JMIR Publications

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  Infodemiology and Infoveillance

  Dec 19, 2025

  Intersection of Big Five Personality Traits and Substance Use on Social Media Discourse: AI-Powered Observational Study

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  Authors List:

  • Julina Maharjan  
  • Ruoming Jin  
  • Jianfeng Zhu  
  • Deric Kenne  

  Abstract:

  Background: Personality traits are known predictors of substance use (SU), but their expression and association with SU in digital discourse remain largely unexamined. During theCOVID-19 pandemic, the online social engagement heightened, and led the amplification in SU rates, thereby creating a unique natural opportunity to investigate these dynamics through a large-scale digital discourse data. In our study we offer insights beyond traditional self-report methods, which is crucial for developing timely and targeted public health interventions. Objective: To evaluate whether the associations between the Big Five personality traits and SU discourse shifted during the 2019–2021 period, and to conduct a focused analysis of how these traits predict SU and relate to specific substance types, emotional expression, and demographic factors. Methods: We analyzed a corpus of several hundred million public posts from a major social media platform from 2019 to 2021. Using a pipeline of natural language processing and deep learning models, we identified SU-related posts and subsequently extracted scores for the Big Five personality traits, emotions, and user demographics. We employed trend analysis to compare annual shifts in trait-SU associations, while detailed 2020 data underwent rigorous modeling using logistic regression, correlation analysis, and topic modeling to elucidate the core relationships. Results: Our analysis revealed that Extraversion (Odd Ratio=3.22) and, most strikingly, Agreeableness (Odd Ratio=4.04) were the strongest positive predictors of being a substance user. In stark contrast to the conventional self-medication hypothesis, Neuroticism emerged as a robust protective factor against SU (Odd Ratio=0.29). This counterintuitive finding was supported by a decreased association between Neuroticism and SU posts at the pandemic's onset in 2020 (Cohen’s value, d=−0.13, 95% Confidence Interval) and a negative correlation with the expression of negative emotions online. Topic modeling further indicated that SU discourse was frequently embedded in social contexts (Social Drinking, Friendly Beverage Choices) rather than themes of solitary coping. Conclusions: Our findings challenge traditional models by demonstrating that in large-scale online discourse, SU expression is more powerfully linked to social-affiliative traits than to negative emotionality. The paradoxical protective role of Neuroticism suggests that established risk profiles may not apply uniformly to digital environments, particularly during a public health crisis. These insights are vital for refining computational methods for public health surveillance and developing interventions that recognize the potent social drivers of substance use in the digital age.

  • J Med Internet Res 2025;27(1):e79454

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  Medicine 2.0: Social Media, Open,...

  Dec 19, 2025

  Time Spent on Social Media Applications in Relation to Depressive Symptoms During Emerging Adulthood and the Mediating Role of Sleep Quality: Cross-Sectional...

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  Authors List:

  • Qi Zou  
  • Jiabi Qin  
  • Lei Zhang  
  • Wenhua Wang  
  • Xiaoxiao Yuan  
  • Tingting Wang  
  • Yuan Peng  
  • Ye Chen  
  • Linfei Dou  
  • Xinghua Yang  
  • Xirennayi Abudurexiti  
  • Mingyang Wu  

  Abstract:

  Background: The link between social media use and depressive symptoms remains bidirectional. Findings in this area are often compromised by methodological limitations related to measurement and sample size. As a result, it is challenging to assess dose-response relationships and potential causal pathways. Objective: To utilize objective measurement methods to assess the dose-response relationship and potential mechanisms between social media use and depressive symptoms. Methods: This study was conducted in six universities in 2022. Social media use duration was assessed based on the monitoring of mobile phone systems, and depressive symptoms were evaluated by the Self-Rating Depression Scale. Logistic regression and restricted cubic spline were employed to assess the relationship between social media use and depressive symptoms. Mediation analysis was used to elucidate the biological pathways of sleep quality in the above-mentioned relationship. Results: A total of 7,401 college students were included in the final analysis, with 5% of moderate to severe depressive symptoms. After adjusting for variables such as sociodemographic characteristics and health-related characteristics, there was a significant association between individuals with longer weekly social media usage time and depressive symptoms (OR>48h, 1.769; 95%CI, 1.303-2.400). Similarly, the association between instant messaging-based social media use duration and depressive symptoms was also significant (OR>24h, 1.728; 95%CI, 1.225-2.437), while no associations were observed for content-based social use (OR>24h, 1.251; 95%CI, 0.932-1.680). Restricted cubic splines regression demonstrated a J-type relationship between social media use duration and depressive symptoms. Additionally, sleep quality played a partial mediating role in the relationship between social media use duration and depressive symptoms, with the mediating effect values ranging from 24.10% to 25.25%. Conclusions: Prolonged social media use duration might be associated with an increased prevalence of depressive symptoms in emerging adulthood, and may increase the odds of depression by affecting sleep quality, suggesting that early prevention and intervention regarding social media use might help to ameliorate depressive symptoms.

