Journal of Medical Internet Research

Patient narratives can reveal aspects of health that standardized measures miss, yet collecting open-ended responses at population scale remains difficult. We evaluated whether speech-to-text (STT) could enable richer responses in online health surveys without the resource demands of interviews. Across four surveys involving people with post-COVID-19 condition, healthy adults, people who engage in sex work, and older adults, participants could answer open-ended questions by typing or using STT. We compared response length, lexical diversity, stopword proportion, and total unique content words, alongside STT uptake and participant feedback. Quantitative comparisons were made using a mixed-effects model including modality (STT vs typed) as fixed effect and a random intercept per participant. STT uptake varied markedly across populations (0.7-80.0%). Spoken responses were estimated to contain more characters than typed responses in two studies, by 245.0 and 891.8 characters on average (both p<0.001), and contained more unique content words, despite lower lexical diversity and higher stopword proportions. Participants generally valued STT for its convenience and spontaneity, but uptake was constrained by context, privacy, interface design and individual preferences. STT may enable population-scale collection of richer health narratives, provided implementation is tailored to the study population and research context.

TikTok has become a significant source of health information, and concern has grown about AI-generated content (henceforth, 'AIGC') as a vehicle for health misinformation. Where AIGC presents realistic-appearing people giving health advice, disclosure labels are the viewer's only reliable cue that what they are watching is synthetic. This research letter compares AI label metadata across 128,016 mental health-related TikTok videos and 4,924 videos from a network of 50 profiles posting exclusively AI-generated mental health content to evaluate how much content reaches audiences undisclosed. In a keywords-based collection, fewer than a percent of TikTok videos about mental health carried an AI label, but in profiles containing purely AI-generated content, just over 9 in 10 videos (90.23%) were neither labelled by the creator nor identified by TikTok's automatic detection. Additionally, in the keyword collection, automatic detection produced the majority of labels, while in confirmed AI-generated content from 50 profiles, it accounted for just three of the 481 labelled videos. These findings highlight the challenging landscape of AI disclosure and labelling and raise questions about where automatic detection is failing.

Seizure diaries are one of the most useful sources of information in the management of epilepsy, however patient engagement with them can be sporadic. Sustained participation with seizure diaries affects the completeness and reliability of self-reported data, so it is vital to be able to measure engagement. To facilitate this, we create a multidimensional engagement metric with which to characterize how patients interact with their seizure diary. We utilise data from the Helpilepsy, a seizure diary application, common features found in application engagement metrics in business settings, and well understood clinical features to do this. Clustering is then performed to isolate different user groups based on how engaged they are, and these groups are studied to understand what drives the differences in engagement. We found three groups emerge from the clustering: low, medium and highly engaged users. Investigating these groups further, we put together a ``profile" for highly-engaged users. We find that they tend to be older at the point of diagnosis, and have had epilepsy for longer than the other users. We also find they tend to have had more medications, have higher doses of common anti-seizure medications, and they have more medications typically given to those with refractory epilepsy. The implications for e-diary design are that more attention should be given to those newer to epilepsy in the onboarding phase. Also, engagement is not necessarily based on just the upload of seizures, with other features of an e-diary being important to be filled in.

Social media language and digital lifestyle behaviours are promoted as scalable signals of population mental health, but predictive models are often evaluated in ways that overstate accuracy. We analysed 20,000 tweets from 72 users, labelled by whether the account belonged to a user identified as depressed, and two synthetic datasets (100,000 and 30,000 records) linking screen time, sleep and social media use with self-reported stress. Under a conventional tweet-level split, a term-weighted logistic regression appeared accurate (area under the receiver operating characteristic curve 0.835), but when whole users were held out its performance fell to near chance (0.595; 95% confidence interval 0.505 to 0.686), because the model largely recognised users and their topics. Depression-associated tweets were more often negative (31.7% versus 24.2%), yet sentiment alone could not identify unseen users (0.539). The two behavioural datasets gave opposite answers: strong discrimination in one (0.926), driven by short sleep, and chance in the other (0.506), so synthetic results reflect how the data were generated. At a population prevalence of 10%, positive predictive values would be 0.10 to 0.30. Digital distress signals should be validated across individuals, on real linked data and at realistic prevalence before informing public health surveillance or prevention.

