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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: Oral drug delivery remains the preferred route of pharmaceutical administration due to patient convenience and cost-effectiveness. However, challenges including poor bioavailability, variable absorption, and unpredictable release patterns continue to limit therapeutic efficacy. The integration of artificial intelligence (AI) technologies offers promising solutions to optimize oral drug formulations and predict pharmacokinetic behavior. Objective: v Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Google Scholar databases from January 2015 to December 2024. Search terms included "artificial intelligence," "machine learning," "oral drug delivery," "formulation optimization," and "bioavailability prediction." Studies were included if they reported AI applications in oral pharmaceutical formulation design, absorption prediction, or pharmacokinetic modeling. Results: The review identified 89 relevant studies demonstrating AI applications across formulation design, absorption prediction, and manufacturing optimization. Machine learning algorithms, particularly artificial neural networks (ANNs), support vector machines (SVMs), and random forests, showed superior performance in predicting critical quality attributes compared to traditional approaches. AI-driven formulation optimization reduced development time by 40-60% and improved bioavailability prediction accuracy by 15-25%. Conclusions: AI technologies demonstrate significant potential for revolutionizing oral drug delivery through enhanced formulation precision, improved pharmacokinetic predictions, and personalized medicine approaches. However, challenges including model interpretability, data standardization, and regulatory acceptance require addressing for broader clinical implementation.
Journal Description
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Background: Oral drug delivery remains the preferred route of pharmaceutical administration due to patient convenience and cost-effectiveness. However, challenges including poor bioavailability, varia...
Background: Oral drug delivery remains the preferred route of pharmaceutical administration due to patient convenience and cost-effectiveness. However, challenges including poor bioavailability, variable absorption, and unpredictable release patterns continue to limit therapeutic efficacy. The integration of artificial intelligence (AI) technologies offers promising solutions to optimize oral drug formulations and predict pharmacokinetic behavior. Objective: v Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Google Scholar databases from January 2015 to December 2024. Search terms included "artificial intelligence," "machine learning," "oral drug delivery," "formulation optimization," and "bioavailability prediction." Studies were included if they reported AI applications in oral pharmaceutical formulation design, absorption prediction, or pharmacokinetic modeling. Results: The review identified 89 relevant studies demonstrating AI applications across formulation design, absorption prediction, and manufacturing optimization. Machine learning algorithms, particularly artificial neural networks (ANNs), support vector machines (SVMs), and random forests, showed superior performance in predicting critical quality attributes compared to traditional approaches. AI-driven formulation optimization reduced development time by 40-60% and improved bioavailability prediction accuracy by 15-25%. Conclusions: AI technologies demonstrate significant potential for revolutionizing oral drug delivery through enhanced formulation precision, improved pharmacokinetic predictions, and personalized medicine approaches. However, challenges including model interpretability, data standardization, and regulatory acceptance require addressing for broader clinical implementation.
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: 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: Online health information seeking is undergoing a major shift with the advent of artificial intelligence (AI)-powered technologies such as voice assistants and large language models (LLMs)...
Background: Online health information seeking is undergoing a major shift with the advent of artificial intelligence (AI)-powered technologies such as voice assistants and large language models (LLMs). While existing heath information seeking behavior (HISB) models have long explained how people find and evaluate health information, less is known about how users engage with these newer tools, particularly tools that provide “one” answer rather than the resources to investigate a number of different sources. Objective: This study aimed to explore how people use and perceive AI- and voice-assisted technologies when searching for health information, and to evaluate whether these tools are reshaping traditional patterns of health information seeking and credibility assessment. Methods: We conducted in-depth qualitative research with 27 participants (ages 19–80) using a think-aloud protocol. Participants searched for health information across three platforms—Google, ChatGPT, and Alexa—while verbalizing their thought processes. Prompts included both a standardized hypothetical scenario and a personally relevant health query. Sessions were transcribed and analyzed using reflexive thematic analysis to identify patterns in search behavior, perceptions of trust and utility, and differences across platforms and user demographics. Results: Participants integrated AI tools into their broader search routines rather than using them in isolation. ChatGPT was valued for its clarity, speed, and ability to generate keywords or summarize complex topics, even by users skeptical of its accuracy. Trust and utility did not always align; participants often used ChatGPT despite concerns about sourcing and bias. Google’s AI overviews were met with caution—participants frequently skipped them to review traditional search results. Alexa was viewed as convenient but limited, particularly for in-depth health queries. Platform choice was influenced by the seriousness of the health issue, context of use, and prior experience. One-third of participants were multilingual and they identified challenges with voice recognition, cultural relevance, and data provenance. Overall, users exhibited sophisticated “mix-and-match” behaviors, drawing on multiple tools depending on context, urgency, and familiarity. Conclusions: Classic HISB models remain applicable in the AI era, but search behavior is becoming more dynamic and context-driven. Users selectively engage with AI and voice tools based on perceived usefulness, not just trustworthiness, challenging assumptions that credibility is the primary driver of technology adoption. Findings highlight the need for digital health literacy efforts that help users evaluate both the capabilities and limitations of emerging tools. Given the rapid evolution of search technologies, longitudinal studies, and real-time observation methods are essential for understanding how AI continues to reshape health information seeking.
Background: Lung cancer is a global health concern, with especially high incidence in Taiwan. Surgical treatment remains a cornerstone for early-stage lung cancer, but patients often struggle to under...
Background: Lung cancer is a global health concern, with especially high incidence in Taiwan. Surgical treatment remains a cornerstone for early-stage lung cancer, but patients often struggle to understand complex surgical options when relying solely on traditional counseling methods. Shared decision making (SDM) promotes patient engagement, and mobile augmented reality (MAR) has the potential to enhance patient comprehension through interactive 3D visualization. However, the use of MAR in patient counseling for thoracic surgery remains underexplored. Objective: This study aimed to evaluate the impact of Mobile Augmented Reality (MAR) counseling compared to traditional methods on shared decision-making (SDM) in thoracic surgery from the perspectives of patients, family members, and healthcare providers. Methods: A randomized clinical trial was conducted at Tri-Service General Hospital, Taipei, Taiwan. Fifty patients or family members were randomly assigned to traditional or MAR counseling groups, and 50 healthcare providers participated in the evaluation. MAR counseling integrated patient-specific 3D anatomical models to enhance comprehension. Questionnaires assessed the impact on SDM, and statistical analyses included Fisher's exact test, chi-square test, and the Wilcoxon rank-sum test. Results: MAR counseling significantly improved SDM compared to traditional methods (P ≤ 0.001). Participants reported higher satisfaction and comprehension with MAR counseling (P ≤ 0.001). Healthcare providers also strongly supported the use of MAR technology for patient counseling. Conclusions: MAR counseling effectively enhances SDM in thoracic surgery by improving patient understanding and engagement. Both patients and healthcare providers favor this innovative digital approach. Multi-center studies are recommended to validate its feasibility, cost-effectiveness, and broader applicability. Clinical Trial: Non applicable.
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: The spread of misleading cancer-related information on social media is growing rapidly. Understanding the research landscape, key focus areas, and the distribution and propagation patterns...
