Journal of Medical Internet Research最新文献

{"title":"Determinants of Digital Health Literacy: International Cross-Sectional Study.","authors":"Connor S Qiu, Tetiana Lunova, Geva Greenfield, Gabriele Kerr, Ömrüm Ergüven, Thomas Beaney, Benedict Hayhoe, Erik Mayer, Azeem Majeed, Ana Luisa Neves","doi":"10.2196/66631","DOIUrl":"10.2196/66631","url":null,"abstract":"<p><strong>Background: </strong>Digital health literacy describes an individual's ability to use digital information and tools to improve their own health. Understanding how digital health literacy varies across populations could help improve health equity. However, the determinants of digital health literacy have been scarcely evaluated.</p><p><strong>Objective: </strong>This study aims to assess the levels of digital health literacy in 4 countries (United Kingdom, Sweden, Italy, and Germany) and explore potential associations between digital health literacy and demographic characteristics and self-perceived health status.</p><p><strong>Methods: </strong>A cross-sectional online survey was disseminated to participants from the United Kingdom, Italy, Germany, and Sweden in December 2020. Digital health literacy was self-reported using the validated eHealth Literacy Scale (eHEALS; range: 0-40); low digital health literacy has been previously defined as an eHEALS score<26. Participant characteristics collected were sex, age group, ethnicity, country, and perceived overall health status. A multivariable linear regression analysis was performed to explore associations between these variables and digital health literacy.</p><p><strong>Results: </strong>A total of 6331 participants were included (51.7% female, n=3272). The mean eHEALS score was 29.2 (SD 6.8). Participant age, sex, health status, and country of residence were included in the final multivariable model. Compared to the 45- to 54-year age group, the 55 years and older age group had lower digital health literacy (β=-1.0; 95% CI -1.4 to -0.5; P<.001), while digital health literacy was higher in those aged 25-34 years (β=0.9; 95% CI 0.3-1.5; P=.002) and 35-44 years (β=0.6; 95% CI 0.1-1.2; P=.03). Better health status was associated with greater digital health literacy (β=0.3; 95% CI 0.2-0.4; P<.001). Compared to participants from Germany, those from the United Kingdom (β=2.1; 95% CI 1.7-2.5; P<.001) and Sweden (β=2.9; 95% CI 2.4-3.4; P<.001) had higher digital health literacy scores, while there was no difference with Italian participants (P=.399). Sex and ethnicity did not have any significant effect on digital health literacy.</p><p><strong>Conclusions: </strong>This study found significant variations in digital health literacy by age, health status, and country of residence. Targeted educational programs for vulnerable groups, particularly those of older age and poorer health status, are essential. Policies fostering accessible digital health solutions and mitigating health technology-related uncertainties for these populations are crucial for achieving optimal health outcomes.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e66631"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528373","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":"Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications.","authors":"Dasheng Wu, Na Liu, Rui Ma, Peilong Wu","doi":"10.2196/71970","DOIUrl":"10.2196/71970","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to investigate the current research status in the use of AI for herpes zoster, offering a comprehensive synthesis of existing advancements.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research on November 17, 2023. Inclusion criteria were as follows: (1) research articles, (2) published in English, (3) involving actual AI applications, and (4) focusing on herpes zoster. Exclusion criteria comprised nonresearch articles, non-English papers, and studies only mentioning AI without application. Two independent clinicians screened the studies, with a third senior clinician resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types; algorithms; data sources; data types; and clinical applications in diagnosis, treatment, and management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (15/26, 57.7%), followed by public databases (6/26, 23.1%) and internet data (5/26, 19.2%). Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71970"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12234400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528372","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":"Online Availability of Diamond Shruumz Before and After FDA Recall Initiation: Qualitative Assessment and Simulated Test Purchasing.","authors":"Tim Mackey, Matthew Nali, Meng Zhen Larsen, Zhuoran Li, Jiawei Li, Joshua Yang","doi":"10.2196/64820","DOIUrl":"10.2196/64820","url":null,"abstract":"<p><strong>Background: </strong>Reports of hospitalization associated with Diamond Shruumz-branded mushroom-containing products in October 2024 led to a manufacturer's recall that restricted the sale, distribution, and accessibility of this new and emerging psychoactive product.