Journal of Medical Internet Research最新文献

{"title":"Perceived Quality of Service in Primary Health Care Based on Google Maps Reviews Before, During, and After the COVID-19 Pandemic: Sentiment Analysis.","authors":"Lili Aunimo, Andreea M Oprescu, Dmitry Kudryavtsev, Luis Munoz Saavedra, Maria Del Carmen Romero Ternero","doi":"10.2196/70410","DOIUrl":"10.2196/70410","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic caused many changes in primary health care systems in Europe. The fast adoption of telemedicine, the shift of health care resources to COVID-19-related tasks, and the tendency of patients to cancel their nonurgent appointments are some examples of these changes. Patient satisfaction is an important outcome of health care services, and the changes caused by COVID-19 in the system may have affected it. Google Maps reviews provide an important channel for patients to communicate about their experiences regarding the primary health care system.</p><p><strong>Objective: </strong>Drawing from research on social media data analytics and text mining, this study set out to investigate the changes in public sentiment regarding primary health care in Finland and Andalusia (Spain) before, during, and after the COVID-19 pandemic.</p><p><strong>Methods: </strong>We collected 55,043 Google Maps reviews from primary health care locations in Finland and Andalusia from January 1, 2013, to May 15, 2024. There are 604 primary health care locations in Finland and 1016 in Andalusia. The total number of Google Maps reviews collected was 12,247 for Finland and 42,796 for Andalusia. First, lexicon-based sentiment analysis using the open-source software AFINN was conducted for the Finnish- and Spanish-language datasets. Thereafter, transformer-based deep learning models for sentiment analysis were applied for both languages. The numeric user ratings and the results of the sentiment analysis were then analyzed. In addition, we conducted a word frequency analysis of the reviews.</p><p><strong>Results: </strong>There were important changes in the ratings and sentiment in the data for Andalusia. The ratings shifted from median 4 (IQR 3) before the COVID-19 pandemic to median 1 (IQR 2) during and median 1 (IQR 3) after the COVID-19 pandemic, on a scale from 1 to 5. The median of the sentiment values of the review texts shifted from neutral before the COVID-19 pandemic to -2 (IQR 0.055) or -1 (IQR 1) during and after the COVID-19 pandemic, depending on which sentiment analysis method was used. Interestingly, changes in numeric ratings and sentiment of the review texts in Finland were only minor, and the median values were the same during all 3 periods. Lexical analysis revealed changes in word frequencies across the periods, reflecting shifts in primary health care experiences during the pandemic, especially among the Spanish-language reviews.</p><p><strong>Conclusions: </strong>The change toward a more negative public discussion on primary health care in Andalusia during the COVID-19 pandemic was considerable. This can be observed both in the numeric user ratings and in the sentiment analysis of the review texts. However, the data for Finland show that the public discourse stayed mostly neutral or slightly positive. The findings have implications on the quality management procedures in primary health care and on the use of ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70410"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131087","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":"Respiratory-Responsive Vocal Biomarker for Asthma Exacerbation Monitoring: Prospective Cohort Study.","authors":"Erik Larsen, Xinyu Song, Dale Joachim, Peter Y Ch'en, Samuel M Green, Emily Hunt, Savneet Kaur, Robin Nag, Olivia Pisani, Sherron Thomas, Victoria Adewunmi, Carlo Lutz, Babak Baghizadeh-Toosi, Jonathan M Feldman, Sunit Jariwala","doi":"10.2196/68741","DOIUrl":"10.2196/68741","url":null,"abstract":"<p><strong>Background: </strong>Asthma exacerbations remain a major challenge in asthma management, often due to delayed recognition and limitations of conventional monitoring tools such as peak flow meters and symptom questionnaires. These tools are typically effort dependent or retrospective, making them less suited for continuous, real-time monitoring. A novel, smartphone-based respiratory-responsive vocal biomarker (RRVB) may offer an accessible and noninvasive approach for dynamic assessment of respiratory health. This RRVB has previously demonstrated generalizable performance in cross-sectional cohorts across multiple respiratory conditions, including asthma, chronic obstructive pulmonary disease, and COVID-19, in populations spanning India and the United States. This study extended this work by evaluating the real-world, longitudinal performance of the same RRVB tool for daily asthma exacerbation monitoring via smartphones in home settings.</p><p><strong>Objective: </strong>This study aimed to evaluate the efficacy of the RRVB as a convenient real-time tool for monitoring asthma exacerbations and respiratory states in a real-world, longitudinal setting.