Background: The spread of misleading cancer-related information on social media is growing rapidly. Understanding the research landscape, key focus areas, and the distribution and propagation patterns of misleading information has become a growing concern in academia. Objective: Aims to reveal the current research landscape and emerging trends, analyzing the topic distribution and propagation mechanisms of cancer-related misleading information, to provide scientific evidence to improve social media governance and enhance public health education. Methods: Bibliometric data were collected from the Web of Science Core Collection (WOSCC) for 2014-2024. Tools such as VOSviewer, Bibliometrix, and CiteSpace analyzed publication trends, collaboration networks, and research hotspots. Social media comment data were gathered from TikTok, YouTube, and Twitter, collecting all first-level comments and user information from the top 1,000 posts on "prostate cancer," "breast cancer," and "cervical cancer." DeepSeek, using Python, was employed to identify and categorize health-related and misleading information into background knowledge, treatment, diagnosis, and prevention. Social network analysis using Gephi identified propagation features, including modularity, community count, network connectivity metrics, path length, network diameter, and proportion of misleading information disseminators. Results: A total of 137 articles and 518,942 social media comments were analyzed. Bibliometric analysis showed that, since 2017, publications in this field have increased significantly, with the Journal of Medical Internet Research publishing the most articles, and the University of Utah leading in contributions. The U.S. dominates in publication volume and international collaboration. Research hotspots include specific cancer types (breast, cervical, prostate), platform-specific information dissemination, the link between misleading information and vaccine hesitancy, and also the impact of COVID-19 on cancer-related misinformation. LLM-Based topic classification revealed significant platform differences in misleading health information spread. TikTok had the most comments (390,449), but YouTube had the highest proportion of health-related comments (53.8%). Twitter had the highest proportion of misleading information (13.0%). TikTok focused on treatment, Twitter on background knowledge, and YouTube on diagnosis. Misleading information was most prevalent in treatment across all cancer types and platforms. Social network analysis showed YouTube had the largest network diameter for prostate cancer dissemination, indicating longer propagation chains and wider spread of misleading information. Misleading information was most concentrated on TikTok and Twitter, where echo chambers amplify its impact. Conclusions: This study summarizes research on misleading cancer information on social media platforms. Through analysis of topic distribution and propagation paths, it provides data to support the prevention and control of such misleading health information.
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.
Digital dermatology is transforming access to skin care by addressing long-standing inequities related to geography, income, and skin type. As many as 70% of individuals with skin conditions do not re...
Digital dermatology is transforming access to skin care by addressing long-standing inequities related to geography, income, and skin type. As many as 70% of individuals with skin conditions do not receive professional care, creating an opportunity for digital tools - ranging from teledermatology and AI to large language models - to bridge gaps in diagnosis, education, and patient empowerment. However, these innovations could worsen existing disparities if not designed intentionally, due to algorithmic bias, unequal digital access, and a lack of culturally responsive models. This paper examines digital dermatology's potential to promote equitable care by focusing on three critical areas: broadening access via digital platforms, ensuring AI fairness through comprehensive datasets, and combating dermatological misinformation using expert-led, AI-powered communication strategies, including a conceptual chatbot that synthesizes and prioritizes dermatologic science for practical use. Drawing on global case studies and insights from the WHO’s AI skin health app and the Skin and Digital Summit, we underscore both the transformative potential and ethical complexities of deploying digital solutions. We advocate for a Radical Dermatology framework that integrates technological advances with community-centered care, equitable data practices, and collaborative design. This vision calls for dermatologists to take the lead, combining scientific rigor with digital innovation to cultivate a more inclusive, intelligent, and trustworthy skin health ecosystem.
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: Rural areas cover vast territories with less convenient transportation compared to urban regions, and have long lacked efficient, inclusive, and high-quality medical resources. Objective:...
Background: Rural areas cover vast territories with less convenient transportation compared to urban regions, and have long lacked efficient, inclusive, and high-quality medical resources. Objective: This work is to powerfully leverage intelligent technologies to drive the shift of healthcare services towards the grassroots and extend high-quality medical resources to the primary level, enabling rural residents to access more equitable, accessible, systematic, and continuous healthcare services locally. Methods: Based on panel data from 31 Chinese provinces between 2013 and 2023, this work empirically examines the impact of the intelligent economy on the development of healthy villages and its underlying mechanisms from the perspective of technology diffusion. Results: This work finds that the intelligent economy can empower the development of Healthy Villages, primarily through the agglomeration and development of health-related industries, which then diffuses and empowers rural health sectors. Simultaneously, the intelligent economy can also promote the development of Healthy Villages in other regions to a certain extent through spatial spillover effects. Conclusions: Recognizing the positive significance of the intelligent economy in promoting Healthy Villages, the policy makers can advance the equitable access to efficient healthcare services that are systematic and continuous across vast rural areas by fostering the agglomeration and development of health industries and leveraging spatial spillover effects. This work provides insights into the direction for building Healthy Villages and offers inspiration and confidence for achieving balanced, equitable, and high-quality rural health levels in the context of the intelligent economy, especially in the Global South.
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: The construction of artificial intelligence (AI) platforms in hospitals forms the basis of the modern healthcare revolution. While traditional hospital information systems have facilitated...
Background: The construction of artificial intelligence (AI) platforms in hospitals forms the basis of the modern healthcare revolution. While traditional hospital information systems have facilitated digitalization, they are still limited by data siloes, fragmented workflows and insufficient clinical intelligence that impede organizations from realizing the promise of data-led decision-making. Objective: This review aims to provide a strategic roadmap for hospitals to build comprehensive AI platforms, moving beyond siloed AI applications toward infrastructure at the system level that supports sustainable, scalable, and interoperable intelligent services across clinical, operational, and administrative domains. Methods: A systematic literature search was performed in Web of Science, EMBASE, PubMed, and Scopus. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were screened and selected for full text review by two independent reviewers with reference to AI platform construction, hospital informatics integration, and institutional deployment strategies. Results: A total of 30 high-quality studies were included in the final analysis. Based on the synthesis of evidence, a five-layer hospital AI platform architecture is proposed, consisting of: (1) infrastructure layer, (2) data layer, (3) algorithm layer, (4) application layer, and (5) security and compliance layer. The review highlights key implementation strategies such as modular deployment, real-world scenario validation, and interdepartmental collaboration. It also identifies critical challenges, including legacy system integration, lack of data standardization, computing resource limitations, organizational resistance, regulatory uncertainty, and economic sustainability. Conclusions: The successful construction of hospital AI platforms requires not only advanced technologies but also institutional readiness, strategic planning, and cultural adaptation. Intelligent hospitals of the future must emphasize privacy-preserving computing, seamless AI integration into clinical workflows, and dynamic performance evaluation systems. Building organizational capacity and fostering cross-disciplinary collaboration will be essential to achieving long-term impact and scalability.
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: 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.
Background: Hospital-at-Home and virtual care models have the potential to improve healthcare delivery but may present unique challenges for ethnocultural communities. Objective: This study explores b...
Background: Hospital-at-Home and virtual care models have the potential to improve healthcare delivery but may present unique challenges for ethnocultural communities. Objective: This study explores barriers and facilitators to Hospital-at-Home adoption among South Asian patients and caregivers. Methods: A qualitative study using semi-structured interviews was conducted with South Asian community members and healthcare providers in the Fraser Health region in British Columbia, Canada. Thematic analysis was used to identify key patterns in perceptions, experiences, and needs. Results: Participants expressed mixed views about Hospital-at-Home care, with concerns centering on digital literacy, language barriers, caregiver readiness, and home environment suitability. Facilitators included comfort at home, trust in providers, and alignment with cultural and religious values. Many participants were unaware of healthcare alternatives beyond emergency departments. Conclusions: Equitable implementation of Hospital-at-Home services for South Asian communities requires culturally tailored strategies that address digital access, health system awareness, and family caregiver roles.
