Journal of Medical Internet Research最新文献

Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/65146

Zhichao Yang, Zonghai Yao, Mahbuba Tasmin, Parth Vashisht, Won Seok Jang, Feiyun Ouyang, Beining Wang, David McManus, Dan Berlowitz, Hong Yu

Background: Recent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. However, the ability of these models to interpret medical images remains underexplored.Objective: This study aimed to comprehensively evaluate the performance, interpretability, and limitations of GPT-3.5 Turbo, GPT-4, and its successor, GPT-4 Vision (GPT-4V), specifically focusing on GPT-4V's newly introduced image-understanding feature. By assessing the models on medical licensing examination questions that require image interpretation, we sought to highlight the strengths and weaknesses of GPT-4V in handling complex multimodal clinical information, thereby exposing hidden flaws and providing insights into its readiness for integration into clinical settings.Methods: This cross-sectional study tested GPT-4V, GPT-4, and ChatGPT-3.5 Turbo on a total of 227 multiple-choice questions with images from USMLE Step 1 (n=19), Step 2 clinical knowledge (n=14), Step 3 (n=18), the Diagnostic Radiology Qualifying Core Exam (DRQCE) (n=26), and AMBOSS question banks (n=150). AMBOSS provided expert-written hints and question difficulty levels. GPT-4V's accuracy was compared with 2 state-of-the-art large language models, GPT-3.5 Turbo and GPT-4. The quality of the explanations was evaluated by choosing human preference between an explanation by GPT-4V (without hint), an explanation by an expert, or a tie, using 3 qualitative metrics: comprehensive explanation, question information, and image interpretation. To better understand GPT-4V's explanation ability, we modified a patient case report to resemble a typical "curbside consultation" between physicians.Results: For questions with images, GPT-4V achieved an accuracy of 84.2%, 85.7%, 88.9%, and 73.1% in Step 1, Step 2 clinical knowledge, Step 3 of USMLE, and DRQCE, respectively. It outperformed GPT-3.5 Turbo (42.1%, 50%, 50%, 19.2%) and GPT-4 (63.2%, 64.3%, 66.7%, 26.9%). When GPT-4V answered correctly, its explanations were nearly as good as those provided by domain experts from AMBOSS. However, incorrect answers often had poor explanation quality: 18.2% (10/55) contained inaccurate text, 45.5% (25/55) had inference errors, and 76.3% (42/55) demonstrated image misunderstandings. With human expert assistance, GPT-4V reduced errors by an average of 40% (22/55). GPT-4V accuracy improved with hints, maintaining stable performance across difficulty levels, while medical student performance declined as difficulty increased. In a simulated curbside consultation scenario, GPT-4V required multiple specific prompts to interpret complex case data accurately.Conclusions: GPT-4V achieved high accuracy on multiple-choice que

{"title":"Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study.","authors":"Zhichao Yang, Zonghai Yao, Mahbuba Tasmin, Parth Vashisht, Won Seok Jang, Feiyun Ouyang, Beining Wang, David McManus, Dan Berlowitz, Hong Yu","doi":"10.2196/65146","DOIUrl":"https://doi.org/10.2196/65146","url":null,"abstract":"Background: Recent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. However, the ability of these models to interpret medical images remains underexplored.Objective: This study aimed to comprehensively evaluate the performance, interpretability, and limitations of GPT-3.5 Turbo, GPT-4, and its successor, GPT-4 Vision (GPT-4V), specifically focusing on GPT-4V's newly introduced image-understanding feature. By assessing the models on medical licensing examination questions that require image interpretation, we sought to highlight the strengths and weaknesses of GPT-4V in handling complex multimodal clinical information, thereby exposing hidden flaws and providing insights into its readiness for integration into clinical settings.Methods: This cross-sectional study tested GPT-4V, GPT-4, and ChatGPT-3.5 Turbo on a total of 227 multiple-choice questions with images from USMLE Step 1 (n=19), Step 2 clinical knowledge (n=14), Step 3 (n=18), the Diagnostic Radiology Qualifying Core Exam (DRQCE) (n=26), and AMBOSS question banks (n=150). AMBOSS provided expert-written hints and question difficulty levels. GPT-4V's accuracy was compared with 2 state-of-the-art large language models, GPT-3.5 Turbo and GPT-4. The quality of the explanations was evaluated by choosing human preference between an explanation by GPT-4V (without hint), an explanation by an expert, or a tie, using 3 qualitative metrics: comprehensive explanation, question information, and image interpretation. To better understand GPT-4V's explanation ability, we modified a patient case report to resemble a typical \"curbside consultation\" between physicians.Results: For questions with images, GPT-4V achieved an accuracy of 84.2%, 85.7%, 88.9%, and 73.1% in Step 1, Step 2 clinical knowledge, Step 3 of USMLE, and DRQCE, respectively. It outperformed GPT-3.5 Turbo (42.1%, 50%, 50%, 19.2%) and GPT-4 (63.2%, 64.3%, 66.7%, 26.9%). When GPT-4V answered correctly, its explanations were nearly as good as those provided by domain experts from AMBOSS. However, incorrect answers often had poor explanation quality: 18.2% (10/55) contained inaccurate text, 45.5% (25/55) had inference errors, and 76.3% (42/55) demonstrated image misunderstandings. With human expert assistance, GPT-4V reduced errors by an average of 40% (22/55). GPT-4V accuracy improved with hints, maintaining stable performance across difficulty levels, while medical student performance declined as difficulty increased. In a simulated curbside consultation scenario, GPT-4V required multiple specific prompts to interpret complex case data accurately.Conclusions: GPT-4V achieved high accuracy on multiple-choice que","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e65146"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Urban-Rural Differences in the Association Between Internet Use Trajectories and Depressive Symptoms in Chinese Adolescents: Longitudinal Observational Study.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/63799

Yujie Liu, Xin Ge, Ying Wang, Xue Yang, Shangbin Liu, Chen Xu, Mi Xiang, Fan Hu, Yong Cai

Background: Internet use exhibits diverse trajectories during adolescence, which may contribute to depressive symptoms. Currently, it remains unclear whether the association between internet use trajectories and depressive symptoms varies between urban and rural areas.

