Journal of Medical Internet Research最新文献

{"title":"Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study.","authors":"Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li","doi":"10.2196/70068","DOIUrl":"https://doi.org/10.2196/70068","url":null,"abstract":"<p><strong>Background: </strong>Fetal growth restriction (FGR) is a common complication of preeclampsia. FGR in patients with preeclampsia increases the risk of neonatal-perinatal mortality and morbidity. However, previous prediction methods for FGR are class-biased or clinically unexplainable, which makes it difficult to apply to clinical practice, leading to a relative delay in intervention and a lack of effective treatments.</p><p><strong>Objective: </strong>The study aims to develop an auxiliary diagnostic model based on machine learning (ML) to predict the occurrence of FGR in patients with preeclampsia.</p><p><strong>Methods: </strong>This study used a retrospective case-control approach to analyze 38 features, including the basic medical history and peripheral blood laboratory test results of pregnant patients with preeclampsia, either complicated or not complicated by FGR. ML models were constructed to evaluate the predictive value of maternal parameter changes on preeclampsia combined with FGR. Multiple algorithms were tested, including logistic regression, light gradient boosting, random forest (RF), extreme gradient boosting, multilayer perceptron, naive Bayes, and support vector machine. The model performance was identified by the area under the curve (AUC) and other evaluation indexes. The Shapley additive explanations (SHAP) method was adopted to rank the feature importance and explain the final model for clinical application.</p><p><strong>Results: </strong>The RF model performed best in discriminative ability among the 7 ML models. After reducing features according to importance rank, an explainable final RF model was established with 9 features, including urinary protein quantification, gestational week of delivery, umbilical artery systolic-to-diastolic ratio, amniotic fluid index, triglyceride, D-dimer, weight, height, and maximum systolic pressure. The model could accurately predict FGR for 513 patients with preeclampsia (149 with FGR and 364 without FGR) in the training and testing dataset (AUC 0.83, SD 0.03) using 5-fold cross-validation, which was closely validated for 103 patients with preeclampsia (n=45 with FGR and n=58 without FGR) in an external dataset (AUC 0.82, SD 0.048). On the whole, urinary protein quantification, umbilical artery systolic-to-diastolic ratio, and gestational week of delivery exhibited the highest contributions to the model performance (c=0.45, 0.34, and 0.33) based on SHAP analysis. For specific individual patients, SHAP results reveal the protective and risk factors to develop FGR for interpreting the model's clinical significance. Finally, the model has been translated into a convenient web page tool to facilitate its use in clinical settings.</p><p><strong>Conclusions: </strong>The study successfully developed a model that accurately predicts FGR development in patients with preeclampsia. The SHAP method captures highly relevant risk factors for model interpretation, alleviating concerns about","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70068"},"PeriodicalIF":5.8,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159553","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":"Toward a Conceptual Framework for Digitally Supported Communication, Coordination, Cooperation, and Collaboration in Interprofessional Health Care: Scoping Review.","authors":"Kim Nordmann, Marie-Christin Redlich, Michael Schaller, Stefanie Sauter, Florian Fischer","doi":"10.2196/69276","DOIUrl":"10.2196/69276","url":null,"abstract":"<p><strong>Background: </strong>Digital tools for communication, coordination, cooperation, and collaboration (D4C), including electronic health records and specialized apps, are increasingly used in health care to ensure continuity of care across professional boundaries. Despite their growing adoption, there is a lack of precise and clear definitions, and no common understanding of D4C within health care.</p><p><strong>Objective: </strong>This study aims to explore the concepts and definitions of digitally supported communication, coordination, cooperation, and collaboration by mapping the individual attributes to build a foundation for the operationalization of these concepts and to generate a clear and precise understanding of these concepts in research, practice, and policy.</p><p><strong>Methods: </strong>A scoping review was conducted across MEDLINE, CINAHL, Embase, PsycINFO, and Scopus to identify studies on D4C. We included peer-reviewed studies in English, French, German, Portuguese, and Spanish published since 2012. Definitions of the modes of interaction (communication, coordination, cooperation, and collaboration) and the digital tool supporting these interactions, along with their definitions in cited references, were extracted and analyzed.