</p><p><strong>Methods: </strong>In this prospective cohort study, 84 adult patients with asthma were enrolled from an academic medical center and followed for 90 days. Participants submitted daily 6-second voice samples and conducted peak expiratory flow measurements and surveys, including symptom reports and asthma control assessments. RRVB scores were generated in real time on the app. Asthma states (normal function, mild event, and exacerbation) were defined based on both peak expiratory flow and self-reported well-being. Risk ratios were calculated to assess the predictive validity of RRVB scores for identifying exacerbation events. Engagement was measured via frequency of completed sessions, and participant experience was evaluated through exit surveys.</p><p><strong>Results: </strong>RRVB scores significantly stratified asthma states. The risk of experiencing an exacerbation was 2.15 times higher (95% CI 1.62-2.85; P<.001) with elevated RRVB scores and 3.57 times higher (95% CI 2.70-4.73; P<.001) using normalized scores adjusted for individual characteristics. RRVB scores did not significantly correlate with the Asthma Control Test (risk ratio=1.17, 95% CI 0.96-1.44; P=.12), highlighting its role as a momentary signal rather than a proxy for longitudinal control. Engagement was moderate or higher (≥26 total app sessions) in 58% (49/84) of participants. Among survey respondents, 93% (43/46) found the app easy to use, 89% (41/46) reported a positive overall experience, and 87% (40/46) indicated that they would use a similar tool in the future. Fewer participants (32/46, 70%) reported understanding the RRVB scores, suggesting a need for improved score interpretability and user guidance in future implementations.</p><p><strong>Conclusions: </strong>The RRVB tool demonstrated effective rea","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68741"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131159","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":"Participatory Design Approach in the Use of Scenario Analysis for Futureproofing Medical Education: Case Study.","authors":"Guido Giunti, Ronan Glynn, Jack Hennessy, Colin P Doherty","doi":"10.2196/73173","DOIUrl":"10.2196/73173","url":null,"abstract":"<p><strong>Background: </strong>Medical education must evolve to prepare health care professionals for a rapidly changing world. Beyond digital literacy, clinicians must develop new competencies to navigate global megatrends, including shifting disease burden, technological advancements, climate change, and demographic shifts. The future job market will introduce novel roles, and educational institutions must remain adaptable to meet the evolving motivations and expectations of students. Megatrends, broad, transformative forces shaping societies, present both challenges and opportunities for health care education.</p><p><strong>Objective: </strong>The present work seeks to understand the implications of megatrends for medical education and explore the use of scenario analysis for curriculum design.</p><p><strong>Methods: </strong>A participatory design approach was employed to conduct a scenario analysis workshop at Trinity College Dublin's School of Medicine in October 2024. Digital connectivity and climate change were selected as key drivers. Participants included medical educators, policymakers, clinicians, and students. Interactive methods such as group discussions, structured boards, and physical cards were utilized to facilitate data collection. Insights were analyzed thematically to identify critical competencies, mindsets, and structural requirements for future medical education.</p><p><strong>Results: </strong>The scenario analysis revealed key competencies and mindsets necessary for future health care professionals. Essential competencies included complex adaptive systems thinking, patient-centeredness, continuous learning, and participatory health, while essential mindsets encompassed sustainability, prevention-focused care, and technological adaptability. Cross-scenario reflections highlighted the increasing need for interdisciplinary collaboration, ethical leadership, and curriculum flexibility. Actionable steps were identified, including integrating sustainability and digital health into curricula, fostering emotional intelligence in student selection, and incorporating adaptive learning models.</p><p><strong>Conclusions: </strong>This study demonstrates the value of participatory design in shaping medical education to align with global megatrends. The findings align with existing foresight research by organizations such as the World Health Organization and the European Commission, emphasizing the need for health care professionals to balance technological proficiency with human-centered care. While the study was limited to a single institutional setting, its insights provide a framework for other medical schools to anticipate future challenges and proactively reform curricula. Future research should explore multi-institutional applications and longitudinal studies to validate these findings.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73173"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131113","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":"Impact of Telemedicine on Health Expenditures During the COVID-19 Pandemic in Japan: Quasi-Experimental Study.","authors":"Hikaru Aihara, Ichiro Kawachi, Benjamin D Sommers","doi":"10.2196/72051","DOIUrl":"10.2196/72051","url":null,"abstract":"<p><strong>Background: </strong>The effects of telemedicine on health expenditures and health outcomes are an important policy question. Many countries loosened regulations on the use of telemedicine during the COVID-19 pandemic, thereby offering an opportunity to evaluate these effects via a natural experiment.