  • J Med Internet Res 2025;27(1):e75337

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  Digital Health Reviews

  Dec 19, 2025

  The Impact of Patient-Generated Health Data From Mobile Health Technologies on Health Care Management and Clinical Decision-Making: Narrative Scoping Review

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  Authors List:

  • Ava Keeling  
  • John Downey  
  • Matthew Halkes  
  • Yinghui Wei  

  Abstract:

  Background: Long-term health conditions and multimorbidity are increasing globally placing an unsustainable pressure on healthcare systems. Mobile health technologies, or mHealth, enable the collection of patient-generated health data outside clinical settings, offering the potential to support personalised care and inform clinical decision-making. However, the ways in which mHealth patient data is being used in clinical practice remains unclear. Objective: To map and synthesise the existing literature on how patient-generated mHealth data is reportedly being used and influencing clinical decision-making for adults with long-term conditions in an outpatient care setting. Methods: A narrative scoping review was conducted on studies published between 2014 and 2025. Studies were eligible for inclusion if they were in English, had data on the use of patient generated mHealth data, went beyond feasibility testing, and had reference to clinician behaviour/patient interactions. Grey literature was not used to maintain a focus on peer reviewed and published evidence. Studies involving paediatric or adolescent populations were excluded. Searches were conducted across the following databases between 2014 and 2025: Embase, MEDLINE, Knowledge and Library Hub, British Nursing Indes, Proquest Health Research Premium Collection. Data were charted systematically and synthesised narratively. Key data included study characteristics, mHealth use, data types and visualisations, patient demographics, and the ways data informed clinical decision-making. Results: 16 studies met the inclusion requirements which were primarily high-income countries focusing on rheumatoid arthritis and diabetes. Studies reported on how mHealth data was integrated into workflows, influenced healthcare decisions, and shaped patient-provider interactions. mHealth patient data was found to support patient-centred care and facilitate proactive holistic care, though in some instances it was shown to reinforce medical agendas removing agency from patients. There is also a gap between the intended use of the data and its implementation in clinical practice. Reported barriers included professional scepticism, integration challenges, and concerns about data accuracy. Evidence was focused on feasibility rather than long-term outcomes, with limited evidence on the impacts of mHealth. Conclusions: PGHD has potential to enhance clinical decision-making and person-centred practices. However, integration into routine practice is hindered by technological challenges, professional hesitancy, and a lack of standardisation. Future research should prioritise supporting integration, improve data presentation, and evaluate the long-term effects on clinical workflows. Addressing these barriers and establishing clear policy frameworks will be crucial for realising the potential of mHealth in healthcare delivery.

  • J Med Internet Res 2025;27(1):e77359

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  Digital Health Reviews

  Dec 19, 2025

  Impact of Telerehabilitation on Rehabilitation Efficacy and Patient Satisfaction After Knee Surgery: Systematic Review and Meta-Analysis of Randomized...