Background: Breast cancer remains the most prevalent cancer among women globally, with significant incidence and mortality rates. Adjuvant therapies, while standard, often lead to adverse effects impa...
Background: Breast cancer remains the most prevalent cancer among women globally, with significant incidence and mortality rates. Adjuvant therapies, while standard, often lead to adverse effects impacting physical and mental health. Exercise has shown potential in mitigating these effects, yet many breast cancer survivors remain insufficiently active. Objective: This study explores a novel 12-week tele-exercise protocol tailored for breast cancer survivors in Hong Kong, integrating psychological theories and behaviour change techniques to foster sustainable exercise habits. Methods: Conducted as a pilot study, this two-group randomized controlled trial involved 24 participants undergoing adjuvant therapy. The intervention group received a progressively supervised tele-exercise regimen, transitioning to unsupervised sessions, combined with psychological counselling. Outcomes were assessed using the RE-AIM framework, focusing on feasibility, acceptability, and preliminary health effects. Results: Results indicated high recruitment (70.6%) and retention (100%) rates, with satisfactory attendance (87.7%) and compliance (69.4%) in the intervention group. Participants reported high acceptability. Exercise-induced improvements in cardiopulmonary fitness (the primary outcome), lower extremity muscular strength, balance, shoulder ROM on the affected side, and health-related quality of life metrics were promising. Conclusions: This pilot demonstrates the acceptability, feasibility, and potential effectiveness of the tele-exercise in breast cancer rehabilitation, suggesting it as a viable alternative to traditional exercise interventions. Future research should explore long-term sustainability and broader applicability of tele-exercise to enhance recovery and quality of life among breast cancer survivors. Clinical Trial: NCT06382441
Background: Despite significant technological progress, patient engagement in digital healthcare remains erratic. Many digital health tools still rely heavily on one-way communication, offering minima...
Background: Despite significant technological progress, patient engagement in digital healthcare remains erratic. Many digital health tools still rely heavily on one-way communication, offering minimal meaningful interaction or personalized advice. This approach often results in patient disengagement and inadequate health outcomes. Objective: This narrative review examines the available evidence on closed-loop digital health systems—those integrating ongoing, personalized feedback—to assess their potential benefits for patient engagement, adherence, clinical outcomes, and patient empowerment. Additionally, the review introduces a conceptual framework, the "Feel-Better Loop,"derived from key themes identified across reviewed studies. Methods: A structured narrative review was performed, targeting peer-reviewed literature from January 2015 to May 2025 across PubMed, PMC, Cochrane Library, and Google Scholar. Seventeen studies that explicitly evaluated digital health interventions with real-time, adaptive feedback were included and analyzed for common themes and outcomes. Results: Reviewed studies indicate that continuous, personalized feedback mechanisms significantly outperform static interventions in fostering patient adherence, improving health literacy, and encouraging lasting behavior changes. Notable outcomes include enhanced clinical metrics, increased patient satisfaction, greater empowerment, and potential healthcare resource optimization. However, the studies also presented common methodological limitations such as small participant groups and variability in outcome measurements. Conclusions: Closed-loop digital health interventions show considerable promise in improving patient engagement and clinical results. The proposed Feel-Better Loop framework integrates core elements identified in the literature into a cohesive model intended for future implementation and evaluation. Further robust research is essential to confirm its efficacy and to thoroughly understand its long-term clinical and economic impact across different healthcare contexts. Clinical Trial: N/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, 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: mHealth apps are increasingly popular, offering tools like health tracking and personalized reminders to support these behaviors. Personalized messaging, tailored to the user’s profile,...
Background: mHealth apps are increasingly popular, offering tools like health tracking and personalized reminders to support these behaviors. Personalized messaging, tailored to the user’s profile, has been shown to improve engagement and retention around health topics. Research links personality traits (based on the Big Five model) with preferred app mechanisms, leading to a preference matrix for personalizing health apps. This preference matrix includes 15 mechanisms, categorized by the Behavior Change Technique and gamification elements, guiding developers to optimize app engagement based on user profiles. Objective: This study aims to validate this preference matrix by assessing whether the associations between mechanisms and Big Five personality profiles proposed in the preference matrix align with the preferences of our population in an experimental context. Methods: This study employs a cross-sectional design. Participants completed an online survey, which collected data on demographic information, mobile health app usage, and personality. Logistic regression and logistic ordinal regression analyses were performed, adjusted using the Bonferroni correction. Results: The average age of the 214 respondents (118 women, 89 men, 5 others), was 29.42. Conscientiousness significantly increased the likelihood of selection for collection (OR = 1.87). For competition, both conscientiousness (OR = 3.22) and altruism (OR = 1.93) emerged as strong predictors. For rewards, conscientiousness (OR = 1.97) and neuroticism (OR = 2.36) also showed a strong predictive value. The study found that four mechanisms, self-monitoring, progression, challenge, and quest, were favored by over half of the participants. Conclusions: Conscientious participants showed a preference for the collection mechanism, while both conscientious and altruistic individuals were drawn to competition. Neurotic and conscientious individuals preferred the reward mechanism. Conscientiousness consistently predicted preferences for all three gamification elements, highlighting its role in influencing engagement with mHealth features.
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: Chronic diseases such as diabetes, cardiovascular disease, and COPD pose significant global health burdens, requiring long-term management strategies. Mobile health (mHealth) technologies...
Background: Chronic diseases such as diabetes, cardiovascular disease, and COPD pose significant global health burdens, requiring long-term management strategies. Mobile health (mHealth) technologies and AI-driven tools have emerged as promising solutions to support patient self-management, improve outcomes, and reduce healthcare costs. However, their long-term effectiveness and accessibility, especially in low-resource settings, remain uncertain. Objective: This review aims to assess the impact of mHealth and AI-enabled interventions on chronic disease management, focusing on patient outcomes, engagement, and implementation challenges. Methods: A systematic literature review was conducted covering studies from 2010 to 2024. Databases searched included PubMed, Scopus, Cochrane Library, Web of Science, and IEEE Xplore. Studies involving adult patients with chronic conditions and addressing mHealth, AI integration, or digital self-management tools were included. Outcomes evaluated comprised self-efficacy, medication adherence, quality of life, and long-term user engagement. Results: Findings indicate that mHealth interventions significantly improve short-term outcomes, including self-management, adherence, and quality of life. AI tools showed potential in personalizing care and supporting early disease detection. However, long-term patient engagement was often poor, and real-world integration of AI remained limited. Digital literacy and technology access were notable barriers in underserved populations, affecting scalability and equity. Conclusions: mHealth and AI technologies can enhance chronic disease management but face limitations in long-term adoption and accessibility. Future research should prioritize equitable implementation, sustained engagement strategies, and real-world validation of AI tools to leverage their potential in diverse healthcare settings. Clinical Trial: It was a literature review, no any clinical trials.
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: Assessment of the quality of the first antenatal consultation in the Malemba Nkulu Health Zone, Haut-Lomami, DRC Objective: This study aims to assess the quality of first prenatal consulta...
Background: Assessment of the quality of the first antenatal consultation in the Malemba Nkulu Health Zone, Haut-Lomami, DRC Objective: This study aims to assess the quality of first prenatal consultations (CPN1) in the Malemba Nkulu Health Zone, by identifying the challenges encountered and proposing areas for improvement to strengthen the quality of prenatal care. Methods: cross-sectional descriptive study was conducted in eight health facilities, involving 248 pregnant women and 14 healthcare providers. The quality of ANC1 was assessed using an observation grid based on national and international standards. Results: conducted by traditional birth attendants with no advanced medical training. 52.4% of pregnant women had their first ANC in the third trimester, limiting access to preventive interventions. Conclusions: Strengthening healthcare provider training, improving access to diagnostic tests, and raising awareness among pregnant women about early antenatal follow-up are crucial for better maternal outcomes.
