Journal of Medical Internet Research

Receipt of Medicines Information From the Internet and Other Information Sources Among Adult Medicine Users in Developed Economies, 2010-2025: Systematic Review

2026-05-20T17:45:09-04:00

Background: The internet, social media, and digital health tools have transformed access and receipt of medicines information (MI), complementing or replacing traditional sources, such as physicians, pharmacists, and package leaflets. Objective: This study aimed to examine the prevalence and patterns of adult medicine users’ receipt of MI from electronic sources (eg, internet and social media) compared with traditional sources, and to assess trends in the receipt of MI in developed economies since 2010. Methods: A systematic search was conducted in CINAHL, the Cochrane Library, ProQuest, Scopus (including Embase and MEDLINE), and Web of Science databases for peer-reviewed studies conducted and published between January 1, 2010, and December 31, 2025. Studies including adults (≥18 years) in developed economies were eligible. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Methodological quality and risk of bias were assessed using the Mixed Methods Appraisal Tool. Due to substantial heterogeneity, findings were synthesized narratively without meta-analysis. Results: Twenty-six studies from 10 countries, involving 19,891 medicine users, were included. Most studies were cross-sectional surveys (n=19), with only one being a national long-term trend study. Physicians, pharmacists, and package leaflets consistently remained the most common MI sources, irrespective of patient group, country, or study design. No clear temporal trends in MI receipt were observed. Variations in the frequency of MI receipt were evident across research methods, medicine user groups, and types of medicines. Individuals with greater familiarity with the internet were more likely to receive MI from electronic sources, such as websites and social media. In most studies, MI obtained from social media was not distinguished from other internet-based sources. Half of the studies focused on heterogeneous medicine user groups (n=14), while the remainder examined specific medicine user groups (n=12). Although the methodological quality of the studies was generally acceptable, only 3 studies reported the use of an explicit theoretical framework. Conclusions: Traditional MI sources remain central for adult medicine users despite the growing role of electronic platforms. While the receipt of MI from electronic sources appears more common among internet-experienced users, no significant temporal trends in using these sources were identified. Further research is needed to better distinguish between different digital MI sources, including artificial intelligence–based MI sources, and to explore their evolving roles, implications for health-related decision-making, and associations with user characteristics and health contexts in the digital era. This review is innovative in systematically comparing traditional and electronic MI sources across developed economies and in highlighting methodological and conceptual gaps in existing research. By synthesizing cross-national evidence and identifying the need for clearer differentiation of digital MI subtypes, the study contributes to the field and informs the development of more targeted, reliable, and user-centered MI strategies in real-world health care settings.

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration

2026-05-20T17:30:15-04:00

Background: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and trust in artificial intelligence (AI)–assisted diagnostics, particularly in oncology. Evidence on how explainability is implemented, validated, and reported in cancer imaging remains fragmented. Objective: This scoping review aimed to systematically map research applying xAI methods to radiologic cancer imaging, summarize methodological and clinical trends, and identify persistent gaps in validation and integration. Methods: We conducted a structured search of PubMed and Scopus (final search executed on October 20, 2025), covering studies published from 2017 to December 2024. Eligible peer-reviewed articles using machine learning or deep learning were analyzed with a focus on xAI components. Data from 371 studies were extracted into predefined categories covering cancer type, imaging modality, AI model, xAI method, terminology, validation, code availability, and decision support system integration. Results: Studies focused primarily on breast (112/371, 30.2%), lung (87/371, 23.5%), and brain (56/371, 15.1%) cancers, with prostate, thyroid, and liver cancers also represented. The primary imaging modalities were computed tomography (139/371, 37.5%) and magnetic resonance imaging (104/371, 28%). Deep learning was used in 70.1% (260/371) of studies, classical machine learning in 18.1% (67/371), hybrid pipeline methods for 10% (37/371), and emerging concept-, prototype-, or causal-based approaches accounted for 1.9% (7/371) of studies. Post hoc xAI methods were dominant (305/371, 82.2%), with visualization (163/371, 53.4%), and feature relevance (111/371, 36.4%) as the most common subcategories. Hybrid post hoc or inherent approaches comprised 12.1% (45/371) and intrinsically interpretable methods 5.7% (21/371). Data sources were mostly public (149/371, 40.2%) or mixed (100/371, 26.9%); 22.9% (85/371) used private institutional datasets, and 7.8% (29/371) did not report data sources. Among validated studies, expert or user-based validation was most common (104/193, 53.9%), followed by mixed methods (74/193, 38.3%), while quantitative metrics (10/193, 5.2%) and clinical knowledge–based (8/193, 4.1%) validation remained rare. Only 17.5% (65/371) of studies provided code and 12.1% (45/371) reported decision support system integration, with few achieving actual clinical deployment. Conclusions: This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance trustworthy AI in oncology imaging.

