JMIR infodemiology最新文献

{"title":"How the General Public Navigates Health Misinformation on Social Media: Qualitative Study of Identification and Response Approaches.","authors":"Sharmila Sathianathan, Adliah Mhd Ali, Wei Wen Chong","doi":"10.2196/67464","DOIUrl":"https://doi.org/10.2196/67464","url":null,"abstract":"<p><strong>Background: </strong>Social media is widely used by the general public as a source of health information because of its convenience. However, the increasing prevalence of health misinformation on social media is becoming a serious concern, and it remains unclear how the general public identifies and responds to it.</p><p><strong>Objective: </strong>This study aims to explore the approaches used by the general public for identifying and responding to health misinformation on social media.</p><p><strong>Methods: </strong>Semistructured interviews were conducted with 22 respondents from the Malaysian general public. The theory of motivated information management was used as a guiding framework for conducting the interviews. Audio-taped interviews were transcribed verbatim and imported into ATLAS.ti software for analysis. Themes were identified from the qualitative data using a thematic analysis method.</p><p><strong>Results: </strong>The 3 main themes identified were emotional responses and impacts of health misinformation, approaches used to identify health misinformation, and responses to health misinformation. The spread of health misinformation through social media platforms has caused uncertainty and triggered a range of emotional responses, including anxiety and feelings of vulnerability, among respondents who encountered it. The approaches to identifying health misinformation on social media included examining message characteristics and sources. Messages were deemed to be misinformation if they contradicted credible sources or exhibited illogical and exaggerated content. Respondents described multiple response approaches to health misinformation based on the situation. Verification was chosen if the information was deemed important, while misinformation was often ignored to avoid conflict. Respondents were compelled to take action if misinformation affected their family members, had been corrected by others, or if they were knowledgeable about the topic. Taking action involved correcting the misinformation and reporting the misinformation to relevant social media, enforcement authorities, and government bodies.</p><p><strong>Conclusions: </strong>This study highlights the factors and motivations influencing the general public's identification and response to health misinformation on social media. Addressing the challenges of health misinformation identified in this study requires collaborative efforts from all stakeholders to reduce the spread of health misinformation and reduce the general public's belief in it.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e67464"},"PeriodicalIF":3.5,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487398","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":"Public Versus Academic Discourse on ChatGPT in Health Care: Mixed Methods Study.","authors":"Patrick Baxter, Meng-Hao Li, Jiaxin Wei, Naoru Koizumi","doi":"10.2196/64509","DOIUrl":"https://doi.org/10.2196/64509","url":null,"abstract":"<p><strong>Background: </strong>The rapid emergence of artificial intelligence-based large language models (LLMs) in 2022 has initiated extensive discussions within the academic community. While proponents highlight LLMs' potential to improve writing and analytical tasks, critics caution against the ethical and cultural implications of widespread reliance on these models. Existing literature has explored various aspects of LLMs, including their integration, performance, and utility, yet there is a gap in understanding the nature of these discussions and how public perception contrasts with expert opinion in the field of public health.</p><p><strong>Objective: </strong>This study sought to explore how the general public's views and sentiments regarding LLMs, using OpenAI's ChatGPT as an example, differ from those of academic researchers and experts in the field, with the goal of gaining a more comprehensive understanding of the future role of LLMs in health care.</p><p><strong>Methods: </strong>We used a hybrid sentiment analysis approach, integrating the Syuzhet package in R (R Core Team) with GPT-3.5, achieving an 84% accuracy rate in sentiment classification. Also, structural topic modeling was applied to identify and analyze 8 key discussion topics, capturing both optimistic and critical perspectives on LLMs.</p><p><strong>Results: </strong>Findings revealed a predominantly positive sentiment toward LLM integration in health care, particularly in areas such as patient care and clinical decision-making. However, concerns were raised regarding their suitability for mental health support and patient communication, highlighting potential limitations and ethical challenges.