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The recent increase in online health information-seeking has prompted extensive user appraisal of encountered content. Information consumption depends crucially on the quality of encountered information and the user's ability to evaluate it; yet, within the context of web-based, organic search behavior, few studies take into account both these aspects simultaneously. We aimed to explore a method to bridge these two aspects and grant even consideration to both the stimulus (web page content) and the user (ability to appraise encountered content). We examined novices and experts in information retrieval and appraisal to demonstrate a novel approach to studying information foraging theory: stimulus-engagement alignment (SEA). We sampled from experts and novices in information retrieval and assessment, asking participants to conduct a 10-minute search task with a specific information goal. We used an observational and a retrospective think-aloud protocol to collect data within the framework of an interview. Data from 3 streams (think-aloud, human-computer interaction, and screen content) were manually coded in the Reproducible Open Coding Kit standard and subsequently aligned and represented in a tabularized format with the R package {rock}. SEA scores were derived from designated code co-occurrences in specific segments of data within the stimulus data stream versus the think-aloud and human-computer interaction data streams. SEA scores represented a meaningful comparison of what participants encountered and what they engaged with. Operationalizing codes as either \"present\" or \"absent\" in a particular data stream allowed us to inspect not only which credibility cues participants engaged with with the most frequency, but also whether participants noticed the absence of cues. Code co-occurrence frequencies could thus indicate case-, time-, and context-sensitive information appraisal that also takes into account the quality of information encountered. Using SEA allowed us to retain epistemic access to idiosyncratic manifestations of both stimuli and engagement. In addition, by using the same coding scheme and designated co-occurrences across participants, we were able to pinpoint trends within our sample and subsamples. We believe our approach offers a powerful analysis encompassing the breadth and depth of data, both on par with each other in the feat of understanding organic, web-based search behavior.

Access to high-quality internet plays an increasingly important role in supporting care delivery and health information access. Although internet access has the potential to alleviate some inequities in health care, the digital divide negatively impacts cancer across the continuum. While subscription to high-speed internet has been previously assessed, satisfaction with home internet to meet the health needs of users is a lesser-known, important indicator of satisfactory access to internet-based health information and digital health technology use. This study aimed to assess differences in perceptions of quality of at-home internet connection and its association to cancer health information-seeking experiences and use of digital health technologies in a nationally representative sample of US adults. Secondary analysis of data from the National Cancer Institute's Health Information National Trends Survey (HINTS) 2022 (n=6252) was conducted. The primary predictor, \"how satisfied are you with your Internet connection at home to meet health-related needs?,\" a novel item on HINTS 6, was dichotomized into \"high\" (extremely satisfied or very satisfied) and \"low\" (somewhat satisfied, not very satisfied, or not at all satisfied) satisfaction. Outcomes variables included 3 items assessing cancer information-seeking experiences and 2 items measuring access to telehealth and patient portals over the past 12 months. Adjusted logistic regression models (P<.05) were performed, including age, race and ethnicity, education, income, health insurance access, geography, and difficulty understanding cancer information, a proxy for health literacy, as covariates. Those reporting low satisfaction with their home internet had higher odds of agreeing that searching for cancer information took a lot of effort (odds ratio [OR] 1.59, 95% CI 1.16-2.19) and that they felt frustrated searching for cancer information (OR 1.46, 95% CI 1.07-1.98). Respondents with lower satisfaction with their home internet had lower odds of accessing their patient portal at least once in the past year (OR 0.54, 95% CI 0.33-0.89). While the relationship between internet satisfaction and concern over information quality was not significant, respondents aged 18-34 years reported higher odds to be concerned compared with those aged 75 years and older (OR 1.74, 95% CI 1.04-2.90), and those with lower education reported less concern over the quality of information compared with those with postbaccalaureate degrees (high school graduate: OR 0.56, 95% CI 0.31-0.99; college graduate: OR 0.67, 95% CI 0.48-0.95). Finally, while the association between satisfaction with internet and telehealth use over the past 12 months was not significant, those without health insurance were significantly less likely to have had a telehealth appointment in the last year (OR 0.39, 95% CI 0.19-0.81). Satisfaction with internet at home to meet health needs is correlated with cancer information-seeking experiences and usage of some available health technology. These findings underscore the value of high-quality internet services toward successful implementation of health care technology and better patient experiences in health information seeking.