Patients with a brain tumour receive evidence-based clinical care in Australia but a focus on supportive care, including social connection, is often deficient. Digital health platforms hold promise to support these patients and their carers. Existing platforms often lack end-user co-design, evidence-based development and rigorous evaluation. Recognising this unmet need, we co-designed Brain Tumours Online, a digital supportive care platform to streamline access to educational resources, symptom management tools, and peer support for patients, carers, and healthcare professionals. In this article, we present our evaluation approach for Brain Tumours Online to advance methodological thinking in the evaluation of multi-faceted, co-designed digital health platforms. In contrast to standardised procedures in clinical trials, digital health interventions such as supportive care platforms are more complex due to their interactive nature, no prescriptive protocols for usage and the dynamic content of web-based information. Thus, traditional evaluation approaches often fall short in evaluating such multi-faceted digital health supportive care platforms. To address these challenges, we developed a bespoke, logic-modelling based evaluation approach to assess the usability, engagement, impact, and economic value of our platform. Our pragmatic but rigourous evaluation approach required the adaptation of existing evaluation frameworks, subject-matter, and lived experience expert knowledge. Our implementation science and co-design approach are shared in different papers. Our study outcomes will also be shared in a separate paper. In the current paper, we share our approach to the evaluation of Brain Tumours Online and provide insights that may be of value for other researchers interested in the nuances of trialing multi-faceted digital health supportive care platforms.

Background HPV-vaccination prevents HPV-related cancers, but uptake remains suboptimal. In the Netherlands, a catch-up campaign invited 18-27-year-olds for an additional cost-free opportunity to initiate or complete their HPV-vaccination in 2023-2024, which led to 24% of this invited population getting vaccinated. As young adults increasingly seek health information online, understanding online questions, concerns and misinformation in response to the campaign may help improve future vaccination efforts. Supplemental to overall evaluation of the vaccination campaign, we piloted online social listening processes to generate an insight report for Public Health Service (PHS) Amsterdam. Objective This study piloted online social listening processes at the PHS Amsterdam to identify actionable insights into questions, concerns and misinformation surrounding the HPV-vaccination catch-up campaign among young adults. Methods Upon inventory of potential platforms and data sources, a retrospective online social listening analysis was conducted across the platforms Instagram, Facebook, and X, collecting data from PHS, the National Public Health Institute for Public Health and the Environment (RIVM), the Dutch public broadcasting foundation (NOS), and LINDA.meiden sources from January 1, 2023, to June 1, 2024. Publicly available posts, comments, and engagement concerning HPV-vaccination were manually collected, and inductively coded. Themes were assessed for risk, based on expected potential harm and observed reach, and used to develop recommendations. Results A total of 821 posts and 5157 comments were collected and analysed. Ten recurring themes were identified across varying levels of risk. Two themes were classified as high risk: an information gap regarding the effectiveness of HPV-vaccination and concerns about the transparency on HPV-vaccine side effects. Three themes were classified as medium risk: questioning HPV-vaccine effectiveness under certain circumstances, reduced general vaccine trust following COVID-19, and accessibility issues related to vaccination locations and service times. Two themes were classified as neutral risk: questions regarding eligibility criteria for the campaign and calls for broader HIPV-prevention measures. Finally, three low risk themes concerned questions regarding HPV-vaccine brand specifics, campaign choices, and questions about scheduling or rescheduling HPV-appointments. Comments also referred to subsequent information sources, including reliable sources (public health institutes), false narratives and misinformation. Recommendations focused on addressing questions and information gaps (i.e. improving HPV risk perception and providing more detailed HPV-vaccine information), improving communication about HPV-vaccination, and reducing practical barriers to vaccination. Conclusions Manual social listening across selected online sources provided actionable insights into the public discourse during the HPV-vaccination catch-up campaign. Results mostly reflected questions, concerns and information gaps. Although some themes were critical of HPV-vaccination, false narratives and misinformation did not dominate the discourse. Recommendations aim to reduce practical barriers and address information needs. This pilot showed that online social listening can be effectively implemented even with limited resources.