A Digital Toolkit for Weight Loss Maintenance in European Adults (NoHoW): 2×2 Factorial Randomized Controlled Trial

2026-05-20T17:15:14-04:00

Background: Digital approaches to weight management have the potential to produce cost-effective and scalable weight management solutions. Effective behavior change interventions typically promote self-regulation of energy balance behaviors, which may be enhanced by incorporating emotion regulation strategies. Objective: This study aimed to evaluate the effectiveness of a digital behavior change toolkit for weight loss maintenance in European adults who had achieved ≥5% weight loss in the previous 12 months. We hypothesized that a combined intervention targeting self-regulation or motivation and emotion regulation would be more effective than either component alone, and that each would outperform an active control. Methods: The Navigating to a Healthier Weight (NoHoW) trial was a 2×2 factorial randomized, single-blind, controlled trial involving 1627 adults who had achieved ≥5% weight loss in the previous 12 months (initial BMI ≥25 kg/m) across 3 European centers (the United Kingdom, Denmark, and Portugal). The trial evaluated a digital toolkit for weight management subsequent to an initial ≥5% weight loss in the prior 12 months. Participants were assigned using adaptive stratified sampling to one of four groups: (1) self-regulation or motivation (n=403), (2) emotion regulation (n=416), (3) combined motivation and emotion (n=408), or (4) active control (generic content, regular self-weighing, and Fitbit use, n=400). The primary outcome was weight change from baseline to 12 months. Prespecified secondary outcomes included cardiometabolic markers. Linear models adjusted for recruitment center, sex, age group, BMI group, and pretrial weight loss. Subgroup analyses were conducted by sex. Results: At 12 months, 76% (364/1627) of participants remained in the study. In the primary ITT analysis in all participants, none of the intervention arms (motivation, emotion, or combined) differed significantly from the active control for weight change at 12 months. Completer and per-protocol analyses produced similar patterns and did not change the overall interpretation. In the per-protocol sample, men regained 0.14 kg, and women regained 0.54 kg of their pretrial weight loss. Subgroup analyses indicated a small effect of the motivation intervention in men, but this was not clinically meaningful and did not alter the primary null findings. Nearly half of ITT participants regained weight, and no significant intervention effects were observed for cardiometabolic secondary outcomes. Conclusions: The NoHoW trial was the first large-scale, multicountry 2×2 factorial randomized controlled trial to evaluate a digital-only toolkit based on self-regulation or motivation and emotion regulation techniques for weight loss maintenance. NoHoW found no evidence in the primary ITT analysis that digital interventions targeting self-regulation or emotion regulation improved weight loss maintenance compared with the active control. A small subgroup effect in men should be interpreted cautiously and does not change this conclusion. The trial provides evidence on both the limitations and potential of digital behavior change interventions for long-term weight outcomes. Future digital interventions may benefit from enhanced engagement and tailored content to improve long-term weight outcomes. Trial Registration: ISRCTN Registry ISRCTN88405328; https://www.isrctn.com/ISRCTN88405328 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-029425

Artificial Intelligence Centrality in Psychotic Delusions and Violence Risk in Forensic Psychiatry: Retrospective Observational Study of Judicial Decisions

