JMIR Formative Research最新文献

{"title":"Remote Patient Monitoring and Digital Therapeutics Enhancing the Continuum of Care in Heart Failure: Nonrandomized Pilot Study.","authors":"Emmanuel Marier-Tétrault, Emmanuel Bebawi, Stéphanie Béchard, Philippe Brouillard, Priccila Zuchinali, Emilie Remillard, Zoé Carrier, Loyda Jean-Charles, John Nam Kha Nguyen, Pascale Lehoux, Marie-Pascale Pomey, Paula A B Ribeiro, François Tournoux","doi":"10.2196/53444","DOIUrl":"https://doi.org/10.2196/53444","url":null,"abstract":"<p><strong>Background: </strong>Heart failure (HF) is the primary cause of hospitalization among Canadian patients aged ≥65 years. Care for HF requires regular clinical follow-ups to prevent readmissions and facilitate medical therapy optimization. Multiple barriers lead to therapeutic medical inertia including limited human resources and regional inequities. Remote patient monitoring (RPM) and digital therapeutics (DTx) solutions have been developed to improve HF management, but their adoption remains limited and underexplored. The Continuum project emerged as a collaborative initiative involving a health care center, a software start-up, and an industrial partner.</p><p><strong>Objective: </strong>We aimed to develop and test the feasibility of the Continuum intervention that seamlessly combined an RPM system with a DTx solution for HF within the same software.</p><p><strong>Methods: </strong>A 3-month pre-post pilot study was conducted from October 2020 to June 2021. Patients with HF who owned a smartphone or tablet (having remote patient monitoring [RPM+]), had (1) access to a self-care app where they could enter their vital signs, weight, and HF symptoms and view educational content; (2) daily monitoring of their data by a nurse; and (3) a DTx module with automated HF medication suggestions based on national guidelines, made available to their treating medical team. Bluetooth devices were offered to facilitate data recording. Nurses on RPM monitoring could call patients and arrange appointments with their medical team. Patients without a mobile device or unable to use the app were followed in another group (without remote patient monitoring [RPM-]).</p><p><strong>Results: </strong>In total, 52 patients were enrolled in this study (32 RPM+ and 20 RPM-). Among patients owning a mobile device, only 14% (5/37) could not use the app. In the RPM+ group, 47% (15/32) of the patients used the app for more than 80% (67 days) of the 12-week study period. The use of our digital solution was integrated into the regular nursing workday and only 34 calls had to be made by the nurse during the study period. Only 6% (2/32) of the patients in the RPM+ group experienced at least 1 all-cause hospitalization versus 35% (7/20) of the RPM- ones during the follow-up (6%, 2/32 vs 25%, 5/20 for HF hospitalization) and patients were more likely to have their HF therapy optimized if the DTx solution was available. Quality of life improved in patients compliant with the use of the mobile app (mean score variation +10.6, SD 14.7).</p><p><strong>Conclusions: </strong>This pilot study demonstrated the feasibility of implementing our digital solution, within the specific context of HF. The seamless integration of Continuum into nursing workflow, mobile app accessibility, and adoption by patients, were the 3 main key learning points of this study. Further investigation is required to assess the potential impacts on hospitalizations, drug optimization, and quality of life.</p","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights From the Development of a Dynamic Consent Platform for the Australians Together Health Initiative (ATHENA) Program: Interview and Survey Study.","authors":"Eddy Xiong, Carissa Bonner, Amanda King, Zoltan Maxwell Bourne, Mark Morgan, Ximena Tolosa, Tony Stanton, Kim Greaves","doi":"10.2196/57165","DOIUrl":"10.2196/57165","url":null,"abstract":"<p><strong>Background: </strong>Dynamic consent has the potential to address many of the issues facing traditional paper-based or electronic consent, including enrolling informed and engaged participants in the decision-making process. The Australians Together Health Initiative (ATHENA) program aims to connect participants across Queensland, Australia, with new research opportunities. At its core is dynamic consent, an interactive and participant-centric digital platform that enables users to view ongoing research activities, update consent preferences, and have ongoing engagement with researchers.</p><p><strong>Objective: </strong>This study aimed to describe the development of the ATHENA dynamic consent platform within the framework of the ATHENA program, including how the platform was designed, its utilization by participants, and the insights gained.</p><p><strong>Methods: </strong>One-on-one interviews were undertaken with consumers, followed by a workshop with health care staff to gain insights into the dynamic consent concept. Five problem statements were developed, and solutions were posed, from which a dynamic consent platform was constructed, tested, and used for implementation in a clinical trial. Potential users were randomly recruited from a pre-existing pool of 615 participants in the ATHENA program. Feedback on user platform experience was gained from a survey hosted on the platform.