Objective: This study aimed to investigate the association between internet use trajectories and adolescent depressive symptoms and to explore variation in this association between urban and rural areas.

Methods: This longitudinal study used 3-wave data from the 2014-2018 China Family Panel Study. Weekly hours of internet use and depressive symptoms were measured using self-reported questionnaires. Latent class growth modeling was performed to identify the trajectories of internet use. Multivariable logistic regressions were used to examine the association between internet use trajectories and depressive symptoms, stratified by rural and urban residence.

Results: Participants were 2237 adolescents aged 10 to 15 years at baseline (mean age 12.46, SD 1.73 years). Two latent trajectory classes of internet use were identified: the low-growth group (n=2008, 89.8%) and the high-growth group (n=229, 10.2%). The high-growth group was associated with higher odds of depressive symptoms (OR 1.486, 95% CI 1.065-2.076) compared to the low-growth group. In the stratified analysis, the association between internet use trajectories and depressive symptoms was significant solely among rural adolescents (OR 1.856, 95% CI 1.164-2.959).

Conclusions: This study elucidates urban-rural differences in the associations between trajectories of internet use and adolescent depressive symptoms. Our findings underscore the importance of prioritizing interventions for rural adolescents' internet use behaviors to mitigate negative effects on their mental health.

{"title":"Urban-Rural Differences in the Association Between Internet Use Trajectories and Depressive Symptoms in Chinese Adolescents: Longitudinal Observational Study.","authors":"Yujie Liu, Xin Ge, Ying Wang, Xue Yang, Shangbin Liu, Chen Xu, Mi Xiang, Fan Hu, Yong Cai","doi":"10.2196/63799","DOIUrl":"https://doi.org/10.2196/63799","url":null,"abstract":"Background: Internet use exhibits diverse trajectories during adolescence, which may contribute to depressive symptoms. Currently, it remains unclear whether the association between internet use trajectories and depressive symptoms varies between urban and rural areas.Objective: This study aimed to investigate the association between internet use trajectories and adolescent depressive symptoms and to explore variation in this association between urban and rural areas.Methods: This longitudinal study used 3-wave data from the 2014-2018 China Family Panel Study. Weekly hours of internet use and depressive symptoms were measured using self-reported questionnaires. Latent class growth modeling was performed to identify the trajectories of internet use. Multivariable logistic regressions were used to examine the association between internet use trajectories and depressive symptoms, stratified by rural and urban residence.Results: Participants were 2237 adolescents aged 10 to 15 years at baseline (mean age 12.46, SD 1.73 years). Two latent trajectory classes of internet use were identified: the low-growth group (n=2008, 89.8%) and the high-growth group (n=229, 10.2%). The high-growth group was associated with higher odds of depressive symptoms (OR 1.486, 95% CI 1.065-2.076) compared to the low-growth group. In the stratified analysis, the association between internet use trajectories and depressive symptoms was significant solely among rural adolescents (OR 1.856, 95% CI 1.164-2.959).Conclusions: This study elucidates urban-rural differences in the associations between trajectories of internet use and adolescent depressive symptoms. Our findings underscore the importance of prioritizing interventions for rural adolescents' internet use behaviors to mitigate negative effects on their mental health.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e63799"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Sharing Statements Requested from Clinical Trials by Public, Environmental, and Occupational Health Journals: Cross-Sectional Study.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/64069

Yingxin Liu, Jingyi Zhang, Lehana Thabane, Xuerui Bai, Lili Kang, Gregory Y H Lip, Harriette G C Van Spall, Min Xia, Guowei Li

Background: Data sharing plays a crucial role in health informatics, contributing to improving health information systems, enhancing operational efficiency, informing policy and decision-making, and advancing public health surveillance including disease tracking. Sharing individual participant data in public, environmental, and occupational health trials can help improve public trust and support by enhancing transparent reporting and reproducibility of research findings. The International Committee of Medical Journal Editors (ICMJE) requires all papers to include a data-sharing statement. However, it is unclear whether journals in the field of public, environmental, and occupational health adhere to this requirement.

Objective: This study aims to investigate whether public, environmental, and occupational health journals requested data-sharing statements from clinical trials submitted for publication.

Methods: In this bibliometric survey of "Public, Environmental, and Occupational Health" journals, defined by the Journal Citation Reports (as of June 2023), we included 202 journals with clinical trial reports published between 2019 and 2022. The primary outcome was a journal request for a data-sharing statement, as identified in the paper submission instructions. Multivariable logistic regression analysis was conducted to evaluate the relationship between journal characteristics and journal requests for data-sharing statements, with results presented as odds ratios (ORs) and corresponding 95% CIs. We also investigated whether the journals included a data-sharing statement in their published trial reports.

Results: Among the 202 public, environmental, and occupational health journals included, there were 68 (33.7%) journals that did not request data-sharing statements. Factors significantly associated with journal requests for data-sharing statements included open access status (OR 0.43, 95% CI 0.19-0.97), high journal impact factor (OR 2.31, 95% CI 1.15-4.78), endorsement of Consolidated Standards of Reporting Trials (OR 2.43, 95% CI 1.25-4.79), and publication in the United Kingdom (OR 7.18, 95% CI 2.61-23.4). Among the 134 journals requesting data-sharing statements, 26.9% (36/134) did not have statements in their published trial reports.