The healthcare landscape is rapidly transforming due to technological advancement, requiring physicians to not only be skilled clinically but also navigate and lead a highly dynamic, innovation-driven...
The healthcare landscape is rapidly transforming due to technological advancement, requiring physicians to not only be skilled clinically but also navigate and lead a highly dynamic, innovation-driven environment. Yet, few medical schools currently provide opportunities for formal training in innovation and entrepreneurship (I&E). In this perspective, we examine the need for I&E education in medical curricula by exploring student interest, effective program models, and implementation strategies. To better understand medical student interest in innovation and willingness to participate in innovation programs during medical school, we surveyed 480 medical students at our institution, the Johns Hopkins University School of Medicine. We observed a strong interest in healthcare innovation, with 97% of respondents valuing knowledge or experience in innovation and 63% expressing intent to incorporate I&E into their careers. To assess the real-world impact of I&E education on medical professionals, we surveyed alumni of the Johns Hopkins Center for Bioengineering Innovation and Design (CBID) master’s program who had also completed medical school. Graduates reported that their experiences cultivated transferable skills—design thinking, interdisciplinary collaboration, and leadership—that shaped their professional trajectories. We propose three models for incorporating I&E education into existing medical curricula—short-term workshops, one-year gap programs, and longitudinal tracks—and discuss their advantages and tradeoffs. Early and structured exposure to I&E education in medical school empowers students to identify unmet clinical needs, collaborate across disciplines, and develop real-world solutions. As the pace of innovation continues to accelerate, integration of I&E education into medical curricula offers a timely opportunity for medical schools to cultivate physician leaders in this space.
Background: Health misinformation is a serious and growing concern for public health. However, the term lacks conceptual clarity, undermining our ability to develop a reliable, replicable evidence bas...
Background: Health misinformation is a serious and growing concern for public health. However, the term lacks conceptual clarity, undermining our ability to develop a reliable, replicable evidence base and develop effective responses. There is a need to examine usages of the term and build a coherent definition that reflects people’s information concerns. Objective: To critically examine the conceptual landscape and implicit tensions around the term “misinformation” in health-related discourses. Methods: We conducted a three-phase hybrid concept analysis; this paper reports the first phase, a systematic literature analysis of sixty multidisciplinary papers. Results: Authors identified rapid technological change, information predators, and cultural issues (e.g. social fragmentation, growing epistemic disagreement, and the loss of information authorities) as antecedents to health misinformation. They characterised health misinformation as a scourge, identifiable by its falseness or deceptiveness, use of persuasive strategies, unscientific subjectivity, and capacity to disrupt community consensus – revealing epistemic and normative assumptions about what information can, or should, be considered “reliable.” Authors highlighted consequences for individuals, communities, and social systems. However, several unresolved conceptual debates and epistemic tensions remained. Conclusions: Several key characteristics of health misinformation emerged from this body of work, but the processes by which these should be judged remain unclear. Additionally, a need exists to establish clearer distinctions between health misinformation and other types of information dysfunction. Findings from this literature phase will inform phase two (stakeholder interviews) and phase three (integrative synthesis).
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: The use of Artificial Intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic hea...
Background: The use of Artificial Intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic healthcare records patient data; however, there is little data about the of the views of palliative care healthcare professionals on the role of AI in practice. Determining the opinions of palliative care healthcare professionals on potential uses of AI in palliative care will be useful for policymakers and practitioners to determine, and inform, meaningful use of AI in palliative care practice. Objective: To explore palliative care healthcare professionals’ views on the use of AI for analysis of patient data in palliative care. Methods: Phenomenological study using qualitative semi-structured interviews of palliative care healthcare professionals with a minimum of one-year clinical experience in a hospice in the Northwest of England. Data were analysed using inductive thematic analysis. Results: We interviewed six palliative care professionals comprising of doctors, nurses and occupational therapies. AI was viewed positively, although most participants had not used this in practice. No participants had received training in AI and said that education in AI would be beneficial. Participants described potential benefits of palliative care AI, which included identification of people requiring palliative care interventions, and to evaluate patient experience. Participants highlighted security and ethical concerns of AI, related to data governance, efficacy, patient confidentiality and consent issues. Conclusions: This study highlights the importance of staff perceptions of AI in palliative care. Our findings support the role of AI in enhancing care, addressing education needs, and tackling trust, ethics, and governance issues. This study lays the groundwork for guidelines on AI implementation, urging further research on methodological, ethical, and practical aspects of AI in palliative care. Clinical Trial: NA
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: Despite substantial progress in maternal health services in Nigeria, a significant proportion of women still deliver without skilled birth attendants, particularly in rural and underserved...
Background: Despite substantial progress in maternal health services in Nigeria, a significant proportion of women still deliver without skilled birth attendants, particularly in rural and underserved areas. Maternal mortality remains unacceptably high, often due to preventable causes that could be mitigated through timely access to skilled care during childbirth. Understanding the demographic, socio-economic, and access-related factors influencing the use of skilled delivery services is crucial for designing targeted interventions Objective: The primary objective of this study was to investigate the factors influencing the utilization of skilled birth attendants among women in Nigeria. Specifically, the study aimed to assess the impact of age, marital status, religion, education, occupation, place of residence, geographical region, and transportation means on skilled delivery service use. Methods: The study utilized data from the Nigeria Demographic and Health Survey (NDHS), applying a cross-sectional design. Descriptive statistics, chi-square tests, and logistic regression analyses were used to examine associations and determine adjusted odds ratios (ORs) with 95% confidence intervals (CI). Data were visualized using bar and column charts for clarity. Variables were categorized and analyzed to compare skilled versus unskilled delivery usage across different subgroups. Results: The results revealed that education, urban residence, employment, and use of motorized transport were strong predictors of skilled birth attendance. Women with post-secondary education had the highest odds of utilizing skilled delivery services (OR=5.39). Urban dwellers and employed women were significantly more likely to access skilled care, while rural residents and those without formal education lagged behind. Religion and region also played substantial roles; Christian women and those from the South had markedly higher utilization rates compared to their northern and Muslim counterparts. Means of transportation emerged as a critical enabler, with women using vehicles or bikes having over 10 times higher odds of skilled delivery compared to pedestrians. Conclusions: The findings underscore the influence of socio-demographic and infrastructural factors on maternal healthcare utilization. Disparities in skilled birth attendance are significantly shaped by education, region, religion, and access to transport. Addressing these inequalities is essential to improve maternal outcomes and reduce preventable mortality. To improve skilled delivery uptake: Enhance girl-child education and women's empowerment; Strengthen rural health infrastructure and transportation systems; Promote awareness through culturally tailored health communication; Develop region-specific interventions targeting vulnerable groups and Foster public-private partnerships to subsidize maternity services. Improving access to skilled delivery services can drastically reduce maternal and neonatal deaths in Nigeria. Policies that bridge gaps in education, infrastructure, and socio-economic inequities are essential for ensuring every woman has the opportunity for a safe and dignified childbirth experience.
Background: The pandemic dramatically increased in the frequency of audiovisual medical visits and the rate of audiovisual visits remains higher than before the pandemic. Theses visits have the potent...