</p><p><strong>Objective: </strong>This study aimed to assess the effect of greater telemedicine use on area-level health expenditures and health outcomes related to common chronic conditions in Japan during the COVID-19 pandemic.</p><p><strong>Methods: </strong>We compared prefectures (area levels of government) with higher prepandemic telemedicine rates (fiscal year [FY] 2019) versus those with lower rates and conducted a difference-in-differences analysis of the change in prefecture-level health expenditures from FY2017 to FY2022 and health outcomes from FY2017 to FY2021. The participants were the total population in Japan from FY2017 to FY2022 (n=126 million), and the exposure was the increase in telemedicine use following the government's relaxation of restrictions on telemedicine use as an exceptional measure during the COVID-19 pandemic. Our main outcomes were the share of outpatient claims that were for telehealth services; total, inpatient, and outpatient annual prefecture-level health expenditures; all-cause mortality, glycated hemoglobin, systolic blood pressure, and low-density lipoprotein cholesterol.</p><p><strong>Results: </strong>Treatment prefectures (n=15, population of 62 million) were defined as those with greater-than-median telemedicine use before the pandemic, while control prefectures (n=32, population of 64 million) were defined as those with less-than-median telemedicine use. Treatment and control prefectures shared similar demographic characteristics before the pandemic. The growth in telemedicine after 2020 as a share of outpatient claims increased among the treatment prefectures by 0.35 percentage points more than among control prefectures, which represented more than a threefold increase in telemedicine use compared to the prepandemic median. In difference-in-differences analyses, this difference was associated with a 1.0% relative decrease (95% CI 0.3%-1.8%) in total health expenditure (P=.006) and a 1.1% relative decrease (95% CI 0.2%-2.0%) in inpatient expenditure (P=.02). Outpatient expenditures showed no significant difference as a result of increased telemedicine adoption. Most health outcomes-all-cause mortality, glycated hemoglobin, systolic blood pressure, diastolic blood pressure, and low-density lipoprotein cholesterol-did not show any significant changes.</p><p><strong>Conclusions: </strong>Areas in Japan with greater expansion of telemedicine use during the pandemic experienced a significant decrease in both inpatient and total health care spending compared with areas with less telemedicine use, without harming health outcomes.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e72051"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131007","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":"Analysis of the Behavioral Performance and Social Support of Patients in Online Health Communities From User Profile Perspectives: Comparative Study.","authors":"Jie Wei","doi":"10.2196/68074","DOIUrl":"10.2196/68074","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With the development of online health care, an increasing number of patients are consulting and exchanging social support through online health communities. People with different diseases have varying needs for information and emotional support. However, comparisons of similarities and differences in behavioral patterns among patients with different disease types and their social support needs require further exploration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Using a large-scale dataset of user-generated posts, we aimed to systematically examine how disease type (acute vs chronic) influences the behavioral patterns, emotional expressions, and support-seeking needs of users in online health communities, providing actionable insights for tailored community interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We identified patients with acute diseases and those with chronic diseases and then crawled corresponding user profiles and post data from the chronic disease online health community (CDOHC) and acute disease online health community (ADOHC). Using a pretrained model, we classified and described the social support performance of users. Subsequently, we conducted a comparative analysis of user behaviors, emotions, and needs by mining behavior patterns and textual content from posts. We performed further social network analysis using user profiles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 492,495 posts from 53,245 users in the CDOHC and 52,047 posts from 23,659 users in the ADOHC. Seeking and providing emotional support were higher in the CDOHC (83,231/492,495, 16.9% and 101,453/492,495, 20.6%, respectively), while seeking and providing information support were higher in the ADOHC (33,993/492,495, 22.8% and 61,128/492,495, 41.0%, respectively). These findings indicate that users with chronic diseases have a higher need for emotional support, while most users with acute diseases want to seek information support. The word co-occurrence network revealed distinct thematic patterns between the 2 communities. In the CDOHC, disease management clusters (8/17, 47%) and emotional clusters (7/17, 41%) showed balanced proportions, reflecting the dual needs of patients with chronic diseases. In contrast, in the ADOHC, posts were overwhelmingly focused on