While the public perceives e-cigarettes as less harmful than combustible tobacco, little is known about their specific health concerns regarding vaping. We demonstrate a data-driven strategy to discover the public's health concerns about vaping e-cigarettes expressed on social media. We obtained all public posts from the largest e-cigarette-related subreddit, r/electronic_cigarette, from its inception on September 17, 2008, through April 1, 2022 (N = 10,403,433). We identified health concerns attributed to vaping by (a) selecting all cause phrases containing "cause" and its inflections, (b) calculating the empirical frequency ratio of words and bi-grams occurring in these phrases relative to random phrases, (c) retaining the 10% of words with the greatest empirical frequency of occurring in cause phrases, and (d) annotating this sample for health-relevant concerns and their subjects. In total, 76,342 posts contained cause phrases, with increased volume over time. Of the 425 words most strongly associated with cause phrases compared to random phrases, 53.4% (95%CI, 48.7-58.1) were identified as health-relevant. The top health-related concern was lipoid pneumonia, cited in 5.9% (95%CI, 5.0-6.8) of all cause phrases, followed by pneumonia (4.1%; 95%CI, 3.3-4.9), and nausea (2.7%;95%CI, 2.0-3.4). The top health concern subjects were respiratory, representing 23.7% (95%CI, 18.5-29.5) of all cause phrases, followed by gastrointestinal (12.7%; 95%CI, 8.8-17.2) and cardiovascular (8.5%; 95%CI, 5.3-12.3) concerns. Other subjects included neurological, dermatological, oral health, sexual health, psychiatric, oncologic, addiction, and sleep concerns. Because our strategy relies on data-driven techniques, our analysis can be integrated into routine social media monitoring and applied across different types of social media and text data, potentially leading to more timely identification of emerging concerns and a broader understanding across platforms. As a result, experts can craft messaging that accounts for current perceptions held by the public using our method.

BackgroundPatient education is critical to safe and effective postoperative bariatric surgery care. Large language models (LLMs) may improve access to personalized patient education but evidence regarding direct patient use in clinical settings remains limited. We evaluated BariBot, an LLM-powered educational application for patients undergoing bariatric surgery. MethodsWe conducted a prospective, single-arm mixed-methods feasibility study at a tertiary academic medical center from May 2024 to May 2025. Adult patients undergoing bariatric surgery used BariBot independently on postoperative day 1. The application provided conversational postoperative education and adapted language complexity to participants educational attainment. Outcomes included feasibility, usability, acceptability, perceived trustworthiness, and anticipated future use. Usability was assessed using the System Usability Scale (SUS). Immediately after BariBot use, participants completed a semi-structured qualitative interview assessing acceptability, perceived trustworthiness, and anticipated future use. Interview transcripts underwent thematic analysis. ResultsSixteen participants completed the BariBot use session, SUS assessment, and post-intervention interview. No technical failures prevented application use, and no participant requested early discontinuation. The median SUS score was 91.3 (IQR, 81.3-98.1), consistent with excellent perceived usability. Qualitative analysis showed that participants valued the applications immediacy, structured responses, conversational context, ability to address follow-up questions, judgement-free nature, and future availability between clinical encounters. Trust was strengthened by alignment with prior clinical guidance and perceived institutional association, although participants emphasized the need for transparency, clinician oversight, and use as an adjunct rather than a replacement of clinician guidance. ConclusionsBariBot was feasible to implement and was associated with high perceived usability and acceptability postoperatively after bariatric surgery. Larger, longitudinal, and comparative studies are needed to evaluate safety during unsupervised use and determine whether LLM-based educational applications improve patient understanding, engagement, and clinical outcomes.

Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: This study evaluated eHealth literacy, technology acceptance, online health information-seeking behaviors, and adoption barriers among Gen Z undergraduates in Sri Lanka, focusing on the interplay between eHealth literacy, AI trust, and digital care preferences. Methods: A cross-sectional survey (N = 172) was conducted among Sri Lankan university undergraduates utilizing adapted, validated instruments: the eHealth Literacy Scale (eHEALS) and the Technology Acceptance Model (TAM). Statistical analysis included scale reliability validation (Cronbach's alpha), descriptive profiling, Chi-Square ({chi}{superscript 2}) contingency tests, Pearson correlations, and Multiple Linear OLS Regression models. Results: Participants demonstrated high overall eHealth literacy (Mean = 3.84 {+/-} 0.58) and strong endorsement of digital health utility (Mean = 3.99 {+/-} 0.59). Online health searches were reported by 86.6% of respondents. AI tools (e.g., ChatGPT, Gemini) emerged as the second most frequent source for health queries (57.6%), surpassing YouTube (44.2%) and social media (26.2%), with medical students showing significantly higher AI utilization ({chi}{superscript 2} = 8.70, p = .003). In multiple regression analysis, digital platform preference over physical clinic visits (R{superscript 2} = .352, p < .001) was significantly predicted by Perceived Ease of Use ({beta} = 0.371, p = .001) and Trust in AI Recommendations ({beta} = 0.370, p < .001), whereas face-to-face consultation preference (76.7%) and personal data privacy risks (50.0%) remained predominant adoption barriers. Conclusion: Gen Z students in Sri Lanka exhibit high digital health readiness and substantial reliance on AI-driven information seeking. However, institutional deployment must address privacy concerns and integrate hybrid clinical workflows to bridge the gap between high perceived utility and physical consultation preferences.

Documentation burden remains a persistent challenge in nursing practice, and Ambient artificial intelligence (Ambient AI) may offer new opportunities to mitigate the burden and improve nursing practice. This brief scientific communication reports survey findings on the current status of Ambient AI implementation in U.S. inpatient nursing settings and nurses' perceptions of its anticipated benefits and concerns. We conducted a cross-sectional online survey of practicing inpatient nurses recruited from nursing-focused social media communities. Descriptive analyses were used to summarize Ambient adoption, user experience, and perceived benefits and concerns. Among 61 eligible respondents, nearly 60% (n = 37) reported that Ambient AI had been piloted or implemented in nursing at their healthcare institutions, and approximately half (49.2 %, n = 30) reported having direct experience using Ambient AI in clinical practice. Most nurses with direct experience (73.3%, n = 22) described positive experience using the technology in practice. The frequently identified perceived benefits of Ambient AI include reduced documentation time and improved patient care workflow, and improved data quality. However, nurses also raised concerns about patients' acceptance of the new technology in their care, potential nursing job displacement, and loss of nursing skills due to overreliance on AI technology. Findings suggest that Ambient AI may help reduce nursing documentation burden and workload, yet implementation strategies should address nurses' concerns about its influence on nursing professions, foster trust among clinicians and patients, and support integration into everyday nursing practice.

Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.