Conclusions: Over one-third of the public, environmental, and occupational health journals did not request data-sharing statements in clinical trial reports. Among those journals that requested data-sharing statements in their submission guidance pages, more than one quarter published trial reports with no data-sharing statements. These results revealed an inadequate practice of requesting data-sharing statements by public, environmental, and occupational health journals, requiring more effort at the journal level to implement ICJME recommendations on data-sharing statements.

{"title":"Data-Sharing Statements Requested from Clinical Trials by Public, Environmental, and Occupational Health Journals: Cross-Sectional Study.","authors":"Yingxin Liu, Jingyi Zhang, Lehana Thabane, Xuerui Bai, Lili Kang, Gregory Y H Lip, Harriette G C Van Spall, Min Xia, Guowei Li","doi":"10.2196/64069","DOIUrl":"https://doi.org/10.2196/64069","url":null,"abstract":"Background: Data sharing plays a crucial role in health informatics, contributing to improving health information systems, enhancing operational efficiency, informing policy and decision-making, and advancing public health surveillance including disease tracking. Sharing individual participant data in public, environmental, and occupational health trials can help improve public trust and support by enhancing transparent reporting and reproducibility of research findings. The International Committee of Medical Journal Editors (ICMJE) requires all papers to include a data-sharing statement. However, it is unclear whether journals in the field of public, environmental, and occupational health adhere to this requirement.Objective: This study aims to investigate whether public, environmental, and occupational health journals requested data-sharing statements from clinical trials submitted for publication.Methods: In this bibliometric survey of \"Public, Environmental, and Occupational Health\" journals, defined by the Journal Citation Reports (as of June 2023), we included 202 journals with clinical trial reports published between 2019 and 2022. The primary outcome was a journal request for a data-sharing statement, as identified in the paper submission instructions. Multivariable logistic regression analysis was conducted to evaluate the relationship between journal characteristics and journal requests for data-sharing statements, with results presented as odds ratios (ORs) and corresponding 95% CIs. We also investigated whether the journals included a data-sharing statement in their published trial reports.Results: Among the 202 public, environmental, and occupational health journals included, there were 68 (33.7%) journals that did not request data-sharing statements. Factors significantly associated with journal requests for data-sharing statements included open access status (OR 0.43, 95% CI 0.19-0.97), high journal impact factor (OR 2.31, 95% CI 1.15-4.78), endorsement of Consolidated Standards of Reporting Trials (OR 2.43, 95% CI 1.25-4.79), and publication in the United Kingdom (OR 7.18, 95% CI 2.61-23.4). Among the 134 journals requesting data-sharing statements, 26.9% (36/134) did not have statements in their published trial reports.Conclusions: Over one-third of the public, environmental, and occupational health journals did not request data-sharing statements in clinical trial reports. Among those journals that requested data-sharing statements in their submission guidance pages, more than one quarter published trial reports with no data-sharing statements. These results revealed an inadequate practice of requesting data-sharing statements by public, environmental, and occupational health journals, requiring more effort at the journal level to implement ICJME recommendations on data-sharing statements.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64069"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/63550

Michael R Ruta, Tony Gaidici, Chase Irwin, Jonathan Lifshitz

Background: ChatGPT, a conversational artificial intelligence developed by OpenAI, has rapidly become an invaluable tool for researchers. With the recent integration of Python code interpretation into the ChatGPT environment, there has been a significant increase in the potential utility of ChatGPT as a research tool, particularly in terms of data analysis applications.

Objective: This study aimed to assess ChatGPT as a data analysis tool and provide researchers with a framework for applying ChatGPT to data management tasks, descriptive statistics, and inferential statistics.

Methods: A subset of the National Inpatient Sample was extracted. Data analysis trials were divided into data processing, categorization, and tabulation, as well as descriptive and inferential statistics. For data processing, categorization, and tabulation assessments, ChatGPT was prompted to reclassify variables, subset variables, and present data, respectively. Descriptive statistics assessments included mean, SD, median, and IQR calculations. Inferential statistics assessments were conducted at varying levels of prompt specificity ("Basic," "Intermediate," and "Advanced"). Specific tests included chi-square, Pearson correlation, independent 2-sample t test, 1-way ANOVA, Fisher exact, Spearman correlation, Mann-Whitney U test, and Kruskal-Wallis H test. Outcomes from consecutive prompt-based trials were assessed against expected statistical values calculated in Python (Python Software Foundation), SAS (SAS Institute), and RStudio (Posit PBC).

Results: ChatGPT accurately performed data processing, categorization, and tabulation across all trials. For descriptive statistics, it provided accurate means, SDs, medians, and IQRs across all trials. Inferential statistics accuracy against expected statistical values varied with prompt specificity: 32.5% accuracy for "Basic" prompts, 81.3% for "Intermediate" prompts, and 92.5% for "Advanced" prompts.

Conclusions: ChatGPT shows promise as a tool for exploratory data analysis, particularly for researchers with some statistical knowledge and limited programming expertise. However, its application requires careful prompt construction and human oversight to ensure accuracy. As a supplementary tool, ChatGPT can enhance data analysis efficiency and broaden research accessibility.

