JMIR Formative Research最新文献

{"title":"Enhancing Diagnostic Accuracy of Ophthalmological Conditions With Complex Prompts in GPT-4: Comparative Analysis of Global and Low- and Middle-Income Country (LMIC)-Specific Pathologies.","authors":"Shona Alex Tapiwa M'gadzah, Andrew O'Malley","doi":"10.2196/64986","DOIUrl":"10.2196/64986","url":null,"abstract":"<p><strong>Background: </strong>The global incidence of blindness has continued to increase, despite the enactment of a Global Eye Health Action Plan by the World Health Assembly. This can be attributed, in part, to an aging population, but also to the limited diagnostic resources within low- and middle-income countries (LMICs). The advent of generative artificial intelligence (AI) within health care could pose a novel solution to combating the prevalence of blindness globally.</p><p><strong>Objective: </strong>The objectives of this study are to quantify the effect the addition of a complex prompt has on the diagnostic accuracy of a commercially available LLM, and to assess whether such LLMs are better or worse at diagnosing conditions that are more prevalent in LMICs.</p><p><strong>Methods: </strong>Ten clinical vignettes representing globally and LMIC-prevalent ophthalmological conditions were presented to GPT-4-0125-preview using simple and complex prompts. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated. Statistical comparison between prompts was conducted using a chi-square test of independence.</p><p><strong>Results: </strong>The complex prompt achieved a higher diagnostic accuracy (90.1%) compared to the simple prompt (60.4%), with a statistically significant difference (χ2=428.86; P<.001). Sensitivity, specificity, PPV, and NPV were consistently improved for most conditions with the complex prompt. The simple prompt struggled with LMIC-prevalent conditions, diagnosing only 1 of 5 accurately, while the complex prompt successfully diagnosed 4 of 5.</p><p><strong>Conclusions: </strong>The study established that overall, the inclusion of a complex prompt positively affected the diagnostic accuracy of GPT-4-0125-preview, particularly for LMIC-prevalent conditions. This highlights the potential for LLMs, when appropriately tailored, to support clinicians in diverse health care settings. Future research should explore the generalizability of these findings across other models and specialties.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e64986"},"PeriodicalIF":2.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and Mitigation Strategies in the Development and Feasibility Assessment of a Digital Mental Health Intervention for Depression (VMood): Mixed Methods Feasibility Study.","authors":"Leena W Chau, Jill K Murphy, Vu Cong Nguyen, Hai Nhu Tran, Harry Minas, Raymond W Lam, Kanna Hayashi, Thi Thanh Xuan Nguyen, Emanuel Krebs, John O'Neil","doi":"10.2196/68297","DOIUrl":"10.2196/68297","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Worldwide, the COVID-19 pandemic contributed to further gaps in mental health care, particularly in low- and middle-income countries such as Vietnam, where care is inaccessible for 90% of those who need it. There has subsequently been a considerable increase in the use of digital mental health interventions such as smartphone apps. Presently, the evidence for such interventions is limited, especially in cases in which the interventions have been adapted from evidence-based in-person formats. Implementation science aims to promote the incorporation of scientific findings into practice. A key determinant of implementation success is an intervention's usability. Hurdles to usability include an intervention being too confusing or time-intensive to use. Facilitators include incorporating a greater number of engagement features and integrating human support.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this implementation science feasibility study was to describe the challenges and mitigation strategies used in the development, usability testing, and implementation of a digital depression intervention (VMood smartphone app) developed in Vietnam. VMood was adapted from an evidence-based in-person intervention originally developed in Canada that is grounded in principles of cognitive behavioral therapy with supportive coaching by a lay health or social services worker. The research team is currently testing the effectiveness and cost-effectiveness of VMood in a randomized controlled trial across 8 provinces in Vietnam informed by the results of this feasibility assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This mixed methods feasibility study was organized using an implementation outcome framework focused on acceptability, adoption, appropriateness, and feasibility. This study involved three data collection components: (1) usability testing (interviews and focus groups with app user and provider participants who tested VMood in 1 Vietnamese province), (2) app metrics (from the early phase of the randomized