</p><p><strong>Results: </strong>In the 13 consumer interviews undertaken, participants were positive about dynamic consent, valuing privacy, ease of use, and adequate communication. Motivators for registration were feedback on data usage and its broader community benefits. Problem statements were security, trust and governance, ease of use, communication, control, and need for a scalable platform. Using the newly constructed dynamic consent platform, 99 potential participants were selected, of whom 67 (68%) were successfully recontacted. Of these, 59 (88%) agreed to be sent the platform, 44 (74%) logged on (indicating use), and 22 (57%) registered for the clinical trial. Survey feedback was favorable, with an average positive rating of 78% across all questions, reflecting satisfaction with the clarity, brevity, and flexibility of the platform. Barriers to implementation included technological and health literacy.</p><p><strong>Conclusions: </strong>This study describes the successful development and testing of a dynamic consent platform that was well-accepted, with users recognizing its advantages over traditional methods of consent regarding flexibility, ease of communication, and participant satisfaction. This information may be useful to other researchers who plan to use dynamic consent in health care research.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.","authors":"Ho Heon Kim, Won Chan Jeong, Kyungran Pi, Angela Soeun Lee, Min Soo Kim, Hye Jin Kim, Jae Hong Kim","doi":"10.2196/58776","DOIUrl":"https://doi.org/10.2196/58776","url":null,"abstract":"<p><strong>Background: </strong>Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment.</p><p><strong>Objective: </strong>This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources.</p><p><strong>Methods: </strong>A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants.</p><p><strong>Results: </strong>The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777).</p><p><strong>Conclusions: </strong>We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Codesigning a Digital Type 2 Diabetes Risk Communication Tool in Singapore: Qualitative Participatory Action Research Approach.","authors":"Jumana Hashim, Lidia Luna Puerta, Pin Sym Foong, E Shyong Tai, Huso Yi, Helen Elizabeth Smith","doi":"10.2196/50456","DOIUrl":"https://doi.org/10.2196/50456","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Diabetes is a serious public health concern worldwide. Despite public health efforts encouraging early screening and improving knowledge of effective interventions for those at increased risk of type 2 diabetes (T2D), the incorporation of preventative behaviors into an individual's daily life remains suboptimal. Successfully and accurately increasing risk perception has been demonstrated to increase behavioral intention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study aims to codesign a T2D risk communication tool by engaging public participants to (1) identify key characteristics that contribute to an effective risk communication tool and (2) test and iterate to develop a culturally sensitive and meaningful risk communication tool that can motivate T2D preventative behaviors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We adopted a novel methodology, \"Patient and Public Involvement (PPI) Hawkers,\" where we approached patrons at hawker centers and public eateries frequented by all local residents to evaluate and test 3 prototypes for the tool. The three prototypes were (1) \"Diabetes Onset\"-estimated age of diabetes onset of T2D based on one's risk factors, (2) \"Relative Risk\"-the relative risk of T2D is presented in a 1-10 scale indicating where one's risk score lie in relation to others, and (3) \"Metabolic Age\"-the median age of the risk category based on one's risk factors, presented to be compared against their chronological age. We gathered reactions and feedback through rapid testing and iteration to understand which risk result presentation would be received the best. All the collected data were revisited and analyzed using an inductive thematic analysis to identify the key characteristics contributing to an effective risk communication tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We engaged with 112 participants (female: n=59, 56%) across 6 hawker centers. The key characteristics that were important to participants emerged in four main themes: (1) appeal and user experience, in terms of format and readability; (2) trust and validity of the institution providing the tool and the accuracy of the risk result; (3) threat appraisal: salience of risk information, which influenced their risk perception; and (4) coping appraisal: facilitators for behavior change, which impacted their intention for implementing T2D preventative behaviors. The predictive nature of the prototype entitled \"Diabetes Onset\" was poorly received and removed after the first iteration. The Relative Risk prototype was valued for being straightforward but feared to be boring. The Metabolic Age prototype was anticipated to be more motivating for behavior change, but there were some concerns that the terminology may not be understood by everyone.