{"title":"ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.","authors":"Michael R Ruta, Tony Gaidici, Chase Irwin, Jonathan Lifshitz","doi":"10.2196/63550","DOIUrl":"https://doi.org/10.2196/63550","url":null,"abstract":"Background: ChatGPT, a conversational artificial intelligence developed by OpenAI, has rapidly become an invaluable tool for researchers. With the recent integration of Python code interpretation into the ChatGPT environment, there has been a significant increase in the potential utility of ChatGPT as a research tool, particularly in terms of data analysis applications.Objective: This study aimed to assess ChatGPT as a data analysis tool and provide researchers with a framework for applying ChatGPT to data management tasks, descriptive statistics, and inferential statistics.Methods: A subset of the National Inpatient Sample was extracted. Data analysis trials were divided into data processing, categorization, and tabulation, as well as descriptive and inferential statistics. For data processing, categorization, and tabulation assessments, ChatGPT was prompted to reclassify variables, subset variables, and present data, respectively. Descriptive statistics assessments included mean, SD, median, and IQR calculations. Inferential statistics assessments were conducted at varying levels of prompt specificity (\"Basic,\" \"Intermediate,\" and \"Advanced\"). Specific tests included chi-square, Pearson correlation, independent 2-sample t test, 1-way ANOVA, Fisher exact, Spearman correlation, Mann-Whitney U test, and Kruskal-Wallis H test. Outcomes from consecutive prompt-based trials were assessed against expected statistical values calculated in Python (Python Software Foundation), SAS (SAS Institute), and RStudio (Posit PBC).Results: ChatGPT accurately performed data processing, categorization, and tabulation across all trials. For descriptive statistics, it provided accurate means, SDs, medians, and IQRs across all trials. Inferential statistics accuracy against expected statistical values varied with prompt specificity: 32.5% accuracy for \"Basic\" prompts, 81.3% for \"Intermediate\" prompts, and 92.5% for \"Advanced\" prompts.Conclusions: ChatGPT shows promise as a tool for exploratory data analysis, particularly for researchers with some statistical knowledge and limited programming expertise. However, its application requires careful prompt construction and human oversight to ensure accuracy. As a supplementary tool, ChatGPT can enhance data analysis efficiency and broaden research accessibility.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e63550"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Good-Quality mHealth Apps for Endometriosis Care: Systematic Search.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/49654

Diksha Sirohi, Cecilia Hm Ng, Niranjan Bidargaddi, Helen Slater, Melissa Parker, M Louise Hull, Rebecca O'Hara

Background: Mobile health (mHealth) apps are increasingly being used by community members to track symptoms and manage endometriosis. In addition, clinicians use mHealth apps for continued medical education and clinical decision-making and recommend good-quality apps to patients. However, poor-quality apps can spread misinformation or provide recommendations that are not evidence-based. Therefore, a critical evaluation is needed to assess and recommend good-quality endometriosis mHealth apps.Objective: This study aimed to evaluate the quality and provide recommendations for good quality endometriosis mHealth apps for the community and clinicians.Methods: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines informed the search of mHealth apps on the Google Play Store and Apple App Store. The search terms included "endometriosis," "adenomyosis," and "pelvic pain." mHealth apps were eligible if they were (1) related to the search terms, (2) were in the English language, and (3) were available free of cost. Only the free content of the eligible mHealth apps was assessed. ENLIGHT, a validated evaluation tool for mobile and web-based interventions, was used to assess the quality across 7 domains such as usability, visual design, user engagement, content, therapeutic persuasiveness, therapeutic alliance, and general subjective evaluation. mHealth apps with a total score of ≥3.5 were classified as "good" according to the ENLIGHT scoring system and are recommended as good-quality mHealth apps for endometriosis care.Results: In total, 42 mHealth apps were screened, and 19 were included in the quality assessment. A total of 6 good-quality mHealth apps were identified (QENDO, Bearable, Luna for Health, Matilda Health, Branch Health: Pain Management, and CHARLI Health). These apps provided symptom-tracking functions and self-management support. A total of 17 apps were designed for community use, while 2 apps provided a digital endometriosis classification tool to clinicians. Most mHealth apps scored well (≥3.5) in the domains of usability (16/19, 84.2%), visual design (14/19, 73.7%), user engagement (11/19, 57.9%), and content (15/19, 78.9%). Few eHealth websites scored well on therapeutic persuasiveness (6/19, 31.6%), therapeutic alliance (9/19, 47.4%), and general subjective evaluation (6/19, 31.6%).Conclusions: Although time and geographical location can influence the search results, we identified 6 "good-quality" endometriosis mHealth apps that can be recommended to the endometriosis community. mHealth apps designed for community use should evaluate their effectiveness on user's endometriosis knowledge, self-recommended management strategies, pain self-efficacy, user satisfaction, and user quality of life. Digital technology should be leveraged to develop mHealth apps for clinicians that contribute to c

{"title":"Good-Quality mHealth Apps for Endometriosis Care: Systematic Search.","authors":"Diksha Sirohi, Cecilia Hm Ng, Niranjan Bidargaddi, Helen Slater, Melissa Parker, M Louise Hull, Rebecca O'Hara","doi":"10.2196/49654","DOIUrl":"10.2196/49654","url":null,"abstract":"Background: Mobile health (mHealth) apps are increasingly being used by community members to track symptoms and manage endometriosis. In addition, clinicians use mHealth apps for continued medical education and clinical decision-making and recommend good-quality apps to patients. However, poor-quality apps can spread misinformation or provide recommendations that are not evidence-based. Therefore, a critical evaluation is needed to assess and recommend good-quality endometriosis mHealth apps.Objective: This study aimed to evaluate the quality and provide recommendations for good quality endometriosis mHealth apps for the community and clinicians.Methods: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines informed the search of mHealth apps on the Google Play Store and Apple App Store. The search terms included \"endometriosis,\" \"adenomyosis,\" and \"pelvic pain.\" mHealth apps were eligible if they were (1) related to the search terms, (2) were in the English language, and (3) were available free of cost. Only the free content of the eligible mHealth apps was assessed. ENLIGHT, a validated evaluation tool for mobile and web-based interventions, was used to assess the quality across 7 domains such as usability, visual design, user engagement, content, therapeutic persuasiveness, therapeutic alliance, and general subjective evaluation. mHealth apps with a total score of ≥3.5 were classified as \"good\" according to the ENLIGHT scoring system and are recommended as good-quality mHealth apps for endometriosis care.Results: In total, 42 mHealth apps were screened, and 19 were included in the quality assessment. A total of 6 good-quality mHealth apps were identified (QENDO, Bearable, Luna for Health, Matilda Health, Branch Health: Pain Management, and CHARLI Health). These apps provided symptom-tracking functions and self-management support. A total of 17 apps were designed for community use, while 2 apps provided a digital endometriosis classification tool to clinicians. Most mHealth apps scored well (≥3.5) in the domains of usability (16/19, 84.2%), visual design (14/19, 73.7%), user engagement (11/19, 57.9%), and content (15/19, 78.9%). Few eHealth websites scored well on therapeutic persuasiveness (6/19, 31.6%), therapeutic alliance (9/19, 47.4%), and general subjective evaluation (6/19, 31.6%).Conclusions: Although time and geographical location can influence the search results, we identified 6 \"good-quality\" endometriosis mHealth apps that can be recommended to the endometriosis community. mHealth apps designed for community use should evaluate their effectiveness on user's endometriosis knowledge, self-recommended management strategies, pain self-efficacy, user satisfaction, and user quality of life. Digital technology should be leveraged to develop mHealth apps for clinicians that contribute to c","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e49654"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies. 为基于传感器的数字健康技术的分析验证选择参考措施的分层框架。

