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JMIR Preprints
A preprint server for pre-publication/pre-peer-review preprints intended for community review as well as ahead-of-print (accepted) manuscripts
Background: Digital transformation is revolutionizing human resource management (HRM) globally, yet its adoption and impact in Nigeria remain underexplored. As the Nigerian workforce becomes increasingly dynamic and digitized, understanding how digital tools influence HRM practices is critical for enhancing organizational effectiveness, employee well-being, and policy formulation. Objective: This study investigates the impact of digital transformation on HRM practices in Nigeria, focusing on four key domains: (1) AI-based recruitment platforms and talent acquisition efficiency, (2) e-learning systems and HRIS in training and development, (3) digital performance management tools and employee performance, and (4) HR analytics dashboards and their role in employee engagement and decision-making. Methods: A synthesis of secondary data was conducted using evidence from reputable sources including McKinsey & Company, Dataphyte, and peer-reviewed journals. Quantitative metrics were extracted and visualized through comparative tables and regression models to illustrate pre- and post-digital transformation outcomes. The analysis focused on Nigerian organizations within the last five years (2019-2024). Results: Findings revealed that AI-based recruitment platforms reduced time-to-hire by 42% and improved quality-of-hire and alignment scores. E-learning systems led to a 25 percentage-point increase in training participation, a 20-point improvement in knowledge retention, and a 40% reduction in training costs. Digital performance management tools explained nearly 47% of the variance in employee performance scores (R² = 0.47, β = 0.69). HR analytics dashboards increased engagement scores, reduced absenteeism by 36%, and lowered turnover rates from 22% to 15%. Conclusions: Digital HR tools demonstrably enhance operational efficiency and strategic HRM outcomes. From recruitment to performance management and employee retention, digital transformation empowers HR professionals with real-time insights, transparency, and scalability. These findings affirm the urgent need to embed digital HR practices into Nigeria’s public and private sector frameworks. Immediate actions include conducting digital HR audits, training HR personnel, and adopting basic digital tools. Mid-term strategies should prioritize policy development, incentivized adoption, and curriculum revision in HRM education. Long-term goals must focus on building national HR analytics infrastructure, enabling informal sector inclusion, and enacting legal reforms for ethical digital HR governance. Digital HRM transformation extends beyond productivity to support employee well-being. Continuous feedback, real-time support, and personalized growth reduce workplace stress and improve engagement. Integrating wellness analytics into HR systems allows early detection of burnout, particularly in high-pressure sectors like healthcare and finance. These tools position digital HRM as a strategic approach to building healthier, more resilient workplaces.
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Background: Digital transformation is revolutionizing human resource management (HRM) globally, yet its adoption and impact in Nigeria remain underexplored. As the Nigerian workforce becomes increasin...
Background: Digital transformation is revolutionizing human resource management (HRM) globally, yet its adoption and impact in Nigeria remain underexplored. As the Nigerian workforce becomes increasingly dynamic and digitized, understanding how digital tools influence HRM practices is critical for enhancing organizational effectiveness, employee well-being, and policy formulation. Objective: This study investigates the impact of digital transformation on HRM practices in Nigeria, focusing on four key domains: (1) AI-based recruitment platforms and talent acquisition efficiency, (2) e-learning systems and HRIS in training and development, (3) digital performance management tools and employee performance, and (4) HR analytics dashboards and their role in employee engagement and decision-making. Methods: A synthesis of secondary data was conducted using evidence from reputable sources including McKinsey & Company, Dataphyte, and peer-reviewed journals. Quantitative metrics were extracted and visualized through comparative tables and regression models to illustrate pre- and post-digital transformation outcomes. The analysis focused on Nigerian organizations within the last five years (2019-2024). Results: Findings revealed that AI-based recruitment platforms reduced time-to-hire by 42% and improved quality-of-hire and alignment scores. E-learning systems led to a 25 percentage-point increase in training participation, a 20-point improvement in knowledge retention, and a 40% reduction in training costs. Digital performance management tools explained nearly 47% of the variance in employee performance scores (R² = 0.47, β = 0.69). HR analytics dashboards increased engagement scores, reduced absenteeism by 36%, and lowered turnover rates from 22% to 15%. Conclusions: Digital HR tools demonstrably enhance operational efficiency and strategic HRM outcomes. From recruitment to performance management and employee retention, digital transformation empowers HR professionals with real-time insights, transparency, and scalability. These findings affirm the urgent need to embed digital HR practices into Nigeria’s public and private sector frameworks. Immediate actions include conducting digital HR audits, training HR personnel, and adopting basic digital tools. Mid-term strategies should prioritize policy development, incentivized adoption, and curriculum revision in HRM education. Long-term goals must focus on building national HR analytics infrastructure, enabling informal sector inclusion, and enacting legal reforms for ethical digital HR governance. Digital HRM transformation extends beyond productivity to support employee well-being. Continuous feedback, real-time support, and personalized growth reduce workplace stress and improve engagement. Integrating wellness analytics into HR systems allows early detection of burnout, particularly in high-pressure sectors like healthcare and finance. These tools position digital HRM as a strategic approach to building healthier, more resilient workplaces.
Background: Self-harm (SH) affects around 20% of all young people in the UK. Treatment options for self-harm remain limited and those available are long and costly and may not suit all young people. T...
Background: Self-harm (SH) affects around 20% of all young people in the UK. Treatment options for self-harm remain limited and those available are long and costly and may not suit all young people. There is an urgent need to develop new scalable interventions to address this gap. IMAGINATOR is a novel imagery-based intervention targeting self-harm initially developed for 16- to 25-year-olds. It is a blended digital intervention delivering Functional Imagery Training (FIT) via therapy sessions and a smartphone app. In this study, we piloted a new version of the app, IMAGINATOR 2.0, extended to adolescents from age 12 and co-produced with a diverse group of young people with lived experience. Objective: Our aim was to test the feasibility and acceptability of delivering IMAGINATOR 2.0 in secondary mental health services. Methods: Four co-design workshops were conducted online with UK-based lived-experience co-designers aged 14-25 to develop the IMAGINATOR 2.0 app. The intervention was then piloted with participants recruited from West London NHS Trust Tier 2 CAMHS and adult Mental health Integrated Network Teams (MINT) teams. Participants received three face-to-face FIT sessions in which the app was introduced, and five brief phone support sessions. Outcome assessments were conducted after completing therapy, approximately three months post-baseline. Two focus groups gathered the therapists' perspectives on IMAGINATOR 2.0’s acceptability and means of improvement. For quantitative data, descriptives are reported. Qualitative data were analysed using a co-produced thematic analysis method with young people with lived experiences. Results: Eighty-three participants were referred and 29 (28 female, 1 transgender, mean age = 18.9) were eligible and completed screening. Of the 27 participants who started, 59% completed therapy per protocol, while only 15 completed the quantitative outcome assessment. There was an overall reduction in number of SH episodes over 3-months from pre- to post-intervention (baseline: median = 6.5, IQR = 35; post-intervention: median = 0, IQR = 7; median diff = -6.5, r = 0.69). Five themes were identified through thematic analysis of therapists’ feedback, including therapy impact, mental imagery efficacy and limitations and need for better integration of the IMAGINATOR 2.0 app with therapy sessions. The app was valued by therapists who highlighted the need for an intervention like IMAGINATOR 2.0 in their services. Conclusions: IMAGINATOR 2.0 can be extended to adolescents, is acceptable and has potential as a brief intervention reducing self-harm in young people under mental health services. A definitive randomised controlled trial is now needed to test the intervention efficacy.
Background: Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disease. Long-term use of immunosuppressive drugs in patients can lead to cellular DNA damage, increasing the risk of develop...
Background: Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disease. Long-term use of immunosuppressive drugs in patients can lead to cellular DNA damage, increasing the risk of developing cancer. 1-3 Studies have shown that SLE patients will develop solid tumors such as lung, breast, bladder, uterus, and hematologic malignant tumor complications such as non-Hodgkin lymphoma within 8~15 years after the onset of the disease. The coexistence of malignancies is one of the leading causes of mortality in SLE patients. 4-7 Therefore, reducing the risk of cancer in patients is crucial for prolonging their lifespan. Currently, clinical prevention of malignant tumor complications in SLE patients involves regular tissue examinations, which do not effectively achieve early prevention. While constructing disease prediction models could forecast patient risk at an earlier stage for early prevention, there is currently a lack of risk prediction models for malignant tumors in SLE patients. 8 This study aims to construct a risk prediction model for malignant tumors in SLE patients by collecting relevant clinical indicators, to enable early prediction and prevention of malignant tumors in SLE patients, thereby improving their survival rate. Objective: Collect and record the signs and relevant laboratory data of systemic lupus erythematosus (SLE) patients from Affiliated Hospital of North Sichuan Medical University and Bazhong Central Hospital, and establish a risk prediction model for malignancy complicating SLE patients. Methods: We selected 504 SLE patients diagnosed and treated at the Affiliated Hospital of North Sichuan Medical University and Bazhong Central Hospital, from January 2016 to January 2023, including 22 patients with concurrent malignancy. Record whether the patient has the presence of Raynaud's, photosensitivity, arthralgia, fever, mouth ulcer, and alopecia. Laboratory data including urinary protein, globulin, albumin, LDH, creatinine, lymphocyte, platelets, neutrophils, monocytes, ESR, CRP, IgA, IgG, IgM, IgE, C3, C4, CEA, CA199, CA125, CA153, AFP, SSA, SSB, dsDNA, Sm antibodies, anti-nucleolar antibodies, anti-histone antibodies, anti-ribosomal P protein antibodies, and anti-phospholipid antibodies were collected. Analyze the risk factors affecting malignancy in SLE patients using Logistic regression, and based on the results, construct a predictive model for malignancy in SLE patients using ROC curves. Results: Compared with SLE patients without malignancy, SLE patients with malignancy showed significant differences in age, Raynaud, ESR, CRP, IgG, C4, CEA, CA199, CA125, CA153, AFP, and Sm antibodies (p < 0.05). The following factors were identified as significant risk factors for malignancy in SLE patients: Age (OR=1.04, 95% CI: 1.02-1.07, p=0.002), Fever (OR=4.29, 95% CI: 1.76-10.42, p=0.001), mouth ulcer (OR=2.43, 95% CI: 1.01-5.83, p=0.048), Lymphocyte count (OR=0.16, 95% CI: 0.05-0.57, p=0.005), ESR (OR=1.02, 95% CI: 1.01-1.04, p < 0.001), CRP (OR=1.02, 95% CI: 1.00-1.03, p=0.006), C4 (OR=1.01, 95% CI: 1.01-1.02, p < 0.001), CEA (OR=74.51, 95% CI: 17.53-316.65, p < 0.001)； CA199 (OR=5.73, 95% CI: 1.76-18.67, p=0.004), CA125 (OR=4.65, 95% CI: 1.80-12.00, p=0.002), Sm antibody (OR=5.73, 95% CI: 2.20-14.94, p < 0.001). The individual AUC values for the above factors were as follows: Age: 0.700, Fever: 0.560, mouth ulcer: 0.608, Lymphocyte count: 0.713, ESR: 0.724, CRP: 0.737, C4: 0.740, CEA: 0.671, CA199: 0.569, CA125: 0.608, Sm antibody: 0.689. When these factors were combined to construct a prediction model, the AUC value increased to 0.917, indicating a high predictive capacity. Conclusions: The factors of age, fever, mouth ulcer, lymphocyte count, ESR, CRP, C4, CEA, CA199, CA125, and Sm antibodies can be used to construct a risk prediction model for malignancy in SLE patients.
Objectives: This study aims to evaluate the effectiveness of health applications on the Google Play Store by analyzing app metadata using machine learning classification models. It investigates which...
Objectives: This study aims to evaluate the effectiveness of health applications on the Google Play Store by analyzing app metadata using machine learning classification models. It investigates which application features—such as AI classification, app category, update status, and version—are associated with higher user ratings.
Methods: A total of 305 health-related applications were selected from the Google Play Store using keyword filters for “Health & Fitness” and “Medical.” Key metadata were extracted and preprocessed, including Classification (AI vs. Non-AI), Category, Reviews, Developer Type, Version, Release Year, and Recent Update. To address class imbalance, the SMOTE technique was applied, and three machine learning models—Naïve Bayes, K-Nearest Neighbors (KNN), and Binomial Logistic Regression—were used to predict user ratings.
Results: The KNN model achieved the most balanced performance with 75.89% accuracy, 82.22% precision, and an AUC of 0.849, while Logistic Regression produced the highest precision (100%) and overall accuracy (76.32%) but lower recall (52.63%). Logistic regression analysis also showed that apps categorized under Health & Fitness, those recently updated, and AI-based apps were more likely to receive high user ratings.
Conclusion: Machine learning models, particularly KNN and Logistic Regression, can reliably predict app effectiveness based on metadata. Regular updates, AI integration, and fitness-focused design are key factors linked to higher user approval, providing useful insights for developers and digital health stakeholders. Future research should consider larger and more diverse datasets and explore additional features (e.g., user sentiment from reviews, app permissions) to further improve model performance.
Background: Healthcare public reporting involves making information about the quality and performance of healthcare providers available to the public; however, however, most individuals (non-healthcar...
Background: Healthcare public reporting involves making information about the quality and performance of healthcare providers available to the public; however, however, most individuals (non-healthcare professionals) are only at the stage of being aware of public reporting information, rather than adopting or acting on it. Objective: This article conducts a systematic review of individuals’ interaction with public reporting systems to explore how people adopt and engage with such information. Methods: A literature search was conducted on five electronic databases: Web of Science, MEDLINE, Embase, PsycINFO and IEEE Xplore. Results: This review includes findings from a total of 44 empirical articles. Our research shows that existing studies on information presentation and patient groups mainly focus on visual dashboards and patient activation. Based on the interaction between systems and individuals, we analyzed the reviewed research topics and identified four key themes of information presentation: data visualization, star ratings, dashboards, and narrative comments. In the process, we identified five themes related to the process: decision-making, timing, communication, personalized care, and family communication. Research finds that information presentation and visualization tools can optimize the usage of public reporting systems from the individual level, helping to fill the gap between system usage and population segmentation. Conclusions: This systematic literature review examines information presentation in health public reporting, analyzing both its presentation formats and population segmentation. By exploring the interaction between public reporting systems and demographic subgroups, this study provides valuable insights into optimizing the usage of public reporting information. The main contributions of this work are as follows: (1) reducing the complexity of public reporting by offering structure and clarity; (2) identifying how overlapping identities influence the use of public reporting information; and (3) reducing the complexity of shared decision-making between patients and providers by offering accessible channels and sources for patients to understand medical information.
Background: Physical inactivity is a critical risk factor for non-communicable diseases (NCDs), particularly among office employees with predominantly sedentary work environments. Addressing this issu...
Background: Physical inactivity is a critical risk factor for non-communicable diseases (NCDs), particularly among office employees with predominantly sedentary work environments. Addressing this issue is critical to improving public health and workplace productivity. Objective: This study aimed to evaluate the effectiveness of a blended intervention, combining web-based intervention and interactive e-workshops, in promoting moderate-to-vigorous physical activity (MVPA) among physically inactive office employees in Hong Kong. Methods: A 24-week randomized controlled trial (RCT) was conducted with 141 participants allocated equally to three groups: blended intervention, web-based intervention, and control. MVPA was objectively measured using accelerometers at baseline, postintervention (12 weeks), and follow-up (24 weeks). Retention, engagement rates, and intervention acceptability were also assessed. Statistical analyses included generalized linear mixed models and paired t-tests. Results: At postintervention, the blended group achieved significantly higher MVPA levels compared to the control group (β = 0.252, 95% CI [0.019, 0.485], p = 0.034) and the web-based group (β = 0.290, 95% CI [0.065, 0.515], p = 0.012). These improvements were sustained at follow-up, with the blended group outperforming both the control (β = 0.376, 95% CI [0.141, 0.610], p = 0.002) and web-based groups (β = 0.364, 95% CI [0.134, 0.594], p = 0.002). Retention rate was high across all groups (83% overall), highlighting its feasibility and acceptability. Conclusions: The blended intervention effectively increased MVPA levels and demonstrated its potential to address physical inactivity among office employees. These findings highlight the value of integrating digital tools with interactive components for sustainable behavior change in workplace settings. Clinical Trial: The blended intervention effectively increased MVPA levels and demonstrated its potential to address physical inactivity among office employees. These findings highlight the value of integrating digital tools with interactive components for sustainable behavior change in workplace settings.
Background: Mobile health (mHealth) applications show promise for delivering physical activity interventions, but uptake remains low due to usability barriers. Beta testing is essential to optimize us...
Background: Mobile health (mHealth) applications show promise for delivering physical activity interventions, but uptake remains low due to usability barriers. Beta testing is essential to optimize user experience before full implementation. Objective: To evaluate the usability, acceptability, and feasibility of the 50K4Life mobile application (50K4Life app) prototype for delivering a two-week walking challenge to public school administrative employees. Methods: Following the Integrate, Design, Assess, and Share (IDEAS) framework, we conducted a single-group beta test with 12 public school administrative employees in El Paso County, Texas. Participants used the 50K4Life app built on the Pathverse platform for a two-week walking challenge. Data collection included acceptability surveys, satisfaction questionnaires, app utilization metrics, and qualitative debriefing sessions. Results: All 12 participants completed the walking challenge. Acceptability was high for app design (91.7%), layout (75.0%), and battery impact (83.3% reported no issues). However, participants experienced difficulties with navigation (58.3%), delays in updating step counts (66.7%), and completion of assigned tasks (41.7% could not locate all features). App utilization was high: 100% accessed the leaderboard and walking challenge page, 91.6% synced step data and set step goals. Qualitative feedback identified needs for improved user engagement features, better synchronization, and enhanced visual appeal. Conclusions: The 50K4Life app demonstrated feasibility for delivering walking challenges with high engagement rates. However, improvements in navigation, data synchronization, and user interface are needed before full-scale implementation. This beta testing approach provides a valuable framework for optimizing mHealth interventions.
Background: Older adults face increased crash risk due to age-related declines in cognitive, visual and physical functioning [1,2]. Maintaining mobility is essential for autonomy, health and wellbeing...
Background: Older adults face increased crash risk due to age-related declines in cognitive, visual and physical functioning [1,2]. Maintaining mobility is essential for autonomy, health and wellbeing in later life, and over 90% of Australians in their 70s continue to drive [3]. Web-based platforms are increasingly used to deliver health and mobility information to older adults, with more than 75% now seeking such content online [4]. However, existing online resources on driving safety often lack age-specific guidance, have usability limitations, or may not be designed with older users in mind [8]. Objective: This study aimed to explore the information needs, preferences, and experiences of older drivers and general practitioners (GPs) to inform the development of a user-centred web-based platform to support driving safety in later life. To ensure the platform met user needs, focus groups were conducted with older adults and clinicians who conduct fitness to drive (FtD) assessments on older drivers to gather feedback on content, usability and accessibility before public launch. The platform seeks to improve access to trusted, accessible information tailored to the needs of older drivers and their support networks. By addressing barriers to engagement and providing relevant resources, this initiative aims to reduce road risk in a group often overrepresented in crash and injury outcomes, ultimately benefiting the safety of all road users. Methods: Website development was informed by a review of 26 Australian websites focused on older driver safety and guided by consultations with stakeholders, including older adults and GPs. Semi-structured interviews were conducted with eight older adults (aged 67+), and a separate focus group was held with ten GPs who conduct FtD assessments for older drivers. Thematic analysis was used to explore participants’ experiences with online health information, along with their perceptions of usability and content relevance. A primary deductive approach was used for reviewing the web platform, whilst inductive analysis identified emergent themes regarding information needs and barriers to engagement. Although the sample was small, it was appropriate for this exploratory qualitative research and enabled rich, formative insights to inform the platforms content and design. Results: The website addressed priority topics including age-related health conditions affecting driving, licensing requirements, vehicle safety features, and strategies for maintaining driving confidence. Older adults appreciated large fonts, plain language, and intuitive navigation, and emphasised the importance of trustworthy sources and practical advice. General practitioners highlighted the websites potential to support clinical discussions with older patients around driving capacity and safety. Gaps identified in existing resources, such as tailored guidance on maintaining driving skills and information about vehicle Advanced Driver Assistance Systems (ADAS) were addressed [15]. Barriers to engagement included digital literacy limitations, accessibility concerns, and uncertainty about the credibility of online information [10]. Conclusions: A co-designed, web-based platform tailored to older adults and clinicians who conduct FtD assessments for older drivers can enhance access to relevant, trustworthy information support driving self-regulation. Findings underscore the importance of involving end users in the development process to optimise usability and ensure the resource aligns with their needs. This study contributes to best-practice approaches for delivering digital health interventions that promote mobility and safety for older road users [9].
Despite the rapid proliferation of artificial-intelligence (AI)-based model development, few studies have prospectively implemented models in practice within the context of ongoing surveillance to pre...
Despite the rapid proliferation of artificial-intelligence (AI)-based model development, few studies have prospectively implemented models in practice within the context of ongoing surveillance to predict clinical deterioration. To our knowledge, the concept of AI-based alert fatigue has not yet been introduced in this context. A necessary first step is to evaluate patterns of alerts to understand the system-level implications of using clinical deterioration alerts in practice and establish a process that enables clinicians to engage in setting alert thresholds.
Storytelling, a timeless art form, is central to the human identity and serves as a bridge across time and place. In medical school admissions, the personal statement prompts applicants to share their...
Storytelling, a timeless art form, is central to the human identity and serves as a bridge across time and place. In medical school admissions, the personal statement prompts applicants to share their story and connect with admission committees through this deeply human medium. However, generative AI tools like ChatGPT blur the line between genuine self-expression and algorithmic assistance, raising concerns about the authenticity of these narratives. This challenge raises questions about the personal statement's role in evaluating candidates and the ethical implications of AI in the medical school admission process. As admission committees adapt to this technology, they must balance fairness, accessibility, and authenticity in evaluating the personal statement while also preparing for its eventual replacement.
Background: Heart disease remains a leading cause of death for women in the United States, yet awareness and knowledge are declining. Artificial intelligence (AI) chatbots have great potential to educ...
Background: Heart disease remains a leading cause of death for women in the United States, yet awareness and knowledge are declining. Artificial intelligence (AI) chatbots have great potential to educate women. Objective: To evaluate the potential efficacy of HeartBot to increase women’s awareness and knowledge of heart attack symptoms and care-seeking behavior. Methods: In this pilot, quasi-experimental study, 92 women aged 25 years or older without history of heart disease completed the HeartBot interaction via Short Message Service. The study was remotely conducted from October 2023 to January 2024. HeartBot, a fully automated AI chatbot, covered 15 topics of heart attack awareness, knowledge, symptoms, and care-seeking in a single session. The mean (SD) length of the HeartBot interaction was 13.0 (7.80) minutes. The primary outcomes consist of 4 questions on (1) recognizing signs and symptoms of a heart attack, (2) telling the difference between the signs and symptoms of a heart attack, (3) calling an ambulance or dialing 911 when experiencing heart attack symptoms, and (4) getting to an emergency room within 60 minutes after the onset of symptoms of a heart attack. Women were asked to answer the 4 questions before and after the HeartBot interaction on a scale of 1-4, with higher score indicating higher levels of awareness and knowledge of heart attack risks and symptoms. Results: The sample mean (SD) age was 45.9 (11.9) years. 55 (59.8%) of the sample represented racial/ethnic minorities. The mean (SD) length of the HeartBot interaction was 13.0 (7.80) minutes. In ordinal logistic regression models, women significantly increased in the 4 self-reported outcomes (i.e., heart attack symptoms, calling 911) even after controlling for potential confounding factors (adjusted odds ratio (AOR) = 7.10, 95% CI: 3.52-13.16 for outcome 1; AOR=5.47, 95% CI: 2.77-10.78 for outcome 2; AOR=5.75, 95% CI: 2.86-11.59 for outcome 3; AOR=2.85, 95% CI: 1.54-5.25 for outcome 4; p < 0.001 for all 4 outcomes). Conclusions: HeartBot led to a significant increase in awareness and knowledge of heart attack risks and symptoms in women. These findings suggest that HeartBot is a promising approach to improve heart health education. A randomized controlled trial of HeartBot is warranted to establish its efficacy and safety for the clinical setting.
Background: Medical imaging remains at the forefront of advancements in adopting digital health technologies in clinical practice. Regulatory approved artificial intelligence (AI) clinical decision su...
Background: Medical imaging remains at the forefront of advancements in adopting digital health technologies in clinical practice. Regulatory approved artificial intelligence (AI) clinical decision support systems are commercially available and being embedded into routine practices for radiologists internationally. These decision support solutions show promising clinical validity compared to standard practice conditions; however, their implementation in practice is poorly understood. Objective: The study presents findings from an end-to-end qualitative implementation-evaluation of an AI clinical decision support tool within a large hospital medical-imaging department. Methods: This prospective implementation-evaluation study was conducted in a large public tertiary referral hospital in Brisbane, Australia. One-to-one participant interviews were conducted across three implementation phases: pre-implementation, peri-implementation and post-implementation. Participants comprised radiology consultants and registrars in addition to radiographers. Eligibility criteria included involvement in chest computed tomography (CT) studies during the study timeframe. Interviews were informed by the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. Results: In total, 43 one on one interviews were conducted across the 2-year study period. This consisted of 6 radiographers (14%), 21 registrar radiologists (49%) and 16 consultant radiologists (37%). Nine (21%) participants were interviewed across multiple timepoints. Perceptions of, and level of engagement with, the AI decision support solution were mixed during real-world image interpretation and reporting in practice. Responses highlighted the importance of addressing the sociotechnical conditions of implementation through early stakeholder involvement, consistent communication, agile training models, workflow-compatible design, and mechanisms for feedback and iterative refinement. Moreover, findings highlight long-term sustainability of such technologies depends on institutional commitment through intentional resourcing and implementation planning. Conclusions: The success, or otherwise, of AI clinical decision support solutions in real-world practice is dependent on many ongoing non-technical factors. Co-design principles that place clinical users at the centre of system development and integration remain as important as ever as we progress into the era of AI-based clinical decision support.
Background: Acquiring relevant knowledge and support is essential for informal caregivers of individuals with early-stage dementia, including awareness, access, and use of comprehensive resources for...
Background: Acquiring relevant knowledge and support is essential for informal caregivers of individuals with early-stage dementia, including awareness, access, and use of comprehensive resources for both individuals with dementia and caregiver support. With appropriate strategies and early-stage support, informal caregivers can play a vital role in enhancing the well-being of individuals with dementia and potentially slowing its progression. While large language models (LLMs) can provide easy access to caregiving knowledge, the risks, perceived challenges, and ways to improve LLM-generated responses in practice remain underexplored. Objective: In this study, we aim to (1) examine the risks and perceived challenges of using a baseline ChatGPT-4o, an internet-accessible artificial intelligence (AI) model, for dementia caregiving support and (2) understand how an enhanced version of ChatGPT-4o, equipped with up-to-date dementia caregiving knowledge, can mitigate these risks and challenges. Methods: We compiled 32 representative questions from informal caregivers seeking guidance on early-stage dementia from a local office specializing in dementia services and researchers in the field. Next, we developed two conditions of ChatGPT-4o: C1, the baseline model available for public use, and C2, an experimental version enhanced through prompt engineering and grounded in a conceptual framework—drawn from health science and gerontology literature—designed to empower caregivers with early-stage dementia support. Using these conditions, we generated 64 responses—32 pairs corresponding to the questions. Twelve experts evaluated LLM-generated responses using validated tools measuring accuracy, reasoning, clarity, usefulness, trust, satisfaction, safety, harm, and relevance. A Mann-Whitney U test compared conditions. After the survey, we conducted interviews to explore experts’ perceived differences, remaining challenges, and design opportunities. Interviews were transcribed and analyzed using descriptive thematic analysis. Results: Responses in C2 showed significant improvements in three criteria—actionability, relevance, and perceived satisfaction—compared to C1. However, no significant differences were found in the remaining five: response accuracy, the model’s ability to understand the question, intelligibility, trustworthiness, response safety, and perceived harm. Qualitative analysis of interview results provided deeper insights into two areas: differences between responses from baseline and experimental conditions, and potential explanations for these differences. Twelve experts commented on dimensions including wordiness, level of detail, empathy, satisfaction, accuracy, relevance, and potential bias. While both models were seen as somewhat verbose, responses from the experimental model were generally viewed as more detailed, relevant, and actionable. Although accuracy was perceived as comparable across models, participants expressed greater satisfaction with the experimental model’s responses. Conclusions: Results indicate that both conditions generated responses perceived as reasonable and intelligible. However, the experimental model offered more relevant, practical guidance on caregiving needs, providing specific information aligned with the 32 testing questions and actionable recommendations. This led to higher perceived satisfaction compared to the baseline model.
Background: There is a gap in mental health care among Latino/x and Spanish speaking communities and the care that is available is often difficult to access, lacks cultural nuance, and results in low...
Background: There is a gap in mental health care among Latino/x and Spanish speaking communities and the care that is available is often difficult to access, lacks cultural nuance, and results in low engagement and satisfaction. Objective: This mixed-methods retrospective pilot study is to evaluate the reach, adoption, and acceptability of digital Spanish-language psychosocial and emotional wellness services among Latino/x adults offered by the digital health company Sanarai. Methods: Data included in this study were collected between Aug 2020 – Sept 2024 by Sanarai as part of its ongoing services. Quantitative data sources included individual customer’s appointments data, individual sessions payment data, and customer satisfaction data. Qualitative data were obtained from transcribed notes of telephone or video-based user interviews conducted by Sanarai staff between Aug 2020 – May 2024. Results: Sanarai served 6,163 users (59.4% female) across all 50 U.S. states with the highest concentration of participants in Texas and California. Results showed 94% of users scheduled a first appointment within one week, with 43% doing so within one day. Over 62% of participants engaged in two or more sessions, attending an average of 8.94 sessions over 110 days. The platform delivered a total of 36,858 appointments, including individual and couples’ sessions. Session satisfaction was high with an average satisfaction rating of 4.88/5.0 and a Net Promoter Score of +85. Nearly all respondents (95.1%) expressed intent to schedule another session. Qualitative interviews with 30 users (70% female) revealed a diverse user base. Many users reported prior mental health service experiences, while one-third were new to care. Participants cited cost, cultural fit, language access, and convenience as key reasons for choosing Sanarai over local services. Users highlighted the platform’s affordability, scheduling flexibility, and provider professionalism as central to their positive experiences. Conclusions: These findings underscore the value of culturally responsive, accessible online mental health care for Spanish-speaking communities. Clinical Trial: n/a
Background: This Is My Story (TIMS) was started by Chaplain Elizabeth Tracey to promote a humanistic approach to medicine. Patients in the TIMS program are the subject of a guided conversation in whic...
Background: This Is My Story (TIMS) was started by Chaplain Elizabeth Tracey to promote a humanistic approach to medicine. Patients in the TIMS program are the subject of a guided conversation in which a chaplain interviews either the patient or their loved one about the patient. The interviewer asks four questions to elicit clinically actionable information that has been shown to improve communication, between the narrator and the medical providers, and increase empathy on part of the medical team. The original recorded conversation is edited into a condensed audio file approximately 1.5 minutes in length and placed in the electronic health record where it is easily accessible by all clinicians caring for the patient. Objective: TIMS is active at the Johns Hopkins Hospital and has shown value in assisting with clinician empathy and communication. As the program expands, there exists a barrier to adoption due to limited time and resources needed to manually edit audio conversations into a more condensed format. To address this, we propose an automated solution using a large language model (LLM) to create meaningful and concise audio summaries. Methods: We analyzed 24 TIMS audio interviews and created three edited versions of each: (1) Expert-edited, (2) AI-edited using a fully automated LLM pipeline, and (3) Novice-edited by two medical students trained by the expert. All versions were evaluated using a within‐subjects design by a second expert who was blinded to both the editor and order each audio was presented. This expert rated all interviews and scored audio quality and content quality on 5-point Likert scales. We quantified transcript similarity to the expert-edited reference using lexical and semantic similarity metrics and qualitatively assessed important information omitted relative to the expert-edited interview. Results: Audio quality (flow, pacing, clarity) and content quality (coherence, relevance, nuance) were each rated on 5-point Likert scales. Expert-edited interviews received highest mean ratings for both audio quality (4.84) and content quality (4.83). Novice-edited scored moderately (3.84 audio, 3.63 content), while AI-edited scored slightly lower (3.49 audio, 3.20 content). Novice and AI edits were rated significantly lower than expert (p <.001), but not significantly different from each other. AI and novice-edited interview transcripts had comparable overlap with the expert reference transcript, while qualitative review found frequent omissions of patient identity, actionable insights, and overall context in both the AI and novice-edited interviews. AI editing was fully automated and significantly reduced the editing time compared to both human editors. Conclusions: AI-based editing pipeline can generate TIMS audio summaries with comparable content and audio quality to novice human editors with one hour of training. AI significantly reduces editing time and removes the need for manual training, while offering a solution to scale TIMS to larger organizations or where expert editors are not readily available. Clinical Trial: Not applicable.
Background: Elderly activity recognition is a critical task in long-term care monitoring, yet it remains challenging due to postural deformities and health-related variability. These factors cause dif...
Background: Elderly activity recognition is a critical task in long-term care monitoring, yet it remains challenging due to postural deformities and health-related variability. These factors cause different activities to appear visually similar, or the same activity to appear dissimilar, undermining the effectiveness of traditional human activity recognition (HAR) models developed for the general population. Objective: This study aims to improve elderly activity recognition by incorporating care assessment information to enable personalized and context-aware monitoring in real-world care environments. Methods: We propose a care-assessment-aware spatio-temporal transformer (CSTT) model that integrates body keypoints, heatmaps, and care level data. The model dynamically adjusts its attention mechanism based on care level context to improve recognition accuracy. CSTT was trained and validated on a real-world elderly motion dataset collected from 28 participants during natural, privacy-preserving mealtime sessions without external intervention. Results: Despite data imbalance and considerable intra-class variation due to differing care needs, the proposed CSTT model achieved an F1 score and accuracy of 0.96. Analysis revealed that movement patterns differ significantly across care levels and that similar motions occur in distinct activities, highlighting the importance of care-aware modeling. Conclusions: Incorporating care level information into activity recognition models significantly enhances performance in elderly care settings. The proposed CSTT framework demonstrates the value of personalized, context-sensitive approaches for accurate and ethical monitoring in long-term care environments.
Background: As healthcare systems increasingly rely on telehealth, older adults face technological and structural, among other barriers that limit their comfort and ability with virtual care platforms...
Background: As healthcare systems increasingly rely on telehealth, older adults face technological and structural, among other barriers that limit their comfort and ability with virtual care platforms. To ensure the success of our clinical trial examining the impact pharmacist-led visits, we employed ethnographic methods to capture a more nuanced understanding of the unique and multiple barriers that older adults face in real-life while interacting with virtual care is needed to implement digital health interventions for older adults. Objective: Our assessment had two primary objectives: 1) To assess and refine our recruitment strategies and technology troubleshooting approaches for a two-site cluster randomized clinical trial (RCT) aimed at delivering pharmacist video visits for medication management. 2) To document the challenges older adults encounter immediately before and during a video visit, and how these challenges affect their overall experience and future willingness to use virtual care. Methods: We applied ethnographic methods, including direct observation and fieldnotes, to shape a technology-focused randomized controlled trial (RCT) for older adults. Our goal was to identify barriers to digital access, usability challenges, and adaptive strategies through in-home observation and interviews. Participants were 20 older community-dwelling veterans (aged 65 and older) engaging in video visits from their home for medication reconciliation with a pharmacist. Data collection included structured fieldnotes from both an ethnographer and clinical pharmacist, and direct in-home observations. Data from fieldnotes and direct observations were systematically analyzed using qualitative rapid analysis. Results: Our findings highlight several key insights: 1) Ethnographic methods aided in improving the RCT’s patient-facing approach and feasibility. We uncovered technological and structural barriers and identified challenges such as device navigation and broadband connectivity. 2) Ethnographic methods revealed insights into digital ageism and strategies to enhance digital inclusion for older adults in video telehealth. Ethnographically informed direct engagement with technology helped to challenge internalized ageism among participants, and personalized adaptations were essential for those with limited digital skills. Socio-cultural and environmental factors significantly influenced participants' virtual care success, with family and caregiver involvement proving pivotal. Conclusions: The novel application of ethnographic methods to shape an RCT for older adults informed the development and tailoring of recruitment and technology trouble shooting approaches for a subsequent RCT to deliver pharmacist video visits for medication management with older adults. Ethnographic methods allowed us to address one component of digital ageism, with an aim to increase participant engagement with research and technology. Our assessment underscores the importance of real-time, ethnographic and qualitative data gathering to improve RCTs and other trials for improving the health of aging populations, with an emphasis in virtual care. Clinical Trial: NA
Retrieval-Augmented Generation models have emerged as a powerful technique for optimizing general large language models in specialized domains, and are being increasingly adopted by researchers in the...
Retrieval-Augmented Generation models have emerged as a powerful technique for optimizing general large language models in specialized domains, and are being increasingly adopted by researchers in the medical field. This article acknowledges the significant potential of RAG to enhance clinical decision-making. However, it argues that researchers and practitioners must proactively address the ethical risks associated with RAG implementation in healthcare. Key considerations include ensuring accuracy, fairness, transparency, and accountability, as well as maintaining essential human oversight, as discussed in detail. We propose that robust data governance, explainable AI techniques, and continuous monitoring are critical components of a responsible RAG implementation strategy. Ultimately, realizing the benefits of RAG while mitigating ethical concerns requires collaboration among healthcare professionals, AI developers, and policymakers, fostering a future where AI supports patient safety, reduces disparities, and improves the quality of nursing care.
Background: In France, clinical data on rare diseases are primarily collected through BaMaRa, a software platform used by national expert centers to populate the BNDMR, the rare disease data warehouse...
Background: In France, clinical data on rare diseases are primarily collected through BaMaRa, a software platform used by national expert centers to populate the BNDMR, the rare disease data warehouse. BaMaRa ensures standardized and structured data collection across all rare disease networks, with a focus on care coordination and epidemiological reporting. In 2024, FREDD, a health data warehouse dedicated to rare eye diseases, was developed within the framework of the third French National Rare Disease Plan, by the SENSGENE sector. Despite overlapping datasets, there is no native interoperability between BaMaRa and FREDD, requiring the development of a dedicated, traceable pipeline to transform BaMaRa exports into data suitable for inclusion in the warehouse. This transformation involves complex business rules to address structural, semantic, and specific differences between the two systems. Objective: This article aims to describe the design and implementation of a robust data transformation pipeline that enables the automated conversion of BaMaRa clinical records into a structured dataset aligned with the FREDD data model. The primary goal is to ensure that data remain semantically consistent and reusable for secondary use of health data. Methods: We developed a Python-based application, called FREDDEX, that integrates several configuration files encoding the domain-specific business rules required to align BaMaRa data with the FREDD schema. These rules include mapping of variable names and values, management of multi-source redundancy and data quality checks. The system was designed to be modular, auditable, and usable by clinical data managers with minimal technical expertise. FREDDEX was tested using synthetic test cases and then validated on real-world data from the CHU de Strasbourg. Results: The application FREDDEX successfully processed and transformed BaMaRa exports from multiple centers, converting patient records into the FREDD format. Business rules were encoded and the tool enabled rapid onboarding of new clinical centers and significantly reduced manual curation time. Importantly, it also established a reproducible framework that can be adapted to other rare disease data reuse contexts, supporting interoperability with national and European platforms such as the ERN-EYE. Conclusions: This automated ETL process ensures the robust, standardized, and traceable reuse of BaMaRa data within FREDD. By integrating complex business rules and quality controls, it strengthens the interoperability and reliability of rare disease datasets, paving the way for large-scale research while reducing the burden on clinical teams.
Background: There is increasing research on machine learning in predicting venous thromboembolism after joint arthroplasty, but the quality and clinical applicability of these models are unclear. Obje...
Background: There is increasing research on machine learning in predicting venous thromboembolism after joint arthroplasty, but the quality and clinical applicability of these models are unclear. Objective: This systematic review and meta-analysis aims to evaluate the predictive performance and methodological quality of machine learning models for venous thromboembolism risk after joint replacement surgery, and to provide insights for further clinical application. Methods: Web of Science, Embase, Scopus, CNKI, Wanfang, Vipro, and PubMed were searched until December 15, 2024. The risk of bias and applicability were evaluated using the prediction model Bias Risk Assessment Tool (PROBAST) checklist. Quantitative synthesis and meta-analysis included models reporting AUC value with 95% confidence intervals. Results: There were 34 prediction models from 9 studies, and the most used machine learning models were extreme gradient boosting and logistic regression. 24 models with reported confidence intervals were incorporated into the meta-analysis, and the total area under the curve was 0.826 (95% CI 0.775-0.876). All studies indicated a high risk of bias and considerable heterogeneity. Age, gender, diabetes, and hypertension were the most frequently used predictive factors. Conclusions: The predictive performance of machine learning models varies greatly, and the AUC value of the report indicates that most of the models have good discriminative ability. These models have a high risk bias, and it is necessary to take this into account when they are used in clinical practice. Future studies should adopt a prospective study design, ensure appropriate data handling, and use external validation to improve model robustness and applicability. Clinical Trial: The protocol for this study is registered with PROSPERO (registration number: CRD42024625842).
Background: Regular physical activity at or above the recommended levels offers significant health benefits. Wearable activity trackers are useful tools to promote activity engagement, especially cons...
Background: Regular physical activity at or above the recommended levels offers significant health benefits. Wearable activity trackers are useful tools to promote activity engagement, especially considering their use in free living environments. Current research shows moderate improvements on step count in wearable tracker users but consistent increases at various intensities of physical activity are inconclusive [1-3]. Many health-related behavior theories highlight the role of social environments in activity engagement, but the relationship between the use of social elements on wearable activity trackers is not well understood. Objective: The purpose of this study was to compare weekly physical activity, approximating moderate-to-vigorous intensity, of adults from the New York City Metropolitan Area assigned to conditions that employed either use or no use of the social engagement physical activity features on their wearable tracker. Additionally, given its importance to activity engagement, exercise self-efficacy was also measured to examine if a relationship existed between self-efficacy and physical activity. Methods: The researchers recruited Apple Watch users living in the NYC area to participate. Eligible participants were randomized into one of two conditions; the condition that employed use of the social engagement physical activity features or the condition that did not use the social engagement features about their physical activity for 8-weeks. Participants submitted objective data from their device (i.e., “exercise minutes”) and completed the Resnick & Jenkins Self-efficacy for Exercise Scale at pre-, mid-, and post-intervention. Participants in the social feature user condition also answered additional questions in the post-intervention survey about which social feature they used the most throughout the study. Upon completion of data collection, 112 participants data sets were analyzed to determine if effects were found. Results: There was not a significant difference between wearable activity tracker social feature users and non-users on weekly physical activity (P = 0.63), but there was an average weekly increase of 62 ± 20.22 minutes of physical activity across all participants. Among those randomized to use their wearable tracker’s social features, those who reported using the feature that highlighted comparing their data to others’ the most increased their activity by 111 ± 142 minutes per week (P = .02) compared to those who used the competition or social support feature the most. Changes in exercise self-efficacy and in physical activity were also positively related (r = .146, P = .03). Conclusions: These results suggest that conscious monitoring of activity on wearable activity trackers can lead to a significant increase in physical activity and comparing one’s own physical activity to others may amplify the effect. With the increased prevalence of device ownership, knowing how these devices can be used to promote increases in physical activity may help those implementing activity interventions. Clinical Trial: N/A
Background: Traditional Chinese Medicine (TCM) clinical decision-making involves complex integration of syndrome differentiation, constitutional assessment, and individualized treatment selection, cre...