Background: The pandemic dramatically increased in the frequency of audiovisual medical visits and the rate of audiovisual visits remains higher than before the pandemic. Theses visits have the potential to be an important clinical modality. Researchers have assessed several aspects of audiovisual visits but they have not investigated their clinical note quality. Objective: The goal of this study is to determine the quality of audiovisual and in-person clinical notes and to compare their quality. Methods: From a population of 1,660 established outpatient primary care type 2 diabetic patient visits occurring in 2021 we randomly selected 100 audiovisual and 100 in-person visits. QNOTE, a validated instrument that measures clinical note quality, was used by seven experienced primary care physicians to assess four key elements of the clinical note: the chief complaint, history of present illness, assessment, and plan. Results: The mean quality scores (out of 100) and their standard deviations (SD) were: overall, audiovisual 75.8 (SD 9.6), in-person 86.5 (SD 4.4), p < 0.0001; chief complaint, audiovisual 72.4 (SD 23.0), in-person 83.0 (SD 14.7), p < 0.0001; history of present illness, audiovisual 63.7 (SD 22.9), in-person 82.5 (SD, 15.8), p < 0.0001; assessment, audiovisual 82.9 (SD 14.8), in-person 89.8 (SD 10.0), p = 0.0002; and plan, audiovisual 84.1 (SD 13.7), in-person 90.9 (SD 8.7), p < 0.0001. The in-person scores are consistent with a previous QNOTE study. The rater intraclass correlation coefficient was excellent (0.81, 95% CI, 0.76 – 0.86). Conclusions: Audiovisual visits demonstrated a lower note quality than in-person visits. To our knowledge, this is the first study to assess the quality of audiovisual visit notes and compare them to the quality of in-person visit notes.
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: Digital health technologies are increasingly used as complementary and/or alternative means of seeking sexual and reproductive health services. Despite their increasing prevalence, there i...
Background: Digital health technologies are increasingly used as complementary and/or alternative means of seeking sexual and reproductive health services. Despite their increasing prevalence, there is an emerging concern that such platforms could inadvertently trigger or perpetuate trauma among end-user patients. Objective: The purpose of this study was to develop a theoretical account of how digital health technologies can cause or perpetuate emotional trauma among people who seek technology-based sexual and reproductive health services Methods: We employed Charmaz’s constructivist grounded theory approach by conducting interviews with 25 participants who have used government and other regulated digital health platforms (i.e., web-based platforms and mobile health applications) to access sexual and reproductive health information or services including STI testing, contraception, and abortion. Data analysis occurred alongside data collection and data were analyzed inductively using open, axial, and theoretical coding Results: We developed a theoretical model that shows that technology-related harm can occur in two main ways – digital platform design features (ie., navigation challenges, data, and security breaches, and inappropriate display of content) and digital platform-facilitated interpersonal interactions (targeted campaigns and depersonalized digital health interactions). While these activities can all cause some harm, these activities are likely to lead to emotional trauma if users have prior trauma. Conclusions: It is increasingly recognized that web-based platforms provide opportunities for advancing sexual and reproductive health and services. At the same time, these technologies can also serve as conduits through which trauma can be triggered, perpetuated, and exacerbated. To mitigate technology-related trauma, both technology developers (particularly designers) and technology implementors (health providers) must adopt patient-centered strategies that not only prevent trauma but promote users' emotional well-bein
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: Early diagnosis and interventional treatment of vascular diseases depend on high-precision medical image analysis. However, existing image analysis methods suffer from issues such as blurr...
Background: Early diagnosis and interventional treatment of vascular diseases depend on high-precision medical image analysis. However, existing image analysis methods suffer from issues such as blurred segmentation edges, overlaps, and difficulties in extracting complex lesion features, limiting their effectiveness. Objective: To develop and improve a convolutional neural network (CNN)-based segmentation and recognition algorithm for vascular interventional treatment images, addressing the challenges of image segmentation accuracy and lesion feature extraction.
Methods: Methods: This study proposes an improved deep CNN by integrating an adaptive convolution layer—combining convolution layers with an adaptive mechanism—to handle vascular images of varying sizes. Additionally, a multi-task learning framework is introduced to jointly optimize vascular image segmentation and lesion recognition, thereby enhancing model efficiency and accuracy. Results: The improved CNN model achieved a Dice coefficient of 92.5% and an intersection-over-union ratio of 89.2% for image segmentation, outperforming traditional CNN and other models. For lesion recognition, the model demonstrated higher accuracy (0.95) and recall (0.96). The model's inference time was also the shortest at 0.035 seconds per image, enabling processing of 28 frames per second. Conclusions: The proposed method significantly enhances early diagnostic capability for vascular lesions and offers strong support for real-time auxiliary tools in vascular interventional treatment, indicating promising clinical applications and practical value. Clinical Trial: Not applicable
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: Cancer is one of the leading causes of death worldwide. Cancer mortality can be reduced by early detection via screening, diagnosis, and effective management. Risk assessment is a vital pa...
Background: Cancer is one of the leading causes of death worldwide. Cancer mortality can be reduced by early detection via screening, diagnosis, and effective management. Risk assessment is a vital part of the cancer screening process, especially for breast, cervical, and esophageal cancers, where early detection improves outcomes. Identifying high-risk individuals based on family history, genetics, lifestyle, and environment makes targeted and personalized screening possible, enhancing accuracy and resource efficiency. The inherent complexity of oncology data, which includes a wide array of clinical observations, laboratory results, radiology images, treatment regimens, and genetic information, poses significant challenges to data interoperability and exchange. Objective: We propose a Fast Healthcare Interoperability Resource (FHIR) standard-based Oncology Data Model (ODM) that enables the capturing, sharing, and processing of oncology data at various phases in cancer care across the health systems. We particularly focus on screening for five types of cancers, i.e., Breast, Cervical, Esophageal, Lung, and Oral, for risk assessment using the FHIR Questionnaire Resource for use in the Meghalaya FIRST Cancer Care (FCC) pilot project in India. Methods: ODM was developed based on the data collected during the cancer patient journey across five key phases: encounter, risk assessment, clinical investigation, treatment, and outcome. Essential oncology data elements were identified and modeled using HL7 FHIR R4 standards. Custom FHIR profiles were created for cancer-specific use cases, along with terminology mapping to standard coding systems such as SNOMED CT, LOINC, and ICD-10. The implementation guide was generated using FHIR Shorthand (FSH), SUSHI, and the HL7 IG Publisher. A demonstration application was also developed to support stakeholder training and facilitate adoption. Results: The data model was developed using HL7 FHIR to enhance interoperability across the cancer care continuum, from screening to treatment. The implementation resulted in the creation of a FHIR Implementation Guide featuring 25 oncology-specific resources and 50 standardized terminology value sets to support consistent and semantically accurate data exchange.
Central to the model were the FHIR Questionnaire and QuestionnaireResponse resources, which were customized to enable interoperable, structured data collection in both clinical and community-based digital health settings. These profiles were designed to support critical cancer screening and assessment workflows. The demonstration tool enabled hands-on exploration of the FHIR profiles and supported engagement with stakeholders. The comprehensive approach supports more integrated, data-driven oncology care within digital health systems. Conclusions: The development of standardized profiles for cancer screening and assessment is a transformative approach to achieving syntactic and semantic interoperability right from screening, diagnosis, to treatment and improving overall cancer care and health service delivery. This work explores the implementation of Questionnaire and Questionnaire Response Resources using digital health standards. When integrated with all the cancer patient journey stages, the approach can accelerate cancer care and support a more responsive and effective healthcare system
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: LGBTQIA+ researchers and participants frequently encounter hostility in online environments, particularly on social media platforms where public commentary on research advertisements can f...