</p><p><strong>Objective: </strong>This study seeks to assess the continuing online availability of a mushroom-containing edible product in a diverse e-commerce landscape, specifically aiming to identify and characterize its online availability before and after recall initiation.</p><p><strong>Methods: </strong>A retrospective online market surveillance of Diamond Shruumz products using structured and automated search queries was employed to identify online product marketing and availability. Online surveillance included the monitoring of multiple social media platforms, cannabis e-commerce websites, and search engine queries between June 22 and June 27, 2024, immediately preceding the manufacturer-initiated recall. Post-recall simulated purchases were then conducted on July 12, 2024, on platforms, websites and domains identified as continuing to actively market and sell the products through online product listings.</p><p><strong>Results: </strong>Prior to product recall, a total of 4117 product listings across 1600 (38.86%) social media posts and user-generated comments, 11 (0.27%) cannabis e-commerce websites, and 2509 (60.94%) hyperlinks from internet search queries were generated for further content analysis. Review of online sources revealed 49 social media posts, 8 e-commerce shops, and 67 domains that were identified as actively marketing and selling products prior to recall. Post-recall, we identified 45 (67.16%) remaining domains that continued to market the product from these different online sources. Simulated purchases revealed that 15 (33.33%) domains successfully transacted test purchases and 30 (66.66%) transactions failed because of account verification or payment failure.</p><p><strong>Conclusions: </strong>The Diamond Shruumz recall exemplifies the ongoing challenge of unknown consumer harm associated with new and emerging substances marketed and sold on the internet, which is especially concerning as these products appeal to younger audiences with a variety of edible flavored products. While a recall was initiated and products became unavailable, our study found that post-recall online vendors continued to market and sell the products. This indicates that there are ongoing challenges to effectuate recalls and online enforcement in a diverse e-commerce landscape that can rapidly bring new and novel psychoactive substances to the market.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64820"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528377","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":"Improvements in Glycemic Control With a Digital Diabetes Logbook: Secondary Analysis of a Randomized Controlled Trial Enriched by Observational, Real-World Data.","authors":"Dominic Ehrmann, Bernhard Ruch, Michael Mitter, Johanna Kober, Norbert Hermanns, Vanessa Schäfer, Bernhard Kulzer, Stephan Silbermann","doi":"10.2196/68933","DOIUrl":"10.2196/68933","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The treatment of diabetes requires substantial self-management. Digital tools can help reduce the burden of self-management and may improve glycemic control.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to determine whether the use of a digital diabetes logbook increased the likelihood of achieving optimal glycemic control (glycated hemoglobin [HbA&lt;sub&gt;1c&lt;/sub&gt;] ≤6.5%) after 3 months, based on a secondary analysis of randomized controlled trial (RCT) data. A secondary objective was to evaluate the long-term impact of the logbook on mean blood glucose levels over 3 and 12 months using observational, real-world data (RWD).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data from 342 participants with type 1 or type 2 diabetes enrolled in the mySugr PRO-RCT were analyzed. A robust logistic regression was performed to examine the likelihood of achieving optimal glycemic control, defined as an HbA1c value ≤6.5% at the 3-month follow-up. The dependent variable was the dichotomous outcome indicating whether this threshold was met. The primary independent variable was group allocation, with baseline HbA1c included as a covariate. For the analysis of RWD, a total of 2861 participants with type 1 or type 2 diabetes were identified using propensity score matching to align their characteristics with those of the RCT participants closely. One-sample t tests were conducted to analyze changes in mean blood glucose separately for each diabetes type, from baseline to 3 months of app use, and from baseline to 12 months of app use (in a subcohort of 1176 participants).