Journal of Medical Internet Research最新文献

{"title":"Using Large Language Models to Abstract Complex Social Determinants of Health From Original and Deidentified Medical Notes: Development and Validation Study.","authors":"Alexandra Ralevski, Nadaa Taiyab, Michael Nossal, Lindsay Mico, Samantha Piekos, Jennifer Hadlock","doi":"10.2196/63445","DOIUrl":"https://doi.org/10.2196/63445","url":null,"abstract":"<p><strong>Background: </strong>Social determinants of health (SDoH) such as housing insecurity are known to be intricately linked to patients' health status. More efficient methods for abstracting structured data on SDoH can help accelerate the inclusion of exposome variables in biomedical research and support health care systems in identifying patients who could benefit from proactive outreach. Large language models (LLMs) developed from Generative Pre-trained Transformers (GPTs) have shown potential for performing complex abstraction tasks on unstructured clinical notes.</p><p><strong>Objective: </strong>Here, we assess the performance of GPTs on identifying temporal aspects of housing insecurity and compare results between both original and deidentified notes.</p><p><strong>Methods: </strong>We compared the ability of GPT-3.5 and GPT-4 to identify instances of both current and past housing instability, as well as general housing status, from 25,217 notes from 795 pregnant women. Results were compared with manual abstraction, a named entity recognition model, and regular expressions.</p><p><strong>Results: </strong>Compared with GPT-3.5 and the named entity recognition model, GPT-4 had the highest performance and had a much higher recall (0.924) than human abstractors (0.702) in identifying patients experiencing current or past housing instability, although precision was lower (0.850) compared with human abstractors (0.971). GPT-4's precision improved slightly (0.936 original, 0.939 deidentified) on deidentified versions of the same notes, while recall dropped (0.781 original, 0.704 deidentified).</p><p><strong>Conclusions: </strong>This work demonstrates that while manual abstraction is likely to yield slightly more accurate results overall, LLMs can provide a scalable, cost-effective solution with the advantage of greater recall. This could support semiautomated abstraction, but given the potential risk for harm, human review would be essential before using results for any patient engagement or care decisions. Furthermore, recall was lower when notes were deidentified prior to LLM abstraction.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e63445"},"PeriodicalIF":5.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675926","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":"Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study.","authors":"Yuhe Ke, Rui Yang, Sui An Lie, Taylor Xin Yi Lim, Yilin Ning, Irene Li, Hairil Rizal Abdullah, Daniel Shu Wei Ting, Nan Liu","doi":"10.2196/59439","DOIUrl":"https://doi.org/10.2196/59439","url":null,"abstract":"<p><strong>Background: </strong>Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field.</p><p><strong>Objective: </strong>This study aimed to explore the role of large language models (LLMs) in mitigating these biases through the use of the multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy compared with humans.</p><p><strong>Methods: </strong>A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 (OpenAI) to facilitate interactions among different simulated agents to replicate clinical team dynamics. Each agent was assigned a distinct role: (1) making the final diagnosis after considering the discussions, (2) acting as a devil's advocate to correct confirmation and anchoring biases, (3) serving as a field expert in the required medical subspecialty, (4) facilitating discussions to mitigate premature closure bias, and (5) recording and summarizing findings. We tested varying combinations of these agents within the framework to determine which configuration yielded the highest rate of correct final diagnoses. Each scenario was repeated 5 times for consistency. The accuracy of the initial diagnoses and the final differential diagnoses were evaluated, and comparisons with human-generated answers were made using the Fisher exact test.</p><p><strong>Results: </strong>A total of 240 responses were evaluated (3 different multi-agent frameworks). The initial diagnosis had an accuracy of 0% (0/80). However, following multi-agent discussions, the accuracy for the top 2 differential diagnoses increased to 76% (61/80) for the best-performing multi-agent framework (Framework 4-C). This was significantly higher compared with the accuracy achieved by human evaluators (odds ratio 3.49; P=.002).</p><p><strong>Conclusions: </strong>The multi-agent framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. In addition, the LLM-driven, multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e59439"},"PeriodicalIF":5.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675924","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":"Telemedicine Integrated Care Versus In-Person Care Mode for Patients With Short Stature: Comprehensive Comparison of a Retrospective Cohort Study.","authors":"Yipei Wang, Pei Zhang, Yan Xing, Huifeng Shi, Yunpu Cui, Yuan Wei, Ke Zhang, Xinxia Wu, Hong Ji, Xuedong Xu, Yanhui Dong, Changxiao Jin","doi":"10.2196/57814","DOIUrl":"10.2196/57814","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Telemedicine has demonstrated efficacy as a supplement to traditional in-person care when treating certain diseases. Nevertheless, more investigation is needed to comprehensively assess its potential as an alternative to in-person care and its influence on access to care. The successful treatment of short stature relies on timely and regular intervention, particularly in rural and economically disadvantaged regions where the disease is more prevalent.