Background: Traditional Chinese Medicine (TCM) clinical decision-making involves complex integration of syndrome differentiation, constitutional assessment, and individualized treatment selection, creating challenges for standardization and quality assurance. While large language models demonstrate remarkable capabilities in medical knowledge integration and clinical reasoning, their application to TCM remains largely unexplored, particularly regarding syndrome differentiation principles and prescription formulation logic. Objective: This study aimed to evaluate five contemporary large language models in TCM clinical decision-making and assess the effectiveness of human-AI collaboration compared to independent decision-making approaches. Specific objectives were to benchmark LLM performance in TCM knowledge assessment, evaluate clinical case analysis capabilities, identify the optimal performing model, and assess the quality, efficiency, and acceptability of human-AI collaborative decision-making. Methods: Five mainstream large language models were evaluated: Claude 3.7 Sonnet-Extended, ChatGPT 4.5, Grok3-DeepSearch, Gemini 2.0 Flash Thinking Experimental, and DeepSeek-R1. The evaluation employed a four-phase methodology: (1) TCM knowledge assessment using 160 standardized examination questions, (2) clinical case analysis of 30 cases representing different disease systems and complexity levels, (3) optimal model selection using weighted scoring (40% knowledge, 60% clinical analysis), and (4) clinical application assessment involving 10 TCM practitioners and 2 experts comparing physician-only, AI-only, and human-AI collaboration approaches across 5 clinical cases. Statistical analysis included descriptive statistics, reliability analysis, comparative testing, and regression analysis. Results: DeepSeek-R1 demonstrated superior performance across both evaluation domains, achieving 96.7% accuracy in knowledge assessment and 17.31/20 mean score in clinical case analysis, significantly outperforming other models (P<.001). Human-AI collaboration achieved significant improvements compared to physician-only decision-making, with 16.1% quality enhancement (mean scores: 33.62 vs 28.97, P<.001) and 66.1% time reduction (162.6s vs 479.2s, P<.001). System usability was rated favorably (SUS score: 76.8, P=.002), with high collaboration acceptance rates (74.25% adoption, 24.0% modification, 1.75% rejection). AI assistance provided greatest benefits in prescription formulation and medication selection domains (P<.001). Conclusions: Large language models, particularly DeepSeek-R1, demonstrate substantial capabilities in TCM knowledge assessment and clinical case analysis. Human-AI collaboration significantly enhanced clinical decision-making quality and efficiency while maintaining high physician acceptance. These findings provide compelling evidence for the clinical value of AI-assisted decision-making in traditional Chinese medicine, suggesting potential solutions to current challenges in knowledge standardization, clinical training, and healthcare delivery efficiency. Strategic implementation of AI assistance could significantly enhance the quality, efficiency, and accessibility of TCM care while preserving fundamental principles of individualized treatment.
Background: Measuring free-living gait with wearable sensors has great potential in supporting personalised rehabilitation. There are challenges meeting the accuracy levels of laboratory-based measure...
Background: Measuring free-living gait with wearable sensors has great potential in supporting personalised rehabilitation. There are challenges meeting the accuracy levels of laboratory-based measurements in detecting initial and final contact, particularly in impaired populations. Objective: To test the criterion validity of a novel temporal gait measurement technique, combining the ActivPAL 4+ (PAL Technologies, Glasgow, UK) and the Teager-Kaiser Energy Operator, to measure stance phase duration in chronic stroke survivors through comparison with the Evoke cluster marker system (Vicon, Oxford, UK). Methods: Stroke participants (n=13, mean age = 59 years 14, time since stroke = 1.5 years 0.5) were assessed using the ACTIVPAL 4+ and a motion capture system. Two 10m walk tests were measured, while wearing two ActivPAL 4+ (located on anterior of both thighs) and clusters on the pelvis and ankles from the motion capture system. The Teager-Kaiser Energy Operator signal processing technique was used to extract the stance durations of the ActivPAL 4+, compared with a previously validated method. Results: There was a good agreement (bias: 0.03s, limits of agreement: -0.22 to 0.28s) between the ACTIVPAL 4+ and motion capture system despite a slight underestimation (mean stance time: 0.850s vs. motion capture system: 0.881s). Conclusions: Findings suggest the ACTIVPAL 4+, combined with Teager-Kaiser Energy Operator technique, provides valid stance time measurements when compared laboratory-based systems, supporting its use in free-living gait analysis and feedback during rehabilitation. Limitations include laboratory-only validation and a small population of chronic stroke patients. Future work should explore free-living gait, and larger, and broader, cross section of stroke populations.
Background: Physical inactivity represents a significant public health issue with substantial socioeconomic costs. In the Autonomous Community of Madrid (CAM), 39.17% of the population does not meet t...
Background: Physical inactivity represents a significant public health issue with substantial socioeconomic costs. In the Autonomous Community of Madrid (CAM), 39.17% of the population does not meet the World Health Organization (WHO) recommendations for physical activity. Gender, sex, and occupational factors are well-established determinants of leisure-time physical activity (LTPA), yet few studies have examined these factors among university staff. Objective: This study aims to analyze the relationship between LTPA and work ability among university staff in the CAM, considering the potential modifying effect of occupational physical activity (OPA). Secondary objectives include examining associations between LTPA, musculoskeletal disorders, health-related quality of life (HRQoL), physical and mental workload, and working conditions, with a focus on sex and gender differences. Methods: A cross-sectional study was designed involving 885 university staff members from the University of Alcalá (UAH), Madrid, Spain. Participants will complete an online survey including sociodemographic questions and validated instruments: Global Physical Activity Questionnaire (GPAQ), Work Ability Index (WAI), Nordic Musculoskeletal Questionnaire, SF-12 Health Survey, and NASA Task Load Index (NASA-TLX). Descriptive and inferential statistics will be performed to assess associations between LTPA, OPA, and work ability, adjusted for relevant covariates. Results: The study was approved by the Ethics Committee of the University of Alcalá in November 2024. Recruitment began in December 2024 and will continue until June 2027. Data analysis will be conducted progressively. Results will be disseminated in peer-reviewed journals and presented at scientific conferences following gender-sensitive and transparent reporting standards. Conclusions: Understanding the determinants of physical activity and their interaction with work ability and gender may inform the development of targeted, culturally sensitive interventions to reduce sedentary behavior and its associated health and economic burdens in university staff. Clinical Trial: NCT06723808
Background: Older adults with chronic disease are key beneficiaries of digital health technologies, yet adoption remains inconsistent, particularly in rural areas and among certain demographic groups,...
Background: Older adults with chronic disease are key beneficiaries of digital health technologies, yet adoption remains inconsistent, particularly in rural areas and among certain demographic groups, such as older women who are less likely to engage with digital health compared to men. Objective: This systematic review aimed to identify barriers and facilitators to digital health adoption among older adults with chronic disease, with particular attention to rural-urban differences, co-design, and equity-relevant factors. Methods: This updated review builds on a previously published review by extending the search to include PsycArticles, Scopus, Web of Science, and PubMed for studies published between April 2022 and September 2024. Grey literature from August 2021 onward was also included. Studies were eligible if they reported barriers and/or facilitators to digital health adoption among adults aged 60+ with chronic disease. Findings were mapped to the Capability, Opportunity, and Motivation Model of Behaviour (COM-B) and analysed using the PROGRESS-Plus equity framework. Quality was assessed using the Mixed Methods Appraisal Tool (MMAT), and all results are reported in line with PRISMA guidelines. Results: Twelve studies from the original review were retained, with 17 new peer-reviewed studies added, yielding a total of 29 studies, in addition to 30 documents identified in the grey literature search. Barriers identified included limited digital literacy, physical and cognitive challenges (Capability), infrastructural deficits and usability challenges (Opportunity), and privacy concerns, mistrust, and high satisfaction with existing care (Motivation). Facilitators included tailored training and accessible design (Capability), provider endorsement and hybrid care models (Opportunity), and recognition of digital health benefits (Motivation). Healthcare providers emerged as both facilitators and barriers—positively influencing adoption when engaged and trained but hindering it when lacking confidence or involvement. Comparative analysis of rural and urban contexts was limited by inconsistent reporting of equity-relevant variables. However, grey literature suggested rural users face additional infrastructural challenges but express higher satisfaction with local care, potentially reducing motivation for digital uptake. Gender differences were observed in three peer-reviewed studies and grey sources, with older women showing lower adoption and differing outcome priorities. Co-design enhanced adoption, especially when involving not just older adults but also healthcare providers and community stakeholders. Conclusions: Digital health adoption among older adults is shaped by capability, opportunity, and motivation factors. Effective and equitable digital health strategies must address infrastructural and literacy barriers, engage healthcare providers through training and co-design, and ensure multi-stakeholder involvement. This review highlights that greater attention to standardised reporting of demographic variables, especially gender and rurality, is essential in digital health research to avoid one-size-fits-all approaches and support inclusive implementation. Clinical Trial: Not applicable
Objective: To provide reference for the transformation of pharmacists’ working mode from the outpatient pharmacists in the public hospitals to community pharmacists by studying the diabetes treatmen...
Objective: To provide reference for the transformation of pharmacists’ working mode from the outpatient pharmacists in the public hospitals to community pharmacists by studying the diabetes treatment education services (DTES) working mode of pharmacists in general hospital in China and evaluating the outcomes.
Methods: A RCT (randomized controlled trial, RCT) study was conducted on 318 patients with type 2 diabetes at the early stage by a Medication Therapy Management (MTM) team mainly built by the pharmacists in the general hospital. a comparison from the aspects of improvement of patients’ adherence, patients’ understanding of diabetes, treatment deviation, treatment outcomes and service satisfaction were evaluated to explore the outcomes of pharmacists’ intervention.
Results: 318 patients took part in this trail. Compared with the control group, patients in the intervention group shown significant differences in the improvement of medication adherence, treatment outcomes, the self-management ability and the service satisfaction. In some aspects, it shown no significant difference and need further exploration. In addition, this paper discussed some communication methods with remarkable advantages by studying the effect of different communication ways applied to different groups of patients in terms of self-management requirements, and this has a practical significance for improving the working efficiency of the pharmacists.
Conclusion: The outpatient pharmacists realize an ideal effect for DTES for patients in this study and conclude a set of practical and cost-effective communication ways through the experimental results for reference by peers.
Background: Older adults in rural areas of Peru encounter many challenges in accessing critical public services sections, such as public health services, education services, and social assistance publ...
Background: Older adults in rural areas of Peru encounter many challenges in accessing critical public services sections, such as public health services, education services, and social assistance public services, due to low levels of digital literacy, lack of technology access, and no formalized and secure identification. This inhibits entry into digital health, education, and social assistance systems and increases their risk of vulnerability and social exclusion. Objective: To design a blockchain technology-based mobile application architecture to help facilitate a secure and inclusive digital ID for older adults in rural areas of Peru to access vital services digitally with a decentralized and privacy-friendly solution. Methods: This study followed the Design Thinking steps. There are five steps in Design Thinking, which include: Empathize, Define, Ideate, Prototype, and Evaluate. A total of thirteen older adults (61 - 85 years of age) were interviewed to determine the usability barriers and trust issues with mobile technology, which will be used to define functional and non-functional requirements. Those requirements were created based on the interviews. The primary features that the target population valued are: blockchain authentication, auxiliary registration, multilingual, and user-friendly. The features were prioritized and prototyped in Figma. The architecture of the application was developed using the C4 model and accounted for sequential development and ensured scalability, modularity and decentralization. Usability was assessed quantitatively by administering the System Usability Scale (SUS) to the same 13 participants after they had interacted with the prototype. Results: The average SUS score was 60.78 (SD = 13.25), this is acceptable usability. The main issues identified were the lack of skills to navigate digital interfaces, poor trust that the data was secure, and challenges with people with disabilities' ability to access the service. Participants provided high ratings for the assisted registration system and notifications. The modular architecture of the system, based on blockchain, showed a great deal of potential to scale and include more people. The prioritization matrix identified that, for adoption, features must contain good design, be multilingual, and require secure authentication. Conclusions: The blockchain-based mobile application model we propose offers a viable technical and socially inclusive model for the secure digital identification of seniors in under-service contexts. Usability tests suggested that the solution was perceived as secure, usable and appropriate for this target population. While not fully deployed, our prototypes and architecture provide a good starting point for future deployment. The findings in this study can contribute to efforts to facilitate digital inclusion, access to services, and respect for people's autonomy in identity management systems, for vulnerable people. Clinical Trial: Not applicable
Background: The postpartum period is a critical phase in a woman’s life following childbirth, marked by significant physical, emotional, and psychological changes. mHealth apps have the potential to...
Background: The postpartum period is a critical phase in a woman’s life following childbirth, marked by significant physical, emotional, and psychological changes. mHealth apps have the potential to offer valuable support during this often-overlooked phase. However, there is limited research evaluating the quality and privacy policy of these apps. Given the need for reliable features and the sensitive nature of user data, it is essential to systematically assess whether these apps are both effective in their purpose and compliant with data protection regulations. Objective: This study aimed to evaluate the quality and privacy policies of mHealth apps for postpartum care available on Google Play and the Apple App Store. Methods: This study utilized a mixed methods approach, combining qualitative and quantitative methods, in three phases: app identification, evaluation, and analysis. In Phase 1, a systematic search was conducted to identify postpartum mHealth apps using defined keywords and selection criteria. In Phase 2, selected apps were evaluated for quality using the Mobile App Rating Scale (MARS) and for privacy policy using the “Fairness of Privacy Policies of Mobile Health Apps” assessment scale, based on the European Union law General Data Protection Regulation (GDPR). Phase 3 involved analysis and interpretation of the results and providing recommendations. Results: In total, 15 mHealth apps met the inclusion criteria and were selected for evaluation. These apps were examined to assess both their quality and privacy policies. However, none of the apps received a maximum score on any of the assessment scales. Overall, MARS scores ranged from 2.1 to 4.0 out of 5. Most apps performed well in functionality (mean = 3.4) and information (mean = 3.3) but scored low in aesthetics (mean = 2.9) and engagement (mean = 2.8). The privacy policy assessment scale categorized nine apps (60%) as "very fair”. While data processing purposes, legal basis, and data subject rights were generally addressed, details on data retention, complaint rights, automated decision-making, and non-EU data transfers were often missing or unclear. Conclusions: The findings suggest that there is room for improvement in the analyzed postpartum mHealth apps freely available on the Google Play and the Apple App Store, particularly in enhancing user engagement, improving app aesthetics, and ensuring privacy policies fully comply with GDPR requirements. Addressing the identified issues is essential to enhance their effectiveness and reliability in supporting the care of new mothers and their infants.
Background: Despite robust empirical support, exposure-based CBT remains one of the least utilized evidence-based practices (EBPs) for anxiety disorders in typical practice settings. Research suggests...
Background: Despite robust empirical support, exposure-based CBT remains one of the least utilized evidence-based practices (EBPs) for anxiety disorders in typical practice settings. Research suggests providers’ negative beliefs about the risk of negative events during exposure delivery are a major predictor of its underutilization. Studies have demonstrated that incorporating experiential learning such as role-playing into conventional didactic training can reduce therapists’ negative beliefs. However, these methods face limitations in terms of accessibility, standardization, and fidelity to real-life experiences. Emerging evidence suggests virtual reality (VR) simulations may be an effective and scalable alternative for improving skills and attitudes pertinent to mental health treatment. Objective: This study examines the initial efficacy of a novel VR simulation-based exposure training program (SET-VRTM) based on (1) perceptions of usability, and (2) degree of change in therapist learning targets (i.e. knowledge, self-efficacy, attitudes). Clinician participants were randomly assigned to a low-immersion desktop version or a high-immersion head-mounted display (HMD) version of the SET-VRTM program to explore the influence of immersion on key outcomes. Methods: Clinician participants (N=41) were recruited from a variety of practice settings. Before randomization, both groups received conventional (4-hour) didactic training for exposure therapy. Next, groups were assigned to immersion modality (desktop or HMD) and began delivering exposures to a virtually simulated patient. Participants practiced titrating exposure intensity (increase, decrease, continue) based on real-time visual and auditory cues from the virtual patient. Participants completed three rounds of exposure delivery to a simulated patient and reviewed their decisions with feedback at the end of each round. Exposure knowledge, exposure self-efficacy, and beliefs about exposures were measured at baseline, post-didactic, and post-VR. Participants also rated the acceptability, usability, and real-world authenticity of VR exposure training. Results: Both groups (desktop, HMD) showed significant improvement in exposure knowledge (p<.01; p<.01), self-efficacy (p<.01; p<.01), and beliefs about exposure (p<.01; p<.01) between baseline and didactic training. There were no significant differences between the low and high immersion groups on any measure at baseline or after didactics. Both groups demonstrated significant improvement in exposure self-efficacy (p<.01; p<.01) and beliefs (P<.001; p=.012) from post-didactic to post-exposure delivery. Neither showed improved knowledge from post-didactic to post-exposure delivery (p>.05; p>.05). Both groups gave highly positive ratings for the acceptability, usability, and authenticity of the simulated training experience. Taken together, results indicate that VR training significantly improved therapists’ self-efficacy and beliefs about exposures beyond gains from didactic training alone. Conclusions: VR exposure therapy training is both well-received and effective in addressing clinician-level barriers to optimal exposure delivery. Supplementing conventional didactic training with experiential learning via VR sessions may be a promising next step in optimizing the standardization, scalability, and effectiveness of exposure training. Clinical Trial: Clinicaltrials.gov Identifier: NCT06706245
Background: Delayed placement of dental implants often leads to bone resorption, particularly in the posterior region, compromising the conditions required for successful implant placement. Osseodensi...
Background: Delayed placement of dental implants often leads to bone resorption, particularly in the posterior region, compromising the conditions required for successful implant placement. Osseodensification (OD), a technique using specialized rotary instruments to compact and densify bone, may improve bone quality by increasing the density and strength. This technique will be proposed to counteract bone resorption and enhance implant stability, osseointegration, and overall clinical outcomes. However, its effectiveness compared to conventional drilling methods has not been fully explored. This study determines whether OD can achieve superior clinical results, ultimately improving the success of dental implant placement. Objective: The objectives of this study are to assess how effectively the OD system works to increase bone volume and density at regions where implants are scheduled to be positioned later. Through clinical and radiographic analyses, the study also strives to evaluate the impact of the OD method on the main stability and osseointegration of dental implants. With an emphasis on bone healing, implant success rates, and the frequency of issues and it further seeks to compare the clinical and radiological results of implants positioned using the OD system. Methods: This will be a nine-month single-arm clinical and radiographic study in 10 systemically healthy patients with edentulous region/s in the anterior or posterior arch. Pre-operative full-mouth ultrasonic scaling, dental hygiene instructions, and pre-operative Cone Beam Computed Tomography (CBCT) imaging will be obtained to measure the bone density at the coronal (mesial and distal), apical regions of the extraction socket in Hounsfield Units (HU). Following local anaesthesia and implant surgery, OD method will be applied to increase the density of the bone during osteotomy site preparation that eventually will help to improve the insertion torque. Simultaneously, improvement in primary stability will be evaluated immediately after implant placement using Ostell device for clinical implant mobility scale (CIMS). Postoperative care will include antibiotics, anti-inflammatory drugs, and follow-up visits. CBCT imaging will be done at baseline, 3 months, and 9months post-surgery to monitor changes in bone density. Results: Increases in HU measures at 3 and 9 months after surgery demonstrated that OD significantly improved bone density at the implant sites. Implant Stability Quotient (ISQ), a measure of primary implant stability, increased right after implantation and kept getting better over the course of the follow-up. There were no significant problems recorded, and clinical results, such as bleeding and plaque indices, were within acceptable bounds. Conclusions: In delayed implant placement, OD improves bone density and implant stability, especially in locations with D2 and D3 bone. The procedure seems to be a viable substitute for traditional drilling techniques because it increases bone-to-implant contact, promotes bone healing, and lowers the risk of implant failure. Clinical Trial: CTRI/2025/03/082513
The COVID-19 pandemic was detrimental to the wellbeing of individuals and families everywhere, with particularly severe consequences for those already susceptible to psychosocial stressors. This proof...
The COVID-19 pandemic was detrimental to the wellbeing of individuals and families everywhere, with particularly severe consequences for those already susceptible to psychosocial stressors. This proof-of-concept study explored the feasibility and acceptability of a virtual respite program, termed Virtual Houseguest, designed for family caregivers of individuals with mild cognitive impairment and early-stage dementia. Conducted between July and December 2021, the study involved nine caregivers who participated in up to six weekly respite visits via Zoom, totaling 122 to 295 minutes. Participants were recruited through Alzheimer’s Association announcements and the Georgia Clinical and Translational Science Alliance. The study's methodology was approved by the University [blinded] Institutional Review Board. The primary themes identified were the acceptability and feasibility of using technology for respite care, along with caregiver and persons’ experiencing dementia experiences. Subthemes included ease of use, appropriateness, facilitators and barriers to technology use, emotional self-care, and activity rapport building. Data analysis involved transcribing Zoom interviews and coding them for themes related to technology use and caregiver experiences. The findings, which indicate that caregivers felt benefited by this intervention, support the potential for future randomized trials with larger samples to further evaluate the Virtual Houseguest program. Limitations such as participant diversity and lack of psychosocial outcome measures were noted. Future studies should address these limitations to enhance the program's effectiveness and inclusivity.
Background: The convergence of AI, Blockchain (BC) technology, and healthcare represents one of the most transformative but technically challenging frontiers in computational medicine. As healthcare s...
Background: The convergence of AI, Blockchain (BC) technology, and healthcare represents one of the most transformative but technically challenging frontiers in computational medicine. As healthcare systems worldwide transition toward data-driven paradigms for precision medicine, clinical decision support, and population health management, the imperative for secure, privacy-preserving, and collaborative learning frameworks has reached critical importance.
This tutorial presents the first comprehensive framework integrating
Federated Learning (FL) and BC} for secure, privacy-preserving healthcare analytics. While FL offers collaborative training across distributed institutions without raw data sharing (aligning with HIPAA/GDPR), it faces vulnerabilities like model poisoning and gradient leakage. We introduce Blockchain-based Federated Learning (BCFL), leveraging BC's immutable ledger and decentralized consensus for enhanced trust, verifiability, and auditability.
Our key contributions include: (1) a systematic taxonomy of diverse medical data types and their FL requirements; (2) three novel integration architectures (fully, semi, loosely coupled) with rigorous analysis of security, scalability, and regulatory compliance; (3) comprehensive security analysis of healthcare-specific vulnerabilities and mitigation via advanced cryptography like zero-knowledge proofs, homomorphic encryption and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and FDA guidelines for AI/Achine-Learning (ML) medical devices. We demonstrate BCFL's effectiveness across critical healthcare applications (e.g., disease prediction, medical imaging, patient monitoring, drug discovery) and identify emerging research frontiers including quantum-resilient cryptography, scalable interoperability, healthcare-specific incentives, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, collaborative AI in healthcare, accelerating privacy-preserving analytics, and ultimately improving patient outcomes. Objective: The objective of the paper is to present the first comprehensive tutorial on integrating Federated Learning (FL) and Blockchain (BC) technologies specifically for secure, privacy-preserving healthcare analytics. The motivation stems from the growing need for collaborative healthcare data analysis that adheres to stringent privacy regulations like HIPAA and GDPR, especially as traditional centralized models pose significant data security risks. The authors aim to address the vulnerabilities of FL, such as model poisoning and gradient leakage, by leveraging BC’s features like decentralization, immutability, and auditability. The tutorial is designed to guide researchers, practitioners, and policymakers in understanding and implementing secure AI systems in the medical domain. Methods: To achieve this goal, the authors develop a multi-faceted framework by first creating a comprehensive taxonomy of medical data types and their specific requirements for FL deployment. They then propose three novel integration architectures—fully coupled, semi-coupled, and loosely coupled—each analyzed for its security, scalability, and compliance with healthcare regulations. The tutorial includes an in-depth security analysis addressing threats unique to healthcare, and explores privacy-enhancing technologies such as zero-knowledge proofs, homomorphic encryption, and differential privacy. It also introduces a regulatory compliance framework aligned with HIPAA, GDPR, and FDA guidelines for AI/ML-based medical devices. Throughout, the methodology integrates technical depth with practical implementation advice. Results: The results of this study are delivered through a set of clearly articulated contributions. The proposed architectures and frameworks are demonstrated to significantly enhance trust, verifiability, and auditability in healthcare FL systems, making them more robust against known threats. The paper effectively showcases how BCFL (Blockchain-based Federated Learning) can be applied to real-world healthcare use cases such as disease prediction, patient monitoring, medical imaging, and drug discovery. Additionally, it outlines emerging research directions, including quantum-resilient cryptography, scalable interoperability, incentive mechanisms for healthcare data sharing, and automated compliance monitoring. These outcomes position the tutorial as a foundational reference for advancing secure and compliant collaborative AI in healthcare. Conclusions: This tutorial presented the first comprehensive framework integrating FL and BC for secure, privacy-preserving healthcare analytics. We demonstrated how FL enables decentralized model training across healthcare institutions while maintaining data locality, and how BC enhances trust, integrity, and auditability through immutable ledgers and decentralized consensus mechanisms.
Our key contributions include: (1) a systematic taxonomy of diverse medical data types and their FL requirements; (2) three novel integration architectures (fully coupled, semi-coupled, and loosely coupled) with rigorous analysis of security, scalability, and regulatory compliance trade-offs; (3) comprehensive security analysis identifying healthcare-specific vulnerabilities and mitigation strategies using advanced cryptographic techniques including zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a practical regulatory compliance framework addressing HIPAA, GDPR, and FDA guidelines for AI}/ML-based medical devices.
We validated BCFL effectiveness across critical healthcare applications including disease prediction, medical imaging analysis, patient monitoring, and drug discovery. Looking ahead, crucial research frontiers involve quantum-resilient cryptography, scalable interoperable infrastructure, healthcare-specific consensus mechanisms, and automated compliance frameworks.
This tutorial serves as a foundational reference for developing trustworthy, interoperable, and patient-centric AI systems that transform healthcare delivery while ensuring privacy protection and regulatory compliance. The successful realization of secure collaborative healthcare analytics through BCFL will drive improved patient outcomes and accelerate medical discoveries in an increasingly connected healthcare ecosystem. Clinical Trial: N/A
Background: Bioacoustics classification plays a crucial role in wildlife monitoring, ecological assessment, and health
diagnostics. However, the presence of environmental noise, signal variability, a...
Background: Bioacoustics classification plays a crucial role in wildlife monitoring, ecological assessment, and health
diagnostics. However, the presence of environmental noise, signal variability, and limited annotated datasets often hinders
model reliability and deployment. Feature extraction and denoising techniques have become critical for improving model
robustness, enabling more accurate interpretation of acoustic events across diverse bioacoustics domains. Objective: This review aims to systematically examine advancements in noise-resilient feature extraction and denoising
techniques used in bioacoustics classification models. Specifically, it explores methodological trends, model types, real-world
deployment, and application areas across ecological and health-related domains. Methods: A systematic review was conducted by searching eight electronic databases, yielding a total of 5,462 records.
Studies were screened for inclusion if they entailed audio-based classification models, applied experimental or computational
methods, and reported empirical performance. A total of 132 studies that fit the eligibility criteria were selected for full review
by two independent reviewers. Risk of bias was assessed using a customized tool, with 87.9% (n = 116) of studies rated as low
risk, 7.6% (n =10) as moderate risk, and 4.5% (n = 6) as high risk. Reporting quality was evaluated using the Transparent
Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results: Out of the 132 included studies, the majority 84.8% (n=112) focused on developing novel classification models, with
deep learning and hybrid approaches being the most dominant. Feature extraction played a critical role, with 96.2% (n=127)
studies clearly demonstrating feature extraction. MFCCs, spectrograms, and filter bank-based representations were the most
common feature representations. Nearly half 47% (n=62) of the studies incorporated noise-resilient methods, such as adaptive
deep models, wavelet transforms, and spectral filtering. However, only 14.4% (n=19) demonstrated real-world deployment
across healthcare, biodiversity monitoring, and environmental surveillance. Conclusions: The integration of advanced deep learning architectures, robust feature engineering techniques and denoising
techniques has significantly improved classification accuracy in bioacoustics. Challenges are however present in real-world
deployment and proper utilization of denoising strategies in various datasets. Future direction in bioacoustics should focus on
deploying noise resilient models into real-world cross domain generalization modules.
Background: Older adults living in rural and underserved areas face significant barriers to healthcare access, often compounded by limited transportation, geographic isolation, and shortages of health...
Background: Older adults living in rural and underserved areas face significant barriers to healthcare access, often compounded by limited transportation, geographic isolation, and shortages of healthcare professionals. Digital and remote health interventions may help address these gaps, yet their implementation and effectiveness in these populations remain underexplored. Objective: To systematically review the current evidence on the use, effectiveness, and challenges of digital and remote health interventions targeting older adults in rural or underserved populations Methods: We conducted a comprehensive literature search in PubMed, Scopus, Web of Science, and Embase for studies published until April 2025. Eligible studies included randomized controlled trials, quasi-experimental studies, and observational designs assessing telehealth, mobile health (mHealth), and other digital interventions among older adults (aged ≥60 years) in rural or underserved settings. Data extraction focused on intervention type, technology used, health outcomes, usability, and implementation barriers. Results: A total of 14 studies met the inclusion criteria. Most were conducted in North America and used telemedicine platforms, mobile applications, or remote monitoring tools. Health outcomes targeted included chronic disease management (e.g., diabetes, hypertension), mental health, and fall prevention. While several studies reported improvements in clinical parameters and user satisfaction, common challenges included digital literacy, limited broadband access, and lack of culturally adapted content. Only a minority of studies incorporated formal usability testing or evaluated long-term adherence. Conclusions: Digital health interventions offer promising strategies to mitigate health disparities among older adults in rural or underserved areas. However, implementation requires addressing technological, cultural, and infrastructural barriers. Future research should prioritize inclusive design, long-term evaluation, and integration into existing health systems. Clinical Trial: CRD420251066174
Background: Self-management plays a vital role in Non-communicable Disease prevention and control. However, it has been challenging for patients and their caregivers to identify how much their lifesty...
Background: Self-management plays a vital role in Non-communicable Disease prevention and control. However, it has been challenging for patients and their caregivers to identify how much their lifestyle affects their health and what level of effort they should make to reduce cardiovascular disease (CVD) risks in everyday life. Therefore, knowing their own CVD risks and daily health-related situations will provide relevant information for self-management by those at risk. The need to help individuals understand their relevant information creates an opportunity to investigate if and how to implement a combined digital and nurse-led self-management intervention in a real-world community setting. Objective: This study evaluates the effectiveness of a combined approach involving digital device support, including a smartwatch, a mobile application, and a salt meter, coupled with nurse-led case management, on self-management behaviors and clinical outcomes. Methods: This study uses a combination of a nurse-led self-management with digital and mobile health innovative approach, including tailored small group face-to-face education sessions, a smartwatch, a smartphone health application, and salt meter, to increase the self-management behaviors to reduce vascular risk through designing and testing an integrated community-based strategy targeted at adults and elderly at risk of cardiovascular in Thailand. The study employs a single-arm pretest-posttest intervention design to assess the effects of the intervention. The intervention will consist of the following components: (1) an interactive face-to-face education session, (2) a real-time knowing your numbers strategy using a smartwatch, a smartphone health application, and a salt meter, (3) a mindfullness-based stress management strategy using SKT Meditation healing exercise: and (4) a self-management diary. Quantitative data will be collected using a smartwatch, smartphone, food diary, and questionnaires at baseline and the post-trial assessment. Results: This study, funded in January 2025, will involve 45 patients. We received ethical approval on May 31, 2024, and began recruitment for participation in May 2023. Researchers will collect, analyze, and synthesize to evaluate the study procedure. We expected to complete data collection by September 2025, with the first results submitted for publication in December 2025. Conclusions: The implementation of a combined Digital Device and Nurse-Led Case Management may identify the use of digital health to support self-management and improve vascular health. The implementation of a combined digital device and nurse-led case management may identify the use of digital health to support self-management and improve vascular health. Clinical Trial: Universal Trial Number (UTN): U1111-1323-8550.
Thai Clinical Trial Registry TCTR20250701003; https://www.thaiclinicaltrials.org/show/TCTR20250701003.
Background: Early rehabilitation in neurocritical care is frequently underutilized due to fragmented workflows, interdisciplinary coordination challenges, and a lack of structured digital decision sup...
Background: Early rehabilitation in neurocritical care is frequently underutilized due to fragmented workflows, interdisciplinary coordination challenges, and a lack of structured digital decision support. Traditional clinical decision support systems (CDSS) often address single domains and do not accommodate the dynamic and multi-professional nature of ICU environments. Objective: This study aimed to design and evaluate the usability of the ERATbi App, a modular, tablet-based CDSS developed to support early rehabilitation planning for patients with moderate-to-severe traumatic brain injury (TBI) in intensive care settings. Methods: The ERATbi App integrates four functional modules—delirium risk management, precision nutrition, stepwise early mobilization, and respiratory care for rib fractures—into a unified interface. A simulation-based usability study was conducted with 18 ICU clinicians. Metrics included System Usability Scale (SUS) scores, task completion rates, error rates, and task durations. Additional feedback was gathered via a 5-point Likert satisfaction scale and open-ended responses. Results: The app demonstrated high usability (mean SUS = 83.6 ± 7.4), 100% task completion, and a low error rate (4.2%). Average module completion time was 6.5 minutes, and participants reported strong satisfaction (mean = 4.7 ± 0.5). Users highlighted the value of the app’s visual logic, real-time alerts, adaptive thresholds, and modular workflow integration for enhancing team coordination and decision consistency. Conclusions: The ERATbi App exhibited strong usability, high user satisfaction, and clinical relevance in simulated ICU workflows. Its logic-driven, workflow-embedded design may support scalable, interdisciplinary implementation of early rehabilitation in neurocritical care environments. Clinical Trial: Not applicable (this study does not meet the WHO definition of a clinical trial)
Background: Background: Large language models (LLMs) like GPT-3.5 and GPT-4 are transforming virtual patient systems in medical education, offering scalable, cost-effective alternatives to standardize...
Background: Background: Large language models (LLMs) like GPT-3.5 and GPT-4 are transforming virtual patient systems in medical education, offering scalable, cost-effective alternatives to standardized patients. However, systematic evaluations of their performance and limitations are limited. Objective: Objective: This review evaluates LLM-based virtual patient systems for medical history-taking, focusing on patient types and disease scope (RQ1), techniques enhancing history-taking (RQ2), experimental designs and metrics (RQ3), and public dataset characteristics (RQ4). Methods: Methods: Following PRISMA guidelines, we analyzed 34 studies (2020–May 2025) from nine databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, SpringerLink, ERIC, arXiv, Springer) using predefined keywords. Results: Results: RQ1: Systems simulate mental health, chronic, neurological, and emergency cases but lack multimorbidity and diverse profiles, limiting applicability. RQ2: Techniques rely on prompt design; few-shot learning and multi-agent frameworks have limited impact. Knowledge graph (KG) integration boosts accuracy by 16.02%, and fine-tuning helps, but further exploration is needed. RQ3: Evaluations use 81.8% Top-1 accuracy, 4.5/5 empathy, 88.1 SUS scores, and 0.9412 robustness but lack standardization and use small samples (10–50 students, 3–5 experts). RQ4: Datasets (e.g., MIMIC-II) are restricted by privacy, hindering comparisons. Conclusions: LLM-based virtual patient systems demonstrate significant potential but face several limitations. Current systems predominantly focus on common diseases, lacking adequate simulation of multimorbidity, cultural diversity, and complex drug interactions, thereby reducing clinical realism. Existing datasets such as MIMIC-III are biased toward single-disease scenarios, English language, and critical care, neglecting broader linguistic and cultural contexts. Methodologically, long prompts suffer from primacy and recency effects, while few-shot learning encounters challenges in maintaining dialogue coherence. To address these issues, incorporating LLM-KG embedding methods into model training can enhance contextual understanding, while combining chain-of-thought reasoning with LoRA improves inference efficiency. Multi-agent frameworks with dialogue compression offer further optimization for real-time interactions. Future research should prioritize the development of open-access, multilingual datasets through ethical data augmentation and international collaboration, supported by regular bias audits to ensure fairness. Establishing unified evaluation frameworks with standardized metrics—such as Top-K accuracy, semantic similarity scores above 0.75, and SUS scores exceeding 80—will be essential for advancing realism, accuracy, and fairness in virtual patient systems. Clinical Trial: -
Background: Loneliness, social isolation, and diminished emotional well-being are increasingly recognized as pressing concerns in later life, often linked to increased risks of depression, cognitive d...
Background: Loneliness, social isolation, and diminished emotional well-being are increasingly recognized as pressing concerns in later life, often linked to increased risks of depression, cognitive decline, and reduced quality of life. Traditional psychosocial interventions often encounter practical barriers, such as limited mobility and geographic dispersion. In response, Virtual Reality (VR) has gained traction as a potentially valuable medium to improve emotional and social well-being in older populations. However, a comprehensive overview of how VR is being deployed in this context is lacking in its formats, objectives, and implementation settings. Objective: This scoping review aimed to systematically map the current empirical landscape of VR-based interventions that aim to improve emotional and social well-being in older adults. Specifically, it sought to identify common modalities, thematic trends, reported outcomes, and contextual factors that shape the design and delivery of these interventions. Methods: Twenty-five peer-reviewed empirical studies published between 2017 and 2025 were selected based on predefined inclusion criteria. Eligible studies included participants 60 years or older, used VR as a central component of the intervention, and reported outcomes related to loneliness, emotional well-being, or social connection. Data were extracted and descriptively synthesized to capture intervention characteristics, delivery formats, and user experiences. Results: Evidence indicates that VR interventions enhance emotional well-being, social connection, and engagement among older adults. Passive experiences, such as 360° videos, often elicit short-term relaxation and enjoyment, while more participatory or symbolic formats, such as co-creative environments or reminiscence-based scenarios, support deeper psychological benefits, including self-expression, identity reinforcement, and emotional connection. Socially interactive VR, particularly through avatar-mediated communication, shows strong potential to reduce loneliness and foster authentic interpersonal engagement. Effectiveness is closely tied to usability, accessibility, and cultural relevance. Although qualitative approaches offer insight into user experience and emotional mechanisms, quantitative research provides measurable outcomes; both contribute complementary perspectives. Assessments suggest that studies with greater methodological rigor tend to report a higher perceived impact, although the variability of the outcomes and the complexity of the interpretation remain. Creative and hybrid VR formats appear especially promising for balancing emotional depth with accessibility. In general, the findings highlight the importance of inclusive, user-centered design and context-sensitive implementation to maximize the psychosocial benefits of VR in later life. Conclusions: VR interventions can support emotional and social well-being in older adults, particularly when they involve multi-user environments, culturally meaningful content, co-design, and trained facilitators. Passive formats offer short-term mood benefits but have limited lasting impact. Future research should emphasize inclusive design, long-term engagement assessment, and integration into existing care models to ensure sustainable and meaningful implementation.
Background: Artificial intelligence (AI) is transforming patient care but also raises ethical questions such as bias and transparency. While a range of well-established frameworks exist to guide respo...
Background: Artificial intelligence (AI) is transforming patient care but also raises ethical questions such as bias and transparency. While a range of well-established frameworks exist to guide responsible AI practice, most were designed for academic or regulatory settings and can be hard to operationalize within fast-moving, resource-limited small and medium-sized enterprises (SMEs). Objective: We introduce the Scalable Agile Framework for Execution in AI (SAFE-AI). SAFE-AI embeds ethical safeguards such as fairness, transparency, and continuous monitoring within standard Agile product-development cycles, while remaining practical for organizations without dedicated ethics teams. Methods: We followed a design-science, practice-oriented approach over 20 weeks. After a needs-finding workshop, a cross-functional team from an SME, ethics researchers, and academic partners met weekly in Agile sprints, continuously reviewing relevant literature and regulations. Through three prototype-feedback cycles the group iteratively refined a four-phase SAFE-AI lifecycle, acceptance/fairness/transparency checklists, and scenario-based responsibility metrics, recording decisions until unanimous consensus. Results: The co-design process produced a four-phase SAFE-AI life-cycle: Discovery, Assessment, Development, Monitoring. SAFE-AI’s phase-specific checklists melds acceptance, fairness, and transparency metrics into each Agile sprint. A novel scenario-based probability-analogy mapping (SPAMM) method was added to translate model risk and uncertainty into plain-language narratives for non-technical stakeholders, forming the framework’s core “responsibility metrics” layer. To keep oversight lightweight, SAFE-AI defines clear triggers that automatically reopen ethical review whenever models are retrained, tuned, or fed new data, ensuring consistent re-evaluation without duplicating earlier work. Conclusions: SAFE-AI shows that meaningful ethical safeguards can be embedded within standard Agile workflows without slowing delivery or requiring a full-time ethics team. Its checklist-driven phases and automatic review triggers provide a lightweight yet defensible way to track fairness, transparency, and responsibility throughout the model lifecycle.
Background: Digital parenting programs offer a promising way to disseminate evidence-based parenting knowledge and support early childhood development, particularly in low- and middle-income countries...