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/58956

Jessie P Bakker, Samantha J McClenahan, Piper Fromy, Simon Turner, Barry T Peterson, Benjamin Vandendriessche, Jennifer C Goldsack

Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs-evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable-is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development.

{"title":"A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies.","authors":"Jessie P Bakker, Samantha J McClenahan, Piper Fromy, Simon Turner, Barry T Peterson, Benjamin Vandendriessche, Jennifer C Goldsack","doi":"10.2196/58956","DOIUrl":"10.2196/58956","url":null,"abstract":"Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs-evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable-is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e58956"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding "Alert Fatigue" in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/62763

Illin Gani, Ian Litchfield, David Shukla, Gayathri Delanerolle, Neil Cockburn, Anna Pathmanathan

Background: The consistency and quality of care in modern primary care are supported by various clinical reminders (CRs), which include "alerts" describing the consequences of certain decisions and "prompts" that remind users to perform tasks promoting desirable clinical behaviors. However, not all CRs are acted upon, and many are disregarded by general practitioners (GPs), a chronic issue commonly referred to as "alert fatigue." This phenomenon has significant implications for the safety and quality of care, GP burnout, and broader medicolegal consequences. Research on mitigating alert fatigue and optimizing the use of CRs remains limited. This review offers much-needed insight into GP attitudes toward the deployment, design, and overall effectiveness of CRs.Objective: This systematic review aims to synthesize current qualitative research on GPs' attitudes toward CRs, enabling an exploration of the interacting influences on the occurrence of alert fatigue in GPs, including the deployment, design, and perceived efficacy of CRs.Methods: A systematic literature search was conducted across the Health Technology Assessment database, MEDLINE, MEDLINE In-Process, Embase, CINAHL, Conference Proceedings Citation Index, PsycINFO, and OpenGrey. The search focused on primary qualitative and mixed methods research conducted in general or family practice, specifically exploring GPs' experiences with CRs. All databases were searched from inception to December 31, 2023. To ensure structured and practicable findings, we used a directed content analysis of the data, guided by the 7 domains of the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, including domains related to Technology, Adopter attitudes, and Organization.Results: A total of 9 studies were included, and the findings were organized within the 7 domains. Regarding Condition and Value Proposition, GPs viewed CRs as an effective way to maintain or improve the safety and quality of care they provide. When considering the attributes of the Technology, the efficacy of CRs was linked to their frequency, presentation, and the accuracy of their content. Within Adopters, concerns were raised about the accuracy of CRs and the risk that their use could diminish the value of GP experience and contextual understanding. From an Organization perspective, the need for training on the use and benefits of CRs was highlighted. Finally, in the context of the Wider system and their Embedding Over Time, suggestions included sharing best practices for CR use and involving GPs in their design.Conclusions: While GPs acknowledged that CRs, when used optimally, can enhance patient safety and quality of care, several concerns emerged regarding their design, content accuracy, and lack of contextual nuance. Suggestions to improve CR adherence included providing coherent training, en

{"title":"Understanding \"Alert Fatigue\" in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders.","authors":"Illin Gani, Ian Litchfield, David Shukla, Gayathri Delanerolle, Neil Cockburn, Anna Pathmanathan","doi":"10.2196/62763","DOIUrl":"10.2196/62763","url":null,"abstract":"Background: The consistency and quality of care in modern primary care are supported by various clinical reminders (CRs), which include \"alerts\" describing the consequences of certain decisions and \"prompts\" that remind users to perform tasks promoting desirable clinical behaviors. However, not all CRs are acted upon, and many are disregarded by general practitioners (GPs), a chronic issue commonly referred to as \"alert fatigue.\" This phenomenon has significant implications for the safety and quality of care, GP burnout, and broader medicolegal consequences. Research on mitigating alert fatigue and optimizing the use of CRs remains limited. This review offers much-needed insight into GP attitudes toward the deployment, design, and overall effectiveness of CRs.Objective: This systematic review aims to synthesize current qualitative research on GPs' attitudes toward CRs, enabling an exploration of the interacting influences on the occurrence of alert fatigue in GPs, including the deployment, design, and perceived efficacy of CRs.Methods: A systematic literature search was conducted across the Health Technology Assessment database, MEDLINE, MEDLINE In-Process, Embase, CINAHL, Conference Proceedings Citation Index, PsycINFO, and OpenGrey. The search focused on primary qualitative and mixed methods research conducted in general or family practice, specifically exploring GPs' experiences with CRs. All databases were searched from inception to December 31, 2023. To ensure structured and practicable findings, we used a directed content analysis of the data, guided by the 7 domains of the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, including domains related to Technology, Adopter attitudes, and Organization.Results: A total of 9 studies were included, and the findings were organized within the 7 domains. Regarding Condition and Value Proposition, GPs viewed CRs as an effective way to maintain or improve the safety and quality of care they provide. When considering the attributes of the Technology, the efficacy of CRs was linked to their frequency, presentation, and the accuracy of their content. Within Adopters, concerns were raised about the accuracy of CRs and the risk that their use could diminish the value of GP experience and contextual understanding. From an Organization perspective, the need for training on the use and benefits of CRs was highlighted. Finally, in the context of the Wider system and their Embedding Over Time, suggestions included sharing best practices for CR use and involving GPs in their design.Conclusions: While GPs acknowledged that CRs, when used optimally, can enhance patient safety and quality of care, several concerns emerged regarding their design, content accuracy, and lack of contextual nuance. Suggestions to improve CR adherence included providing coherent training, en","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e62763"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/59524