</p><p><strong>Conclusions: </strong>This study underscores the transformative potential of LLMs in public health while emphasizing the need to address ethical and practical concerns. By comparing public discourse with academic perspectives, our findings contribute to the ongoing scholarly debate on the opportunities and risks associated with LLM adoption in health care.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e64509"},"PeriodicalIF":3.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478139","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":"Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.","authors":"Muhammad Ahmad, Ildar Batyrshin, Grigori Sidorov","doi":"10.2196/70525","DOIUrl":"https://doi.org/10.2196/70525","url":null,"abstract":"<p><strong>Background: </strong>The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixed with other illicit substances. Various strategies have been developed by federal and local governments and health organizations to address this crisis. One of the most significant objectives is to understand the epidemic through better health surveillance, and machine learning techniques can support this by identifying opioid users at risk of overdose through the analysis of social media data, as many individuals may avoid direct testing but still share their experiences online.</p><p><strong>Objective: </strong>In this study, we take advantage of recent developments in machine learning that allow for insights into patterns of opioid use and potential risk factors in a less invasive manner using self-reported information available on social platforms.</p><p><strong>Methods: </strong>This study used YouTube comments posted between December 2020 and March 2024, in which individuals shared their self-reported experiences of opioid drugs mixed with other substances. We manually annotated our dataset into multiclass categories, capturing both the positive effects of opioid use, such as pain relief, euphoria, and relaxation, and negative experiences, including nausea, sadness, and respiratory depression, to provide a comprehensive understanding of the multifaceted impact of opioids. By analyzing this sentiment, we used 4 state-of-the-art machine learning models, 2 deep learning models, 3 transformer models, and 1 large language model (GPT-3.5 Turbo) to predict overdose risks to improve health care response and intervention strategies.</p><p><strong>Results: </strong>Our proposed methodology (GPT-3.5 Turbo) was highly precise and accurate, helping to automatically identify sentiment based on the adverse effects of opioid drug combinations and high-risk drug use in YouTube comments. Our proposed methodology demonstrated the highest achievable F1-score of 0.95 and a 3.26% performance improvement over traditional machine learning models such as extreme gradient boosting, which demonstrated an F1-score of 0.92.</p><p><strong>Conclusions: </strong>This study demonstrates the potential of leveraging machine learning and large language models, such as GPT-3.5 Turbo, to analyze public sentiment surrounding opioid use and its associated risks. By using YouTube comments as a rich source of self-reported data, the study provides valuable insights into both the positive and negative effects of opioids, particularly when mixed with other substances. The proposed methodology significantly outperformed traditional models, contributing to more accurate predictions of overdose risks and enhancing health care responses to the opioid crisis.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e70525"},"PeriodicalIF":3.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334584","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":"Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis.","authors":"Akito Nakanishi, Masao Ichikawa, Yukie Sano","doi":"10.2196/69321","DOIUrl":"https://doi.org/10.2196/69321","url":null,"abstract":"<p><strong>Background: </strong>As the global population ages, concerns about older drivers are intensifying. Although older drivers are not inherently more dangerous than other age groups, traditional surveys in Japan reveal persistent negative sentiments toward them. This discrepancy suggests the importance of analyzing discourse on social media, where public perceptions and societal attitudes toward older drivers are actively shaped.</p><p><strong>Objective: </strong>This study aimed to quantify long-term public discourse on older drivers in Japan through Twitter (subsequently rebranded X), a leading social media platform. The specific objectives were to (1) examine the sentiments toward older drivers in tweets, (2) identify the textual contents and topics discussed in the tweets, and (3) analyze how sentiments correlate with various variables.