treatment (25/28, 89%), with minimal emotional vocabulary clusters (2/28, 7%). Social network analysis further highlighted these differences. The CDOHC showed the highest edge density in the seeking emotional support subnetwork and reciprocal interactions in 68.0% (83,025/122,095) of providing emotional support connections, indicating robust emotional support exchanges. Meanwhile, the ADOHC exhibited significantly faster post velocity in treatment discussions, consistent with its acute care context. These structural differences aligned with user behavior patterns. Users with chronic diseases maintained strong community bonds (averaging 8.2 connections/user), while users with acute di","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68074"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131000","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":"Integrating Immersive Virtual Reality With Savoring to Promote the Well-Being of Patients With Chronic Respiratory Diseases: Pilot Randomized Controlled Trial.","authors":"Elisa Pancini, Alessia Fumagalli, Sveva Maggiolini, Clementina Misuraca, Davide Negri, Luca Bernardelli, Daniela Villani","doi":"10.2196/67395","DOIUrl":"10.2196/67395","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Chronic respiratory diseases (CRDs) are widespread pathologies that cause nonreversible airflow limitations as well as extrapulmonary adverse effects. These pathologies are related to frequent hospitalizations and consequently high levels of anxiety, depression, and stress. In this respect, immersive virtual reality (IVR) relaxation integrated with savoring, which is the ability to generate and amplify positive emotions, can enhance well-being and relaxation in patients with CRDs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This pilot randomized controlled trial aimed to investigate the effectiveness of a 2-week IVR-based relaxation intervention integrated with savoring in patients with CRDs for increasing emotional and psychological well-being, positive emotions, relaxation, and peripheral oxygen saturation (SpO2), and decreasing negative emotions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study included 45 hospitalized patients with CRDs from the Pulmonary Rehabilitation Unit of Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Nazionale di Ricovero e Cura per Anziani (INRCA) Casatenovo. Alongside traditional pulmonary rehabilitation, the experimental group (n=23) took part in a 4-session IVR-based intervention, while the active control group (n=22) listened to relaxing music. In each session, the experimental group experienced a relaxing virtual scenario followed by a savoring exercise. Both groups completed self-reported questionnaires at 3 time points-preintervention/baseline (T0), postintervention (T1), and 1-month follow-up (T2)-as well as before and after each session. The experimental group's IVR acceptance and sense of presence were also measured.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Regarding the primary outcomes, taking T0 and T1 into account, repeated measures analysis of covariance revealed significant increases for the experimental group in emotional well-being (P&lt;.001; partial η²=0.398), psychological well-being (P&lt;.001; partial η²=0.559), positive emotions (P&lt;.001; partial η²=0.407), and relaxation (P&lt;.001; partial η²=0.598), and a significant decrease in negative emotions (P&lt;.001; partial η²=0.456) compared to the control group. Moreover, 2-tailed paired t tests, considering T0 and T2, revealed significant long-term psychological changes at T2 for the experimental group in emotional well-being (P=.046), psychological well-being (P=.03), and positive emotions (P=.005), whereas the control group reported no significant changes. Concerning secondary outcomes, no significant differences in SpO2 between the 2 groups were found, and patients in the experimental group reported high IVR acceptance and sense of presence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;These results suggest that relaxing IVR integrated with savoring may promote well-being not only after the intervention but also in the long term. Savoring may have played a role in enhancing the positive effects of the IVR experience by helping patients ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e67395"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130989","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":"Validating the Efficacy of a Mobile Digital Therapeutic for Insomnia (WELT-I): Randomized Controlled Decentralized Clinical Trial.","authors":"Kyung Mee Park, Suonaa Lee, Yujin Lee, Daa Un Moon, Eun Lee","doi":"10.2196/70722","DOIUrl":"10.2196/70722","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cognitive behavioral therapy for insomnia (CBT-I) has proven to be an effective treatment; however, its accessibility is limited. To address this issue, digital therapeutics for insomnia (DTx-Is), which are software-driven interventions designed to treat insomnia based on CBT-I, have emerged as a potential solution to enhance access.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to verify the efficacy and safety of WELT-I, a DTx-I. Due to the impact of the global pandemic during the study period, we thought that a decentralized clinical trial (DCT) design that does not require visits to institutions would be appropriate for a clinical study of a digital therapeutic for patients with insomnia. Thus, we also examined the potential of the DCT design as an effective method for validating DTx-Is.