</p><p><strong>Results: </strong>Of the 407 identified papers addressing D4C, 6.1% (n=25) defined the digital concept and 6.6% (n=27) defined the interaction supported by the digital tool, with even fewer being backed by a reference. The analysis of the definitions revealed a hierarchical framework, detailing dimensions, requisites, and goals for each mode of interaction and the digital tool. It delineates progression from communication to collaboration: communication enables the exchange of information; coordination involves organizing people, resources, and activities; cooperation focuses on dividing tasks to achieve shared goals; and collaboration, at the apex, involves jointly addressing care needs. Each mode of interaction can be supported by digital tools.</p><p><strong>Conclusions: </strong>The proposed framework offers a structured approach to establish a shared understanding of the concept of D4C. This unified understanding can serve as a foundation for developing objectives related to the implementation and evaluation of digital tools aimed at fostering interprofessional interactions in health care. As such, it can inform stakeholders in their understanding of D4C, possibly improving workflows and patient care. Further research is needed to operationalize and validate the framework across health care settings.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.2196/45179.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69276"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150740","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":"Patients' and Health Care Professionals' Perspectives on Remote Patient Monitoring in Chronic Obstructive Pulmonary Disease Exacerbation Management: Initiating Cocreation.","authors":"Atena Mahboubian, Marise J Kasteleyn, Enna Bašić, Niels H Chavannes, Jiska J Aardoom","doi":"10.2196/67666","DOIUrl":"https://doi.org/10.2196/67666","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Chronic obstructive pulmonary disease (COPD) exacerbations cause physiological and psychological distress, affecting overall health and quality of life. Early diagnosis of exacerbations is crucial for preserving lung function, preventing hospitalizations, and reducing health care costs. While remote patient monitoring (RPM) offers the potential for early exacerbation detection, challenges remain in recognizing symptoms in a timely manner. A noninvasive breath analysis device is under development to monitor patients with COPD and detect exacerbations before symptoms arise by measuring breath biomarkers through volatile organic compounds. This study encompassed the initial cocreation phase to align the use of the breath analysis device and corresponding care process with current COPD exacerbation management and user needs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to explore perspectives on COPD care processes, exacerbation management, and RPM in the Netherlands through 3 objectives: (1) identify stakeholders in COPD exacerbation care, (2) understand existing COPD care, and (3) explore stakeholder experiences and expectations regarding RPM in COPD care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Following the Center for eHealth Research and Disease Management Roadmap, 4 research activities were conducted between March 2024 and September 2024 for the initial cocreation phase: (1) desk research, (2) interviews, (3) project group meeting, and 4) coanalysis focus group. Desk research involved reviewing literature and COPD (exacerbation) care guidelines. Semistructured interviews (N=34) were conducted with 18 patients, 14 health care professionals (HCPs), 1 caregiver, and 1 hospital policy adviser. Topics included COPD diagnosis, exacerbation management, stakeholder roles in COPD care, and RPM experiences or expectations. The project group meeting between interviews and the focus group verified interim findings and guided the focus group content. In total, 6 patients participated in a coanalysis focus group to review interview quotes on exacerbations and RPM. The framework method was used to analyze the interviews and the focus group through abductive coding.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Seven key stakeholders were identified in COPD care, patients, pulmonologists, general practitioners, nurse practitioners, nurse specialists, physiotherapists, and informal caregivers. We observed a lack of uniformity in COPD care, exacerbation management, and information provision across HCPs. Patients reported struggling to recognize exacerbations. Although patients with experience in RPM reported positive experiences, they questioned the added value in early detection of exacerbations. Those without RPM experience were receptive to its use for symptom tracking but were concerned about reduced in-person care and overreliance on data. HCPs reported seeing value in RPM for between-visit monitoring and efficiently allocating resources but stresse","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e67666"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150719","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":"Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study.","authors":"Jingchao Lei, Jia Zhai, Yao Zhang, Jing Qi, Chuanzheng Sun","doi":"10.2196/66733","DOIUrl":"10.2196/66733","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosis challenging. Traditional diagnostic tools are often limited, and machine learning techniques offer promising solutions for predicting adverse outcomes in patients with sepsis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop an explainable machine learning model, incorporating stacking techniques, to predict the occurrence of liver injury in patients with