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  Authors List:

  • Yuang Wang  
  • Xinge Liu  
  • Qingyi Wu  
  • Qiming Zhu  
  • Ming Zhang  

  Abstract:

  Background: Postoperative rehabilitation after knee surgery is crucial for functional recovery, but traditional in-person methods can impose burdens on patients, particularly those with mobility limitations or living remotely. Telerehabilitation, leveraging digital platforms, offers a potential alternative, yet its comparative efficacy and acceptability remain debated, especially across surgery types. Objective: To evaluate if telerehabilitation improves postoperative rehabilitation satisfaction and efficacy compared to traditional methods for knee joint surgery patients. Methods: Six databases (Web of Science, PubMed, MEDLINE, ScienceDirect, EMBASE, Cochrane Library) were searched from inception to September 27, 2025. Eligibility criteria included randomized controlled trials (RCTs) comparing telerehabilitation with traditional rehabilitation in adult postoperative knee surgery patients, reporting patient satisfaction and/or efficacy outcomes. Risk of bias was assessed using the Cochrane Risk of Bias 1 tool. Data were synthesized using random-effects meta-analysis with the Hartung-Knapp-Sidik-Jonkman method for confidence intervals, reporting standardized mean differences or mean differences, τ2, τ, and prediction intervals where applicable. Heterogeneity was assessed with τ2, τ and PIs. Certainty of evidence was evaluated using GRADE. Results: 19 RCTs were included. Overall, on average, patient satisfaction showed no significant difference between telerehabilitation and traditional rehabilitation (SMD = 0.15; 95% CI = -0.48 to 0.78; P = .48; τ2 = 0.30; τ = 0.55; PI = -1.17 to 1.47). Subgroup analysis revealed on average lower satisfaction with synchronous telerehabilitation (k=4; SMD = -0.52; 95% CI = -1.02 to -0.02; P = .04; τ2 = 0.17; τ = 0.41) and higher with asynchronous (k=6; SMD = 0.56; 95% CI = 0.08 to 1.03; P = .02; τ2 = 0.30; τ = 0.55). Telerehabilitation showed significant improvements on total WOMAC (k=4; SMD = -0.76; 95% CI = -1.38 to -0.14; P = .02; τ2 = 0.08; τ = 0.29; PI = -1.85 to 0.33), KOOS (k=5; SMD = 0.58; 95% CI = 0.47 to 0.70; P = .01; τ2 = 0; τ = 0; PI = 0.36 to 0.80), TUG (k=4; MD = -2.73 s; 95% CI = -4.50 to -0.96; P = .04; τ2 = 1.14; τ = 1.07; PI = -7.17 to 1.72) and knee extension range (k=3; MD=9.64°; 95% CI = 6.89 to 12.39; P=.049; τ2 = 2.45; τ = 1.56; PI = 0.60 to 18.68). Risk of bias was low to moderate; heterogeneity moderate. Conclusions: The pooled average effects suggest that telerehabilitation is noninferior to traditional care for patient satisfaction on average and may improve pain and function and some objective measures. However, bootstrapped prediction intervals and between-study variability indicate that effects vary by context; implementation should therefore be individualized with attention to modality, patient digital literacy, and technical support. Targeted trials with standardized measures are recommended to increase certainty and narrow the expected distribution of effects. Clinical Trial: PROSPERO CRD420251025461; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251025461.

  • J Med Internet Res 2025;27(1):e76844

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  Public (e)Health, Digital...

  Dec 19, 2025

  Social Media, Health Consciousness, and Cultural Influences on Sugar Reduction Behaviors in Chinese Youth: Extending the Stimulus-Organism-Response Model

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  Authors List:

  • Bing Hu  
  • Yi Zhu  
  • Ruxiang Bao  
  • Ziying Zhao  
  • Chao Liu  
  • Aomi Lin  

  Abstract:

  Background: The rising prevalence of sugar-related diseases, such as obesity and diabetes, has intensified efforts to reduce sugar intake, particularly among youth. In China, social media is playing an increasingly significant role in shaping health behaviors, including habits related to sugar consumption as sugar reduction has become a prominent youth-led movement. Objective: This study extends the Stimulus-Organism-Response (SOR) model by incorporating the distinct cultural influence of "face" to investigate the impact of social media on sugar reduction behaviors among Chinese youth, as well as the mediating role of health consciousness and conformity, and the moderating effects of face concern and eHealth literacy. Methods: We conducted a national web-based cross-sectional survey through proportionate probability sampling of 883 Chinese youth in July 2024. Descriptive statistics, Pearson correlations, Model fit indices and PLS (Partial Least Squares) SEM were employed to examine and explore the relationships among all the variables. Results: Nearly half the 883 participants were female (460/883, 52.1%) , 91.9% (812/883)of the sample ages fall within the 15-30 range. Most of the participants (602/883, 68.2%) had undergraduate education levels; the majority of participants (688/883, 77.9%) had a bachelor's degree or higher, and a normal Body Mass Index (BMI) (654/883, 74.1%). Most (575/883, 74.1%) had used social media for 3–10 years. Chinese youth reported relatively high sugar-reduction behavior scores in sugar reduction behaviors (mean score: 3.621/5, SD 0.990). Male participants achieved notably higher scores in sugar reduction behaviors.(mean score 3.725/5, SD 0.933), Participants at the age of 15–18 showed significantly lower sugar reduction behavior scores (mean score 3.508/5, SD 1.052). Structural equation modeling revealed that social media usage positively influenced conformity (β=.508, P<.001) and health consciousness (β=.353, P<.001). These factors in turn significantly predicted sugar reduction behaviors (β=.139 and β=.498, respectively; both P<.001). The influence of social media usage on sugar reduction behaviors is primarily facilitated through two mediating pathways.Health consciousness mediated the relationship between social media usage and sugar reduction behaviors (VAF=51.5%), while conformity's mediation was less pronounced (VAF=21.05%), indicating a secondary influence. Face concern (β=0.089, P=.02) and eHealth literacy (β=0.055, P=.04) moderated the respective relationships. Conclusions: This study demonstrates that social media effectively promotes sugar reduction behaviors among Chinese youth. By embedding cultural influences like face concern alongside enabling competencies like eHealth literacy an extended SOR model, we enhance our understanding of social media's influence on health behaviors. The findings highlight cultural nuances in health communication and position the enhanced SOR model as a framework for health promotion. Furthermore, The study underscores the primary mediating effect of health consciousness—surpassing that of conformity—while also delineating the moderating roles of face concern and eHealth literacy, offering actionable insights for digital-age public health strategies.

  • J Med Internet Res 2025;27(1):e68180

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  News and Perspectives

  Dec 18, 2025

  Beyond Waiting: How Patients Are Reshaping Digital Health

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  • Anshu Ankolekar

  Abstract:

  • J Med Internet Res 2025;27(1):e89205

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  Public (e)Health, Digital...

  Dec 17, 2025

  Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study

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  Authors List:

  • Jialin Meng  
  • Ruiming Dai  
  • Xiaolan Huang  
  • Yi Gu  
  • Shixing Yan  
  • Xiaoke Wang  
  • Jingrong Gao  
  • Tian-Tian Zhang  

  Abstract:

  Background: Precision health promotion, which aims to tailor health messages to individual needs, is hampered by the lack of structured metadata in vast digital health resource libraries. This bottleneck prevents scalable, personalized content delivery and exacerbates information overload for the public. Objective: This study aimed to develop, deploy, and validate an automated tagging system using a large language model (LLM) to create the foundational metadata infrastructure required for tailored health communication at scale. Methods: We developed a comprehensive, 3-tier health promotion taxonomy (10 primary, 34 secondary, and 90,562 tertiary tags) using a hybrid Delphi and corpus-mining methodology. We then constructed a hybrid inference pipeline by fine-tuning a Baichuan2-7B LLM with low-rank adaptation for initial tag generation. This was then refined by a domain-specific named entity recognition model and standardized against a vector database. The system’s performance was evaluated against manual annotations from nonexpert staff on a test set of 1000 resources. We used a “no gold standard” framework, comparing the artificial intelligence–human (A-H) interrater reliability (IRR) with a supplemental human-human (H-H) IRR baseline and expert adjudication for cases where artificial intelligence provided additional tags (“AI Additive”). Results: The A-H agreement was moderate (Cohen κ=0.54, 95% CI 0.53-0.56; Jaccard similarity coefficient=0.48, 95% CI 0.46-0.50). Critically, this was higher than the baseline nonexpert H-H agreement (Cohen κ=0.32, 95% CI 0.29-0.35; Jaccard similarity coefficient=0.35, 95% CI 0.27-0.43). A granular analysis of disagreements revealed that in 15.9% (159/1000) of the cases, the “AI Additive” tags were not identified by human annotators. Expert adjudication of these cases confirmed that the “AI Additive” tags were correct and relevant with a precision of 90% (45/50; 95% CI 78.2%-96.7%). Conclusions: A fine-tuned LLM, integrated into a hybrid pipeline, can function as a powerful augmentation tool for health content annotation. The system’s consistency (A-H κ=0.54) was found to be superior to the baseline human workflow (H-H κ=0.32). By moving beyond simple automation to reliably identify relevant health topics missed by manual annotators with high, expert-validated accuracy, this study provides a robust technical and methodological blueprint for implementing artificial intelligence to enhance precision health communication in public health settings.