controlled trial in the same province but from different municipalities), and (3) discourse data (notes from various team meetings, communications, and reports on VMood's development and implementation). Qualitative data were analyzed using thematic content analysis. App use data were analyzed using basic descriptive statistics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The findings of the 3 data components showed that there were seven main challenges: (1) challenges with recruitment and uptake of the app, (2) challenges with use and engagement, (3) screening challenges, (4) digital divide, (5) limitations to digital applications for mental health, (6) technological challenges, and (7) funding and policy constraints. Various solutions to help mitigate the challenges were used by the team.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings contribute important evidence on the challenges to the development and feasibility assessment of ","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e68297"},"PeriodicalIF":2.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Effectiveness of Smart Glasses in Reducing Patient Care Time in Emergency Departments: Cohort Study From the Hangzhou Asian Games.","authors":"Xinwei Jiang, Bangbo Xia, Mohammad Mostafa Ansari, Huiquan Jiang, Jianjiang Qi, Zhongheng Zhang, Sheng Dai, Pingping Zheng, Yang He, Ning Liu, Pengpeng Chen, Ronghua Luo, Xuchang Qin, Yansong Miao, Junru Dai, Xiaoyu Zhou, Changliang Wang, Hui Chen, Wenbin Xu, Tao Wu, Qiang Shi, Zhonghua Chen, Liping Zhou, Hao Zhang, Yun Xie, Quan Zhang, Bifa Zhou, Xiaohong Pan, Zixi Chen, Libo Zhen, Yaqing Sun, Zelin Lu, Yihao Loh, Shameera Sayer, Jennifer Mochtar, Pannika Wongpraewit, Yifan Wang, Yucai Hong","doi":"10.2196/65617","DOIUrl":"10.2196/65617","url":null,"abstract":"<p><strong>Background: </strong>Challenges in emergency medicine include overcrowding, insufficient emergency care resources, and extended emergency department (ED) waiting times. These issues contribute to delays in treatment and unfavorable outcomes. This situation worsens in events with large crowds and particularly worsened during the COVID-19 pandemic. The integration of augmented reality (AR) smart glasses could potentially enhance patient care in the ED.</p><p><strong>Objective: </strong>This study aims to assess the effectiveness of AR smart glasses in reducing patient care time in the ED during the 19th Asian Games and the Fourth Asian Para Games Hangzhou 2022 (HAG2022). The study specifically compares the prepreparation time (PPT) and consult response time (CRT) in patients receiving teleconsultations via AR smart glasses versus those receiving standard care without AR.</p><p><strong>Methods: </strong>This retrospective study was conducted between September 13, 2023, and October 28, 2023, during HAG2022. The data were gathered from AR smart glasses using 5G technology at the HAG2022 village and electronic health records at Sir Run Run Shaw Hospital, China. The study included 2 groups: the teleconsultation by augmented reality telemedicine system (ARTS) group and the non-ARTS group. The main data assessed were PPT and CRT in ED.</p><p><strong>Results: </strong>During the research period, 80 patients were divided into 2 cohorts: the ARTS cohort (n=10) and the non-ARTS cohort (n=70). Gender and age demographics showed no significant differences between the cohorts. The ARTS cohort had a significantly lower average PPT of 23 minutes compared to the non-ARTS cohort's 40.3 minutes (P<.001). In addition, CRT in the ARTS cohort was significantly lower at 15.6 minutes compared to the non-ARTS cohort's 164.8 minutes (P=.03). The outcomes suggest that smart glasses are effective in decreasing PPT and CRT.</p><p><strong>Conclusions: </strong>AR smart glasses have the potential to enhance patient admission efficiency and reduce care time in EDs. However, despite these benefits, further research is needed to confirm their effectiveness, and additional studies are essential to identify the challenges and barriers to their successful implementation in emergency medicine.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e65617"},"PeriodicalIF":2.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12234398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Artificial Intelligence ChatGPT to Access Medical Information about Chemical Eye Injuries: A Comparative Study.","authors":"Layan Yousef Alharbi, Rema Rashed Alrashoud, Bader Shabib Alotaibi, Abdulaziz Meshal Al Dera, Raghad Saleh Alajlan, Reem Rashed AlHuthail, Dalal Ibrahim Alessa","doi":"10.2196/73642","DOIUrl":"https://doi.org/10.2196/73642","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Background: Chemical ocular injuries are a major public health issue. They cause eye damage from harmful chemicals and can lead to severe vision loss or blindness if not treated promptly and effectively. Although medical knowledge has advanced, accessing reliable and understandable information on these injuries remains a challenge. This is due to unverified online content and complex terminology. Artificial Intelligence (AI) tools like ChatGPT provide a promising solution by simplifying medical information and making it more accessible to the general public.