sepsis and provide decision support for early intervention and personalized treatment strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective multicenter cohort study adhered to the TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Extended for Artificial Intelligence) guidelines. Data from 8834 patients with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used for training and internal validation, while data from 4236 patients in the eICU-Collaborative Research Database (eICU-CRD) database were used for external validation. SALI was defined as an international normalized ratio &gt;1.5 and total bilirubin &gt;2 mg/dL within 1 week of intensive care unit admission. Nine machine learning models-decision tree, random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine, elastic net, logistic regression, multilayer perceptron, and k-nearest neighbors-were trained. A stacking ensemble model, using LightGBM, XGBoost, and RF as base learners and Lasso regression as the meta-model, was optimized via 10-fold cross-validation. Hyperparameters were tuned using grid search and Bayesian optimization. Model performance was evaluated using accuracy, balanced accuracy, Brier score, detection prevalence, F1-score, Jaccard index, κ coefficient, Matthews correlation coefficient, negative predictive value, positive predictive value, precision, recall, area under the receiver operating characteristic curve (ROC-AUC), precision-recall AUC, and decision curve analysis. Shapley additive explanations (SHAP) values were used to quantify feature importance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the training set, LightGBM, XGBoost, and RF demonstrated the best performance among all models, with ROC-AUCs of 0.9977, 0.9311, and 0.9847, respectively. These models exhibited minimal variance in cross-validation, with tightly clustered ROC-AUC and precision-recall area under the curve distributions. In the internal validation set, LightGBM (ROC-AUC 0.8401) and XGBoost (ROC-AUC 0.8403) outperformed all other models, while RF achieved an ROC-AUC of 0.8193. In the external validation set, LightGBM (ROC-AUC 0.7077), XGBoost (ROC-AUC 0.7169), and RF (ROC-AUC 0.7081) maintained strong performance, alt","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e66733"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144142693","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":"Beyond Benchmarks: Evaluating Generalist Medical Artificial Intelligence With Psychometrics.","authors":"Luning Sun, Christopher Gibbons, José Hernández-Orallo, Xiting Wang, Liming Jiang, David Stillwell, Fang Luo, Xing Xie","doi":"10.2196/70901","DOIUrl":"10.2196/70901","url":null,"abstract":"<p><strong>Unlabelled: </strong>Rigorous evaluation of generalist medical artificial intelligence (GMAI) is imperative to ensure their utility and safety before implementation in health care. Current evaluation strategies rely heavily on benchmarks, which can suffer from issues with data contamination and cannot explain how GMAI might fail (lacking explanatory power) or in what circumstances (lacking predictive power). To address these limitations, we propose a new methodology to improve the quality of GMAI evaluation using construct-oriented processes. Drawing on modern psychometric techniques, we introduce approaches to construct identification and present alternative assessment formats for different domains of professional skills, knowledge, and behaviors that are essential for safe practice. We also discuss the need for human oversight in future GMAI adoption.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70901"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12129431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150613","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":"The Value of Remote Vital Signs Monitoring in Detecting Clinical Deterioration in Patients in Hospital at Home Programs or Postacute Medical Patients in the Community: Systematic Review.","authors":"Su-Ann Cheng, Shijie Ian Tan, Samuel Li Earn Goh, Stephanie Q Ko","doi":"10.2196/64753","DOIUrl":"https://doi.org/10.2196/64753","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Vital signs monitoring (VSM) is used in clinical acuity scoring systems (APACHE [Acute Physiology and Chronic Health Evaluation], NEWS2 [National Early Warning Score 2], and SOFA [Sequential Organ Failure Assessment]) to predict patient outcomes for early intervention. Current technological advances enable convenient remote VSM. While the role of VSM for ill, hospital ward-treated patients is clear, its role in the community for acutely ill patients in the hospital at home (HAH) or postacute setting (patients who have just been discharged from an acute hospital stay and at increased risk of deterioration) is less well defined.