  • J Med Internet Res 2025;27(1):e83219

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  News and Perspectives

  Dec 17, 2025

  The Potential and Peril of Artificial Intelligence in the Emergency Department

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  Authors List:

  • Wendy Glauser

  Abstract:

  • J Med Internet Res 2025;27(1):e89200

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  Digital Health Reviews

  Dec 17, 2025

  Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review

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  Authors List:

  • Musitapa Maimaitiaili  
  • Yiershatijiang Jiamaliding  
  • Guangle Dai  
  • Hui Xiao  
  • Warisijiang Kuerbanjiang  
  • Yuexiong Yi  

  Abstract:

  Background: The construction of artificial intelligence (AI) platforms in hospitals forms the basis of the modern healthcare revolution. While traditional hospital information systems have facilitated digitalization, they are still limited by data siloes, fragmented workflows and insufficient clinical intelligence that impede organizations from realizing the promise of data-led decision-making. Objective: This review aims to provide a strategic roadmap for hospitals to build comprehensive AI platforms, moving beyond siloed AI applications toward infrastructure at the system level that supports sustainable, scalable, and interoperable intelligent services across clinical, operational, and administrative domains. Methods: A systematic literature search was performed in Web of Science, EMBASE, PubMed, and Scopus. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were screened and selected for full text review by two independent reviewers with reference to AI platform construction, hospital informatics integration, and institutional deployment strategies. Results: A total of 30 high-quality studies were included in the final analysis. Based on the synthesis of evidence, a five-layer hospital AI platform architecture is proposed, consisting of: (1) infrastructure layer, (2) data layer, (3) algorithm layer, (4) application layer, and (5) security and compliance layer. The review highlights key implementation strategies such as modular deployment, real-world scenario validation, and interdepartmental collaboration. It also identifies critical challenges, including legacy system integration, lack of data standardization, computing resource limitations, organizational resistance, regulatory uncertainty, and economic sustainability. Conclusions: The successful construction of hospital AI platforms requires not only advanced technologies but also institutional readiness, strategic planning, and cultural adaptation. Intelligent hospitals of the future must emphasize privacy-preserving computing, seamless AI integration into clinical workflows, and dynamic performance evaluation systems. Building organizational capacity and fostering cross-disciplinary collaboration will be essential to achieving long-term impact and scalability.

  • J Med Internet Res 2025;27(1):e79788

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  Web-based and Mobile Health...

  Dec 17, 2025

  Digital Versus In-Person Physical Therapy in Adults With Musculoskeletal Conditions: Retrospective Matched-Cohort Analysis of Surgery and Low-Value Surgical...

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  Authors List:

  • Beatriz Domingues  
  • Ana P Pereira  
  • Akshat Pradhan  
  • Catarina Zidde  
  • Dora Janela  
  • Carolina Marramaque  
  • Virgílio Bento  
  • Vijay Yanamadala  
  • Steven P Cohen  
  • Luke Belz  
  • Kevin Wang  
  • Fernando Dias Correia  
  • Fabíola Costa  

  Abstract:

  Background: Musculoskeletal (MSK) disorders are leading causes of disability worldwide, with clinical guidelines recommending physical therapy–based interventions. Digital MSK programs offer an alternative to address logistical and socioeconomic barriers to regular in-person care. However, evidence comparing surgical use between digital and in-person physical therapy remains limited, particularly for low-value procedures. Objective: This study aimed to evaluate the 12-month incidence of MSK surgery and low-value surgical procedures among participants initiating a multimodal Digital Care Program (DCP) versus a matched-cohort initiating in-person physical therapy. Methods: Retrospective, matched-cohort study, using exact and propensity matching, with a Health Insurance Portability and Accountability Act (HIPAA)–deidentified US nationwide merged claims dataset (July 2022-February 2025). Eligible adults had spine, knee, hip, or shoulder conditions, ≥24 months uninterrupted health insurance coverage to an employer-sponsored DCP, and no MSK surgery in the prior year. The intervention group (IG) participated in a DCP combining exercise, education, and cognitive behavioral therapy, with real-time biofeedback and remote physical therapist oversight. The comparator group (CG) initiated in-person physical therapy, identified from a third-party claims database, using relevant MSK ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes as primary diagnosis. The primary outcome was the incidence of any MSK surgery within 12 months; the secondary outcome was the incidence of low-value surgery based on Choosing Wisely–aligned definitions. Cohort characteristics were compared using t test and chi-square test. Risk ratios (RRs) were calculated overall and by pain site, age group, and Social Deprivation Index. Results: In a matched cohort of 4190 individuals, predominantly middle-aged (~52 years old) women (1335/2095, 63.7%) with spinal pain (1123/2095, 53.6%), participation in the digital program was linked to a 58% (95% CI 49-66) lower relative risk of surgery at 12 months compared to those initiating in-person physical therapy (RR 0.42, 95% CI 0.34-0.52; E-value=4.19 [lower CI 3.29]). For surgeries categorized as low-value, IG was associated with 82% (95% CI 68-90) lower relative risk (RR 0.17, 95% CI 0.09-0.31; E-value=11.24 [lower CI 5.91]). Overall MSK surgical trends were consistent across pain sites, with greatest relative differences for knee (IG: 40/414 9.7% vs CG: 122/414, 29.5%; RR 0.26; 95% CI 0.17-0.38) followed by hip (19/203, 9.4% vs 42/203, 20.7%; RR 0.40; 95% CI 0.22-0.71). Lower surgery incidences in the IG (overall and low-value) were found across all socioeconomic and age strata. Conclusions: This real-world study demonstrated, for the first time, that participation in a digital MSK program was associated with substantially lower incidences of both overall and low-value surgeries compared to those who opted for in-person physical therapy among commercially-insured adults. These findings suggest that digital MSK programs can mitigate access barriers, promote adherence to guideline-concordant care, and reduce unnecessary procedures, including among underserved populations.

  • J Med Internet Res 2025;27(1):e82573

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  Medicine 2.0: Social Media, Open,...

  Dec 17, 2025

  An Exploratory Typology of Tobacco-Related Misleading Content on Social Media: Qualitative Analysis of Instagram and TikTok

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  Authors List:

  • Eileen Han  
  • Joanne Chen Lyu  
  • Pamela M Ling  

  Abstract:

  Background: Tobacco-related misinformation on social media platforms presents growing challenges to digital health communication and public health. Although prior studies have focused on platform-specific patterns, a unified framework for categorizing and comparing misinformation across platforms is lacking. Such a framework is essential for improving infodemiological surveillance and designing targeted digital interventions. Objective: This study was an exploratory analysis aimed to build a cross-platform typology to categorize tobacco-related misinformation. Methods: Data from Instagram and TikTok between January 2020 and August 2023 were collected using a third-party data collection platform (CrowdTangle) and the TikTok Research application programming interface (API). We reviewed a total of 4850 Instagram posts using a combination of generative artificial intelligence (AI) and human validation by two independent reviewers. In addition, 719 TikTok videos were reviewed manually using qualitative analysis. We iteratively developed and refined the exploratory typology informed by the literature integrating our prior analysis of Twitter data and these new datasets. Results: Of the 22 (71%) Instagram posts and 9 (29%) TikTok videos we analyzed closely to classify misinformation, 2 (6.5%) were about cigarettes, 22 (71%) were about electronic cigarettes (e-cigarettes), 1 (3.2%) was about heated tobacco products (HTPs), 2 (6.5%) were about nicotine (not mentioning specific products), and 3 (9.7%) were about cannabidiol (CBD) products. 1 (3.2%) post did not mention any type of products. These categories could overlap in a single post. The resulting typology consisted of five core narrative archetypes: false or misleading health claims (A1), wellness and lifestyle appeal (A2), conspiracy-driven policy agenda (A3), undermining trust in science and medicine (A4), and recreational nicotine use normalization (A5). Each archetype has attributes of false claim types and sources. Among the posts we analyzed, A1 and A2 were most likely to be found on Instagram. A3 was most frequently found on Twitter. A4 was commonly seen on both Twitter and TikTok, and A5 was most frequently found on TikTok. Two additional dimensions—type of falsehood and source—were also added to characterize a given misinformation post. This exploratory typology paved the way for a structured lens to view how misinformation is tailored to digital environments and target audiences. Conclusions: This cross-platform typology building supports digital health research by integrating AI and qualitative methods to categorize tobacco-related misinformation. It can inform the development of automated misinformation detection models, enhance real-time infodemiological monitoring, and guide digital public health campaigns to build tailored countermessaging.