Background: Digital parenting programs offer a promising way to disseminate evidence-based parenting knowledge and support early childhood development, particularly in low- and middle-income countries with limited resources. They help reduce costs while improving scalability and fidelity. However, their successful implementation is context-dependent, and existing research offers limited guidance on how implementation of digital parenting interventions unfolds across diverse settings. Objective: Guided by the RE-AIM framework, this study examines the reach, adoption, implementation, and maintenance of a digital (chatbot-led) parenting program, in both urban educational and rural healthcare settings in China. It aims to identify the common and unique facilitators and barriers affecting each aspect, as well as differentiated mechanisms for the effective implementation and institutionalization of digital parenting support, across these settings. Methods: A multiple-case study approach compared the implementation in the two settings, with consistent digital intervention content but different contexts and formats of local human-led support. Data were collected through program documents, field observations, semi-structured interviews, and focus group discussions with 83 stakeholders. Thematic analysis was conducted using ATLAS.ti, guided by the RE-AIM framework. Results: Regarding reach, strong relationships between parents and implementers and the credibility of program developers were common facilitators. However, parenting conservatism and limited understanding of the program were barriers. In rural healthcare settings, parents’ perception of village doctors as lacking parenting expertise posed an additional challenge. For adoption, trust between managers and program developers, program alignment with organizational functions, and organizational empowerment supported implementation. At the individual level, task-driven motivation helped, while time constraints hindered adoption. Teachers adopted the program due to its relevance to their roles, unlike village doctors who did not see it as part of their core duties. For implementation, supportive management and clear guidelines were facilitators, while lack of purpose and psychological pressure were barriers. Timing the program during off-seasons and providing standardized workflows helped rural delivery, whereas flexible workflows were essential in the urban educational setting. Regarding maintenance, alignment with organizational functions and internal resources facilitated sustainability, while heavy reliance on government authorization was a challenge. Urban education settings required contextual adaptation, while rural healthcare settings needed more content adaptation. Conclusions: Implementing digital parenting programs is a complex process, influenced by multilevel facilitators and barriers that vary across regions (rural vs. urban) and settings (educational vs. healthcare). This study highlights the importance of context-specific implementation strategies and proposes differentiated delivery models tailored to local structures and needs. Clinical Trial: The research protocols were prospectively registered on the Chinese Clinical Trial Registry (ChiCTR2400081911 and ChiCTR2400092609).
Background: Video games are becoming increasingly accessible and occupy a position of prominence among students' leisure activities. Recent studies have demonstrated that engagement with video games c...
Background: Video games are becoming increasingly accessible and occupy a position of prominence among students' leisure activities. Recent studies have demonstrated that engagement with video games can facilitate the development of specific abilities in users, including visual-spatial representation and coordination. Objective: The objective of this study was to ascertain whether dental students exhibiting characteristics associated with the gamer profile exhibited divergent fundamental technical skills in comparison to students with characteristics associated with the non-gamer profile. This preliminary study aims to validate or amend our measurement tools for a subsequent prospective study. Methods: A total of 92 second-year dental students, who were novices in the field of videogame practice, were divided into two groups: one designated "non-players" and the other "players". The visual motor and cognitive coordination of the students was assessed using three different tests. The initial assessment focused on evaluating spatial ability, while the subsequent assessments addressed arm-hand coordination and the velocity of execution. The study data were collected in September 2021. Results: The findings of the study revealed that there was no statistically significant discrepancy between the two groups, i.e. "players" and "non-players", when the three distinct tests were administered. Conclusions: The present study did not demonstrate a significant discrepancy between the profiles of dental students who participated in the study and those who did not, with regard to their fundamental technical abilities in a preclinical training environment. Nevertheless, it facilitated the validation of a methodology for a future longitudinal study that would concentrate on the evolution of acquiring technical skills during pre-clinical training in these two populations. Consequently, it is imperative to observe the impact of video games on the acquisition of surgical skills, including in dentistry, and further investigations are required to conclude this matter.
Background: Digital Health Technologies (DHTs) used in the English NHS must demonstrate clinical safety assurance under two national standards: DCB0129 (for manufacturers) and DCB0160 (for deploying o...
Background: Digital Health Technologies (DHTs) used in the English NHS must demonstrate clinical safety assurance under two national standards: DCB0129 (for manufacturers) and DCB0160 (for deploying organisations). NHS bodies have a statutory duty to ensure all DHTs in use meet these requirements. However, compliance is neither routinely monitored nor enforced. This study assessed the assurance status of deployed DHTs and organisational compliance with these standards across the English NHS. Objective: Explore and quantify compliance with clinical risk management standards DCB0129 and DCB0160 in the NHS in England. Methods: In February–March 2025, 239 NHS organisations in England were issued a Freedom of Information (FOI) request regarding compliance of their deployed DHTs with DCB0129 and DCB0160. Results: Of the organisations contacted, 204 (85·4%) responded, with 179 (87·8%) providing either full or partial data. The mean number of deployed DHTs per organisation was 82.8 (median 27·5, IQR 81·5), with substantial variation between secondary care providers, Integrated Care Boards (ICBs), and ambulance trusts.
Compliance was low: on average, only 34·7% (median 25·6%) of DHTs that an organisation deployed were fully assured against both standards. Thirteen organisations reported full compliance, while sixteen reported that none of their deployed DHTs were assured.
14,747 DHT deployments were reported across responding organisations. Of these, 17·4% were fully assured, 13·3% partially assured (compliant with either DCB0129 or DCB0160), and 70·1% had no documented assurance. Conclusions: More than 10,000 DHTs currently in use in the NHS lack documented compliance with clinical safety standards. For a typical NHS patient attending hospital, three in four of the digital tools influencing their care do not demonstrate the minimum legal or clinical safety requirements. These findings raise significant concerns about the safety of digital technologies in the NHS and the potential risk of patient harm arising from inadequate assurance.
Background: The aging population and increasing burden of chronic diseases underscore the need for effective community-based health management strategies. This study aimed to evaluate the clinical eff...
Background: The aging population and increasing burden of chronic diseases underscore the need for effective community-based health management strategies. This study aimed to evaluate the clinical effectiveness of digital health interventions on health outcomes among community-dwelling middle-aged and elderly adults in Taiwan. Objective: This study aimed to evaluate the effectiveness of a digital health intervention using smart devices and physician feedback on health outcomes among community-dwelling middle-aged and elderly adults in Taiwan. Methods: Four communities in Taoyuan City, Taiwan, were voluntarily recruited. Eligible participants were adults aged ≥50 years who had resided in the community for at least six months. Of the 308 individuals assessed for eligibility, 199 provided informed consent and completed baseline evaluations. During the 6-month intervention period, participants used smart devices to collect monthly physiological data, which were automatically synchronized with a centralized health management platform. Physicians reviewed the data monthly and provided personalized consultations and health education. Primary outcomes—assessed at baseline, 3 months, and 6 months—included anthropometric measures, biochemical indices, electrocardiograms, and health-related questionnaires. Results: After 6 months, significant improvements were observed in the proportion of participants with abnormal indicators: blood pressure (53.67% to 34.46%; P<.001), blood glucose (55.93% to 37.85%), uric acid (22.60% to 7.34%; P<.001), total cholesterol (1.69% to 0.00%), BMI (44.07% to 40.11%; P<.001), body fat percentage (62.15% to 55.37%; P<.001), visceral fat rating (42.94% to 39.55%; P<.001), skeletal muscle mass index (4.52% to 2.82%; P<.001), poor self-rated health (11.30% to 5.65%; P<.001), and poor sleep quality (50.28% to 40.11%; P<.001). Conclusions: The use of cloud-integrated smart devices for community health promotion significantly improved both physical and mental health outcomes in middle-aged and elderly residents.
Background: Axial spondyloarthritis (axSpA), a chronic autoinflammatory disease characterized by heterogeneous clinical manifestations, presents significant challenges in long-term patient self-manage...
Background: Axial spondyloarthritis (axSpA), a chronic autoinflammatory disease characterized by heterogeneous clinical manifestations, presents significant challenges in long-term patient self-management. Despite growing applications of large language models (LLMs) in healthcare, their efficacy in providing axSpA-specific health advice remains unassessed. Objective: To construct a patient-oriented needs assessment tool and conduct a systematic evaluation of LLM-generated health advice quality for axSpA patients. Methods: A three-round Delphi consensus process was employed to develop the questionnaire, which were subsequently distributed to 84 axSpA patients and 26 rheumatologists. Patient-identified concerns were processed through five LLM platforms (ChatGPT-4, DeepSeek R1, Hunyuan T1, Kimi k1.5, Wenxin X1). Responses were assessed using guideline-based accuracy scoring and AlphaReadabilityChinese analysis tools. Results: The validated questionnaire revealed age-related differences in priorities: younger patients expressed significantly greater concern than those over 40 regarding AS symptom management and medication side effects. Divergent priorities between clinicians and patients were observed regarding diagnostic mimics and drug mechanisms. LLM performance varied by domain—accuracy peaked in Diagnosis/Examination (avg. 20.4/25) but dipped in Treatment/Medication (19.3). ChatGPT-4 and Kimi demonstrated superior performance in readability, safety remained high overall (disclaimer rates: ChatGPT-4/DeepSeek-R1 100%, Kimi 88%). Conclusions: The observed age-stratified needs and clinician-patient communication gaps highlight the necessity for tailored patient education programs. LLMs demonstrated robust performance across evaluation metrics, particularly ChatGPT-4 which achieved 94% overall compliance with clinical guidelines. These AI tools show potential as scalable adjuncts for ongoing axSpA patient support, though human oversight remains crucial for complex clinical decisions.
Background: Game-based learning has emerged as an effective strategy for enhancing knowledge and engagement in healthcare education. However, they have not been specifically designed to support cognit...
Background: Game-based learning has emerged as an effective strategy for enhancing knowledge and engagement in healthcare education. However, they have not been specifically designed to support cognitive improvements for diverse learning styles in oral microbiology and immunology. Objective: This study aimed to develop and evaluate an educational card game designed to support diverse learning styles in oral microbiology and immunology, using a duel-style format. Methods: A mixed-methods study was conducted with 40 third-year dental students, where half of them were assigned to the first group starting as the host, while those in the other groups began as the microbe. Participants alternated between the microbe and host roles during gameplay. Active engagement through playing as the microbe facilitated knowledge acquisition and recall. On the other hand, the host role aimed to promoted decision-making and the application of knowledge. Quantitative data were collected using pre- and post-knowledge assessments and satisfaction questionnaires. Qualitative insights were obtained through semi-structured interviews exploring learning experiences when playing as the microbe compared to the host. Results: Students demonstrated significant improvements in knowledge scores across the three assessments (P<.01), with no difference between groups (P>.05). They also perceived the game positively in all three aspects (usefulness, ease of use, and enjoyment). Qualitative findings revealed that role variation supported both inductive and deductive learning processes. Participants valued the combination of pedagogical and entertaining components, leading to the game motivation and engagement. A conceptual framework demonstrated key emerging themes relevant to the game design and implementation, including learner profile, learning setting, game design, learning process, and learning outcomes. Conclusions: The card game effectively enhanced knowledge acquisition, strategic thinking, and student engagement in oral microbiology and immunology. Role-switching between the host and microbe facilitated multiple learning pathways, meeting diverse learner styles. Integrating such educational card games in dental education may bridge theoretical understanding and clinical reasoning. Further research is recommended to investigate long-term retention and broader practicality.
Background: The growing global burden of mental health disorders has intensified the search for scalable, accessible, and cost-effective interventions. Conversational agents in the form of digital hum...
Background: The growing global burden of mental health disorders has intensified the search for scalable, accessible, and cost-effective interventions. Conversational agents in the form of digital humans have emerged as promising tools to deliver mental health support across diverse populations and settings. Objective: The scoping review seeks to provide a comprehensive analysis of digital humans' roles in depression management, identifying their specific applications in both diagnostic processes and therapeutic interventions. Additionally, this study aims to evaluate the design choices implemented in digital human systems, including their appearance, interaction modalities, back-end intelligence systems, and the various roles they assume. Methods: Following the PRISMA-ScR guidelines, we systematically searched peer-reviewed literature across major databases including ACM Digital Library, IEEE Xplore, Web of Science, and PubMed to capture both psychological and technological perspectives. The search query used was to include a wide variety of synonyms for digital humans and depression: ("avatar" OR "virtual agent" OR "embodied conversational agent" OR "relational agent" OR "digital human" OR "virtual human" OR "virtual character") AND ("Major Depressive Disorder" OR "Depression"). Studies were included if they described the development, implementation, or evaluation of digital humans designed to support mental health outcomes. Data were charted on agent design, therapeutic approach, target population, delivery context, and reported effectiveness. Results: Twenty studies (2010-2024) were included. Depression assessment studies comprised 35% (n=7), interventions 55% (n=11), and combined approaches 10% (n=2). Assessment protocols included questionnaires (PHQ-9, CES-D-VAS-VS), semi-structured interviews based on DSM-5 criteria, and interactive tasks designed to elicit emotional responses. Intervention approaches employed Cognitive Behavioral Therapy, psychoeducation, Compassion-Focused Therapy, and Avatar Therapy. Digital humans assumed five distinct roles: interviewer (n=6), facilitator (n=3), counselor (n=3), educator (n=3), and actor (n=5). Interviewers primarily appeared in assessment studies, presenting structured questions. Counselors engaged in therapeutic dialogues, while educators delivered psychoeducational content. Facilitators assisted participants in achieving system goals. Actors portrayed specific emotions or dysfunctional beliefs to facilitate therapeutic processes. Studies highlighted digital humans' utility in enhancing diagnostic processes and therapeutic interventions, noting potential for transformation through physiological data integration. Conclusions: This scoping review demonstrates that digital humans represent a transformative advancement in depression management, offering innovative applications across both assessment and intervention phases. The evidence reveals digital humans' effectiveness in replicating traditional therapeutic roles while providing unique advantages including 24/7 accessibility, reduced stigma, consistent care delivery, and personalized support. Digital humans successfully function across multiple roles with demonstrated capability to establish therapeutic alliances and elicit meaningful engagement comparable to human providers. Findings underscore the need for continued research to fully realize digital humans' potential in addressing depression-specific needs, advocating for expansion into diverse therapeutic scenarios and exploration of unexplored digital human applications.
Background: Advanced cancer patients often experience poorly managed pain. Objective: We evaluated the feasibility and acceptability of MyPainPal, a mobile health (mHealth) application that combines d...
Background: Advanced cancer patients often experience poorly managed pain. Objective: We evaluated the feasibility and acceptability of MyPainPal, a mobile health (mHealth) application that combines daily symptom and opioid-use surveys, algorithmic self-management support, psychoeducation, and clinician monitoring. Methods: This single-arm pilot study enrolled adults with advanced malignancies using opioids for moderate-to-severe pain from a major cancer center’s palliative care clinic. Participants used the app for 28 days; nurses monitored symptoms on a secure portal. Patients assessed usability and acceptability assessments. Semi-structured patient debriefing interviews explored app utilization, perceived impact, and optimization strategies. Results: Twenty participants (mean age 57[SD=12.3], 55% female, 80% White with mixed cancer types enrolled, used MyPainPal a median of 14 times (IQR:8,17), and completed 8 symptom surveys (IQR:5,14) reflecting 36% (SD:20%) of eligible (out-of-hospital) days on study. Participants found the app acceptable; mean system usability scale=78.3/100 (SD=16.2); 79% rated their overall satisfaction as ≥4/5. Twenty percent of surveys generated an alert, prompting nurse outreach. In response, five participants had symptom medications changed and two had medication errors corrected. Participants described reduced barriers to pain reporting and facilitated constructive interactions with care teams, and several noted it validated their pain experience, reduced opioid stigma, and promoted self-management. Patients recommended featuring educational resources more prominently, modifying symptom surveys, and introducing MyPainPal earlier in their pain trajectory. Conclusions: MyPainPal demonstrated feasibility, acceptability, and preliminary evidence of clinical impact. In response, the app is being adapted for a future efficacy study. Clinical Trial: NCT03717402
This study aims to explore how artificial intelligence-generated content (AIGC) impacts the critical thinking skills of medical students through a systematic review. It also aims to develop a framewor...
This study aims to explore how artificial intelligence-generated content (AIGC) impacts the critical thinking skills of medical students through a systematic review. It also aims to develop a framework for coping strategies. The study focuses on AIGC's use in clinical diagnosis, evidence-based medicine, ethical decision-making, and scientific research, while examining challenges and ways to enhance critical thinking.
The study followed the PRISMA 2020 guidelines, searching English literature from November 2022 to June 2025 in PubMed using keywords like "AIGC," "medical students," and "critical thinking." Two reviewers evaluated and analyzed relevant studies qualitatively.
Additionally, the research predominantly emphasizes short-term effects and lacks follow-up evaluations regarding the long-term impacts of AIGC. AIGC in medical education has both benefits and drawbacks. It provides rich learning resources and tools, speeding up knowledge acquisition. However, overreliance on AIGC may reduce critical thinking skills. Strategies like tailored AI tools, virtual patients, and evaluating AI limitations can help maintain and improve critical thinking. no
Background: Induction training for junior doctors in otolaryngology (ENT) must address a wide range of prior experience. Artificial intelligence (AI) avatars offer a novel approach to deliver educatio...
Background: Induction training for junior doctors in otolaryngology (ENT) must address a wide range of prior experience. Artificial intelligence (AI) avatars offer a novel approach to deliver educational content. This study evaluated whether an AI avatar-delivered ENT induction course could improve trainee confidence in key ENT clinical skills. Objective: To evaluate the feasibility, acceptability, and educational impact of an AI avatar-delivered induction course on junior doctors’ self-reported confidence in key ENT clinical skills. Methods: A modular online ENT induction course was developed using AI-generated avatar instructors (video-based, non-interactive) via the HeyGen platform. The course content covered otoscopic examination, endoscopic anatomy and pathology of the upper aerodigestive tract, management of ENT emergencies, triaging referrals, and acute airway management. Thirty junior doctors (Foundation Year 2, general practice trainees, core surgical trainees, and clinical fellows) at a tertiary hospital ENT department completed the course. Participants rated their confidence in seven ENT skills before and after the course on a 10-point Likert scale (1 = not confident, 10 = extremely confident). A post-course survey collected feedback on the AI tutors’ understandability, willingness to use AI-based learning in the future, and comparisons of the learning experience and content retention versus traditional methods. Paired t-tests were used to analyze changes in confidence. No objective skill assessment was performed. Results: All 30 participants completed both pre- and post-course assessments. Mean self-confidence scores improved significantly in all seven ENT skill domains after the course (mean increases ranging from +2.5 to +4.3 points on the 10-point scale; p<0.001 for each). The largest gains were in identifying normal endoscopic anatomy and in triaging ENT referrals. The AI avatar tutors were generally well understood (mean clarity rating 7.8/10). A majority of trainees (57%, 17/30) expressed willingness to take further AI-delivered courses, with 30% unsure and 13% unwilling. However, most participants (66.7%) reported no difference in their overall learning experience compared to traditional instructor-led videos, and 20% felt the AI format was inferior to traditional methods (only 13.3% reported an enhanced learning experience). Similarly, 70% perceived no impact of the AI tutors on their ability to retain material (13.3% reported enhanced retention, 16.7% reported worse retention). Conclusions: An AI avatar-delivered induction course substantially increased junior doctors’ self-reported confidence across a range of essential ENT skills. The intervention was generally well received and accepted by trainees. Nevertheless, despite objective confidence gains, most participants did not perceive the AI avatars to improve their learning experience relative to conventional teaching, highlighting an important gap between confidence and perceived educational value. AI avatar tutors show promise as scalable tools in surgical education to supplement training, but further refinement—such as increasing interactivity—and evaluation (including objective performance measures) are warranted to optimize their effectiveness. Clinical Trial: n/a
Artificial intelligence (AI) is increasingly being explored for its potential to assist clinical decision-making, particularly in triage. Large language models (LLMs) like GPT-4 have demonstrated the...
Artificial intelligence (AI) is increasingly being explored for its potential to assist clinical decision-making, particularly in triage. Large language models (LLMs) like GPT-4 have demonstrated the ability to synthesize medical knowledge. However, comparative assessments against experienced clinicians in structured scenarios remain limited.
This study aimed to compare the triage accuracy of GPT-4 with that of experienced emergency physicians using standardized clinical vignettes.
Two hundred anonymized clinical vignettes were sourced from open-access databases, representing a variety of emergency care presentations. GPT-4 was prompted with each vignette and asked to assign one of four triage levels: emergent, urgent, routine, or self-care. Separately, three board-certified emergency physicians independently rated the same cases. A consensus panel established a gold-standard triage level for each case. The primary outcome was agreement with the gold standard, assessed using Cohen’s kappa (κ). Sensitivity and specificity were calculated for identifying high-acuity cases (emergent or urgent).
GPT-4 achieved a κ of 0.85 with the gold standard, comparable to the clinicians’ κ of 0.83 (P = .42). GPT-4’s sensitivity and specificity for high-acuity cases were 0.92 and 0.88, respectively. For clinicians, sensitivity and specificity were 0.90 and 0.89, respectively. GPT-4 showed a slight over-triage tendency (6% vs 4% in clinicians), while under-triage rates were similar (4% vs 5%).
In this simulation-based evaluation, GPT-4 demonstrated triage accuracy equivalent to that of emergency physicians. These findings support the potential utility of LLMs in augmenting digital triage workflows. However, their performance in real-time, complex clinical environments remains uncertain and warrants further study.
Background: To clarify the pathogenesis of changes in protein metabolism in the setting of liver ischemia and to study the role of the antioxidant defense system. Objective: To clarify the pathogenesi...
Background: To clarify the pathogenesis of changes in protein metabolism in the setting of liver ischemia and to study the role of the antioxidant defense system. Objective: To clarify the pathogenesis of changes in protein metabolism in the setting of liver ischemia and to study the role of the antioxidant defense system. Methods: It is important to clarify the role of the antioxidant defense system in the pathogenesis of changes in blood protein metabolism depending on the duration of liver ischemia in ag rats, a model of chronic toxicosis.The studies were conducted at the Scientific Research Center of Azerbaijan Medical University using 85 white rats divided into 4 groups. The concentration of total protein, albumin, α1, α2, β, γ globulins, and LDH activity were determined in the blood samples collected from the white rats using reagent kits manufactured by Roche on a Bioscreen MS-2000 microanalyzer. Results: Chronic intoxication was created using 20% hydrochloric acid (HCl). To create an ischemia model against its background, the abdominal cavity was opened under anesthesia, and the artery leading to the liver was ligated. Blood was taken from the experimental animals on the 3rd, 7th, 15th, and 30th days, 5 heads each. In order to strengthen the antioxidant defense system, 0.2 ml of riditox solution was injected into the tail vein of the 4th-5th groups of animals once a day for 7 days. The obtained quantitative indicators were statistically processed using a non-parametric method, taking into account modern recommendations Conclusions: The results of our experiments showed that, following the induction of the ischemia model and treatment, several changes occurred in blood protein metabolism. LDH activity increased, while the concentrations of total protein and albumin decreased. The levels of α1- and α2-globulins varied—increasing in some cases and decreasing in others—when compared to both the intact condition and chronic intoxication.
Background: Acute exacerbations of Chronic Obstructive Pulmonary Disease (COPD) are a major clinical challenge, often leading to frequent emergency room visits and significantly reducing patients' qua...
Background: Acute exacerbations of Chronic Obstructive Pulmonary Disease (COPD) are a major clinical challenge, often leading to frequent emergency room visits and significantly reducing patients' quality of life. Early detection through wearable devices could facilitate timely interventions at the community level, reducing hospital admissions, and disease-related morbidity and mortality. Objective: This study seeks to retrospectively assess the feasibility, sensitivity, and reliability of the Frontier X2 wearable device to monitor clinically relevant physiological signals in volunteers with COPD who experience acute exacerbations. Methods: This is a single-center, retrospective, observational feasibility study, monitoring 30 COPD volunteers (mMRC Grades 1- 4) who had been previously hospitalized due to acute exacerbations in the past 12 months. The study will last up to 18 months, focusing on physiological changes occurring within 168 hours prior to any COPD exacerbation. Qualitative data will be gathered through self-administered questionnaires every two weeks to correlate subjective symptoms with device-captured physiological metrics, as well as a separate questionnaire on device adherence and usability. Results: Recruitment for this study started in June 2024 and it is anticipated that the data will be collected within 18 months of study initiation with data analysis completed by December 2025. Final results will be published in January 2026. Conclusions: The BREATH-TRACHER 2 study will validate the use of the Frontier X2 device in subjects with COPD in home settings. The Frontier X2 device, if successful, has the potential to transform COPD management and support proactive care leading to enhanced clinical outcomes and reduced disease mortality and morbidity. Clinical Trial: ClinicalTrials.gov NCT06419062; https://clinicaltrials.gov/study/NCT06419062
Background: Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide and often involve a complex interplay of physiological, psychological, and behavioral factors [1]. While tradit...
Background: Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide and often involve a complex interplay of physiological, psychological, and behavioral factors [1]. While traditional risk factors such as genetics, lifestyle, and medical history are well-established, increasing attention has been given to the role of psychological states and personality traits in the development and progression of CVDs. Type D personality, characterized by high levels of negative affectivity and social inhibition, has been associated with poor mental health outcomes, including depression, anxiety, and chronic stress—factors that independently and collectively contribute to adverse cardiac events [2,3].
Research has shown that depression doubles the risk of myocardial infarction and significantly increases overall cardiac morbidity and mortality. Anxiety, post-traumatic stress, and chronic stress are also linked to the onset and exacerbation of heart disease. These findings are supported by studies such as those by Chauvet-Gelinier and Bonin (2017) [4], who emphasized the importance of psycho-biological mechanisms in understanding the brain-heart connection. Their review also highlighted the critical window that cardiac rehabilitation offers for identifying and addressing emotional distress in patients.
Furthermore, individuals with Type D personalities tend to engage in fewer health-promoting behaviors, are less likely to seek medical help, and often experience reduced quality of life [5]. Studies by Denollet and colleagues suggest that these traits not only increase vulnerability to CVDs but also negatively influence disease management and prognosis [6,7]. Mols and Denollet (2010) [8] additionally note that early life experiences and social environments contribute to the development of this personality pattern, which may persist into adulthood and increase the risk of various chronic conditions, including cardiovascular disease. Other research demonstrates that stress and negative emotional states contribute to physiological changes—such as inflammation, autonomic dysfunction, and hormonal imbalances—that exacerbate cardiovascular risk [9].
The INTERHEART study ranked psychosocial stress, including depression, as one of the top three risk factors for coronary artery disease [10]. These insights underline the need for a holistic approach to cardiac care that incorporates psychological assessment and intervention.
Despite the growing body of literature linking psychological factors to cardiovascular health, gaps remain in fully understanding the specific role of Type D personality and its interaction with stress, anxiety, and depression in cardiac populations. While many studies have explored these factors separately, fewer have examined them concurrently in the context of actual cardiac events or disease progression. Moreover, the underlying mechanisms—behavioral and biological—through which these psychological characteristics affect cardiovascular outcomes are still being uncovered.
This study aims to address these gaps by providing a more integrated view of the psychological profile of cardiac patients, with an emphasis on Type D personality. The research holds significant clinical relevance, as early identification of at-risk individuals could lead to the implementation of targeted interventions, such as psychological screening, stress management programs, and personalized cardiac rehabilitation strategies. By improving our understanding of how personality and emotional distress contribute to heart disease, this study may help optimize prevention efforts and improve both short- and long-term outcomes in cardiac care.
The present study seeks to examine the relationship between Type D personality traits and the presence of psychological distress—including symptoms of depression, anxiety, and stress—among individuals diagnosed with cardiovascular disease. Specifically, the research aims to determine whether individuals with Type D personality report higher levels of emotional distress compared to those without this personality pattern.
In light of previous research findings and theoretical frameworks linking personality and emotional regulation to cardiovascular health, the following hypotheses are proposed:
1. Individuals with Type D personality will report significantly higher levels of depression, anxiety, and stress than those without Type D personality traits.
2. Type D personality will be a significant predictor of psychological distress, even after controlling for demographic and medical variables.
By addressing these objectives, the study aims to contribute to a more nuanced understanding of the psychological profile of cardiac patients and to inform early screening and intervention efforts within cardiovascular care settings. Objective: The study aims to contribute to a more nuanced understanding of the psychological profile of cardiac patients and to inform early screening and intervention efforts within cardiovascular care settings. Methods: Study Design
This study employed a cross-sectional quantitative research design aimed at examining associations between Type D personality traits, depressive symptoms, and cardiovascular history among adults aged 30 to 85 years. Convenience and snowball sampling methods were used to recruit participants via online social media platforms.
Sample and Population
The study sample consisted of 146 participants, including 49 men (33.6%) and 97 women (66.4%), with ages ranging from 30 to 85 years (M = 52.4, SD = 12.3). Participants’ ages ranged from 30 to 85 years, with a mean age of 52.4 years (SD = 12.3).
Of these, 40 participants (27.4%) reported a history of cardiovascular disease (CVD) or related cardiac events, while 106 participants (72.6%) reported no such history. Participants represented a broad demographic spectrum in terms of religion, education, and socio-economic background.
Research Variables
Dependent Variables
• Type D Personality: Measured by the DS14 questionnaire [7], which assesses two core dimensions: Negative Affectivity (NA) and Social Inhibition (SI). The questionnaire consists of 14 items, each rated on a 5-point Likert scale (1 = "not at all" to 5 = "very much"). A participant is classified as Type D if scoring 10 or higher on both NA and SI subscales, following Denollet’s established cutoff criteria.
• Depression, Anxiety, and Stress: Assessed by the Depression Anxiety Stress Scales (DASS-21) [11], a validated 21-item self-report instrument. The scale includes three subscales (Depression, Anxiety, Stress), each with seven items rated from 0 ("did not apply to me") to 3 ("applied to me very much or most of the time"). A composite distress score was calculated by averaging all items.
Independent Variable
• Cardiovascular History: Determined by self-report to a direct question regarding diagnosed cardiovascular disease or cardiac events, coded dichotomously as "Yes" or "No."
Research Instruments
Demographic and Medical Background Questionnaire: Developed for this study to capture key demographic characteristics (age, gender, marital status, number of children, education, religion, self-identification) and cardiovascular history.
DS14 Type D Personality Scale [7]: Demonstrates high internal consistency (Cronbach’s alpha > 0.80) for both NA and SI subscales, widely validated across populations with and without cardiac conditions.
DASS-21 [11]: Has demonstrated excellent psychometric properties, with high reliability for each subscale (Cronbach’s alpha typically > 0.85) and validated for assessing emotional distress in both clinical and non-clinical populations.
Data Collection Procedure
Ethical approval was obtained from the Ruppin Academic Center Ethics Committee (Approval Code: 251-L/22). The questionnaire was implemented using Google Forms and distributed online via social media channels (Facebook, WhatsApp). Recruitment continued until April 2022. Informed consent was obtained electronically from all participants before they accessed the survey. Participation was voluntary, anonymous, and confidential. Instructions and contact information for support were provided.
Statistical Analysis
Data analyses were conducted using SPSS version 28. Initial data screening included checks for missing data, outliers, and assumptions of normality. Descriptive statistics summarized demographic variables, cardiovascular history, and psychological measures.
Group Comparisons: Independent-samples t-tests and chi-square tests examined differences between participants with and without cardiovascular history, and between those classified as Type D versus non-Type D, on continuous and categorical variables respectively.
Correlation Analysis: Pearson’s correlation coefficients assessed relationships between continuous measures of Type D subscales (NA, SI) and depression scores.
Classification of Type D Personality: Followed the standard criterion of scoring 10 or higher on both DS14 subscales (NA and SI). Prevalence rates were calculated accordingly.
Adjustment for Confounders: Exploratory analyses controlled for potential confounding variables such as age and gender using ANCOVA or logistic regression, to isolate the effect of Type D personality on depressive symptoms and cardiovascular status.
Reliability Analysis: Cronbach’s alpha coefficients were computed to assess internal consistency of the DS14 and DASS-21 scales in this sample.
All statistical tests were two-tailed with significance set at p < 0.05. Effect sizes (Cohen’s d or Cramér’s V) were reported to provide context for the magnitude of observed differences. Results: Results
Sample Characteristics
The sample consisted of 146 participants, of whom 40 (27.4%) reported a history of cardiovascular disease, while 106 (72.6%) did not. Participant ages ranged from 30 to 85 years. Among those without a cardiac history, 50% were younger than 40, whereas in the cardiac history group, 50% were younger than 60. Overall, 66.4% (n = 97) of the sample were female, and 95.9% identified as Jewish. Most participants identified as secular (over 70%), were married (75%), and were parents (85%). Additionally, approximately 80% had attained a higher education degree.
Descriptive Statistics and Reliability of Measures
Table 1 displays descriptive statistics and internal consistency values for the primary variables. The average score for Type D personality was 1.21 (SD = 0.55), while mean scores for depression, anxiety, and stress were 0.93 (SD = 0.71), 1.05 (SD = 0.74), and 1.18 (SD = 0.65), respectively, suggesting generally low levels of distress in the sample. Internal consistency was high for all scales, with Cronbach’s alpha coefficients ranging from 0.83 to 0.94.
Table 1. Descriptive Statistics and Internal Consistency for Study Variables
Cronbach’s α SD Mean Variable
0.83 0.55 1.21 Type D Personality
0.87 0.71 0.93 Depression
0.89 0.74 1.05 Anxiety
0.91 0.65 1.18 Stress
0.94 0.65 1.06 Distress (Total)
Type D Personality Classification
Using the standard criterion (scores ≥10 on both the Negative Affectivity [NA] and Social Inhibition [SI] subscales), 62 participants (42.5%) were classified as having a Type D personality, while 84 participants (57.5%) were classified as Non-Type D.
Table 2. Prevalence of Type D Personality
Percentage (%) Number of Participants Classification
42.5 62 Type D Personality
57.5 84 Non-Type D
Independent samples t-tests showed that individuals classified with a Type D personality exhibited significantly higher levels of depression, anxiety, and stress compared to Non-Type D individuals (all p < .001).
Table 3: Group Differences in Psychological Distress
p t Non-Type D (n = 84) Mean (SD) Type D (n = 62) Mean (SD) Variable
<.001 8.12 11.3 (4.5) 19.2 (5.0) Depression
<.001 7.89 9.7 (3.9) 16.5 (4.6) Anxiety
<.001 8.48 12.1 (4.8) 21.0 (5.3) Stress
Group Differences Based on Cardiac History and Type D Personality
To test the first hypothesis, t-tests compared participants by cardiac history and Type D personality classification. Participants with a history of cardiovascular disease reported significantly higher Type D scores (M = 1.42, SD = 0.61) than those without (M = 1.13, SD = 0.50), t(144) = 2.97, p < .01. Additionally, individuals with cardiovascular disease reported significantly higher levels of depression (M = 1.23, SD = 0.82 vs. M = 0.81, SD = 0.63), anxiety (M = 1.33, SD = 0.78 vs. M = 0.94, SD = 0.68), and stress (M = 1.40, SD = 0.73 vs. M = 1.08, SD = 0.57); all comparisons were statistically significant (p < .05).
Among participants classified as Type D, scores for depression (M = 1.28, SD = 0.73), anxiety (M = 1.36, SD = 0.76), stress (M = 1.44, SD = 0.69), and overall distress (M = 1.35, SD = 0.67) were all significantly higher than those of Non-Type D individuals (M = 0.69, SD = 0.56; M = 0.89, SD = 0.63; M = 1.01, SD = 0.52; M = 0.86, SD = 0.53, respectively), with all p-values < .001.
Regression Analysis
To explore associations, a hierarchical multiple regression was conducted to evaluate the extent to which Type D personality is associated with psychological distress after accounting for demographic variables and cardiovascular history.
In Step 1, demographic factors (age, gender, education) and cardiac history were associated with 14.6% of the variance in distress, F(4, 141) = 6.05, p < .001. In Step 2, the addition of Type D personality was significantly associated with additional variance in distress, accounting for an additional 15.1% of the variance, ΔF(1, 140) = 29.64, p < .001. The final model was statistically significant, F(5, 140) = 14.87, p < .001, R² = .297.
Type D personality showed a strong association with psychological distress (β = .46, p < .001), and cardiovascular history was also significantly associated with distress (β = .18, p = .008), after controlling for demographic variables.
The mean score for the Negative Affectivity (NA) subscale was 12.1 (SD = 4.0), and for the Social Inhibition (SI) subscale was 11.3 (SD = 4.3), indicating moderate levels across the sample. Conclusions: The findings of this study confirm the hypothesis that Type D personality is associated with elevated levels of depression, anxiety, and stress. Participants with cardiovascular disease were more likely to report higher Type D scores and greater emotional distress compared to those without cardiac history. These results are consistent with existing literature suggesting that Type D personality increases vulnerability to psychological and physiological stressors that can exacerbate cardiovascular conditions [11,12,13].
Furthermore, participants with Type D personality traits, regardless of cardiac history, exhibited significantly higher levels of psychological distress than non-Type D participants. The strength and consistency of these group differences—confirmed through both t-tests and regression—highlight the clinical relevance of Type D personality in assessing psychological risk [12,15]. The use of validated measures such as the Depression Anxiety Stress Scales (DASS) helped ensure the reliability of psychological distress assessments in this study [11].
Regression analysis confirmed that Type D personality significantly predicts psychological distress even after controlling for demographic and medical variables. This supports the hypothesis that Type D traits are independently associated with distress and not merely a byproduct of illness status. These findings align with Denollet’s theoretical model of Type D personality as a risk factor for poor emotional and cardiac outcomes [8].
These results underscore the importance of incorporating psychological screening into cardiovascular care. Identifying patients with high psychological vulnerability can help clinicians implement early interventions, such as stress management training and tailored psychosocial support, potentially improving both emotional well-being and cardiac prognosis [16]. Our findings also reinforce the relevance of integrating psychological and behavioral health assessments into cardiac rehabilitation programs, as suggested by Chauvet-Gelinier and Bonin and echoed by recent clinical reviews [4,17,18].
Additionally, the association between chronic stress and cardiovascular morbidity is well established, and studies continue to demonstrate that unmanaged psychological distress significantly contributes to adverse cardiac events [14–16]. Chronic stress mechanisms—such as heightened autonomic arousal, systemic inflammation, and behavioral risk factors—may partially mediate the relationship between Type D traits and poor cardiovascular outcomes [14,15].
In line with Mols and Denollet’s assertion that early life experiences contribute to enduring personality patterns [12], our results underscore the necessity of adopting a lifespan approach to cardiovascular risk prevention that includes psychological factors. The strong association between Type D personality and distress in this study further demonstrates the need to incorporate personality assessment tools into standard cardiovascular evaluations.
In summary, this study adds to the growing body of evidence suggesting that Type D personality traits may contribute to elevated psychological distress, particularly among individuals with cardiovascular disease. These findings point to the potential value of incorporating psychological assessments into cardiovascular care—especially for those identified with Type D characteristics—as a way to support both emotional well-being and clinical outcomes. However, further longitudinal research is needed to clarify causal relationships and to evaluate the effectiveness of targeted interventions in this population [13,19].
This study contributes a novel perspective by directly comparing psychological distress levels between individuals with and without cardiovascular disease, while simultaneously accounting for Type D personality traits. Unlike previous studies that often focus solely on cardiac populations, our approach clarifies the independent and additive effects of Type D personality across both clinical and non-clinical groups. By doing so, the study addresses a gap in the literature regarding whether Type D traits are primarily a consequence of chronic illness or represent a stable risk factor across health statuses. Furthermore, the integration of validated psychological instruments and rigorous regression analyses enhances the methodological robustness of our findings. This research thus advances understanding of the psychological dimensions of cardiovascular risk and supports the development of more personalized approaches to prevention and intervention.
Author Contributions
Conceptualization, KG; methodology, KG and YS; formal analysis, KG and YS , writing—original draft preparation, KG and YS, writing—review and editing, KG and YS. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board (IRB) Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Faculty of Social and Community Sciences, Ruppin Academic Center
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Conflicts of Interest
The author declares no conflict of interest. Clinical Trial: NA
Background: The extension of life expectancy has increasingly highlighted the importance of physical exercise in active aging. However, adherence to traditional exercise among community-dwelling older...
Background: The extension of life expectancy has increasingly highlighted the importance of physical exercise in active aging. However, adherence to traditional exercise among community-dwelling older adults is generally low. Virtual Reality (VR) and Mixed Reality (MR) Tai Chi exergames, as novel health promotion tools, show significant potential, particularly for older adults exercising in a home setting. Objective: This study aimed to systematically evaluate the overall usability and feasibility of a VR/MR Tai Chi exergame designed for community-dwelling older adult users. It specifically focused on its potential to promote home-based physical activity, including subjective experience, physiological comfort, and objective interaction performance. The study also explored the relationships between key usability factors and user characteristics to provide empirical evidence for design optimization. Methods: Of the 86 community-dwelling older adults recruited for this study, data from 70 participants were considered valid after an initial screening in which 18.6% were excluded due to issues with VR adaptation. The study employed a between-groups design where participants were assigned to one of four conditions combining two display modes (VR vs. MR) and two feedback intensities (Soothing vs. Intense). Primary outcome measures included the Game Experience Questionnaire (GEQ), Virtual Reality Sickness Questionnaire (VRSQ), and objective game performance logs data. Results: The VR/MR Tai Chi game demonstrated good overall usability and acceptability among the screened community-dwelling older adult participants, suggesting its potential as a home-based exercise tool. Subjective experience was highly positive (mean Positive Affect M=3.74, mean Competence M=3.53; P<.001), with low perceived challenge (M=1.43) and high competence. Physiological comfort in the post-screening sample was acceptable, with common mild symptoms being dizziness with eyes closed (20.00%) and vertigo (18.57%), both of low severity; however, the initial exclusion of 18.6% of participants due to VR discomfort is noteworthy. Accuracy showed significant positive correlations with flow (ρ=0.342) and competence (ρ=0.322), while the VRSQ total score was significantly negatively correlated with positive affect (ρ= -0.334, P=.005). Conclusions: Tai Chi exergames based on immersive technologies offer a feasible and attractive pathway for promoting physical exercise among community-dwelling older adults, particularly within the home environment, thereby supporting their ability to age in place. Analysis of the key usability factors provides guidance for specific design choices, while indicating directions for future research, such as longitudinal evaluations, extension to more diverse populations, and application in real-world home settings.
Background: Clinical reasoning is crucial in psychology education, yet traditional training methods provide limited practical experience. Virtual patients (VPs), enhanced by generative artificial inte...
Background: Clinical reasoning is crucial in psychology education, yet traditional training methods provide limited practical experience. Virtual patients (VPs), enhanced by generative artificial intelligence (GAI), may effectively bridge this gap, offering realistic simulations that promote diagnostic and reasoning skills in a controlled environment Objective: To evaluate the impact of GAI-powered conversational virtual patients on active learning, student satisfaction, participation levels, and overall educational experience in an undergraduate psychopathology course. Methods: The study involved 160 second-year psychology undergraduates at Miguel Hernández University, who engaged in structured text-based interviews with virtual patients generated by ChatGPT (gpt-4o model). Each student participated in one to six sessions, resulting in 1,832 recorded interactions. AI temperature settings (0.1, 0.5, 0.9) were systematically varied to examine their effect on interactions and perceptions. Sentiment analysis was conducted using Python's "pysentimiento" library, and quantitative data were analyzed with R software. Results: Participants rated the platform highly, with median ratings close to 10 across different conditions. Statistical analysis revealed no significant correlation between age (p = 0.42) or number of questions asked (p = 0.42) and user ratings. A moderate negative correlation was found between AI errors and ratings (r = –0.31, p < 0.001). Temperature settings significantly influenced ratings (Kruskal-Wallis test, p = 0.031), with higher ratings at the 0.9 temperature compared to 0.1 (Dunn's test, p = 0.037). Sentiment analysis showed predominantly negative sentiment in AI responses (median negativity = 0.8903), reflecting clinical realism. Conclusions: GAI-powered conversational VPs significantly enhance clinical training in psychopatology skills, providing realistic, engaging simulations that improve student satisfaction and clinical reasoning skills. Optimizing AI temperature settings can further enhance educational effectiveness, highlighting the value of carefully tailored simulation parameters.
