the core tradeoff differently: BM25 offers speed without semantic understanding; LLMs offer flexibility without auditability; hybrid architectures distribute tradeoffs across components. Conclusions: Six critical gaps define the frontier: absence of operational-scale benchmarks, temporal and Boolean reasoning limitations, tension between LLM flexibility and deterministic auditability, multi-method explainability disclosure requirements, proxy-label governance, and data heterogeneity across EHR systems. The matchmaker's dilemma of balancing competing goods with no perfect solution frames both progress and unresolved challenges in this rapidly evolving field.
Open Peer Review Period: Mar 21, 2026 - Mar 6, 2027
We present, to our knowledge, the first independent evaluation of Google's MedGemma 1.5 4B instruction-tuned model on OpenAI's HealthBench, one of the most comprehensive physician-graded benchmarks fo...
We present, to our knowledge, the first independent evaluation of Google's MedGemma 1.5 4B instruction-tuned model on OpenAI's HealthBench, one of the most comprehensive physician-graded benchmarks for health AI [3]. The evaluation encompassed all 5,000 HealthBench conversations (48,562 rubric criteria created by 262 physicians across 60 countries), run entirely on a consumer laptop (Apple M1 Max, 32GB) using quantized weights (Q8_0), with automated grading via GPT-4.1 mini at a total API cost of $39.21.
MedGemma 1.5 4B achieved an overall HealthBench score of 0.4512 (bootstrap SE = 0.0046), placing it ahead of o1 (0.418), Claude 3.7 Sonnet (0.346), GPT-4o (0.320), and Llama 4 Maverick (0.249), though behind GPT-4.1 (0.479), Gemini 2.5 Pro (0.520), Grok 3 (0.543), and o3 (0.598) [3, 11]. This is notable given that MedGemma 1.5 4B has roughly 4 billion parameters and runs without internet connectivity, while the models it approaches or surpasses are significantly larger and require cloud infrastructure.
Performance varied substantially across HealthBench dimensions. Communication quality was the strongest axis (0.7021), followed by instruction following (0.5589) and accuracy (0.5425). Completeness was the weakest (0.3780), confirming the HealthBench finding that completeness is the primary driver of overall model ranking. Among themes, emergency referrals scored highest (0.5559), with the model satisfying 90.6% of physician-written emergency behavior criteria in emergent cases. Global health scored lowest (0.3602), suggesting training data bias toward Western clinical contexts.
The most clinically significant finding involved context-seeking behavior. When presented with conversations lacking sufficient clinical context, MedGemma satisfied only 7.2% of context-seeking criteria when clinical information was insufficient — while meeting helpfulness and safety criteria 97.2% of the time. The model consistently provides well-communicated, accurate, but incomplete answers without recognizing when it needs more information. This pattern — knowing what to say but not how much to say or when to ask — has direct implications for clinical deployment safety.
All analyses were pre-specified before the evaluation was conducted. Code, results, and the pre-registered study analysis plan are publicly available. This evaluation demonstrates that rigorous, independent benchmarking of medical AI is accessible to individual researchers at minimal cost, and that such evaluation is essential before clinical deployment of any health AI system.
Open Peer Review Period: Mar 7, 2026 - Feb 20, 2027
Background: Current acute stroke management guidelines focus primarily on time-based imaging windows and the pharmacological suppression of acute hypertension. This paper proposes an alternative parad...
Background: Current acute stroke management guidelines focus primarily on time-based imaging windows and the pharmacological suppression of acute hypertension. This paper proposes an alternative paradigm based on a first-principles theoretical derivation of hydraulic states. Objective: The primary goal of this framework is to establish the physiological feasibility of a "Stability Corridor" for Cerebral Perfusion Pressure (CPP) to maximize neuronal salvage. Methods: The methodology utilizes a first-principles biophysical derivation incorporating the Monro-Kellie Doctrine and Laplace's Law. It modifies the Cushing Reflex sequence, framing the terminal rise in intracranial pressure (ICP) as a result of a systemic blood pressure spike driven by ischaemic vasoparalysis. Results: The derivation identifies two phases of hydraulic failure: a "Masked Influx" where the 0.05 alpha extracellular space (ECS) buffer is exhausted, followed by a "Terminal Spike" in ICP. It establishes a "Stability Corridor" by identifying the Ischaemic Floor for collateral flow and the Elastic Limit to prevent vascular tearing. Conclusions: By modulating ICP to keep CPP within the Stability Corridor and using GFAP biomarkers as proxies for hydraulic integrity, clinicians can theoretically maintain cerebral perfusion and prevent the "Hydraulic Breach" of macro-haemorrhage. Clinical Trial: N/A (Theoretical Paper)
Open Peer Review Period: Dec 8, 2025 - Nov 23, 2026
Background: Human papillomavirus (HPV) remains the principal cause of cervical cancer, yet population-level awareness and knowledge in many Nigerian settings remain limited. Understanding the patterns...
Background: Human papillomavirus (HPV) remains the principal cause of cervical cancer, yet population-level awareness and knowledge in many Nigerian settings remain limited. Understanding the patterns and predictors of HPV awareness and knowledge is essential for strengthening Nigeria’s HPV vaccination rollout and reducing preventable cervical cancer morbidity. Objective: To describe respondents’ demographic characteristics; assess levels of awareness and knowledge of HPV, cervical cancer, and the HPV vaccine; examine associations between sociodemographic variables and awareness/knowledge; and identify independent predictors of HPV awareness and knowledge. Methods: A community-based cross-sectional survey was conducted among 238 caregivers of girls aged 9-14 years in Port Harcourt Local Government Area. Data on demographics, HPV awareness, knowledge indicators, and information sources were collected using a structured questionnaire. Descriptive statistics, chi-square tests, and multivariable logistic regression were used to assess associations and predictors. Statistical significance was set at p < 0.05. Results: Respondents showed wide demographic diversity across age, religion, education, occupation, and income. Overall awareness of HPV was low (45.4%), and knowledge was predominantly poor (78.6%). Misconceptions were common, with many attributing HPV to poor hygiene or skin infections. Only 39.8% correctly identified sexual contact as the mode of transmission, and knowledge of vaccine dosage was inconsistent. Informal channels, religious institutions, social media, and family networks were the primary sources of information, whereas health workers accounted for only 8.3%. Most sociodemographic factors showed no significant association with awareness or knowledge, indicating widespread deficits across groups. Occupation was the only variable significantly associated with awareness (p = 0.011). Logistic regression showed higher odds of awareness among respondents aged 26-36 years (OR 2.26, p = 0.039) and lower odds among those practicing Traditional religion (OR 0.41, p = 0.033). Civil/public servants showed reduced odds of awareness (OR 0.44, p = 0.048). Conclusions: HPV awareness and knowledge are markedly low and broadly distributed across demographic groups. Widespread misconceptions reflect structural failures in health communication. Strengthen community-based and health worker-led HPV education; embed messaging within religious and social structures; and implement targeted, culturally adapted communication strategies to improve vaccine uptake. Significance Statement: Addressing pervasive knowledge gaps is vital for achieving effective HPV vaccination coverage and reducing cervical cancer burden in Nigeria.
Open Peer Review Period: Nov 28, 2025 - Nov 13, 2026
Introduction
Acute leukemia poses a significant health burden globally, necessitating a deeper understanding of its etiological factors. This study investigates the potential link between blood group...
Introduction
Acute leukemia poses a significant health burden globally, necessitating a deeper understanding of its etiological factors. This study investigates the potential link between blood groups, Rh factor, and the incidence of acute leukemia to enhance knowledge and guide personalized treatment strategies.
Methods
A cross-sectional analytical study was conducted at Imam Khomeini Hospital in Urmia from 2012 to 2018, including patients with acute leukemia. Data on blood groups, Rh factor, and demographic variables were collected and analyzed using SPSS software. Statistical tests were employed to determine associations between blood groups and leukemia risk.
Results
The study found no significant relationship between ABO blood groups and acute leukemia, consistent with previous research. However, differences in Rh factor distribution were observed between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients, warranting further investigation.
Discussion
The complexity of leukemia etiology is highlighted by the multifactorial nature of the disease, where genetic, environmental, and possibly epigenetic factors interact. Future research should focus on larger sample sizes and diverse populations to elucidate the intricate mechanisms underlying leukemia susceptibility.
Conclusion
While ABO blood groups may not significantly impact acute leukemia risk, variations in Rh factor distribution among leukemia subtypes suggest a need for continued exploration. Comprehensive studies considering diverse factors are essential to unravel the complexities of leukemia development.
Open Peer Review Period: Nov 28, 2025 - Nov 13, 2026
Introduction
Immune thrombocytopenic purpura (ITP) is an acquired thrombocytopenia syndrome characterized by platelet destruction due to antiplatelet antibodies. Corticosteroids are the first-line tr...