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A double-blind, sham-controlled randomized DCT was conducted with participants who met the diagnostic criteria for insomnia. Participants were recruited through advertisements posted on an open-access website. WELT-I is a DTx-I based on CBT-I. A sham app was engineered to mirror WELT-I's installation, login, user engagement, and content delivery processes while maintaining double-blind protocols. After randomization, participants were asked to use WELT-I or the sham app for 6 weeks. All treatment processes were fully automated. Sleep parameters were measured through an app-based sleep diary. Self-report questionnaires on sleep, depression, and anxiety were administered via the app at baseline and the end of the study. The primary outcome was sleep efficiency. To investigate the feasibility of the DCT design, compliance, retention rate, participant satisfaction, and time to reach the recruitment goal were evaluated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 89 participants provided consent and underwent screening, and 68 participants were randomly assigned to the WELT-I group (n=33) or control group (n=35). Among them, 14 participants discontinued the trial, leaving 54 participants who completed the study and were included in the final analysis (28 in the WELT-I group and 26 in the control group). WELT-I significantly improved sleep efficiency (least-squares difference=8.28; P=.04) and dysfunctional beliefs about sleep (least-squares difference=-1.03; P=.008) compared with the sham app. The study completed recruitment in 73 days, and the compliance rate was 95% (186/196) in the WELT-I group and 91% (165/182) in the control group. Moreover, the retention rate was 82% (23/28), and the average satisfaction score was 7.2 out of 10.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;WELT-I showed significant therapeutic efficacy and safety in improving sleep efficiency and sleep-related dysfunctional attitudes in cases of insomnia. In addition, this study demonstrated the feasibility of DCTs, and the findings of rapid recruitment, high compliance and retention rates, and strong participant satisfaction suggest ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70722"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131133","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":"Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models.","authors":"Fuzhen Zhang, Zilong Yang, Xiaonan Geng, Yu Dong, Shanshan Li, Cong Yao, Yuanyuan Shang, Weicong Ren, Ruichao Liu, Haobin Kuang, Liang Li, Yu Pang","doi":"10.2196/69998","DOIUrl":"10.2196/69998","url":null,"abstract":"<p><strong>Background: </strong>Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognoses and preventing the transmission of this deadly disease. However, the absence of validated predictive models remains a significant challenge.</p><p><strong>Objective: </strong>This study compared a conventional logistic regression model with machine learning (ML) models using demographic and clinical data to predict outcomes at 2 and 6 months of treatment for MDR/RR-TB. The goal was to advance model applications, refine control strategies, and boost MDR/RR-TB cure rates.</p><p><strong>Methods: </strong>This retrospective study encompassed an internal cohort of 744 patients with MDR/RR-TB examined between January 2017 and June 2023, as well as an external cohort comprising 137 patients with MDR/RR-TB examined between March 2021 and June 2022. Data on culture conversion were collected at 2 and 6 months, and culture conversion was tracked in the external cohort at the same time points. The internal cohort was assigned as the training set, whereas the external cohort was used as the validation set. Logistic regression and 7 ML models were developed to predict the culture conversion of patients with MDR/RR-TB at 2 and 6 months of treatment. Model performance was evaluated using the area under the curve, accuracy, sensitivity, and specificity.</p><p><strong>Results: </strong>In the internal cohort, culture conversion rates for MDR/RR-TB were 81.9% (485/592) at 2 months and 87.1% (406/466) at 6 months. The odds ratio for treatment success was 8.55 (95% CI 3.31-22.08) at 2 months and 20.33 (95% CI 6.90-59.86) at 6 months after conversion, with sensitivities of 86.5% and 92.2% and specificities of 57.1% and 63.2%, respectively. The artificial neural network model was the best for culture conversion at both 2 and 6 months of treatment, with areas under the curve of 0.82 (95% CI 0.77-0.86) and 0.90 (95% CI 0.86-0.93), respectively. The accuracy, sensitivity, and specificity of the model were 0.74, 0.74, and 0.75 at 2 months of treatment and 0.80, 0.79, and 0.87 at 6 months of treatment, respectively.</p><p><strong>Conclusions: </strong>The ML models based on 2- and 6-month culture conversion could accurately predict treatment outcomes for patients with MDR/RR-TB. ML models, particularly the artificial neural network model, outperformed the logistic regression model in both stability and generalizability and offer a rapid and effective tool for evaluating therapeutic efficacy in the early stages of MDR/RR-TB treatment.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69998"},"PeriodicalIF":6.