Beng Heng Ang, Sujatha Das Gollapalli, Mingzhe Du, See-Kiong Ng

Background: Early maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of online mental health communities (OMHCs), how EMSs manifest in these online support-seeking environments remains unclear. Understanding these characteristics could inform the design of more effective interventions powered by artificial intelligence to address online support seekers' unique therapeutic needs.Objective: We aimed to uncover associations between EMSs and mental health problems within OMHCs and examine features of EMSs as they are reflected in OMHCs.Methods: We curated a dataset of 29,329 posts from widely accessed OMHCs, labeling each with relevant schemas and mental health problems. To identify associations, we conducted chi-square tests of independence and calculated odds ratios (ORs) with the dataset. In addition, we developed a novel group-level case conceptualization technique, leveraging GPT-4 to extract features of EMSs from OMHC texts across key schema therapy dimensions, such as schema triggers and coping responses.Results: Several associations were identified between EMSs and mental health problems, reflecting how EMSs manifest in online support-seeking contexts. Anxiety-related problems typically highlighted vulnerability to harm or illness (OR 5.64, 95% CI 5.34-5.96; P<.001), while depression-related problems emphasized unmet interpersonal needs, such as social isolation (OR 3.18, 95% CI 3.02-3.34; P<.001). Conversely, problems with eating disorders mostly exemplified negative self-perception and emotional inhibition (OR 1.89, 95% CI 1.45-2.46; P<.001). Personality disorders reflected themes of subjugation (OR 2.51, 95% CI 1.86-3.39; P<.001), while posttraumatic stress disorder problems involved distressing experiences and mistrust (OR 5.04, 95% CI 4.49-5.66; P<.001). Substance use disorder problems reflected negative self-perception of failure to achieve (OR 1.83, 95% CI 1.35-2.49; P<.001). Depression, personality disorders, and posttraumatic stress disorder were also associated with 12, 9, and 7 EMSs, respectively, emphasizing their complexities and the need for more comprehensive interventions. In contrast, anxiety, eating disorder, and substance use disorder were related to only 2 to 3 EMSs, suggesting that these problems are better addressed through targeted interventions. In addition, the EMS features extracted from our dataset averaged 13.27 (SD 3.05) negative features per schema, with 2.65 (SD 1.07) features per dimension, as supported by existing literature.Conclusions: We uncovered various associations between EMSs and mental health problems among online support seekers, highlighting the prominence of specific EMSs in each pro

{"title":"Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities.","authors":"Beng Heng Ang, Sujatha Das Gollapalli, Mingzhe Du, See-Kiong Ng","doi":"10.2196/59524","DOIUrl":"https://doi.org/10.2196/59524","url":null,"abstract":"Background: Early maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of online mental health communities (OMHCs), how EMSs manifest in these online support-seeking environments remains unclear. Understanding these characteristics could inform the design of more effective interventions powered by artificial intelligence to address online support seekers' unique therapeutic needs.Objective: We aimed to uncover associations between EMSs and mental health problems within OMHCs and examine features of EMSs as they are reflected in OMHCs.Methods: We curated a dataset of 29,329 posts from widely accessed OMHCs, labeling each with relevant schemas and mental health problems. To identify associations, we conducted chi-square tests of independence and calculated odds ratios (ORs) with the dataset. In addition, we developed a novel group-level case conceptualization technique, leveraging GPT-4 to extract features of EMSs from OMHC texts across key schema therapy dimensions, such as schema triggers and coping responses.Results: Several associations were identified between EMSs and mental health problems, reflecting how EMSs manifest in online support-seeking contexts. Anxiety-related problems typically highlighted vulnerability to harm or illness (OR 5.64, 95% CI 5.34-5.96; P<.001), while depression-related problems emphasized unmet interpersonal needs, such as social isolation (OR 3.18, 95% CI 3.02-3.34; P<.001). Conversely, problems with eating disorders mostly exemplified negative self-perception and emotional inhibition (OR 1.89, 95% CI 1.45-2.46; P<.001). Personality disorders reflected themes of subjugation (OR 2.51, 95% CI 1.86-3.39; P<.001), while posttraumatic stress disorder problems involved distressing experiences and mistrust (OR 5.04, 95% CI 4.49-5.66; P<.001). Substance use disorder problems reflected negative self-perception of failure to achieve (OR 1.83, 95% CI 1.35-2.49; P<.001). Depression, personality disorders, and posttraumatic stress disorder were also associated with 12, 9, and 7 EMSs, respectively, emphasizing their complexities and the need for more comprehensive interventions. In contrast, anxiety, eating disorder, and substance use disorder were related to only 2 to 3 EMSs, suggesting that these problems are better addressed through targeted interventions. In addition, the EMS features extracted from our dataset averaged 13.27 (SD 3.05) negative features per schema, with 2.65 (SD 1.07) features per dimension, as supported by existing literature.Conclusions: We uncovered various associations between EMSs and mental health problems among online support seekers, highlighting the prominence of specific EMSs in each pro","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e59524"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Description of Weight-Related Content and Recommended Dietary Behaviors for Weight Loss Frequently Reposted on X (Twitter) in English and Japanese: Content Analysis.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/64739