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Objective: This study aims to assess the use of ChatGPT in providing reliable, accurate, and accessible medical information on chemical ocular injuries. It evaluates the correctness, thematic accuracy, and coherence of ChatGPT's responses compared to established medical guidelines and explores its potential for patient education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Methods: Nine questions were entered to ChatGPT regarding various aspects of chemical ocular injuries. These included the definition, prevalence, etiology, prevention, symptoms, diagnosis, treatment, follow-up, and complications. The responses provided by ChatGPT were compared to the ICD-9 and ICD-10 guidelines for chemical (alkali and acid) injuries of the conjunctiva and cornea. The evaluation focused on criteria such as correctness, thematic accuracy, coherence to assess the accuracy of ChatGPT's responses. The inputs were categorized into three distinct groups, and statistical analyses, including Flesch-Kincaid readability tests, ANOVA, and trend analysis, were conducted to assess their readability, complexity and trends.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Results: The results showed that ChatGPT provided accurate and coherent responses for most questions about chemical ocular injuries, demonstrating thematic relevance. However, the responses sometimes overlooked critical clinical details or guideline-specific elements, such as emphasizing the urgency of care, using precise classification systems, and addressing detailed diagnostic or management protocols. While the answers were generally valid, they occasionally included less relevant or overly generalized information. This reduced their consistency with established medical guidelines. The average FRES was 33.84 ± 2.97, indicating a fairly challenging reading level, while the FKGL averaged 14.21 ± 0.97, suitable for readers with college-level proficiency. Passive voice was used in 7.22% ± 5.60% of sentences, indicating moderate reliance. Statistical analysis showed no significant differences in FRES (p = .385), FKGL (p = .555), or passive sentence usage (p = .601) across categories, as determined by one-way ANOVA. Readability remained relatively constant across the three categories, as determined by trend analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Conclusions: ChatGPT shows strong potential in providing accurate and","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coaching Patients to Understand and Use Patient-Reported Outcome Data: Intervention Design and Evaluation.","authors":"Martha Burla, Brocha Z Stern, Andrew Bl Berry, Sarah Pila, Patricia D Franklin","doi":"10.2196/65931","DOIUrl":"10.2196/65931","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Providing patients with information about their health and treatment options is important to ensure care that best reflects patient needs, values, and preferences. Patient-reported outcomes (PROs), measures of health status, are regularly collected in clinical contexts and scores can be returned to patients in personalized decision aids. One example of a PRO-based decision aid is the Arthritis care through Shared Knowledge (ASK) report, which shares individual PRO data on knee and hip arthritis-related pain and functional limitations with patients. However, given that the use of such data in clinical consultations is unfamiliar to many patients, support may be required to ensure this information is understood and used as intended.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This paper describes ASK coaching, an online 1-hour group session designed to ensure patients understood the ASK report, including their PRO scores, and how to use the information in conversations with their clinicians. We present (1) quantitative evaluation results associated with attendance and self-assessment of learning and (2) qualitative evaluation results on motivation to attend, acceptability of the session format, and achievement of session goals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The session was designed and refined collaboratively with clinical experts and patient advisers. Patients in one arm of a pragmatic cluster-randomized trial evaluating the ASK report were invited to attend this session. To understand the profile of attendees (N=438) sociodemographic and clinical data were compared with all participants invited to coaching (N=1545) and a patient-reported assessment of self-efficacy was collected on a subset (N=692). In addition, a postsession survey was used to self-assess learning. Qualitative data were synthesized from semistructured postcoaching interviews, paired pre- and postcoaching