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The RCT data showed that the likelihood of achieving optimal glycemic control was nearly doubled in the intervention group compared with the control group (odds ratio 2.24, 95% CI 1.12-4.47; P=.02). RWD indicated that mean blood glucose levels significantly improved over 3 months of app use in both groups (type 1: -16.3 mg/dL; 95% CI -20.6 to -12.4; P&lt;.001 and type 2: -27.3 mg/dL, 95% CI -28.7 to -25.9; P&lt;.001). Participants with an estimated HbA&lt;sub&gt;1c&lt;/sub&gt;&gt;8.5% at baseline (before app use) showed the greatest reductions in mean blood glucose (type 1: -82.2 mg/dL; 95% CI -102.0 to -61.8; P&lt;.001; type 2: -104.6 mg/dL, 95% CI -109.1 to -100.3; P&lt;.001). Long-term analyses revealed a sustained reduction in mean blood glucose over a 12-month period, with a mean decrease of -19.8 mg/dL (95% CI -21.8 to -17.9; P&lt;.001) after 12 months of app use in the total RWD sample.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The secondary analysis of the RCT demonstrated a significant increase in the likelihood of achieving optimal glycemic control after 3 months of using the mySugr logbook. This finding was supported by observational, real-world data, which showed significant reductions in mean blood glucose after 3 and 12 months of app use-particularly among individuals with elevated baseline HbA1c levels.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;German Clinical Trials","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68933"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528375","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":"Relationship Between Short Video Addiction Tendency and Depression Among Rural Older Adults: Cross-Sectional Study.","authors":"Ping Dong, Xianqi Zhang, Wenqiang Yin, Yongli Shi, Mengyuan Xu, Haoqi Li, Xianglan Zhuge, Ziyuan Li, Kui Sun, Zhongming Chen","doi":"10.2196/75938","DOIUrl":"https://doi.org/10.2196/75938","url":null,"abstract":"<p><strong>Background: </strong>Depression, a prevalent mental illness among older people, is associated with some adverse health problems and lower quality of life. Against the backdrop of a growing aging population, coping with late-life depression has become an important public health priority. Emerging evidence suggests that short video addiction tendency may be a new risk factor for depression. However, there has been limited discussion on the potential association between short video addiction tendency and depression among older adults.</p><p><strong>Objective: </strong>We aimed to investigate the relationship between short video addiction tendency and depression in a sample of rural older adults. In addition, we aimed to examine the mediating roles of asthenopia and sleep efficiency in the relationship between short video addiction tendency and depression.</p><p><strong>Methods: </strong>A face-to-face interview design was used to collect valid data from 872 rural older adults aged ≥60 years from October 2024 to January 2025 in 2 provinces of China. Participants were requested to complete self-report measures on short video addiction tendency (Short Video Addiction Scale), asthenopia (11-item Asthenopia Survey Questionnaire), sleep efficiency (actual sleep time and time in bed at night), and depression (Center for Epidemiologic Studies Depression Scale). Linear regression analyses were performed using model 6 of the PROCESS 4.1 macro in SPSS 26.0 to assess the relationship between short video addiction tendency and depression and to examine the mediating roles of asthenopia and sleep efficiency in this relationship, adjusting for sex, age, education, and marital status.</p><p><strong>Results: </strong>We observed that the prevalence of depression was 27.8% (242/872) in this study. There was a significant positive relationship between short video addiction tendency and depression (β=.263; P<.001). Short video addiction tendency affected depression through 3 different pathways: the mediating role of asthenopia (β=.084, 95% CI .059-.114); the mediating role of sleep efficiency (β=.021, 95% CI .001-.043); and the chain mediating role of asthenopia and sleep efficiency (β=.017, 95% CI .010-.026). The effect values of the 3 pathways accounted for 31.94%, 7.99%, and 6.46% of the total effect, respectively.</p><p><strong>Conclusions: </strong>We highlighted a direct and statistically substantial relationship between short video addiction tendency and depression, with asthenopia and sleep efficiency serving as potential mediating factors. Our findings predicted that guiding and assisting rural older adults to use short video apps appropriately, addressing asthenopia, and enhancing sleep efficiency may be a valuable approach to improve their mental health, preventing and delaying the occurrence and development of depression.