  • J Med Internet Res 2025;27(1):e78854

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  Consumer & Patient Education and...

  Dec 16, 2025

  Supporting Informed Vaccine Decision-Making and Communication in Pregnancy Through the Vaccines in Pregnancy Canada Intervention: Multimethod Co-Design Study

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  Authors List:

  • Eliana Castillo  
  • Andrea M Patey  
  • Medea Myers-Stewart  
  • Monica Santosh Surti  
  • Maria Castrellon Pardo  
  • Marcia Bruce  
  • Zaileen Jamal  
  • Madison Kennedy  
  • Julie A Bettinger  
  • Jessica Kaufman  
  • Margie Danchin  
  • Maoliosa Donald  

  Abstract:

  Background: Vaccination in pregnancy (VIP) protects pregnant individuals and their newborns; yet, uptake remains suboptimal. Pregnant individuals face unique decision-making challenges, and communication with their health care provider (HCP) is crucial for uptake. While there is extensive data on barriers to VIP, interventions applying evidence-based behavior change strategies and co-designed with end users are scarce. Our prior work indicated that a new Canadian intervention was needed. Objective: This study aimed to co-design a multicomponent intervention to support informed decision-making and vaccine communication in pregnancy. Methods: Our multimethod study followed the Double Diamond phases (ie, Discover, Define, Develop, and Deliver) and partnered with a diverse patient advisory council and a multidisciplinary team of HCPs. During the Discover and Define phases, our previous work, we explored gaps and barriers to VIP in Canada and defined the behavior change strategies to address those needs. During the Develop phase, we co-designed and conducted iterative prototyping of four intervention components: (1) a pregnancy-specific communication approach, (2) a skills course for HCPs, (3) a practice change plan, and (4) a website with evidence-based resources for patients and HCPs. We used online and in-person participatory co-design sessions and peer-to-peer, patient-oriented online focus groups and semistructured in-depth interviews. During the Deliver phase, we refined the intervention components through functionality and usability testing. Results: The Vaccines in Pregnancy Canada (VIP Canada) intervention consists of four integrated components: (1) DECIDE (Determine, Elicit, Consent, Interactive discussion, Deliver, and Empower): a patient-centered, pregnancy-specific communication approach for providers to deliver a clear vaccine recommendation while respecting autonomy. (2) Skills course for HCPs: 4 self-paced, online modules to learn the rationale for VIP and the DECIDE communication approach and 2 group sessions. Providers found the skills course clear, practical, and applicable across diverse clinical roles and settings. Feedback led to enhancements, including improved audio-visual synchronization, consistent closed captioning, and the addition of downloadable reference materials to support learning. (3) Practice change plan: an action plan HCPs make to integrate vaccine communication into their practice. (4) VIP Canada website: an evidence-based website with resources to support informed vaccine decision-making for patients and providers. Patient feedback informed iterative refinements to the layout and content of the website to enhance navigation, readability, and representation of diverse identities. Functionality and usability testing demonstrated that patients found the VIP Canada website visually appealing, easy to navigate, and supportive of informed decision-making. Conclusions: The VIP Canada is a promising intervention co-designed to drive behavior change by addressing key barriers to vaccine communication and informed decision-making around our patient partners’ and HCPs’ perspectives and lived experiences to bridge theoretical frameworks with real-world relevance. Next steps include a feasibility study for further refinement and a subsequent effectiveness study. Trial Registration:

  • J Med Internet Res 2025;27(1):e77446

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