Background: LGBTQIA+ researchers and participants frequently encounter hostility in online environments, particularly on social media platforms where public commentary on research advertisements can foster stigmatization. Despite a growing body of work on researcher harassment, little empirical research has examined the actual content and emotional tone of public responses to LGBTQIA+-focused research recruitment. Objective: This study aimed to analyze the thematic patterns and sentiment of social media comments directed at LGBTQIA+ research recruitment advertisements, in order to better understand how online stigma is communicated and how it may impact both researchers and potential participants. Methods: A total of 994 publicly visible Facebook comments posted in response to LGBTQIA+ recruitment ads (January–May 2024) were collected and analyzed. Text preprocessing included tokenization, stop-word removal, and lemmatization. Latent Dirichlet Allocation (LDA) was used to identify latent themes across the dataset. Sentiment analysis was conducted using the Bing Liu and NRC lexicons, with scores ranging from -1 (most negative) to 1 (most positive). Linguistic Inquiry and Word Count (LIWC) was employed to quantify psychological and moral language features. Comments were also manually coded into four audience target groups (researchers, LGBTQIA+ community, general public, other commenters), and language category differences were analyzed using one-way ANOVAs with Bonferroni corrections. Results: Topic modeling identified three key themes: (1) “Transitions, Health, and Gender Dysphoria,” (2) “Negative and Confrontational Language,” and (3) “Religious and Ideological Debates.” Topic 2 had the highest average prevalence (γ = 0.486). Sentiment analysis revealed negative mean sentiment scores for all three topics: Topic 1 (-0.41), Topic 2 (-0.21), and Topic 3 (-0.35). No topic exhibited a statistically significant predominance of positive sentiment. A one-way ANOVA showed significant differences in linguistic tone across target groups: negative tone (F(3, 990) = 12.84, P < .001), swearing (F(3, 990) = 16.07, P < .001), and anger-related language (F(3, 990) = 9.45, P < .001), with the highest levels found in comments directed at researchers. Comments targeting LGBTQIA+ individuals showed higher references to mental illness, morality, and threats to children. While a small proportion of comments offered affirming responses, they were embedded in adversarial exchanges and did not offset the broader negativity. Conclusions: This study documents a persistently hostile online environment for LGBTQIA+ research, where researchers are frequently dehumanized and LGBTQIA+ identities are pathologized. These findings reinforce stigma communication models and suggest a need for institutional responses that include mental health support, enhanced moderation tools, and policy advocacy. Future research should investigate how hostile discourse affects researcher well-being and recruitment outcomes, and evaluate interventions to foster more respectful engagement with LGBTQIA+ studies.
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: Digital tools have been shown to play a role in promoting public health interventions. The recommendation that healthcare providers (HCPs) use vaccination-related mobile or web-based appli...
Background: Digital tools have been shown to play a role in promoting public health interventions. The recommendation that healthcare providers (HCPs) use vaccination-related mobile or web-based applications has contributed to improving vaccine awareness and acceptance among vaccine-eligible individuals. In the United States, the state of Texas, which has one of the lowest HPV vaccination rates, has seen a significant increase in HPV vaccination hesitancy during the COVID-19 pandemic. Objective: This study examined the association between changes in the HPV vaccine hesitancy observed by HCPs among patients in Texas and the promotion of vaccination-related applications at their healthcare facilities during the COVID-19 pandemic. Methods: A population-based cross-sectional survey administered to HCPs working in Texas between January and April 2021 by The University of Texas MD Anderson Cancer Center was used for this study. We described the observed changes in the HPV vaccination hesitancy reported by the HCPs among patients using descriptive statistics. We conducted a logistic regression analysis to examine the association between the decrease in the HPV vaccine hesitancy observed by HCPs among patients and the promotion of vaccination-related applications at their facility. Results: A total of 1283 HCPs completed the survey. Among the 730 HCPs who reported changes in the HPV vaccination hesitancy, 7.0% observed a decrease in the HPV vaccine hesitancy among patients during the COVID-19 pandemic. Among the 576 HCPs who responded to the survey question regarding vaccination-related applications, 20.7% reported that vaccination-related applications were promoted at their facilities. The respondents were predominantly aged 35-54 years (62.6%), females (77.3%), non-Hispanic Whites (50.5%), and working in group practice (39.6%). Compared to HCPs who did not promote vaccination-related applications, those who promoted vaccination-related applications at their healthcare facility had significantly higher odds of observing a decrease in HPV vaccine hesitancy among patients during the COVID-19 pandemic (Adjusted Odds Ratio [aOR]: 2.53, 95% CI: 1.18-5.41). Compared to HCPs who worked at university/teaching hospitals, HCPs working at Federally Qualified Health Clinics (aOR: 8.96, 95% CI: 1.74-46.10), public facilities (aOR: 12.53, 95% CI: 2.01-78.26) and Employed Physician Practices (aOR: 8.63, 95% CI: 1.37-54.39) had significantly higher odds to observe a decrease in the HPV vaccine hesitancy among patients during the COVID-19 pandemic. Conclusions: Our findings demonstrate the benefits of promoting vaccination-related applications at healthcare facilities in areas with high HPV vaccine hesitancy, such as Texas. Digital health interventions could provide a platform for promoting and increasing HPV vaccination uptake in a context of pandemic preparedness. Clinical Trial: N/A
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: Large language models (LLMs) such as ChatGPT have shown promise in medical education assessments, but the comparative effects of prompt engineering across optimized variants and relative p...
Background: Large language models (LLMs) such as ChatGPT have shown promise in medical education assessments, but the comparative effects of prompt engineering across optimized variants and relative performance against medical students remain unclear. Objective: To systematically evaluate the impact of prompt engineering on five ChatGPT variants (GPT-3.5, GPT-4.0, GPT-4o, GPT-4o1mini, GPT-4o1) and benchmark their performance against fourth-year medical students in midterm and final examinations. Methods: A 100-item examination dataset covering multiple-choice, short-answer, clinical case analysis, and image-based questions was administered to each model under no-prompt and prompt-engineered conditions over five independent runs. Student cohort scores (n=143) were collected for comparison. Responses were scored using standardized rubrics, converted to percentages, and analyzed in SPSS Statistics 29 with paired t-tests and Cohen’s d (p<0.05). Results: Baseline midterm scores ranged from 59.2% (GPT-3.5) to 94.1% (GPT-4o1); final scores from 55.0% to 92.4%. Fourth-year students averaged 89.4% (midterm) and 80.2% (final). Prompt engineering significantly improved GPT-3.5 (+10.6%, p<0.001) and GPT-4.0 (+3.2%, p=0.002) but yielded negligible gains for optimized variants (p=0.066–0.94). Optimized models matched or exceeded student performance on both exams. Conclusions: Prompt engineering enhances early-generation model performance, whereas advanced variants inherently achieve near-ceiling accuracy, surpassing medical students. As LLMs mature, emphasis should shift from prompt design to model selection, multimodal integration, and critical use of AI as a learning companion. Clinical Trial: IRB #CSMU-2024-075
Background: Lifestyle modification involving both patients and their significant others (dyads) is critical in the long-term management of chronic kidney disease (CKD). However, achieving sustained be...