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluated the clinical outcomes, health-seeking behaviors, and cost of telemedicine integrated into care for children with short stature in China.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Our study involved 1241 individuals diagnosed with short stature at the pediatric outpatient clinic of Peking University Third Hospital between 2012 and 2023. Patients were divided into in-person care (IPC; 1183 patients receiving only in-person care) and telemedicine integrated care (TIC; 58 patients receiving both in-person and virtual care) groups. For both groups, the initial 71.43% (average of 58 percentages, with each percentage representing the ratio of patients in the treatment group) of visits were categorized into the pretelemedicine phase. We used propensity score matching to select individuals with similar baseline conditions. We used 7 variables such as age, gender, and medical insurance for the 1:5 closest neighbor match. Eventually, 115 patients in the IPC group and 54 patients in the TIC group were selected. The primary clinical outcome was the change in the standard height percentage. Health-seeking behavior was described by visit intervals in the pre- and post-telemedicine phases. The cost analysis compared costs both between different groups and between different visit modalities of the TIC group in the post-telemedicine phase.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In terms of clinical effectiveness, we demonstrated that the increase in height among the TIC group (Δz&lt;sub&gt;TIC&lt;/sub&gt;=0.74) was more substantial than that for the IPC group (Δz&lt;sub&gt;IPC&lt;/sub&gt;=0.51, P=.01; paired t test), while no unfavorable changes in other endpoints such as BMI or insulin-like growth factor 1 (IGF-1) levels were observed. As for health-seeking behaviors, the results showed that, during the post-telemedicine phase, the IPC group had a visit interval of 71.08 (IQR 50.75-90.73) days, significantly longer than the prior period (51.25 [IQR 34.75-82.00] days, P&lt;.001; U test), whereas the TIC group's visit interval remained unchanged. As for the cost per visit, there was no difference in the average cost per visit between the 2 groups nor between the pre- and post-telemedicine phases. During the post-telemedicine phase, within the TIC group, in-person visits had a higher average total cost, elevated medical and labor expenses, and greater medical cost compared with virtual visits.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We contend that the rise in medical visits facil","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e57814"},"PeriodicalIF":5.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667865","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":"Added Value of Medical Subject Headings Terms in Search Strategies of Systematic Reviews: Comparative Study.","authors":"Victor Leblanc, Aghiles Hamroun, Raphaël Bentegeac, Bastien Le Guellec, Rémi Lenain, Emmanuel Chazard","doi":"10.2196/53781","DOIUrl":"https://doi.org/10.2196/53781","url":null,"abstract":"<p><strong>Background: </strong>The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically.</p><p><strong>Objective: </strong>The objective of this study was to estimate the added value of using MeSH terms in PubMed queries for systematic reviews (SRs).</p><p><strong>Methods: </strong>SRs published in 4 high-impact medical journals in general medicine over the past 10 years were selected. Only SRs for which a PubMed query was provided were included. Each query was transformed to obtain 3 versions: the original query (V1), the query with free-text terms only (V2), and the query with MeSH terms only (V3). These 3 queries were compared with each other based on their sensitivity and positive predictive values.</p><p><strong>Results: </strong>In total, 59 SRs were included. The suppression of MeSH terms had an impact on the number of relevant articles retrieved for 24 (41%) out of 59 SRs. The median (IQR) sensitivities of queries V1 and V2 were 77.8% (62.1%-95.2%) and 71.4% (42.6%-90%), respectively. V1 queries provided an average of 2.62 additional relevant papers per SR compared with V2 queries. However, an additional 820.29 papers had to be screened. The cost of screening an additional collected paper was therefore 313.09, which was slightly more than triple the mean reading cost associated with V2 queries (88.67).</p><p><strong>Conclusions: </strong>Our results revealed that removing MeSH terms from a query decreases sensitivity while slightly increasing the positive predictive value. Queries containing both MeSH and free-text terms yielded more relevant articles but required screening many additional papers. Despite this additional workload, MeSH terms remain indispensable for SRs.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e53781"},"PeriodicalIF":5.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675921","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":"AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.","authors":"Pin Zhong Chan, Eric Jin, Miia Jansson, Han Shi Jocelyn Chew","doi":"10.2196/58892","DOIUrl":"https://doi.org/10.2196/58892","url":null,"abstract":"<p><strong>Background: </strong>Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.