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75938"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528378","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":"Development and Effect of Prenatal Education Programs Using Virtual Reality for Pregnant Women Hospitalized With Preterm Labor: Experimental Study.","authors":"SeoA Park, Hyeyoung Kim","doi":"10.2196/75585","DOIUrl":"10.2196/75585","url":null,"abstract":"<p><strong>Background: </strong>Pregnant women hospitalized due to preterm labor often experience anxiety, stress, and physical discomfort, which may influence uterine contractions and cervical changes, underscoring the need for effective prenatal management. Virtual reality (VR)-based prenatal education programs can enhance interaction and engagement for these women. The Cox Interaction Model of Client Health Behavior (IMCHB) provides an appropriate framework for nursing interventions addressing their complex needs. This study developed a VR prenatal education program based on the IMCHB and evaluated its effectiveness.</p><p><strong>Objective: </strong>This study aims to develop, implement, and evaluate the effects of a prenatal educational program using VR technology for pregnant women hospitalized with preterm labor.</p><p><strong>Methods: </strong>This program was developed based on the Cox IMCHB. To guide program development, we applied the Analyze, Design, Develop, Implement, and Evaluate (ADDIE) model, following its 5 stages: analysis, design, development, implementation, and evaluation. The study used a pre- and posttest design with a nonequivalent control group. A total of 15 participants in the experimental group and 16 participants in the control group, all pregnant women hospitalized with preterm labor, were analyzed. Data were analyzed using descriptive statistics; homogeneity tests; the chi-square test; the Fisher exact test; and independent, unpaired, 2-tailed t tests.</p><p><strong>Results: </strong>The experimental group that participated in the VR-based prenatal education program showed significantly lower levels of state anxiety (P=.009), stress related to preterm labor (P=.002), frequency of uterine contractions (P=.004), and intensity of uterine contractions (P<.001) compared with the control group. Additionally, the experimental group demonstrated a significantly greater increase in cervical length (P=.009). Practice behavior (P=.047) and self-efficacy in pregnancy health care (P<.001) were also significantly higher in the experimental group than in the control group.</p><p><strong>Conclusions: </strong>Prenatal education using VR was shown to be effective across physical, emotional, and educational domains by delivering a professional, integrated intervention tailored to the complex nursing needs of hospitalized patients with preterm labor.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75585"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12234401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528374","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":"Evolving Perceptions and Attitudes to Adopting Generative AI in Professional Settings: Multicenter Longitudinal Qualitative Study of Senior Chinese Hospital Leaders.","authors":"Zheng Zhi, Jing Zhao, Qiang Li, Qingxia Li, Meifang Xu, Yan Zuo, Ming Wang, Jiankang Liu, Jingyi Guan, Jia Wang","doi":"10.2196/75531","DOIUrl":"10.2196/75531","url":null,"abstract":"<p><strong>Background: </strong>The rapid evolution of generative artificial intelligence (GenAI) is transforming health care globally. In China, hospitals are rapidly embracing digital transformation. Senior leaders are pivotal in influencing and deciding the adoption of GenAI technologies in professional settings. However, evidence on their perceptions and attitudes and how they evolve over time is lacking.</p><p><strong>Objective: </strong>This study aims to investigate senior Chinese hospital leaders' perceptions relating to GenAI and their attitudes toward adopting GenAI technologies in professional settings and to document how their perspectives evolve.</p><p><strong>Methods: </strong>A longitudinal, descriptive qualitative study was conducted using a phenomenological approach. Senior leaders, including hospital directors/deputies and department heads/deputies, from 3 tertiary hospitals across the Hebei, Guangdong, and Sichuan provinces were purposively sampled. Data were collected through semistructured telephone interviews at two time points (baseline and 6 months later). Interview transcriptions were analyzed using Colaizzi's method to extract themes related to knowledge acquisition, attitudinal shifts, and evolving barriers and facilitators to GenAI adoption.