Background: Lifestyle modification involving both patients and their significant others (dyads) is critical in the long-term management of chronic kidney disease (CKD). However, achieving sustained behavioral changes remains challenging. Digital interventions, particularly using widely adopted platforms like instant messaging apps, present a promising approach to support CKD dyads in lifestyle modification. Objective: This study aimed to develop, optimize, and test the usability of a digital dyadic empowerment platform named “Kidney Lifestyle” using the LINE instant messaging app. The platform was designed based on the Digital Dyadic Empowerment Framework (DDEF) to facilitate collaborative lifestyle modification among CKD dyads. Methods: We adopted a three-phase Agile-based iterative development cycle: (1) iterative development and trial use, (2) heuristic evaluation, and (3) usability testing. In Phase 1, platform prototype was co-developed with healthcare professionals and technical partners, then trialed by CKD dyads who provided qualitative and quantitative feedback on interface clarity, ease of use, acceptance, intention to continue usage, and overall satisfaction. In Phase 2, multidisciplinary experts independently conducted heuristic evaluations, rating platform compliance with Nielsen’s ten usability principles on a scale from -1 (does not comply) to 1 (complies), and provided suggestions for improvement. In Phase 3, experienced CKD dyads from Phase 1 performed six representative tasks using the platform. Task success rates, completion times, and operational errors were quantitatively recorded, and usability perceptions were assessed using the After-Scenario Questionnaire (ASQ; 1-7 scale) and the System Usability Scale (SUS; 0-100 scale). Results: Phase 1 results indicated high user acceptance and satisfaction (overall platform satisfaction: mean 4.1 out of 5) among 10 CKD dyads (19 individuals). Participants valued real-time interaction, convenience in monitoring health data, and educational resources. In Phase 2, five experts conducted heuristic evaluation, revealing high overall usability compliance (average compliance scores ranged from 89% to 93%), although issues related to navigation complexity and the need for enhanced interactive feedback were identified. In Phase 3, usability testing was conducted with 5 CKD dyads (10 individuals), showing high task success rates (60%-100%) and task completion times ranging from 1 to 5 minutes. However, navigation difficulties within the LINE Official Account were noted, resulting in a marginally acceptable average SUS score of 67.5. The ASQ indicated higher usability satisfaction for the extended App tasks (mean average = 5.64) compared to those within LINE (mean average = 3.87). Conclusions: The LINE-based digital dyadic empowerment platform “Kidney Lifestyle” (LINE ID: @509kgajt) demonstrated promising usability, high user engagement, and strong clinical potential for supporting lifestyle modification among CKD dyads. However, usability issues within the instant messaging interface highlight the importance of simplifying navigation pathways and enhancing user feedback. Future research should include a larger-scale feasibility trial and further optimization to enhance usability and clinical integration.
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: The rise of AI and accessible audio equipment has led to a proliferation of recorded conversation transcripts datasets across various fields. However, automatic mass recording and transcri...
Background: The rise of AI and accessible audio equipment has led to a proliferation of recorded conversation transcripts datasets across various fields. However, automatic mass recording and transcription often produce noisy, unstructured data. First, these datasets naturally include unintended recordings, such as hallway conversations, background noise and media (e.g., TV programs, radio, phone calls). Second, automatic speech recognition (ASR) and speaker diarization errors can result in misidentified words, speaker misattributions, and other transcription inaccuracies. As a result, large conversational transcript datasets require careful preprocessing and filtering to ensure their research utility. This challenge is particularly relevant in behavioral health contexts (e.g., therapy, treatment, counselling): while these transcripts offer valuable insights into patient-provider interactions, therapeutic techniques, and client progress, they must accurately represent the conversations to support meaningful research. Objective: We present a framework for preprocessing and filtering large datasets of conversational transcripts and apply it to a dataset of behavioral health transcripts from community mental health clinics across the United States. Within this framework we explore tools to efficiently filter non-sessions – transcripts of recordings in these clinics that do not reflect a behavioral treatment session but instead capture unrelated conversations or background noise. Methods: Our framework integrates basic feature extraction, human annotation, and advanced applications of large language models (LLMs). We begin by mapping transcription errors and assessing the distribution of sessions and non-sessions. Next, we identify key features to analyze how outliers help in characterizing the type of transcript. Notably, we use LLM perplexity as a measure of comprehensibility to assess transcript noise levels. Finally, we use zero-shot LLM prompting to classify transcripts as sessions or non-sessions, validating LLM decisions against expert annotations. Throughout, we prioritize data security by selecting tools that preserve anonymity and minimize the risk of data breaches. Results: Our findings demonstrated that basic statistical outliers, such as speaking rate, are associated with transcription errors and are observed more frequently in non-sessions versus sessions. Specifically, LLM perplexity can flag fragmented and non-verbal segments and is generally lower in sessions (permutation test mean difference = -258, p<0.05), thus can serve as a filtering aiding tool. Additionally, LLM algorithms have shown an ability to distinguish between sessions and non-sessions with high validity (κ=0.71), while also capturing the nature of the meeting. Conclusions: This study’s hybrid approach effectively characterizes errors, evaluates content, and distinguishes different text types within unstructured conversational datasets. It provides a foundation for research on conversational data, providing key methods and practical guidelines that serve as crucial first steps in ensuring data quality and usability, particularly in the context of mental health sessions. We highlight the importance of integrating clinical experts with AI tools while prioritizing data security throughout the process.
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.
Background: Digital health platforms that integrate patient-reported outcome measures (PROMs) with wound image submissions offer new opportunities for remote wound surveillance. However, the alignment...
Background: Digital health platforms that integrate patient-reported outcome measures (PROMs) with wound image submissions offer new opportunities for remote wound surveillance. However, the alignment between patient-reported symptoms and physician clinical judgment remains underexplored, particularly in real-world settings. Objective: This study aimed to evaluate the diagnostic performance of PROM-reported wound infection in predicting physician-initiated callbacks and to explore the symptom features associated with patients' perception of infection. Methods: We conducted a retrospective observational study at a tertiary medical center in Taipei, Taiwan. Patients with acute or chronic wounds were enrolled in a chatbot-assisted digital monitoring program between June 30, 2022, and March 1, 2023. Using their mobile devices, patients submitted wound photographs and completed a structured symptom checklist, including indicators such as redness, darkness, and infection. A senior plastic surgeon independently reviewed each image to determine the need for clinical follow-up (callback), which served as the reference outcome. The presence of “infection” in the PROM checklist served as the primary predictor. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess predictive accuracy. A secondary analysis examined associations between symptom features and infection reporting using logistic regression. Results: Among 2,297 wound image entries from 270 patients, PROM-reported infection showed high sensitivity (94.0%) and an AUC of 0.9575 (95% CI: 0.9502–0.9648) for predicting physician callbacks. In the acute wound subgroup, the AUC remained high (0.9335). Redness was the strongest correlate of infection reporting (OR = 31.6; 95% CI: 23.1–43.2), while skin darkness was negatively associated with perceived infection in acute wounds (OR = 0.415; 95% CI: 0.203–0.850), indicating potential misinterpretation. Conclusions: Patient-reported infection through a digital platform demonstrated high sensitivity in identifying wounds requiring medical attention. However, notable false-negative rates and symptom misinterpretation underscore the need for improved patient education and real-time decision support. These findings support the utility of PROM-based systems for remote triage and highlight the importance of integrating patient-clinician feedback loops to enhance wound care safety.
Background: Clinical reasoning is a key skill of the medical profession. In many virtual patient environments, the students enter the diagnoses, and all students receive the same feedback with an expl...