</p><p><strong>Objective: </strong>This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.</p><p><strong>Methods: </strong>A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.</p><p><strong>Results: </strong>A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.</p><p><strong>Conclusions: </strong>Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e58892"},"PeriodicalIF":5.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675922","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":"Author's Reply: Expanding the Scope: Reflections on Digital Smoking Cessation Strategies for Diverse Age Groups.","authors":"Margaret C Fahey","doi":"10.2196/67749","DOIUrl":"10.2196/67749","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e67749"},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667943","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":"Digital Health Platform for Improving the Effect of the Active Health Management of Chronic Diseases in the Community: Mixed Methods Exploratory Study.","authors":"Zhiheng Zhou, Danian Jin, Jinghua He, Shengqing Zhou, Jiang Wu, Shuangxi Wang, Yang Zhang, Tianyuan Feng","doi":"10.2196/50959","DOIUrl":"10.2196/50959","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;China is vigorously promoting the health management of chronic diseases and exploring digital active health management. However, as most medical information systems in China have been built separately, there is poor sharing of medical information. It is difficult to achieve interconnectivity among community residents' self-testing information, community health care information, and hospital health information, and digital chronic disease management has not been widely applied in China.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to build a digital health platform and improve the effectiveness of full-cycle management for community chronic diseases through digital active health management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was a single-arm pre-post intervention study involving the development and use of a digital health platform (2-year intervention; 2020 to 2022). The digital health platform included the \"i Active Health\" applet for residents and the active health information system (cardio-cerebrovascular disease risk management system) for medical teams. The digital active health management of chronic diseases involved creating health streets, providing internet-assisted full-cycle active health services for residents, implementing internet-based community management for hypertension and diabetes, and performing real-time quantitative assessment and hierarchical management of residents' risks of cardio-cerebrovascular disease. After the 2-year intervention, management effectiveness was evaluated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We constructed a digital health platform with interconnected health information and implemented a digital active health management model. After the intervention, the 2-way referral between community health care institutions and hospitals increased. Residents' health literacy rate increased from 30.6% (3062/10,000) in 2020 to 49.9% (4992/10,000) in 2022, with improvements in health knowledge, health behavior, and health skills. Moreover, the risk of cardio-cerebrovascular disease decreased after the intervention. The community hypertension and diabetes standardized management rates increased from 59.6% (2124/3566) and 55.8% (670/1200) in 2020 to 75.0% (3212/4285) and 69.4% (1686/2430) in 2022, respectively. The control rates of blood pressure in patients with hypertension and blood sugar in patients with diabetes increased from 51.7% (1081/2091) and 42.0% (373/888) in 2020 to 81.2% (1698/2091) and 73.0% (648/888) in 2022, respectively. The intervention improved patients' BMI, waist circumference, blood uric acid levels, and low-density lipoprotein cholesterol levels. The drug compliance rate of patients with hypertension and diabetes increased from 33.6% (703/2091) and 36.0% (320/888) in 2020 to 73.3% (1532/2091) and 75.8% (673/888) in 2022, respectively. The intervention greatly improved the diet behavior, exercise behavior, and drinking behavior of patients with hypertension and","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e50959"},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667957","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 Vast Potential of ChatGPT in Pediatric Surgery.","authors":"Ran Tang, Shi-Qin Qi","doi":"10.2196/66453","DOIUrl":"10.2196/66453","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e66453"},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667869","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":"Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study.","authors":"Christopher Slade, Roberto M Benzo, Peter Washington","doi":"10.2196/55694","DOIUrl":"10.2196/55694","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F&lt;sub&gt;1,144&lt;/sub&gt;=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (χ&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;1&lt;/sub&gt;=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e55694"},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667955","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":"Expanding the Scope: Reflections on Digital Smoking Cessation Strategies for Diverse Age Groups.","authors":"Bin Wei, Xin Hu, XiaoRong Wu","doi":"10.2196/65929","DOIUrl":"10.2196/65929","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e65929"},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667962","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}