interviews, and free-text responses to a postsession survey. A qualitative descriptive approach was used for analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Compared with nonattendees, patients reporting higher education, greater health literacy, Medicare insurance, and lower self-efficacy for managing treatments were more likely to attend ASK coaching when invited. Participants' self-assessment of learning showed an improved understanding of current and projected osteoarthritis symptoms and where to find additional information. Qualitatively, patients reported attending coaching to gain information that could benefit their treatment or aid in research. The online group format was generally described as acceptable, and the session goals related to understanding the report and preparing for future conversations with clinicians were met. Suggestions for improvement, such as providing more opportunities for within-group interaction, were also provided.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our results highlight the value of coaching as an intervention to help patien","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e65931"},"PeriodicalIF":2.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Impact on Pain Perceptions, Pain Intensity, and Physical Activity of a Mobile App to Empower Employees With Musculoskeletal Pain: Mixed Methods Pilot Study.","authors":"Stijn Keyaerts, Maxwell Szymanski, Lode Godderis, Vero Vanden Abeele, Liesbeth Daenen","doi":"10.2196/67886","DOIUrl":"10.2196/67886","url":null,"abstract":"<p><strong>Background: </strong>Mobile apps present opportunities to empower employees with musculoskeletal pain and reduce long-term absenteeism. However, adoption remains limited because of a lack of empirical evidence and challenges in user-friendly design.</p><p><strong>Objective: </strong>This pilot study aimed to evaluate the potential effects of a fully automated, app-based pain management intervention tailored for employees. Specifically, the study aimed to (1) assess the effect of the intervention on maladaptive pain perceptions, pain intensity, and physical activity and (2) identify factors influencing its effectiveness.</p><p><strong>Methods: </strong>A total of 66 employees from a Belgian university hospital who had been experiencing musculoskeletal pain for at least 6 weeks participated in a 24-week intervention. The app-based intervention focused on reducing maladaptive pain perceptions, providing work-related guidance, and promoting healthy activity habits through interactive modules, real-time recommendations, and goal-setting features. Every 6 weeks, participants completed a questionnaire measuring maladaptive pain perceptions (pain catastrophizing and fear-avoidance beliefs). Pain intensity was recorded daily using a visual analog scale, and step count was tracked daily using an activity tracker. In addition, semistructured interviews were conducted with 12 participants to explore how they engaged with the intervention and perceived its impact.</p><p><strong>Results: </strong>Quantitative analysis showed a significant reduction in pain catastrophizing (B=-0.83, P<.001, d=-0.27), with greater decreases observed among participants with higher baseline scores (σ=-0.38; P=.09). No significant overall change was found in fear-avoidance beliefs (B=-0.35; P=.15), although individual trajectories varied (σ²=1.34; P=.04). Pain intensity also showed significant variability across participants (σ²=17.29; P=.03) despite no overall effect (B=-0.37; P=.67). No significant change was observed in the daily step count (B=107.50; P=.23). Qualitative analysis revealed that the effectiveness of the intervention was hindered by content and design choices that did not adequately account for diverse work settings and the busy lives of employees. Cognitive biases and nonsupportive work environments further complicated the successful implementation of the intervention in the workplace.</p><p><strong>Conclusions: </strong>This pilot study demonstrates the potential of an app-based intervention to support employees with musculoskeletal pain by reducing pain-related fear and promoting active coping strategies. While promising for some, digital interventions alone may be insufficient for employees with more complex needs. Blended approaches and integration within supportive workplace environments are likely essential to enhance effectiveness and promote sustainable work participation.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e67886"},"PeriodicalIF":2.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12254710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing Patient Complaints in Web-Based Reviews of Private Hospitals in Selangor, Malaysia, Using Large Language Model-Assisted Content Analysis: Mixed Methods Study.","authors":"Muhammad Hafiz Sulaiman, Nora Muda, Fatimah Abdul Razak","doi":"10.2196/69075","DOIUrl":"10.2196/69075","url":null,"abstract":"<p><strong>Background: </strong>Large language model (LLM)-assisted content analysis (LACA) is a modification of traditional content analysis, leveraging the LLM to codevelop codebooks and automatically assign thematic codes to a web-based reviews dataset.