IMPORTANCE With a growing global user base of 3.5 billion and users spending nearly as much time gaming as on social media, video gaming's effects on mental well-being have attracted scholarly and public interest. Despite the WHO's inclusion of gaming disorder in ICD-11 and government-mandated restrictions in multiple countries, the causal evidence supporting such policies remains limited. OBJECTIVE To investigate the causal effect of video gaming on mental well-being in the post-COVID period. DESIGN A natural experiment of game console lottery was used to identify the causal effect of video gaming on mental well-being. The intention-to-treat effect was estimated using multivariate regression and propensity score matching. Causal effects of game engagement were estimated using the instrumental variable method (two-stage least squares) and a causal machine learning algorithm, instrumental forest. SETTING Online surveys were conducted between September 2022 and March 2023, covering all 47 prefectures in Japan. PARTICIPANTS A total of 71,435 participants aged 10-69 answered the surveys. 6,911 individuals participated in the natural experiment. EXPOSURES Video game engagement, including video game console ownership, use of the console in the last 30 days, and video gaming duration. MAIN OUTCOMES AND MEASURES Psychological distress and life satisfaction. RESULTS The intention-to-treat effects of winning game console lotteries on mental well-being were positive (0.1 SD). Game console ownership improved mental well-being by 0.1-0.2 SD, and past-month play improved it by 0.2-0.3 SD. An extra hour of daily video game play led to 0.3-0.5 SD improvements in mental well-being. CONCLUSIONS AND RELEVANCE This study found that video gaming had a positive effect on mental well-being in the post-COVID period. The consistency of the effect size with that of a related COVID-period study adds robustness to our findings. Our findings add to the growing evidence that digital media screen time has diverse effects on well-being and support public health policies that recognize the potential mental well-being benefits of appropriate levels of video gaming.

Background: Adverse events (AEs) after hospitalization are common and disproportionately affect adults with multiple chronic conditions (MCC). Capturing patient-reported symptoms and self-assessed health may enable earlier detection of post-discharge AEs. Objective: To identify and test user requirements for an automated remote monitoring system to enhance AE surveillance during the transition home following discharge. Methods: We conducted a mixed-methods study using an iterative, user-centered design approach. Semi-structured interviews with patients and clinicians informed system requirements, followed by real-world field testing in 20 patients who used the system for up to 7 days after discharge. The prototype leveraged interoperable electronic health record data services, delivered automated post-discharge check-ins using a combined questionnaire assessing new or worsening symptoms and patient-reported outcomes (PROs), provided risk-stratified health advice (when and with whom to initiate contact), and escalated high-risk symptoms to clinicians in real-time. Descriptive statistics assessed feasibility and utilization; conventional content analysis identified user needs and implementation considerations. Results: Thirty-seven patients with MCC and 23 clinicians participated. Key requirements for patients included clear communication of personalized risk based on red-flag symptoms, and actionable guidance aligned with discharge instructions. Key requirements for clinicians included explicit delineation of responsibility across inpatient and outpatient setting, and selective escalation to minimize burden. Field testing patients completed 60% of the combined questionnaires. Seven patients received Level 2 or Level 3 health advice after reporting new or worsening symptoms. Three patients triggered Level 3 alerts, resulting in one-time, secure escalation emails to clinicians. Four of the 7 patients who received Level 2 or 3 health advice had chart-confirmed emergency department visits within 1 week of discharge. Patients found the system understandable and helpful, while clinicians noted challenges interpreting PRO trends. Conclusions: These observations support the feasibility and acceptability among patients and clinicians of collecting patient-reported symptoms and PROs during the early post-discharge period. Future iterations should prioritize clear risk communication, role clarity, and interpretable patient-reported data. Formal validation is required to assess predictive performance and clinical utility of symptom-based escalation for post-discharge AE surveillance.

Pain assessment remains challenging in clinical practice. Automated pain assessment using artificial intelligence (AI) and facial expression analysis has emerged as a potential approach to support more objective and continuous pain monitoring, particularly for patients who are unable to communicate effectively. However, successful implementation depends on clinician acceptance. We conducted a cross-sectional survey of physicians, advanced practice providers (APPs), and nurses at a tertiary academic medical center to evaluate current pain assessment practices and perceptions of a facial-analysis-based pain assessment system. Eighty-seven eligible clinicians participated. For current assessment practices, clinicians commonly relied on observational indicators of pain, including vital signs (88.5%), physical examination findings (85.1%), and facial expressions (79.3%). Attitudes toward automated pain assessment were generally neutral to positive. Half of respondents agreed that such a system would be useful for monitoring patients over time (50.6%), and many perceived value for nonverbal patients (86.5%), patients with dementia or cognitive impairment (78.4%), and patients with language barriers (62.2%). Most clinicians reported requiring high levels of accuracy before routine use, with nearly 60% requiring at least 90% accuracy. Key concerns included alert fatigue and unequal performance across patient populations. These findings suggest clinicians view automated facial-analysis systems as potentially valuable adjuncts for pain monitoring, particularly in communication-impaired populations, while emphasizing the importance of accuracy, fairness, explainability, and workflow integration during system development and implementation.