Background: A key challenge for schools throughout the COVID-19 pandemic was finding ways to monitor and prevent cases of COVID-19. While diagnostic testing and connecting students and their families...
Background: A key challenge for schools throughout the COVID-19 pandemic was finding ways to monitor and prevent cases of COVID-19. While diagnostic testing and connecting students and their families to appropriate resources to mitigate spread of COVID-19 was recommended, few schools had scalable infrastructure including information technology systems to implement these types of measures. Objective: The current study tested a new approach to COVID-19 testing (SCALE-UP Counts) in school settings that employed automated bidirectional text messages provided to the school community that alerted parents of students to COVID-19 testing options and guidance on when to test. The primary outcome was the proportion of parents whose households tested for COVID-19 and the secondary outcome was the number of missed school days. Methods: The SCALE-UP Counts trial was designed as a Sequential Multiple Assignment Randomized Trial and final analyses compared results from parents who received intensive, fully automated, bi-directional text messaging about COVID-19 testing or usual care (control; fully automated unidirectional text messaging about COVID-19 testing) unblinded interventions. From the 16 selected schools, all eligible participants who did not opt out of the study were enrolled. Schools from both arms of the trial were provided with free at-home COVID-19 test kits. Parents were asked to respond to self-report measures on testing outcomes and missed school days through online questionnaires. Results: The study included 7122 parents of students from 16 schools, half of which were Title 1 schools; 2588 were randomized to usual care and 4534 to bidirectional text messaging. The SCALE-UP Counts intervention led to increased self-reported testing when compared with the control condition (22.8% vs 13.5%, relative testing rate = 1.64, 95% CI 1.31-2.02, P<.001). There was not an observed difference in missed school days between the study arms (0.43/month vs 0.28 in usual care, relative missed days rate = 1.55, 95% CI 0.98 - 2.45, P=.06). Conclusions: SCALE-UP Counts worked closely with schools and the state’s public health system to implement and test a scalable health information technology approach that delivered automated text messages to students’ parents around COVID-19 testing and provided access to free at-home test kits. Such an approach can help facilitate COVID-19 testing among school communities including those that provide education and resources to students and their families from racial/ethnic minorities and with low SES. Similar health information technology approaches could be used to increase ease of access to testing, reduce testing burden, and provide tailored information on health measures in school communities for a variety of illnesses or public health concerns. Clinical Trial: ClinicalTrials.gov NCT05112900; http://clinicaltrials.gov/ct2/show/NCT05112900
Background: With the increasing aging global population trend, the demand and dissemination of health information for older adults has gradually become an important issue in the field of public health...
Background: With the increasing aging global population trend, the demand and dissemination of health information for older adults has gradually become an important issue in the field of public health. The older adults gradually become the users and disseminators of electronic health information. However, due to low eHealth literacy, many older adults are vulnerable to misinformation when identifying and relaying online health information, which can lead to the spread of misinformation. The situational problem-solving theory (STOPS) emphasizes that specific problem perception and referent criterion will affect individuals' information behaviour. As the core basis for judging the relevance and reliability of the information, it may significantly affect the health information forwarding decisions of older adults. Objective: This study aims to explore how the four dimensions of eHealth literacy (functional, communicative, critical, and translational) influence the health information forwarding behaviour of older adults in social media environments through the mediating role of referent criterion. Methods: This study used a cross-sectional survey design to collect data from a population over 50 years old in Shanghai, China, through a structured questionnaire. eHealth literacy was measured through the framework of the Transactional Model of eHealth Literacy (TMeHL), which includes four dimensions: functional eHealth literacy, communicative eHealth literacy, critical eHealth literacy, and translational eHealth literacy. In addition, referent criterion and health information forwarding behaviour were assessed using the Kim and Grunig (2011) scale. A total of 606 valid questionnaires were collected. SPSS and SmartPLS were used for statistical analysis, including a two-stage evaluation of the measurement and structural models. The reliability and validity of the measurement model were assessed. PLS-SEM method and Bootstrapping were used to test the path coefficient and mediation effect in the structural model. Results: The study found that the overall e-health literacy level of older adults in Shanghai was high (M=3.91, SD=0.56). Communication eHealth literacy (β=0.324, p<.001) and critical eHealth literacy (β=0.312, p<.001) significantly promoted the establishment of the referent criterion. Thus, it improved their health information forwarding behaviour (β=0.467, p<.001). Referent criterion significantly mediates eHealth literacy and information-forwarding behaviour (β=0.207, p<.001). Functional and translational eHealth literacy had no significant direct impact on the referent criterion. Conclusions: This study explores the influence of eHealth literacy on health information forwarding behaviour in older adults and confirms that the referent criterion plays a crucial mediating role in this relationship. This suggests that targeted strategies should be adopted to improve eHealth literacy for older adults to maximize its positive impact. At the same time, it further highlighted the key role of referent criterion in health behaviour, indicating that social media, community influence, and trust mechanisms cannot be ignored in health information dissemination. Clinical Trial: This study did not involve a randomized controlled trial or clinical intervention. Thus, trial registration was not applicable under the ICMJE policy.
Background: Automatic sleep staging technology based on wearable photoplethysmography can provide a non-invasive and continuous solution for large-scale sleep health monitoring. Objective: This study...
Background: Automatic sleep staging technology based on wearable photoplethysmography can provide a non-invasive and continuous solution for large-scale sleep health monitoring. Objective: This study accordingly developed a novel cross-scale dynamically coupled extended long short-term memory network (SleepXLSTM) to realize automatic sleep staging based on heart rate signals collected by wearable devices. Methods: SleepXLSTM models the relationship between heart rate fluctuations and sleep stage labels by correlating physiological features with clinical semantics using a knowledge graph neural network. Furthermore, an excitation–inhibition dual-effect regulator is applied in an improved multiplicative long short-term memory network along with memory mixing in a scalar long short-term memory network to extract and strengthen the key heart rate timing features while filtering out noise produced by motion artifacts, thereby facilitating subsequent high-precision sleep staging. The benefits and functions of this comprehensive heart rate feature extraction were demonstrated through sleep staging prediction and ablation experiments. Results: The proposed model exhibited a superior accuracy of 91.25% and Cohen’s kappa coefficient of 0.876 compared to an extant state-of-the-art neural network sleep staging model with an accuracy of 69.80% and kappa coefficient of 0.040. Conclusions: The dynamic coupling strategy employed by SleepXLSTM for automatic sleep staging using the heterogeneous temporal representation of heart rate data can promote the development of smart wearable devices to provide early warning of sleep disorders and realize cost-effective technical support for sleep health management.
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.
Background: Gestational diabetes (GDM) is one of the most common complications in pregnancy. Optimal glycemic control is key to reduce the risk of adverse pregnancy outcomes. If glycemic control is in...
Background: Gestational diabetes (GDM) is one of the most common complications in pregnancy. Optimal glycemic control is key to reduce the risk of adverse pregnancy outcomes. If glycemic control is inadequate, additional medications are needed. A large body of evidence has shown that metformin is also an effective medication in GDM. Compared to immediate-release (IR) formulation, the prolonged-release (PR) formulation of metformin offer some advantages with once-daily dosage and less frequent side effects, leading to better compliance. To date, there is no study specifically report the effectiveness of the use of metformin PR in the treatment of GDM, as well as the time to achieve glycemic control after treatment. Objective: To evaluate the effectiveness of metformin PR in the treatment of GDM in terms of glycemic control within 6 weeks, time to achieve glycemic control, and associated factors. Methods: A randomized, double-blind, placebo-controlled clinical trial will be conducted among 80 pregnant women diagnosed with GDM who had inadequate glycemic control. The women will be randomized into 2 equal group, either receiving metformin PR or placebo, in addition to nutritional therapy and behavioral modification. Dosage adjustment will be made every 2 weeks as per obstetrician’s discretion. If glycemic target is not achieved within 6 weeks, insulin therapy will be initiated. All the participants and the investigators are blinded to the treatment provided. The primary outcome is the rate of achievement of glycemic control and secondary outcomes are time to achievement of glycemic control, rate of insulin therapy, and factors associated with the success of metformin PR use. Results: As of June 2025, the study has recruited 12 participants. Conclusions: The results of this study will provide additional information on the use of metformin in the treatment of GDM, including use of different formulations, rate of glycemic control, time to achieve glycemic control, and associated factors. This will help physicians plan better care of pregnant women with GDM, especially when glycemic control is inadequate, including choices of metformin formulation, dosage and follow-up schedule, identification of women at risk for treatment failure, etc. Clinical Trial: This protocol has been registered to the Thai Clinical Trial Registry (Trial registration number TCTR20250525007).
Background: Although continuous glucose monitoring (CGM) devices are now widely used among people with type 1 diabetes (PwT1D), the utility of these devices has not been specifically optimized for ado...
Background: Although continuous glucose monitoring (CGM) devices are now widely used among people with type 1 diabetes (PwT1D), the utility of these devices has not been specifically optimized for adolescents and young adults (AYAs). Objective: We hypothesized that predictive alerting for hypo- and hyperglycemia would improve the user experience among young adult PwT1D using CGM. Methods: We engaged in an iterative co-design and pilot testing process of our own novel predictive CGM alerting app – BeaGL – with a cohort of six young adults over 5 months. Results: Qualitative feedback from participants emphasized the importance of simplicity and customizability within the app, which then led to experienced benefits of improved agency and reduced cognitive burden related to their T1D self-management. Although the pilot was not powered to detect statistically significant changes in CGM metrics, all participants demonstrated a trend toward reduced time in hypoglycemia (<70 mg/dl), severe hyperglycemia (>250 mg/dl), or both. Conclusions: Future research should evaluate the benefits of customizable predictive CGM alerting among AYA PwT1D for both glycemic outcomes and quality of life via a larger, randomized trial.
Background: Mobile health (mHealth) applications have become more common in healthcare, including in orthopaedics. However, for these mHealth apps to be efficient, patients should be willing to use th...
Background: Mobile health (mHealth) applications have become more common in healthcare, including in orthopaedics. However, for these mHealth apps to be efficient, patients should be willing to use them. Objective: This study evaluated the acceptability of using an mHealth app for post-operative care following total hip and knee arthroplasty. Methods: This pre-operative cohort study with 100 patients measured acceptability using the theoretical framework for acceptability (TFA) pre-operatively and at three months post-operation. We also measured satisfaction with app usage post-operatively using the USE questionnaire as well as PROMs pre-operatively and post-operatively using the Oxford hip and knee scores and the VAS. Patients included were 18 or older, underwent unilateral primary total hip, total knee or partial knee arthroplasty, spoke and read French or English and had a smartphone with Internet access. Participants used mymobility® (Zimmer-Biomet) in addition to standard government funded physiotherapy. Results: Overall result for pre-operative TFA was 4.2 ± 0.6 out of 5. When comparing TFA results for patients who filled both pre-operative and post-operative TFAs, there was a statistically significant decrease in post-operative TFAs. Subgroup analysis revealed higher levels of self-efficacy in pre-operative TFAs with university level education compared to non-university, and lower levels of acceptability in post-operative TFAs with TKA compared to THA. USE questionnaire revealed a good level of satisfaction with usage of the app and PROMs showed improvement in THA but not in TKA at average 31.2 days follow-up. Conclusions: There was a good level of acceptability with the use of mymobility® for the post-operative management in THA and TKA, although acceptability decreased post-operatively. Higher education was associated with higher acceptability, whereas TKA as the procedure was associated with lower acceptability. Reduction in acceptability post-operatively could signify high expectations towards the app pre-operatively, higher than expected difficulties and pain in the early post-operative period, or the need for app improvement.
Background: Frailty constitutes a growing challenge for health and social care systems around the world. In England, 35% of adults aged 65 and over live with frailty, with international estimates indi...
Background: Frailty constitutes a growing challenge for health and social care systems around the world. In England, 35% of adults aged 65 and over live with frailty, with international estimates indicating that almost half of all hospital inpatients within the same age group are frail. This population often experience multimorbidity and frequent care transitions. Written documentation and verbal handovers may lack the precision and nuance required to understand an older adult’s presentation and support needs. Video-recordings of individual patients, capturing aspects of their functional abilities and condition, may help to enhance multidisciplinary team communication and care continuity, yet little is known about their use in the care of older inpatients with frailty. Objective: We aimed to evaluate the acceptability, feasibility of implementation, and potential effectiveness of video-based patient records (the Isla Health Digital Pathway Platform) for supporting the assessment and care of older inpatients with frailty within the acute hospital setting. Methods: A non-randomized mixed-methods pilot study was conducted within three acute medicine wards for older adults. The video-based patient records intervention, permitting videos to be embedded securely within the electronic patient record, was implemented over a three-month period alongside usual care. Patient enrollment and retention figures; qualitative interviews with patients, carers, and clinical staff; and video capture and view metrics were used to address study objectives. The Theoretical Framework of Acceptability of Healthcare Interventions was applied to the framework analysis of interview data, capturing concepts such as intervention ethicality, burden, and coherence. Patient and public involvement and engagement informed each research stage. Results: Twenty-nine patients were enrolled (56.9%); one patient withdrew before receiving the intervention. Modal reasons given by patients for non-participation included not wanting to take part in research (n = 8) or feeling too unwell (n = 2). Staff identified multiple opportunities for capturing patient videos, including documentation of mobility assessments or seizures. The intervention was considered acceptable, on the grounds that safeguards were always in place, including secure data storage and upholding of patient dignity. Implementation barriers and facilitators were identified; factors such as difficulties in capturing videos within busy ward environments and scheduling issues were voiced by participants. Video view metrics and anecdotal data from interviews collectively suggested low rates of engagement with videos by clinical staff once captured. Potential intervention impacts included perceived enhancements to clinical assessment and person-centered care. Conclusions: Our findings suggest that the intervention is largely acceptable to patients, carers, and clinical staff. Conclusions as to intervention feasibility were mixed, with limited engagement with videos suggesting further work is required to promote sufficient uptake amongst staff. Finally, this research presents promising patient, carer, and clinical opinion as to the potential effectiveness of video-based patient records for improving aspects of patient care. Clinical Trial: ClinicalTrials.gov: NCT06504641
Background: Although large language models (LLMs) have demonstrated promising diagnostic performance, it is uncertain whether their use improves diagnostic reasoning of medical students. Objective: To...
Background: Although large language models (LLMs) have demonstrated promising diagnostic performance, it is uncertain whether their use improves diagnostic reasoning of medical students. Objective: To investigate the impact of an LLM on medical students’ diagnostic performance in rheumatology compared with traditional resources. Methods: This randomized controlled trial was conducted from January 7 to March 30, 2025, and recruited medical students from University Marburg, Germany. Participants provided a main diagnosis with corresponding diagnostic confidence and up to four additional differential diagnoses for three rheumatic vignettes. Participants were randomized to either use the LLM in addition to traditional diagnostic resources or traditional resources only. The primary outcome was the proportion of cases with a correct top diagnosis. Secondary outcomes included the proportion of cases with a correct diagnosis among top 5 suggestions, a cumulative diagnostic score, diagnostic confidence and case completion time. Diagnostic suggestions were rated by blinded expert consensus. Results: A total of 68 participants (mean [SD] age, 24.8 [2.6]) were randomized. Participants using the LLM identified the correct top diagnosis significantly more often than those in the control group (77.5% vs 32.4%), corresponding to an adjusted odds ratio of 7.0 (95% CI: [3.8, 14.4], P<.001) and also outperformed the LLM alone (77.5% vs 71.6%). Mean cumulative diagnostic scores were significantly higher in the LLM group (mean [SD], 12.3 [12.3]) compared with the control group (6.7 [3.2]; Welch t₆₀.₂₂ = 8.1; P<.001). Diagnostic confidence was greater in the LLM group (mean 7.0 [SD 1.3]) than in the control group (mean 6.1 [SD 1.2]; P<.001). Case completion time was significantly longer in the LLM group (mean 505 seconds [SD 131]) compared to the control group (mean 287 seconds [SD 106]; P<.001). Conclusions: In this randomized clinical trial, medical students using an LLM achieved significantly higher diagnostic accuracy than those using conventional resources. Students assisted by the LLM also outperformed the model alone, highlighting the potential of human-AI collaboration. These findings suggest that LLMs may help improve clinical reasoning in complex fields such as rheumatology. Clinical Trial: ClinicalTrials.gov Identifier: NCT06748170
A retrospective online social listening analysis of the 2023-2024 HPV vaccination catch-up campaign identified insights regarding HPV vaccination among young adults aged 18-27 years in the Netherlands...
A retrospective online social listening analysis of the 2023-2024 HPV vaccination catch-up campaign identified insights regarding HPV vaccination among young adults aged 18-27 years in the Netherlands, i.e.: netizens expressed questions and concerns regarding eligibility, effectiveness and safety, and other product information (vaccine brand); reduced general trust in vaccination since COVID-19; and access barriers.
Background: Virtual Hospital at Home services have the potential to improve care access and outcomes, but rapid implementation during COVID-19 lacked patient-centered development, raising concerns abo...
Background: Virtual Hospital at Home services have the potential to improve care access and outcomes, but rapid implementation during COVID-19 lacked patient-centered development, raising concerns about equity and engagement. Objective: To co-design solutions that support equitable, patient-centered Virtual Hospital at Home services using an Experience-Based Co-Design (EBCD) approach. Methods: We conducted a five-stage improvement process in Fraser Health Authority, British Columbia, including (1) forming a multi-disciplinary steering committee; (2) reviewing provider experiences; (3) interviewing South Asian patients and caregivers; (4) hosting a co-design workshop to develop solutions; and (5) sharing back findings. Results: Participants identified barriers including digital literacy, language, and trust in virtual care. Co-designed solutions focused on culturally tailored education, hybrid digital training, caregiver inclusion, and community-driven engagement strategies. Conclusions: EBCD enabled the development of inclusive and actionable strategies to improve Virtual Hospital at Home services. Findings highlight the importance of ongoing community collaboration to ensure equity in virtual care innovation.
Background: Knee braces may improve symptoms and physical function following anterior cruciate ligament reconstruction (ACLR). However, their effectiveness depends on adherence, which typically relies...
Background: Knee braces may improve symptoms and physical function following anterior cruciate ligament reconstruction (ACLR). However, their effectiveness depends on adherence, which typically relies on self-reported wear time that is prone to recall and response bias. Objective measures of adherence, such as temperature sensors, which are already validated in footwear and orthotics research, offer a potentially more accurate alternative to self-reporting. Despite this, there is no research comparing self-reported and sensor-measured wear times in a knee brace. Objective: To determine how well self-reported wear times reflect sensor-measured data in a slim-fit knee brace. Methods: Young adults (18-45 years), 1-8 years post-ACLR with early-onset knee osteoarthritis symptoms (KOOS4 score <80/100) wore a slim-fit brace during a 6-week feasibility trial. Self-reported wear times were recorded in daily logs. An undisclosed, embedded temperature sensor recorded temperature once every 10 minutes. A wear detection algorithm identified brace donning and doffing. These data were used to calculate aggregated measures (cumulative wear time over the 6-week intervention period, average daily wear time, total number of days worn) and repeated measures (daily wear duration, 3- and 7-day rolling averages). Agreement between self-reported and sensor-based measures was assessed using concordance correlation coefficients (CCC) and limits of agreement (LoA). Results: Of 14 randomised participants, 10 (30% male, age 33±6 years, 4±1 years post-ACLR) had both temperature sensor and self-reported wear data. Six participants (60%) under-reported average daily wear time (mean 29±24 minutes across all 10 participants), while nine (90%) over-reported the number of days worn (mean 9±6 days across all 10 participants). Daily wear time showed moderate agreement between the sensor and self-reporting (CCC 0.70, 95%CI 0.58 to 0.79), but wide LoA (-223 to 217 minutes). Using 3- or 7-day rolling averages narrowed LoA (-47 to 36 minutes per day and -14 to 10 minutes per day, respectively) and slightly improved CCCs (0.74, 95%CI 0.58 to 0.85, and 0.73, 95%CI 0.51 to 0.86, respectively). Greater agreement was observed with more aggregated outcomes; for total 6-week wear time, the CCC was 0.84 (95%CI 0.50 to 0.95). When expressed as daily average wear time, the CCC was excellent (0.92, 95%CI 0.73 to 0.98), although daily LoA still remained wide (-68 to 32 minutes), indicating substantial individual variability between self-reported and sensor-based measures. For the total number of days worn, the CCC was moderate (0.64, 95%CI 0.15 to 0.88) and LoA wide (-10 to 22 days). Conclusions: Self-reported daily brace wear time is inaccurate compared to objectively measured adherence using a temperature sensor. Aggregated data and rolling averages showed better agreement. Future intervention studies should consider objective adherence measures. Failing this, averaging self-reported wear time across the intervention period could improve accuracy. Clinical Trial: ACTRN12623001027606
Background: For actigraphic devices to collect valid and reliable data from children and young people, consistent wear is essential. Adherence in primary-school aged children may be particularly chall...
Background: For actigraphic devices to collect valid and reliable data from children and young people, consistent wear is essential. Adherence in primary-school aged children may be particularly challenging due to developmental factors and designs considerations. Despite the growing use of these devices in research and clinical settings, no previous review has attempted to quantify adherence in this age group. Objective: To provide the first pooled-estimate of adherence to actigraphic devices in primary-school aged children and examine the impact of sociodemographic, clinical and device related factors on adherence rates. Methods: The electronic databases Embase, MEDLINE, PsycInfo, Social Policy and Practice, Education Resources Information Center, British Education Index and CINAHL were queried using database specific pre-defined search strategies. Empirical studies that reported on wearable actigraph adherence in children (aged 5-11 years old) were included in this review. Data were extracted for 235 studies, which were all narratively synthesised. Of these, 135 studies contained adherence data which were pooled in a proportional meta-analysis. Meta-regression was used to examine the impact of individual, device-specific and study-related factors on adherence. The main outcome was actigraphic device adherence derived as the proportion of children that met the analytic analysis wear-time threshold compared to the number of children invited to use the device at baseline. Results: Overall adherence, measured over a range of 1 to 140 days, was 81.6% (95% CI: 78.7%–84.4%). Children with a health diagnosis, particularly neurodevelopmental, demonstrated higher adherence to these devices (b=.395, p =.004, 95% CI = [.125-.665]). No other individual, device- or study-related factors had a statistically significant impact on adherence. Conclusions: This review demonstrates high adherence to actigraphic devices in 5-11 years old children, and more so in those with health conditions. However, questions remain about long-term adherence, particularly due to the over-reliance on commercial devices and methodological reporting quality. Clinical Trial: PROSPERO registration: CRD42021232466
Background: Artificial intelligence (AI) chatbots, including ChatGPT-4o, Google Gemini, and Microsoft Copilot, are increasingly utilized to deliver healthcare-related information. Their potential to a...
Background: Artificial intelligence (AI) chatbots, including ChatGPT-4o, Google Gemini, and Microsoft Copilot, are increasingly utilized to deliver healthcare-related information. Their potential to assist in pharmaceutical care and drug information services is gaining attention globally. However, their ability to provide accurate, complete, and safe drug-related information in non-English contexts, particularly in Thai, remains underexplored. Objective: This study aimed to evaluate the performance of these AI systems in responding to drug-related questions written in Thai. Methods: An analytical cross-sectional study was conducted using 76 public drug-related questions compiled from medical databases and social media sources between November 1st, 2019, and December 31st, 2024. These questions were categorized into 18 distinct types along with one mixed-type category, with each category comprising four questions (n=19 categories × 4 questions=76). The responses generated by ChatGPT-4o, Google Gemini, and Microsoft Copilot were evaluated in terms of correctness, completeness, risk, and reproducibility. All AI models were queried using identical input text in Thai, and responses were independently assessed by clinical pharmacists using standardized evaluation criteria. Results: ChatGPT-4o demonstrated a higher proportion of fully correct responses (50.0%) compared to Microsoft Copilot (35.5%) and Google Gemini (34.2%), although these differences did not reach statistical significance (P=.078). All three AI models provided generally complete responses, with no significant difference in completeness scores among them (P=.080). While high-risk answers were observed across all systems, the overall risk levels were not significantly different (P=.123). The category of drug-related questions significantly influenced the correctness of AI responses (P=.002), but not completeness (P=.230). ChatGPT-4o generally yielded the highest proportion of fully correct and complete answers across most categories. However, in the pharmacology category, Google Gemini and Microsoft Copilot outperformed ChatGPT in correctness. Question type also statistically significantly affected the risk level of the answers (P=.039); in particular, the pregnancy and lactation category showed the highest high-risk response rate (1.32% per system). Regarding reproducibility, all three AI models demonstrated consistent response patterns when the same questions were re-queried after 1, 7, and 14 days, with no significant deviation from the initial responses. Conclusions: The evaluated AI chatbots were able to answer the queries with generally complete content; however, we found limited accuracy and occasional high-risk errors in responding to drug-related questions in Thai. However, all models exhibited good reproducibility, with consistent response patterns observed across multiple time points. Further improvements are necessary to provide safe, reliable, and language-specific pharmaceutical information.
Background: Obesity is a major risk factor for many chronic diseases. However, weight loss benefits may diminish if accompanied by skeletal muscle loss. Objective: This study evaluates muscle changes...
Background: Obesity is a major risk factor for many chronic diseases. However, weight loss benefits may diminish if accompanied by skeletal muscle loss. Objective: This study evaluates muscle changes during mobile health (mHealth) app-based weight loss in overweight/obese individuals and identifies muscle gain predictors. Methods: Among the 38,209 participants achieving weight loss via the mHealth app were divided into two groups: weight loss without muscle gain (WL-NG) and with muscle gain (WL-MG, defined by ≥ 5% skeletal muscle rate [SMR] increase over 6 months). Women showed slower fat loss (8.36, IQR: 7.04 vs -12.24, IQR: 10.00%, P < 0.0001) and muscle gain (4.46, IQR: 3.77 vs 4.64, IQR: 3.85%, P < 0.001) than men, despite higher measurement frequency and weight-loss rates. WL-MG participants had lower baseline age/SMR but higher baseline BMI/body fat (PBF) versus WL-NG (P < 0.001). Weight-loss rate, fat-loss rate, and measurement frequency were higher in WL-MG (P < 0.0001). PBF change rate strongly correlated with SMR changes (woman: rs = -0.977, P < 0.0001; man: rs = -0.936, P < 0.0001). Multivariable regression identified higher measurement frequency as the strongest muscle-gain predictor (OR = 1.030, 95% CI: 1.028–1.031, χ²-df = 1658.983, P < 0.0001). Results: In conclusion, this study demonstrates that during the weight loss process, men tend to lose more fat and gain more muscle than women. The rate of weight change was negatively correlated with the rate of SMR change. Conclusions: Frequent mHealth self-monitoring enhances weight loss efficacy and muscle preservation, highlighting its critical role in optimizing body composition.
Background: Artificial Intelligence (AI)-powered medical question-answering systems significantly improve access to reliable healthcare information, yet challenges remain, particularly hallucinations...
Background: Artificial Intelligence (AI)-powered medical question-answering systems significantly improve access to reliable healthcare information, yet challenges remain, particularly hallucinations in large language models (LLMs), where AI produces misleading or fabricated content. These risks are especially critical in clinical contexts. Ensuring accuracy and trustworthiness requires validated, domain-specific datasets and robust semantic alignment. Objective: This study aims to develop and evaluate an AI-driven question-answering system for cardiovascular diseases using a validated dataset, knowledge graph and advanced semantic models to improve the accuracy, interpretability and trustworthiness of responses. Each answer’s reliability is demonstrated and supported by the source of the information, a reliability rating based on the CRAAP method, relevant references and disease-related images to support transparency and user trust. Methods: We have curated a dataset from trusted medical sources (Mayo Clinic, Cleveland Clinic, NHS, British Heart Foundation, MedlinePlus), validated using the CRAAP method. The data is structured into a knowledge graph via an automated pipeline. Semantic matching is performed using PubMedBERT, BioBERT and Sentence Transformer, while a T5-based model is employed to rephrase low-confidence queries for improved alignment. Results: The system is evaluated using 100 queries across five categories: straightforward, complex (long queries), general spelling mistakes, misspelled disease names, and out-of-data queries. It demonstrates high accuracy for straightforward (99.64 ± 1.6%), complex (98.78 ± 2.68%), and general spelling mistake queries (99.1 ± 2.79%). Accuracy declines with misspelled disease names (54.56 ± 50.67%) due to limitations in correcting domain-specific errors. For out-of-data queries (11.99 ± 29.28%), the system refrains from speculative outputs and transparently indicates when no satisfactory answer is available. Conclusions: This study demonstrates the successful development of a trustworthy and accurate AI-driven question-answering system for cardiovascular medicine. By integrating a validated knowledge graph, multi-model language embeddings and a T5-based paraphrasing model, the system delivers contextually appropriate responses supported by source ratings, references and medical images. While performance is strong across most query types, challenges with misspelled disease names and multi-intent queries remain. Future work will address these limitations through spelling correction, query segmentation and expansion to additional disease domains.
Background: eHealth technologies have shown promise in improving the accessibility and quality of nursing research and practice. Less is known about nurses' perceptions of eHealth technology that are...
Background: eHealth technologies have shown promise in improving the accessibility and quality of nursing research and practice. Less is known about nurses' perceptions of eHealth technology that are prerequisites for the implementation of eHealth-based care and studies. Objective: To validate the Perception of eHealth Technology Scale in Chinese Brief (PETS-C Brief) in Chinese nurses. Methods: Participants were 1409 nurses (96.8% female; mean age [SD] 34.6 [8.6] years). Confirmatory factor analysis (CFA) verified the previously reported four-factor structure of PETS-C Brief. Convergent validity was examined by analyzing correlations with scores of the General Self-Efficacy Scale (GSE) and Information Literacy Scale (ILS). Known-group validity and test-retest reliability were also assessed. Cronbach's α was calculated for internal consistency reliability. Sociodemographic and working-related characteristics were analyzed. Results: The goodness-of-fit of the four-factor PETS-C Brief was acceptable (CFI =0.933, SRMR=0.064, RMSEA=0.085). Internal consistency was good (Cronbach's α=0.912). The scale showed stable test-retest reliability over 1 month (intraclass correlation coefficient=0.684, 95% CI: 0.548, 0.778). Good convergent validity was demonstrated by positive correlations with scores on the GSE (r=0.25, P<0.001) and ILS (r=0.56, P<0.001). Known-group validity was supported by higher PETS-C Brief scores observed in younger age (P=0.006) and higher educational attainment (P=0.023). No significant associations were observed between working-related characteristics and PETS-C Brief score. Conclusions: The satisfactory validity and reliability suggested the PETS-C Brief could be deployed for assessing perception of eHealth technology in Chinese nurses. Studies in large and random samples and in other cultural settings are warranted to increase the generalizability of our results.
Background: Background: The growing use of artificial intelligence (AI) chatbots for seeking health-related information is concerning, as they were not originally developed for delivering medical guid...
Background: Background: The growing use of artificial intelligence (AI) chatbots for seeking health-related information is concerning, as they were not originally developed for delivering medical guidance. The quality of chatbots’ responses relies heavily on their training data and is often compromised in medical contexts due to their lack of specific training data in medical literature. Objective: Objectives: This study examined AI chatbots responses to human papillomavirus (HPV)-related questions by analyzing structure and patterns, linguistic features, information accuracy and currency. Methods: Methods: We conducted a qualitative content analysis to examine four selected AI chatbots (ChatGPT-4, Claude 3.7-Sonnet, DeepSeek-V3, and Docus [General AI Doctor]) in answering HPV vaccine questions adapted from Vaccine Conspiracy Beliefs Scale (VCBS) items and Google Trends query. Results: Results: All AI chatbots cited evidence-based sources from reputable health organizations. We found no fabricated information or inaccuracies in numerical data. For complex questions, all AI chatbots appropriately deferred to healthcare professionals’ suggestions. All chatbots maintained a neutral or pro-vaccine stance, corresponding with the scientific consensus. The mean response lengths varied (word count; ChatGPT: 436.4, Claude: 188.0, DeepSeek: 510.0, Docus: 159.4), as did readability (Flesch-Kincaid Grade-Level; ChatGPT: 10.7, Claude: 13.2, DeepSeek:11.3, Docus:12.2). ChatGPT and Claude offered personalized responses, while DeepSeek and Docus lacked this. Occasionally, some responses included broken or irrelevant links and medical jargon. Conclusions: Conclusion: Amidst an online environment saturated with misinformation, AI chatbots have the potential to serve as alternative sources of accurate HPV-related information to conventional online platforms (websites, social media), though improvements in readability, personalization, and link accuracy are still needed. Clinical Trial: N/A
Background: Dementia presents significant challenges for informal caregivers. A gap remains in technology-driven personalized support tailored to caregivers' needs. Objective: To develop a theory-driv...
Background: Dementia presents significant challenges for informal caregivers. A gap remains in technology-driven personalized support tailored to caregivers' needs. Objective: To develop a theory-driven, multi-component mobile application specifically designed for caregivers of individuals with dementia, and test its usability among end users. Methods: We developed "CareBuddy," a mobile care ecosystem based on the Stress Process Model and user-centered design. The app includes personalized assessments and tailored solutions, an AI-driven chatbot, GPS-based location monitoring, peer support, a helpline, telemedicine, healthcare provider integration and caregiver self-care resources. Development was informed by interviews with caregivers and stakeholders, followed by two-phase pilot testing to assess usability and acceptability involving 18 and 10 participants respectively Results: In phase 1, the mean system usability scores (SUS) increased from 65.4 (S.D. 11.8) in round 1 to 77.5 (S.D. 7.2) in round 2, and 73.8 (S.D. 15.9) in round 3, with rounds 2 and 3 exceeding the benchmark SUS of 68.
In phase 2, caregivers rated the app highly with mHealth app usability questionnaire (MAUQ) overall total mean score of 95.4 (SD 8.5). The domains of ease of use (mean 24.1; SD 2.9), user interface and satisfaction (mean 40.3; SD 3.4), and usefulness (mean 31; SD 3.9) received high MAUQ ratings. Participants valued the content focused on dementia management and caregiver well-being. Caregivers appreciated the interactive features -social networking portal, service directory, and conversational large language model. Feedback highlighted areas for improvement, including reducing textual overload and addressing navigational challenges. Conclusions: CareBuddy offers a multifaceted digital solution for dementia caregivers, with high usability and satisfaction. An ongoing trial is evaluating the app’s effectiveness in improving caregiver outcomes.
Intergenerational service-learning is increasingly being developed and implemented to address issues of a rapidly aging society and the need to cultivate the younger generation to better understand, a...
Intergenerational service-learning is increasingly being developed and implemented to address issues of a rapidly aging society and the need to cultivate the younger generation to better understand, appreciate and serve this population. Developing and implementing meaningful intergenerational service-learning experiences, however, requires careful consideration to achieve valuable contribution to the older adult with educational takeaways for the learners.
TriGen@SGH HomeCare is a longitudinal intergenerational service learning programme consisting of healthcare volunteers leading youth volunteers to conduct monthly home visits to older adults in the community with the aim of preserving continuity of care and addressing the community needs of the older adult holistically as a multidisciplinary team. The programme’s key pillars include intergenerational connectedness, interprofessional collaboration and community-based integrated care. Singapore General Hospital (SGH) is a renowned healthcare institution in Singapore, providing top-tier medical services, education, and research since 1821.
This paper describes a case series from the TriGen@SGH HomeCare programme demonstrating the benefits to community-dwelling older adults and volunteers and its potential as a feasible intergenerational service-learning programme. The study highlights the importance of programme design, patient-centric care, youth engagement and mentoring, facilitators’ commitment and relationship with both generations in achieving valuable contribution to the older adult alongside educational takeaways for the learners.
Background: Heart Failure (HF) is a complex clinical syndrome with a high morbidity and mortality rate. Despite advancements in treatment, the recurrence of HF remains a significant challenge, often l...
Background: Heart Failure (HF) is a complex clinical syndrome with a high morbidity and mortality rate. Despite advancements in treatment, the recurrence of HF remains a significant challenge, often leading to deteriorating health conditions and increased pressure on the healthcare system. Early detection of recurrence is pivotal in mitigating and managing the adverse outcomes associated with HF. Objective: The primary objective of this study is to collect data that facilitates the identification of digital biomarkers that may indicate deterioration of the heart, and ultimately develop algorithms that can predict HF. Methods: This prospective cohort study is conducted in Copenhagen, Denmark, and will recruit individuals diagnosed with decompensated HF. Participants will be followed for a period of one year, during which they will undergo a Quarterly Assessment Period (QAP) every three months. Each QAP spans seven days and involves continuous monitoring using an ambulatory electrocardiogram (ECG) sensor. Throughout each QAP, participants will also complete daily assessments and questionnaires. All data will be collected using a dedicated mobile application installed on the participant's personal smartphone and securely stored in a cloud-based system. Results: This study is part of the ‘Cardio-Share Model for Cross-Sectoral Ambulatory Treatment of Congestive Heart Disease based on Personal Health Technology (CATCH)' project. Technical and regulatory preparation started in 2023. Recruitment for this study started in January 2025 and is expected to be completed during the spring of 2026. The dataset will be anonymized and published for further research. Conclusions: This study aims to provide a comprehensive longitudinal open-source dataset of HF recorded in real-world ambulatory conditions that enhances our understanding of HF signs and symptoms. This dataset will provide an important source for detailed analysis and understanding of HF based on ambulatory and contextual physiological data. Such insight has the potential to enhance the clinical management of individuals with HF and enable them to handle their condition at home.
Background: Empathy is a crucial aspect of nursing practice that enhances the quality of the relationship between nurses and patients. The empathy-based approach grounded in Swanson's Theory of Caring...
Background: Empathy is a crucial aspect of nursing practice that enhances the quality of the relationship between nurses and patients. The empathy-based approach grounded in Swanson's Theory of Caring has improved nurses' empathetic skills and the quality of care provided to patients. Therefore, it is essential to investigate how this empathy-based approach improves nurses' caring behaviour. Objective: This scoping review explores and analyses the impact of an empathy-based approach grounded in Swanson's Theory of Caring on enhancing nurses' caring behavior Methods: The method used is a scoping review following the five stages outlined by Arksey & O'Malley (2005). These stages include defining the research question, identifying relevant studies, selecting studies for inclusion, mapping data from the included studies, and synthesizing and summarizing the findings. The studies analyzed were published in the last ten years and sourced from PubMed, ProQuest, and Scopus databases. Results: The findings from the scoping review indicate that the application of Swanson's Theory of Caring in empathy-based nursing care significantly contributes to improving nurses' caring behavior. The reviewed studies revealed that training based on this theory enhances patient satisfaction and care quality and helps nurses respond more to patients' emotional needs. Conclusions: The empathy-based approach implemented through Swanson's Theory of Caring effectively improves the quality of care and the nurse-patient relationship. Therefore, it is crucial for nursing education institutions to integrate empathy-based training into their curricula and to support nurses with specific training to enhance their communication and empathy skills.
Background: Advances in data science and technology have transformed lifestyle studies by enabling the integration of multimodal information and generation of large volumes of data. Despite the growin...
Background: Advances in data science and technology have transformed lifestyle studies by enabling the integration of multimodal information and generation of large volumes of data. Despite the growing interest in machine learning (ML) in health behaviour research, significant methodological gaps remain. Objective: The study aims to systematically review the applications of supervised ML algorithms in analyzing healthy lifestyle (HL) data, with a specific focus on the methodological approach employed. The specific objectives are to explore the types and sources of data used in health outcomes, examine the ML processes employed, including explainability artificial intelligence (XAI) methods, and review the software tools utilized. Additionally, this review aims to provide practical guidelines to enhance the quality and transparency of future ML research in health. Methods: Following the PRISMA-ScR recommendations, the search was conducted across PubMed, PsychINFO, and Web of Science, resulting in 48 studies that meet the inclusion criteria. Results: Most studies (37, 77%), integrated multidomain data from physical activity, diet, sleep, and stress. Data sources were split between self-acquired (25, 52.08%) and health repositories (23, 47.92%). Single items measurements were common, particularly for physical activity, diet and sleep. Despite a multimodel approach in 28 studies, random forest was the most frequently used algorithm. Only 10 studies (20.83%) employed XAI methods, with 9 using SHapley Additive exPlanation (SHAP) values and 1 using Local Interpretable Model-agnostic Explanations (LIME). R was the most widely used software, with variations in the libraries employed. Conclusions: This review highlights methodological gaps in the application of supervised ML to HL data. The ML workflow should span from data acquisition to explainability, with iterative steps to improve the process. Multidomain approaches in data acquisition enhance understanding of health issues related to lifestyle but are constrained by low data representativeness due to methodological limitations in acquisition. While random forest was prevalent, a multimodel approach is recommended for comprehensive comparison. Lifestyle components consistently ranked among the top features in studies that incorporated XAI. Integrating XAI methods into the ML pipeline can support personalized interventions, provided the data is accurately collected. The R metapackage tidymodels facilitates process evaluation through unified syntax, improving replicability. Methodological and reporting guidelines are provided to enhance transparency and replicability in multidisciplinary ML research.
Background: Renal tumors represent one of the most common malignancies worldwide, with incidence rates continuing to rise. Early detection and precise treatment are crucial for effective disease manag...
Background: Renal tumors represent one of the most common malignancies worldwide, with incidence rates continuing to rise. Early detection and precise treatment are crucial for effective disease management. Accurate segmentation of renal tumors in CT images plays a critical role in lesion localization and radiotherapy planning. However, current segmentation methods largely depend on manual delineation by radiologists, which is both time-consuming and subject to inter-observer variability due to tumor heterogeneity, posing significant challenges for automation. Objective: This study aims to develop and validate an automated renal tumor segmentation algorithm that addresses the challenges of blurred tumor boundaries and false positives in CT imaging, thereby enhancing clinical decision-making and treatment planning. Methods: We propose GAM-DeepLabV3+, an automatic segmentation model built upon the DeepLabV3+ encoder-decoder framework. The architecture integrates three key innovations. First, an enhanced MobileNetV2 backbone combined with a spatial pyramid pooling layer is employed to extract comprehensive low-level features and critical tumor information from CT scans. Second, a Global Attention Mechanism (GAM) module is incorporated into the decoder to efficiently fuse deep and shallow features, improving boundary delineation. Third, multi-scale feature integration enables the network to adaptively focus on tumor regions with varying sizes and shapes. The model was trained and evaluated on both a private dataset and the publicly available KiTS19 dataset. Results: Experimental evaluation demonstrates that GAM-DeepLabV3+ achieves superior segmentation performance, with Dice coefficients of 0.92 on the private dataset and 0.98 on the KiTS19 dataset. These results significantly outperform conventional methods, particularly in cases with complex tumor morphology. Furthermore, we developed a freely accessible online platform (http://www.cppdd.cn/KAI) to facilitate clinical application and support preoperative planning. Conclusions: The proposed GAM-DeepLabV3+ model provides accurate, efficient, and fully automated renal tumor segmentation, reducing dependence on manual annotation while maintaining clinical-grade precision. By addressing key challenges in renal tumor imaging, our approach offers valuable support for surgical planning and treatment, and holds promise for broader integration into clinical workflows.