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We assessed the efficacy of remote VSM for patients in the HAH or postacute setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched studies in PubMed (MEDLINE), Embase, and Scopus. Studies focused on the postacute phase were included, as only 2 case series addressed the HAH setting. Risk of bias (ROB) was evaluated using the Cochrane Risk of Bias Tool for randomized controlled trials (RCTs), the Newcastle-Ottawa scale for observational studies, and the case methods outlined by Murad et al for case reports. The GRADE (Grading Recommendations Assessment, Development, and Evaluation) framework was used to assess the certainty of evidence. Outcomes of interest included hospital readmissions, mortality, patient satisfaction, and compliance. Risk ratios (RR) were used to measure effect sizes for readmission and mortality, with patient satisfaction and compliance reported descriptively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The search yielded 5851 records, with 28 studies meeting eligibility criteria (8 RCTs, 7 cohort studies, and 13 case series). Two focused on HAH, while 26 studies addressed the postacute phase. Nineteen studies looked at heart failure, 3 studied respiratory conditions, and 6 studies studied other conditions. Meta-analysis was conducted with 6 studies looking at hospital readmission within 60 days and 4 studies at mortality within 30 days. Readmissions did not significantly decrease (RR 0.81, 95% CI 0.61-1.09; P=.16). Significant heterogeneity was observed for readmissions (I&lt;sup&gt;2&lt;/sup&gt;=58%). Conversely, mortality reduced significantly (RR 0.65, 95% CI 0.42-0.99; P=.04). There was no significant heterogeneity in mortality (I&lt;sup&gt;2&lt;/sup&gt;=0%). There was high heterogeneity in the study populations, interventions, and outcomes measured. Many studies were of poor quality, with 50% (4/8) of RCTs exhibiting a high ROB. The certainty of evidence for both readmission and mortality was very low.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Published data on the effects of remote VSM in acutely ill patients at home remains scarce. Future studies evaluating all common vital signs (heart rate, blood pressure, oxygen saturation, and temperature) with co","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64753"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150724","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":"Effectiveness of Digital Health Interventions for Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.","authors":"Miaoqing Zhuang, Intan Idiana Hassan, Wan Muhamad Amir W Ahmad, Azidah Abdul Kadir, Xiaodong Liu, Furong Li, Yinuo Gao, Yang Guan, Shuting Song","doi":"10.2196/76323","DOIUrl":"10.2196/76323","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Chronic obstructive pulmonary disease (COPD), marked by dyspnea, cough, and sputum production, significantly impairs patients' quality of life and functionality. Effective management strategies, particularly those empowering patients to manage their condition, are essential to reduce this burden and health care use. Digital health interventions-such as mobile apps for symptom tracking, wearable sensors for vital sign monitoring, and web-based pulmonary rehabilitation programs-can enhance self-efficacy and promote greater patient engagement. By improving self-management skills, these interventions also help alleviate pressure on health care systems.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic review and meta-analysis assesses the clinical effectiveness of smartphone apps, wearable monitors, and web-delivered platforms in four COPD management areas: (1) quality of life (measured by the COPD Assessment Test [CAT] and St George's Respiratory Questionnaire), (2) self-efficacy (assessed by the General Self-Efficacy Scale), (3) functional capacity (evaluated via the 6-minute walk test and Modified Medical Research Council Dyspnea Scale), and (4) health care use (indicated by hospital and emergency department visits).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A systematic review was conducted using predefined search terms in PubMed, Embase, Cochrane, and Web of Science up to January 26, 2025, to identify randomized trials on digital health interventions for COPD. Two reviewers independently screened studies and extracted data. Outcomes included quality of life, self-efficacy, functional status, and health care use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;This review included 17 studies with 2027 participants from 11 countries. Eleven trials involved health care professionals in digital platform use, and 12 reported adherence strategies. Digital tools for COPD primarily focused on telerehabilitation (eg, video-guided exercises) and self-management systems (eg, artificial intelligence-driven exacerbation alerts). The study participants were predominantly older adults. Meta-analysis results indicated that digital health interventions significantly improved quality of life at 3 months on the CAT (mean difference [MD] -1.65, 95% CI -3.17 to -0.14; P=.03); at 6 months on the CAT (MD -2.43, 95% CI -3.93 to -0.94; P=.001) and St George's Respiratory Questionnaire (MD 3.25, 95% CI 0.69-5.81; P=.01); at 12 months on the CAT (MD -2.53, 95% CI -3.91 to -1.16; P&lt;.001), EQ-5D (MD 0.04, 95% CI 0.01-0.07; P=.02), and EQ-5D visual analogue scale (MD 5.88, 95% CI 0.38-11.37; P=.04); the General Self-Efficacy Scale at 3 months (MD 1.65, 95% CI 0.62-2.69; P=.002) and 6 months (MD 1.94, 95% CI 0.83-3.05; P&lt;.001); and the Modified Medical Research Council Dyspnea Scale at more than 3 months (MD -0.23, 95% CI -0.36 to -0.11; P=.003). However, no significant differences were observed in the 6-minute walk test, emergency department admissions, hospital admiss","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e76323"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144142689","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":"The