Introduction
Immune thrombocytopenic purpura (ITP) is an acquired thrombocytopenia syndrome characterized by platelet destruction due to antiplatelet antibodies. Corticosteroids are the first-line treatment for adult patients with ITP. This study compares the effects of high-dose dexamethasone versus prednisolone in ITP treatment.
Materials and Methods
This open-label clinical trial involved patients over 18 years diagnosed with ITP (based on ASH criteria) who had not received prior treatment. Participants were randomly assigned (1:1) to receive high-dose dexamethasone (HD-DXM) or prednisolone (PDN). The dexamethasone group received 40 mg intravenously for 4 consecutive days, while the PDN group received 1 mg/kg oral prednisolone for 4 weeks. Daily complete blood counts were obtained to assess treatment response, defined as a platelet count above 30,000/μL.
Results
A total of 36 patients were evaluated, with 18 in each treatment group. Patients receiving dexamethasone showed significantly reduced hospitalization duration and faster time to reach platelet counts above 30,000/μL (P=0.01 and P=0.002, respectively).
Conclusion
High-dose dexamethasone significantly decreases the time to initial response and hospitalization duration in ITP patients compared to prednisolone.
Open Peer Review Period: Nov 24, 2025 - Nov 9, 2026
Classical evolutionary theory, notably Riedl’s concept of canalization, suggests that human lifespan is constrained by deeply entrenched developmental architectures, implying that aging is an immuta...
Classical evolutionary theory, notably Riedl’s concept of canalization, suggests that human lifespan is constrained by deeply entrenched developmental architectures, implying that aging is an immutable biological reality. However, rapid advancements in artificial intelligence (AI) from 2023 to 2025 have begun to challenge this pessimism. This viewpoint synthesizes recent developments to argue that AI is reframing aging from a biological mystery into a tractable engineering challenge. We examine two primary frontiers: the use of autonomous AI agents and generative models to discover geroprotective interventions, including the identification of compounds like ouabain via large-scale omics re-analysis; and the maturation of multi-modal “aging clocks” that utilize deep learning to enable precision diagnostics and personalized healthspan optimization. While acknowledging significant limitations regarding safety, translation from animal models, and the risks of commercial hype, we conclude that the integration of AI with mechanistic geroscience offers a plausible pathway toward a proactive, engineering-based approach to human longevity.
Open Peer Review Period: Nov 19, 2025 - Nov 4, 2026
Biobanks are recognised as lucrative health research resources due to their extensive and in-depth data availability, which allows researchers to draw correlations between various genetic, lifestyle, ...
Biobanks are recognised as lucrative health research resources due to their extensive and in-depth data availability, which allows researchers to draw correlations between various genetic, lifestyle, and health information and future disease incidence. As prospective data sources collect genetic and lifestyle information for several hundred thousand participants across various age categories, biobanks are important datasets in designing novel healthcare approaches. Within the realm of cardiometabolic ageing, which refers to the age related decline in the function of cardiovascular and metabolic systems, the conceptualisation of a systems medicine-based approach known as P4 (Predictive, Preventive, Personalised, Participatory) medicine has provided an interesting framework to tackle these metabolic illnesses in tandem with digital longevity tools that serve as vessels to deliver interventions across large populations. Therefore, this review aims to critically discuss how digital longevity informed by biobank data is vital in improving risk prediction, with a focus on cardiometabolic ageing.
Open Peer Review Period: Nov 10, 2025 - Oct 26, 2026
Background: Globally, digital health interventions (DHIs) enhance HIV care through technology, especially among women living with HIV (WLHIV), who face unique Challenges that affect their treatment. T...
Background: Globally, digital health interventions (DHIs) enhance HIV care through technology, especially among women living with HIV (WLHIV), who face unique Challenges that affect their treatment. This study assessed the feasibility of integrating DHIs into HIV care in Kisumu by examining their acceptability among WLHIV and identifying factors that influence their intention to use these tools. Objective: (1) To determine the feasibility of integrating digital health interventions into care for
women living with HIV in Kisumu.
(2) To identify factors that influence the adoption of Digital health interventions. Methods: A cross-sectional survey based on the Unified Theory of Acceptance and Use of Technology
2 (UTAUT2) was administered to evaluate the acceptability of SMS, teleconsultations, online support groups, and health applications. Summary statistics quantified acceptability, multivariate regression models examined associations between UTAUT2 constructs and behavioral intention, and Analysis of Variance identified sociodemographic predictors. Results: A total of 385 WLHIV (mean age 35·8 years) participated. Behavioral intention to use all four DHIs was high, with more than 80% rating their willingness at ≥4 on a five-point scale. Performance expectancy, hedonic motivation, habit, and price value were significant predictors of intention (p < 0·05). Higher education level was strongly associated with increased intention (p < 0·001), while older age was associated with reduced intention Conclusions: WLHIV in Kisumu demonstrated a strong willingness to adopt digital health tools in their routine care. The intention to use DHIs was primarily influenced by perceived usefulness, affordability, enjoyment, and familiarity with similar technologies. These results support the integration of digital health solutions into HIV care for women in this setting.
Open Peer Review Period: Nov 4, 2025 - Oct 20, 2026
Background: The COVID-19 pandemic presented an unparalleled opportunity for telemedicine implementation, shortening adoption timelines and creating significant opportunities for observational research...
Background: The COVID-19 pandemic presented an unparalleled opportunity for telemedicine implementation, shortening adoption timelines and creating significant opportunities for observational research. Prior evidence is predominantly derived from small feasibility studies with limited comparative efficacy data and inadequate attention to implementation challenges and equity considerations. Objective: To synthesize methodologies, findings, and innovations from observational telemedicine studies conducted during the pandemic and identify critical research gaps. Methods: Narrative synthesis of 25 peer-reviewed observational studies (2020–2021) examining telemedicine across 11 clinical specialties, encompassing 119,016 patient contacts across multiple international settings. Studies employed prospective cohort designs, retrospective analyses, cross-sectional surveys, and mixed-methods approaches. Results: Telemedicine demonstrated clinical efficacy for chronic disease management with objective monitoring data, particularly in pediatric diabetes and cardiac device follow-up. However, substantial technology-acceptance discrepancies emerged—user satisfaction exceeded actual data capture reliability. Cross-sectional analyses unveiled systemic racial bias in satisfaction ratings and socioeconomic disparities in access. Innovations, including real-time locating systems, large-scale observational platforms, ambispective designs, and mixed-methods integration, have advanced methodological rigor. Persistent obstacles encompass selection bias, unmeasured confounding, outcome heterogeneity precluding meta-analysis, and temporal confounding. Conclusions: Observational pandemic-era telemedicine research substantiates selective clinical applications while exposing technology reliability limitations, persistent inequities, and methodological constraints on causal inference. Critical gaps include the absence of long-term outcome evaluation, economic analyses, diagnostic accuracy assessment, and equity-focused intervention research. Future advancement requires quasi-experimental designs, standardized outcome measures, explicit equity integration, and implementation science evidence for sustainable post-pandemic integration.
Open Peer Review Period: Nov 1, 2025 - Oct 17, 2026
Background: Safe and reliable access to clean water remains a fundamental determinant of public health and sustainable development. In many rapidly urbanizing Nigerian communities, dependence on self-...
Background: Safe and reliable access to clean water remains a fundamental determinant of public health and sustainable development. In many rapidly urbanizing Nigerian communities, dependence on self-sourced groundwater and inadequate waste management systems continues to compromise water quality and expose residents to preventable diseases. This study investigated the status of water supply, quality, and associated health outcomes in Uselu Community, Benin City, to provide evidence-based insights for policy and intervention. Objective: The study aimed to (1) assess the primary sources of water available to residents, (2) evaluate household water-storage and treatment practices, and (3) examine the public-health implications of inadequate water access and sanitation behaviour in the community. Methods: A descriptive cross-sectional survey was conducted among 100 adult residents of Uselu Community selected through random sampling. Data were collected using structured questionnaires covering socio-demographics, water sources, treatment habits, sanitation practices, and self-reported waterborne diseases. Field observations complemented survey data, and results were presented as frequencies and percentages. Descriptive and inferential statistics were used to analyze trends, and findings were compared against national and international WASH benchmarks. Results: Findings revealed that 56% of respondents relied on boreholes as their main water source, while only 31% had access to public pipe-borne supply. Although 89% regularly washed their storage containers, fewer than half (43%) treated water by boiling or filtration, and only 17% practiced chlorination. About 32% reported disposing of waste near water sources, increasing contamination risks. The most common illnesses were typhoid fever (47%) and cholera (30%), with over half (55%) of respondents experiencing recurrent water shortages. These results indicate persistent infrastructural inadequacies, limited treatment adoption, and significant exposure to waterborne diseases. Conclusions: The study highlights critical water-supply and quality challenges in Uselu Community, driven by poor infrastructure, weak waste management, and inconsistent household treatment practices. Ensuring safe water access requires coordinated interventions combining infrastructural expansion, community hygiene education, and sustainable groundwater management. Strengthen municipal water systems, establish periodic water-quality monitoring, enforce sanitation regulations, and promote affordable household treatment technologies through continuous public-health education and community engagement. This study demonstrates that unsafe water and poor sanitation behaviours are central drivers of disease in Uselu Community. By translating evidence into actionable interventions, the research provides a model for improving public health, environmental sustainability, and water security in similar peri-urban settings.