With the development of digital health platforms, patients with breast cancer are increasingly relying on web-based resources to search for disease-related information. Proper usage of web-based health information by patients with breast cancer is crucial for understanding disease information and participating in treatment decisions. However, in the face of the large amount and complexity of information, it is still unclear how patients can make psychological adjustments and behavioral responses. Problems such as variable information quality and conflicting information are also affecting the cognitive and treatment decision-making process of patients with breast cancer. This study aims to explore the real experiences of Chinese patients with breast cancer in their search for web-based health information from a phenomenological perspective, providing insights for optimizing future web-based health information support for patients. This qualitative study used semistructured, in-depth face-to-face interviews to collect data. Through purposive and convenience sampling, 18 female patients with breast cancer were recruited from a tertiary cancer hospital in China. The data saturation principle was observed to determine the endpoint of data collection. The collected data were analyzed using thematic analysis. From 18 original interview documents, three themes and 11 subthemes were categorized as follows: (1) driving force of information search (emotion-based information search, problem-solving-oriented information search), (2) cognitive judgments amidst the information fog (interweaving of multichannel information, judgment of information authenticity, information applicability assessment, cognitive confusion in the context of information conflict, and construction of information meaning), and (3) adaptation under the pressure of web-based information (transform information into action, emotional regulatory coping, build a support network, and acceptance and adjustment of expectations). This study reveals that the experiences of patients with breast cancer within web-based health information environments resemble an information navigation journey. Patients continuously search, evaluate, and adjust within the sea of information to maintain cognitive clarity and emotional equilibrium. The findings offer valuable insights for clinical health care providers, health information platform developers, and policymakers. They can help optimize digital health services and design personalized information support that better meets patients' needs.

The rise of generative artificial intelligence (AI) tools such as ChatGPT is rapidly transforming how people access information online. In the health context, generative AI is seen as a potentially disruptive information source due to its low entry barriers, conversational style, and ability to tailor content to users' needs. However, little is known about whether and how individuals use generative AI for health purposes, and which groups may benefit or be left behind, raising important questions of digital health equity. This study aimed to assess the current relevance of generative AI as a health information source and to identify key factors predicting individuals' intention to use it. We applied the Unified Theory of Acceptance and Use of Technology 2, focusing on 6 core predictors: performance expectancy, effort expectancy, facilitating conditions, social influence, habit, and hedonic motivation. In addition, we extended the model by including health literacy and health status. A cross-national design enabled comparison across 4 European countries. A representative online survey was conducted in September 2024 with 1990 participants aged 16 to 74 years from Austria (n=502), Denmark (n=507), France (n=498), and Serbia (n=483). Structural equation modeling with metric measurement invariance was used to test associations across countries. Usage of generative AI for health information was still limited: only 39.5% of respondents reported having used it at least rarely. Generative AI ranked last among all measured health information sources (mean 2.08, SD 1.66); instead, medical experts (mean 4.77, SD 1.70) and online search engines (mean 4.57, SD 1.88) are still the most frequently used health information sources. Despite this, performance expectancy (b range=0.44-0.53; all P<.001), habit (b range=0.28-0.32; all P<.001), and hedonic motivation (b range=0.22-0.45; all P<.001) consistently predicted behavioral intention in all countries. Facilitating conditions also showed small but significant effects (b range=0.12-0.24; all P<.01). In contrast, effort expectancy, social influence, health literacy, and health status were unrelated to intention in all countries, with one marginal exception (France: health status, b=-0.09; P=.007). Model fit was good (comparative fit index=0.95; root mean square error of approximation=0.03), and metric invariance was confirmed. Generative AI use for health information is currently driven by early adopters-those who find it useful, easy to integrate, enjoyable, and have the necessary skills and infrastructure to do so. Cross-national consistency suggests a shared adoption pattern across Europe. To promote equitable adoption, communication efforts should focus on usefulness, convenience, and enjoyment, while ensuring digital access and safeguards for vulnerable users.

Human papillomavirus (HPV) is a leading cause of cervical cancer. It has a substantial impact on global public health, with low- and middle-income countries, including India, facing the highest burden. In 2022, India reported 127,526 new cases and 79,906 deaths due to cervical cancer, projected to increase by 61% by 2040. Although the National Technical Advisory Group on Immunization recommended the HPV vaccine for cervical cancer prevention, it is yet to be a part of India's universal immunization program. This study aims to examine online interest in the HPV vaccine in India from January 2010 to April 2024 using Google Trends. A cross-sectional analysis of Google Trends data was performed, using the relative search volume to track interest on a scale of 0-100. Trends were analyzed annually using 1-way ANOVA and joinpoint regression to identify significant changes in search behavior related to public health events. Statistical significance was set at P<.05. The average annual growth in HPV vaccine-related searches was 13.7% (95% CI 7.9%-19.1%), with the highest relative search volume in 2024 (49.5) and the lowest in 2017 (3.38). Spikes in search interest aligned with key events like the 2018 National Technical Advisory Group on Immunization recommendation and the 2022 launch of the indigenous HPV vaccine. The results highlight online search data's value in tracking public interest, which fluctuates in response to health policy changes or developments on social media. In India, targeted digital strategies will be vital for addressing vaccine hesitancy and increasing HPV vaccine uptake. Google Trends data can inform public health strategies by identifying periods of high interest, aiding in the promotion of HPV vaccination in India.