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Participants were divided on which of the 2 prototypes, \"Metabolic Age\" or \"Relative Risk,\" they would favor adopting. Further testing is now required to determine which prototype will be mo","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Views and Needs of Students, Parents, and Teachers on Closed-Circuit Television, Proximity Trackers, and Access Cards to Facilitate COVID-19 Contact Tracing in Schools: Thematic Analysis of Focus Groups and Interviews.","authors":"Sofia Chantziara, Ian J Craddock, Claire H McCallum, Amberly L C Brigden","doi":"10.2196/67607","DOIUrl":"https://doi.org/10.2196/67607","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/44592.].</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.","authors":"Masato Tagi, Yasuhiro Hamada, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki, Takeshi Konishi, Jun Hirose","doi":"10.2196/55218","DOIUrl":"https://doi.org/10.2196/55218","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R&lt;sup&gt;2&lt;/sup&gt; value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P&lt;.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.&lt;","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Claims-Based Algorithm to Identify Pre-Exposure Prophylaxis Indications for Tenofovir Disoproxil Fumarate and Emtricitabine Prescriptions (2012-2014): Validation Study.","authors":"Patrick Sean Sullivan, Robertino M Mera-Giler, Staci Bush, Valentina Shvachko, Eleanor Sarkodie, Daniel O'Farrell, Stephanie Dubose, David Magnuson","doi":"10.2196/55614","DOIUrl":"10.2196/55614","url":null,"abstract":"<p><strong>Background: </strong>To monitor the use of tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) and related medicines for pre-exposure prophylaxis (PrEP) as HIV prevention using commercial pharmacy data, it is necessary to determine whether TDF/FTC prescriptions are used for PrEP or for some other clinical indication.</p><p><strong>Objective: </strong>This study aimed to validate an algorithm to distinguish the use of TDF/FTC for HIV prevention or infectious disease treatment.</p><p><strong>Methods: </strong>An algorithm was developed to identify whether TDF/FTC prescriptions were for PrEP or for other indications from large-scale administrative databases. The algorithm identifies TDF/FTC prescriptions and then excludes patients with International Classification of Diseases (ICD)-9 diagnostic codes, medications, or procedures that suggest indications other than for PrEP (eg, documentation of HIV infection, chronic hepatitis B, or use of TDF/FTC for postexposure prophylaxis). For evaluation, we collected data by clinician assessment of medical records for patients with TDF/FTC prescriptions and compared the assessed indication identified by the clinician review with the assessed indication identified by the algorithm. The algorithm was then applied and evaluated in a large, urban, community-based sexual health clinic.</p><p><strong>Results: </strong>The PrEP algorithm demonstrated high sensitivity and moderate specificity (99.6% and 49.6%) in the electronic medical record database and high sensitivity and specificity (99% and 87%) in data from the urban community health clinic.</p><p><strong>Conclusions: </strong>The PrEP algorithm classified the indication for PrEP in most patients treated with TDF/FTC with sufficient accuracy to be useful for surveillance purposes. The methods described can serve as a basis for developing a robust and evolving case definition for antiretroviral prescriptions for HIV prevention purposes.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Extended Reality to Enhance Effectiveness and Group Identification in Remote Group Therapy for Anxiety Disorders: A Critical Analysis.","authors":"Ayoub Bouguettaya, Elias Aboujaoude","doi":"10.2196/64494","DOIUrl":"https://doi.org/10.2196/64494","url":null,"abstract":"<p><p>Group therapy is a scalable and effective treatment for anxiety disorders. However, when performed online, the reduced ability to identify with group members and the reduced interactivity can limit its appeal and effectiveness. Extended reality (XR) technology, including virtual reality and augmented reality, may help address these limitations, thereby enhancing the reach of online group therapy and the benefits that can be drawn from it. To understand how the incorporation of XR technology may improve online group therapy for anxiety disorders, this viewpoint paper examines evidence related to the treatment of anxiety disorders using offline group therapy, online group therapy, and virtual reality, as well as ways to increase social identification and interactivity with the platform, the therapist, and other users. This viewpoint paper suggests ways to integrate these research streams to leverage the strengths of XR platforms and improve group therapeutic offerings.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Body Positivity, Physical Health, and Emotional Well-Being Discourse on Social Media: Content Analysis of Lizzo's Instagram.","authors":"Stephanie L Albert, Rachel E Massar, Omni Cassidy, Kayla Fennelly, Melanie Jay, Philip M Massey, Marie A Bragg","doi":"10.2196/60541","DOIUrl":"https://doi.org/10.2196/60541","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Weight stigma is a fundamental cause of health inequality. Body positivity may be a counterbalance to weight stigma. Social media is replete with weight-stigmatizing content and is a driver of poor mental health outcomes; however, there remains a gap in understanding its potential to mitigate the prevalence and impact of harmful messaging and to promote positive effects on a large scale.