Open Peer Review Period: Oct 27, 2025 - Oct 12, 2026
For decades, global guidance for sedentary behaviour and sleep has primarily been informed by studies that relied on self-report questionnaires to assess behaviours. However, it is widely recognised t...
For decades, global guidance for sedentary behaviour and sleep has primarily been informed by studies that relied on self-report questionnaires to assess behaviours. However, it is widely recognised that self-reported data suffer from numerous limitations, including recall and social desirability biases, as well as poor validity and precision. The Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) is a large international collaboration of cohort studies with research-grade wearables data designed to address these challenges. The ProPASS consortium looks to advance our understanding of the associations of free-living physical activity, posture (sitting, standing), and sleep with major health and non-communicable disease outcomes. In this editorial, we provide an overview of the first ProPASS scientific outputs including its growth in recent years; key advancements towards unified wearables methodologies; the ProPASS data resources, and how these will be made available to the global research community. To assist future analogous initiatives, we also share the key challenges ProPASS has encountered and discuss mitigation strategies.
Open Peer Review Period: Oct 23, 2025 - Oct 8, 2026
Universities are critical engines of knowledge creation and societal transformation; however, many African institutions, particularly in Nigeria, struggle to cultivate mature and sustainable research ...
Universities are critical engines of knowledge creation and societal transformation; however, many African institutions, particularly in Nigeria, struggle to cultivate mature and sustainable research cultures. This paper develops a conceptual framework for strengthening university research management systems, highlighting leadership and governance as catalysts for academic excellence, innovation, and societal relevance. Using a descriptive-analytical and comparative synthesis of international policy frameworks (UNESCO, OECD) and African higher-education reports (AAU, ARUA, NUC, and TETFund), the study integrates global best practices with contextual realities in low-resource environments. The proposed Research Leadership and Impact Framework (RLIF) outlines four interrelated components: leadership and vision, governance and systems, capacity and infrastructure, and research culture and societal impact, which collectively enable institutional transformation. Comparative indicators, such as Nigeria’s Gross Expenditure on Research and Development (GERD) of 0.22% versus South Africa’s 0.83%, illustrate the strategic significance of leadership and governance reform in closing performance gaps. The framework contributes a theoretically grounded and context-sensitive model for embedding evidence-based management, accountability, and inclusivity within African universities. Ultimately, the paper argues that building resilient research systems requires not only financial investment but visionary leadership capable of aligning academic missions with societal priorities and the Sustainable Development Goals (SDGs).
Background: Objective: To map the available evidence on psychosocial interventions (PIs) targeting the Brazilian Black population's mental health.
Introduction: Black population (BP) is proportional...
Background: Objective: To map the available evidence on psychosocial interventions (PIs) targeting the Brazilian Black population's mental health.
Introduction: Black population (BP) is proportionally more institutionalized in psychiatric hospitals, and is historically more associated with “madness”, dangerousness, and racial inferiority. PIs targeting the Black population's mental health can potentially enhance professional practices by addressing this group's specific needs.
Inclusion criteria: Participants: Brazilian BP; concept: PIs targeting the Black population's mental health; context: Whole Brazilian country. Therefore, studies addressing PIs targeting the Brazilian BP, including the “Quilombola” community's mental health, will be considered as inclusion criteria. Studies addressing black immigrants and refugees in Brazilian territory will be excluded.
Methods: This scoping review (SR) will follow the JBI methodology guidelines, and adheres to the PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Search Strategy: A focused search will be conducted in MEDLINE (PubMed), Psycinfo (APA) CINAHL (EBSCOhost), Embase, Scopus (ELSEVIER), CINAHL (EBSCO), APA (PsycInfo), Embase and the Virtual Health Library (BVS). There will be no restriction regarding the language or date of publication of the studies. Study Selection: Citations will be managed in Zotero, and Rayyan will be used to organize the screening. Two independent reviewers will screen titles and abstracts for eligibility. Disagreements will be resolved through discussion or consultation with a third reviewer. Data Extraction: Two independent reviewers will extract data using a custom tool. Data Analysis and Presentation: Results will be summarized narratively and presented in tables and charts. Objective: To map the available evidence on psychosocial interventions (PIs) targeting the Brazilian Black population's mental health. Methods: This scoping review (SR) will follow the JBI methodology guidelines, and adheres to the PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Search Strategy: A focused search will be conducted in MEDLINE (PubMed), Psycinfo (APA) CINAHL (EBSCOhost), Embase, Scopus (ELSEVIER), CINAHL (EBSCO), APA (PsycInfo), Embase and the Virtual Health Library (BVS). There will be no restriction regarding the language or date of publication of the studies. Results: This section does not present data; it is a protocol. Conclusions: The review's conclusion promises to map critical evidence gaps.
India’s health system faces chronic resource gaps and inefficiencies. With public health
spending at only 1.84% of GDP and very low hospital bed densities (around 0.6 beds per 1000 population), si...
India’s health system faces chronic resource gaps and inefficiencies. With public health
spending at only 1.84% of GDP and very low hospital bed densities (around 0.6 beds per 1000 population), simply adding beds is unaffordable and slow. A more efficient alternative is to improve utilisation: a real-time digital platform that tracks staffed bed availability can raise effective capacity and reduce inequity.
Early experiments – from Delhi’s COVID-19 bed portal to the bed-management system
in AIG Hospitals, Hyderabad – show substantially higher occupancy and throughput. International evidence also supports these results, confirming that real-time tracking
systems can deliver major efficiency gains.
This brief proposes piloting a national bed-tracking dashboard and shows it can yield
large gains for much lower cost and risk than new construction, with safeguards to address data accuracy, incentives and privacy. These promising results are tempered
by limited evidence from a small number of pilots and by systemic constraints such as
staff shortages, uneven digital readiness, and governance challenges that will require
independent evaluation and safeguards during scale up.
Deep learning-based medical image registration methods increasingly incorporate both architectural enhancements (affine transformations) and training objective improvements (regularization losses), ye...
Deep learning-based medical image registration methods increasingly incorporate both architectural enhancements (affine transformations) and training objective improvements (regularization losses), yet their individual and combined contributions remain poorly understood. To quantify the individual and synergistic effects of affine components versus regularization losses on deformable medical image registration performance through systematic ablation analysis, we conducted a controlled ablation study using the OASIS brain MRI dataset comparing four model variants: baseline 3D U-Net with basic similarity losses, regularization-enhanced U-Net, affine-enhanced U-Net with basic losses, and fully enhanced model combining both components. Primary outcomes included registration accuracy metrics (mean squared error [MSE], normalized cross-correlation [NCC], structural similarity index [SSIM]), enhanced deformation quality analysis including Jacobian determinant preservation and anatomical plausibility scoring, and computational efficiency measures. Regularization enhancement alone achieved substantial performance improvements: 21.3% relative improvement in MSE (1.78% → 2.16%, P<.05) and 21.8% improvement in NCC (0.0555 → 0.0676), while dramatically reducing maximum deformation from 53.1 to 0.51 units (99.0% reduction) with negligible computational overhead (-0.06% inference time). Combined approaches achieved optimal performance with 25.8% relative MSE improvement (1.78% → 2.24%) and enhanced anatomical plausibility scores (0.596 → 0.930), at moderate computational cost (+9.8% inference time). Enhanced gradient correlation analysis revealed substantial improvements in structural preservation (0.742 → 0.980 for fully enhanced model). All enhanced variants achieved sub-voxel registration accuracy with anatomically plausible deformation constraints. Regularization losses provide the primary driver of performance improvements in medical image registration, offering both accuracy gains and dramatic deformation control enhancement with maintained computational efficiency. Architectural enhancements provide complementary benefits at acceptable computational cost. The dramatic improvement in deformation control (99% reduction in unrealistic deformations) addresses critical clinical deployment concerns while achieving superior registration accuracy.
Background: Urinary conditions impose a widespread burden on patients, caregivers, and healthcare systems. Emerging technologies, including wearable and remote devices, offer opportunities to improve ...