Influenza vaccine effectiveness (VE) is estimated from a limited number of clinics using a test-negative design. These standard estimates face geographic, temporal, and operational constraints. Using Twitter/X data, we applied few-shot chain-of-thought prompting to identify self-reported vaccination status and influenza test results, then implemented a test-negative-like design to estimate VE. Our estimates fell within the range of interim reports and could complement current systems, improving feasibility, timeliness, and scalability.

Objective: The Alimetry Gut-Brain Wellbeing (AGBW) Survey is a validated 10-item scale developed to assess mental health in patients with chronic gastroduodenal symptoms. While the original scale demonstrated strong psychometric properties overall, as part of an ongoing commitment to continuous instrument optimization, this study evaluated a revised, unidirectional version of the scale (AGBW-II) to enhance measurement precision. Methods: The psychometric properties of the revised AGBW-II were evaluated via an anonymous online questionnaire in a sample of 161 patients with chronic gastroduodenal symptoms. Score distributions, factor analysis, reliability, and validity were analyzed and compared with data from the validation study of the original scale (n= 311). Results: The AGBW-II total and subscale scores demonstrated robust reliability and validity, outperforming the original AGBW Survey. Notable improvements in the reliability and validity of the stress subscale were observed following item rewording. Confirmatory factor analysis verified the three-factor model separating the depression, stress, and anxiety subscales, with the AGBW-II showing improved model fit indices. Conclusions: The revised, unidirectional AGBW-II demonstrated improved reliability and validity, while maintaining the underlying psychometric properties of the original scale. Consequently, the AGBW-II provides a reliable, valid, and brief mental health assessment for patients with chronic gastroduodenal symptoms.

The COVID-19 pandemic triggered an unprecedented volume of real-time discourse on social media platforms, with Twitter serving as a global forum for public reactions, fears, and evolving narratives. Traditional sentiment analysis approaches treat tweets as independent, static samples, failing to capture the temporal evolution and geographic heterogeneity of public opinion. This paper presents a comprehensive spatio-temporal framework that integrates fine-grained sentiment classification using COVID-Twitter-BERT with dynamic topic modeling via BERTopic to automatically discover and track evolving narratives. Using a corpus of 2.4 million geolocated tweets collected between January 2020 and June 2022, our analysis reveals distinct pandemic phases: early fear-driven narratives about mask shortages (Q1 2020), vaccine optimism followed by polarization (2021), and pandemic fatigue (2022). Regional comparisons show significant differences, with US discourse dominated by freedom-versus-mandate debates while European discussions emphasized collective solidarity. Our framework achieved 76% F1-score in sentiment classification and successfully identified 50 distinct narratives with high coherence scores. This work provides a powerful methodology for real-time epidemiological narrative surveillance and crisis communication monitoring.

Introduction: Vaping public education campaigns are a central prevention strategy for young adults (YAs), but message effectiveness varies widely and formative testing typically evaluates one message attribute at a time. This study examined how four perceptual dimensions, including cognitive engagement, social relevance, emotional arousal, and emotional valence, relate to perceived message effectiveness (PME), and whether multiple combinations of these dimensions correspond to similarly high PME. Methods: In a cross-sectional online message-testing study conducted from March to April 2025, 540 U.S. YAs who vape each evaluated 15 messages randomly sampled from a corpus of 229 text-image vaping public education messages, yielding 8,100 message evaluations. Cross-classified multilevel models estimated associations between each perceptual dimension and PME. An Explainable Boosting Machine probed nonlinear associations, and k-means clustering of high-predicted-PME perceptual configurations identified distinct profiles. Analyses were conducted in 2026. Results: Cognitive engagement (b = 0.20, 95% CI [0.18, 0.22]) and social relevance (b = 0.15, 95% CI [0.13, 0.17]) showed the strongest associations with PME, followed by emotional arousal (b = 0.06, 95% CI [0.04, 0.08]); emotional valence showed no linear association (b = 0.00, 95% CI [-0.02, 0.02]). The Explainable Boosting Machine revealed a U-shaped valence pattern, with higher predicted PME at both the negative and positive ends of the scale. Clustering yielded five distinct perceptual profiles with similarly high predicted PME (4.10-4.25). Conclusions: Multiple perceptual configurations correspond to similarly high PME, supporting a configurational approach to formative message testing rather than a single optimal message formula.

