Journal of Medical Internet Research最新文献

{"title":"Analyzing Public Google Search Interest in Measles Within Canada: Identifying Key Moments for Targeted Risk Communication.","authors":"Mohammad Jokar, Diego Nobrega","doi":"10.2196/75025","DOIUrl":"10.2196/75025","url":null,"abstract":"<p><strong>Unlabelled: </strong>We analyzed Google Trends data on measles-related searches in Canada from January 1 to May 21, 2025; web, news, and YouTube search trends increased significantly across provinces (all P values were <.05), aligning with rising case numbers. Our findings emphasize the importance of timely, targeted risk communication for enhancing public awareness and responses during this outbreak.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75025"},"PeriodicalIF":5.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144600717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data.","authors":"Ari Z Klein, Shriya Kunatharaju, Su Golder, Lisa D Levine, Jane C Figueiredo, Graciela Gonzalez-Hernandez","doi":"10.2196/66097","DOIUrl":"10.2196/66097","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Preterm birth, defined as birth at &lt;37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pregnancy is associated with an increased risk of preterm birth; however, data remain limited by trimester of infection. The ability to study COVID-19 infection during the earlier stages of pregnancy has been limited by available sources of data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective of this study was to use self-reports in large-scale social media data to assess the association between the trimester of COVID-19 infection and preterm birth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this retrospective cohort study, we used natural language processing and machine learning, followed by manual validation, to identify self-reports of pregnancy on Twitter and to search these users' collection of publicly available tweets for self-reports of COVID-19 infection during pregnancy and, subsequently, a preterm birth or term birth outcome. Among the users who reported their pregnancy on Twitter, we also identified a 1:1 age-matched control group, consisting of users with a due date before January 1, 2020-that is, without COVID-19 infection during pregnancy. We calculated the odds ratios (ORs) with 95% CIs to compare the frequency of preterm birth for pregnancies with and without COVID-19 infection and by the timing of infection: first trimester (1-13 weeks), second trimester (14-27 weeks), or third trimester (28-36 weeks).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Through August 2022, we identified 298 Twitter users who reported COVID-19 infection during pregnancy, a preterm birth or term birth outcome, and maternal age: 94 (31.5%) with first-trimester infection, 110 (36.9%) with second-trimester infection, and 95 (31.9%) with third-trimester infection. In total, 26 (8.8%) of these 298 users reported preterm birth: 8 (8.5%) with first-trimester infection, 7 (6.4%) with second-trimester infection, and 12 (12.6%) with third-trimester infection. In the 1:1 age-matched control group, 13 (4.4%) of the 298 users reported preterm birth. Overall, the odds of preterm birth were significantly higher for pregnancies with COVID-19 infection compared to those without (OR 2.08, 95% CI 1.06-4.28; P=.046). In particular, the odds of preterm birth were significantly higher for pregnancies with COVID-19 infection during the third trimester (OR 3.16, 95% CI 1.36-7.29; P=.007). The odds of preterm birth were not significantly higher for pregnancies with COVID-19 infection during the first trimester (OR 2.05, 95% CI 0.78-5.08; P=.12) or second trimester (OR 1.50, 95% CI 0.54-3.82; P=.44) compared to those without infection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Based on self-reports in large-scale social media data, the results of our study suggest that COVID-19 infection particularly during the third trimester is associated ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e66097"},"PeriodicalIF":5.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144600719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a Framework for Online Review-Based Health Care Service Quality Assessment: Text-Mining Study.","authors":"Xue Zhang, Jianshan Sun, Xin Li, Yezheng Liu, Chenwei Li","doi":"10.2196/66141","DOIUrl":"10.2196/66141","url":null,"abstract":"<p><strong>Background: </strong>With the development of online health care platforms, patient reviews have become an important source for assessing medical service quality. However, the critical aspects of quality dimensions in textual reviews remain largely unexplored.</p><p><strong>Objective: </strong>This study aims to establish a comprehensive medical service quality assessment framework by leveraging online review data. Such a framework would support large service providers, such as online platforms, to assess the quality of many doctors efficiently.</p><p><strong>Methods: </strong>We adopted a text-mining approach with theory-driven topic extraction from online reviews to develop a service quality assessment framework. The framework is based on topic and sentiment classification methods. We conducted an empirical analysis to assess the validity of the framework. Specifically, we examined if patients' sentiments regarding our extracted dimensions affect demand (number of consultation requests) due to quality signals reflected in these dimensions.