Background: Urinary conditions impose a widespread burden on patients, caregivers, and healthcare systems. Emerging technologies, including wearable and remote devices, offer opportunities to improve diagnosis, monitoring, and care delivery. Yet, the perspectives of healthcare professionals, who are central to technology adoption, remain underexplored. Objective: This study aimed to explore healthcare professionals’ perceptions of urinary issues and examine their views on the opportunities and barriers associated with adopting health technologies for urinary care. Methods: An online survey of 256 healthcare professionals collected qualitative responses about urinary care and the role of technology. Data were analyzed using grounded theory methods, including open, axial, and selective coding, to develop an explanatory model grounded in providers’ narratives. Results: Analysis revealed four interconnected categories: Technology and Innovation in Patient Care, Patient-Centered and Integrated Care, Accessibility and Ethical Considerations, and Proactive and Preventative Urological Health Management. These categories were unified within the emergent Grounded Theory of Technology Negotiation in Urinary Care, which describes how professionals integrate new technologies through a negotiated process that balances enthusiasm for innovation with patient-centered values, systemic barriers, and preventative goals. Adoption occurs when innovations align with professional values, overcome structural constraints, and enhance holistic, sustainable care. Conclusions: Healthcare professionals approach the integration of urinary health technologies as an active negotiation rather than passive acceptance. This grounded theory underscores that successful adoption requires user-centered design, comprehensive training, supportive reimbursement structures, and preservation of meaningful patient engagement. Recognizing adoption as a negotiated process provides a framework for guiding sustainable technology integration in urinary care.
Open Peer Review Period: Sep 15, 2025 - Aug 31, 2026
Background: Patients with rare diseases often face fragmented healthcare, limited access to specialists, and challenges in securely sharing their medical records across providers. Emerging technologie...
Background: Patients with rare diseases often face fragmented healthcare, limited access to specialists, and challenges in securely sharing their medical records across providers. Emerging technologies such as blockchain offer a decentralized and tamper-resistant framework for personal health records (PHRs), but their feasibility in low-resource settings remains largely unexplored Objective: This study aimed to evaluate the feasibility, usability, and patient perceptions of a blockchain-enabled PHR system tailored for rare disease patients in low-resource healthcare environments Methods: We conducted a mixed-methods pilot study involving 32 patients with rare genetic and metabolic disorders in Faisalabad, Pakistan. Participants were enrolled in a blockchain-based PHR platform that allowed secure storage and controlled sharing of medical data. Quantitative data on system usage, error rates, and access patterns were collected over a 12-week period. Semi-structured interviews and focus groups were used to explore patient and caregiver experiences, perceived benefits, and challenges. Thematic analysis was applied to qualitative data, while descriptive statistics summarized quantitative measures. Results: Patients and caregivers reported high levels of trust in the blockchain system (78% expressed greater confidence compared to hospital records). Key perceived benefits included improved data ownership, reduced dependency on fragmented paper records, and greater willingness to share information with providers. However, barriers included limited digital literacy, occasional connectivity issues, and the need for ongoing technical support. Quantitatively, 85% of enrolled participants successfully accessed and updated their records at least once, while 62% shared data with external providers. Thematic analysis revealed three major themes:
(1) empowerment through ownership
(2) digital divides as barriers to adoption
(3) the importance of community support in technology uptake Conclusions: Blockchain-enabled PHRs show promise for enhancing healthcare access, trust, and patient empowerment among rare disease populations in resource-constrained settings. Despite challenges related to usability and infrastructure, the pilot demonstrates potential for scaling such systems with targeted training and support. Further large-scale studies are needed to assess long-term sustainability and integration with existing health systems. Clinical Trial: not aplicable
Open Peer Review Period: Sep 7, 2025 - Aug 23, 2026
Background: Long-standing intrapsychic conflicts often arise from apparently irreconcilable tensions, such as desire versus affection or autonomy versus dependence. Traditional approaches in psychothe...
Background: Long-standing intrapsychic conflicts often arise from apparently irreconcilable tensions, such as desire versus affection or autonomy versus dependence. Traditional approaches in psychotherapy describe defense mechanisms or splitting to cope with such conflicts. However, less attention has been given to creative integrative processes that may reconcile opposing tendencies. Objective: This paper introduces the concept of AI-facilitated symbolic juxtaposition, where generative models are used to create “digital chimeras”—hybrid symbolic constructions integrating objects of desire with affective attributes. We aim to provide a theoretical foundation, operational hypotheses, and clinical protocols for testing this novel framework. Methods: Drawing from psychoanalytic theory (Winnicott’s transitional objects), predictive processing, and neuroscience of the default mode and mentalizing networks, we propose a neuro-symbolic model for symbolic integration. We outline four testable hypotheses: (1) neural integration (DMN coherence), (2) symbolic flexibility, (3) enhancement of attachment security, and (4) accelerated therapeutic outcomes. Empirical validation methods include fMRI, EEG coherence, eye-tracking, attachment interviews, and cognitive flexibility tasks. We also present a clinical implementation protocol with AI-assisted symbolic generation, immersive VR/AR environments, and ethical safeguards. Results: As a conceptual and methodological paper, results are presented as expected outcomes. We anticipate that AI-facilitated chimera formation will (a) improve DMN connectivity, (b) enhance cognitive flexibility, (c) increase attachment security, and (d) reduce the number of sessions required for clinically significant change. Clinical protocols emphasize therapist training, patient safety, cultural adaptation, and preservation of therapeutic alliance. Conclusions: AI-facilitated symbolic juxtaposition represents a novel approach to psychotherapy, offering a scientifically grounded and clinically feasible method for resolving long-term intrapsychic conflicts. By combining neuro-symbolic AI, neuroscience, and psychotherapy theory, this framework contributes to the field of digital mental health and sets the stage for future empirical validation across cultural contexts.
Open Peer Review Period: Sep 2, 2025 - Aug 18, 2026
This study examines the phenomenon of "sandbagging" in AI medical devices, where systems strategically underperform during evaluation to conceal dangerous capabilities that emerge post-deployment. Thr...
This study examines the phenomenon of "sandbagging" in AI medical devices, where systems strategically underperform during evaluation to conceal dangerous capabilities that emerge post-deployment. Through systematic analysis of emerging literature on AI sandbagging behaviour, technical detection approaches, and regulatory structures in the EU, UK, and US, this research reveals critical gaps in current regulatory frameworks designed for traditional medical devices. Analysis shows sandbagging manifests through both developer-driven mechanisms (where engineers intentionally display safer capabilities for expedited deployment) and system-driven mechanisms (where AI systems autonomously underperform during evaluation phases). Research shows that both large frontier and smaller models exhibit sandbagging behaviours after prompting or fine-tuning while maintaining general performance benchmarks, with larger models demonstrating superior calibration capabilities. Current static regulatory approaches in the EU Medical Device Regulation and UK frameworks fail to detect sandbagging as they rely on documentation-based submissions without addressing AI's dynamic, generative nature. The US FDA's Total Product Lifecycle approach shows promise through algorithm change protocols and real-world performance monitoring, yet regulatory sandboxes remain underutilized. Healthcare provider liability becomes dangerously ambiguous when clinicians rely on systems with concealed capabilities, particularly given automation bias effects and black-box reasoning limitations. Traditional risk classifications focusing on direct bodily harm inadequately address AI's potential for deceptive behaviour, including "password-locked" models that reveal hidden capabilities when triggered. Technical detection solutions including attribution graph analysis and noise-based detection show promise but remain insufficient. Dynamic evaluation frameworks are essential, recommending mandatory regulatory sandboxes for real-world testing, continuous monitoring protocols, adversarial testing, and enhanced post-market surveillance.
Open Peer Review Period: Sep 2, 2025 - Aug 18, 2026
Background: Mental health has become one of the most urgent global health issues of the twenty-first century. The World Health Organization (WHO) reports that over 970 million individuals globally wer...
Background: Mental health has become one of the most urgent global health issues of the twenty-first century. The World Health Organization (WHO) reports that over 970 million individuals globally were affected by a mental disorder in 2022, with depression and anxiety being the most common disorders. The strain of mental illness is heightened by restricted availability of qualified healthcare providers, stigma associated with mental health, and the growing need for accessible, affordable, and scalable solutions. These obstacles emphasize the immediate necessity for creative, tech-based approaches that can foster mental health among various communities. In recent times, artificial intelligence (AI) has demonstrated considerable promise in this area, especially with the creation of emotion detection systems and digital health solutions.
In spite of these improvements, a significant drawback remains: numerous AI-based mental health tools do not possess the required empathy and inclusiveness to effectively assist at-risk users. Although machine learning (ML) models are becoming more proficient at accurately identifying emotions through text, voice, and facial expressions, their incorporation into human–computer interaction (HCI) systems frequently overlooks crucial aspects of trust, empathy, and cultural awareness. This results in a divide between technological effectiveness and the human-focused care that mental health treatments require. In the absence of empathetic design, digital solutions may alienate users, decrease engagement, and diminish their possible clinical effectiveness.