Background: Patients waiting in many emergency departments (EDs) face multiple barriers to care, including complex operations, long wait times, and limited communication with staff. These barriers dri...
Background: Patients waiting in many emergency departments (EDs) face multiple barriers to care, including complex operations, long wait times, and limited communication with staff. These barriers drive patient dissatisfaction, increasing the likelihood that patients leave without being seen or completing treatment. This study piloted the feasibility of using text message (or SMS) communication to improve care transparency and to address common patient frustrations. We hypothesized that scaling an automated text message program would be feasible and that patients would wish to have this service. Objective: To assess the feasibility of implementing an automated, event-triggered text message (SMS) communication program in the emergency department (ED) and to evaluate patient interest in receiving such messages during their ED visit. Methods: Text message content and event timing were designed by a multidisciplinary working group of both clinical and non-clinical stakeholders. An existing arrival workflow was modified to support SMS enrollment of ambulatory patients on arrival to the ED. All messages were designed to be programmatically triggered on events generated in the electronic health record (EHR), with no staff intervention required. Nursing and registration staff were trained on message content and workflow. Patients received an optional, anonymous survey at the conclusion of the ED visit to solicit feedback. Results: 13 automated messages were implemented. Just over 4,100 patients enrolled in the program between July of 2023 and October of 2024, of whom 168 provided structured and qualitative feedback through the automated survey. 88.7% of survey respondents expressed a desire to receive text messages in the ED. Conclusions: This program demonstrated that SMS is a technically and operationally feasible intervention and that patients wish to receive such messages. Future directions should explore additional settings, message content, patient population targets, and the effect of messages on the patient experience.
Background: Prolonged postures, such as sustained sitting and standing, contribute to musculoskeletal disorders, yet daily monitoring approaches vary widely, limiting comparability and the development...
Background: Prolonged postures, such as sustained sitting and standing, contribute to musculoskeletal disorders, yet daily monitoring approaches vary widely, limiting comparability and the development of standardized posture monitoring strategies. Objective: This systematic review aimed to summarize current practices in prolonged posture monitoring using accelerometer-based wearable sensors and to identify gaps across four stages: data collection, data preprocessing, data analysis, and user feedback. Methods: This systematic review synthesized 79 studies (82.3% rated low risk of bias) extracted from 16645 records in 10 databases, including PubMed, IEEE Xplore, Scopus, Web of Science, EI, ACM, ProQuest, CINAHL, SPORTDiscus, and EMBASE. The risk of bias was assessed using NOS, QUADAS-2, ROBINS-I, RoB 2, and a self-developed tool for algorithmic studies, with 87.7% rated as low risk. Due to methodological heterogeneity, results were synthesized qualitatively. Results: Thigh- and waist-worn sensors were most common, particularly activPAL and ActiGraph. Sensor placement, population characteristics, and preprocessing methods varied widely. Most studies used manually engineered features with statistical analysis, yet few leveraged deep learning or biomechanical metrics. Only 11 studies implemented user feedback strategies, typically visual or vibratory. Personalized user feedback remains underexplored, and few studies have considered specific free-living contexts or privacy concerns. Conclusions: Findings highlight the promising potential of accelerometer-based posture monitoring systems. However, a lack of standardization in sensor placement and data processing, underrepresentation of diverse populations, and limited real-world feedback mechanisms constrain scalability. Future research should develop standard protocols for sensor placement, include diverse population groups, and prioritize the use of interpretable machine learning to support robust classification. Developers should design mobile-compatible systems with personalized, real-time feedback strategies and embed privacy-preserving methods to enable ethically sound deployment in daily scenarios. Clinical Trial: Registered with PROSPERO (ID: CRD42025637387).
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
Background: Background: Background: Large language models (LLMs) like GPT-3.5 and GPT-4 are transforming virtual patient systems in medical education, offering scalable, cost-effective alternatives to...
Background: Background: Background: Large language models (LLMs) like GPT-3.5 and GPT-4 are transforming virtual patient systems in medical education, offering scalable, cost-effective alternatives to standardized patients. However, systematic evaluations of their performance and limitations are limited. Objective: Objective: Objective: This review evaluates LLM-based virtual patient systems for medical history-taking, focusing on patient types and disease scope (RQ1), techniques enhancing history-taking (RQ2), experimental designs and metrics (RQ3), and public dataset characteristics (RQ4). Methods: Methods: Methods: Following PRISMA guidelines, we analyzed 34 studies (2020–May 2025) from nine databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, SpringerLink, ERIC, arXiv, Springer) using predefined keywords. Results: Results: Results: RQ1: Systems simulate mental health, chronic, neurological, and emergency cases but lack multimorbidity and diverse profiles, limiting applicability. RQ2: Techniques rely on prompt design; few-shot learning and multi-agent frameworks have limited impact. Knowledge graph (KG) integration boosts accuracy by 16.02%, and fine-tuning helps, but further exploration is needed. RQ3: Evaluations use 81.8% Top-1 accuracy, 4.5/5 empathy, 88.1 SUS scores, and 0.9412 robustness but lack standardization and use small samples (10–50 students, 3–5 experts). RQ4: Datasets (e.g., MIMIC-II) are restricted by privacy, hindering comparisons. Conclusions: Conclusions: LLM-based virtual patient systems demonstrate significant potential but face several limitations. Current systems predominantly focus on common diseases, lacking adequate simulation of multimorbidity, cultural diversity, and complex drug interactions, thereby reducing clinical realism. Existing datasets such as MIMIC-III are biased toward single-disease scenarios, English language, and critical care, neglecting broader linguistic and cultural contexts. Methodologically, long prompts suffer from primacy and recency effects, while few-shot learning encounters challenges in maintaining dialogue coherence. To address these issues, incorporating LLM-KG embedding methods into model training can enhance contextual understanding, while combining chain-of-thought reasoning with LoRA improves inference efficiency. Multi-agent frameworks with dialogue compression offer further optimization for real-time interactions. Future research should prioritize the development of open-access, multilingual datasets through ethical data augmentation and international collaboration, supported by regular bias audits to ensure fairness. Establishing unified evaluation frameworks with standardized metrics—such as Top-K accuracy, semantic similarity scores above 0.75, and SUS scores exceeding 80—will be essential for advancing realism, accuracy, and fairness in virtual patient systems.
Background: Atopic dermatitis (AD) affects up to 30.48% of Chinese infants, causing significant health and economic burdens. While patient education is crucial for management, traditional models face...
Background: Atopic dermatitis (AD) affects up to 30.48% of Chinese infants, causing significant health and economic burdens. While patient education is crucial for management, traditional models face limitations in scalability, personalization, and relapse prevention. Digital tools offer potential solutions, but evidence for pediatric AD relapse reduction remains limited. Objective: This multicenter randomized clinical trial (RCT) assessed whether a smartphone-based digital education program reduces relapse rates versus conventional outpatient care alone in children aged 0–6 years with moderate-to-severe AD. Methods: In this parallel RCT across 12 Chinese tertiary hospitals, 615 children (SCORAD ≥25) were randomized 1:1 to: (1) Intervention: Access to the “Skin Care E Station” smartphone platform managed by a contract research organization (CRO), delivering a structured Education Action Plan via CRO-pushed notifications thrice-weekly (Mondays, Wednesdays, Fridays) over 12 weeks, plus 54 weekly multimedia modules (illustrated texts, images, videos, animated stories) covering AD pathophysiology, behaviors, lifestyle, daily care, treatment, and mental health support, alongside real-time clinician access via encrypted telemedicine portal (response time <8 hours) during exacerbations and standard care; or (2) Control: Conventional 15-minute face-to-face counseling at scheduled visits only. All participants received 2-week standardized acute-phase topical therapy before maintenance. Primary outcome was 12-week relapse rate (SCORAD increase ≥10 points post-acute phase), analyzed by intention-to-treat. Secondary outcomes included disease severity (SCORAD, PP-NRS, POEM) and quality of life (IDQOL/CDLQI, DFI) over 52 weeks. Results: The digital group demonstrated significantly lower 12-week relapse rates (16.6% vs 24.0%; RR=0.69, 95%CI 0.50–0.96, p = .02) and reduced relapse risk (HR=0.688, 95%CI 0.490–0.966, p = .03), though differences at other timepoints (4–52 weeks) were non-significant. Intervention adherence (58.0%) exceeded control clinic attendance (49.0%, p = .03), with 26.7% utilizing expedited clinician access. No significant between-group differences occurred in disease severity or quality-of-life scores at any follow-up. Attrition at 12 weeks was lower in the digital group (20.2% vs 27.6%, p = .04), though overall 52-week attrition reached 51.38%. Conclusions: The digital program reduced early relapse rates by 31% at 12 weeks and demonstrated higher adherence than standard care, likely through improved caregiver knowledge and timely intervention. While it didn't alter long-term disease severity or quality of life, it provides a scalable strategy for short-term relapse prevention in regions with high smartphone penetration. Future research should focus on sustaining long-term engagement and equitable access. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2000031474; https://www.chictr.org.cn/bin/project/edit?pid=32400
Background: Transgender and gender diverse (TGD) individuals face health disparities linked to social determinants, including lack of support. Objective: This study evaluated social media use and soci...
Background: Transgender and gender diverse (TGD) individuals face health disparities linked to social determinants, including lack of support. Objective: This study evaluated social media use and social support among TGD patients at a transgender clinic. Methods: A survey assessing social media use, social support, and demographics was emailed to patients at a tertiary care TGD clinic. Results: Of 48 respondents (20% response rate), 50% identified as transfeminine, 29% as transmasculine, and 8% as non-binary. Nearly 70% reported monthly transphobia; 35% reported it weekly. Primary support came from significant others or friends (49%), with 13% citing online friends. Social media was primarily used to connect with queer/TGD communities, mainly via Discord, Reddit, and Instagram. Over half had never attended a gender-related support group, though 60% expressed interest. Conclusions: TGD individuals experience frequent transphobia and seek social support from personal and online connections. Many are open to support groups, suggesting a potential avenue to improve care
Background: Transgender Latina women in the U.S. are disproportionately affected by HIV due to intersecting social and structural vulnerabilities that increase both risk of transmission and barriers t...
Background: Transgender Latina women in the U.S. are disproportionately affected by HIV due to intersecting social and structural vulnerabilities that increase both risk of transmission and barriers to care. Although gender-affirming, culturally responsive services and eHealth strategies offer promise for improving access to HIV-related services, social media-based approaches remain underutilized. While there are community-based organizations (CBOs) that offer bilingual and culturally responsive HIV-related services for Latino LGBTQ+ communities, awareness of these resources can be limited. Objective: This study aimed to develop and pilot a culturally tailored social media campaign to increase awareness of culturally relevant HIV prevention and care services offered by a CBO in King County, WA for transgender Latina women. We assessed the campaign’s feasibility and acceptability. Methods: Using a multi-phase, community-engaged design, phase 1 involved conducting in-depth interviews (n=20) with transgender Latina women recruited through the CBO to identify priorities and preferences related to the social media campaign. Guided by the Unified Theory of Behavior, interview findings informed the development of six draft campaign concepts, which were refined through a focus group conducted with seven transgender Latina women. In phase 2, the finalized campaign was piloted on Facebook and Instagram. A REDCap survey with a subset of transgender Latina women who viewed the campaign (n=100) assessed campaign reach, participant demographics, and perceptions of feasibility and acceptability. Descriptive analyses were conducted. Results: In-depth interviews (n=20) revealed four key themes that guided the development of the campaign: (1) importance of HIV prevention and awareness; (2) accessibility of HIV services; (3) provision of culturally tailored care; and (4) need for confidentiality. The focus group reviewed six draft campaign concepts and recommended incorporating personal stories, cultural references, and messages centered on empowerment and community. Six focus group participants joined the project team to co-create campaign content. The finalized campaign was launched on Facebook and Instagram, and a REDCap survey was completed by 100 transgender Latina women who engaged with the campaign (mean age: 29.7). Most survey respondents (97%) had previously been tested for HIV, with 44% reporting a test within the past six months. Three respondents reported living with HIV, all of whom were on antiretroviral therapy. Nearly all participants (91%) indicated that the campaign motivated them to take some form of action, such as getting tested or seeking additional information or services. Conclusions: This study demonstrates the feasibility and acceptability of a culturally tailored social media campaign to promote HIV prevention and care among transgender Latina women. The participatory development process strengthened the campaign’s cultural relevance and resonance. Findings highlight variations in social media use, access to digital surveys, and levels of engagement with HIV-related services across subgroups of transgender Latina women. These results underscore the importance of recognizing and addressing heterogeneity within this population when designing and implementing digital outreach strategies for HIV prevention.
Background: Myopia has emerged as a major threat to the visual health of adolescents worldwide. Early intervention can effectively slow down the progression of myopia in adolescents. Tuina, a signific...
Background: Myopia has emerged as a major threat to the visual health of adolescents worldwide. Early intervention can effectively slow down the progression of myopia in adolescents. Tuina, a significant therapeutic method in traditional Chinese medicine, has shown promising clinical efficacy in delaying the progression of myopia; however, it lacks robust, large-scale, and standardized randomized controlled trials. Objective: This study aims to explore the efficacy and safety of Tuina therapy in managing myopia in adolescents, thereby providing solid evidence for the application of Tuina in the clinical treatment of myopia. Methods: The design of this study is a multicenter, single-blind, randomized controlled clinical trial. We will include 192 myopic children from four hospitals, who will be randomly assigned in a 1:1 ratio to a Tuina experimental group and a drug-positive control group (tropicamide eye drops). Treatments in each group will be three times a week, for a total of 8 weeks. The Tuina therapy experimental group will receive 20 minutes of Tuina therapy per session, while the drug-positive control group will use tropicamide eye drops, administered every other day, with two drops per session. The primary outcome measures include uncorrected visual acuity and axial length, with secondary measures including refractive power and accommodative amplitude. Data will be collected on the day of enrollment and treatment (week 0), on the day of completion of the 4th and 8th weeks of treatment, and at the end of the 10-week follow-up. Adverse events will be monitored and recorded throughout the study, and statisticians will be blinded. Data will be analyzed using SPSS 28.0. Results: This study has been funded, and data collection is expected to take place between June 2026 and June 2026. Final manuscript submission should happen by August 2026. Conclusions: This study aims to evaluate the efficacy and safety of massage therapy in the treatment of myopic adolescents. We hypothesize that the therapeutic effect of massage therapy is non-inferior to that of topical atropine eye drops, with the added advantages of fewer side effects and stable long-term efficacy, thereby providing reliable evidence and support for the application of massage therapy in the management of myopia in adolescents.
Background: Wearable biomedical devices are increasingly used in clinical and personal health monitoring, but they may not perform equally across all demographics. Studies show that factors such as sk...
Background: Wearable biomedical devices are increasingly used in clinical and personal health monitoring, but they may not perform equally across all demographics. Studies show that factors such as skin pigmentation can affect the accuracy of sensor readings, particularly in pulse oximeters and other light-based technologies. Objective: To evaluate how wearable biomedical device performance varies across ethnic groups, with a focus on measurement accuracy, disparities in clinical interpretation, and potential strategies for reducing bias. Methods: We reviewed literature and studies that analyzed wearable device accuracy by skin pigmentation or self-identified ethnicity. Devices included pulse oximeters, wrist-worn monitors, remote photoplethysmography (rPPG), electrocardiograms (ECGs), and perinatal monitoring devices. Performance metrics such as error margins, rates of occult hypoxemia, and area under the curve (AUC) for diagnostic accuracy were compared. Results: Wrist-worn wearables occasionally show reduced heart rate accuracy for darker skin, whereas remote photoplethysmography tends to perform well under controlled conditions. An AI-enhanced ECG algorithm demonstrated consistently high performance (AUC 0.93) across major ethnic groups. In perinatal devices, observed overestimation has led to screening errors that could prompt unnecessary interventions. Proposed strategies include algorithmic recalibration and adjustments in device design to account for varied skin pigmentation. Conclusions: Ethnic disparities in wearable device accuracy can impact clinical decision-making and patient safety. Strategies such as algorithmic recalibration and inclusive device design are necessary to improve equity and reliability across diverse populations.
Background: Effective self-management of type 1 diabetes mellitus (T1DM) in children and adolescents remains challenging despite advances in insulin delivery and glucose monitoring technologies. Mobil...
Background: Effective self-management of type 1 diabetes mellitus (T1DM) in children and adolescents remains challenging despite advances in insulin delivery and glucose monitoring technologies. Mobile health (mHealth) interventions have emerged as promising tools to support pediatric diabetes care. However, their clinical impact and the behavioral mechanisms through which they operate—particularly those grounded in Social Cognitive Theory (SCT)—are not well established. Objective: This scoping review assesses mHealth applications focused on management of T1DM in the pediatric population and looks into the underlying behavioral frameworks in accordance with the Social Cognitive Theory (SCT) Methods: We conducted a scoping review of five databases (PubMed, Cochrane Library, EMBASE, CINAHL, Scopus) for English-language studies published between January 2000 and July 2024. Eligible studies evaluated mHealth applications for pediatric patients with T1DM (≤18 years) and reported outcomes including glycemic control, self-efficacy, adherence, self-management, or quality of life. Data were extracted and synthesized according to clinical outcomes and the presence of SCT constructs—namely self-efficacy, behavioral capability, expectations, reinforcements, and reciprocal determinism. Results: Of 5607 studies screened, 12 met inclusion criteria. These comprised 4 randomized controlled trials, 4 pilot studies, 2 pre-post intervention studies, 1 retrospective cohort study, and 1 double crossover trial. App features included glucose logging, insulin tracking, bolus calculators, reminders, gamification, and structured educational content. Nine studies reported HbA1c outcomes; four demonstrated statistically significant improvements, while others reported stability or no change. Several studies also reported improvements in treatment adherence and perceived self-efficacy. Eleven of twelve studies incorporated at least one SCT construct, with most integrating behavioral capability and self-efficacy as core components. Interventions employing multiple SCT constructs showed greater promise in supporting sustained behavior change. Conclusions: mHealth applications for pediatric T1DM are complex behavioral interventions that often leverage key principles of Social Cognitive Theory to promote effective self-management. While the evidence supports modest benefits in glycemic control and behavioral outcomes, heterogeneity in study design and outcome measurement limits broader generalizability. Future research should prioritize the development and evaluation of SCT-informed digital interventions with standardized outcome frameworks to improve pediatric diabetes care.
Background: Internet hospitals are playing a significant role in medical care with its potential to provide widely accessible outpatient service delivery via information technologies. Current research...
Background: Internet hospitals are playing a significant role in medical care with its potential to provide widely accessible outpatient service delivery via information technologies. Current research on patients’ satisfaction of Internet hospitals mainly focused on physician-patient relationship and patient demand, and less is considered about the whole process of online consultation. Objective: This study aims to identity the factors influencing patient satisfaction considering the entire process of online consultation to help physicians deliver better online medical services and physical hospitals operate Internet hospital more effectively. Methods: Based on SERVQUAL theoretical model, questionnaire items were designed for the five dimensions of reliability, assurance, responsiveness, empathy and tangibility. 355 patients on the Internet hospital platform operated by a tertiary general hospital in Beijing were taken as the research samples for data collection. Confirmatory factor analysis was carried out for dimensions of the measurement model, and the path analysis was carried out for the structural model. Results: The current consultation process of internet hospitals as perceived by patients did not yet meet their expectations, and the overall satisfaction rate of patients was only 7.15. Five dimensions of reliability, responsiveness, empathy, tangibility and assurance all had different positive predictive effects on patient satisfaction in Internet hospitals to a certain extent. Among them, tangibility exerted the greatest impact on patient satisfaction in Internet hospitals() but obtained the lowest score among the sampled participants (Mean=6.78). Conclusions: Internet hospitals and physicians should focus on the factors of the above dimensions especially tangibility of medical services in the multistage of online consultation, thus better serving patients and promoting the sustainable development of Internet healthcare.
Background: Standard urotherapy for childhood incontinence involves traditional tools like paper bladder diaries, timer watches, wetting alarms and uroflowmeters. However, little is known about how th...
Background: Standard urotherapy for childhood incontinence involves traditional tools like paper bladder diaries, timer watches, wetting alarms and uroflowmeters. However, little is known about how these tools are experienced by today’s digitally native children. Objective: This study aimed to explore how children undergoing urotherapy perceive and experience these commonly used tools, with the goal of informing more engaging and child-centered design approaches. Methods: A qualitative focus group design was used with purposive sampling of children undergoing in-clinic urotherapy group training. Nineteen participants (13 boys, 6 girls) aged 9–13 years, took part in focus groups of 3 to 4 children, held at the hospital. A child-friendly focus group toolkit was used to facilitate discussion through creative and playful exercises. Seven focus groups were conducted, including two repeated sessions, until thematic saturation was reached. All sessions were held in Dutch, video- and audio-recorded, and transcribed verbatim. An inductive conventional content analysis was conducted using a dual-coder approach to identify and iteratively refine emerging themes. Results: Four themes emerged: (1) Attitudes and Motivation: ranging from willingness to engage in urotherapy and use tools, to reluctance or resistance; (2) Social Acceptance: highlighting the impact of peer perception, fear of being bullied, and opportunities to break the taboo and reframe tools as socially desirable; (3) Contextual Influences: including dissatisfaction with school toilets and limited child involvement at doctor visits, contrasted with the positive peer support experienced during group therapy; (4) Digital Integration: children saw many traditional tools as outdated and suggested gamified, smart alternatives. The drawings created by children during the exercises served as a creative reflection of these thematic findings. Conclusions: Involving children in research and development is essential for creating interventions that are truly child-centered. Through creative, qualitative methods, this study uncovered rich insights into children’s experiences with urotherapy tools, pointing to four key design priorities: personalization, stigma-free design, adaptability, and digital innovation.
Background: The COVID-19 pandemic accelerated telehealth adoption, significantly transforming healthcare delivery in Australia, particularly for mental health and allied health services. The Governmen...
Background: The COVID-19 pandemic accelerated telehealth adoption, significantly transforming healthcare delivery in Australia, particularly for mental health and allied health services. The Government of Australia introduced Medicare-subsidized telehealth consultations to improve accessibility, especially in rural and remote areas. This study examines the economic impact of telehealth expansion on mental health service costs, focusing on rural, remote, and very remote populations. Objective: The objectives of the study were to investigate the implications of telehealth expansion on costs and the drivers of costs post-telehealth expansion. Methods: A retrospective observational study analysed Medicare Benefits Schedule (MBS) claims data (November 2017 to February 2023). Costs per 100,000 population for clinical psychology and psychology services delivered in-person and via video consultations in Modified Monash Model (MMM) 4–7 regions were assessed. Analytical methods included Interrupted Time Series Analysis (ITSA), and other multivariate regression methods, considering demographic, geographic, and pandemic-related factors. Results: Telehealth expansion significantly increased video consultation costs per 100,000 population for clinical psychology (AU$169.5 to AU$1,557.6) and psychology (AU$117.9 to AU$1,313.5) while reducing in-person service costs. Service utilisation rose in MMM 4–7 regions, improving access. Cost drivers included younger age, female gender, and state-level differences, with peaks during COVID-19 waves like Omicron. Conclusions: Telehealth expansion improved access to mental health services in rural areas but increased costs due to higher demand. Policy adjustments are necessary to ensure equitable access while maintaining financial sustainability. Further research is required to optimise telehealth utilisation and cost-efficiency.
Background: Bipolar disorders (BD) represent a significant global health challenge, with frequent and severe affective episodes that impair quality of life. Accurate, early prediction of these episode...
Background: Bipolar disorders (BD) represent a significant global health challenge, with frequent and severe affective episodes that impair quality of life. Accurate, early prediction of these episodes remains difficult. Recent advances in mobile sensing offer new possibilities to detecThis study aimed to examine whether spatial exploratory behavior, assessed via passive GPS data, can predict depressive and manic episodes in individuals with BD. Specifically, we evaluated the predictive value of unique places visited and related mobility metrics, using statistical process control (SPC) techniques to identify early deviations indicative of prodromal states.t prodromal changes via smart digital phenotypes, such as geolocation data. Objective: This study aimed to examine whether spatial exploratory behavior, assessed via passive GPS data, can predict depressive and manic episodes in individuals with BD. Specifically, we evaluated the predictive value of unique places visited and related mobility metrics, using statistical process control (SPC) techniques to identify early deviations indicative of prodromal states. Methods: Using high-resolution GPS data from the BipoSense dataset, we applied Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to extract behavioral mobility indicators: number of unique places visited, frequency of location changes, and time spent per location. We implemented exponentially weighted moving average (EWMA)-based SPC to identify 'out-of-bounds' deviations from individual baselines. We then tested the alignment of these deviations with affective episodes and prodromal periods. Optimization of SPC parameters (lambda and control limit L) was performed to enhance predictive accuracy. Results: The analysis included 28 participants with BD and a total of 10,213 observation days, covering 26 depressive and 20 (hypo)manic episodes. While EWMA-SPC detected behavioral deviations during affective episodes, no single variable consistently met predefined thresholds for both sensitivity and specificity. Optimized SPC settings improved performance, but the number of unique places alone did not robustly predict prodromal or acute episodes. No statistically significant predictive accuracy (e.g., sensitivity >70% and specificity >70%) was achieved for any individual indicator (P > 0.05). However, SPC charts showed temporal patterns potentially useful for future multimodal models. Conclusions: Although unique places visited alone may not suffice as a predictive marker, the application of EWMA-based SPC to GPS data holds promise for the development of smart digital phenotypes. This approach may contribute to early detection of affective episodes in BD and support more timely interventions. Further research is needed to refine these digital biomarkers and validate their clinical utility in reducing the frequency and severity of illness phases.
Background: The rising prevalence of diabetes necessitates continuous monitoring and treatment, especially for type 2 diabetes. Patient education plays a crucial role in enabling self-management, and...
Background: The rising prevalence of diabetes necessitates continuous monitoring and treatment, especially for type 2 diabetes. Patient education plays a crucial role in enabling self-management, and web-based educational portals can support patients effectively. While literature highlights various issues impacting self-management system design, few studies explore the usability aspects that either facilitate or hinder such systems for this patient group. Objective: This study investigated usability issues related to a web-based educational portal for diabetes patients, in Razavi-Khorasan province, Iran. Additionally, it sought to develop a framework of design principles to address critical usability concerns for diabetes self-management. Methods: The literature on diabetes self-management was analyzed. The analysis focused on design and usability issues affecting self-management. A think-aloud study was carried out with ten patients using a web-based educational portal designed for diabetes patients. The portal comprises nine sections, with patients performing tasks related to each one. Participants’ task completion times were measured, and problem severity ratings were calculated. Results: Five design principles were proposed that cover: (1) understanding and learning about one’s condition, (2) motivation and fostering sustained practices, (3) autonomy and confidence, (4) interaction and collaboration, and (5) privacy and security. The study identified 111 usability problems related to the portal’s nine sections. Feedback from participants was considered to refine the principles, and participants’ considerations were recorded for any issues that worked against the principles. A total of 16 suggestions for improvement were extrapolated from the data. Conclusions: This study highlights critical usability and design aspects to consider when developing web-based educational portals to support self-management in type 2 diabetes.
Background: Effective team communication is critical in pediatric cardiopulmonary arrest management, where delays or miscommunication can jeopardize survival. TeamScreen, a web-based interface display...
Background: Effective team communication is critical in pediatric cardiopulmonary arrest management, where delays or miscommunication can jeopardize survival. TeamScreen, a web-based interface displayed on a large screen, was developed to enhance cardiopulmonary resuscitation (CPR) by providing real-time visualization of clinical data and resuscitation steps aligned with American Heart Association (AHA) Pediatric Advanced Life Support algorithms. Objective: This study evaluated the usability of the TeamScreen Figma prototype, evaluating how efficiently and accurately experienced emergency physicians and nurses retrieved critical information during a simulated pediatric in-hospital cardiac arrest (IHCA) scenario. Although no strict time constraints were imposed, participants were instructed to perform the tasks as spontaneously and as quickly as possible. Methods: Usability testing involved 20 pediatric emergency physicians and nurses with varied CPR experience. Participants performed 21 information-retrieval tasks within a simulated pediatric cardiac arrest scenario. Data collection included audio/video recordings via the “think-aloud” method, the Post-Study System Usability Questionnaire (PSSUQ), and a post-test survey. Effectiveness was measured by task completion rates, efficiency by time-on-task, and satisfaction via PSSUQ scores. Think-aloud data were analyzed for usability issues using Nielsen Norman Group’s severity ratings and Bastien and Scapin’s ergonomic criteria. Results: 5 physicians and 15 nurses achieved an 81% task success rate, with a mean completion time of 8.13 seconds, calculated across all 21 tasks and all participants. PSSUQ scores reflected high satisfaction (mean: 2.40/7, lower is better), notably for information clarity and system utility. Qualitative analyses identified 21 usability issues, 8 deemed critical, primarily involving information visibility, navigation, and density, highlighting areas for interface and workflow enhancement. Conclusions: The usability evaluation confirmed TeamScreen’s potential to improve real-time information access during pediatric CPR, with strong task success and satisfaction scores supporting its role in aiding decision-making. Challenges with visibility, navigation, and information density require further refinement. These findings will guide improvements and inform the design of multicenter trials to assess TeamScreen’s efficacy in simulation-based resuscitation settings.
Background: Effective preoperative digital health education (DHE) for patients undergoing knee arthroplasty (KA) requires optimising cognitive load and learning performance, particularly among older a...
Background: Effective preoperative digital health education (DHE) for patients undergoing knee arthroplasty (KA) requires optimising cognitive load and learning performance, particularly among older adults with age-related cognitive limitations. Although multimedia modalities and learning motivation are recognised as influential factors, their combined effects and underlying cognitive mechanisms remain insufficiently explored in KA education. Objective: This study aims to explore how learning motivation influences cognitive load and learning performance across different modalities (text, text-graphic composite, and video-based) in the preoperative health education of KA. Methods: A 2×3 factorial experiment was conducted with 62 KA patients stratified by learning motivation (high vs. low). Participants engaged with DHE materials in three modalities (text, text-graphic composite and video-based). Cognitive load was assessed using subjective measures (NASA-TLX) and objective eye-tracking metrics (average fixation duration, number of fixation points, duration before the first fixation). Learning performance was evaluated via knowledge retention and transfer tests. Data were analyzed using general linear models, Two-way ANOVA analyzing and Pearson correlations. Results: High-motivation learners exhibited significantly lower cognitive load (NASA-TLX: F= 37.625, p < 0.001) and superior learning performance (F= 34.000, p < 0.001) compared to low motivation patients. Video-based materials induced the lowest cognitive load, while text-graphic composites promoted deeper learning despite higher load. Cognitive load negatively correlated with learning performance (r = -0.32, p< 0.001). Eye-tracking revealed that high-motivation learners adapted attention strategies across modalities, whereas low-motivation learners struggled with extraneous load in text-graphic formats. Conclusions: Learning motivation and multimedia modality interact to influence cognitive load and learning effectiveness in preoperative DHE for KA patients. Video-based materials support cognitive efficiency, while text-graphic composites may benefit motivated learners by enhancing germane load. To optimise patient education outcomes, DHE should incorporate motivational scaffolding, adaptive modality matching, and real-time cognitive load monitoring.
Background: Advancements in information technology have significantly enhanced self-service delivery and transformed the service landscape, enabling organizations to deploy various wearable medical de...
Background: Advancements in information technology have significantly enhanced self-service delivery and transformed the service landscape, enabling organizations to deploy various wearable medical devices that foster increased customer engagement. Wearable medical devices, a form of self-service technology in the healthcare sector, have gained widespread attention. Objective: This study investigates the key factors influencing the intention to adopt wearable medical devices, leveraging the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods: The study explores the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on users’ behavioral intentions. It also examines the moderating roles of age, gender, experience, and voluntariness of use. A Dominance-based Rough Set Approach (DRSA) is employed to infer decision rules for adoption intention. Data were collected from 382 current and potential users of wearable medical devices, and the analysis is conducted using a taxonomy of induction-related activities through the DRSA method. Results: The analysis yielded interpretable decision rules that highlight the relationships among UTAUT factors and adoption intentions, as well as the influence of moderating variables. These insights help to clarify user behavior in adopting wearable medical devices. Conclusions: The findings provide valuable theoretical contributions to technology adoption research and offer practical implications for the development, marketing, and implementation strategies of wearable medical technologies.
Background: Personality traits are known predictors of substance use (SU), but their expression and association with SU during a global crisis remain largely unexamined at a population scale. The COVI...
Background: Personality traits are known predictors of substance use (SU), but their expression and association with SU during a global crisis remain largely unexamined at a population scale. The COVID-19 pandemic, which amplified both SU rates and online social engagement, created a unique natural experiment to investigate these dynamics through digital discourse. This approach offers insights beyond traditional self-report methods, which is crucial for developing timely and targeted public health interventions. Objective: To evaluate whether the associations between the Big Five personality traits and SU discourse shifted during the 2019–2021 period, and to conduct a focused analysis of how these traits predict SU and relate to specific substance types, emotional expression, and demographic factors. Methods: We analyzed a corpus of several hundred million public posts from a major social media platform from 2019 to 2021. Using a pipeline of natural language processing and deep learning models, we identified SU-related posts and subsequently extracted scores for the Big Five personality traits, emotions, and user demographics. We employed trend analysis to compare annual shifts in trait-SU associations, while detailed 2020 data underwent rigorous modeling using logistic regression, correlation analysis, and topic modeling to elucidate the core relationships. Results: Our analysis revealed that Extraversion (OR=3.22) and, most strikingly, Agreeableness (OR=4.04) were the strongest positive predictors of being a substance user. In stark contrast to the conventional self-medication hypothesis, Neuroticism emerged as a robust protective factor against SU (OR=0.29). This counterintuitive finding was supported by a decreased association between Neuroticism and SU posts at the pandemic's onset in 2020 (d=−0.13) and a negative correlation with the expression of negative emotions online. Topic modeling further indicated that SU discourse was frequently embedded in social contexts (Social Drinking, Friendly Beverage Choices) rather than themes of solitary coping. Conclusions: Our findings challenge traditional models by demonstrating that in large-scale online discourse, SU expression is more powerfully linked to social-affiliative traits than to negative emotionality. The paradoxical protective role of Neuroticism suggests that established risk profiles may not apply uniformly to digital environments, particularly during a public health crisis. These insights are vital for refining computational methods for public health surveillance and developing interventions that recognize the potent social drivers of substance use in the digital age.
Background: Scientific misinformation remains a major barrier to effective health communication. Bridging the gap between academic research and public understanding requires tools that simplify scient...
Background: Scientific misinformation remains a major barrier to effective health communication. Bridging the gap between academic research and public understanding requires tools that simplify scientific language and adapt content to diverse audiences. Objective: This study presents Maria Ciência, a specialized GPT-based assistant for science communication. The tool supports researchers in translating peer-reviewed scientific findings through simple prompts into accessible, ethically appropriate materials tailored for children, the general public, health professionals, and policymakers. Methods: The tool was configured using prompt engineering techniques and guided by curated reference materials on inclusive and non-stigmatizing scientific language. Materials derived from 47 public health articles resulted in 188 outputs, which were assessed by 121 evaluators using four criteria: clarity, level of detail, language suitability, and content quality. Results: Globally, mean scores were high: clarity (4.90), language suitability (4.78), content quality (4.72), and level of detail (4.56), on a 5-point scale. Materials for children and the general public consistently achieved the highest ratings across all criteria. Conclusions: A targeted comparison with the base large language model (ChatGPT 4o) demonstrated superior performance of Maria Ciência in contextual stability. Maria Ciência demonstrates the potential of AI-assisted tools to enhance knowledge translation and counter scientific misinformation by producing scalable, audience-specific content.
Background: Digital platforms, particularly social media including Instagram, present unique opportunities for health promotion among adolescents due to their widespread use with interactive features...
Background: Digital platforms, particularly social media including Instagram, present unique opportunities for health promotion among adolescents due to their widespread use with interactive features supporting high user engagement. However, the feasibility of effectively utilizing platforms like Instagram for health interventions requires careful consideration of adolescent engagement patterns. Objective: This pilot study evaluated the leduin program – designed to foster essential life skills and functional social media use among adolescents – while also exploring the broader feasibility of using Instagram to deliver complex social and psychological interventions in this population. Methods: The study adapted Bowen’s feasibility framework and used a mixed-methods approach. Quantitatively, Instagram interaction metrics of 99 participants (62 women (62.6%) and 37 men (37.4%), aged 14–18; mean = 15.2, SD = 0.74) were analyzed descriptively (means, medians, SDs) and inferentially (Welch’s ANOVA, Kruskal-Wallis, Pearson and Spearman correlations, linear and segmented regressions) using RStudio. Metrics included story views, retention rates, feature engagement (e.g., polls, question stickers, quizzes), and drop-off rates. Recruitment efforts were also analyzed descriptively. Qualitatively, 13 post-program semi-structured interviews were conducted with 11 women (64.7%) and 6 men (35.3%) (mean age = 15.29, SD = 0.99). Participants were sampled to reflect varying engagement levels (six high, five medium, six low). The mean interview duration was 25:11 minutes (SD = 6:34). Content analysis, with high inter-coder reliability (κ = .90), comprehensively explored participants’ experiences and the program’s impact. Results: Quantitative results indicate that the recruitment process was challenging, with 101 schools and 10 youth centers contacted, resulting in a participation rate of 12.8% (99 out of 775 students). On Instagram, story views ranged from 34 to 81 per post, with an average daily retention rate of 87.7% (SD = 7.8%). By week 4, 76.0% of the total drop in views had occurred (mean views declined from 66.1 to 53.4); by week 6, 97.3% of the drhad been reached (49.9 views), indicating sustained viewer interest over the 14-week program. Features requiring minimal user effort including polls (56.8%-54.4%), quizzes (56.6%), and sliders (51.2%) showed significantly higher interaction rates than more demanding features such as challenges (21.7%) and question stickers (20.6%) (P<.001). Qualitative findings revealed that adolescents valued the program, its design and methods for its relevance to their daily lives and its support in developing essential life skills. Suggestions for improvements were made. Conclusions: The study underlines the potential of various Instagram features and content posting schedules for health interventions to meet adolescent preferences and interests. Challenges in reaching the target group effectively emphasize the need for targeted recruitment strategies and optimizing initial content to boost engagement, underscoring the critical implications for prevention research and policy in leveraging digital platforms to enhance adolescent health.
Background: Digital health interventions (DHI) deliver health-related services in a digital manner. To be efficient and adopted in particular by older adults, they must be tailored to address their ne...
Background: Digital health interventions (DHI) deliver health-related services in a digital manner. To be efficient and adopted in particular by older adults, they must be tailored to address their needs which could be realized by an inclusive design approach. Inclusive design is an approach that aims to accommodate the needs of a broad spectrum of users, taking into account factors such as socioeconomic status, gender, age, ethnicity, and language diversity. Objective: To develop a set of inclusive design heuristics for DHIs designed for older adults. Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, this scoping review examined peer-reviewed papers from databases including IEEE Xplore, Scopus and PubMed collected on January 12, 2025. Studies were included if they (1) described a DHI specifically designed for individuals aged 60 years or older and (2) described an inclusive design approach. Data extraction included information on the DHI and its design process, facilitators and barriers for adopting DHI by older adults. Results: Out of 944 records, 34 papers were included and considered for data synthesis. DHI are provided through broad range of technical platforms (e.g. web-based, mobile, voice assistant). Sometimes, their design process included older adults but also clinicians, engineers and researchers. Design elements to be considered for inclusive design comprise 11 aspects covering multiple dimensions: visual design and readability, navigation, accessibility, customization and personalization, social engagement and support, learnability, multi-platforms and device compatibility, motivation, feedback and user engagement, security and privacy, inclusive language and costs. Barriers range from age-related health issues to technical hurdles related to access or connectivity. Conclusions: Inclusive design of DHI for older adults goes beyond usability and user interface design. The older adults have to be placed into the center of development, with their needs and challenges to be identified and addressed in the solution. Future work will have to validate our results from a practical perspective. Adoption of our heuristics in practice could be fostered by developing concrete methods considering the heuristics.
Background: People living with HIV (PLWH) are at increased risk of cardiovascular disease but in low- and middle-income countries, screening and diagnosing cardiovascular disease requires expertise an...
Background: People living with HIV (PLWH) are at increased risk of cardiovascular disease but in low- and middle-income countries, screening and diagnosing cardiovascular disease requires expertise and equipment unavailable in most HIV treatment centres. Objective: We aim to evaluate an alternative non-invasive approach using low-cost wearable pulse oximeters and machine learning in asymptomatic PLWH attending routine outpatient clinics in Vietnam. Methods: Eighty asymptomatic PWLH without prior history of cardiovascular disease were screened for cardiovascular disease risk using Framingham and D:A:D modified Framingham scores. Cardiovascular disease was screened for from ECG and echocardiograms performed in all participants at baseline, with ECG repeated at 2-3 follow-up visits over a 12 month period. Continuous pulse oximetry waveforms were collected at baseline using low-cost wearable oximeters from which heart rate variability waveform morphology features were extracted from a 5 minute segment. These features and variables from risk scores were used to develop machine learning models predicting cardiovascular risk and cardiovascular disease. Model features were ranked to deliver further insight. Participants perspectives were collected by questionnaire at a follow up visit. Results: Acceptable quality data was available from all participants. Overall 52 (65%) of participants were classified as low risk using the standard Framingham score, whereas 36 (45%) were classified as low risk according to D:A:D modified score. Baseline findings suggestive of cardiovascular disease were seen in 4 (11%) participants in the low risk group and 4 (11%) in the higher risk group. Elastic Net machine learning model achieved the best performance predicting Framingham or D:A:D modified risk groups, with an accuracy of 91% for risk groups alone and 85% and 86% respectively for the composite endpoint of risk or evidence of cardiovascular disease. Feature ranking particularly identified pulse waveform morphology features as important predictors of risk group, and whereas heart rate variability features were included in composite endpoint prediction. Overall participants expressed positive sentiments about wearable devices. Conclusions: Low-cost pulse oximeters linked to machine learning algorithms are a feasible non-invasive approach for diagnosing and screening cardiovascular disease in PLWH in a low resource setting.
Background: People who are incarcerated face significantly higher health risks than the general population, yet deaths in custody remain underreported and poorly monitored by public health systems. Al...