</p><p><strong>Results: </strong>A total of 32 participants were interviewed in August 2024 and February 2025, including 11 (34.4%) participants from Hebei, 12 (37.5%) from Sichuan, and 9 (28.1%) from Guangdong. Their roles included 8 (25%) hospital directors/deputies and 24 (75%) department heads/deputies. The analysis of the interview transcriptions revealed three primary themes. First, participants' understanding of GenAI improved markedly over time as they engaged with diverse information sources and gained practical experience. Second, despite widespread support for personal use, senior leaders shifted from initial reluctance to formal institutional adoption. Third, initial obstacles, such as limited technical literacy and resource constraint concerns, diminished over time, while new drivers, including peer influence and technological breakthroughs such as DeepSeek, emerged to catalyze adoption.</p><p><strong>Conclusions: </strong>Senior Chinese hospital leaders' perceptions and attitudes toward GenAI have evolved significantly over time. Improved knowledge through diverse information channels has enhanced their comprehension and shifted their stance from cautious personal experimentation toward formal institutional adoption. The dynamic interplay between diminishing obstacles and emerging catalysts, notably the influence of peer practices and technological breakthroughs such as DeepSeek, underscores the potential for transformative change in health care management. Targeted educational initiatives, pilot projects, and robust policy frameworks are needed to facilitate GenAI integration.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75531"},"PeriodicalIF":5.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12227188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511997","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":"Examining the Impact of Digital Inclusion on Depression Among Older Adults in China: Mediating Role of Noncognitive Abilities.","authors":"Xinru Li, Chengyu Chen, Xiyan Li, Yuyang Li, Shujuan Xiao, Jianan Han, Yanan Wang, Chichen Zhang","doi":"10.2196/71441","DOIUrl":"10.2196/71441","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In the digital and intelligent era, a considerable number of older adults in China still have a low level of digital inclusion. Although existing literature has explored the relationship between the use of the internet and depression among older adults to some extent, it mainly focused on surface aspects rather than delving into the underlying mechanism of action among digital inclusion, depression, and noncognitive abilities, which remains unclear.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to explore the mediating role of noncognitive abilities between digital inclusion and depression among older adults in China, as well as the parallel mediating roles of each dimension of the Big Five personality traits in the relationship between them.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We extracted cross-sectional data from a nationally representative survey, namely the China Family Panel Studies, which centered on older adults (aged 60 years or older). The Center for Epidemiologic Studies Depression Scale (CES-D 8), which consists of 8 items, was used to determine the presence of depression. The 15-item short version of the Big Five Personality Scale was used to measure the noncognitive abilities of older adults. Model 4 of the PROCESS macro (Andrew F. Hayes) program was applied to test, respectively, the mediating role of noncognitive abilities between digital inclusion and depression status, as well as the parallel mediating roles of each dimension of the Big Five personality traits in the relationship between them.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;This study found that digital inclusion was negatively related to depression in older adults (β=-.054, t&lt;sub&gt;6545&lt;/sub&gt;=-4.804; P&lt;.01). After adding noncognitive abilities as a mediating variable, depression was found to be negatively related to digital inclusion (β=-.022, t&lt;sub&gt;6544&lt;/sub&gt;=-1.972; P&lt;.05). Noncognitive abilities play a significant mediating role in the relationship between the level of digital inclusion and depression, and their effect accounts for 59.44% of the total effect. In the parallel mediation model, conscientiousness, extraversion, openness, and emotional stability all partially mediated the association between digital inclusion and depression. The parallel mediation effects of conscientiousness (β=-.0045, 95% CI -0.0068 to -0.0024; P&lt;.05), extraversion (β=-.0067, 95% CI -0.0096 to -0.0043; P&lt;.05), openness (β=.0085, 95% CI 0.0042 to 0.0128; P&lt;.05), and emotional stability (β=-.0073, 95% CI -0.0131 to -0.0017; P&lt;.05) of noncognitive abilities in the relationship between digital inclusion and depression were significant, accounting for 8.33%, 12.41%, -15.74%, and 13.52% of the total effect, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our results demonstrate that digital inclusion is a negative predictor of depression among older adults, and noncognitive abilities play a partial mediating role between digital inclusion and depression status. Moreov","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71441"},"PeriodicalIF":5.