</p><p><strong>Objective: </strong>This study aims to develop and validate the use of LACA for analyzing hospital web-based reviews and to identify themes of issues from web-based reviews using this method.</p><p><strong>Methods: </strong>Web-based reviews for 53 private hospitals in Selangor, Malaysia, were acquired. Fake reviews were filtered out using natural language processing and machine learning algorithms trained on yelp.com validated datasets. GPT-4o mini model application programming interface (API) was then applied to filter out reviews without any quality issues. In total, 200 of the remaining reviews were randomly extracted and fed into the GPT-4o mini model API to produce a codebook validated through parallel human-LLM coding to establish interrater reliability. The codebook was then used to code (label) all reviews in the dataset. The thematic codes were then summarized into themes using factor analysis to increase interpretability.</p><p><strong>Results: </strong>A total of 14,938 web-based reviews were acquired, of which 1121 (9.3%) were fake, 1279 (12%) contained negative sentiments, and 9635 (88%) did not contain any negative sentiment. GPT-4o mini model subsequently inducted 41 thematic codes together with their definitions. Average human-GPT interrater reliability is perfect (κ=0.81). Factor analysis identified 6 interpretable latent factors: \"Service and Communication Effectiveness,\" \"Clinical Care and Patient Experience,\" \"Facilities and Amenities Quality,\" \"Appointment and Patient Flow,\" \"Financial and Insurance Management,\" and \"Patient Rights and Accessibility.\" The cumulative explained variance for the six factors is 0.74, and Cronbach α is between 0.88 and 0.97 (good and excellent) for all factors except factor 6 (0.61: questionable). The factors identified follow a global pattern of issues identified from the literature.</p><p><strong>Conclusions: </strong>A data collection and processing pipeline consisting of Python Selenium, the GPT-4o mini model API, and a factor analysis module can support valid and reliable thematic analysis. Despite the potential for collection and information bias in web-based reviews, LACA of web-based reviews is cost-effective, time-efficient, and can be performed in real time, helping hospital managers develop hypotheses for further investigations promptly.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e69075"},"PeriodicalIF":2.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12254706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mediating Effects of Psychological Independence and Social Support on the Association Between Family Strength and Depression in Young Korean Adults: Cross-Sectional Study.","authors":"Sunyoung Kim, Suin Park, Hyunlye Kim, Dabok Noh","doi":"10.2196/71485","DOIUrl":"10.2196/71485","url":null,"abstract":"<p><strong>Background: </strong>Although family strength is potentially associated with a reduced risk of depression, little is known about the underlying pathways and mediating factors.</p><p><strong>Objective: </strong>This study aimed to investigate the mediating effects of psychological independence and social support on the relationship between family strength and depression in young adults.</p><p><strong>Methods: </strong>A cross-sectional web-based survey was conducted among 1,000 young Korean adults aged 19 to 24 years. We used a web-based survey agency to recruit participants using an independent panel and quota sampling, with stratification based on gender and age. The participants completed self-reported questionnaires that assessed family strength, psychological independence, social support, and depression. To examine the mediating effects of psychological independence and social support on the relationship between family strength and depression, we performed path analysis with AMOS 26 software (IBM Corp) using maximum standard likelihood estimation.</p><p><strong>Results: </strong>The path analysis revealed that gender (female) had a direct positive effect on depression (β=.09, P=.004) and an indirect negative effect on depression through social support (β=-.03, P=.001). Although there were no significant direct effects of living status (with parents) on depression, it had a significant and positive indirect effect through psychological independence (β=.03, P=.001). Family strength had a significant and negative direct effect on depression (β=-0.19, P=.001) and significant indirect and negative effects through psychological independence and social support (β=-0.17, P=.001). Therefore, the overall effect of family strength on depression was significantly negative (β=-0.37, P=.001). Psychological independence influenced depression both directly (β=-0.16, P=.001) and indirectly through social support (β=-0.12, P=.001), and social support influenced depression directly (β=-0.21, P=.001). The overall model explained 23% of the total variance in depression.