Background: People who are incarcerated face significantly higher health risks than the general population, yet deaths in custody remain underreported and poorly monitored by public health systems. Although the federal Death in Custody Reporting Act (DCRA) requires states to report all deaths in correctional facilities to the U.S. Department of Justice, reporting has been inconsistent, delayed, and often inaccessible to the public. As a result, researchers have turned to press releases issued by correctional agencies as one of the few timely sources of information on individual deaths in custody. These press releases, however, vary widely in content and structure, making it difficult to extract standardized information. Manually reviewing and coding these documents is time consuming and hard to scale. Crowdsourcing platforms like Amazon Mechanical Turk (MTurk) may offer a faster, low-cost method for gathering data, but their utility in this setting remains untested. Objective: This pilot study evaluated whether MTurk could be used to extract structured information from press releases about deaths in custody, as part of a broader effort to improve the timeliness and transparency of health data in correctional systems. Methods: We selected 144 press releases describing individual deaths that occurred between 2000 and 2023 across 35 U.S. prison systems. Each press release was assigned to three MTurk workers, for a total of 432 participants. Workers completed a 16-question form aligned with DCRA variables, including age, race and ethnicity, date of death, and facility location. We assessed how often workers agreed on responses, reviewed common types of errors, and recorded the time to complete tasks. Results: All 144 data abstraction tasks were completed within 48 hours, illustrating the efficiency of the MTurk platform. However, interrater agreement was low, with concordance rates of 14.2 percent for age, 12.3 percent for race or ethnicity, and 11.4 percent for date of birth. Qualitative analysis revealed frequent errors, omissions, and indications of inattentive or automated responses. Workers often misinterpreted system-specific terminology and, in some cases, submitted placeholder text rather than extracting information directly from the source material. Conclusions: Although MTurk allowed for rapid task completion, the quality of the extracted data was consistently low when applied to press releases about deaths in prisons and jails. These findings suggest that general crowdsourcing platforms may not be well suited for extracting accurate and detailed health information from unstructured or inconsistent sources without additional training, oversight, or quality checks. Even so, this remains a promising area for further research. With improved task design and support from artificial intelligence tools, crowdsourcing may help address important gaps in public health surveillance of deaths in custody. Long term progress, however, will require correctional agencies to implement consistent, transparent, and standardized systems for reporting deaths, similar to those in healthcare and public health systems. Clinical Trial: N/A
Background: : Non-alcoholic fatty liver disease (NAFLD) is a common comorbidity in patients with Type 2 Diabetes Mellitus (T2DM), contributing significantly to hepatic and cardiovascular morbidity. De...
Background: : Non-alcoholic fatty liver disease (NAFLD) is a common comorbidity in patients with Type 2 Diabetes Mellitus (T2DM), contributing significantly to hepatic and cardiovascular morbidity. Despite increasing global prevalence, limited local data are available regarding NAFLD among diabetics in Pakistan. Objective: To determine the prevalence of NAFLD in patients with T2DM attending a tertiary care hospital in Islamabad. Methods: : A cross-sectional study was conducted from January to June 2024 in the Medicine Department of a tertiary care hospital in Islamabad. A total of 300 patients with confirmed T2DM were enrolled using convenience sampling. Patients underwent abdominal ultrasonography for NAFLD diagnosis. Data on demographics, glycemic control, BMI, and lipid profile were collected. NAFLD prevalence and its association with clinical parameters were analyzed using SPSS version 26. Results: Of the 300 T2DM patients, 192 (64%) were diagnosed with NAFLD. A higher prevalence was noted in patients with BMI ≥27 kg/m² (p < 0.001) and poor glycemic control (HbA1c ≥ 7.5%) (p = 0.002). No significant gender difference was found (p = 0.21). Dyslipidemia was significantly associated with NAFLD (p = 0.01). Conclusions: The prevalence of NAFLD among T2DM patients in our setting is notably high. Early screening and integrated management are crucial to mitigate hepatic complications in diabetic patients.
Background: Leadership development programs in healthcare often fail due to their lack of adaptability to the schedules of busy clinicians. This study addresses the need for scalable, flexible program...
Background: Leadership development programs in healthcare often fail due to their lack of adaptability to the schedules of busy clinicians. This study addresses the need for scalable, flexible programs tailored to nurse leaders. Objective: This case study evaluated the acceptability, appropriateness, and feasibility of the Relational Playbook, an evidence-based leadership development program developed in the Veterans Health Administration, delivered through the Whistle Systems employee recognition web and mobile applications. Methods: A one-year case study approach was deployed using descriptive survey data and qualitative interview analysis. The Playbook’s educational content and interventions were hosted on the Whistle platform, which integrates behavioral science and gamification strategies. Content was delivered weekly via app-based nudge notifications and email. Engagement metrics included activity completion rates. User experience data were collected through weekly reflection surveys (with Likert scale responses and open-text options), monthly check-ins, and a post-implementation acceptability, appropriateness, and feasibility survey and interview. Descriptive statistics summarized engagement levels and trends, while qualitative data were analyzed using content analysis to identify recurring concepts. Quantitative and qualitative data were analyzed sequentially for comprehensive insights. Results: Five cardiology nurse practitioners (NPs) from a large academic medical center, providing both inpatient and outpatient care, participated. The Whistle platform was deemed an acceptable, appropriate and feasible technology for delivering the Playbook content. Participants valued the weekly nudges, microlearning content, and flexibility of web and mobile applications. The Playbook content supported personal growth and fostered positive shifts in attitudes toward work. Conclusions: Delivering leadership development content through the Whistle platform is an acceptable approach to support the growth and well-being of busy nurse leaders.
Background: Mobile health (mHealth) applications offer considerable potential for health promotion. However, their adoption and sustained use remain limited. While existing studies have explored aspec...
Background: Mobile health (mHealth) applications offer considerable potential for health promotion. However, their adoption and sustained use remain limited. While existing studies have explored aspects such as app design and user engagement, the early psychological and practical barriers to initial adoption are less understood. Factors such as perceived usefulness, ease of use, and users' health status are thought to influence adoption. Understanding the relative importance of these factors—especially in comparison to user attitudes toward other types of apps—can offer valuable insights into the psychological mechanisms underlying app adoption; however, such comparative studies are rare. Objective: This study aimed to delineate the key determinants of user attitudes toward health app adoption, focusing on Perceived Usefulness and Psychological Resistance. We further explored the relative influence of smartphone proficiency versus health status in shaping these perceptions, and contextualized our findings through comparisons with other commonly used applications. Methods: A cross-sectional online survey was conducted with a quota-sampled cohort of 717 smartphone users in Japan. Key measures included Perceived Usefulness (10-point Numerical Rating Scale; NRS) for five app categories (e.g., news, social networking) and Psychological Resistance (5-point NRS) to installing a physician-recommended health app versus a discount-incentivized shopping app. Data on smartphone proficiency, reasons for resistance, health status (including WHO-5), and sociodemographics were also collected. Non-parametric tests (e.g., Friedman test, Kruskal-Wallis test with Dunn's post-hoc tests, McNemar's test) were used for statistical analysis. Results: Health apps were perceived as moderately useful (mean = 6.2), significantly less so than News apps (mean = 6.8; P<.001). Psychological resistance to installing a physician-recommended health app (mean = 2.69) was comparable to that for a discount-incentivized shopping app (mean = 2.66). Concern over smartphone storage was a primary reason for resistance for both types of apps (Health: 43.1%; Shopping: 48.3%). Critically, neither Perceived Usefulness nor resistance toward health apps showed a significant association with users' self-rated health status (P=.88 and P=.18, respectively). In stark contrast, smartphone proficiency was associated with these user attitudes: higher proficiency was associated with higher Perceived Usefulness and significantly lower resistance (both Kruskal-Wallis tests, P<.001), demonstrating a graded relationship. Conclusions: Smartphone users do not generally perceive health apps as uniquely useful compared to other app types. This "moderate" Perceived Usefulness, when weighed against practical burdens like smartphone storage, suggests users may not prioritize mHealth tools, resulting in initial resistance. Such resistance can hinder digital health interventions from reaching their intended audiences. Furthermore, smartphone proficiency was more strongly associated with user attitudes than self-rated health. These findings emphasize that to ensure equitable mHealth adoption, public health strategies must go beyond creating user-friendly apps and focus on enhancing digital competencies.
Background: College students are at heightened risk for mental health problems but often demonstrate low rates of seeking professional help. Although digital mental health tools can improve accessibil...
Background: College students are at heightened risk for mental health problems but often demonstrate low rates of seeking professional help. Although digital mental health tools can improve accessibility and reduce stigma, most are narrowly focused and lack integration with campus-based services. Multi-domain platforms that integrate diverse support features offer personalized, scalable solutions; however, their usability and effectiveness remain largely underexplored. Objective: This study evaluated “Fruto,” a multi-domain digital platform designed to support help-seeking behaviors among university students. We investigated students’ interaction with its integrated features, tracked changes in their attitudes and beliefs over time, and identified design elements that influenced these outcomes. Methods: We conducted a two-phase, mixed-methods study. Phase 1 involved vignette-based semi-structured interviews (n = 16) to explore user experiences with a prototype version of Fruto, with thematic analysis guiding platform refinement. In Phase 2, a single-group pre-post study design was used, involving 70 students who used the app over eight weeks. Surveys assessed help-seeking attitudes, beliefs about counseling, and perceived app quality. Paired t-tests examined pre-post changes, and stepwise regression identified predictors of outcomes. Results: Significant improvements were observed in student’s positive attitudes toward help-seeking (t = -2.89, p = .005) and counseling expectations (t = -2.91, p = .005). However, no significant changes were observed in negative attitudes or socially supportive beliefs. Regression analyses indicated that subjective satisfaction with the app significantly predicted positive help-seeking attitudes (β = 0.227, p < .05), while perceived information credibility predicted positive counseling expectations (β = 0.237, p < .05). Qualitative findings emphasized the importance of trusted content providers, seamless feature integration, and relatable self-discovery content in reducing psychological barriers and enhancing user engagement. Conclusions: Fruto shows potential as a campus-integrated, multi-domain platform that supports student mental health through a user-centered, integrated design. Such platforms may be better equipped to address the evolving and personalized needs of students. Future research should incorporate control groups, long-term follow-up, and objective usage data to confirm efficacy and inform broader implementation. Clinical Trial: Clinical Research Information Service (CRIS) KCT0010622; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=30274&status=5&seq_group=30274&search_page=M
Caffeine consumption is a common strategy to enhance alertness, particularly among medical students managing intense academic demands. This study examines caffeine intake across different stages of me...
Caffeine consumption is a common strategy to enhance alertness, particularly among medical students managing intense academic demands. This study examines caffeine intake across different stages of medical training—first-year (M1), second-year (M2), and third-year (M3) medical students—to determine whether intake increases as students progress. M1–M3 students at a California medical school completed an anonymous survey (8/14/25–8/28/25) on weekly caffeine intake. Likert-scale questions assessed consumption and impact. SPSS 28 was used; nonparametric tests and Spearman’s correlation identified significant differences (adjusted p ≤ 0.05). Caffeine totals were calculated per item.Among 122 respondents, M3s consumed more caffeine from coffee than M1s (p = .028) and M2s (p = .010), and more from OTC drugs than M1s (p = .010) and M2s (p = .006). Higher modified CAGE scores (1–3) were linked to greater caffeine intake than score 0 (p < .001–.040).Caffeine use increased with training level, highest in M3s, likely due to rising demands. Tea remained stable; soft drink use declined. M3s consumed more energy drinks and chocolate. Findings align with stress-related stimulant use. Limitations include single-site, self-report data, and lack of longitudinal or confounding variable control.
Background: Academic institutions face increasing challenges in grant writing due to evolving federal and state policies that restrict the use of specific language. Manual review processes are labor-i...
Background: Academic institutions face increasing challenges in grant writing due to evolving federal and state policies that restrict the use of specific language. Manual review processes are labor-intensive and may delay submissions, highlighting the need for scalable, secure solutions that ensure compliance without compromising scientific integrity. Objective: To develop a secure, AI-powered tool that assists researchers in writing grants consistent with evolving state and federal policy requirements. Methods: GrantCheck was built on a private AWS Virtual Private Cloud, integrating a rule-based natural language processing engine with large language models (LLMs) accessed via Amazon Bedrock. A hybrid pipeline detects flagged terms and generates alternative phrasing, with validation steps to prevent hallucinations. A secure web-based front end enables document upload and report retrieval. Usability was assessed using the System Usability Scale. Results: GrantCheck achieved high performance in detecting and recommending alternatives for sensitive terms, with a precision of 1.000, recall of 0.73, and an F1 score of 0.84—outperforming general-purpose models including GPT-4o (F1 = 0.43), Deepseek R1 (F1 = 0.40), Llama 3.1 (F1 = 0.27), Gemini 2.5 Flash (F1 = 0.58), and even Gemini 2.5 Pro (F1 = 0.72). Usability testing among 16 faculty and staff participants yielded a mean System Usability Scale (SUS) score of 82.2, indicating a positive user satisfaction with the tool’s interface, functionality, and workflow integration. Conclusions: GrantCheck demonstrates the feasibility of deploying institutionally hosted, AI-driven systems to support compliant and researcher-friendly grant writing. Its hybrid architecture ensures high performance and privacy while reducing administrative burden in navigating shifting language policies.
Background: Right ventricular dysfunction (RVD) is an important predictor of outcomes in patients with heart disease, which has significant prognostic implications for a range of diseases, including i...
Background: Right ventricular dysfunction (RVD) is an important predictor of outcomes in patients with heart disease, which has significant prognostic implications for a range of diseases, including ischaemic and non-ischaemic cardiomyopathy, valvular heart disease, congenital heart disease, and pulmonary hypertension. The routine diagnosis of right heart structural abnormalities at an earlier stage, ideally when only structural abnormalities are present but patients are not yet symptomatic, represents an elusive but critical goal in the field of cardiology. Objective: The objective of this study is to apply deep learning (DL) analysis to chest X-rays (CXRs) in order to accurately detect specific structural abnormalities, thus facilitating the early identification of patients exhibiting RVD and improving outcomes. Methods: Approximately 10,000 adult CXRs with corresponding echocardiographic labels of right ventricular function, including right ventricular fractional area change (RVFAC), tricuspid annular plane systolic excursion (TAPSE) and right ventricular myocardial performance index (RVMPI), are currently being collected within a 12-month period. The study will employ DenseNet-121 for the interpretation of CXRs with the objective of identifying the presence of RVD. The DL model will be evaluated against independently collected and labelled sets of CXRs. Subsequently, three main assessments of the model will be performed: validation of the trained model on a separate dataset of patients who have been treated in The emergency department, validation on another independent test dataset obtained from healthy volunteers, and comparison of the model's performance against that of five radiologists on a sample of CXRs will be conducted. The primary objective of the study protocol is the creation of a DL model that is capable of accurately identifying RVD from inexpensive and prevalent examinations of CXRs. Results: The collected data will be synthesized to evaluate the program's acceptability and the feasibility of study procedures. Ethical approval was obtained in November 2024, followed by the initiation of participant recruitment. Data collection is scheduled to continue through December 2025. Conclusions: This feasibility trial may confirm deep learning can identify right ventricular dysfunction from chest X-rays Clinical Trial: Trial registration number Chinese Clinical Trial Registry, ChiCTR2500095838.
Background: Over 1.9 billion people are classified as being obese or overweight. Digital technologies have varying effects in affecting weight loss in participants. Precision health exploits the benef...
Background: Over 1.9 billion people are classified as being obese or overweight. Digital technologies have varying effects in affecting weight loss in participants. Precision health exploits the benefits of digital health and data by being able to provide highly tailored, or personalised, pathways to service users. Obesity is a multi-morbid condition caused in part by lifestyle risk factors including dietary choices, sleep hygiene, exercise and mental health. Objective: The objective was to assess the effect of participants choosing their health “focus” on joining the NHS-certified Gro Health app to support holistic remote weight management. Methods: Participants were invited to engage with a precision behavioural change tool that addresses the four pillars of health-mental health/wellbeing, nutrition, sleep and exercise-via the Gro Health app. Participants were referred by primary care teams or local authorities and invited to use the tool’s education programmes, access multidisciplinary team (MDT) health coaching and track their health. Gro Health onboards users to self-select a “Focus” on either: Sleep, Exercise, Nutrition, or Wellbeing. Outcome variables-weight (kg), HbA1c (%), PHQ-8 (depression), Karolinska Sleepiness Scale (KSS) and Patient Activation Measure (PAM)-were compared across “focus” groups. Results: A total of 438 participants (mean age: 41.9 ± 13 years; mean starting weight: 95.59 kg ± 5.6) downloaded the app. 72% selected nutrition as their focus. The greatest average weight loss of 7.0 kg was observed in this group. Improvements in sleepiness and depression were highest among those selecting sleep as their focus. Conclusions: User-driven focus selection influences health outcomes. Precision behavioural change tools like Gro Health can support sustainable, tailored health improvements across multiple domains and represent a scalable method for delivering personalised weight management.
Background: Cancer survivors are likely to face physical, mental, financial, social, and emotional difficulties, regardless of whether or when they receive treatment. Many cancer survivors report an i...
Background: Cancer survivors are likely to face physical, mental, financial, social, and emotional difficulties, regardless of whether or when they receive treatment. Many cancer survivors report an inability to understand the explanations of health care professionals as well as other poor communication. However, empirical evidence for such “poor communication” remains scarce. Objective: The purpose of this study was to clarify the information sources that are trusted by cancer survivors according to patient attributes. Specifically, we classified patients according to sex, treatment status, and cancer type to determine the best approach for disseminating appropriate information according to patient trends. Methods: We administered a cross-sectional survey to 350 cancer survivors aged 20–80 years according to the Checklist for Reporting Results of Internet E-Surveys. Items in the preliminary survey included sociodemographic information, “cancer stage,” “current treatment status,” “date of cancer diagnosis,” and “date of termination of cancer treatment” in the preliminary survey, and those in the main survey included “what you have researched about cancer,” “what are your cancer information sources,” “what social media sites or applications do you use to collect cancer information,” “information seeking difficulties,” “reliable information sources,” “intention to use hospital-recommended counseling support and information gathering applications and services,” “advantages of using hospital-recommended counseling support and information gathering applications and services,” “communication with surroundings,” and the Japanese version of the 10-Item Personality Inventory (which measures the Big Five characteristics of extraversion, conscientiousness, agreeableness, openness, and neuroticism). Data were analyzed using latent class analysis (LCA), and Kruskal-Wallis and Dunn-Bonferroni tests were used to compare the latent classes. Results: The LCA identified three classes: a group of women under follow-up, a group of men under follow-up, and a group under treatment. There were significantly more people who reported that they “could not ask the doctor questions” in the group under treatment than in the group of men under follow-up (P = .01), the latter of whom also had a higher tendency for neuroticism (P = .02). The male group undergoing follow-up care had significantly higher responses for “my doctor was easy to consult” (P < .001) and “I felt my doctor was knowledgeable and experienced” (P = .01) than the other groups, which confirmed their tendency to value smooth communication with their doctors. Conclusions: We revealed differences in trust tendencies and psychological characteristics of information sources among sex and treatment stage groups. These findings indicate that cancer survivors seek different types of support in regard to information gathering depending on their treatment status and sex.
Background: Hypertension is associated with a high rate of disability and mortality, leads to a substantial social-economic burden. Moxibustion is an external treatment in traditional Chinese medicine...
Background: Hypertension is associated with a high rate of disability and mortality, leads to a substantial social-economic burden. Moxibustion is an external treatment in traditional Chinese medicine, which was used to treat mild to moderate hypertension in individuals with phlegm-dampness constitution, and had acupoint specificity. Objective: a standard large-scale randomized clinical trial to verify its effectiveness is still needed. This study is proposed to examine the clinical effectiveness and potential cardio-protective benefits of moxibustion at home as a treatment for individuals with phlegm-dampness hypertension. Methods: This study is a multi-center, randomized, controlled trial. A total of 120 patients with mild to moderate hypertension and phlegm-dampness constitution will be recruited and randomly assigned in a 1:1 ratio to the treatment group (acupoint: Zusanli, ST36) or the control group (acupoint: Xuanzhong, GB39). All patients will receive 12 weeks of treatment and 12-week follow-up period. Results: The primary outcome measure is the change in morning systolic blood pressure from baseline to week 12. The secondary outcome measures include blood pressure-related indicators (morning diastolic blood pressure, average systolic blood pressure, average diastolic blood pressure, nighttime systolic blood pressure, nighttime diastolic blood pressure, blood pressure circadian rhythm) and short-term blood pressure variability coefficient, all of which will be measured by 24-hour ambulatory blood pressure monitoring. Additionally, cardiac-related indicators measured by 24-hour Holter monitoring, metabolic disorder-related indicator, liver and kidney function indicators, transformed scores of the TCM phlegm-dampness constitution scale, and the Montreal Cognitive Assessment (MoCA) will also be evaluated. Conclusions: This multi-center, randomized, controlled clinical trial will provide evidence on the clinical treatment effectiveness and potential cardio-protective benefits of moxibustion at home as a treatment for individuals with phlegm-dampness type of hypertension. Clinical Trial: This study was registered on Chinese Clinical Trial Registrat ,registry name:Clinical efficacy of moxibustion at Zusanli(St36) in protection cardiovascular and cerebrovascular diseases on phlegm dampness type hypertension;Trial registration number:ChiCTR2400086582);Register date:July 5,2024;https://www.chictr.org.cn/showpro.ChiCTR2400086582
Background: Indigenous men in Australia face the highest rates of morbidity and mortality, coupled with the lowest use of healthcare services. Despite this, their attendance at Emergency Departments (...
Background: Indigenous men in Australia face the highest rates of morbidity and mortality, coupled with the lowest use of healthcare services. Despite this, their attendance at Emergency Departments (EDs) is double that of any other demographic group in the country. Additionally, Indigenous men are often discharged from EDs more rapidly than other groups, a pattern linked to adverse health outcomes and possibly reflecting dissatisfaction with the services on offer. Objective: Braun and Clarke's (2006) six stages of reflexive thematic analysis will guide the data analysis. The Health Citizenship Framework will facilitate the exploration of autonomy, participation, and respect in healthcare interactions. The SEWB Framework will ensure the research reflects a culturally grounded, holistic understanding of wellbeing, incorporating connections to family, culture, spirit, and community. Grounded in the Health Citizenship Framework and the Social and Emotional Wellbeing (SEWB) model, this study aims to explore the experiences of Indigenous men in EDS and identify culturally appropriate and responsive pathways to improve care and engagement. Methods: Semi-structured interviews will be conducted with 10 to 15 Indigenous men aged 18 years and older who have been referred to Alcohol and Other Drugs (AOD) services after presenting to the ED. Results: Preliminary analysis revealed five key themes influencing Indigenous men’s experiences in Emergency Departments (ED): cultural safety and stigma, disempowerment due to a lack of communication, limited access to Aboriginal liaison services, feelings of shame associated with alcohol use, and systemic barriers to follow-up care. Participants emphasised the importance of trust, culturally competent staff, and stronger referral pathways to Alcohol and Other Drugs (AOD) services and community support. Conclusions: This study highlights the urgent need to embed cultural safety principles and Indigenous-led care models within ED settings. Addressing communication gaps, providing consistent access to Aboriginal support services, and strengthening continuity of care are essential for improving health outcomes for Indigenous men. Finding offers practical guidance for developing more inclusive, respectful, and effective service delivery pathways across ED and AOD care settings.
Background: The enhancement of Primary care and the prevalence of chronic diseases are key issues worldwide, especially in Canada. As the incidence of chronic illnesses rises, they have emerged as the...
Background: The enhancement of Primary care and the prevalence of chronic diseases are key issues worldwide, especially in Canada. As the incidence of chronic illnesses rises, they have emerged as the foremost cause of mortality worldwide. This trend has led to a surge in demand for healthcare services, placing significant pressure on primary care systems. The evolving and multidimensional nature of the chronic disease situation creates challenges that can affect the quality of care offered to patients. The lack of communication directly affects relational continuity, i.e., the sharing of information from previous events and circumstances, to ensure that care is appropriate to the individual and his or her problem. Patients living with chronic disease may also perceive contradictory recommendations from different professionals, which undermines their potential for self-management. These challenges highlight the importance of establishing clear patient pathways within interprofessional teams, ensuring that information is shared efficiently, and that the continuity of care is coordinated effectively, especially in a telehealth context. In 2019, with the arrival of the pandemic, the demanded of telehealth emerged as a crucial resource for patients with chronic illnesses. This resource was implemented with no specific infrastructure, often without patient support, and left to the discretion of individual professionals. Interprofessional collaboration plays a critical role in the use of telehealth in managing chronic diseases. Despite its advantages, telehealth can have negative effects on interprofessional teamwork if used sub-optimally Objective: This study aims to understand the interprofessional collaboration (IPC) process as experienced by patients in a telehealth context within primary care, with a focus on patient engagement. More specifically, the study's objectives are: 1) to describe the IPC process in telehealth within primary care from the perspective of patients living with chronic conditions; 2) to identify, in collaboration with patients living with chronic disease, the barriers and facilitating factors of this process; 3) to understand the engagement of these patients in relation to the IPC process in a telehealth context. Methods: To describe the process of interprofessional collaboration in the telehealth context in primary care from the perspective of patients living with chronic disease, this qualitative research is based on a constructivist research methodology. The research team constructs knowledge derived from the interpretation of information that was obtained during the interviews with participants. To meet the study's objectives, a qualitative journey mapping data collection will be carried out, following the approach of Trebbel et al., (2010). Individual interviews will be analyzed iteratively. This method is useful for this research as it visually and collaboratively captures patients lived experiences. Results: Data collection was completed between May 2024 and November 2024. A total of 22 interviews were conducted. The project is currently in progress, with multiple papers being drafted for publication in peer reviewed journals. Conclusions: The results of this study will support and improve the interprofessional collaboration process in the telehealth context by providing concrete insights into patients’ experiences, identifying gaps and strengths in current collaborative practices, and offering evidence-based recommendations. Journey mapping will help identify potential facilitating factors for improving primary care in the telehealth context according to the patient's journey. Results will be used to build a practical guide (in phase 2) supporting interprofessional collaboration in the primary care telehealth context.
Background: Sepsis is a major global health concern, particularly given its high morbidity and mortality rates. Despite its clinical significance, the public awareness of sepsis remains limited. Objec...
Background: Sepsis is a major global health concern, particularly given its high morbidity and mortality rates. Despite its clinical significance, the public awareness of sepsis remains limited. Objective: Therefore, since short videos are increasingly becoming a vital medium for health education, we aimed to systematically assess sepsis-related short videos’ content quality, information coverage, and dissemination performance across major social media platforms, as well as to identify the key factors influencing their communication effectiveness and educational utility. Methods: This mixed-methods study integrated questionnaire data and video content analyses. The questionnaires were distributed among 200 participants to assess sepsis awareness and short video usage preferences. Meanwhile, 140 sepsis-related videos were collected from TikTok, Bilibili, and WeChat and evaluated using the Global Quality Score (GQS), the modified DISCERN tool, and a six-dimension content coverage framework. Finally, communication performance was assessed through user engagement metrics and other related indicators. Results: Compared to videos from the media or individual publishers, physician-produced videos had significantly higher GQS and DISCERN scores (p < 0.001). Additionally, the Intensive Care Unit (ICU) and chief physicians produced the highest-quality content. Furthermore, high-quality videos (GQS > 3, DISCERN > 3) correlated with greater content retention and diffusion. We also noted a mismatch between the content provided and public information needs. Specifically, practical topics such as symptoms and prevention were underrepresented. Additionally, although emotional elements and clickbait-style titles moderately enhanced engagement, they did not substitute for content quality. Moreover, various platform-specific benefits were identified including TikTok facilitating rapid exposure, Bilibili supporting structured learning, and WeChat enabling socially driven redistribution. Conclusions: Although short video platforms hold great promise for sepsis education, challenges of inconsistent quality, limited coverage, and misalignment with audience needs persist in current content. Therefore, enhancing professional accuracy, optimizing structural design, and tailoring strategies to platform characteristics would improve the educational impact of the videos, ultimately promoting early sepsis diagnosis and treatment.
Background: Information gathering is the foundational skill of clinical reasoning. However, residents and attending physicians have no objective insights into the competencies of residents in developi...
Background: Information gathering is the foundational skill of clinical reasoning. However, residents and attending physicians have no objective insights into the competencies of residents in developing skills in information gathering in the electronic health record (EHR). The EHR audit logs, time stamped records of user activities, can provide a wealth of information about how residents gather information about patients at the time of admissions and throughout daily rounding. Objective: In this study, our goals were to: 1. Understand and delineate attending physician expectations of residents’ EHR-based information gathering activities at different stages of residency, 2. Develop a system, referred to as the Trainee Digital Growth Chart (a.k.a Growth Chart), using the EHR audit logs to audit and feed back information gathering performance to residents and their attending physicians, and 3. Pilot the Growth Chart among pediatric residents on pediatric hospital medicine (PHM) rotations to understand whether audit and feedback data on EHR-based information gathering is helpful in supporting resident learning and assessment. Methods: We convened a focus group of PHM attending physicians to establish information gathering benchmarks for residents at each stage of their training. Residents and attendings were involved in the co-design of an information gathering performance electronic dashboard called the Trainee Digital Growth Chart. This dashboard was piloted in an observational cohort study among PHM residents and attending physicians during the 2023-24 academic year. Results: Considerable variability was observed as focus group attendings established training-stage specific benchmarks. During the pilot, resident and attending logged into the Growth Chart to observe performance at moderate to high rates. However, despite their involvement in its co-design, most participants did not find great value in the Growth Chart. However, as an intervention, viewing prior Growth Chart information gathering performance had a positive impact on future information gathering performance among first year residents on daily rounds when that performance was also discussed with an attending physician. Conclusions: Information gathering is at the foundation of clinical reasoning. However, no competency-based benchmarks for information gathering in the EHR exist. Opportunities to leverage the EHR audit logs exist to feed back performance information to trainees, thereby influencing future information gathering behaviors. This is particularly powerful when done early in training before habits become formed, and when done in conjunction with verbal review with an attending physician. Such tools must find their way into routine clinical workflows and be capable of providing real time or near real time feedback before perceived educational value will be realized. Nevertheless, these approaches have broad potential to scale across specialties and allied health disciplines.
Background: Endometriosis is a gynecological condition that involves the implantation of endometrial tissue outside the uterine cavity. About 1.2 million women are suffering from this disease in Bangl...
Background: Endometriosis is a gynecological condition that involves the implantation of endometrial tissue outside the uterine cavity. About 1.2 million women are suffering from this disease in Bangladesh Objective: The purpose of this study was to explore the factors associated with endometriosis, symptoms, and clinical treatment in Bangladesh. Methods: In this case-control study, out of 162 women, 82 had endometriosis confirmed with laparoscopy/ transvaginal ultrasound and 80 were included in the control group with normal pelvic ultrasound. All were asked to fill out a questionnaire containing demographics, reproductive, and menstrual status. Comparisons between the two groups were done using an Independent t-test, Chi-square test and logistic regression model. P-value < .05 was considered statistically significant. Results: The prevalence of endometriosis was higher with, age 25.78 ± 5.36 (P = .01), marital status, and BMI (P < .05). The most common symptoms were dysmenorrhea, excessive bleeding, cramping etc. Infertility (OR:2.21; %95CI: 1.07–4.53; P = .03), thyroid imbalance (OR:3.44; %95CI: 1.47–8.03; P = .004), irregular menstruation (OR:5.76; %95CI: 2.12–15.60; P = .001), age at menarche (OR:2.54; %95CI: 1.04–6.21; P = .04) were the factor that associated with endometriosis. Endometriosis was diagnosed most frequently by TVS (transvaginal ultrasound) at 45.1%, NSAI (nonsteroidal aromatase inhibitors) at 22.8% was the most commonly utilized medicine, and 17.9% of patients undergo laparoscopy for surgical treatment Conclusions: This study identified several factors significantly associated with endometriosis among infertile women in Bangladesh. The prevalence of endometriosis was notably higher among women with increased age, specific marital statuses, and elevated BMI. Common symptoms included dysmenorrhea, excessive bleeding, and cramping. Key associated factors were infertility, thyroid imbalance, irregular menstruation and early age at menarche. Transvaginal ultrasound emerged as the most frequently used diagnostic method, while nonsteroidal aromatase inhibitors were the most common form of medical treatment. These findings emphasize the importance of early screening and targeted interventions, as well as the need to enhance clinical awareness and access to care for women at risk of endometriosis.
Background: Background: Traumatic brain injuries (TBIs) caused by gunshot wounds present complex clinical challenges with high mortality and disability rates. Early and structured rehabilitation may e...
Background: Background: Traumatic brain injuries (TBIs) caused by gunshot wounds present complex clinical challenges with high mortality and disability rates. Early and structured rehabilitation may enhance recovery, especially in resource-limited settings like Bangladesh. Objective: In this case study, the rehabilitation care of a 16-year-old boy who was shot in the head during Bangladesh's 2024 anti-discrimination movement is detailed. The emphasis is on improving functional outcomes by early mobilisation and an organised physiotherapy program. Methods: Case Presentation: This report describes a 16-year-old boy who sustained a penetrating brain injury during a peaceful student protest in the 2024 anti-discrimination movement in Bangladesh. Following emergency neurosurgical intervention, he presented with severe neurological impairments, including right-sided hemiplegia, left lower limb paresis, spasticity, and postural instability. Intervention: The patient underwent a three-month multidisciplinary rehabilitation program comprising 36 sessions. Interventions included early mobilisation, balance and vestibular training, neuromuscular electrical stimulation, manual therapy, and task-specific functional training. Rehabilitation followed post-concussion guidelines and was personalised based on symptom progression and functional response Results: Outcomes: By the end of the rehabilitation program, pain levels decreased from 6–7/10 to 0/10. The patient’s Sitting Balance Scale score improved from 0/44 to 31/44. He progressed from being wheelchair-bound to walking with moderate assistance, with enhanced trunk control, postural balance, and mobility. Conclusions: Conclusion: This case highlights the potential for significant recovery through early, individualised rehabilitation following severe gunshot-related TBI. It underscores the importance of integrating structured neurorehabilitation into trauma care, particularly in low-resource environments affected by sociopolitical unrest.
Background: Functional rehabilitation is commonly used for patients with chronic ankle instability (CAI). Digital training systems have become increasingly popular in postoperative rehabilitation; how...
Background: Functional rehabilitation is commonly used for patients with chronic ankle instability (CAI). Digital training systems have become increasingly popular in postoperative rehabilitation; however, their effectiveness for CAI patients after modified Brostrom surgery is uncertain. Objective: This trial aimed to evaluate whether individually tailored physiotherapeutic ankle-specific training (PAST) delivered via a digital training system is noninferior to conventional face-to-face physiotherapy for CAI patients following modified Brostrom surgery in ChinA two-arm, single-assessor blinded, randomized controlled trial was conducted at Huashan Hospital from January 2022 to January 2024, enrolling 84 patients. Participants were randomly allocated to either the digital training system group (DT group, n=42), receiving a 12-week individualized PAST program via digital system, or the conventional face-to-face training group (PT group, n=42), undergoing standard physiotherapy for 12 weeks. Assessments occurred at baseline, 12 weeks, and 24 weeks postoperatively. Primary outcomes were two subscales of the Foot and Ankle Ability Measure (FAAM). Secondary outcomes included balance tests (Time-in-Balance Test, Foot-Lift Test, Star Excursion Balance Test), functional tests (ankle dorsiflexion range of motion, Side-Hop Test, Figure-8 Hop Test), and quality of life assessed by the FAAM scale. Statistical analyses included inferential statistics and bootstrapping for incremental cost-effectiveness ratio (ICER).a. Methods: A two-arm, single-assessor blinded, randomized controlled trial was conducted at Huashan Hospital from January 2022 to January 2024, enrolling 84 patients. Participants were randomly allocated to either the digital training system group (DT group, n=42), receiving a 12-week individualized PAST program via digital system, or the conventional face-to-face training group (PT group, n=42), undergoing standard physiotherapy for 12 weeks. Assessments occurred at baseline, 12 weeks, and 24 weeks postoperatively. Primary outcomes were two subscales of the Foot and Ankle Ability Measure (FAAM). Secondary outcomes included balance tests (Time-in-Balance Test, Foot-Lift Test, Star Excursion Balance Test), functional tests (ankle dorsiflexion range of motion, Side-Hop Test, Figure-8 Hop Test), and quality of life assessed by the FAAM scale. Statistical analyses included inferential statistics and bootstrapping for incremental cost-effectiveness ratio (ICER). Results: Baseline demographic and clinical characteristics were similar between groups, except for the Foot-Lift Test. At the 24-week follow-up, the between-group differences for FAAM improvements, adjusted for baseline values, indicated noninferiority with near-zero differences: FAAM-activities of daily living (FAAM-ADL), 0.36 (95% CI: -1.01 to 1.72); FAAM-sport (FAAM-S), 1.67 (95% CI: -0.61 to 3.96). Secondary outcome measures (Time-in-Balance Test, ankle dorsiflexion range of motion, Side-Hop Test) also showed no significant differences. The average intervention costs per patient were lower in the DT group (53,551.36 CNY) compared to the PT group (59,372.04 CNY), with incremental costs of -14,450.57 CNY, leading to ICER values of -16,396.25 for FAAM-ADL and -114,130.78 for FAAM-S.
Conclusions: Individually tailored PAST delivered via a digital training system is noninferior and more cost-effective compared to conventional face-to-face training, supporting its use as a reliable rehabilitation alternative for CAI patients following modified Brostrom surgery. Conclusions: Individually tailored PAST delivered via a digital training system is noninferior and more cost-effective compared to conventional face-to-face training, supporting its use as a reliable rehabilitation alternative for CAI patients following modified Brostrom surgery. Clinical Trial: Chinese Clinical Trial Registry (Number: ChiCTR2300075292)
Background: Mild cognitive impairment (MCI) is a prevalent condition among older adults, often progressing to dementia and imposing significant burdens on healthcare systems and informal caregivers. D...
Background: Mild cognitive impairment (MCI) is a prevalent condition among older adults, often progressing to dementia and imposing significant burdens on healthcare systems and informal caregivers. Digital health interventions, such as the Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD) tablet application, have been proposed to support people living with MCI (PwMCI) and their caregivers by facilitating daily routines and improving quality of life (QoL). However, evidence regarding their long-term cost-effectiveness remains limited. Objective: This study aimed to evaluate the 18-month cost-effectiveness of the SMART4MD tablet-based intervention, in addition to standard care, compared to standard care alone for PwMCI and their informal caregivers, from the perspective of healthcare providers in Sweden and Spain. Methods: A pragmatic, multicenter randomized controlled trial was conducted between December 2017 and September 2020 across sites in Sweden and Spain. Dyads consisting of PwMCI and their informal caregivers were randomized to receive either the SMART4MD intervention plus standard care or standard care alone. The primary outcome was health-related quality of life, measured by quality-adjusted life years (QALYs) derived from the EQ-5D-3L instrument. Secondary outcomes included disease-specific QoL (QoL-AD), cognitive function (MMSE), and caregiver burden (Zarit Burden Interview, ZBI). Cost data were collected from healthcare provider registries, and economic evaluation followed the CHEERS guidelines. Incremental cost-effectiveness ratios (ICERs) and net monetary benefit (NMB) were calculated, with sensitivity and subgroup analyses performed to assess the uncertainties. Results: A total of 345 dyads were included in the Swedish cost-effectiveness analysis. After 18 months, there were no statistically significant differences in total costs or QALYs between the intervention and control groups for PwMCI, informal caregivers, or dyads. For PwMCI, the intervention was associated with slightly higher costs (€9) and lower QALYs (–0.015) compared to standard care, resulting in the intervention being dominated by standard care (negative NMB). For informal caregivers, the intervention group showed a small, non-significant QALY gain (0.006) at higher cost (€468), with an ICER above the Swedish willingness-to-pay threshold, indicating the intervention was not cost-effective. Scenario analysis in the Spanish site showed the intervention could be cost-effective for PwMCI (ICER €3,337/QALY), but differences were not statistically significant. Notably, the intervention group showed a statistically significant improvement in MMSE scores, but no significant differences in other outcomes. Conclusions: Over 18 months, the SMART4MD intervention did not result in significant improvements in quality of life for PwMCI or their informal caregivers compared to standard care. The intervention was not cost-effective from a healthcare provider perspective, except in a scenario analysis for one Spanish site. Further research with larger sample sizes, longer follow-up, and strategies to enhance engagement and minimize dropout is warranted to clarify the potential of digital interventions in this population. Clinical Trial: ClinicalTrials.gov: NCT03325699
Background: Falls are the primary cause of fatal and non-fatal accidental injuries in older adults. The World Falls Prevention Guidelines recommend balance-challenging, functional exercise programmes...
Background: Falls are the primary cause of fatal and non-fatal accidental injuries in older adults. The World Falls Prevention Guidelines recommend balance-challenging, functional exercise programmes as a key strategy for falls prevention but access, uptake and adherence to these programmes in community settings remain suboptimal. Keep-On-Keep-Up (KOKU), a digital, National Health Service (NHS) approved programme was co-developed with older adults and therapists, to provide progressive, evidence-based exercises and to raise awareness of fall prevention strategies. Objective: This trial aims to investigate the effectiveness and cost-effectiveness of the KOKU digital strength and balance programme for improving balance, enhancing physical function and reducing falls risk among community dwelling older adults. Methods: This is a two-arm, parallel group randomised controlled trial. A total of 196 community dwelling older adults aged 60 years and older will be randomised to either the intervention group comprising a digital strength and balance programme (KOKU) alongside standard care (strength and balance exercise advice and a falls prevention leaflet) or to a control group, receiving standard care only. Participants receiving the intervention will be asked to exercise three times per week following the tailored and progressive programme. Randomisation will take place after recruitment and baseline data collection. The trial’s primary outcome measure is balance function (Berg Balance Score) at twelve weeks post-randomisation. Secondary trial outcomes include: lower limb strength; healthcare utilisation and health-related quality of life; self-reported concerns about falling; self-reported physical activity; falls risk, pain, mood, fatigue, self-reported falls, acceptability and usability of the KOKU programme. Intention to treat analysis and a cost-effectiveness analysis will be employed for trial data analysis. Qualitative interviews and focus groups will be undertaken with around 10 care providers and 13 participants to further understand views of the intervention and trial processes. Results: This study began recruitment in July 2024 and concluded in March 2024 recruiting a total of 202 participants (102 intervention and 100 control). Following protocol publication, data compilation and analysis will be conducted, with results anticipated to be published in 2027. Conclusions: This trial will provide important evidence on whether a digital strength and balance programme can improve balance and related outcomes in older adults compared to usual care. Clinical Trial: ClinicalTrials.gov: NCT06687135
Background: Cisplatin resistance remains a significant obstacle in cancer therapy, frequently driven by translesion DNA synthesis (TLS) mechanisms that utilize specialized polymerases such as human DN...