</p><p><strong>Methods: </strong>We collected Japanese tweets related to older drivers from 2010 to 2022. Each quarter, we (1) applied to the Japanese version of the Linguistic Inquiry and Word Count dictionary for sentiment analysis, (2) employed 2-layer nonnegative matrix factorization for dynamic topic modeling, and (3) applied correlation analyses to explore the relationships of sentiments with crash rates, data counts, and topics.</p><p><strong>Results: </strong>We obtained 2,625,807 tweets from 1,052,976 unique users discussing older drivers. The number of tweets has steadily increased, with significant peaks in 2016, 2019, and 2021, coinciding with high-profile traffic crashes. Sentiment analysis revealed a predominance of negative emotions (n=383,520, 62.42%), anger (n=106,767, 17.38%), anxiety (n=114,234, 18.59%), and risk (n=357,311, 58.15%). Topic modeling identified 29 dynamic topics, including those related to driving licenses, crash events, self-driving technology, and traffic safety. The crash events topic, which increased by 0.28% per year, showed a strong correlation with negative emotion (r=0.76, P<.001) and risk (r=0.72, P<.001).</p><p><strong>Conclusions: </strong>This 13-year study quantified public discourse on older drivers using Twitter data, revealing a paradoxical increase in negative sentiment and perceived risk, despite a decline in the actual crash rate among older drivers. These findings underscore the importance of reconsidering licensing policies, promoting self-driving systems, and fostering a more balanced understanding to mitigate undue prejudice and support continued safe mobility for older adults.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e69321"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310920","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":"Measurement, Characterization, and Mapping of COVID-19 Misinformation in Spain: Cross-Sectional Study.","authors":"Javier Alvarez-Galvez, Carolina Lagares-Franco, Esther Ortega-Martin, Helena De Sola, Antonio Rojas-García, Paloma Sanz-Marcos, José Almenara-Barrios, Angelos P Kassianos, Ilaria Montagni, María Camacho-García, Maribel Serrano-Macías, Jesús Carretero-Bravo","doi":"10.2196/69945","DOIUrl":"https://doi.org/10.2196/69945","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The COVID-19 pandemic has been accompanied by an unprecedented infodemic characterized by the widespread dissemination of misinformation. Globally, misinformation about COVID-19 has led to polarized beliefs and behaviors, including vaccine hesitancy, rejection of governmental authorities' recommendations, and distrust in health institutions. Thus, understanding the prevalence and drivers of misinformation is critical for designing effective and contextualized public health strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;On the basis of a tailored survey on health misinformation, this study aims to assess the prevalence and distribution of COVID-19-related misinformation in Spain; identify population groups based on their beliefs; and explore the social, economic, ideological, and media use factors associated with susceptibility to misinformation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A cross-sectional telephone survey was conducted with a nationally representative sample of 2200 individuals in Spain. The study developed the COVID-19 Misinformation Scale to measure beliefs in misinformation. Exploratory factor analysis identified key misinformation topics, and k-means clustering classified participants into 3 groups: convinced, hesitant, and skeptical. Multinomial logistic regression was used to explore associations between misinformation beliefs and demographic, social, and health-related variables.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Three population groups were identified: convinced (1078/2200, 49%), hesitant (666/2200, 30.27%), and skeptical (456/2200, 20.73%). Conspiracy theories, doubts about vaccines, and stories about sudden death emerged as the most endorsed current misinformation topics. Higher susceptibility to misinformation was associated with the female sex, lower socioeconomic status, use of low-quality information sources, higher levels of media sharing, greater religiosity, distrust of institutions, and extreme and unstated political ideologies. Frequent sharing of health information on social networks was also associated with membership in the skeptical group, regardless of whether the information was verified. Interestingly, women were prone to COVID-19 skepticism, a finding that warranted further research to understand the gender-specific factors driving vulnerability to health misinformation. In addition, a geographic distribution of hesitant and skeptical groups was observed that coincides with the so-called empty Spain, areas where political disaffection with the main political parties is greater.