</p><p><strong>Results: </strong>We develop a 5-dimensional health care service quality framework (HSQ-5D model). In the empirical study, patient demand is affected by these dimensions, including expertise (coefficient=1.12; P<.001), service delivery process (coefficient=5.60; P<.001), attitude (coefficient=0.82; P<.001), empathy (coefficient=2.65; P<.001), and outcome (coefficient=0.26; P<.001; through patients' perceived quality from reviews). The 5 dimensions can explain 85.52% of the variance in patient demand, while all information from online reviews can explain 85.67%. The results show the validity and the potential practical value of the proposed HSQ-5D model.</p><p><strong>Conclusions: </strong>This study explores how online reviews can be used to evaluate health care services, offering significant implications for health care management. Theoretically, we extend existing service quality frameworks by integrating text-mining analysis of online reviews, thereby enhancing the understanding of service quality assessment in the digital health context. Practically, the framework can allow health care platforms to identify and reveal doctors' service quality to reduce patients' information asymmetry and strengthen patient-provider relationships, ultimately contributing to a more effective and patient-centered health care system.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e66141"},"PeriodicalIF":5.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144600720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.","authors":"Daun Shin, Hyoseung Kim, Seunghwan Lee, Younhee Cho, Whanbo Jung","doi":"10.2196/79198","DOIUrl":"https://doi.org/10.2196/79198","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/54617.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e79198"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591460","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}

{"title":"School-Based Online Surveillance of Youth: Systematic Search and Content Analysis of Surveillance Company Websites.","authors":"Alison O'Daffer, Wendy Liu, Cinnamon S Bloss","doi":"10.2196/71998","DOIUrl":"10.2196/71998","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;School-based online surveillance of students has been widely adopted by middle and high school administrators over the past decade. Little is known about the technology companies that provide these services or the benefits and harms of the technology for students. Understanding what information online surveillance companies monitor and collect about students, how they do it, and if and how they facilitate appropriate intervention fills a crucial gap for parents, youth, researchers, and policy makers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The two goals of this study were to (1) comprehensively identify school-based online surveillance companies currently in operation, and (2) collate and analyze company-described surveillance services, monitoring processes, and features provided.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We systematically searched GovSpend and EdSurge's Education Technology (EdTech) Index to identify school-based online surveillance companies offering social media monitoring, student communications monitoring, or online monitoring. We extracted publicly available information from company websites and conducted a systematic content analysis of the websites identified. Two coders independently evaluated all company websites and discussed the findings to reach 100% consensus regarding website data labeling.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our systematic search identified 14 school-based online surveillance companies. Content analysis revealed that most of these companies facilitate school administrators' access to students' digital behavior, well beyond monitoring during school hours and on school-provided devices. Specifically, almost all companies reported conducting monitoring of students at school, but 86% (12/14) of companies reported also conducting monitoring 24/7 outside of school and 7% (1/14) reported conducting monitoring outside of school at school administrator-specified locations. Most online surveillance companies reported using artificial intelligence to conduct automated flagging of student activity (10/14, 71%), and less than half of the companies (6/14, 43%) reported having a secondary human review team. Further, 14% (2/14) of companies reported providing crisis responses via company staff, including contacting law enforcement at their discretion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study is the first detailed assessment of the school-based online surveillance industry and reveals that student monitoring technology can be characterized as heavy-handed. Findings suggest that students who only have school-provided devices are more heavily surveilled and that historically marginalized students may be at a higher risk of being flagged due to algorithmic bias. The dearth of research on efficacy and the notable lack of transparency about how surveillance services work indicate that increased oversight by policy makers of this industry may be warranted. Dissemination of our findings can improve pare","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71998"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usefulness of Mixed Reality in Surgical Treatment: Delphi Study.","authors":"Renato Magalhães, Ana Carolina Lima, António Marques, Javier Pereira, Lúcio Lara Santos","doi":"10.2196/69964","DOIUrl":"https://doi.org/10.2196/69964","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mixed reality (MR) combines real and virtual elements and has shown promise in diverse fields, including surgical procedures. MR headsets may support surgical navigation, planning, and training. It is crucial to determine whether medical professionals consider this technology indispensable. This study uses the Delphi method, facilitated by the Welphi web-based platform, to assess the utility of MR in surgical settings and analyzes the results of the first round using a systematic approach modeled on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to examine the feasibility and advantages of MR technology in surgical contexts. The findings are intended to inform and direct health care professionals, researchers, and developers in advancing MR integration into surgical environments to optimize treatment quality and safety.