Background: Cisplatin resistance remains a significant obstacle in cancer therapy, frequently driven by translesion DNA synthesis (TLS) mechanisms that utilize specialized polymerases such as human DNA polymerase η (hpol η). Although small-molecule inhibitors like PNR-7-02 have demonstrated potential to disrupt hpol η activity, current compounds often lack sufficient potency and specificity to effectively combat chemoresistance. The vastness of chemical space further limits traditional drug discovery approaches, underscoring the need for advanced computational strategies such as machine learning (ML)-enhanced Quantitative Structure-Activity Relationship (QSAR) modeling. Objective: This study aimed to develop and validate ML-augmented QSAR models to accurately predict hpol η inhibition by indole thio-barbituric acid (ITBA) analogs, with the goal of accelerating the discovery of potent and selective inhibitors to overcome cisplatin resistance. Methods: A curated library of 85 ITBA analogs with validated hpol η inhibition data was used, excluding outliers to ensure data integrity. Molecular descriptors spanning 1D to 4D were computed, resulting in 220 features. Seventeen ML algorithms—including Random Forests, XGBoost, and Neural Networks—were trained using 80% of the data for training and evaluated with 14 performance metrics. Robustness was ensured through hyperparameter optimization and 5-fold cross-validation. Results: Ensemble methods outperformed other algorithms, with Random Forest achieving near-perfect predictive performance (training MSE = 0.0002, R² = 0.9999; testing MSE = 0.0003, R² = 0.9998). SHAP analysis revealed that electronic properties, lipophilicity, and topological atomic distances were the most important predictors of hpol η inhibition. Linear models exhibited higher error rates, highlighting the non-linear relationship between molecular descriptors and inhibitory activity. Conclusions: Integrating machine learning with QSAR modeling provides a robust framework for optimizing hpol η inhibition, offering both high predictive accuracy and biochemical interpretability. This approach accelerates the identification of potent, selective inhibitors and represents a promising strategy to overcome cisplatin resistance, thereby advancing precision oncology.
Background: The linea alba connects the fascia that covers the rectus abdominis muscles serving as the central seam. It also serves as central insertion point for RA and the 3 major abdominal muscles...
Background: The linea alba connects the fascia that covers the rectus abdominis muscles serving as the central seam. It also serves as central insertion point for RA and the 3 major abdominal muscles – transversus abdominis, external obliques and internal obliques. Within the entire length of the rectus abdominis (RA), the inter-recti spacing can vary from 2-3 CM in width and 2cm to 5cm long to 20cm wide Objective: To describe a study protocol comparing the effectiveness of Core stability exercise, static abdominal contraction, yogic diaphragmatic breathing in Reducing Gap between Diastasis Recti Abdominis in Postnatal women Methods: Randomized Controlled Trial with block randomized sampling design will be used for the study. Postnatal women having Diastasis Recti Abdominis will be assigned to experimental or comparison group. Results: Among the groups it’s anticipated that reduction in the gap between recti muscle in the abdomen of postnatal women as a result of intervention. Measurement will be done before and after the intervention which is 8 weeks. Conclusions: This research will provide evidence for various exercises can lead to more personalized treatment plans for postpartum women. This study would provide valuable insights into the comparative efficacy of these methods, helping healthcare professionals make informed decisions about postpartum rehabilitation programs. Clinical Trial: CTRI/2025/06/088140
Background: An estimated 18 million people are living with Rheumatoid Arthritis (RA) in the world (1). The disease comes with significant morbidity for patients, including the increased risk of fractu...
Background: An estimated 18 million people are living with Rheumatoid Arthritis (RA) in the world (1). The disease comes with significant morbidity for patients, including the increased risk of fractures compared to the general population due to the chronic inflammation associated with RA, which can lead to reduced bone mineral density (2).
Presently, disease severity and progression have been helped by the rapid evolution of anti-rheumatic medications. These medications are broadly categorized into two main types: non-steroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs) (3).
Patients with RA have an increased risk of post-operative complications of orthopaedic surgery because of its chronic impact on bone as well as the use of immunomodulatory medications that may interfere with bone healing (4). Drugs with immunosuppressive action can lead to potential complications with both wound healing and bone union during elective osteotomy. This is particularly important in foot and ankle surgery, where corrective osteotomies are commonly performed, and the risk of wound breakdown is high. Decisions to continue or suspend taking these medications need to be based on evidence weighing the potential for post-operative complications versus potentiating a disease flare.
There is an abundance of literature that highlights the adverse effects of NSAIDs on bone healing and fracture union, but there is little robust evidence surrounding the use of DMARDs in orthopaedic surgery (3, 5). Patients requiring surgery in trauma or elective settings will often be on one or more of these medications. Therefore, it is vital to understand the effect of DMARDs on postoperative outcomes to improve their recovery and rehabilitation, and if required, to suspend the medications in the perioperative period (6). Current guidance published by the American College of Rheumatology has been formulated for elective hip and knee surgery with a focus on preventing wound complications, typically restarting DMARDs at the 14-day mark when the wound has healed. These guidelines do not consider the time to union of bone, which is typically 6 weeks.
The currently available research has been in vivo or in vitro studies, with little to few studies assessing the clinical implications of DMARDs on bone healing in the rheumatoid patient. Objective: This literature review aims to synthesise and evaluate current evidence on the impact of DMARDs on bone healing. Methods: A literature search was conducted on PubMed, Embase, and Medline. An initial search was conducted looking at the effect of DMARDs or anti-rheumatic medications on bone healing in foot and ankle osteotomies. We used the keywords ‘DMARDs OR anti-rheumatic medications’, ‘foot and ankle surgery OR osteotomy’, AND ‘Bone healing OR bone union’. It yielded only four original papers for review after removing duplicates, case reports, conference abstracts, and non-English Language material. Due to the limited data in this field, we expanded our search and question to look at the effects of DMARDs on bone union in elective and trauma patients.
The keywords were subsequently refined to ‘rheumatic disease OR rheumatoid arthritis’ AND ‘anti-rheumatic medication OR disease-modifying anti-rheumatic medications OR DMARDs’ AND ‘fracture healing OR bony union OR malunion or non-union’. As there was still a limited number of original studies on this theme, we decided to include any study design apart from case studies. These were excluded due to their potential bias and limited generalisability. We also only included papers written in the English language and within the last 50 years. We selected papers that looked specifically at either DMARDs or methotrexate and their effect on bone healing, fracture union, or bone metabolism. The search resulted in 80 papers for review. After applying the above inclusion and exclusion criteria with two independent reviewers, a total of 9 papers were included for a narrative analysis. Results: The effect of methotrexate (MTX) on bone appears to be dose-dependent. Satoh et al showed that new bone formation in a fracture gap in rats did not differ significantly between low-dose MTX and control groups (7). However, there was a marked reduction in bone formation in the high-dose MTX group, particularly periosteal bone formation de novo in the fracture gap site in the first week. The study showed no difference between the three groups for intramedullary bone formation or chondroid tissue formation. A key limitation of this study was that it only looked at bone formation rather than bone strength or mineral density. Several other animal studies support the finding that high-dose MTX has a greater adverse effect on bone metabolism than low-dose MTX (8, 9, 10).
Pountos et al’s systematic review analysed 70 papers of in vivo and animal studies on the effect of MTX on fracture healing (11). The review gave rise to contradictory evidence. Some in vitro studies concluded that MTX reduces mitochondrial activity, bone cell metabolism, and turnover. Other studies showed no effect on osteoblast proliferation, which is a crucial step in bone healing (3). Some studies also showed there was a reduction in biochemical markers of osteogenesis, such as ALP (alkaline phosphatase), while in others, ALP increased (12).
In clinical studies, the impact of DMARDs on bone healing has been studied for patients undergoing elective spinal surgery (13). One study looked at bone fusion rates after craniovertebral junction surgery and found that those who continued DMARDs showed higher radiographic fusion outcomes than those who discontinued (92.8% vs 75%, P value = 0.276). However, the study was not statistically significant due to its small sample size of 30 patients in total (14). Guadiani et al studied revision spinal surgery rates for patients using DMARDs and TNF-alpha inhibitors compared to a control group not on either medication. The reoperation rate within 1 year was 19% for the TNF-alpha inhibitor group and 11% for the DMARD group compared to 6% for the control group. According to the Cox proportional hazard model they used the TNF-alpha group had a 3.1-fold increased risk compared to the control group (95% CI 1.4-7.0), while the DMARD group showed a 2.2-fold increase (95% CI: 0.96-5.3). The reasons for revision surgery were due to infection (40%) or other causes (60%), such as failure to fuse in the DMARDs group, while in the TNF-alpha inhibitor group, it was 47% for infection and 53% for other causes (15). This implies there is a higher rate of infection for the TNF-alpha inhibitor cohort. The authors concluded that continuing DMARDs, especially TNF-alpha inhibitors, 90 days before surgery, appeared to have a higher rate of revision spinal surgery than those who discontinued.
In 2017, the American College of Rheumatology and American Association of Hip and Knee Surgery (ACR/AAHK) performed an extensive meta-analysis of the literature around the use of DMARDs in orthopaedic surgery (10). They advised that conventional DMARDs, which include methotrexate, leflunomide, hydroxychloroquine, and sulfasalazine, can be continued in orthopaedic surgery as they did not lead to adverse post-operative outcomes. However, they recommended holding biologics for two weeks before surgery as there was an increased risk of poor wound healing. The effect on bone healing itself was not studied in this review. There is, in fact, very limited data on the effect of biologic DMARDs on bone healing, but in an in vivo study, they have shown an inhibitory effect on osteoblast proliferation (3). This is particularly true of TNF-alpha inhibitors such as infliximab, which showed a reduction in overall osteoblast cell numbers. This suggests it could interfere with the bone repair and remodelling (3).
Furthermore, the 2021 critical analysis review by Saunders et al on the perioperative management of antirheumatic drugs in foot and ankle surgery also concluded that conventional DMARDS are generally safe to use throughout the perioperative period, while biologics should be held typically before surgery (16). Conclusions: Our narrative review has highlighted an important literature gap within the field of DMARDs and bone healing, whether in a traumatic or elective setting. Much of the original research is in vivo or animal studies, and although they show statistically significant results, they cannot accurately predict human outcomes due to significant differences in physiology and biology (3,8,9,12). Clinical studies are even fewer, and the ones conducted so far have included small study populations. Moreover, they are antiquated and often do not examine the latest anti-rheumatic drugs. For instance, an important study we included, Elia et al, included only 30 patients, which reduced the statistical power of the results (14). All the clinical studies we have included so far are for elective procedures such as spinal surgery or foot and ankle surgery (13,14, 16, 17). To our knowledge, there are currently no randomised controlled trials that study the effect of DMARDs on bone healing, either in a trauma or elective setting.
Nevertheless, there is a greater number of publications available to consider for the effect of DMARDs on wound healing in orthopaedic surgery. This is of important consequence as surgical site infections, especially when involving the bone, can lead to impaired fracture healing, causing malunion or non-union (18). Current evidence suggests MTX has no adverse effect on wound healing in orthopaedic surgery and can be safely continued pre- and postoperatively (10,11). However, biologics are recommended to be held perioperatively due to the increased risk of surgical site infections and impaired wound healing. The current guidance is to schedule surgery at the end of their dosing cycle (10). Some hospital trusts have advised only restarting when most of the wound has healed (19). Considering a wider evidence base, biologics have shown an increased risk of serious infection, so there is certainly a research gap to explore on how these medications impact patient outcomes in orthopaedic surgery (20).
Any decision to stop anti-rheumatic medications in the preoperative period should be a carefully considered decision, with patients fully informed as to the risks and benefits of stopping such therapy. Patients on DMARDs tend to have more severe disease, and withholding them may result in disease flares, which can cause significant morbidity. Flares may lead to joint swelling, stiffness, pain, and increased cardiovascular risk (21). This can ultimately impair rehabilitation following major surgery, which predisposes the patient to further post-operative complications such as venous thromboembolism, hospital-acquired infections, or reduced functional baseline from a prolonged hospital stay (22).
Grennan et al found that those who discontinued MTX two weeks before and after surgery showed a higher rate of flare-ups than those who continued their medication. Patients who continued MTX before surgery had even fewer post-operative complications than the control group that was not on any MTX (23). The 2017 American College of Rheumatology study also concluded that continuing glucocorticoids and DMARDs perioperatively for hip and knee arthroplasty resulted in better function, a greater range of motion, and improved post-operative pain (10).
Therefore, we advise that the decisions around anti-rheumatic medications in patients undergoing orthopaedic surgery should be determined on an individual basis, with consideration given to their disease severity, functional baseline, and risk factors for poor bone healing, as we currently do not have enough evidence to suggest that they should be held.
Our literature review, however, has some limitations. Firstly, we used specific terminology to capture the effect of anti-rheumatic medications on bone healing, so we may have missed articles that contain this information, which did not include our keywords. Secondly, there is such little data available for our topic that the papers we have selected for review have small study populations or no controls. None of the studies we included showed any randomisation. Therefore, results were interpreted with caution as there is a potential for bias and reduced generalisability. Finally, many papers we included were animal studies, so their findings cannot be applied directly to humans.
In conclusion, the effect of DMARDs on bone union remains largely unstudied, especially considering human studies and large randomised controlled trials. Our literature review has identified that MTX may be safe to continue before orthopaedic surgery, as it does not appear to affect bone union at low doses that are used in RA. However, biologics should be withheld as there is evidence to suggest they can cause an increased risk of infection or wound breakdown. The effect of biologics specifically on bone healing, has not been studied to our knowledge. Given that millions of patients suffer from rheumatoid arthritis, and many will at one point undergo a joint procedure, it is important to further understand the clinical impact of DMARDs on bone so we can recommend evidence-based guidance. Until then, we advise a multi-disciplinary approach in determining which anti-rheumatic medications to withhold before any orthopaedic surgery.
Background: Global aging presents significant socio-economic and health challenges, particularly for older adults who face an increased risk of chronic diseases and reduced physical activity levels. A...
Background: Global aging presents significant socio-economic and health challenges, particularly for older adults who face an increased risk of chronic diseases and reduced physical activity levels. Although physical activity is crucial for maintaining health, most elderly individuals do not meet the recommended guidelines. Gamification and mobile health (mHealth) technologies offer innovative solutions to motivate physical activity; however, research focusing on older adults is limited, especially regarding the effectiveness and sustainability of such interventions. Objective: To synthesizes evidence on the effectiveness of mHealth-based gamified interventions for improving physical activity in older adults. Methods: This systematic review followed PRISMA and MOOSE guidelines and analyzed studies from several online databases, including PubMed, Embase, Web of Science, CINAHL, Scopus, and Wiley, covering relevant literature from their inception up to May 2025. Inclusion criteria focused on gamified mHealth interventions for adults aged 60+, excluding serious games. Quality assessment was conducted according to the Joanna Briggs Institute standards, with data extracted on study design, gamification elements, and outcomes such as step counts and moderate-to-vigorous physical activity (MVPA). Results: Gamified interventions significantly increased daily step counts and time spent in moderate-to-vigorous physical activity among older adults. Goal setting and rewards were the most frequently components. The combined use of mobile and wearable devices offered greater flexibility and accessibility. However, the heterogeneity in study designs, small sample sizes, and lack of long-term follow-up studies limited the generalizability of the findings. Half of the studies employed a theoretical framework, suggesting that there is a gap in systematic design approaches. Conclusions: mHealth-based gamification shows promise in enhancing physical activity among older adults. However, future research should address scalability, theoretical integration, and caregiver involvement to improve sustainability and inclusivity. This review highlights the need for tailored, theory-driven interventions that bridge the gap between technology and the health needs of the elderly. Clinical Trial: This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420251056689).
Background: Alerts, a key feature of Electronic Health Record (EHR) systems, intend to improve patient safety by providing timely information at the point of care. However, many EHR systems generate e...
Background: Alerts, a key feature of Electronic Health Record (EHR) systems, intend to improve patient safety by providing timely information at the point of care. However, many EHR systems generate excessive alerts that are not immediately clinically relevant and that contribute to alert fatigue. Despite growing recognition of alert fatigue as a safety concern, clinicians’ experiences of alert fatigue and the broader system-level factors that contribute to it being experienced are not well understood. Objective: Use a human factors approach to comprehensively explore how alert fatigue is experienced by doctors, identify alert fatigue’s contributing factors, perceived influences and impacts, and strategies to address it in practice. Methods: Semi-structured interviews were conducted with junior doctors working in hospitals across Australia. Data were thematically analysed using a hybrid inductive and deductive approach, informed by the Safety Engineering Initiative for Patient Safety (SEIPS) and an information processing model. Results: Twenty doctors were interviewed. Alert fatigue was described to occur at different stages of information processing, including when alerts were not detected, superficially processed using mental shortcuts, or required excessive cognitive effort to interpret. When alerts were not detected or thoroughly processed, participants more often perceived impacts on patient safety and care quality, whereas when alerts required excessive cognitive effort interruptions, frustration, and time and effort loss were frequently reported. Contributors to alert fatigue were reported to include technology, task, and environmental factors such as the interface design and clinical relevance of alerts, and information overload from system alerts as well as other alerts and tasks. Alert fatigue was described to be experienced differently depending on provider characteristics, such as experiences with and knowledge of alerts, mood, and personality, and organisational factors including culture, shift type and time of day. Conclusions: Alert fatigue is not a binary concept but instead experienced on a continuum and influenced by interacting individual, technical and contextual factors. Addressing alert fatigue requires tailored interventions that target its different causes and outcomes. These could include technical and design improvements, changes to organisational practices, and individual customisation to reduce experiences of fatigue and accommodate differences in clinicians’ needs.
Background: The growing trend of integrated healthcare services within physician groups has improved care delivery by enhancing convenience, efficiency, and care coordination. However, it has also rai...
Background: The growing trend of integrated healthcare services within physician groups has improved care delivery by enhancing convenience, efficiency, and care coordination. However, it has also raised concerns about financial incentives potentially driving overutilization. Objective: We examine the impact of distribution method (traditional third-party referral versus physician-managed via Rx Redefined technology platform) on the quantity of urinary catheters supplied to Medicare patients. Methods: We analyzed utilization patterns for urological catheters (HCPCS codes A4351, A4352, and A4353) using 2021 Medicare claims data. We identified 54 urology specialists in core metropolitan areas who were enrolled in the Rx Redefined platform throughout 2021 and compared their utilization patterns with unenrolled urologists in the same regions. For enrolled physicians, who managed approximately 40 percent of their prescriptions through the platform, we also compared utilization between physician-managed and third-party distribution methods. Results: For catheter services A4351 and A4352, when distribution was managed by third parties, we found no significant differences in utilization (i.e. units supplied) between enrolled and unenrolled physicians. However, physician-managed distribution through Rx Redefined resulted in significantly lower utilization compared to third-party vendor distribution by non-enrolled physicians (p < 0.001 for both codes). In paired analysis of enrolled physicians, direct management showed significantly lower utilization compared to third-party distribution for A4351 (p = 0.014), but this difference was not significant for A4352 (p = 0.62). Conclusions: These findings demonstrate that physician-managed catheter distribution does not lead to increased utilization. In fact, for certain catheter types, physician-managed distribution may result in lower utilization compared to traditional third-party referral methods, suggesting a potential reduction in oversupply and improved efficiency.
Background: Current methods for analyzing and matching shapes frequently struggle to distinguish subtle structural variations, particularly under conditions involving noise, deformation, or articulati...
Background: Current methods for analyzing and matching shapes frequently struggle to distinguish subtle structural variations, particularly under conditions involving noise, deformation, or articulations. Existing algorithms often lack robustness and flexibility, relying heavily on local curvature, which may inadequately represent complex structural details essential for precise shape classification and matching. Objective: To develop a robust and versatile three-tier shape representation pipeline that enhances intra-group similarity and amplifies inter-group differences, thereby providing an invariant representation resilient to noise, articulations, and mechanical deformations. Methods: We propose a novel approach comprising three steps: (1) a manifold-reduction step employing stress minimization to neutralize shape deformations, (2) application of the eccentricity transform (Ecc) to incorporate internal structural information, and (3) integral invariants (II) for robust boundary description. This tripartite framework synergizes differential geometry, topology, and scale-space theory, rigorously evaluated on standard datasets such as the Kimia database. Results: Our method significantly outperformed existing shape-matching algorithms, demonstrating notably improved intra-group matching accuracy and effectively enhancing inter-group discrimination. The approach provided substantial resilience against noise, articulations, and bending-induced shape distortions, verified through extensive experimentation and statistical evaluation. Conclusions: The proposed three-tier invariant representation delivers a robust and mathematically sound pipeline suitable for precise shape matching and classification tasks. Its resilience to common shape-analysis challenges makes it highly suitable for practical applications in computational anatomy, biomechanics, medical imaging, and computer-aided geometric design. Clinical Trial: Not applicable (omit if required).
Background: Diabetic foot ulcers (DFU) represent a severe complication that can increase morbidity and mortality in diabetic patients. Effective management of DFU requires accurate and prompt wound as...
Background: Diabetic foot ulcers (DFU) represent a severe complication that can increase morbidity and mortality in diabetic patients. Effective management of DFU requires accurate and prompt wound assessment. However, the need for proper management of DFU necessitates wound assessments that are both swift and accurate, a challenge that persists in current clinical practice. Objective: This study explores the application of AI-based assessment models in evaluating DFU conditions, aiming to enhance detection accuracy, transparency in medical decision-making, and the effectiveness of real-time patient monitoring and care. Methods: A scoping review methodology based on the PRISMA-ScR framework was used to identify, select, and summarize literature on the use of AI in DFU assessment. Literature was sourced from PubMed, ProQuest, and Scopus using keywords like diabetic foot ulcer, Artificial Intelligence, and wound assessment. Results: AI models demonstrate high accuracy in risk prediction, detection, segmentation, and classification of diabetic foot ulcers (DFU), with some models achieving up to 99% accuracy. Smart applications and deep learning-based systems have proven to be reliable and comparable to clinical evaluations, enhancing efficiency and transparency in DFU management. Conclusions: The development and application of AI-based models in DFU assessment and monitoring improve diagnostic effectiveness and accuracy while supporting more transparent and timely medical decisions.
Background: Acute aortic syndrome is a rare but life-threatening clinical syndrome that can rapidly progress to aortic rupture and death. Symptoms are vague and non-specific, making it challenging to...
Background: Acute aortic syndrome is a rare but life-threatening clinical syndrome that can rapidly progress to aortic rupture and death. Symptoms are vague and non-specific, making it challenging to identify. Objective: We aimed to evaluate prediction models to help clinicians identify acute aortic syndrome based on the data available at the time of presentation. Methods: We combined two existing national datasets of signs and symptoms gathered from patients with and without acute aortic syndrome, from over 30 UK healthcare centres (n = 6,168). Sample incidence was 10.1% (n = 634) against a symptomatic population incidence of 0.26%. We fitted 4,776 prediction models to an 80% ‘training’ split of the data, and then tested on the remaining 20% ‘test’ split. Sensitivity, overall net benefit, and informedness (using Youden’s J) were calculated to represent the perspectives of the clinician, the patient, and the decision modeller. Results: The most-common performance was for models to show little to no sensitivity or informedness (< 0.1) and negative overall net benefit. Models with high sensitivity (>0.8) had a range of informedness values, including 0. The only models that had a positive overall net benefit all used the same rule that labelled everyone as having acute aortic syndrome. These “yes to all” models had a sensitivity of 100%, an overall net benefit of only 10%, and informedness value of 0. Conclusions: The perspectives of the clinician, the patient, and the decision modeller need to be considered when developing prediction models for decision support. No model performed well on all evaluation statistics. Difficult trade-offs are revealed, which are exacerbated for rare and severe conditions, such as acute aortic syndrome.
Background: Background: Tetraplegia, often resulting from cervical spinal cord injury (SCI), may lead to significant motor and sensory loss, severely impacting independence and quality of life. Assist...
Background: Background: Tetraplegia, often resulting from cervical spinal cord injury (SCI), may lead to significant motor and sensory loss, severely impacting independence and quality of life. Assistive technologies (ATs), such as wheelchair-mounted robotic arms (WMRAs), offer potential to enhance autonomy in daily living. However, adoption remains limited due to high costs, complex controls, and insufficient end-user involvement. Robust evidence on their real-world effectiveness, particularly post-hospitalisation, is still lacking. Objective: Objektives: This study explores the real-life use of a WMRA for individuals with tetraplegia. It aims to evaluate its support in activities of daily living (ADLs), assess usability and satisfaction, and conduct a preliminary health economic analysis comparing cost-effectiveness and quality of life outcomes with standard care. Methods: Methods: This study will be conducted in post-hospitalisation settings in Switzerland. Up to 15 participants with upper limb impairments (SCI C0–Th1, AIS A–D) using powered wheelchairs will be recruited. They will use the robotic arm for six consecutive days. An equal number of participants will be recruited for the economic analysis group. A mixed methods approach will combine quantitative data collected via standardised questionnaires (PSSUQ, NASA-TLX, EQ-5D-5L, VAS, aCOMP, CSSRI-EU) at baseline and post-intervention, along with qualitative feedback gathered through an informal questionnaire and semi-structured interviews. Feasibility will be assessed through task performance and health economic analysis. The latter will include quality-adjusted life years (QALY), which quantify quality and length of life, and modelling the Incremental Cost-Effectiveness Ratio (ICER), which compares the cost-effectiveness of the intervention based on cost per QALY gained. Results: Results: Recruitment was initiated in April 2025, with the enrolment period expected to conclude in December 2025. As of June 2025, no participants have been enrolled. We expect the robotic system to reduce caregiver time and associated costs, while enhancing autonomy, quality of life, and mental well-being. Potential technical and recruitment challenges have been identified and mitigation strategies planned. By evaluating real-life use of a WMRAs, this study may support the broader adoption of assistive robotic technologies. Conclusions: Conclusion: This research offers key insights into the feasibility, usability, and economic value of robotic assistance for individuals with tetraplegia and will help inform future development and scale-up studies.
Background: Sri Lanka has a well-established National Blood Transfusion
Service that provides quality assured blood bank service.
However, the information flow is inefficient and less utilized for...
Background: Sri Lanka has a well-established National Blood Transfusion
Service that provides quality assured blood bank service.
However, the information flow is inefficient and less utilized for
evidence-based decision-making. The statistics unit of National
Blood Centre is unable to produce Annual Statistics Report
timely due to the difficulty in analysing and making reports
manually utilizing the considerable amount of data collected
throughout the year. To address this, an electronic Health
Information Management System was proposed as a solution for
the inefficiency of the data flow for statistical purposes. Objective: 1. General Objective
Facilitate decision-making by developing, implementing and
evaluating an electronic information management system to
capture monthly statistics data from island wide blood banks.
2. Specific Objectives
Identify the requirements of the system (MSR-NBTS)
Customize DHIS2 to fulfil the identified
requirements
Testing and hosting the system at National Blood
Centre Narahenpita
Evaluation of usability and cost-effectiveness of the
system Methods: A Monthly Statistics Reporting System was designed and
developed using DHIS2, which is a Free and Open Source
Software (FOSS) to fulfil the requirements of the National Blood
Transfusion Service. To evaluate the new system, a qualitative
study was conducted using semi-structured interviews amongst
a selected study population of 17 participants within the NBC
Cluster, which includes 11 blood banks in Colombo area. The
gathered data was analysed using a thematic analysis techniques
and the emerging categories and themes were used in the
subsequent discussions. Results: Problems of calculation, usability, reliability, utilization of
data and availability of reports were identified in the paper
based system. Results shows that the new electronic system has
high usefulness, ease of use, ease of learn, satisfaction and cost
effectiveness with accepted enhanced features of the interface.
According to the interviews, participants expressed that the
likelihood of using this system in the future is high. Conclusions: Almost all the participants in this research readily accepted
new electronic information management system. Therefore, it
will assure the sustainability of the new system. Because of the
real time updated dashboard, it will help most of the blood bank
functions by facilitating administrative decision-making
efficiently.
Background: Systems psychodynamics provide valuable insights into organizational development. To date, instruments that can reliably assess organizations based on systems psychodynamic theories are la...
Background: Systems psychodynamics provide valuable insights into organizational development. To date, instruments that can reliably assess organizations based on systems psychodynamic theories are lacking, though. The Systematic Multidimensional Organisational Assessment (SyMOA) is a qualitative instrument that provides an in-depth, systems psychodynamic analysis of organizational dynamics by using a semi-structured interview guide. To complement the method, a standardized, quantitative self-assessment questionnaire will be developed and validated. Objective: The aim of this study is to develop and psychometrically validate an instrument for assessing organizational health based on the SyMOA diagnostic system. The questionnaire is intended to provide a scientifically grounded yet practical diagnostic tool applicable in both research and corporate practice. The findings aim to contribute to the advancement of systems psychodynamic theory and serve as a foundation for evidence-based interventions in organizational change processes. Methods: The study follows a multi-stage development and validation process. First, the SyMOA construct will be transformed into a questionnaire battery and the items will be evaluated by experts (expert validity). The items will be tested through factor and item analyses in an online panel (n=150) and iteratively refined. Subsequently, factorial validity, discriminant validity, and test-retest reliability will be examined before standardizing the instrument with a larger sample (n=800). Results: As of April 2025, a first draft of 158 items was developed based on Dimension I of the SyMOA framework. The draft underwent an expert review process with two experts in psychodynamics, who provided feedback on content validity and conceptual alignment. Approximately 20% of the items were revised to improve clarity and theoretical precision. Data collection using a panel is scheduled for the coming months, with iterative item analysis to be conducted thereafter. Results are expected to be published in late 2025. Conclusions: Parallel field application of the SyMOA framework in organizational settings complements the quantitative development by offering insights into its real-world relevance and usability. This integration underscores the instrument’s translational value, while also illustrating the practical challenges of applying systems psychodynamic diagnostics in organizational contexts. Clinical Trial: Freiburger Register Klinischer Studien FRKS005727
Background: Most medical schools do not require anesthesiology as part of their clerkship curricula, limiting student exposure to the specialty. Objective: This study aims to investigate whether the C...
Background: Most medical schools do not require anesthesiology as part of their clerkship curricula, limiting student exposure to the specialty. Objective: This study aims to investigate whether the California Anesthesiology Medical Student Symposium (CAMSS), a one-day conference composed of anesthesiology lectures and workshops led by residency program leaders, can increase student knowledge or interest in anesthesiology. Methods: The Annual CAMSS of 2022 was organized at University of California Irvine School of Medicine by medical students and residency program leaders. An online survey was distributed to all registered students three days prior to the conference and immediately afterwards. Student exposure, knowledge, and interest in anesthesiology were evaluated using Likert-scales. Pre-conference versus post-conference results were analyzed using two-sample t-tests with a p-value < 0.05 considered as statistically significant. Results: The pre-conference survey was emailed to all 96 students who registered for the conference, 68 of which completed the survey (response rate 70.8%). The post-conference survey was emailed to all 83 students who attended the conference, 51 of which completed the survey (response rate 61.4%). On a Likert scale of 1-10, post-conference survey responses revealed a statistically significant increase in self-perceived knowledge of anesthesiology compared to pre-conference surveys (mean 6.44, SD 1.79 vs. mean 4.71, SD 2.07 respectively; p < 0.001). Conclusions: A one-day anesthesiology-focused conference can increase medical students’ self-perceived knowledge of the specialty’s multifaceted role in the hospital setting. Clinical Trial: This prospective cohort observational study was approved by University of California, Los Angeles Medical Institutional Review Board (IRB) # 21-001825.
Background: Digital shared medication records (DSMRs) are promoted to improve medication management across care settings, but implementation remains slow and challenging. Existing systems often fail t...
Background: Digital shared medication records (DSMRs) are promoted to improve medication management across care settings, but implementation remains slow and challenging. Existing systems often fail to reflect patient-led changes, raising questions about why national initiatives do not allow patients or family caregivers to be directly involved in updating shared information. At the same time, little is known about how patients perceive these tools and what they expect. Public and patient involvement in the design of such systems has been minimal, leaving a critical gap in user-centered evidence to guide implementation. Objective: This study aimed to develop and pilot test a discrete choice experiment (DCE)-based survey instrument to assess patient preferences and estimated uptake of DSMRs. The tool is intended to inform the co-design of digital medication records that align with patient needs and support broader stakeholder decision-making. Methods: We developed the survey instrument in three phases. First, we identified relevant DSMR features from scientific literature and Swiss policy and technical documents. Second, we conducted a stakeholder and expert prioritization exercise to select attributes for the DCE. Third, we refined the attributes and levels through think-aloud interviews with patients. The final survey included the DCE, items on potential adoption factors, and questions addressing current policy concerns. We pilot-tested it online with 300 patients who regularly take multiple medications. Results: An initial list of 31 concepts was refined into 17 dimensions, ultimately yielding seven key DSMR attributes for the pilot: content, update responsibility, access rights, tool purpose, additional features, data protection, and financial incentives. Choice model estimations confirmed expected preference directions. Financial incentives, responsibility for updating, and data protection had the strongest influence on uptake, followed by content and primary purpose. Access rights and extra features were less impactful. Respondents favored collaborative medication plan management involving both patients and professionals over professional-only approaches. Conclusions: The instrument demonstrated strong potential for larger-scale use in Switzerland, with minor adaptations recommended for other settings. Health authorities and innovators can use this tool to test DSMR design and implementation strategies while generating context- and population-specific insights that would otherwise require costly and time-intensive evaluations. This approach supports strategic planning, including simulations to tailor implementation across subgroups. Such foresight can help optimize investments and reduce the risk of widening health inequities and digital divides. More broadly, the instrument provides a practical method for engaging the public in digital health policymaking and co-creating patient-centered services.
Background: Cervical spondylosis (CS), a progressive degenerative disorder often leading to neurological impairment, remains poorly characterized in terms of its association with routine biochemical m...
Background: Cervical spondylosis (CS), a progressive degenerative disorder often leading to neurological impairment, remains poorly characterized in terms of its association with routine biochemical markers. This multicenter study aimed to identify novel CS subtypes through unsupervised clustering of clinical and laboratory biomarkers, subsequently developing a predictive model for postoperative recurrence. Objective: This study aimed to leverage unsupervised machine learning to delineate clinically actionable cervical spondylosis (CS) subtypes based on preoperative biomarker profiles, and further establish a predictive nomogram for postoperative recurrence risk stratification. By integrating clustering-driven phenotyping with supervised feature selection, we sought to bridge the gap between heterogeneous inflammatory signatures and surgical complexity, ultimately guiding subtype-specific therapeutic decision-making. Methods: In this study, 884 cervical spondylopathy patients who underwent Cervical spondylosis surgery were enrolled at the Department of Spine Osteopathology, the First Affiliated Hospital of Guangxi Medical University from June 2012 to June 2021. After screening, 715 patients were eventually included. After 7:3 training-validation split, k-means clustering stratified patients into subtypes based on 37 preoperative variables. Feature selection integrated LASSO regression and Random Forest algorithms, with subsequent nomogram construction via multivariable logistic regression. Model performance was evaluated through ROC analysis and calibration curves. Results: Unsupervised clustering delineated two subtypes with distinct profiles: Subtype 1 (n=215) exhibited milder inflammation (CRP: 2.1±1.1 mg/L) versus Subtype 2 (n=580) demonstrating marked systemic inflammation (CRP: 8.7±3.2 mg/L, p<0.001). The nomogram incorporating neutrophil count, lymphocyte levels, eosinophil percentage, basophils, and cystatin C showed exceptional discrimination (AUC=0.996, 95% CI: 0.985-1.000). Despite equivalent JOA improvement rates (>25% in both subtypes), Subtype 2 required more multilevel decompressions (35.5% vs. 17.5%, p<0.001). Conclusions: Our machine learning framework successfully identifies inflammatory-driven CS phenotypes with differential surgical complexity. The validated nomogram enables preoperative risk stratification, potentially guiding personalized rehabilitation strategies. Prospective validation across diverse populations is warranted to confirm clinical utility.
Background: In Mexico, the maternal and child population continues to face a high burden of malnutrition, posing a persisted public health challenge. The healthcare system plays a crucial role, not on...
Background: In Mexico, the maternal and child population continues to face a high burden of malnutrition, posing a persisted public health challenge. The healthcare system plays a crucial role, not only in addressing existing cases but also in preventing and detecting malnutrition early. Mobile health (mHealth) technologies have the potential to strengthen maternal and child health services by improving the quality, accessibility, and timeliness of nutritional care. Objective: The aim was to develop and validate the design and content of a mobile application—CANMI (Calidad de la Atención Nutricional Materno Infantil, by its Spanish acronym) — to monitor the quality of maternal and child nutritional care in primary health care units in Mexico. Methods: The framework of the CANMI app was based on the 16 validated indicators designed to assess the quality of nutritional care during the preconception, pregnancy, postpartum, early childhood, and preschool stages. The application was developed for both iOS and Android systems using a user-centered design approach. Following development, a pilot usability study was conducted in a randomized sample of 18 primary health care units in Guanajuato, Mexico. Trained nutritionists implemented the app and collected usability data at the end of the initial usage period and again six weeks later. To further explore user experience, semi-structured online interviews were conducted to identify barriers, facilitators, and overall satisfaction with the app. Results: The CANMI app allows the systematic registration of key indicators to assess the quality of nutritional care in primary health care settings. Users described the app as simple, intuitive, and visually appealing. Overall usability was rated positively, with a mean score of 71.13 on the System Usability Scale (SUS) indicating good acceptability. The app’s offline functionality, streamlined interface, and efficiency in data collection were identified as key facilitators of use. Reported benefits included reduced time for data entry and perceived improvements in the quality of nutritional care. Identified barriers to integration included the need to use personal devices, user fatigue due to prolonged screen time, inconsistent clinical records, and limited time to incorporate the app into routine workflows. Importantly, the app encouraged promoted improvements in documentation practices and heightened awareness among health personnel regarding the precision and clarity of their nutritional recommendations. Conclusions: The CANMI app provides a feasible and effective solution for monitoring the quality of maternal and child nutritional care in primary health settings. Its high usability and offline capabilities make it particularly suitable for low-connectivity environments. Beyond facilitating data collection, the app contributed to improved clinical documentation practices and enhanced provider awareness of care quality. As such, the application can represent a promising digital tool to support the implementation evidence-based, user-centered strategies aimed at strengthening maternal and child health services in resource- limited contexts. Clinical Trial: The study protocol was reviewed and approved by the Research Ethics Committee of the Universidad Iberoamericana in Mexico City (172/2022).
Background: Promoting individual resilience – i.e., maintaining or regaining mental health despite stressful circumstances – is often regarded as important endeavor to prevent mental illness. Howe...
Background: Promoting individual resilience – i.e., maintaining or regaining mental health despite stressful circumstances – is often regarded as important endeavor to prevent mental illness. However, digital resilience interventions designed to enhance mental health outcomes, including stress levels and self-perceived resilience, have yielded mixed results. Such heterogeneous effects reflect a variety of unsolved conceptual challenges in interventional resilience research. These range from grounding interventions in genuine resilience frameworks, using theory or targeting etiologically important resilience factors as intervention content, to a lack of knowledge about the mechanisms underlying effects, and employing techniques specifically developed to foster psychosocial resources. The web- and app-based resilience intervention RESIST was designed to address these challenges, mainly by utilizing both the Positive Appraisal Style Theory of Resilience as its theoretical foundation and interventional techniques from Strengths-based Cognitive Behavioral Therapy. Objective: The study’s primary aim was to evaluate the effectiveness of RESIST in a general working population as a means of universal prevention, relative to a waitlist control group. A secondary study aim was to explore the resilience factors of self-efficacy, optimism, perceived social support, and self-compassion the intervention targets as potential mediators of its effect on stress and self-perceived resilience. Methods: In total, 352 employees were randomly assigned to either a self-help version of RESIST or waitlist control group. Data were collected at baseline, post-intervention, and at 3- and 6-month (intervention group only) follow-up. The primary outcome was perceived stress, measured with the Perceived Stress Scale-10. Secondary outcomes included self-perceived resilience, the resilience factors targeted, and other mental and work-related health outcomes. Results: The intervention group reported significantly less stress than controls post-intervention (Δ=-3.14; d=-0.54, 95%CI -0.75 to -0.34, and P<.001) and at 3-month follow-up (Δ=-2.79; d=-0.47, 95%CI -0.71 to -0.22, and P=.002). These improvements in the intervention group were maintained at 6-month follow-up. Favorable between-group differences also were detected for self-perceived resilience and the resilience factors. Effects on other mental and work-related outcomes were mixed. Parallel mediation analyses revealed significant indirect effects of optimism (a2b2=-0.34, 95% CI -0.63 to -0.06) and self-compassion (a4b4=-0.66, 95% CI -1.15 to -0.17) on perceived stress, whereas indirect effects through self-efficacy and social support were not found. A similar pattern emerged for self-perceived resilience as mediation outcome. Conclusions: In a sample of employees experiencing heightened work-burden levels, RESIST was effective in reducing perceived stress, and increasing self-perceived resilience as well as the targeted resilience factors. Mediation analyses suggested that developing a positive future outlook and a self-compassionate attitude toward oneself may be key drivers to enhance resilience. Changing the quality of social relationships and strengthening the belief in one’s abilities may require more time, the involvement of others, or personal support from a mental health professional, such as an e-coach, to ensure sufficient learning opportunities. Clinical Trial: German Clinical Trials Register DRKS00017605; https://drks.de/search/de/trial/DRKS00017605
Background: The relationships between circadian rhythm syndrome, physical function, and muscle strength remain unclear. Objective: This study aimed to demonstrate the separate and combined deleterious...
Background: The relationships between circadian rhythm syndrome, physical function, and muscle strength remain unclear. Objective: This study aimed to demonstrate the separate and combined deleterious effects of solid fuel use and circadian rhythm syndrome on physical function and muscle strength. Methods: We used data from the China Health and Retirement Longitudinal Study cohort. The study population consisted of participants who underwent comprehensive assessments of metabolism, circadian rhythm, indoor air pollution, physical function, and muscle strength at the initial evaluation. Muscle strength was assessed using repeated grip strength measurements, and physical function was assessed using a composite score of muscle strength, physical performance, and balance. Circadian rhythm syndrome was derived from the five diagnostic components of metabolic syndrome, combined with sleep duration and depression. Logistic regression and linear mixed models were used to assess the relationships between solid fuel use, circadian rhythm syndrome, physical function and muscle strength. Furthermore, we analyzed the mediating role of circadian rhythm syndrome and its combined effect with solid fuel use on physical function and muscle strength. Results: A total of 7954 participants were included in the study, most of whom used solid fuels. solid fuel use was positively associated with circadian rhythm syndrome (OR: 1.078; 95% CI: 1.031–1.125). Circadian rhythm syndrome was found to be a significant risk factor for impairment of physical function and muscle strength. Participants who used solid fuels and had circadian rhythm syndrome needed to pay more attention to changes in physical function (β: -0.698, 95% CI: -0.813, -0.584) and muscle strength (β: -0.332, 95% CI: -0.387, -0.277). Circadian rhythm syndrome partially mediated the association between solid fuel use and physical function. Conclusions: Circadian rhythm syndrome exacerbates the adverse effects of solid fuel use on physical function and muscle strength. Fuel cleanliness and regular work and rest habits are crucial for the health of middle-aged and older adults. Clinical Trial: Not applicable
Background: The Japanese National Medical Licensing Examination (NMLE) is mandatory for all medical graduates to become licensed physicians in Japan. Given the cultural emphasis on summative assessmen...