2026-05-20T16:31:36-04:00

Background: Artificial intelligence (AI)–themed delusions are increasingly observed in psychotic-spectrum disorders, reflecting the incorporation of contemporary sociotechnical elements into delusional systems. However, it remains unclear whether the structural role of AI within these belief systems is associated with increased violence risk or more restrictive forensic outcomes. Given the importance of dynamic clinical factors (eg, insight and treatment adherence) in forensic risk assessment, clarifying the role of AI centrality has clinical and legal relevance. Objective: This study examined whether AI centrality within psychotic delusional systems is associated with (1) violence toward others and (2) judicial findings of significant public safety risk in forensic psychiatric decisions. Methods: This retrospective observational study used jurisprudential data from the Société québécoise d’information juridique database, including all publicly available Quebec tribunal and court decisions up to December 31, 2025. Eligible cases (N=29) involved psychotic-spectrum disorders with explicit AI-related delusional content and judicial consideration of dangerousness or disposition. The unit of analysis was the judicial decision. AI centrality was coded as central (n=15, 51.7%) or noncentral (n=14, 48.3%) using a structured, text-based framework. The primary outcome was documented violence toward others; secondary outcomes included direct AI-linked violence attribution and judicial findings of significant public safety risk. Covariates included impaired insight, treatment nonadherence, substance use disorder, and prior violence history. Data were extracted through full-text review using a standardized coding grid. Bivariate associations were analyzed using Fisher exact tests (α=.05), and odds ratios (ORs) with 95% CIs were calculated. Exploratory logistic regression models were performed to assess adjusted associations. Results: Violence toward others was documented in 20/29 (69%) cases. AI centrality was not significantly associated with violence (12/15, 80.0%, vs 8/14, 57.1%; OR 2.91, 95% CI 0.63-13.45; P=.26) but was strongly associated with direct AI-linked violence attribution (9/15, 60.0%, vs 2/14, 14.3%; OR 9.00, 95% CI 1.48-54.6; P=.01). Judicial findings of significant public safety risk were more frequent in AI-central cases (13/15, 86.7%, vs 9/14, 64.3%; OR 3.60, 95% CI 0.63-20.5; P=.24), although not statistically significant. AI-central cases demonstrated higher prevalence of impaired insight (13/15, 86.7%, vs 8/14, 57.1%; OR 4.89, 95% CI 0.79-30.1) and treatment nonadherence (9/15, 60.0%, vs 4/14, 28.6%; OR 3.75, 95% CI 0.74-18.9). Conclusions: AI centrality within delusional systems appears to be not independently associated with increased violence toward others but is strongly associated with AI-based attribution of behavior and markers of epistemic vulnerability, including impaired insight and treatment nonadherence. The findings suggest that AI-themed delusions function as structural organizers of meaning and agency rather than novel criminogenic risk factors. Clinically and legally, this underscores the importance of prioritizing dynamic risk variables over thematic novelty, informing more proportionate forensic decision-making and risk assessment in an era of rapidly evolving digital environments.

Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study

2026-05-20T16:30:15-04:00

Background: The clinical value of artificial intelligence (AI)–based diagnostic systems depends not only on their accuracy but also on how well their outputs integrate with clinicians’ judgments in practice. Critical knowledge gaps remain regarding diagnostic concordance between AI and clinicians in stress echocardiography interpretation, patient characteristics predicting discordance, and how cardiologists respond when AI recommendations conflict with their clinical diagnoses. Objective: This study examined the diagnostic alignment between an AI-driven stress echocardiography system (EchoGo Pro [EGP]) and cardiologists’ diagnoses of coronary artery disease (CAD), identified predictors of concordance and AI scan rejection, and explored cardiologists’ decision-making strategies when disagreements arise. Methods: We conducted mixed methods research. The quantitative study analyzed concordance between EGP and cardiologists using data from 854 participants with suspected CAD in the multicenter PROTEUS randomized controlled trial. Logistic regression identified predictors of agreement, disagreement, and scan rejection, adjusting for age, sex, smoking status, BMI, and cardiovascular risk factors (hypertension, hypercholesterolemia, diabetes, family history of CAD, and prior CAD events). To gain deeper insight into discordance, we conducted a qualitative study analyzing survey responses from 61 UK consultant cardiologists recruited via Qualtrics, exploring their perceptions of AI tools, the risks of following discordant AI recommendations, and their typical responses to AI-clinician disagreement. Results: EGP and cardiologists agreed in 60% (512/854) of the cases, but agreement was significantly lower among patients with hypertension (OR 0.58, 95% CI 0.38‐0.89; =.01), diabetes (OR 0.56, 95% CI 0.35‐0.90; =.02), and pre-existing CAD (OR 0.48, 95% CI 0.30‐0.77; =.002). EGP rejected 26.1% (222/854) of the scans due to insufficient image quality, with rejection significantly more common in male patients (=0.35; =.03) and those with a family history of CAD. If a positive CAD diagnosis was assigned when either cardiologists or EGP identified CAD, the proportion of positive cases increased from 17.9% (153/854) to 22.1% (189/854), potentially identifying additional at-risk patients. Survey respondents (50/60, 85% male; 26/57, 46% aged 40-49 years; 39/61, 64% White) required 65% to 69% confidence in their initial diagnosis to justify disregarding contradictory AI recommendations. The survey findings revealed cardiologists treated AI recommendations as advisory rather than definitive. When facing discordance, they retained confidence in their judgment and sought corroboration through additional testing, data review, or second opinions rather than deferring to AI. Paradoxically, cardiologists with higher confidence in AI tools required greater confidence in their own diagnosis to disregard AI recommendations (=7.73; =.02). Cardiologists attributed discordance primarily to AI’s inability to incorporate patient history, comorbidities, and broader clinical context. Conclusions: EGP shows promise as an adjunctive tool but struggles with multimorbid patients and exhibits high, uneven rejection rates. Cardiologists use AI to prompt scrutiny, not replace judgment. Future systems need to integrate wider patient data with imaging and minimize bias through representative training to avoid exacerbating inequities. Trial Registration: ClinicalTrials.gov NCT05028179; https://clinicaltrials.gov/study/NCT05028179 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2023-079617