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A 3-round Delphi approach was implemented to ascertain consensus on the utility of MR in surgical treatment. Participants (n=22) were purposefully selected from among experts with professional experience in technologies such as virtual reality, augmented reality, 3D laparoscopy, and robotics. In the first round, participants provided insights into the potential applications of MR in surgical procedures through open-ended questions structured across 5 distinct sections. Responses were analyzed to develop the second-round questionnaire, which was hierarchically organized into main topics and subtopics. In the third round, the questions were identical to those in the second round, including the percentage results, allowing participants to reconsider their responses. A consensus round was subsequently conducted. The majority consensus level was defined as agreement by ≥70% of the participants in a given round.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The study was conducted from January to May 2024. All 22 invited experts provided responses in both the first and second rounds (100% response rate). In the third and consensus rounds, 20 (91%) of the 22 experts participated. The consensus round, conducted to present the results, yielded a majority consensus (19/20, 95%) on the usefulness of MR in surgical treatment. The primary benefits of MR in surgery were identified as surgical navigation (15/20, 75%), planning (15/20, 75%), and teaching and training (14/20, 70%). In addition, 75% (15/20) of the experts identified cost and investments as primary constraints. We used the Kendall tau-b coefficient for correlation analysis, and significant correlations were identified between distinct aspects.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;MR technology is most beneficial in surgical navigation, planning, and training. However, the costs and investments required for implementation may present a potential limitation for the integration of this technology into surgical procedures. Moreover, it is of cruc","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69964"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591463","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}

{"title":"External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.","authors":"Jeong Hoon Lee, Pyeong Hwa Kim, Nak-Hoon Son, Kyunghwa Han, Yeseul Kang, Sejin Jeong, Eun-Kyung Kim, Haesung Yoon, Sergios Gatidis, Shreyas Vasanawala, Hee Mang Yoon, Hyun Joo Shin","doi":"10.2196/72097","DOIUrl":"10.2196/72097","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Artificial intelligence (AI) is increasingly used in radiology, but its development in pediatric imaging remains limited, particularly for emergent conditions. Ileocolic intussusception is an important cause of acute abdominal pain in infants and toddlers and requires timely diagnosis to prevent complications such as bowel ischemia or perforation. While ultrasonography is the diagnostic standard due to its high sensitivity and specificity, its accessibility may be limited, especially outside tertiary centers. Abdominal radiographs (AXRs), despite their limited sensitivity, are often the first-line imaging modality in clinical practice. In this context, AI could support early screening and triage by analyzing AXRs and identifying patients who require further ultrasonography evaluation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to upgrade and externally validate an AI model for screening ileocolic intussusception using pediatric AXRs with multicenter data and to assess the diagnostic performance of the model in comparison with radiologists of varying experience levels with and without AI assistance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective study included pediatric patients (≤5 years) who underwent both AXRs and ultrasonography for suspected intussusception. Based on the preliminary study from hospital A, the AI model was retrained using data from hospital B and validated with external datasets from hospitals C and D. Diagnostic performance of the upgraded AI model was evaluated using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). A reader study was conducted with 3 radiologists, including 2 trainees and 1 pediatric radiologist, to evaluate diagnostic performance with and without AI assistance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Based on the previously developed AI model trained on 746 patients from hospital A, an additional 431 patients from hospital B (including 143 intussusception cases) were used for further training to develop an upgraded AI model. External validation was conducted using data from hospital C (n=68; 19 intussusception cases) and hospital D (n=90; 30 intussusception cases). The upgraded AI model achieved a sensitivity of 81.7% (95% CI 68.6%-90%) and a specificity of 81.7% (95% CI 73.3%-87.8%), with an AUC of 86.2% (95% CI 79.2%-92.1%) in the external validation set. Without AI assistance, radiologists showed lower performance (overall AUC 64%; sensitivity 49.7%; specificity 77.1%). With AI assistance, radiologists' specificity improved to 93% (difference +15.9%; P&lt;.001), and AUC increased to 79.2% (difference +15.2%; P=.05). The least experienced reader showed the largest improvement in specificity (+37.6%; P&lt;.001) and AUC (+14.7%; P=.08).