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We selected musical artist Lizzo, whose brand emphasizes body positivity and empowerment, for an instrumental case study on the discourse on social media and specifically Instagram. We focused on 3 domains, including body positivity, physical health, and emotional well-being. These domains challenge social norms around weight and body size and have the potential to positively affect the physical and psychological health of people with diverse body sizes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We evaluated posts by Lizzo, comments from Instagram users, and replies to comments over a 2-month period (October 11 to December 12, 2019). Two coders rated Lizzo's posts and Instagram users' comments for their sentiments on the 3 domains. Replies to Instagram users' comments were assessed for their reactions to comments (ie, did they oppose or argue against the comment or did they support or bolster the comment). Engagement metrics, including the number of \"likes,\" were also collected.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The final sample included 50 original posts by Lizzo, 250 comments from Instagram users, and 1099 replies to comments. A proportion of Lizzo's content included body positive sentiments (34%) and emotional well-being (18%); no posts dealt explicitly with physical health. A substantial amount Instagram users' comments and replies contained stigmatizing content including the use of nauseated and vomiting emojis, implications that Lizzo's body was shameful and should be hidden away, accusations that she was promoting obesity, and impeachments of Lizzo's health. In spite of the stigmatizing content, we also discovered content highlighting the beneficial nature of having positive representation of a Black woman living in a larger body who is thriving. Moreover, analysis of the discourse between users illustrated that stigmatizing expressions are being combated online, at least to some degree.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates that Lizzo has exposed millions of social media users to messages about body positivity and provided more visibility for conversations about weight and shape. Future research should examine the extent to which body positive messages can lead to greater acceptance of individuals living in larger bodies. Instagram and other social media platforms should consider ways to reduce body-shaming content while finding ways to promote content that features diverse bodies. Shifting the landscape of social media could decrease stereotypes about weight and shape while incre","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Views on Online Sexual Health Symptom Checker Tool: Qualitative Research.","authors":"Alicia Jean King, Jade Elissa Bilardi, Janet Mary Towns, Kate Maddaford, Christopher Kincaid Fairley, Eric P F Chow, Tiffany Renee Phillips","doi":"10.2196/54565","DOIUrl":"https://doi.org/10.2196/54565","url":null,"abstract":"<p><strong>Background: </strong>Delayed diagnosis and treatment of sexually transmitted infections (STIs) contributes to poorer health outcomes and onward transmission to sexual partners. Access to best-practice sexual health care may be limited by barriers such as cost, distance to care providers, sexual stigma, and trust in health care providers. Online assessments of risk offer a novel means of supporting access to evidence-based sexual health information, testing, and treatment by providing more individualized sexual health information based on user inputs.</p><p><strong>Objective: </strong>This developmental evaluation aims to find potential users' views and experiences in relation to an online assessment of risk, called iSpySTI (Melbourne Sexual Health Center), including the likely impacts of use.</p><p><strong>Methods: </strong>Individuals presenting with urogenital symptoms to a specialist sexual health clinic were given the opportunity to trial a web-based, Bayesian-powered tool that provides a list of 2 to 4 potential causes of their symptoms based on inputs of known STI risk factors and symptoms. Those who tried the tool were invited to participate in a once-off, semistructured research interview. Descriptive, action, and emotion coding informed the comparative analysis of individual cases.</p><p><strong>Results: </strong>Findings from interviews with 14 people who had used the iSpySTI tool support the superiority of the online assessment of STI risk compared to existing sources of sexual health information (eg, internet search engines) in providing trusted and probabilistic information to users. Additionally, potential users reported benefits to their emotional well-being in the intervening period between noticing symptoms and being able to access care. Differences in current and imagined urgency of health care seeking and emotional impacts were found based on clinical diagnosis (eg, non-STI, curable and incurable but treatable STIs) and whether participants were born in Australia or elsewhere.</p><p><strong>Conclusions: </strong>Online assessments of risk provide users experiencing urogenital symptoms with more individualized and evidence-based health information that can improve their health care-seeking and provide reassurance in the period before they can access care.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}