Fumi Oono, Mai Matsumoto, Risa Ogata, Mizuki Suga, Kentaro Murakami

Background: Both obesity and underweight are matters of global concern. Weight-related content frequently shared on social media can reflect public recognition and affect users' behaviors and perceptions. Although X (Twitter) is a popular social media platform, few studies have revealed the content of weight-related posts or details of dietary behaviors for weight loss shared on X.Objective: This study aims to describe body weight-related content frequently reposted on X, with a particular focus on dietary behaviors for weight loss, in English and Japanese.Methods: We collected English and Japanese X posts related to human body weight having over 100 reposts in July 2023 using an application programming interface tool. Two independent researchers categorized the contents of the posts into 7 main categories and then summarized recommended weight loss strategies.Results: We analyzed 815 English and 1213 Japanese posts. The most popular main category of the content was "how to change weight" in both languages. The Japanese posts were more likely to mention "how to change weight" (n=571, 47.1%) and "recipes to change weight" (n=114, 9.4%) than the English posts (n=195, 23.9% and n=10, 1.2%, respectively), whereas the English posts were more likely to mention "will or experience to change weight" (n=167, 20.5%), "attitudes toward weight status" (n=78, 9.6%), and "public health situation" (n=44, 5.4%) than Japanese posts. Among 146 English and 541 Japanese posts about weight loss strategies, the predominant strategies were diet (n=76, 52.1% in English and n=170, 31.4% in Japanese) and physical activities (n=56, 38.4% and n=295, 54.5%, respectively). The proportion of posts mentioning both diet and physical activity was smaller in Japanese (n=62, 11.5%) than in English (n=31, 21.2%). Among 76 English and 170 Japanese posts about dietary behaviors for weight loss, more than 60% of posts recommended increasing intakes of specific nutrients or food groups in both languages. The most popular dietary component recommended to increase was vegetables in both English (n=31, 40.8%) and Japanese (n=48, 28.2%), followed by protein and fruits in English and grains or potatoes and legumes in Japanese. Japanese posts were less likely to mention reducing energy intake; meal timing or eating frequency; or reducing intakes of specific nutrients or food groups than the English posts. The most popular dietary component recommended to decrease was alcohol in English and confectioneries in Japanese.Conclusions: This study characterized user interest in weight management and suggested the potential of X as an information source for weight management. Although weight loss strategies related to diet and physical activity were popular in both English and Japanese, some differences in the details of the strategies were present, indicating that X users are exposed t

{"title":"Description of Weight-Related Content and Recommended Dietary Behaviors for Weight Loss Frequently Reposted on X (Twitter) in English and Japanese: Content Analysis.","authors":"Fumi Oono, Mai Matsumoto, Risa Ogata, Mizuki Suga, Kentaro Murakami","doi":"10.2196/64739","DOIUrl":"10.2196/64739","url":null,"abstract":"Background: Both obesity and underweight are matters of global concern. Weight-related content frequently shared on social media can reflect public recognition and affect users' behaviors and perceptions. Although X (Twitter) is a popular social media platform, few studies have revealed the content of weight-related posts or details of dietary behaviors for weight loss shared on X.Objective: This study aims to describe body weight-related content frequently reposted on X, with a particular focus on dietary behaviors for weight loss, in English and Japanese.Methods: We collected English and Japanese X posts related to human body weight having over 100 reposts in July 2023 using an application programming interface tool. Two independent researchers categorized the contents of the posts into 7 main categories and then summarized recommended weight loss strategies.Results: We analyzed 815 English and 1213 Japanese posts. The most popular main category of the content was \"how to change weight\" in both languages. The Japanese posts were more likely to mention \"how to change weight\" (n=571, 47.1%) and \"recipes to change weight\" (n=114, 9.4%) than the English posts (n=195, 23.9% and n=10, 1.2%, respectively), whereas the English posts were more likely to mention \"will or experience to change weight\" (n=167, 20.5%), \"attitudes toward weight status\" (n=78, 9.6%), and \"public health situation\" (n=44, 5.4%) than Japanese posts. Among 146 English and 541 Japanese posts about weight loss strategies, the predominant strategies were diet (n=76, 52.1% in English and n=170, 31.4% in Japanese) and physical activities (n=56, 38.4% and n=295, 54.5%, respectively). The proportion of posts mentioning both diet and physical activity was smaller in Japanese (n=62, 11.5%) than in English (n=31, 21.2%). Among 76 English and 170 Japanese posts about dietary behaviors for weight loss, more than 60% of posts recommended increasing intakes of specific nutrients or food groups in both languages. The most popular dietary component recommended to increase was vegetables in both English (n=31, 40.8%) and Japanese (n=48, 28.2%), followed by protein and fruits in English and grains or potatoes and legumes in Japanese. Japanese posts were less likely to mention reducing energy intake; meal timing or eating frequency; or reducing intakes of specific nutrients or food groups than the English posts. The most popular dietary component recommended to decrease was alcohol in English and confectioneries in Japanese.Conclusions: This study characterized user interest in weight management and suggested the potential of X as an information source for weight management. Although weight loss strategies related to diet and physical activity were popular in both English and Japanese, some differences in the details of the strategies were present, indicating that X users are exposed t","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64739"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effectiveness of Continuous Glucose Monitoring on Short-Term, In-Hospital Mortality Among Frail and Critically Ill Patients With COVID-19: Randomized Controlled Trial.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2025-02-07 DOI: 10.2196/67012

Jiawei Shang, Ziming Yuan, Zuoyan Zhang, Quanhong Zhou, Yan Zou, Wei Wang

Background: The use of continuous glucose monitoring (CGM) in the hospital setting is growing, with more patients using these devices at home, especially during the COVID-19 pandemic. Frail and critically ill patients with COVID-19 and previously normal glucose tolerance are also associated with variability in their glucose levels during their intensive care unit (ICU) stay. However, very limited evidence supports the use of CGM in ICU settings, especially among frail patients with COVID-19.