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124004","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":"Establishing a Net-Zero Emissions Kidney Care Center: A Model Proposal for Taiwan.","authors":"Mei-Yi Wu, Wei-Cheng Lo, Min-Kuang Tsai, Yuan-Leng Lin, Yih-Giun Cherng, Ming-Che Lee, Mai-Szu Wu","doi":"10.2196/73942","DOIUrl":"10.2196/73942","url":null,"abstract":"<p><p>Green nephrology has emerged as a crucial strategy to address the health care sector's role in the climate crisis, particularly due to the high carbon intensity of dialysis-related services. Aligned with global net-zero commitments, sustainable kidney care can reduce environmental impact while maintaining high standards of patient care. This viewpoint paper proposes a net-zero carbon emissions kidney care center model to address global climate change challenges and advance health care sustainability goals. Based on the United Nations Sustainable Development Goals, we developed a 4D framework: digital transformation, low-carbon health care, circular economy, and preventive medicine. The digital transformation dimension features a precision kidney health system integrating acute and chronic kidney injury digital care models. The low-carbon health care dimension focuses on increasing the rates of kidney transplantation and choosing optimal dialysis modality. The circular economy dimension involves dialysis wastewater recycling, repurposing of medical materials, and integration of renewable energy into facility operations. The preventive medicine dimension incorporates telehealth education, behavioral interventions, and health inequality improvements. This net-zero carbon emissions kidney care model represents an environmental, social, and governance approach to ensuring implementation and continual improvement. It also provides actionable steps for implementing sustainable kidney care and serves as a reference model for net-zero emissions health care systems.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73942"},"PeriodicalIF":6.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124876","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":"Large Language Models in Neurological Practice: Real-World Study.","authors":"Natale Vincenzo Maiorana, Sara Marceglia, Mauro Treddenti, Mattia Tosi, Matteo Guidetti, Maria Francesca Creta, Tommaso Bocci, Serena Oliveri, Filippo Martinelli Boneschi, Alberto Priori","doi":"10.2196/73212","DOIUrl":"10.2196/73212","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) such as ChatGPT (OpenAI) and Gemini (Google) are increasingly explored for their potential in medical diagnostics, including neurology. Their real-world applicability remains inadequately assessed, particularly in clinical workflows where nuanced decision-making is required.</p><p><strong>Objective: </strong>This study aims to evaluate the diagnostic accuracy and appropriateness of clinical recommendations provided by not-specifically-trained, freely available ChatGPT and Gemini, compared to neurologists, using real-world clinical cases.</p><p><strong>Methods: </strong>This study consisted of an experimental evaluation of LLMs' diagnostic performance presenting real-world neurology cases to ChatGPT and Gemini, comparing their performance with that of clinical neurologists. The study was conducted simulating a first visit using information from anonymized patient records from the Neurology Department of the ASST Santi Paolo e Carlo Hospital, ensuring a real-world clinical context. The study involved a cohort of 28 anonymized patient cases covering a range of neurological conditions and diagnostic complexities representative of daily clinical practice. The primary outcome was diagnostic accuracy of both neurologists and LLMs, defined as concordance with discharge diagnoses. Secondary outcomes included the appropriateness of recommended diagnostic tests, interrater agreement, and the extent of additional prompting required for accurate responses.</p><p><strong>Results: </strong>Neurologists achieved a diagnostic accuracy of 75%, outperforming ChatGPT (54%) and Gemini (46%). Both LLMs demonstrated limitations in nuanced clinical reasoning and overprescribed diagnostic tests in 17%-25% of cases. In addition, complex or ambiguous cases required further prompting to refine artificial intelligence-generated responses. Interrater reliability analysis using Fleiss Kappa showed a moderate-to-substantial level of agreement among raters (κ=0.47, SE 0.077; z=6.14, P<.001), indicating agreement between raters.</p><p><strong>Conclusions: </strong>While LLMs show potential as supportive tools in neurology, they currently lack the depth required for independent clinical decision-making when using freely available LLMs without previous specific training. The moderate agreement observed among human raters underscores the variability even in expert judgment and highlights the importance of rigorous validation when integrating artificial intelligence tools into clinical workflows. Future research should focus on refining LLM capabilities and developing evaluation methodologies that reflect the complexities of real-world neurological practice, ensuring effective, responsible, and safe use of such promising technologies.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73212"},"PeriodicalIF":6.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123985","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}