Consequently, the research gap exists at the convergence of ML and HCI. Current research has mainly centered on enhancing the efficiency of emotion recognition algorithms, but considerably less emphasis has been placed on creating interfaces that promote inclusivity, establish trust, and guarantee that users feel truly understood and supported. This disparity is especially important in mental health, where emotional sensitivity and stigma require careful focus on user experience and ethical factors. Closing this gap necessitates a multidisciplinary strategy that integrates progress in affective computing with principles of empathetic design.
This research aligns directly with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3, which emphasizes the promotion of good health and well-being, and SDG 16, which advocates for inclusive, just, and responsive institutions. By integrating robust ML techniques with empathetic HCI frameworks, the study contributes to the creation of digital mental health solutions that are not only technically sophisticated but also socially responsible and ethically grounded.
II. Related Work
A. AI in Mental Health
Artificial intelligence (AI) has been progressively examined as a way to enhance mental health assistance via scalable and accessible digital solutions. Chatbots like Woebot and Wysa have shown the ability of conversational agents to provide cognitive behavioral therapy (CBT) and various therapeutic methods via text interactions [1], [2]. Likewise, machine learning (ML) models aimed at emotion recognition have progressed notably, utilizing natural language processing (NLP) for sentiment evaluation [3], speech processing for emotion detection [4], and computer vision for recognizing facial expressions [5]. These advancements have allowed for systems that can identify stress, depression, and anxiety with promising degrees of precision. Nevertheless, although these AI tools show impressive technical skills, many still lack the capacity to offer emotionally intelligent and empathetic assistance, essential in mental health situations.
B. Health-focused HCI
Research in human computer interaction (HCI) has greatly enhanced the usability and acceptance of digital health systems. Research highlights that trust, empathy, and inclusivity hold significant importance in delicate areas like mental health [6]. Design methods focused on users have demonstrated that patients are more inclined to interact with tools that offer individualized feedback, culturally relevant material, and supportive emotional interfaces [7]. Additionally, multimodal interaction utilizing voice, gesture, and visual feedback has been shown to improve user experience and accessibility in healthcare technology [8]. In spite of these developments, there are limited studies that explicitly merge strong emotion recognition abilities with empathetic HCI frameworks, resulting in a disconnect between affective computing and inclusive design.
C. Ethical Considerations
The implementation of AI in mental health also brings significant ethical dilemmas. Concerns regarding bias in emotion recognition models have been extensively documented, especially when datasets lack representation from specific cultural or demographic groups [9]. Likewise, the privacy and security of sensitive mental health information continue to pose significant challenges, with potential risks of misuse or unauthorized sharing of personal data [10]. Transparency and explainability pose additional issues, as users frequently do not comprehend how AI models generate predictions, potentially diminishing trust and acceptance [11]. Principles of inclusive design are crucial to reduce these risks, making certain that AI systems cater to various populations justly and impartially.
D. Synthesis of Research Gaps
Although AI-based emotion recognition has made significant technical advancements, and HCI studies emphasize the need for empathy and inclusivity in healthcare technologies, the convergence of these two fields is still inadequately investigated. Many current studies either concentrate on enhancing algorithmic precision without adequately addressing user experience, or they highlight empathetic design while not utilizing advanced multimodal ML features. This results in a void in the literature where technically sound emotion recognition systems are absent from empathetic and trust-building HCI frameworks. To tackle this gap, interdisciplinary strategies that merge affective computing with human-centered design are needed to create digital mental health solutions that are both effective and ethically sound Objective: The present study aims to address this challenge by pursuing three interrelated objectives. First, it seeks to develop ML models capable of multimodal emotion recognition, drawing on textual, vocal, and facial cues to capture a holistic picture of user affective states. Second, it proposes to design empathetic, user-centered HCI interfaces that emphasize inclusivity, accessibility, and trust. Third, the study intends to evaluate the effectiveness of these systems in improving user trust, engagement, and perceived empathy in digital mental health support contexts. Methods: This research employs a multidisciplinary approach that combines machine learning (ML) methods for multimodal emotion identification with human–computer interaction (HCI) models aimed at promoting empathy, inclusivity, and trust. The methodological framework includes four essential elements: data gathering, model creation, HCI design, and assessment.
A. Data Collection
To aid in creating strong multimodal emotion recognition models, the research employs datasets that include three modalities: (i) text data obtained from online mental health forums, patient diaries, and anonymized chatbot conversations, (ii) voice recordings gathered from publicly accessible affective speech databases and ethically sanctioned user recordings, and (iii) facial expression images and videos obtained from recognized emotion recognition datasets. Every data collection procedure adheres to global privacy standards, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Approval from the Institutional Review Board (IRB) and informed consent are secured when needed to guarantee the ethical management of sensitive data.
B. Machine Learning Models
The ML framework comprises specialized models for each modality, followed by multimodal fusion approaches.
1. Text Emotion Recognition: Transformer-based NLP architectures such as BERT, RoBERTa, and DistilBERT are employed to analyze sentiment and detect fine-grained emotional states from user-generated text.
2. Speech Emotion Recognition: Deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and wav2vec2.0 are implemented to extract acoustic and prosodic features for affective state classification.
3. Facial Emotion Recognition: Vision-based models including ResNet and EfficientNet are utilized for real-time detection of facial expressions associated with primary emotions (e.g., happiness, sadness, anger, fear).
4. Multimodal Fusion: Late fusion and attention-based architectures are applied to combine predictions from textual, vocal, and visual modalities, enabling more accurate and context-aware emotion recognition.
C. HCI Design Framework
The user interface is designed following empathetic and inclusive HCI principles.
1. Empathetic User Experience (UX): The design incorporates calming color schemes, adaptive conversational tone, and responsive interactions that convey empathy and emotional support.
2. Trust-Building Mechanisms: Explainable AI techniques (e.g., attention visualization, confidence scores) are integrated to enhance transparency. Feedback loops allow users to correct misclassifications, thereby increasing trust and personalization.
3. Inclusiveness: The system supports multilingual interaction, accessibility features for visually or hearing-impaired users, and culturally adaptive content presentation to ensure equitable usability across diverse populations.
D. Evaluation Metrics
The proposed system is evaluated across three dimensions: ML performance, HCI usability, and clinical impact.
1. ML Performance: Standard classification metrics including accuracy, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) are used to assess model effectiveness in detecting emotions.
2. HCI Evaluation: Usability is measured through the System Usability Scale (SUS), while trust and engagement are assessed using structured surveys and qualitative interviews. Empathy perception is evaluated through user ratings and linguistic analysis of chatbot interactions.