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study highlights the important role of determinants of susceptibility to COVID-19 misinformation that go beyond purely socioeconomic and ideological factors. Although these factors are relevant in explaining the social reproduction of this phenomenon, some determinants are linked to the use of social media (ie, searching and sharing of alternative health information) and ","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e69945"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310919","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":"Availability and Use of Digital Technology Among Women With Polycystic Ovary Syndrome: Scoping Review.","authors":"Pamela J Wright, Charlotte Burts, Carolyn Harmon, Cynthia F Corbett","doi":"10.2196/68469","DOIUrl":"10.2196/68469","url":null,"abstract":"<p><strong>Background: </strong>Polycystic ovary syndrome (PCOS) is a common endocrinopathy among women that requires self-management to improve mental and physical health outcomes and reduce risk of comorbidity. Digital technology has rapidly emerged as a valuable self-management tool for people with chronic health conditions. However, little is known about the digital technology available for and used by women with PCOS.  .</p><p><strong>Objective: </strong>The purpose of this scoping review was to identify what is known about digital technology currently available and used by women with PCOS for PCOS-specific knowledge, self-management, or social support.</p><p><strong>Methods: </strong>The databases PubMed, Embase, CINAHL, and Compendex were searched using Medical Subject Headings terms for PCOS, digital technology, health knowledge, self-management, and social support. Inclusion criteria were full-text, peer-reviewed publications of primary research from 2010 to 2025 in English about digital technology used for PCOS-specific knowledge, self-management, or social support by women aged 18 years and older with PCOS. Exclusion criteria were articles about pediatric populations and digital technology used for intervention recruitment or by health care providers to diagnose or treat patients.</p><p><strong>Results: </strong>In total, 34 full-text articles met the inclusion criteria. Given the scope of digital technology, eligible studies were grouped into 7 domains: mobile apps (n=14), internet-based programs (eg, Google; n=6), social media (n=6), SMS text message (n=2), machine learning (n=2), artificial intelligence (eg, ChatGPT [OpenAI]; n=3), and web-based intervention platforms (n=1). Findings highlighted participants' varied perceptions of technology usefulness based on reliability of health care information, application features, accuracy of PCOS or fertility prediction, social group engagement, user-friendly interfaces, cultural sensitivity, and accessibility.</p><p><strong>Conclusions: </strong>There is potential for digital technology to transform PCOS self-management, but further design and development are needed to optimize the technologies for women with PCOS. Future research should focus on including end users during the design phase of digital technology, refining predictive models, improving app inclusivity, conducting frequent reliability testing, and enhancing user engagement and support via additional features to promote more comprehensive self-management of PCOS.   .</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e68469"},"PeriodicalIF":3.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12178569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Reliability and Validity of Principles for Health-Related Information on Social Media (PRHISM) for Evaluating Breast Cancer Treatment Videos on YouTube: Instrument Validation Study.","authors":"Hiroki Kusama, Yoshimitsu Takahashi, Shunichiro Orihara, Kayo Adachi, Yumiko Ishizuka, Ryoko Semba, Hidetaka Shima, Yoshiya Horimoto, Hiroshi Kaise, Masataka Taguri, Sho Inoue, Takeo Nakayama, Takashi Ishikawa","doi":"10.2196/66416","DOIUrl":"10.2196/66416","url":null,"abstract":"<p><strong>Background: </strong>There is breast cancer-related medical information on social media, but there is no established method for objectively evaluating the quality of this information. Principles for Health-Related Information on Social Media (PRHISM) is a newly developed tool for objectively assessing the quality of health-related information on social media; however, there have been no reports evaluating its reliability and validity.</p><p><strong>Objective: </strong>The purpose of this study was to statistically examine the reliability and validity of PRHISM using videos about breast cancer treatment on YouTube (Google).</p><p><strong>Methods: </strong>In total, 60 YouTube videos were selected on January 5, 2024, with the Japanese words for \"breast cancer,\" \"treatment,\" and \"chemotherapy,\" and assessed by 6 Japanese physicians with expertise in breast cancer. These evaluators independently evaluated the videos using PRHISM and an established tool for assessing the quality of health-related information, DISCERN, as well as through subjective assessments. We calculated interrater and intrarater agreement among evaluators with CIs, measuring agreement using weighted Cohen kappa.