</p><p><strong>Conclusions: </strong>The findings highlight that gender, living with parents, family strength, psychological independence, and social support in reduce depression among young adults. Additionally, the mediating effects of psychological independence and social support on the relationship between family strength and depression were significant in this population. Therefore, strategies to increase psychological independence and social support could reduce the risk of depression in young adults who have low family strength.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e71485"},"PeriodicalIF":2.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Health Care Professional Communication via a Secure Web-Based Portal in Severe Mental Health Conditions: Qualitative Analysis of Secure Messages.","authors":"Eva Meier-Diedrich, Carolyn Turvey, Jonas Maximilian Wördemann, Justin Speck, Mareike Weibezahl, Julian Schwarz","doi":"10.2196/63713","DOIUrl":"10.2196/63713","url":null,"abstract":"<p><strong>Background: </strong>Patients' web-based access to their medical records and secure messaging (SM) via patient portals is becoming increasingly prevalent worldwide. SM offers several potential benefits, including improved health outcomes and increased patient engagement. However, SM also raises concerns about effects on the therapeutic relationship and may be constrained by factors such as limited digital literacy and access to digital devices. Evidence on the use of SM in mental health is limited, and results are inconclusive.</p><p><strong>Objective: </strong>This study aimed to examine (1) the purposes for which health care professionals (HCPs) and patients with psychiatric disorders use SM to communicate and (2) the specific use patterns associated with both patients and HCPs.</p><p><strong>Methods: </strong>The secure messages (n=274) of 38 patients with psychiatric disorders and 4 HCPs (psychiatrists) from 3 psychiatric outpatient clinics in Brandenburg, Germany, was analyzed using thematic analysis. The data selected for this study represent a subsample from a larger study comprising a total of 116 patients. The subsample consists of the patients and HCPs who used SM.</p><p><strong>Results: </strong>A total of 274 messages were analyzed: 22.3% (61/274) were initial notes from HCPs, 44.5% (122/274) were patient responses, and 33.2% (91/274) were HCP replies. Patients sent between 1 and 15 messages (mean 4.16, SD 3.42) and logged in 1 to 42 times (mean 10.78, SD 9.38). Most messages were sent during the day, although some were also sent at night and in the early morning. Regarding the purposes of SM, 4 core functions of SM were identified: reporting and feedback, interpersonal uses, intrapersonal uses, and organizational uses. Both patients and HCPs used SM to share treatment-relevant information and elicited feedback on treatment and medication. Furthermore, secure messages included expressions of gratitude by the patients, in addition to well-wishes and emotional support from the HCPs. SM allowed patients to reflect on their treatment and provide self-encouragement. Finally, secure messages were used to address organizational aspects such as scheduling, appointments, and administrative tasks.</p><p><strong>Conclusions: </strong>SM in outpatient mental health care is multifaceted and holds the potential to enhance therapeutic contact and improve access to care by enabling quick, low-threshold communication between patients and HCPs, allowing treatment-related concerns to be addressed promptly and effectively. However, the asynchronous nature of SM also poses new challenges, particularly in managing acute mental health crises and in setting boundaries to prevent HCPs from being perceived as constantly available. Therefore, specific training for HCPs-both during medical education and in clinical practice-is essential, along with clear guidelines on handling crises and managing sensitive information.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e63713"},"PeriodicalIF":2.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12254702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.","authors":"Mohammed A Almanna, Lior M Elkaim, Mohammed A Alvi, Jordan J Levett, Ben Li, Muhammad Mamdani, Mohammed Al-Omran, Naif M Alotaibi","doi":"10.2196/60859","DOIUrl":"10.2196/60859","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P&lt;.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the \"broadcasting\" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the \"scientific\" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influenti","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e60859"},"PeriodicalIF":2.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144496736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}