Objective: We aimed to investigate the effectiveness of CGM on ICU-related outcomes among frail and critically ill patients with confirmed COVID-19.

Methods: This was an exploratory, prospective, open-label, parallel, single-center, randomized controlled trial. A total of 124 patients was finally analyzed. The primary outcome was 28-day, in-ICU mortality. The secondary outcome included the length of ICU stay as well as the occurrence of hypoglycemia and severe hypoglycemia events.

Results: The mean age was 78.3 (SD 11.5) years. The mean fasting glucose level and hemoglobin A_1c level at baseline were 8.12 (SD 1.54) mmol/L and 7.2% (SD 0.8%), respectively. The percentage of participants with diabetes was 30.6% (38/124). The corresponding hazard ratio of the primary outcome in the intermittently scanned CGM (isCGM) group when compared with the point-of-care testing (POCT) group was 0.18 (95% CI 0.04-0.79). The average length of ICU stay was 10.0 (SD 7.57) days in the isCGM group and 14.0 (SD 6.86) days in the POCT group (P=.02). At the end of study period, the mean value of fasting glucose in the isCGM group and the POCT group was 6.07 (SD 0.63) mmol/L and 7.76 (SD 0.62) mmol/L, respectively (P=.01). A total of 207 hypoglycemia events (<3.9 mmol/L) was detected, with 43 in the isCGM group and 164 in the POCT group (P<.001). A total of 81 severe hypoglycemia events (<2.8 mmol/L) was detected, with 16 in the isCGM group and 65 in the POCT group (P<.001). The major adverse event in this study was bleeding in the puncture site, with a total of 6 occurrences in the isCGM group. During the follow-up, none of the participants dropped out because of bleeding in the puncture site.

Conclusions: We found a significant clinical benefit from the use of CGM among frail and critically ill patients with COVID-19. These findings support the use of CGM in the ICU and might help with the extension of application in various in-hospital settings.

Trial registration: Chinese Clinical Trial Registry ChiCTR2200059733; https://www.chictr.org.cn/showproj.html?proj=169257.

{"title":"Effectiveness of Continuous Glucose Monitoring on Short-Term, In-Hospital Mortality Among Frail and Critically Ill Patients With COVID-19: Randomized Controlled Trial.","authors":"Jiawei Shang, Ziming Yuan, Zuoyan Zhang, Quanhong Zhou, Yan Zou, Wei Wang","doi":"10.2196/67012","DOIUrl":"10.2196/67012","url":null,"abstract":"Background: The use of continuous glucose monitoring (CGM) in the hospital setting is growing, with more patients using these devices at home, especially during the COVID-19 pandemic. Frail and critically ill patients with COVID-19 and previously normal glucose tolerance are also associated with variability in their glucose levels during their intensive care unit (ICU) stay. However, very limited evidence supports the use of CGM in ICU settings, especially among frail patients with COVID-19.Objective: We aimed to investigate the effectiveness of CGM on ICU-related outcomes among frail and critically ill patients with confirmed COVID-19.Methods: This was an exploratory, prospective, open-label, parallel, single-center, randomized controlled trial. A total of 124 patients was finally analyzed. The primary outcome was 28-day, in-ICU mortality. The secondary outcome included the length of ICU stay as well as the occurrence of hypoglycemia and severe hypoglycemia events.Results: The mean age was 78.3 (SD 11.5) years. The mean fasting glucose level and hemoglobin A1c level at baseline were 8.12 (SD 1.54) mmol/L and 7.2% (SD 0.8%), respectively. The percentage of participants with diabetes was 30.6% (38/124). The corresponding hazard ratio of the primary outcome in the intermittently scanned CGM (isCGM) group when compared with the point-of-care testing (POCT) group was 0.18 (95% CI 0.04-0.79). The average length of ICU stay was 10.0 (SD 7.57) days in the isCGM group and 14.0 (SD 6.86) days in the POCT group (P=.02). At the end of study period, the mean value of fasting glucose in the isCGM group and the POCT group was 6.07 (SD 0.63) mmol/L and 7.76 (SD 0.62) mmol/L, respectively (P=.01). A total of 207 hypoglycemia events (<3.9 mmol/L) was detected, with 43 in the isCGM group and 164 in the POCT group (P<.001). A total of 81 severe hypoglycemia events (<2.8 mmol/L) was detected, with 16 in the isCGM group and 65 in the POCT group (P<.001). The major adverse event in this study was bleeding in the puncture site, with a total of 6 occurrences in the isCGM group. During the follow-up, none of the participants dropped out because of bleeding in the puncture site.Conclusions: We found a significant clinical benefit from the use of CGM among frail and critically ill patients with COVID-19. These findings support the use of CGM in the ICU and might help with the extension of application in various in-hospital settings.Trial registration: Chinese Clinical Trial Registry ChiCTR2200059733; https://www.chictr.org.cn/showproj.html?proj=169257.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e67012"},"PeriodicalIF":5.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0