Research on the effectiveness of digital health campaign strategies is lacking. Understanding performance outcomes is essential for the successful implementation of campaigns. Two studies examined platforms, tactics, and content of digital health campaigns using paid media performance data. This analysis compared 2 digital advertising methods (social media and banner or display) using click-through rate (CTR) and cost-per-click (CPC) as performance measures. Performance differences by state, community type, message approach, format, and image type were assessed. CTR and CPC served as measures in determining performance differences between social media and banner or display. This cross-sectional secondary analysis examined campaign performance for the HEALing (Helping to End Addiction Long-Term) Communities Study, which served 85,875,105 impressions. Data were collected from media buy reports, entered into templates that included method (display or banner and social media) and key performance indicators (impressions, clicks, and media spend), and CTR and CPC were calculated. Study 1 assessed differences in CTR and CPC for social media and banner or display by state (KY, NY, MA, and OH) and community type (urban and rural). Study 2 assessed differences in CTR for social media and banner or display by state (KY, NY, MA, and OH), community type (urban and rural), message approach (testimonial and information-based), format (motion graphic or graphics interchange format, video, and static image), and image type (local and stock). Separate analyses were conducted for each advertising method. Study 1 found significant differences between advertising methods, where social media had higher CTR compared to banner or display. Social media had a significant main effect for state, where OH had the highest CTR. There was a statistically significant difference in CPC based on advertising method, where social media had a lower CPC compared to banner or display. Social media had a significant main effect for state, where OH had the lowest CPC. Banner or display had a significant main effect for state and community type, where OH and urban communities had the highest CPC. Study 2 found significant differences between advertising methods, where social media had higher CTR than banner or display. For social media, urban communities, static format, and local spokespersons had the highest CTR. There were significant differences between all pairs of states, where OH had the highest CTR. For display or banner, static format and local spokespersons had the highest CTR. This analysis provides guidance for digital health campaigns. It examined the performance of opioid use disorder campaigns using CTR and CPC measures, demonstrating utility in future campaign evaluations. Social media was more related to stimulating responses to campaign messages compared to banner or display. State-to-state variations emphasized the importance of message pilot testing. Using local spokespersons versus stock spokespersons is recommended.

Many people use peer online forums to seek support for health-related problems. More research is needed to understand the impacts of forum use and how these are generated. However, there are significant ethical and practical challenges with the methods available to do the required research. We examine the key challenges associated with conducting each of the most commonly used online data collection methods: surveys, interviews, forum post analysis, and triangulation of these methods. Based on our learning from the Improving Peer Online Forums (iPOF) study, an interdisciplinary realist-informed mixed methods evaluation of peer online forums, we outline strategies that can be used to address key issues pertaining to assessing important outcomes, facilitating participation, validating participants (users who consent to take part in one or more parts of the study), protecting anonymity, gaining consent, managing risk, multistakeholder engagement, and triangulation. We share this learning to support researchers, reviewers, and ethics committees faced with deciding how best to address these challenges. We highlight the need for open, transparent discussion to ensure the research field keeps pace with evolving technology design and societal attitudes to online data use.

Singapore is a multicultural society characterized by a diverse array of ethnic groups, including Chinese, Malay, Indians, and others. A considerable percentage of Singaporeans are active users of the internet. The internet has become a significant resource for health education, particularly for women who wish to learn about a healthy lifestyle during pregnancy. However, it is still unclear how pregnant women search for information online, particularly within specific groups. This study aimed to explore the relationship between healthy lifestyle practices, online health information-seeking behaviors, and internet usage (IU) among 1905 pregnant women. Structural equation modeling (SEM) was used to evaluate the relationships between the appropriate intake of food groups, healthy diet practices (HD), internet for dietary advice (ID), internet for physical activity advice (IP), and IU, based on 5 hypotheses rooted in theoretical concepts. We used a multigroup SEM approach to examine these hypotheses across various ages, ethnicities, BMI, and categories of pregnant groups. Our results confirmed 5 hypotheses, indicating significant relationships among the variables: appropriate intake of food groups was positively linked to HD (β=0.262; P<.001); HD was positively linked to ID (β=.168; P<.001); ID was positively linked to IP (β=0.185; P<.001); IP was positively linked to IU (β=0.190; P<.001); and HD was negatively linked to IU (β=-0.208; P<.001). The multigroup SEM analyses yielded significant differences in Hypotheses 2 and 3 when comparing different age groups (P=.009), BMI categories (P=.03), and number of pregnancies (P=.003). Our findings offer valuable insights into developing customized online interventions aimed at encouraging a healthy lifestyle during pregnancy.