</p><p><strong>Results: </strong>The interrater agreement for PRHISM overall quality was κ=0.52 (90% CI 0.49-0.55), indicating that the expected level of agreement, statistically defined by the lower limit of the 90% CI exceeding 0.53, was not achieved. However, PRHISM demonstrated higher agreement compared with DISCERN overall quality, which had a κ=0.45 (90% CI 0.41-0.48). In terms of validity, the intrarater agreement between PRHISM and subjective assessments by breast experts was κ=0.37 (95% CI 0.14-0.60), while DISCERN showed an agreement of κ=0.27 (95% CI 0.07-0.48), indicating fair agreement and no significant difference in validity.</p><p><strong>Conclusions: </strong>PRHISM has demonstrated sufficient reliability and validity for evaluating the quality of health-related information on YouTube, making it a promising new metric. To further enhance objectivity, it is necessary to explore the use of artificial intelligence and other approaches.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e66416"},"PeriodicalIF":3.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Surveillance of Public Interest in Cosmetic Tourism for Aesthetic Eyelid Surgery Abroad: Cross-Sectional Infodemiology Investigation of Internet Search Trends and Social Media Content.","authors":"Daniel B Azzam, Yi Ling Dai, Victoria S North, Alison B Callahan, Katrinka L Heher, Mitesh K Kapadia, M Reza Vagefi","doi":"10.2196/64639","DOIUrl":"10.2196/64639","url":null,"abstract":"<p><strong>Background: </strong>Global medical tourism for aesthetic surgery has become a popular phenomenon through ease of access in the digital era, though such services are not without potential risks. The application of infodemiology for global health surveillance may provide unique insights into unknown patient travel patterns and surgeon workforce dynamics abroad.</p><p><strong>Objective: </strong>This study aimed to evaluate American cosmetic tourism trends in oculofacial plastic surgery, including demand profile and qualifications of the most sought-after international eyelid surgeons on social media.</p><p><strong>Methods: </strong>This cross-sectional infodemiology study queried Google Trends to assess US interests in aesthetic eyelid surgery abroad in 25 destination countries from 2013 to 2023. The highest-rated content posted by 55 eyelid surgeons (US: n=11; international: n=44) on a social media platform (Instagram; Meta Platforms) was evaluated. The main outcomes included Google search volumes for aesthetic eyelid surgery for each destination country, as well as specialty training and professional medical society affiliations of popular eyelid surgeons on social media in each of these countries.</p><p><strong>Results: </strong>The top 5 destinations Americans sought for aesthetic eyelid surgery abroad were South Korea, Mexico, Canada, Turkey, and China. Interest in eyelid surgery abroad remained stable over the last decade despite 118% growth in blepharoplasty searches. Social media indicated eyelid surgeons abroad were more often general plastic surgeons than in the United States (30/44, 68% vs 2/11, 18%; P=.003). US surgeons more frequently completed oculofacial plastics, facial plastics, or aesthetic plastics fellowships compared with international surgeons (9/11, 82% vs 10/44, 23%; P<.001) and had membership in professional medical societies (11/11, 100% vs 22/44, 50%; P=.002).</p><p><strong>Conclusions: </strong>American demand for international eyelid surgery remained stable over the past decade despite a 2-fold increase in the US interest for blepharoplasty. Digital epidemiology data reveal a shortage of international surgeons with specialized aesthetic eyelid fellowship training or professional society affiliations on social media among the preferred destinations for Americans seeking aesthetic eyelid surgery. These findings may provide beneficial insights for patients interested in traveling abroad for eyelid surgery, as well as for surgeons or academic societies seeking to increase social media presence or patient-directed educational content via social media engagement.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e64639"},"PeriodicalIF":3.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12148249/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Digital Health Equity Audits in Preventing Harmful Infodemiology.","authors":"Massimiliano Biondi, Fabio Filippetti, Giorgio Brandi, Elsa Ravaglia, Sofia Filippetti, Pamela Barbadoro","doi":"10.2196/75495","DOIUrl":"10.2196/75495","url":null,"abstract":"<p><strong>Background: </strong>Health disparities persist and are influenced by digital transformation. Although digital tools offer opportunities, they can also exacerbate existing inequalities, a problem amplified by the COVID-19 pandemic and the related infodemic. Health equity audit (HEA) tools, such as those developed in the United Kingdom, provide a framework to assess equity but require adaptation for the digital context. Digital determinants of health (DDoH) are increasingly recognized as crucial factors influencing health outcomes in the digital era.