Background: Clinical reasoning is a key skill of the medical profession. In many virtual patient environments, the students enter the diagnoses, and all students receive the same feedback with an explanation why a certain diagnosis is considered correct. Results of meta-analyses highlight the benefits of feedbacking information to students based on their individual answers. Such adaptive feedback is time and resource demanding. Objective: We propose computer-supported adaptive feedback as an interactive, resource-optimised and scalable alternative. Methods: In the current study we compare static expert feedback agains computer supported adaptive feedback in two learning modes, individual and collaborative learning modes. Overall 105 students completed a pre and post test, consisting of 10 multiple choice items and 12 key feature items. In the meantime they diagnosed 8 virtual patients with either adaptive feedback or static feedback, either in the collaborative or individual learning mode. Results: Results indicate that students who received computer supported adaptive feedback outperformed students who received static feedback in the posttest independent from the learning mode. Students who worked in the collaborative learning mode had a higher diagnostic accuracy in the learning phase, but not in the posttest, independent from the feedback given. Conclusions: Considering the novelty of the system in itself and the presentation of the adaptive feedback to the students the results are promising. With future development and implementation of artificial intelligence in the generation of answers the learning of medical students. Until then an NLP-based system, such as the one presented in this study, seems to be a viable solution to provide a large number of students with elaborated adaptive feedback. Clinical Trial: 17-250
Background: Screen-based media use among children has been increasing, particularly in lower socioeconomic groups. As this behavior is linked to obesogenic habits, such as physical inactivity, poor di...
Background: Screen-based media use among children has been increasing, particularly in lower socioeconomic groups. As this behavior is linked to obesogenic habits, such as physical inactivity, poor dietary habits, and disrupted sleep patterns, it is crucial to examine the associations between screen-based media use and adiposity in primary school children, particularly those from socially vulnerable contexts. Objective: To examine the associations between screen-based media use and adiposity in primary school children from socially vulnerable contexts. Methods: This study, part of the BeE-school Project, included 735 children (mean age 7.7 ± 1.2 years) from 10 primary schools located in vulnerable contexts in northern Portugal. Researchers recorded weight, height, and waist circumference, and then Body mass index z-scores (BMIz) and waist-to-height ratio (WHtR) were calculated. Screen-based media use was also reported by parents using the ScreenQ tool that includes four domains (screen access, frequency of use, media content and caregiver-child co-viewing). Sociodemographic and anthropometric data of parents were reported via questionnaire. Generalized Linear Models (GLM) were applied. Results: Higher screen-based media use score was associated with higher BMIz and WHtR (b = 0.064, 95% CI 0.034 to 0.094; b = 0.002, 95% CI 0.001 to 0.003, respectively) even after adjusting for relevant variables. Similar associations were observed for the domains of screen access, frequency of use, and media content. Conclusions: Screen-based media use is linked to increased adiposity in vulnerable children. Reducing screen access, limiting usage frequency, and curating media content could improve health outcomes. Interventions for obesity prevention should consider these factors. Clinical Trial: This study is part of the BeE-school project, a cluster randomized controlled trial registered on ClinicalTrials.gov (identifier NCT05395364).
Background: Transcatheter aortic valve implantation (TAVI) has become a potential treatment modality for symptomatic patients with severe aortic stenosis (AS) across all surgical risk profiles. Howeve...
Background: Transcatheter aortic valve implantation (TAVI) has become a potential treatment modality for symptomatic patients with severe aortic stenosis (AS) across all surgical risk profiles. However, peri-procedural stroke remains a persistent and serious complication with significant implications for patient outcomes and healthcare systems. Reported incidence ranges between 2-7%. As the benefit of cerebral protection devices and the optimal antithrombotic regime following TAVI remain unclear, understanding contemporary risks and timings of stroke are important in order to tailor peri/post procedural stroke risk reduction strategies. Objective: To evaluate the incidence, timing, and predictors of stroke and transient ischaemic attack (TIA) within 30 days post TAVI in a contemporary real-world all-comers registry. Methods: Consecutive patients undergoing TAVI (n=980) between January 2020 and February 2024 were included in this retrospective study. A stroke diagnosis was made based on the Valve Academic Research Consortium-2 (VARC-2), defined as a focal or global neurological deficit >24 hours or <24 hours if haemorrhage or infarct was found on neuroimaging. TIA was defined as the duration of a focal or global neurological deficit <24 hours. Those with documented evidence of stroke or TIA were sub-divided into acute (<24 hours post procedure) and subacute (1-30 days post procedure). Patients from outside our catchment area were excluded (n=46) due to the lack of access to patient records. Two patients were excluded as no valve was deployed. Results: A total of 932 patients (41% female, mean age 81.6±6.9 years) were included in the study. TAVI was performed for severe AS in the context of degenerative calcific disease of native valves in 94% (n=873), 6% (n=57) of TAVIs were valve-in-valve procedures, and only one patient was treated for severe stenosis of a congenital (Bicuspid) aortic valve. 84% (n=779) had no prior history of stroke and 26% had a history of diabetes mellitus. 60% (n=555) of patients were in sinus rhythm prior to TAVI, 35% (n=326) were in atrial fibrillation or flutter and 5% (n=51) were in a paced rhythm. Self-expanding valves were implanted in 58% (n=542) of cases and Balloon-expanding valves were used in the remainder. The majority of cases were performed transfemorally (96%). Pre-dilatation balloon aortic valvuloplasty was performed in 16% (n=150) of cases and the median procedure time was 76 mins [IQR 66.0, 89.0]. Vascular closure device successfully achieved haemostasis in 94% (n=877) of procedures. The thirty-day incidence of stroke/TIA was 3.2% (n=30), with 35% (n=11) occurring within 24 hours and the majority occurring within the first 48 hours (58%, n=18). The median number of days of stroke/TIA post-TAVI was 1.0 [0.0, 3.0]. Most (80%, n=24) were ischaemic strokes and of these one had a haemorrhagic transformation. Diabetes is the only variable predictive of stroke at 30 days HR 2.14 (95% CI 1.01 – 4.56), p=.049 using logistic regression. Conclusions: Most cerebrovascular events occurred early post TAVI. Effective stroke prevention strategies, including optimized antithrombotic regimens and the role of cerebral protection devices, warrant further evaluation. Clinical Trial: n/a
Background: Serious games are increasingly recognized as effective tools for healthcare interventions, particularly for adolescents with behavioral and developmental needs. However, inconsistent desig...
Background: Serious games are increasingly recognized as effective tools for healthcare interventions, particularly for adolescents with behavioral and developmental needs. However, inconsistent design frameworks and limited integration of theoretical concepts challenge their scalability and impact. Understanding how these concepts are applied in serious game design is essential for enhancing their real-world impact. Objective: The objective of this systematic review is to examine the current state of the art in the use of serious gaming interventions in healthcare for adolescents with behavioral or developmental issues. The review will focus on elucidating the elements involved in how these games are designed and can contribute to learning. The review is conducted from the theoretical framework perspectives of boundary crossing, transfer and a model of reality. Methods: A total of five databases (PubMed, Scopus, ERIC, PsycINFO and EMBASE) were searched for relevant titles and abstracts. The databases were identified as relevant and cover a wide range of published research into health and social science. Results: A total of 34 relevant studies were included in the review, which covered a range of serious gaming artefacts with the objective of identifying learning or development opportunities for adolescents with behavioral or developmental issues. Conclusions: This review highlights the transformative potential of serious games in healthcare, particularly for individuals with developmental and behavioral needs, by fostering skill acquisition, collaboration, and real-world application. Despite their potential, the development of serious games requires a more structured integration of theoretical frameworks to ensure scalability, replicability, and sustained impact. Future research should prioritize standardized methodologies, longitudinal evaluations, and a focus enhanced collaboration.
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.