Impact of Internet Hospital Follow-Up on the Quality of Life of Patients With Epilepsy: Randomized Controlled Trial.","authors":"Ting Ting Liu, Min Zhang, Hong Ying Li, Yu Wen Zhang, Lu Liang, Xin Yue Huang, Xia Gan, Lan Mou, Chen Shi Liu, Ming Ming Zhang, Jie Liu","doi":"10.2196/70665","DOIUrl":"10.2196/70665","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;As the second most common neurological disorder, epilepsy requires long-term management to ensure better seizure control and improved patient outcomes. Health education and sustained care significantly help people with epilepsy manage their condition effectively. Internet hospitals (IHs) have emerged as a promising approach to enhancing health care accessibility. These digital platforms can significantly improve the quality of life for patients with epilepsy. However, despite their growing adoption, research on the application of IHs in the follow-up management of epilepsy remains limited, highlighting the need for further investigation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study has 2 primary aims. The first aim was to assess and compare the differences in quality of life, anxiety, and depression between IH follow-up and traditional outpatient follow-up for patients with epilepsy. The second aim was to explore chronic disease management models that are tailored to meet the needs of patients with epilepsy, improving their overall care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Eligible patients diagnosed with epilepsy were recruited at Sichuan Provincial People's Hospital and randomly assigned to the intervention or control group. Data collected included demographic information, clinical characteristics, and scores from the Quality of Life in Epilepsy-31 (QOLIE-31), Generalized Anxiety Disorder-7 Scale (GAD-7), and Neurological Disorders Depression Inventory for Epilepsy (NDDI-E). The control group received traditional outpatient follow-up, while the intervention group was managed via the IH. Both groups received epilepsy health education. After 6 months, changes in quality of life, anxiety, and depression were assessed using the same scales. Data analysis followed the intention-to-treat principle, and a linear mixed model was used to examine the intervention effect on primary and secondary outcomes. The effect sizes of group differences were calculated using Hedges g (0.2-0.4: small, 0.5-0.7: medium, and ≥0.8: large).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 214 patients with epilepsy participated in the study (intervention group: N=107; control group: N=107). At the end of the study, 94.4% (101/107) in the intervention group and 93.5% (100/107) in the control group had completed the follow-up visits. After the intervention, the intention-to-treat analysis revealed evidence for improved quality of life (QOLIE-31 total score; F&lt;sub&gt;216.53&lt;/sub&gt;=13.10, P&lt;.001) with small between-group effects (Hedges g=0.49, 95% CI 0.22-0.76) in favor of the intervention group. We also found evidence of reduced depression, as well as improved seizure worry, overall quality of life, emotional well-being, energy or fatigue, medication side effects, with effects ranging from small to medium (Hedges g=0.42-0.79).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Long-term follow-up through IHs can effectively improve the quality of life and reduce anxiety ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70665"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144142151","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":"Eleven-Year Trajectories of Internet Usage Time and Depression Scores Among Middle-Aged and Older Adults in China: Latent Class Mixed Model Analysis.","authors":"Mengyao Li, Zhongliang Zhou, Jing Wang, Dan Wang, Rebecca Mitchell, Wenhua Wang","doi":"10.2196/64581","DOIUrl":"https://doi.org/10.2196/64581","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mental health issues have emerged as a global challenge, particularly affecting middle-aged and older adults. Research has shown that internet use can potentially promote mental health. Substantial research investigated the relationship between mental health and internet usage time or purposes. However, few studies have examined the association between internet usage time trajectories and mental health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective of this study was to identify distinct trajectories of internet usage time over a span of 11 years and assess their relationship with depressive scores among middle-aged and older adults.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using longitudinal data from the China Family Panel Studies spanning from 2010 to 2020 and consisting of 5 waves. Participants older than 45 years with internet usage data available for at least 3 waves, including wave 5, were included in the analysis. Internet usage time was operationalized as the number of hours spent on the internet per week, while depressive scores were assessed using the 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8). A latent class mixed model was used to identify distinct trajectories of internet usage time over the course of this period. Mixed-effect models were used to test the relationship between distinct trajectories of internet usage time and depressive scores.