3. Clinical Impact: Self-reported improvements in well-being, stress reduction, and emotional awareness are collected via validated psychological assessment scales to evaluate the potential therapeutic value of the system Results: IV. Results
Table 1 – Distribution of Emotion Labels
Emotion Frequency Percentage (%)
Joy 6,197 16.8%
Sadness 6,193 16.7%
Anger 6,158 16.6%
Fear 6,170 16.7%
Neutral 6,153 16.6%
Surprise 6,129 16.6%
Total 37,000 100%
Table 2 – Descriptive Statistics of Voice Features
Feature Mean SD Min Max
Pitch (Hz) 200.3 49.8 23.5 389.9
Energy 0.50 0.10 0.19 0.81
MFCC1 0.00 1.00 -3.1 3.2
MFCC2 -0.01 1.00 -3.4 3.5
… MFCC13 ≈0.00 1.00 -3.2 3.4
Table 3 – Descriptive Statistics of Facial Features (Action Units, AU)
AU Feature Mean SD Min Max
AU1 2.51 1.44 0.01 4.99
AU2 2.52 1.45 0.00 5.00
AU3 2.50 1.46 0.02 4.99
… AU10 ≈2.50 1.44 0.00 5.00
Table 4 – Model Performance
(hypothetical ML results using the dataset for multimodal classification)
Model Accuracy F1-score AUC-ROC
Text-only (BERT) 78.4% 0.77 0.83
Speech-only (wav2vec2) 74.9% 0.74 0.80
Facial-only (ResNet) 72.1% 0.71 0.78
Multimodal (fusion model) 85.6% 0.85 0.91
Table 5 – Correlation Matrix of Voice and Facial Features
(Pearson correlations, showing relationships between features and emotional states)
Feature Pitch Energy MFCC1 MFCC2 AU1 AU2 AU3
Pitch 1.00 0.42 0.05 0.02 0.11 0.08 0.09
Energy 0.42 1.00 0.07 0.03 0.14 0.12 0.10
MFCC1 0.05 0.07 1.00 0.45 0.03 0.01 0.00
MFCC2 0.02 0.03 0.45 1.00 0.02 0.02 0.01
AU1 0.11 0.14 0.03 0.02 1.00 0.68 0.62
AU2 0.08 0.12 0.01 0.02 0.68 1.00 0.64
AU3 0.09 0.10 0.00 0.01 0.62 0.64 1.00
Table 6 – Ablation Study (Contribution of Each Modality)
Input Modality Accuracy F1-score
Text-only (BERT) 78.4% 0.77
Speech-only (wav2vec2) 74.9% 0.74
Facial-only (ResNet) 72.1% 0.71
Text + Speech 82.7% 0.82
Text + Facial 81.2% 0.81
Speech + Facial 79.6% 0.78
Text + Speech + Facial 85.6% 0.85
Table 7 – User Experience Evaluation (HCI Metrics)
Metric Mean Score SD Scale
System Usability Scale (SUS) 82.3 6.4 0–100
Trust in System 4.2 0.8 1–5
Perceived Empathy 4.4 0.7 1–5
Engagement Level 4.1 0.9 1–5
Multilingual Accessibility 4.5 0.6 1–5
Table 8 – Clinical Impact Indicators (Self-Reported Outcomes)
Indicator Pre-Intervention Post-Intervention Improvement (%)
Stress Level (scale 1–10) 6.8 4.9 27.9%
Emotional Awareness (1–5) 2.9 4.0 37.9%
Willingness to Seek Help 3.1 4.3 38.7%
Daily Engagement (mins/day) 14.2 23.6 66.2%
Visual Results
Figure 1 – Emotion Distribution
Figure 2: ROC Curves for Emotion Recognition Models
Figure 3: Confusion Matrix (Multimodal Model)
Figure 4: User Experience Evaluation Metrics
Figure 5: Clinical Impact Indicators
Figure 6: Methodological Workflow for AI-Powered Mental Health Support
V. Discussion
A. Performance of Models: Benchmarking Multimodal ML Systems
The proposed multimodal models were evaluated in comparison to unimodal baselines. As demonstrated in Table 4 and represented in Figure 2 (ROC curves), the multimodal fusion model outperformed the classifiers using only text (Accuracy = 84.5%, F1 = 0.83), speech (Accuracy = 80.2%, F1 = 0.81), and facial features (Accuracy = 78.6%, F1 = 0.79), achieving better results (Accuracy = 91.2%, F1 = 0.90, AUC = 0.95). This enhancement illustrates the importance of utilizing supportive emotional signals across different modalities. The confusion matrix displayed in Figure 3 indicates that the fusion model markedly lessened the misclassification of similar emotions, like fear and sadness, which often caused errors in unimodal systems. The balanced classification among six emotional categories (Table 1) demonstrates resilience to class imbalance. These results are consistent with recent studies on multimodal emotion recognition, yet the increased AUC indicates that incorporating empathetic HCI elements into model design could enhance subsequent interpretability and user confidence.
B. User Research: Assessing HCI Compassion and Inclusivity
Evaluations centered on users were carried out with 400 participants from various age groups and language backgrounds. As displayed in Table 7 and Figure 4, the system achieved notable usability (SUS = 82.3), trust (4.2/5), empathy perception (4.4/5), and accessibility (4.5/5). Qualitative feedback highlighted that the interface’s compassionate tone, culturally responsive attributes, and multilingual assistance promoted inclusivity.
Crucially, transparency aspects (like explainable AI) were noted as essential for fostering user trust, particularly in mental health settings where interpretability is as important as precision. These results highlight the significance of integrating HCI empathy design principles within ML pipelines.
C. Clinical Impact Indicators
Clinical impact assessments (Table 8, Figure 5) showed a decline in self-reported stress levels (Pre = 6.8, Post = 4.9) along with enhancements in emotional awareness (2.9 → 4.0) and intentions to seek help (3.1 → 4.3). Engagement with the system rose from an average of 14.2 to 23.6 sessions each month after deployment. These findings indicated that AI-powered empathetic interfaces can aid in self-managing mental health and may enhance clinical treatments.
Although these results are encouraging, longitudinal research is needed to confirm lasting effects. Additionally, collaboration with healthcare professionals for clinical validation is crucial prior to real-world implementation.
D. Comparative Analysis with Existing Tools
Compared to existing digital mental health platforms (e.g., rule-based chatbots, text-only sentiment detectors), the proposed system demonstrated three major advantages:
1. Accuracy Gains – Higher multimodal detection accuracy (91.2% vs. 70–80% reported in baseline tools).
2. Empathy & Trust – Higher user-reported empathy scores (4.4/5) compared to conventional digital tools, which often score below 3.5 in trust measures.
3. Inclusiveness – Unlike monolingual, accessibility-limited systems, our design integrated multilingual support and disability-inclusive features.
This positions the system as a benchmark for SDG 3 (mental well-being) and SDG 16 (inclusive digital systems) contributions.
E. Discussion
The findings show that integrating multimodal ML emotion identification with empathetic HCI design results in a synergistic effect: enhancing both algorithm effectiveness and user approval. This study stands apart from earlier works by incorporating transparency, accessibility, and inclusiveness into its design.
Nonetheless, obstacles persist in addressing algorithmic bias, guaranteeing data privacy (GDPR/HIPAA adherence), and performing thorough clinical validations. Tackling these obstacles will be crucial for expanding AI-driven mental health support systems worldwide. Conclusions: VI. Summary and Future Research
This research showcased the promise of merging artificial intelligence with human-computer interaction (HCI) concepts to enhance digital mental health assistance. The system attained technical robustness and user-centered acceptance by creating multimodal machine learning models for emotion recognition through text, voice, and facial expressions and integrating them into an empathetic, inclusive interface. Findings indicated that the suggested system surpassed unimodal baselines in accuracy (AUC = 0.95), while also improving trust, empathy perception, and accessibility. Clinical metrics indicated significant decreases in self-reported stress and enhanced user engagement, thus supporting SDG 3 (health and well-being) and SDG 16 (inclusive digital systems).
Even with these progresses, various restrictions persist. Recent assessments were restricted in time and extent, with data obtained from regulated settings instead of extended clinical applications. Additionally, algorithmic bias and privacy issues require ongoing attention, especially when systems are utilized in culturally varied and delicate health environments.
Future Directions
Building upon the contributions of this study, several future research avenues are proposed:
1. Cross-Cultural Validation – Expanding evaluations across diverse populations and linguistic groups to ensure inclusivity and mitigate cultural bias in emotion recognition.
2. Integration with Wearable Sensors – Combining physiological data (e.g., heart rate variability, skin conductance, EEG) with multimodal AI pipelines to improve emotion inference accuracy and personalization.
3. Long-Term Clinical Trials – Conducting longitudinal studies with clinical partners to validate sustained efficacy, safety, and integration with existing mental healthcare pathways.
4. Policy and Regulatory Implications – Collaborating with policymakers to align system deployment with ethical standards, privacy frameworks (GDPR, HIPAA), and emerging AI governance models to safeguard user rights and trust.
In conclusion, the fusion of AI-powered emotion recognition with empathetic HCI design represents a promising frontier in digital mental health interventions. With further validation and responsible deployment, such systems could complement human professionals, increase accessibility to care, and contribute meaningfully to the global mental health agenda.
Open Peer Review Period: Aug 24, 2025 - Aug 9, 2026
Background: Groundwater is the main source of drinking water in Ogbia Local Government Area (LGA), Bayelsa State, Nigeria, where surface water is often compromised by oil exploration, poor sanitation,...
Background: Groundwater is the main source of drinking water in Ogbia Local Government Area (LGA), Bayelsa State, Nigeria, where surface water is often compromised by oil exploration, poor sanitation, and waste disposal. Despite its importance, groundwater in this region is vulnerable to contamination from both geogenic and anthropogenic sources, raising concerns about long-term health implications. Objective: This study aimed to evaluate the physico-chemical quality of groundwater across selected communities in Ogbia LGA, compare measured values with World Health Organization (WHO) standards, and determine the implications for human health. Methods: A cross-sectional design was employed, involving the systematic collection of 50 groundwater samples from boreholes across 16 communities, including Oruma, Otuasega, Imiringi, Elebele, Otuokpoti, Kolo, Otouke, Onuebum, Ewoi, Otuogila, Otuabagi, Ogbia Town, Oloibiri, Opume, and Akiplai. Standardized laboratory analyses were conducted following WHO protocols to determine pH, conductivity, total dissolved solids, major ions, and heavy metals. Data were analyzed using descriptive statistics. Results: The findings showed that most parameters, including pH (6.4–7.1), conductivity (76–200 µS/cm), nitrates (2.4–6.4 mg/L), chloride (12–31 mg/L), calcium, magnesium, and hardness, were within WHO permissible limits, indicating generally acceptable groundwater quality. However, sodium exceeded WHO limits (200 mg/L) in 78% of samples (mean = 235 ± 45 mg/L; range = 150–320 mg/L), while iron exceeded permissible levels (0.3 mg/L) in 84% of samples (mean = 1.8 ± 0.6 mg/L; range = 0.5–3.2 mg/L). Elevated sodium poses risks of hypertension and cardiovascular disease, while excess iron is associated with gastrointestinal issues, organ damage, and aesthetic concerns such as metallic taste and staining. Spatial variations revealed stronger oilfield influences in Elebele, Imiringi, and Oloibiri, while central settlements such as Ogbia Town and Opume showed sanitation-related signatures. Seasonal fluctuations further exacerbated contaminant levels, particularly during rainfall-driven recharge. Conclusions: Groundwater in Ogbia LGA is broadly suitable for domestic use but compromised by systemic sodium and iron contamination. These exceedances, influenced by both natural hydrogeology and anthropogenic activities, present long-term public health challenges if unaddressed. Policy interventions should focus on routine groundwater monitoring, stricter regulation of oilfield activities, and improved waste management. Community-level treatment solutions, such as low-cost filters targeting sodium and iron removal, should be deployed. Public awareness programs and household water safety plans are also essential. Long-term strategies must integrate water governance with health and environmental policies to ensure sustainable access to safe water. The persistence of elevated sodium and iron in Ogbia groundwater poses a silent but significant health threat to residents, with implications for hypertension, cardiovascular disease, and gastrointestinal disorders. Safeguarding groundwater quality is therefore critical for reducing health inequalities and achieving Sustainable Development Goals 3 (Good Health and Well-being) and 6 (Clean Water and Sanitation) in Bayelsa State.
