Journal of Medical Internet Research最新文献

{"title":"AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.","authors":"David Owen, Amy J Lynham, Sophie E Smart, Antonio F Pardiñas, Jose Camacho Collados","doi":"10.2196/59225","DOIUrl":"10.2196/59225","url":null,"abstract":"<p><strong>Background: </strong>Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care.</p><p><strong>Objective: </strong>This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness.</p><p><strong>Methods: </strong>A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review.</p><p><strong>Results: </strong>Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone.</p><p><strong>Conclusions: </strong>Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e59225"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638402","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":"Correction: Effects of Message Framing on Human Papillomavirus Vaccination: Systematic Review.","authors":"Jie Gong, Dandan Gu, Suyun Dong, Wangqin Shen, Haiou Yan, Juan Xie","doi":"10.2196/68783","DOIUrl":"10.2196/68783","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/52738.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e68783"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Comment Slant and Comment Tone on Digital Health Communication Among Polarized Publics: A Web-Based Survey Experiment.","authors":"Fangcao Lu, Caixie Tu","doi":"10.2196/57967","DOIUrl":"10.2196/57967","url":null,"abstract":"<p><strong>Background: </strong>Public attitudes toward health issues are becoming increasingly polarized, as seen in social media comments, which vary from supportive to oppositional and frequently include uncivil language. The combined effects of comment slant and comment tone on health behavior among a polarized public need further examination.</p><p><strong>Objective: </strong>This study aims to examine how social media users' prior attitudes toward mask wearing and their exposure to a mask-wearing-promoting post, synchronized with polarized and hostile discussions, affect their compliance with mask wearing.</p><p><strong>Methods: </strong>The study was a web-based survey experiment with participants recruited from Amazon Mechanical Turk. A total of 522 participants provided consent and completed the study. Participants were assigned to read a fictitious mask-wearing-promoting social media post with either civil anti-mask-wearing comments (130/522, 24.9%), civil pro-mask-wearing comments (129/522, 24.7%), uncivil anti-mask-wearing comments (131/522, 25.1%), or uncivil pro-mask-wearing comments (132/522, 25.3%). Following this, the participants were asked to complete self-assessed questionnaires. The PROCESS macro in SPSS (model 12; IBM Corp) was used to test the 3-way interaction effects between comment slant, comment tone, and prior attitudes on participants' presumed influence from the post and their behavioral intention to comply with mask-wearing.</p><p><strong>Results: </strong>Anti-mask-wearing comments led social media users to presume less influence about others' acceptance of masks (B=1.49; P<.001; 95% CI 0.98-2.00) and resulted in decreased mask-wearing intention (B=0.07; P=.03; 95% CI 0.01-0.13). Comment tone with incivility also reduced compliance with mask-wearing (B=-0.44; P=.02; 95% CI -0.81 to -0.07). Furthermore, polarized attitudes had a direct impact (B=0.86; P<.001; 95% CI 0.45-1.26) and also interacted with both the slant and tone of comments, influencing mask-wearing intention.</p><p><strong>Conclusions: </strong>Pro-mask-wearing comments enhanced presumed influence and compliance of mask-wearing, but incivility in the comments hindered this positive impact. Antimaskers showed increased compliance when they were unable to find civil support for their opinion in the social media environment. The findings suggest the need to correct and moderate uncivil language and misleading information in online comment sections while encouraging the posting of supportive and civil comments. In addition, information literacy programs are needed to prevent the public from being misled by polarized comments.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e57967"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638823","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":"EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study.","authors":"Samira Abbasgholizadeh Rahimi, Richa Shrivastava, Anita Brown-Johnson, Pascale Caidor, Claire Davies, Amal Idrissi Janati, Pascaline Kengne Talla, Sreenath Madathil, Bettina M Willie, Elham Emami","doi":"10.2196/63356","DOIUrl":"10.2196/63356","url":null,"abstract":"<p><strong>Background: </strong>Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle.</p><p><strong>Objective: </strong>This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems.</p><p><strong>Methods: </strong>This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops' insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development.</p><p><strong>Results: </strong>In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care.