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12254705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511939","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":"ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis.","authors":"Weihong Huang, Wudi Wei, Xiaotao He, Baili Zhan, Xiaoting Xie, Meng Zhang, Shiyi Lai, Zongxiang Yuan, Jingzhen Lai, Rongfeng Chen, Junjun Jiang, Li Ye, Hao Liang","doi":"10.2196/74423","DOIUrl":"10.2196/74423","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Influenza in mainland China results in a large number of outpatient and emergency visits related to influenza-like illness (ILI) annually. While deep learning models show promise for improving influenza forecasting, their technical complexity remains a barrier to practical implementation. Large language models, such as ChatGPT, offer the potential to reduce these barriers by supporting automated code generation, debugging, and model optimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the predictive performance of several deep learning models for ILI positive rates in mainland China and to explore the auxiliary role of ChatGPT-assisted development in facilitating model implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;ILI positivity rate data spanning from 2014 to 2024 were obtained from the Chinese National Influenza Center (CNIC) database. In total, 5 deep learning architectures-long short-term memory (LSTM), neural basis expansion analysis for time series (N-BEATS), transformer, temporal fusion transformer (TFT), and time-series dense encoder (TiDE)-were developed using a ChatGPT-assisted workflow covering code generation, error debugging, and performance optimization. Models were trained on data from 2014 to 2023 and tested on holdout data from 2024 (weeks 1-39). Performance was evaluated using mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;ILI trends exhibited clear seasonal patterns with winter peaks and summer troughs, alongside marked fluctuations during the COVID-19 pandemic period (2020-2022). All 5 deep learning models were successfully constructed, debugged, and optimized with the assistance of ChatGPT. Among the 5 models, TiDE achieved the best predictive performance nationally (MAE=5.551, MSE=43.976, MAPE=72.413%) and in the southern region (MAE=7.554, MSE=89.708, MAPE=74.475%). In the northern region, where forecasting proved more challenging, TiDE still performed best (MAE=4.131, MSE=28.922), although high percentage errors remained (MAPE&gt;400%). N-BEATS demonstrated the second-best performance nationally (MAE=9.423) and showed greater stability in the north (MAE=6.325). In contrast, transformer and TFT consistently underperformed, with national MAE values of 10.613 and 12.538, respectively. TFT exhibited the highest deviation (national MAPE=169.29%). Extreme regional disparities were observed, particularly in northern China, where LSTM and TFT generated MAPE values exceeding 1918%, despite LSTM's moderate performance in the south (MAE=9.460).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Deep learning models, particularly TiDE, demonstrate strong potential for accurate ILI forecasting across diverse regions of China. Furthermore, large language models like ChatGPT can substantially enhance modeling efficiency and accessibility by assisting nontechnical users in model development. These findings support the i","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e74423"},"PeriodicalIF":5.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12227151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511996","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":"Virtual Reality in Obesity Management: Addressing Key Limitations in Clinical Application.","authors":"Xin Hu, Bin Wei","doi":"10.2196/68269","DOIUrl":"10.2196/68269","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68269"},"PeriodicalIF":5.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12227153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511940","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}