Health literacy, the ability to understand and act on health information, is critical for patient outcomes and health care system effectiveness. While plain language guidelines enhance text-based communication, audio-based health information remains underexplored, despite the growing use of digital assistants and smart devices in health care. Traditional readability formulas, such as Flesch-Kincaid, provide limited insights into the complexity of health-related texts and fail to address challenges specific to audio formats. Factors like syntax and semantic features significantly influence comprehension and retention across modalities. This study investigates features that affect comprehension of medical information delivered via text or audio formats. We also examine existing readability formulas and their correlation with perceived and actual difficulty of health information for both modalities. We developed a parallel corpus of health-related information that differed in delivery format: text or audio. We used text from the British Medical Journal (BMJ) Lay Summary (n=193), WebMD (n=40), Patient Instruction (n=40), Simple Wikipedia (n=243), and BMJ journal (n=200). Participants (n=487) read or listened to a health text and then completed a questionnaire evaluating perceived difficulty of the text, measured using a 5-point Likert scale, and actual difficulty measured using multiple-choice and true-false questions (comprehension) as well as free recall of information (retention). Questions were generated by generative artificial intelligence (ChatGPT-4.0). Underlying syntactic, semantic, and domain-specific features, as well as common readability formulas, were evaluated for their relation to information difficulty. Text versions were perceived as easier than audio, with BMJ Lay Summary scoring 1.76 versus 2.1 and BMJ journal 2.59 versus 2.83 (lower is easier). Comprehension accuracy was higher for text across all sources (eg, BMJ journal: 76% vs 58%; Patient Instructions: 86% vs 66%). Retention was better for text, with significant differences in exact word matching for Patient Instructions and BMJ journal. Longer texts increased perceived difficulty in text but reduced free recall in both modalities (-0.23,-0.25 in audio). Higher content word frequency improved retention (0.23, 0.21) and lowered perceived difficulty (-0.20 in audio). Verb-heavy content eased comprehension (-0.29 in audio), while nouns and adjectives increased difficulty (0.20, 0.18). Readability formulas' outcomes were unrelated to comprehension or retention, but correlated with perceived difficulty in text (eg, Smog Index: 0.334 correlation). Text was more effective for conveying complex health information, but audio can be suitable for easier content. In addition, several textual features affect information comprehension and retention for both modalities. Finally, existing readability formulas did not explain actual difficulty. This study highlighted the importance of tailoring health information delivery to content complexity by using appropriate style and modality.

Preliminary research has suggested that internet use data could offer digital signals of early disease and has the potential to facilitate early detection and improve patient outcomes. However, there are significant challenges in linking individual-level internet use data with health outcomes. One key aspect is that the public might not be willing to share data for research or that selective data sharing might create bias in datasets and increase inequalities. Our study aimed to investigate the willingness of the public to share their internet use data for medical research and to identify key criteria that affect willingness to share. We conducted a web-based, cross-sectional online survey with 2390 UK adults with and without a history of cancer, heart disease, and depression using quota sampling. Participants were randomly assigned to explore willingness to share different types of internet use data for 1 of 3 health conditions (cancer, heart disease, and depression) and for provision of a pictorial example of internet use data. Logistic regression analysis (α=.05) for each condition was used to determine key factors of willingness to share, including sociodemographics and attitudes toward sharing. Open-ended comments regarding facilitators of sharing and concerns were analyzed thematically. Willingness to share internet use data was high across conditions (74%-77%, 95% CI 70.5%-80.3%), especially for health app data (73%-76%, 95% CI 69.8%-79.1%). The pictorial example of browsing history did not affect willingness to share. For all conditions, factors consistently associated with willingness to share were perceived benefits (odds ratios [ORs] 5.692-8.850; all P<.001) and concerns (ORs 0.343-0.432; all P<.001). Key concerns were data privacy, potential for misuse, and lack of relevance. Suggestions to increase willingness to share included contributing to society and research, data security assurances, clarification of research purposes, and monetary incentives. Familiarity with internet use data was related to lower willingness to share for heart disease detection (OR 0.740, 95% CI 0.561-0.976). Asian ethnicity was associated with lower willingness to share internet use for cancer detection (OR 0.234, 95% CI 0.076-0.723). Younger age (OR 0.975, 95% CI 0.951-0.999) and male gender (OR 2.615, 95% CI 1.511-4.526) were associated with higher willingness to share data for depression detection. This first large-scale assessment of public willingness to share internet use data for early disease detection adds novel insights by comparing conditions and examining sociodemographic factors alongside perceived benefits and risks. It highlights that understanding of internet data is limited yet willingness to share for research is high. Clear communication of benefits, strong privacy protections, and incentives may increase participation and reduce bias. The findings inform consent design, targeted outreach to underrepresented groups, and data governance for safe use of personal digital data. Future research should focus on improving public communication, particularly among less willing groups at risk of inequality.