</p><p><strong>Conclusions: </strong>The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e63356"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of the Offer of the Smoke Free Smartphone App Compared With No Intervention for Smoking Cessation: Pragmatic Randomized Controlled Trial.","authors":"Sarah Jackson, Dimitra Kale, Emma Beard, Olga Perski, Robert West, Jamie Brown","doi":"10.2196/50963","DOIUrl":"https://doi.org/10.2196/50963","url":null,"abstract":"<p><strong>Background: </strong>Digital technologies offer the potential for low-cost, scalable delivery of interventions to promote smoking cessation.</p><p><strong>Objective: </strong>We aimed to evaluate the effectiveness of the offer of Smoke Free-an evidence-informed, widely used app-for smoking cessation versus no support.</p><p><strong>Methods: </strong>In this 2-arm randomized controlled trial, 3143 motivated adult smokers were recruited online between August 2020 and April 2021 and randomized to receive an offer of the Smoke Free app plus follow-up (intervention arm) versus follow-up only (comparator arm). Both groups were shown a brief message at the end of the baseline questionnaire encouraging them to make a quit attempt. The primary outcome was self-reported 6-month continuous abstinence assessed 7 months after randomization. Secondary outcomes included quit attempts in the first month post randomization, 3-month continuous abstinence assessed at 4 months, and 6-month continuous abstinence at 7 months among those who made a quit attempt. The primary analysis was performed on an intention-to-treat (ITT) analysis basis. Sensitivity analyses included (1) restricting the intervention group to those who took up the offer of the app, (2) using complete cases, and (3) using multiple imputation.</p><p><strong>Results: </strong>The effective follow-up rate for 7 months was 41.9%. The primary analysis showed no evidence of a benefit of the intervention on rates of 6-month continuous abstinence (intervention 6.8% vs comparator 7.0%; relative risk 0.97, 95% CI 0.75-1.26). Analyses of all secondary outcomes also showed no evidence of a benefit. Similar results were observed on complete cases and using multiple imputation. When the intervention group was restricted to those who took up the offer of the app (n=395, 25.3%), participants in the intervention group were 80% more likely to report 6-month continuous abstinence (12.7% vs 7.0%; relative risk 1.80, 95% CI 1.30-2.45). Equivalent subgroup analyses produced similar results on the secondary outcomes. These differences persisted after adjustment for key baseline characteristics.</p><p><strong>Conclusions: </strong>Among motivated smokers provided with very brief advice to quit, the offer of the Smoke Free app did not have a detectable benefit for cessation compared with follow-up only. However, the app increased quit rates when smokers randomized to receive the app downloaded it.</p><p><strong>Trial registration: </strong>ISRCTN ISRCTN85785540; https://www.isrctn.com/ISRCTN85785540.</p><p><strong>International registered report identifier (irrid): </strong>RR2-https://onlinelibrary.wiley.com/doi/full/10.1111/add.14652.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e50963"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638742","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":"Impact of Image Content on Medical Crowdfunding Success: A Machine Learning Approach.","authors":"Renwu Wang, Huimin Xu, Xupin Zhang","doi":"10.2196/58617","DOIUrl":"10.2196/58617","url":null,"abstract":"<p><strong>Background: </strong>As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience.</p><p><strong>Objective: </strong>This paper aims to explore the relationship between images and crowdfunding success in cancer-related crowdfunding projects.</p><p><strong>Methods: </strong>We used the Alibaba Cloud platform to detect individual features in images. In addition, we used the Recognize Anything Model to label images and obtain content tags. Furthermore, the discourse atomic topic model was used to generate image topics. After obtaining the image features and image content topics, we built regression models to investigate the factors that influence the results of crowdfunding success.</p><p><strong>Results: </strong>Images with a higher proportion of young people (β=0.0753; P<.001), a larger number of people (β=0.00822; P<.001), and a larger proportion of smiling faces (β=0.0446; P<.001) had a higher success rate. Image content related to good things and patient health also contributed to crowdfunding success (β=0.082, P<.001; and β=0.036, P<.001, respectively). In addition, the interaction between image topics and image characteristics had a significant effect on the final fundraising outcome. For example, when smiling faces are considered in conjunction with the image topics, using more smiling faces in the rest and play theme increased the amount of money raised (β=0.0152; P<.001). We also examined causality through a counterfactual analysis, which confirmed the influence of the variables on crowdfunding success, consistent with the results of our regression models.</p><p><strong>Conclusions: </strong>In the realm of web-based medical crowdfunding, the importance of uploaded images cannot be overstated. Image characteristics, including the number of people depicted and the presence of youth, significantly improve fundraising results. In addition, the thematic choice of images in cancer crowdfunding efforts has a profound impact. Images that evoke beauty and resonate with health issues are more likely to result in increased donations. However, it is critical to recognize that reinforcing character traits in images of different themes has different effects on the success of crowdfunding campaigns.