</p><p><strong>Objective: </strong>This editorial proposes an approach to extend HEA principles to create a specific framework, the digital health equity audit (DHEA), designed to systematically assess and address health inequities within the design, implementation, and evaluation of digital health technologies, with a focus on DDoH.</p><p><strong>Methods: </strong>We propose a cyclical DHEA model based on existing HEA principles, integrating them with digital health equity frameworks. The DHEA cycle comprises six phases: (1) scoping the audit and mobilizing the team (including community members); (2) developing the digital health equity profile and identifying inequities (assessing DDoH at individual, interpersonal, community, and societal levels); (3) identifying high-impact actions to address DDoH and inequities; (4) prioritizing actions for maximum equity impact; (5) implementing and supporting change; and (6) evaluating progress and impact, and refining. This method emphasizes multilevel interventions and stakeholder engagement.</p><p><strong>Results: </strong>The main result is the articulation of the DHEA framework: a structured, 6-phase cyclical model to guide organizations in the analysis and proactive mitigation of digital health-related disparities. The framework explicitly integrates the assessment of DDoH across multiple levels (individual, interpersonal, community, societal) and promotes the development of targeted interventions to ensure digital solutions promote equity.</p><p><strong>Conclusions: </strong>The DHEA model offers an integrated approach to consider social, epidemiological, health, and technological variables, aiming to reduce health inequities through the conscious use of new technologies. It is emphasized that digital technologies can be the cause or the solution to inequalities; DHEAs are proposed as a tool to foster equity. Its systematic adoption, along with a collaborative approach (co-design) and trust building, can help ensure that the benefits of health digitization are equitably distributed while strengthening trust in institutions. Continued attention is needed to manage emerging challenges such as infodemiology in the era of big data and artificial intelligence.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e75495"},"PeriodicalIF":3.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Media and the Evolution of Vaccine Preferences During the COVID-19 Pandemic: Discrete Choice Experiment.","authors":"Robbie Maris, Zack Dorner, Stephane Hess, Steven Tucker","doi":"10.2196/66081","DOIUrl":"10.2196/66081","url":null,"abstract":"<p><strong>Background: </strong>Vaccine information and misinformation are spread through social media in ways that may vary by platform. Understanding the role social media plays in shaping vaccine preferences is crucial for policymakers and researchers.</p><p><strong>Objective: </strong>This study aims to test whether social media use is associated with changes in vaccine preferences during the COVID-19 pandemic in New Zealand, and whether trust in sources of information has a moderating role.</p><p><strong>Methods: </strong>Our data consist of a balanced panel of 257 web-based respondents in New Zealand in August 2020, October-November 2020, and March-April 2021. We use a novel approach with stated choice panel data to study transitions between different vaccine preference groups. We analyze the associations between these transitions and social media use. We classify respondents as resistant (never chose a vaccine), hesitant (chose a vaccine between 1 and 5 times), and provaccine (chose a vaccine 6 out of 6 times) in each wave of data.</p><p><strong>Results: </strong>We found a positive or neutral association between social media use and vaccine uptake. Facebook, Twitter (pre-2022), and TikTok users who are provaccine are less likely to become hesitant or resistant. Facebook and Instagram users who are hesitant are more likely to become pro. Some social media platforms may have a more positive association with vaccine uptake preferences for those who do not trust the government.</p><p><strong>Conclusions: </strong>The paper contributes to the wider literature, which shows social media can be associated with reinforcing both pro and antivaccination sentiment, and these results depend on where individuals get their information from and their trust in such sources.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e66081"},"PeriodicalIF":3.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165271/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}