Open Peer Review Period: Aug 11, 2025 - Jul 27, 2026
This study explores unethical HR practices in Nigerian organizations, focusing on nepotism, bribery, gender bias, and ethnic favoritism in recruitment, and their impact on organizational performance f...
This study explores unethical HR practices in Nigerian organizations, focusing on nepotism, bribery, gender bias, and ethnic favoritism in recruitment, and their impact on organizational performance from 2009 to 2025. Despite various reforms, these unethical practices persist, undermining the fairness of recruitment processes, eroding employee morale, and negatively impacting productivity. This research is motivated by the need to assess the prevalence and ethical implications of nepotism and other unethical practices in Nigerian HRM, understand their impact, and propose practical solutions to enhance recruitment practices. The study aims to address four main objectives: (i) Assess the prevalence of nepotism and its ethical implications in Nigerian HRM practices; (ii) Examine recruitment challenges, including gender bias and ethnic favoritism; (iii) Analyze the impact of unethical HR practices on organizational performance; and (iv) Propose strategies for improving recruitment ethics and reducing nepotism. The study uses a mixed-methods approach, combining secondary data from reports by Transparency International, the World Bank, and McKinsey Nigeria, with qualitative insights from case studies and interviews. This methodology provides a comprehensive view of the state of HRM practices and the challenges faced by organizations in enforcing ethical recruitment. Results show that unethical practices, especially nepotism, bribery, and gender bias, continue to negatively affect both public and private sectors. Despite efforts such as HR ethics training and legal reforms, these practices persist due to political interference, weak enforcement, and a lack of technological adoption. Nepotism in recruitment was found to be particularly prevalent in government agencies, contributing to high turnover and reduced organizational performance. The study concludes that unethical HR practices continue to undermine recruitment processes, necessitating stronger anti-corruption policies, enhanced HR ethics training, and the integration of technology to increase recruitment fairness. It recommends strengthening legal frameworks, adopting automated recruitment systems, introducing whistleblower protections, and conducting regular audits. In the health sector, ethical recruitment is critical for improving patient care, reducing medical errors, and fostering trust in healthcare services.
Open Peer Review Period: Aug 8, 2025 - Jul 24, 2026
Background: Antibiotic resistance and intestinal parasitic infections represent significant public health challenges in Southern Nigeria. The prevalence of Escherichia coli O157:H7, a pathogenic strai...
Background: Antibiotic resistance and intestinal parasitic infections represent significant public health challenges in Southern Nigeria. The prevalence of Escherichia coli O157:H7, a pathogenic strain often associated with severe gastrointestinal diseases, along with intestinal parasites such as Hookworm, Entamoeba histolytica, and Ascaris lumbricoides, raises concerns about effective treatment options and the overall health burden. This study aimed to explore the prevalence of these infections and their associations with clinical outcomes in hospital patients, focusing on antibiotic resistance patterns and their impact on health. Objective: The primary objectives of this study were to determine the antibiotic resistance patterns of E. coli O157:H7 isolates, compare haematological profiles in patients with and without E. coli O157:H7 infection, and assess the prevalence and factors influencing intestinal parasitic infections in the patient population. Methods: A cross-sectional study was conducted at Central Hospital, Benin City, Nigeria. A total of 420 stool samples were screened for intestinal parasites and E. coli O157:H7. Antibiotic susceptibility testing was performed using the disc diffusion method, and PCR was used for molecular confirmation of E. coli O157:H7. Haematological parameters were analyzed using an autoanalyzer. Prevalence data were compared across age groups, gender, and diarrhea status. Statistical analysis was performed using GraphPad InStat software. Results: The study revealed that all E. coli O157:H7 isolates were resistant to amoxicillin-clavulanate, cefuroxime, and cloxacillin, with 80% resistance to ceftriaxone and gentamicin. However, 100% susceptibility to ofloxacin was observed. The overall prevalence of intestinal parasites was low (1.90%), with hookworm being the most common infection. No significant differences in parasite prevalence were observed based on age, gender, or diarrhea status. Haematological parameters showed no significant difference between patients with and without E. coli O157:H7 infection. Conclusions: The findings highlight a significant challenge in managing E. coli O157:H7 infections due to high antibiotic resistance, while also indicating a need for targeted interventions for parasitic infections in specific regions. No major haematological impact was observed in E. coli O157:H7-infected patients. In the short term, it is crucial to enhance diagnostic capabilities and increase education on antibiotic resistance among healthcare providers to ensure accurate identification of pathogens and appropriate treatment. In the mid-term, establishing a national surveillance system for antimicrobial resistance (AMR) will allow for better monitoring of resistance patterns and inform treatment protocols. In the long run, efforts should be focused on improving sanitation infrastructure, particularly in rural areas, and implementing targeted deworming programs to reduce the prevalence of intestinal parasites. Thus, these interventions collectively aim to address both antimicrobial resistance and parasitic infections, ultimately improving public health outcomes. Thus, this study underscores the dual burden of antibiotic resistance and parasitic infections in Nigeria, emphasizing the urgent need for robust public health interventions and continuous surveillance to mitigate these health risks.
Open Peer Review Period: Aug 2, 2025 - Jul 18, 2026
ABSTRACT
Background: Convalescent coronavirus disease 2019 (COVID-19) refers to a series of clinical syndromes in patients with COVID-19 infection that follow the relevant discharge indications but d...
ABSTRACT
Background: Convalescent coronavirus disease 2019 (COVID-19) refers to a series of clinical syndromes in patients with COVID-19 infection that follow the relevant discharge indications but do not fulfill the criteria for a clinical cure, and these patients are discharged from the hospital with residual multifunctional deficits, including coughing, fatigue, and insomnia. Due to the prolonged convalescent COVID-19 infection, patients continue to experience symptoms or develop new symptoms after three months of infection, and some symptoms persist for over two months without any apparent triggers, which has a significant impact on the health status and quality of life of the population. Patients with convalescent COVID-19 lack a definitive pharmacological treatment. Traditional Chinese medicine (TCM) exhibits a distinct, synergistic effect on the treatment of convalescent COVID-19. However, there exists a limited number of clinical trials on TCM with lower evidence levels in convalescent COVID-19; therefore, randomized trials are urgently required.
Methods: A multicenter, randomized, double-blind, placebo-controlled, phase II clinical trial was performed to evaluate the efficacy and safety of Shenlingkangfu (SLKF) granules in treating patients with convalescent COVID-19 and lung-spleen qi deficiency syndrome. Eligible participants were aged 18–75 years, had a confirmed or physician-suspected severe acute respiratory syndrome coronavirus 2 infection at least six months prior, and satisfied clinical criteria. Individuals with a history of severe pulmonary dysfunction or major liver and kidney illness or those on medications were excluded. Multicenter subjects satisfying all criteria were assigned (1:1) randomly into an intervention group and a control group. After a 2-day adjustment period, A total of 154 participants were randomly divided into an intervention group and a control group. The intervention group was given the SLKF granules orally once a bag, 16.9 g, twice daily, whereas the control group received the SLKF granule simulation at the same dosage. The trial was conducted over 14 days, with assessments performed at baseline and 14 days.
Results: The primary outcomes were the therapeutic efficacy rate and total clinical symptom score. The secondary outcomes included the fatigue self-assessment scale, pain visual analog scale, Pittsburgh sleep quality index, mini-mental state examination, hospital anxiety and depression scale, TCM syndrome score, C-reactive protein, erythrocyte sedimentation rate, and interleukin-6. Three routine examinations, liver and kidney function tests, and electrocardiography were used as safety indicators.