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The upgraded AI model improved diagnostic performance for screening ileocolic intussusception on pediatric AXRs. It effectively enhanced the specificity and overa","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e72097"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144626519","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}

{"title":"Psychometric Evaluation of Large Language Model Embeddings for Personality Trait Prediction.","authors":"Julina Maharjan, Ruoming Jin, Jianfeng Zhu, Deric Kenne","doi":"10.2196/75347","DOIUrl":"10.2196/75347","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Recent advancements in large language models (LLMs) have generated significant interest in their potential for assessing psychological constructs, particularly personality traits. While prior research has explored LLMs' capabilities in zero-shot or few-shot personality inference, few studies have systematically evaluated LLM embeddings within a psychometric validity framework or examined their correlations with linguistic and emotional markers. Additionally, the comparative efficacy of LLM embeddings against traditional feature engineering methods remains underexplored, leaving gaps in understanding their scalability and interpretability for computational personality assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluates LLM embeddings for personality trait prediction through four key analyses: (1) performance comparison with zero-shot methods on PANDORA Reddit data, (2) psychometric validation and correlation with LIWC (Linguistic Inquiry and Word Count) and emotion features, (3) benchmarking against traditional feature engineering approaches, and (4) assessment of model size effects (OpenAI vs BERT vs RoBERTa). We aim to establish LLM embeddings as a psychometrically valid and efficient alternative for personality assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a multistage analysis using 1 million Reddit posts from the PANDORA Big Five personality dataset. First, we generated text embeddings using 3 LLM architectures (RoBERTa, BERT, and OpenAI) and trained a custom bidirectional long short-term memory model for personality prediction. We compared this approach against zero-shot inference using prompt-based methods. Second, we extracted psycholinguistic features (LIWC categories and National Research Council emotions) and performed feature engineering to evaluate potential performance enhancements. Third, we assessed the psychometric validity of LLM embeddings: reliability validity using Cronbach α and convergent validity analysis by examining correlations between embeddings and established linguistic markers. Finally, we performed traditional feature engineering on static psycholinguistic features to assess performance under different settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;LLM embeddings trained using simple deep learning techniques significantly outperform zero-shot approaches on average by 45% across all personality traits. Although psychometric validation tests indicate moderate reliability, with an average Cronbach α of 0.63, correlation analyses spark a strong association with key linguistic or emotional markers; openness correlates highly with social (r=0.53), conscientiousness with linguistic (r=0.46), extraversion with social (r=0.41), agreeableness with pronoun usage (r=0.40), and neuroticism with politics-related text (r=0.63). Despite adding advanced feature engineering on linguistic features, the performance did not improve, suggesting that LLM embeddings inherently capture ke","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75347"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical and Cost-Effectiveness of Telehealth-Supported Home Oxygen Therapy on Adherence, Hospital Readmission, and Health-Related Quality of Life in Patients With Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials.","authors":"Cuirong Hu, Xinqi Liao, Yi Fang, Shu Zhu, Xia Lan, Guilan Cheng","doi":"10.2196/73010","DOIUrl":"10.2196/73010","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder frequently requiring oxygen therapy to relieve symptoms and improve survival. In recent years, telehealth-supported interventions have emerged as promising strategies to optimize home oxygen therapy by promoting adherence, reducing hospitalizations, and enhancing health-related quality of life. However, evidence regarding their effectiveness remains inconsistent and equivocal, underscoring the need for further rigorous evaluation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the clinical and cost-effectiveness of telehealth-supported home oxygen therapy on adherence, hospital readmission, and health-related quality of life in patients with COPD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive search was conducted across 6 databases (PubMed, Cochrane Central, Embase, Web of Science, PsycINFO, and CINAHL) up to October 1, 