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among 9163 middle-aged and older adults were included in the analysis. The trajectory analysis identified 3 clusters: \"Never use,\" \"Slow increase,\" and \"Rapid increase.\" The \"Never use\" cluster indicated no internet use for one decade. In the slow increase cluster, internet use rose slowly with an average of 7.69 hours per week in 2020. In contrast, the \"Rapid increase\" cluster exhibited a sharp increase, reaching 15.13 hours per week in 2020. Compared to the \"Never use\" cluster, the \"Slow increase\" cluster was significantly negatively associated with depressive scores among middle-aged and older adults (coefficient -0.20, 95% CI -0.34 to -0.06), while the \"Rapid increase\" cluster showed no significant association. The benefits of internet use were more pronounced among females and older adults with chronic diseases than among their male and older adult counterparts without chronic diseases. The sensitive analysis confirmed the robustness of the results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study identified 3 trajectory clusters of internet usage time among middle-aged and older adults in China from 2010 to 2020. Compared to the older adults who never used the internet, those whose internet usage increases gradually over time exhibited slightly lower depressive scores. Notably, the \"Slow increase\" cluster exhibited a negative association with depressive scores, with this association being statistically significant in females and older adults with chronic diseases, but not in males or those without chronic diseas","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64581"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150616","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":"Developing a Core Outcome Set for Pediatric and Adult Acute and Chronic Pain Extended Reality Trials: Delphi Consensus-Building Process.","authors":"Courtney W Hess, Deirdre E Logan, Brittany N Rosenbloom, Giulia Mesaroli, Laura E Simons, Carley Ouellette, Cynthia Nguyen, Fahad Alam, Jennifer N Stinson","doi":"10.2196/58947","DOIUrl":"10.2196/58947","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Appropriate outcome assessment strategies and high-quality trials are critical to advancing care of patients with acute and chronic pain. Using extended reality (XR), namely, virtual and augmented reality, as a nonpharmacological treatment for pain has accelerated in the last decade. XR allows users to engage completely in immersive, gamified, sensorial digital experiences. Currently, no standardized approach to assessing outcomes of XR-based interventions for pain exists.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Our aim was to recommend a core set of outcomes for pediatric and adult acute and chronic pain XR intervention trials.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To identify core outcomes, we conducted a multiphase process. In phase 1, we conducted systematic reviews on XR in pediatric and adult acute and chronic pain trials to identify the most common core outcome domains assessed in existing published studies. Primary outcome domains were identified and informed the development of the survey for phase 2, a Delphi survey of clinicians and researchers who were actively researching or using XR for pain treatment. Together, results from the systematic reviews and Delphi survey responses were collated, and in phase 3, a 2-day in-person meeting was held to reach consensus on recommended outcome domains for adult and pediatric acute and chronic pain XR clinical trials. This was followed by 2 additional rounds of the Delphi survey to broaden consensus and refine the domains and definitions. Following the Outcome Measures in Rheumatology guidelines for consensus building, outcomes were organized into 3 categories: mandatory, important to consider but optional, and research agenda.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A systematic review including XR trials for adult and pediatric acute and chronic pain was conducted in March 2023, and 90 pediatric and 104 adult studies were included. The round 1 Delphi survey, completed by 66 respondents, revealed the following commonly measured outcomes: pain intensity or quality, distraction, anxiety or fear, satisfaction, and adverse events. Respondents indicated the following domains to be of highest importance to measure in studies: safety, feasibility, and acceptability; pain intensity or quality; pain interference or functioning; emotional functioning; and user experience or immersion. By unanimous vote at the consensus conference, pain severity, adverse events, user experience, and psychological constructs were identified as mandatory domains to be assessed in all XR trials for acute and chronic pain, with the addition of pain interference for chronic pain trials. Physiological markers and physical function were deemed important-to-consider but optional domains. Additional emerging areas for future research did not obtain sufficient support in the consensus process but were noted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;An established core outcome set will help strengthen the emerging evidence base","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e58947"},"PeriodicalIF":5.8,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127654","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}