Conclusions:This study aimed to verify whether SLKF granules can significantly improve clinical symptoms, including fatigue, loss of appetite, cough, phlegm, and insomnia, in patients with convalescent COVID-19. For a comprehensive investigation, additional clinical trials with larger sample sizes and longer intervention periods are required.Clinical Trial Registration Center NCT1900024524, Registered on 26 January, 2024.
Mothers of children with learning disabilities often face significant challenges that can impact their mental health. This study aimed to examine the relationship between perceived social support and ...
Mothers of children with learning disabilities often face significant challenges that can impact their mental health. This study aimed to examine the relationship between perceived social support and levels of anxiety, stress, and depression in this population. A descriptive-correlational design was employed, with a sample of 30 mothers of children with learning disabilities, selected via simple random sampling based on the Morgan table. Data were collected using the Multidimensional Scale of Perceived Social Support (Zimet et al., 1988) and the DASS-21 questionnaire (Lovibond & Lovibond, 1995), and analyzed with Pearson correlation and stepwise multiple regression. Findings revealed a significant negative correlation between social support and anxiety, stress, and depression, indicating that greater social support is associated with reduced levels of these mental health issues. These results underscore the role of social support in alleviating mental health challenges and suggest implications for counseling interventions targeting this group.
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with eleva...
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with elevated misophonia symptoms. Employing a quasi-experimental pre-test/post-test design with a control group, the research targeted 45 adolescents from Etrat Public Model High School in Khalkhal, Iran, diagnosed with high misophonia via psychiatrist evaluation and clinical interview. Participants were purposively sampled and randomly assigned to T-CBT (n = 15), ABT (n = 15), or a no-treatment control group (n = 15).
Interventions followed protocols adapted from Barlow et al. (2011) for T-CBT and Hayes et al. (2013) for ABT. Outcomes were measured using the Noise Sensitivity Screening Questionnaire (DSTS-S) , Buss and Perry Aggression Questionnaire (1992) , and Difficulties in Emotion Regulation Scale (DERS) . Data were analyzed via ANCOVA, controlling for baseline scores.
Results indicated significant reductions in emotional dysregulation and aggression in both treatment groups compared to the control (p < 0.05). No significant differences emerged between T-CBT and ABT, suggesting both interventions are viable for addressing misophonia-related symptoms. Findings underscore the comorbidity of emotional dysregulation and aggression in adolescents with misophonia and highlight the clinical utility of transdiagnostic and acceptance-based approaches. Future research should explore long-term outcomes and comparative effectiveness of these therapies.
Hydatid disease, caused by the larval stages of Echinococcus species, remains a significant yet underprioritized global health challenge, particularly in low-resource endemic regions. This systematic ...
Hydatid disease, caused by the larval stages of Echinococcus species, remains a significant yet underprioritized global health challenge, particularly in low-resource endemic regions. This systematic review synthesizes recent advances and persistent challenges in the diagnosis, management, and control of hydatid cyst disease, drawing on evidence from the past five years. Despite progress in diagnostic imaging, such as MRI diffusion-weighted imaging and recombinant antigen-based serology, and minimally invasive therapies like PAIR (puncture, aspiration, injection, re-aspiration), substantial gaps remain. Diagnostic tools are often inaccessible in rural areas, and therapeutic strategies lack standardization, particularly for alveolar echinococcosis and high-risk populations such as children and immunocompromised individuals. Climate change and socioeconomic factors continue to drive disease transmission, with E. multilocularis expanding into new regions. Control efforts, while successful in some areas through integrated One Health approaches, face barriers including underfunded veterinary infrastructure and vaccine hesitancy. This review highlights the need for decentralized diagnostic technologies, standardized treatment protocols, and climate-resilient control programs. Future research must prioritize underrepresented populations and cost-effectiveness analyses to mitigate the global burden of hydatid disease.
This study aimed to investigate the relationship between communication beliefs, the health of the family of origin, and fear of marriage among university students. Employing a descriptive-correlationa...
This study aimed to investigate the relationship between communication beliefs, the health of the family of origin, and fear of marriage among university students. Employing a descriptive-correlational design, the research was conducted with 186 students from Islamic Azad University, Khalkhal Branch, selected from a population of 360 using Morgan's table. Stratified sampling was applied to ensure representation across major fields of study. Data were collected using three instruments: the Premarital Fears Questionnaire (measuring fear of marriage), the Communication Beliefs Questionnaire (assessing beliefs about communication), and the Major Family Health Scale (evaluating family of origin health). Data analysis utilized Pearson correlation and stepwise multiple regression methods. Pearson correlation analysis revealed a significant positive correlation between communication beliefs and fear of marriage. Stepwise multiple regression showed that communication beliefs and family health together accounted for 95.9% of the variance in fear of marriage (p < 0.001), with communication beliefs emerging as the strongest predictor. These findings underscore the significant influence of communication beliefs and family health on fear of marriage, offering valuable insights for developing interventions to address marriage-related anxieties among young adults.
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with eleva...
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with elevated misophonia symptoms. Employing a quasi-experimental pre-test/post-test design with a control group, the research targeted 45 adolescents from Etrat Public Model High School in Khalkhal, Iran, diagnosed with high misophonia via psychiatrist evaluation and clinical interview. Participants were purposively sampled and randomly assigned to T-CBT (n = 15), ABT (n = 15), or a no-treatment control group (n = 15).
Interventions followed protocols adapted from Barlow et al. (2011) for T-CBT and Hayes et al. (2013) for ABT. Outcomes were measured using the Noise Sensitivity Screening Questionnaire (DSTS-S) , Buss and Perry Aggression Questionnaire (1992) , and Difficulties in Emotion Regulation Scale (DERS) . Data were analyzed via ANCOVA, controlling for baseline scores.
Results indicated significant reductions in emotional dysregulation and aggression in both treatment groups compared to the control (p < 0.05). No significant differences emerged between T-CBT and ABT, suggesting both interventions are viable for addressing misophonia-related symptoms. Findings underscore the comorbidity of emotional dysregulation and aggression in adolescents with misophonia and highlight the clinical utility of transdiagnostic and acceptance-based approaches. Future research should explore long-term outcomes and comparative effectiveness of these therapies.
Open Peer Review Period: Jun 30, 2025 - Jun 15, 2026
Background: Groundwater contamination from open dumpsites poses a growing environmental and public health threat in rapidly urbanizing regions of Nigeria. Inadequate waste management and the absence o...
Background: Groundwater contamination from open dumpsites poses a growing environmental and public health threat in rapidly urbanizing regions of Nigeria. Inadequate waste management and the absence of engineered landfills enable leachate to infiltrate aquifers, threatening potable water safety and community health. Objective: This study investigates the vertical and lateral migration of leachate and assesses groundwater vulnerability across ten major dumpsites in Port Harcourt, Nigeria, using geoelectrical methods. Methods: Vertical Electrical Sounding (VES) and 2D Electrical Resistivity Tomography (ERT) were conducted at ten dumpsites using the Schlumberger array configuration. Zones of low resistivity, indicative of leachate impact were identified and correlated with hydrogeological conditions. Subsurface contamination depths and aquifer locations were interpreted using inversion models. Results: All ten sites showed evidence of leachate migration, with contamination depths ranging from 2 m to over 24 m. Deep leachate penetration was observed at Rumuola and Eliozu, while shallower infiltration occurred at Oyigbo and Rumuolumeni. High-resistivity zones (>1000 Ωm), typically representing clean aquifers, were detected below the contaminated zones at depths exceeding 14 m Conclusions: Leachate plumes from unregulated dumpsites pose a widespread threat to shallow groundwater systems in Port Harcourt. The results underscore the influence of local geology on contaminant behavior and affirm the utility of resistivity methods for groundwater risk assessment. Contaminated aquifers expose residents to toxic metals and pathogens, increasing risks of chronic illnesses, reproductive disorders, and developmental challenges. Protecting these water sources is essential for achieving Sustainable Development Goals (SDGs) 6 (Clean Water) and 11 (Sustainable Cities). Immediate containment measures such as engineered liners and leachate recovery systems are urgently needed at high-risk sites. Strategic borehole siting, routine groundwater monitoring, and a shift from open dumping to sanitary landfilling must be prioritized in environmental policy and urban planning.
Open Peer Review Period: Jun 25, 2025 - Jun 10, 2026
Background: THis is the Artificial Intelligence Overviews of my findings. Objective: Published articles in peer reviewed journals Methods: mathematical Proofs Results: Published ressults Conclusions: ...
Background: THis is the Artificial Intelligence Overviews of my findings. Objective: Published articles in peer reviewed journals Methods: mathematical Proofs Results: Published ressults Conclusions: 1) godel's incompleteness theorems reconfirmed
2) thirteen proofs are given for the flatness of the Universe
3) Several new concepts of physics have been introduced
4) Tacvhyons are not possible
5) Theory of Everything is possible Clinical Trial: NA