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e58617"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638805","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":"Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.","authors":"Phawinpon Chotwanvirat, Aree Prachansuwan, Pimnapanut Sridonpai, Wantanee Kriengsinyos","doi":"10.2196/51432","DOIUrl":"https://doi.org/10.2196/51432","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;To accurately capture an individual's food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client's memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This review aims to fill the gap by providing a current overview of AI's integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems' strengths and weaknesses and propose enhancements to improve IADA's accuracy and adoption in the nutrition community.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve diet","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e51432"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638481","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":"Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.","authors":"Annette Brons, Shihan Wang, Bart Visser, Ben Kröse, Sander Bakkes, Remco Veltkamp","doi":"10.2196/47774","DOIUrl":"10.2196/47774","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A scoping review was conducted based on the framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorizat","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e47774"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638821","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":"Correction: Assessing the Feasibility and Acceptability of Smart Speakers in Behavioral Intervention Research With Older Adults: Mixed Methods Study.","authors":"Kelly Quinn, Sarah Leiser Ransom, Carrie O'Connell, Naoko Muramatsu, David X Marquez, Jessie Chin","doi":"10.2196/66813","DOIUrl":"10.2196/66813","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/54800.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e66813"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638403","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":"Comparing the Quality of Direct-to-Consumer Telemedicine Dominated and Delivered by Public and Private Sector Platforms in China: Standardized Patient Study.","authors":"Faying Song, Xue Gong, Yuting Yang, Rui Guo","doi":"10.2196/55400","DOIUrl":"https://doi.org/10.2196/55400","url":null,"abstract":"<p><strong>Background: </strong>Telemedicine is expanding rapidly, with public direct-to-consumer (DTC) telemedicine representing 70% of the market. A key priority is establishing clear quality distinctions between the public and private sectors. No studies have directly compared the quality of DTC telemedicine in the public and private sectors using objective evaluation methods.</p><p><strong>Objective: </strong>Using a standardized patient (SP) approach, this study aimed to compare the quality of DTC telemedicine provided by China's public and private sectors.</p><p><strong>Methods: </strong>We recruited 10 SPs presenting fixed cases (urticaria and childhood diarrhea), with 594 interactions between them and physicians. The SPs evaluated various aspects of the quality of care, effectiveness, safety, patient-centeredness (PCC), efficiency, and timeliness using the Institute of Medicine (IOM) quality framework. Ordinary least-squares (OLS) regression models with fixed effects were used for continuous variables, while logistic regression models with fixed effects were used for categorical variables.</p><p><strong>Results: </strong>Significant quality differences were observed between public and private DTC telemedicine. Physicians from private platforms were significantly more likely to adhere to clinical checklists (adjusted β 15.22, P<.001); provide an accurate diagnosis (adjusted odds ratio [OR] 3.85, P<.001), an appropriate prescription (adjusted OR 3.87, P<.001), and lifestyle modification advice (adjusted OR 6.82, P<.001); ensure more PCC (adjusted β 3.34, P<.001); and spend more time with SPs (adjusted β 839.70, P<.001), with more responses (adjusted β 1.33, P=.001) and more words (adjusted β 50.93, P=.009). However, SPs on private platforms waited longer for the first response (adjusted β 505.87, P=.001) and each response (adjusted β 168.33, P=.04) and paid more for the average visit (adjusted β 40.03, P<.001).</p><p><strong>Conclusions: </strong>There is significant quality inequality in different DTC telemedicine platforms. Private physicians might provide a higher quality of service regarding effectiveness and safety, PCC, and response times and words. However, private platforms have longer wait times for their first response, as well as higher costs. Refining online reviews, establishing standardized norms and pricing, enhancing the performance evaluation mechanism for public DTC telemedicine, and imposing stricter limitations on the first response time for private physicians should be considered practical approaches to optimizing the management of DTC telemedicine.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e55400"},"PeriodicalIF":5.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621207","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}