2024, and updated on April 28, 2025. Randomized controlled trials involving patients with COPD comparing telehealth-supported home oxygen therapy with usual care, and reporting outcomes on adherence, hospital readmissions, or health-related quality of life, were included. In addition, 2 reviewers independently screened the studies, extracted data, assessed the risk of bias using the Cochrane Risk of Bias 2 tool, and evaluated the certainty of evidence with the Grading of Recommendations Assessment, Development, and Evaluation approach. Meta-analyses and heterogeneity assessments were conducted using R software (R Core Team). Standardized mean differences with 95% CIs were calculated to evaluate the intervention effects under a random-effects model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 8 studies comprising 1275 patients were included in the review. Telehealth-supported home oxygen therapy significantly reduced hospital readmissions (standardized mean difference [SMD]=-0.40, 95% CI -0.60 to -0.21) and improved health-related quality of life (SMD=0.49, 95% CI 0.25-0.73). No significant effect was observed on therapy adherence (SMD=0.19, 95% CI -0.76 to 1.14). Furthermore, 3 economic evaluations suggested that although telehealth interventions may incur higher initial costs, they are likely to result in long-term savings by reducing hospital admissions. Sensitivity analyses confirmed the robustness of the findings for hospital readmissions and health-related quality of life, for which the quality of evidence was rated as high, whereas the evidence for therapy adherence was rated as low.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Telehealth-supported home oxygen therapy significantly reduces hospital admissions and improves health-related quality of life in patients with COPD, but does not significantly improve therapy adherence. Tailored interventions that consider patient demographics, combined with supportive policies, may further enhance clinical outcomes. Future research should incorporate economic evaluati","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73010"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study.","authors":"Helena Wang, Norman Farb, Bechara Saab","doi":"10.2196/56086","DOIUrl":"10.2196/56086","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Before meaningful progress toward precision psychiatry is possible, objective (unbiased) assessment of patient mental well-being must be validated and adopted broadly.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to compare the fidelity of a precision psychiatry therapy recommendation algorithm when trained with an objective quantification of psychological stress versus subjective ecological momentary assessments (EMAs) of stress and mood.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;From 2786 unique individuals engaging between March 2015 and December 2022 in English language psychotherapy sessions and providing pre- and postsession self-report and facial biometric data via a mobile health platform (Mobio Interactive Pte Ltd, Singapore), analysis was conducted on 67 \"super users\" that completed a minimum of 28 sessions with all pre- and postsession measures. The platform used has previously demonstrated reduced psychiatric symptom severity and improved overall mental well-being. Psychotherapy recordings (\"sessions\") within the platform, available asynchronously and on demand, span mindfulness, meditation, cognitive behavioral therapy, client-centered therapy, music therapy, and self-hypnosis. The platform also has the unusual ability to rapidly assess mental well-being without bias via an easy-to-use objective measure of psychological stress derived from artificial intelligence-based analysis of facial biomarkers (objective stress level [OSL]). In tandem with the objective measure, EMAs obtain self-reported values of stress (SRS) and mood (SRM). ∆OSL, ∆SRS, and ∆SRM (with delta referring to the presession subtracted from the postsession measurement) were used to independently train a therapy recommendation algorithm designed to predict what future sessions would prove most efficacious for each individual. Algorithm predictions were compared against the efficacy of the individual's self-selected sessions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The objective measure of psychological stress provided a differentiated delta for the measurement of therapeutic efficacy compared to the 2 EMA deltas, as shown by clear divergence in ∆OSL vs ∆SRS or ∆SRM (r&lt;0.03), while the EMA deltas showed significant convergence (r=0.53, P&lt;.01). The recommendation algorithm selected increasingly efficacious therapy sessions as a function of training data when trained with either ∆OSL (F&lt;sub&gt;1,16&lt;/sub&gt;=5.37, P=.03) or ∆SRM data (F&lt;sub&gt;1,16&lt;/sub&gt;=3.69, P&lt;.05). However, the sequential improvement in prediction efficacy only surpassed the efficacy of self-selected therapy when the algorithm was trained using objective data (P&lt;.01). Training the algorithm with EMA data showed potential trends that did not reach significance (∆SRS: P=.09; ∆SRM: P=.12). As a final insight, self-selected therapy sessions were overrepresented among the algorithmically recommended sessions, an effect most pronounced when the algorithm was trained with ∆OSL data (F&lt;sub&gt;1,14&lt;/sub&gt;=","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e56086"},"PeriodicalIF":5.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144575663","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}