Background: The Japanese National Medical Licensing Examination (NMLE) is mandatory for all medical graduates to become licensed physicians in Japan. Given the cultural emphasis on summative assessment, the NMLE has had a significant impact on Japanese medical education. Although the NMLE Content Guidelines have been revised approximately every five years over the last two decades, there is an absence of objective literature analyzing how the actual exam itself has evolved. Objective: To provide a holistic view of the trends of the actual exam over time, this study used a combined rule-based and data-driven approach. We primarily focused on classifying the questions according to the perspectives outlined in the NMLE Content Guidelines, while complementing this approach with a natural language processing technique called topic modeling to identify latent topics. Methods: Publicly available NMLE data from 2001 to 2024 were collected. Six exam iterations (2,880 questions) were manually classified from three perspectives (Level, Content, and Taxonomy) based on pre-established rules derived from the guidelines. Temporal trends within each classification were evaluated using the Cochran-Armitage test. Additionally, topic modeling was conducted for all 24 exam iterations (11,540 questions) using the BERTopic framework. The temporal trends of each topic were traced using linear regression models of topic frequencies to identify topics growing in prominence. Results: In Level classification, the proportion of questions addressing common or emergent diseases increased from 60% to 76% (p < 0.001). In Content classification, the proportion of questions assessing knowledge of pathophysiology decreased from 52% to 33% (p < 0.001), whereas the proportion assessing practical knowledge of primary emergency care increased from 20% to 29% (p < 0.001). In Taxonomy classification, the proportion of questions that could be answered solely through simple recall of knowledge decreased from 51% to 30% (p < 0.001), while the proportion assessing advanced analytical skills, such as interpreting and evaluating the meaning of each answer choice according to the given context, increased from 4% to 19% (p < 0.001). Topic modeling identified 25 distinct topics, and 10 topics exhibited an increasing trend. Non-organ-specific topics with notable increases included “Comprehensive Clinical Questions,” “Accountability in Medical Practice and Patients’ Rights,” “Care, Daily Living Support, and Community Healthcare,” and “Infection Control and Safety Management in Basic Clinical Procedures.” Conclusions: This study identified significant shifts in the Japanese NMLE over the past two decades, suggesting that Japanese undergraduate medical education is evolving to place greater importance on practical problem-solving abilities than on rote memorization. This study also identified latent topics that showed an increase, possibly reflecting underlying social conditions. Clinical Trial: NA
Abstract
Background
Cholestatic liver disease (CLD) is associated with various hereditary and acquired liver diseases. However, its outcomes are often poor and lack proper treatment. Danning tablets...
Abstract
Background
Cholestatic liver disease (CLD) is associated with various hereditary and acquired liver diseases. However, its outcomes are often poor and lack proper treatment. Danning tablets (DNT) have been widely used to treat CLD and have achieved favorable outcomes in clinical practice. However, there is currently a lack of clinical trials verifying the efficacy of DNT. Therefore, we investigated whether DNT combined with ursodeoxycholic acid capsules (UDCA) is more effective than UDCA alone in treating CLD of the damp heat stagnation type.
Methods
This was an open-label, multicenter, randomized controlled trial (RCT). A total of 186 patients diagnosed with damp heat stagnation type CLD were enrolled. They were stratified according to the severity of CLD and randomly assigned in a 1:1 ratio to receive either UDCA treatment alone or combined with DNT for 3 months. The primary endpoints of the study were improvement in clinical symptoms and liver function after treatment. The secondary endpoints included liver stiffness, survival status during hospitalization and follow-up, and adverse events.
Conclusion
This RCT will provide high-quality evidence to demonstrate the potential benefit of DNT combined with UDCA in patients with CLD of the damp heat stagnation type.
Background: Chronic obstructive pulmonary disease (COPD) is a globally prevalent respiratory disorder characterized by progressive airflow limitation, leading to substantial disability and being among...
Background: Chronic obstructive pulmonary disease (COPD) is a globally prevalent respiratory disorder characterized by progressive airflow limitation, leading to substantial disability and being among the leading causes of chronic morbidity and mortality worldwide. However, there remains a notable lack of convenient and effective home-based intervention programs to support COPD self-management. Objective: This study aimed to develop and evaluate the usability of "COPD CarePro," a WeChat-based mini-program designed to improve home-based self-management for COPD patients. Methods: Utilizing a mixed-methods design, we first conducted semi-structured interviews on 15 COPD patients and their caregivers following the consolidated criteria for reporting qualitative research (COREQ) guidelines to identify user needs. A multidisciplinary team then co-developed a five-module intervention featuring: 1) secure authentication, 2) symptom monitoring, 3) health diary, 4) multimedia education library, and 5) clinician communication portal. A two-stage usability assessment was implemented: (i) PSSUQ testing (n=10) uncovered navigational and functional pain points for prototype optimization, followed by (ii) SUS administration (n=52) to quantify usability of the production-ready version. Results: The final prototype demonstrated good usability with mean SUS score of 76.15±6.58, meeting the acceptability threshold (>70). Key functional outcomes included successful implementation of real-time symptom monitoring using CAT/mMRC thresholds for exacerbation alerts, and high engagement with video-based pulmonary rehabilitation guidance. Conclusions: COPD CarePro represents a clinically relevant health solution that successfully bridges critical gaps in COPD homecare through technology-enabled self-management support. The development process highlights the value of participatory design in creating patient-centered digital health interventions. Clinical Trial: NO
Background: Exergaming refers to video gaming with/without virtual reality that requires the use of physical activity during gameplay, and has been utilized as an emerging type of physical activity in...
Background: Exergaming refers to video gaming with/without virtual reality that requires the use of physical activity during gameplay, and has been utilized as an emerging type of physical activity in improving older adults’ physical and mental health. Exergaming can also be considered as esports when a competitive and interactive element is embedded in the gameplay. To date, the impact of exergaming-based esports on older adults’ health and well-being has been less investigated. Objective: This study aims to examine the effectiveness of an exergaming-based esports intervention program in promoting older adults’ physical, psychological, and cognitive health outcomes in Hong Kong. Methods: A total of 54 older adults were recruited and 48 (male = 12; female = 36) were retained for data analysis (six did not attend the post-test). All participants were allocated to either an esports group (EG = 24) or a control group (CG = 24). EG participants were invited to participate in an eight-week exergaming-based esports intervention program consisting of 16 training sessions to learn and play the Nintendo Switch™ Fitness Boxing game. A fitness boxing competition was embedded in the final three sessions. CG participants, in contrast, were instructed to maintain their normal daily activities. Outcome measures including the Senior Fitness Test, the University of California, Los Angeles (UCLA) Loneliness Scale (ULS-8), the Chinese version of the Physical Activity Enjoyment Scale (PACES), the Number Comparison Test (NCT), the Trail Making Test (TMT), and the Short Form-36 (SF-36) Health Survey were used to assess physical, psychological, and cognitive conditions. A repeated-measures ANCOVA was conducted, controlling for baseline values and demographic covariates. Results: The results showed that after the 8-week intervention, EG participants had better lower body strength, higher aerobic endurance, higher enjoyment level, and higher cognitive functioning than those in the CG. Conclusions: This study provides a theoretical contribution by filling the research gap regarding the beneficial effects of exergaming-based esports in enhancing older adults’ health in Hong Kong. Game designers are encouraged to design game types with competitive and interactive elements for older adults to play, thereby promoting their emotional and cognitive well-being. Clinical Trial: The trial design was registered on the Chinese Clinical Trial Registry (ChiCTR) on 13 November 2024 (TRN: ChiCTR2400092284). This study was retrospectively registered, as registration took place after the first participant was enrolled.
Background: Outdoor play has always been a fundamental part of childhood. Children’s participation in outdoor play connects them to nature, the land and supports their role in the natural world. Ear...
Background: Outdoor play has always been a fundamental part of childhood. Children’s participation in outdoor play connects them to nature, the land and supports their role in the natural world. Early learning and child care (ELCC) centres provide important opportunities for outdoor play, however, barriers towards the provision of outdoor play opportunities exist, including educator attitudes, existing policies and procedures, outdoor space limitations and adverse weather conditions. Objective: The PROmoting Early Childhood Outside (PRO-ECO) 2.0 study is a community-based research partnership with Indigenous Knowledge Keepers and Elders, Indigenous and early childhood organizations, early childhood education faculty, ELCC centres and families, aiming to expand outdoor play in ELCC centres. This paper provides a detailed overview of the community-based design process, guided by the 5 R’s – Respect, Relevance, Responsibility, Reciprocity and Relationship – and the resulting study protocol for the mixed methods wait-list control cluster randomized trial. Methods: The PRO-ECO program and study protocol are implemented in partnership with 10 ELCC centres delivering licensed full-day, year-round care to children aged 2.5-6 years in rural and urban areas of British Columbia, Canada. The PRO-ECO program includes four components to address the common barriers to outdoor play in ELCC settings. Primary outcome measures include the proportion and diversity of observed nature play behaviour during dedicated outdoor times at ELCC centres as measured through observational behaviour mapping. Secondary outcomes include changes in educator attitudes, quality of ELCC outdoor play space, and children’s perspectives of their experiences at ELCC centres. Outcome data are collected at baseline, and 6-months and 12-months post-baseline. The community’s perspectives (educators, children, families) of the project are assessed qualitatively to understand the acceptability and effect of the PRO-ECO program. Mixed-effect models will test the effect of the PRO-ECO program on quantitative outcomes. Qualitative data will support interpretation of quantitative findings and provide evidence on project acceptability. Results: Participant recruitment for this study began in August 2023 and data collection was completed at participating ELCC centres in March 2025. A total of 227 children, 90 early childhood educators and 40 family members were recruited to participate in this study. Conclusions: The PRO-ECO 2.0 study ruses a rigorous and robust experimental design within a community-based research project. The 5 R’s approach grounded our work in shared values, disrupting traditional academic power relations and weaving together Indigenous and Western worldviews in the context of academic research. Clinical Trial: NCT05626595
Background: Selecting suitable healthcare professionals remains a challenge for patients due to information asymmetry and limited guidance provided by online consultation platforms. Existing doctor re...
Background: Selecting suitable healthcare professionals remains a challenge for patients due to information asymmetry and limited guidance provided by online consultation platforms. Existing doctor recommendation systems often overlook the importance of "patient expectations" in assessing medical service quality, leading to suboptimal matching. Objective: To address this gap, we propose a personalized doctor recommendation system that integrates patient preferences and doctor profiles using the SERVQUAL framework. Methods: This system builds comprehensive bilateral profiles through feature extraction and sentiment analysis of user data from an online health community. Key dimensions, including tangibility, reliability, responsiveness, empathy, and assurance, are operationalized alongside additional factors like price and disease specialization.
A matching algorithm is developed to align patient expectations with doctor service attributes systematically. Results: Evaluation through scenario-based simulations demonstrated high match accuracy and high participant satisfaction. Conclusions: This approach enhances recommendation accuracy, reduces decision-making complexity, and improves user experiences on online healthcare platforms, optimizing resource allocation and patient outcomes.
Background: The World Health Organization (WHO) reported in 2020 that approximately 50% of all mental health disorders in adolescents manifest before the age of 14. However, the literature on mental w...
Background: The World Health Organization (WHO) reported in 2020 that approximately 50% of all mental health disorders in adolescents manifest before the age of 14. However, the literature on mental well-being and programmes designed and implemented by nurses for adolescents in low- middle-income countries (LMICs) is limited. This scoping review explores the development and implementation of psychosocial support programmes targeting high school learners in LMICs. Objective: This prospective scoping review will explore how psychosocial support programmes have been developed and implemented for high school learners from LMICS. Methods: Using the Joanna Briggs Institute (JBI) scoping review framework, primary research articles will be identified through systematic searches of ERIC, MEDLINE, Science Direct, PubMed, and PsycINFO. Grey literature will also be sourced from Google Scholar. Two independent reviewers will apply pre-determined inclusion criteria to select studies. Data will be charted, analyzed narratively, and presented in tables and figures Results: Data collection started in January 2024. Results yet to be published. Conclusions: This scoping review will synthesize evidence on psychosocial support programmes in LMICS and guide the development of targeted interventions to address the mental health needs of high school learners. Clinical Trial: Additional supplemental material is available from the Open Science repository.
Background: Unskilled birth delivery significantly contributes to maternal and neonatal mortality in Sub-Saharan Africa, especially Nigeria, due to cultural beliefs, poverty, poor health access, and w...
Background: Unskilled birth delivery significantly contributes to maternal and neonatal mortality in Sub-Saharan Africa, especially Nigeria, due to cultural beliefs, poverty, poor health access, and weak policies. Despite efforts to promote skilled attendance, many women still use traditional birth attendants (TBAs) and home deliveries. This study explores the socio-demographic, cultural, and systemic factors driving this trend, offering evidence for better policies and health interventions. Objective: This study examined the socio-demographic and socio-cultural barriers to the utilization of skilled delivery services among women of reproductive age in Nigeria. Methods: A cross-sectional design utilizing both quantitative surveys and qualitative interviews was employed. The study involved 1,200 expectant and recently delivered women across urban, semi-urban, and rural regions in Nigeria. Data on socio-demographics, beliefs, access factors, and healthcare usage were collected. Policy documents and intervention records were reviewed, while focus groups provided depth to cultural and systemic themes. Descriptive and inferential statistics were applied using SPSS, and thematic analysis was used for qualitative data. A literature triangulation approach was used to validate findings with existing research. Results: The study revealed that low maternal education, poverty, and rural residence strongly predicted unskilled delivery service usage. Cultural norms that regard childbirth as a domestic or spiritual event influenced avoidance of hospitals. Access barriers included poor transport, cost, and distrust in formal healthcare. Geographic inequality was evident, with rural regions lacking health infrastructure. Policy review showed limited reach and weak enforcement of maternal care programs. However, when community-based midwives or mobile clinics were available, skilled birth attendance improved significantly. Conclusions: The persistence of unskilled deliveries is a multifaceted issue driven by intersecting socio-cultural, economic, geographic, and institutional factors. Despite policy efforts, gaps remain in cultural sensitivity, resource allocation, and infrastructure coverage. To address maternal health effectively, interventions must be locally adapted, multidimensional, and equity-focused. To address unskilled delivery use, maternal health education should leverage community programs with local languages and cultural context. Rural healthcare infrastructure must expand via mobile clinics and trained midwives to improve access. Skilled delivery costs should be subsidized or covered by insurance to remove financial barriers. Traditional birth attendants could be trained and integrated into the formal health system under supervision. Finally, maternal health policies require regular review, adequate funding, and strict monitoring to ensure impact. These steps are vital to reducing maternal mortality in Nigeria and Sub-Saharan Africa. Unskilled delivery service utilization represents a critical barrier to maternal and neonatal health improvements in Nigeria and Sub-Saharan Africa. Addressing this issue through targeted socio-cultural, structural, and policy interventions is essential to reduce preventable maternal deaths and achieve Sustainable Development Goal 3 on maternal health.
Background: With technological advancement, the internet has become the most convenient and vital source of information for many young people, most especially with the influx of mobile health (mHealth...
Background: With technological advancement, the internet has become the most convenient and vital source of information for many young people, most especially with the influx of mobile health (mHealth) platforms, which prevent many hurdles associated with young people’s access to SRH information and services. Hence, there is a gradual drift from in-person and constant visits to health facilities to a more convenient and easy way with just a tap. Again, unpleasant experiences such as attitudes of healthcare providers, proximity of health facilities, and cost implications further deter youth access to SRH in Ghana. In the bid to surf towards the new wave, novel approaches such as digital platforms, among which included the ‘You Must Know App’ by the Ghana Health Service (GHS), the Flow App, and many other digital tools, were introduced to help address this menace and facilitate access to quality and inclusive SRH among the Ghanaian youth. Objective: The study assesses the viability of digital tools as a means for sexual reproductive health (SRH) access among young people in the Greater Accra Region. Methods: A cross-sectional descriptive design was employed in the study. Following informed consent, a structured questionnaire was administered through an online platform to obtain information on socio-demographic and background characteristics, knowledge of available digital health platforms, sources of sexual reproductive health and any health-related information, and services, including participants' level of knowledge of mHealth and challenges to access. Results: The study found that 53.5% of participants had never used any digital health. Specifically, 66.8% indicated zero knowledge and awareness of the ‘You Must Know App’, and had they used any mobile applications to access healthcare before. On the other hand, 43.1% stated that they have ever used mobile health applications in their life, while 3.5% of the respondents did not know if they have ever used the applications or not. The results further suggested that despite technological advances in Ghana and parts of Africa, there remains a significantly low level of knowledge of mHealth tools and thus, the need for sensitization about mHealth Platforms, as a majority of the Ghanaian youth may not be aware of these applications at all. Conclusions: This study brought to light the emerging approaches to accessing sexual reproductive health information and services by the youth in Ghana, particularly with the onset of technology. However, it also revealed the emerging gaps or challenges faced by young people when using the available mobile health (mHealth) platforms as a source of SRH information in Ghana, particularly the You Must Know App, and suggested key innovations that can be implemented to enhance young people’s experiences with mhealth tools in Ghana.
Background: Meniscal injuries are prevalent knee pathologies. However, the public increasingly relies on online video platforms for health information, where content quality and reliability vary signi...
Background: Meniscal injuries are prevalent knee pathologies. However, the public increasingly relies on online video platforms for health information, where content quality and reliability vary significantly, posing risks of misinformation, particularly in China with its extensive platform usage. Objective: This study aimed to evaluate the quality (GQS), reliability (mDISCERN), understandability (PEMAT-U), and actionability (PEMAT-A) of meniscal injury video content on major Chinese platforms (Bilibili and Douyin/TikTok). It also sought to identify key predictive factors for these dimensions and, innovatively, to understand user perspectives, engagement patterns, and feedback through sentiment analysis and topic modeling of user comments. Methods: In this cross-sectional study, 200 top-ranked meniscal injury-related videos (100 from Bilibili, 100 from TikTok) were collected using a specific keyword and assessed by medical experts using GQS, mDISCERN, and PEMAT-A/U. Statistical analyses, performed with SPSS 27.0, included descriptive statistics, Mann-Whitney U tests, Spearman correlations, and stepwise regression. Approximately 22,000 user comments were analyzed using a fine-tuned BERT model for sentiment classification and BERTopic for thematic structure mining. Results: TikTok videos exhibited higher engagement metrics but shorter durations (P < .001). For GQS scores, professional sources were significantly higher than non-professional sources (P < .001), though no significant platform difference was found (P = 0.455). Regarding mDISCERN scores, Bilibili was significantly superior to TikTok (P < .05), yet no significant difference was observed between professional and non-professional sources (P = 0.23). PEMAT-U scores were significantly higher on TikTok compared to Bilibili (P < .001), but actionability (PEMAT-A) was consistently low across all platforms and sources, with no significant differences (P > .05). Regression analysis indicated that content reliability was the strongest predictor of quality, while video duration and quality significantly predicted reliability. Comment sentiment was predominantly neutral (72.4%), followed by positive (18.9%), with negative being the lowest (8.7%). Topic modeling revealed "Functional and Rehabilitation Discussion," "Discussion on Disease," and "Discussion on Treatment" as key themes. Conclusions: Content quality and reliability vary on Chinese video platforms regarding meniscal injury. While professional sources provide higher quality content, their reliability is not statistically superior to non-professional sources in this context. A universal deficiency in video actionability across all platforms and sources highlights a critical "understandable but not actionable" gap. Content creators should prioritize information accuracy and actionability to better empower public health management in the digital age.
Background: Chinese patent ethnomedicines(CPMs) are a form of traditional Chinese patent medicine that originate from the traditional medicines of ethnic minorities and are widely used in clinical pra...
Background: Chinese patent ethnomedicines(CPMs) are a form of traditional Chinese patent medicine that originate from the traditional medicines of ethnic minorities and are widely used in clinical practice. However, existing evidence for their application remains unclear. Therefore, to address this gap, this comprehensive scoping review will be performed to provide an overview of the available evidence from Chinese patent ethnomedicine preparations. Objective: This review aims to provide the evidence profile of oral CPMs. This study will elucidate the current state of the evidence with respect to these medicines and identify research gaps. The detailed steps for conducting this review are outlined in this protocol. This review will contribute to a better understanding of CPMs. Methods: This review will include clinical studies of CPMs irrespective of study design. The frameworks described by Arksey and O'Malley, Levac, and the Joanna Briggs Institute will be used to guide the current scoping review. This review will include six steps: (1) identify the research question;(2) collect information about Chinese patent ethnomedicines from national related drug catalogues; (3) search the MEDLINE (via PubMed), Embase, Web of Science, Cochrane Library and Chinese databases from inception to February 2025 to identify relevant publications; (4) screen the literature against the eligibility criteria; (5) extract data by using a predefined standardized data extraction form; and (6) summarize, discuss, analyse, and report the results. We will also present the results via data visualization techniques. Results: We will synthesize data on CPMs by conducting the Scoping Review, drawing the evidence maps, identifying the clinical research characteristics related to AEs features identifying , as well as highlighting the limitations and gaps in the literature. We expect to publish the results in 2026. Conclusions: The information obtained through this review could inform future research involving CPMs. Clinical Trial: Review registration number https://osf.io/e763b.
Background: Necrotizing enterocolitis (NEC) is the most common gastrointestinal emergency affecting preterm infants with high mortality and morbidity. With suboptimal and incomplete methods of prevent...
Background: Necrotizing enterocolitis (NEC) is the most common gastrointestinal emergency affecting preterm infants with high mortality and morbidity. With suboptimal and incomplete methods of prevention of NEC, early diagnosis and treatment can potentially mitigate the impact of NEC. This study explores the application of machine learning techniques, specifically Random Forest and Extreme Gradient Boosting (XG Boost), to improve early and accurate NEC and FIP diagnosis. Objective: To evaluate the effectiveness of sampling techniques in addressing class imbalance and to identify the optimal machine learning (ML) classifiers for predicting necrotizing enterocolitis (NEC) and focal intestinal perforation (FIP) in preterm infants. Methods: We developed ML models using 49 clinical variables from a retrospective cohort of 3,463 preterm infants, using clinical data from the first two weeks of life as input features. We applied various sampling strategies to address the inherent class imbalance, and then combined various sampling strategies with different ML algorithms. Parsimonious models with selected key predictors were evaluated to maintain predictive performance comparable to the full-featured (complex) models. Results: The parsimonious generalized linear model (GLM) with SMOTE sampling achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 for NEC prediction, closely approximating the complex model's AUROC of 0.76. For FIP prediction, parsimonious models of GLM with ADASYN sampling and XG Boost with TOMEK sampling achieved AUROC values exceeding 0.90, comparable to those of the corresponding complex models. For both NEC and FIP, the area under the precision-recall curve (AUPRC) surpassed the respective prevalence rates, indicating strong performance in identifying rare outcomes. Conclusions: We demonstrate that targeted sampling strategies can effectively mitigate class imbalance in neonatal datasets, and simplified models with fewer variables can offer comparable predictive power, enhancing the performance of ML-based prediction models for NEC and FIP.
Background: Workplace stress has emerged as a pressing public health issue in Nigeria, where approximately 75% of employees experience work-related stress significantly higher than the global average....
Background: Workplace stress has emerged as a pressing public health issue in Nigeria, where approximately 75% of employees experience work-related stress significantly higher than the global average. This stress, exacerbated by systemic labor policy gaps, cultural stigma, and economic instability, contributes to burnout, reduced productivity, and economic losses. Despite emerging HRM interventions, mental health remains underprioritized in organizational strategies, particularly within sectors such as healthcare, banking, construction, and the informal economy. There is a critical need for evidence-based, culturally adapted HRM strategies that address these unique challenges in Nigeria’s workforce. Objective: This study seeks to examine the prevalence and sector-specific drivers of workplace stress in Nigeria, evaluate the effectiveness and limitations of current HRM interventions, identify key socio-cultural and structural barriers hindering mental health program implementation, and propose actionable, evidence-based strategies that are contextually tailored to Nigeria’s diverse workforce. Through a synthesis of localized research and global best practices, the study aims to provide a strategic roadmap for enhancing mental health resilience in Nigerian workplaces. Methods: A narrative review methodology was employed, guided by qualitative synthesis and thematic analysis frameworks. Literature was sourced from global and regional databases (PubMed, PsycINFO, AJOL, Scopus) spanning 2018–2024, including peer-reviewed articles, policy reports, and grey literature. Inclusion focused on empirical and policy studies relevant to Nigerian HRM practices. NVivo 12 was used for thematic coding, and a gap analysis framework was applied to identify unaddressed areas. A total of 42 studies met the inclusion criteria. Expert validation and triangulation with global data enhanced rigor. Results: Burnout rates in Nigeria are among the highest globally, with 35% in healthcare, 32% in retail, and 29% in banking. Women and younger workers face disproportionate stress burdens. HRM strategies such as Employee Assistance Programs (EAPs) and Flexible Work Arrangements showed the highest effectiveness but had limited adoption due to cost, stigma, and infrastructure gaps. Digital mental health tools, though cost-effective, had low uptake (23%) due to digital illiteracy. Barriers included cultural stigma, weak labor policies, leadership apathy, and lack of ROI measurement. Promising strategies identified include faith-based EAPs, peer networks, mobile clinics, and stigma-reduction campaigns, particularly when culturally embedded and supported by community leaders. Conclusions: Workplace stress in Nigeria is a systemic challenge rooted in socio-economic, cultural, and organizational structures. Although several HRM interventions show promise, their effectiveness is hindered by low adoption, poor contextual fit, and limited legal enforcement. Evidence suggests that when mental health strategies are localized and culturally endorsed via faith leaders, digital tools, or flexible work, they yield improved employee retention, lower absenteeism, and better organizational resilience.
Lung cancer continues to pose a global health burden, with delayed diagnosis contributing significantly to mortality. This study aimed to identify the most predictive behavioural, physiological, and p...
Lung cancer continues to pose a global health burden, with delayed diagnosis contributing significantly to mortality. This study aimed to identify the most predictive behavioural, physiological, and psychosocial factors associated with lung cancer in a young adult population using a multivariate logistic regression framework. A dataset of 276 respondents was analysed after removing duplicates from an original sample of 309. The dependent variable was self-reported lung cancer status, while independent variables included smoking behaviour, symptoms such as fatigue and coughing, and indicators of chronic disease and psychosocial stress. Univariate and bivariate analyses were conducted prior to model development. Nine predictors demonstrated statistical significance and were retained in the final model. The model exhibited strong predictive performance, achieving an AUC of 0.9625 and Tjur’s R² of 0.566, with no evidence of multicollinearity among predictors. Fatigue, chronic disease, coughing, and swallowing difficulty emerged as the most influential risk factors, while smoking had a comparatively smaller effect size, likely due to the young age profile of participants. Peer pressure and yellow fingers were also significant, offering novel contextual insights into behavioural risk adoption. The findings support the integration of multidimensional, low-cost, self-reported indicators into lung cancer screening protocols, especially in resource-limited settings. This study provides a data-driven foundation for developing early detection models and public health interventions tailored to younger populations. Future research should incorporate longitudinal and biomarker data to enhance causal inference and predictive accuracy.
Background: Successful Research and MedTech collaborations depend on six key components: talent and workforce development, innovative solutions, robust research infrastructure, regulatory compliance,...
Background: Successful Research and MedTech collaborations depend on six key components: talent and workforce development, innovative solutions, robust research infrastructure, regulatory compliance, patient-centered care, and rigorous evaluation.
Institutional leaders frequently navigate multiple professional identities; simultaneously serving as educators, researchers, clinicians, and innovators; creating bridges between academic rigor and practical application that accelerate the translation of research into meaningful solutions. Institutions and organizations may also need to broaden their identities.
The contemporary landscape presents significant challenges as institutions balance the pursuit of academic excellence with the need for rapid responsiveness to technological and commercial innovation. Traditional research processes, while ensuring quality, often impede the pace of advancement necessary in today's rapidly evolving environment. This tension necessitates structural reforms across multiple dimensions of institutional operation.
To cultivate a thriving research and innovation ecosystem, several essential components must be established:First, institutions require agile research infrastructure with cutting-edge laboratories and collaboration spaces, specialized equipment, and certified research professionals specifically trained in device development and regulatory compliance. Robust clinical management platforms can expedite trials and streamline data extraction for publication and dissemination. Objective: The Orange County (OC) Impact Conference, held in November 2024, convened 180 key stakeholders from the life sciences, technology, medical device, and healthcare sectors. CHOC Research in collaboration with University Lab Partners (ULP) and the University of California, Irvine, provided this platform for leaders, decision-makers, and experts to discuss the intersection of innovation in research, healthcare, biotechnology, and data science. Methods: We convened a multidisciplinary symposium (180 participants) to examine advancements in life sciences and medical device research development. The structured forum incorporated moderated panel discussions and a keynote speaker. Participants represented diverse stakeholder categories including research scientists, clinicians, investors and financiers, and executive research and healthcare leadership. The event design facilitated both structured knowledge exchange and strategic networking opportunities aimed at identifying implementation pathways to enhance clinical impact. Results: The 2024 OC Impact Conference Proceedings outline a strategy for healthcare innovation, demonstrating how targeted collaboration between patients, families, researchers, clinicians, engineers, data scientists, and industry is reshaping the healthcare innovation ecosystem. This integrated approach ensures every stakeholder's voice contributes to meaningful advancement, guiding resource allocation and partnership development across the life science and medical device sectors. Our findings demonstrate that success requires moving beyond traditional approaches to patient-driven research priorities, augmented design principles for medical device development, and direct engagement between innovators, research participants, industry and healthcare centers throughout the research development cycle. Conclusions: The insights gained through participation in the OC Impact Conference contribute to the ongoing discourse in these fields, emphasizing collaborative efforts to enhance pediatric and adult healthcare outcomes. Clinical Trial: N/A
Background: Nigeria faces severe economic losses ($14 billion annually) and high youth unemployment (33.3%) due to persistent skills gaps, exacerbated by sectoral disparities (e.g., 68% ICT shortages...
Background: Nigeria faces severe economic losses ($14 billion annually) and high youth unemployment (33.3%) due to persistent skills gaps, exacerbated by sectoral disparities (e.g., 68% ICT shortages vs. 63% agricultural deficits) and systemic inequities in education and vocational access. Despite growing HRM interventions, empirical evidence on their efficacy remains limited, necessitating a comprehensive review to guide policy. Objective: This study analyzes Nigeria’s sector-specific skills gaps, evaluates the effectiveness of HRM interventions (apprenticeships, digital upskilling, PPPs), and proposes actionable frameworks to align workforce development with labor market demands. Methods: A narrative review of peer-reviewed literature (2015–2023), institutional reports (World Bank, PwC, NBS), and case studies (e.g., Andela’s model) was conducted. Data were synthesized to compare regional benchmarks (Kenya’s TVET, South Africa’s HRM reforms) and Nigeria’s performance (talent readiness score: 42/100). Results: Key findings include: (1) Vocational training (60% readiness) outperforms tertiary education (40%); (2) Apprenticeships and PPPs show high impact (30% job placement increase); (3) Urban-rural and gender disparities persist (women 30% less likely to access training). Private-sector models demonstrate scalability but require policy support. Conclusions: Nigeria’s skills crisis demands urgent, context-sensitive interventions. Blended strategies (e.g., industry-aligned curricula, gender-inclusive vocational programs) could unlock 5% annual GDP growth. Prioritize: (1) National skills councils to standardize certifications; (2) Tax incentives for employer-led training; (3) Digital infrastructure for rural upskilling. Closing Nigeria’s skills gaps would mitigate economic losses, reduce inequality, and enhance global competitiveness, transforming its youth bulge into a sustainable demographic dividend.
Background: Central venous catheterization (CVC) is a very common procedure performed across medical and surgical wards as well as intensive care units. It provides relatively extended vascular access...
Background: Central venous catheterization (CVC) is a very common procedure performed across medical and surgical wards as well as intensive care units. It provides relatively extended vascular access for critically ill patients, in order to the administer intricate life-saving medications, blood products and parenteral nutrition.
Major vascular catheterization provides a risk of easy accessibility and dissemination of catheter related infections as well as venous thromboembolism. Therefore, its crucial to ensure following standardized practices while insertion and management of CVC in order to minimize the infection risks and procedural complications. The aim of these central line insertion guidelines is to address the primary concerns related to predisposition of Central line associated blood stream infections (CLABSI). These guidelines are evidence based and gathered from pre-existing data associated with CVC insertion.
The most common used sites for central venous catheterization are internal jugular and subclavian veins as compared to femoral veins. Catheterization of these vessels enables healthcare professionals to monitor hemodynamic parameters while ensuring lower risks of CLABSI and thromboembolism. Femoral vein is less preferred due to advantage of invasive hemodynamic monitoring and low risk of local infection and thromboembolic phenomena.
CVC can be inserted using Landmark guided technique and ultrasound guided techniques. Following informed consent, the aseptic technique for CVC insertion includes performing appropriate hand hygiene and ensuring personal protective measures, establishing and maintaining sterile field, preparation of the site using chlorhexidine, and draping the patient in a sterile manner from head to toe. Additionally, the catheter is prepared by pre-flushing and clamping all unused lumens, and the patient is placed in the Trendelenburg position. Throughout the procedure, maintaining a firm grasp on the guide wire is essential, which is subsequently removed post-procedure. It is followed by flushing and aspirating blood from all lumens, applying sterile caps, and confirming venous placement. Procedure is ended with cleaning the catheter site with chlorhexidine, and application of a sterile dressing.
Hence, formal training and knowledge of standardized practices of CVC insertion is essential for health care professionals in order to prevent CLABSI. Our audit assesses the current practices of doctors working at a tertiary care hospital to analyze their background knowledge of standard practices to prevent CLABSI during insertion of CVC. Objective: This study was aimed to audit and re-audit residents’ practices of central venous line insertion in medical and nephrology units of A Tertiary Care Hospital of Rawalpindi, Pakistan and to assess the adherence of residents to checklist and practice guidelines of CVC insertion implemented by John Hopkins Hospital and American Society of Anesthesiologists. Methods: This audit was conducted as a cross sectional direct observational study and two-phase quality improvement project in the Medical and Nephrology Units of a Tertiary Care Hospital of Rawalpindi from December 2023 to February 2024.
After taking informed consent from patients and residents, CVC insertion in 34 patients by 34 individual residents was observed. Observers were given a purposely designed observational tool made from John Hopkins Medicine checklist and ASA practice guidelines for central line insertion, for assessment of residents’ practices.
First part contained questions regarding the demographic details of residents such as age, gender, year of post graduate training, and parent department, and data related to the procedure such as date and time of procedure, need of CVC discussion during rounds, site of CVC insertion, catheter type and type of procedure (Landmark guided CVC or Ultrasound guided CVC insertion). Second part included direct observational checklist based on checklist provided for prevention of intravascular catheter-associated bloodstream infections to audit the practices of residents during CVC insertion that included: adequate hand hygiene before insertion, adherence to aseptic techniques, using sterile personal protective equipment and sterile full body drape of patient, choosing the best insertion site to minimize infections based on patient characteristics.
The parameters observed to be done completely were scored "1" and the items not done were scored "0". The cumulative percentage of performed practices according to checklist, was satisfactory if it was 80% or more and unsatisfactory if it was less than 80%.
After initial audit, participants were given pamphlets with checklist incorporating John Hopkins Medicine checklist and ASA practice guidelines for CVC insertion. Re audit was performed one month after the audit, including same participants who participated in initial audit. The results of audit and re-audit were analyzed using SPSS version 25. Mean +/- SD was calculated for quantitative variables and Number (N) percentage was calculated for qualitative variables. Z- Test was applied on proportions of parameters and test scores to calculate Z –score and P value (<0.05 was significant). Results: Among the 34 participants, 44% of the participants belonged to Nephrology Department and 56% of participants belonged to Department of Internal Medicine.
32.3% residents were in their first year, 14.7% in second, 14.7 in third year, 17.6% in fourth year and 17.6% in 5th/Final year of training.
47% of the participants were male and 53% were female. Participants were aged between 27 and 34 years old, the median age at the time of audit was 29 years.
Landmark guided CVC insertion was performed in Subclavian Vein (73.5%) and Internal Jugular Vein (26.5%).
Post audit practices were improved from 73.5% to 94%. Conclusions: Our audit found that many of the residents adopted inadequate practices because of lack of proper training and institutional guidelines for CVC insertion. Our re-audit elaborated an improvement in the practices of residents following intervention with educational material. Our study underscores the importance of structured quality improvement initiatives in enhancing clinical practices and patient outcomes.
Background: Social media has profoundly transformed consumer behavior and marketing practices within the hospitality industry. Understanding how these changes influence hotel selection and booking dec...
Background: Social media has profoundly transformed consumer behavior and marketing practices within the hospitality industry. Understanding how these changes influence hotel selection and booking decisions, the effectiveness of social media strategies, and shifts in reputation management practices is crucial for hotels aiming to enhance their digital presence and customer engagement. Objective: The study aims to analyze the influence of social media on consumer behavior, audience engagement, and reputation management in hotel selection and booking decisions as well as compare pre- and post-social media reputation management practices. Methods: Data was collected through surveys and interviews with hotel guests and marketing professionals. The analysis included descriptive statistics and comparative assessments of pre- and post-social media reputation management practices. The effectiveness of various social media strategies was evaluated based on respondent feedback. Results: The findings indicate that promotional offers, user reviews, and visual content significantly influence consumer behavior in hotel selection and booking decisions. Collaboration with influencers, user-generated content, live video content, and social media advertising are the most effective strategies for audience engagement and brand building, each with a 100% effectiveness rate. There is a notable shift in reputation management practices, with a decrease in promptly addressing issues and providing compensation, and an increase in seeking private resolutions through direct messages post-social media. Conclusions: Social media plays a critical role in shaping consumer behavior and brand perception in the hotel industry. Effective social media strategies, particularly those involving influencers and user-generated content, are essential for engaging audiences and building brand identity. The transition to social media has also led to changes in reputation management, emphasizing the importance of balancing transparency with discreet conflict resolution. Hotels should prioritize comprehensive social media strategies that include collaboration with influencers, regular updates, and engaging content. Encouraging positive user-generated content and implementing robust monitoring and response systems are essential. Training staff on social media engagement and conflict resolution can further improve reputation management. Ongoing adaptation to emerging social media trends is crucial for maintaining effectiveness. This study provides valuable insights into the impact of social media on consumer behavior and marketing in the hospitality industry. By identifying effective social media strategies and examining changes in reputation management, it offers practical guidance for hotels seeking to enhance their digital presence and customer engagement. The findings underscore the importance of leveraging social media to achieve greater business success and maintain a positive brand reputation.
Background: Noncommunicable diseases (NCDs) pose a significant burden in the Philippines, with cardiovascular and cerebrovascular diseases among the leading causes of mortality. The Department of Heal...
Background: Noncommunicable diseases (NCDs) pose a significant burden in the Philippines, with cardiovascular and cerebrovascular diseases among the leading causes of mortality. The Department of Health implemented the Philippine Package of Essential Non-Communicable Disease Interventions (Phil PEN) to address this issue. However, healthcare professionals faced challenges in implementing the program due to the cumbersome nature of the multiple forms required for patient risk assessment. To address this, a mobile medical app, the PhilPEN Risk Stratification app, was developed for community health workers (CHWs) using the extreme prototyping framework. Objective: This study aimed to assess the usability of the PhilPEN Risk Stratification app using the (User Version) Mobile App Rating Scale (uMARS) and to determine the utility of uMARS in app development. The secondary objective was to achieve an acceptable (>3 rating) score for the app in uMARS, highlighting the significance of quality monitoring through validated metrics in improving the adoption and continuous iterative development of medical mobile apps. Methods: The study employed a qualitative research methodology, including key informant interviews, linguistic validation, and cognitive debriefing. The extreme prototyping framework was used for app development, involving iterative refinement through progressively functional prototypes. CHWs from a designated health center participated in the app development and evaluation process – providing feedback, using the app to collect data from patients, and rating it through uMARS. Results: The uMARS scores for the PhilPEN Risk Stratification app were above average, with an Objective Quality rating of 4.05 and a Personal Opinion/Subjective Quality rating of 3.25. The mobile app also garnered a 3.88-star rating. Under Objective Quality, the app scored well in Functionality (4.19), Aesthetics (4.08), and Information (4.41), indicating its accuracy, ease of use, and provision of high-quality information. The Engagement score (3.53) was lower due to the app's primary focus on healthcare rather than entertainment. Conclusions: The study demonstrated the effectiveness of the extreme prototyping framework in developing a medical mobile app and the utility of uMARS not only as a metric, but also as a guide for authoring high-quality mobile health apps. The uMARS metrics were beneficial in setting developer expectations, identifying strengths and weaknesses, and guiding the iterative improvement of the app. Further assessment with more CHWs and patients is recommended. Clinical Trial: N/A
Among the countless decisions healthcare providers make daily, many clinical scenarios do not have clear guidelines, despite a recent shift towards the practice of evidence-based medicine. Even in cli...
Among the countless decisions healthcare providers make daily, many clinical scenarios do not have clear guidelines, despite a recent shift towards the practice of evidence-based medicine. Even in clinical scenarios where guidelines do exist, these guidelines do not universally recommend one treatment option over others. As a result, the limitations of existing guidelines presumably create an inherent variability in provider decision-making and the corresponding distribution of provider behavioral variability in a clinical scenario, and such variability differs across clinical scenarios. We define this variability as a marker of provider uncertainty, where scenarios with a wide distribution of provider behaviors have more uncertainty than scenarios with a narrower provider behavior distribution. We propose four exploratory analyses of provider uncertainty: (1) field-wide overview; (2) subgroup analysis; (3) provider guideline adherence; and (4) pre-/post-intervention evaluation. We also propose that uncertainty analysis can also be used to help guide interventions in focusing on clinical decisions with the highest amounts of provider uncertainty and therefore the greatest opportunity to improve care.
This study investigates the behavioral dynamics of sociopaths, focusing on their reliance on glibness (superficial charm) as a primary manipulation tactic and aggressiveness as a secondary strategy wh...
This study investigates the behavioral dynamics of sociopaths, focusing on their reliance on glibness (superficial charm) as a primary manipulation tactic and aggressiveness as a secondary strategy when charm fails. Sociopathy, characterized by manipulative tendencies and a lack of empathy, often manifests in adaptive yet harmful behaviors aimed at maintaining control and dominance.
Using the Deenz Antisocial Personality Scale (DAPS-24) to collect data from 34 participants, this study examines the prevalence and interplay of these dual strategies. Findings reveal that sociopaths employ glibness to disarm and manipulate, transitioning to aggressiveness in response to resistance. The implications for understanding sociopathic manipulation are discussed, emphasizing the importance of early detection and intervention in both clinical and social contexts.