Background Naturalistic audiovisual recordings of caregiver-child interactions contain rich developmental signals. However, extracting interpretable clinical measures requires resource-intensive manual coding. To address this bottleneck, we evaluated natural-language queries for retrieving specific behavioral moments from these recordings, applying multimodal embeddings as an automated evidence-selection layer. Methods We compared three embedding models (Jina Embeddings v5 Omni, LanguageBind, and Wave7B) for natural-language retrieval directly from audio and video streams, bypassing transcript text. We assessed performance across 27 behavioral targets in 277 caregiver-child recordings (14, 24, and 36 months of age) from the Early Head Start Talkbank corpus, yielding 7,479 recording-target queries. Results Jina Embeddings v5 Omni achieved the highest top-10 retrieval success (text-to-audio 38.3%; text-to-video 36.4%), ahead of LanguageBind (37.0%; 34.5%) and Wave7B (36.1%; 35.0%). Across models, retrieval was substantially more successful for common targets than for rare vocal and gestural behaviors, such as pointing and babbling. By analyzing the spoken words within the retrieved audio clips, we found that Jina accurately ranked the children by their relative vocabulary size at each age (Spearman = 0.68, 0.82, and 0.90 at 14, 24, and 36 months). However, the model severely underestimated the total number of unique words each child used throughout the full session. Conclusion Multimodal embeddings can successfully pinpoint important developmental behaviors and speech patterns within lengthy caregiver-child recordings. However, these systems still struggle to locate rare events. Additionally, while they can accurately rank children by relative vocabulary size, they fail to measure a child's complete vocabulary. We conclude that these models are currently best suited for automated evidence-selection to prioritize relevant segments for expert interpretation rather than acting as an independent replacement for manual behavioral coding or language assessment. Improving the detection of infrequent behaviors and validating these models across external datasets are essential next steps before real-world clinical deployment.

Background People with lower levels of literacy or health literacy may struggle to understand conventional health information. Video animations show promise as information tools, yet it is unclear whether video animations help reduce these inequalities in understanding. This study examined whether the effectiveness of video animations in health settings differs according to level of literacy or health literacy. Methods We drew on trials from a recent systematic review of video animations about healthcare or public health topics for patients or the public. We extracted available data on literacy, health literacy, or proxy indicators. One reviewer extracted data and a second checked all entries. Where possible, we conducted subgroup analyses of low and high literacy levels or interaction meta-analyses comparing low versus high literacy groups; otherwise, results were summarised narratively. Results From 88 eligible trials, we extracted health literacy data for 12. Across nine trials reporting knowledge, animations mostly improved knowledge compared with controls in both lower and higher health literacy groups. Effects on attitudes and behaviours were mixed and often small, with few studies reporting results by health literacy level. Across the subgroup analyses available, there was no consistent evidence of a pooled interaction effect of animations according to low and high literacy groups, but both statistical heterogeneity and small subgroup sizes limited precision of estimates. Across 88 trials, 54 (61%) reported education level, 22 (25%) did not, and 12 (14%) involved children or adolescents likely to have similar education levels. Conclusions Overall, the available data suggest that video animations can improve knowledge outcomes in both lower and higher health literacy groups, but their impact on attitudes and behaviour is less clear. Because literacy was rarely reported or analysed in the trials, it remains uncertain whether animations help to reduce literacy-related inequalities in access to, and use of health information.