Virtual Consultations for People With Intellectual Disabilities in General Practice and Community Care: Mixed Methods Qualitative Study

2026-05-20T16:30:03-04:00

Background: Virtual consultations (VCs) using video or telephone were embraced at speed in general practice (GP) and community care during the COVID-19 pandemic. People with intellectual disabilities, their families, and support workers (SWs), along with health care professionals (HCPs), had to adapt quickly to this change in provision, but little is known about how this new way of working was experienced. Objective: This study aims to explore the views and experiences of people with intellectual disabilities, their families, SWs, GP, and community care professionals on the quality and safety of VCs. Methods: This paper reports on users’ experiences of VCs, as part of a larger Experience-Based Co-design study. This paper relates to 2 stages of data collection. Observed video consultations in GP and community care (n=3), and semistructured interviews with people with intellectual disabilities, their family members or SWs, GP, and community care professionals (n=34). The 30-month study was conducted from November 2021 ending in April 2024. Data were analyzed using framework analysis. Results: Integrated results are presented through 5 themes, encompassed under an overarching theme of safety and quality. The five themes highlight critical factors in planning, delivery, and aftercare of VCs in GP and community services for people with intellectual disabilities in the United Kingdom: (1) context, space, and purpose—showing the importance of safe spaces to talk, and having clear consultation objectives and purpose; (2) choice—facilitating choice over time about modality of health care contact; (3) familiarity, online relationships, and trust—the building blocks for quality consultations; (4) prepare and personalize—to ensure that HCPs are aware of reasonable adjustments, and recognition of caregiver involvement; and (5) continued connection—where patients or families are offered continued contact with a named or same HCP enhancing access to regular or ongoing care. All participants were aware of the limitations of VC, which may impact safety, such as gaps in home monitoring due to the absence of appropriate equipment or recording, inability to identify vital risk indicators, and limited field of vision on screen. However, participants were also aware of the distinct benefits they offer in terms of quality provision, such as timeliness of care, building and sustaining comfortable relationships, support for more frequent attendance, and continuous connection to health teams. Conclusions: VCs offer an opportunity to improve digital inclusion in health care for people with intellectual disabilities. However, the quality and safety of VCs for this population are dependent on continuous review of patients’ needs over time and ensuring that their choices and preferences are considered when planning and providing ongoing care.

Frailty-Based Remote Monitoring in Older Adults With Heart Failure: Conceptual Framework for Adaptive Digital Health Strategies

2026-05-20T16:15:05-04:00

Remote monitoring is increasingly used in heart failure care, but most programs rely on uniform models that insufficiently reflect the heterogeneity of older adults, particularly with respect to frailty, cognitive impairment, functional dependency, and caregiver availability. This viewpoint argues that frailty should be considered a central determinant of remote monitoring design in older adults with heart failure, rather than a secondary modifier of conventional digital health pathways. Drawing on evidence from heart failure telemonitoring, geriatric medicine, and real-world cardiogeriatric experience, we propose a frailty-adaptive framework structured around four clinical trajectories: robust, prefrail, frail, and palliative. Each trajectory is associated with distinct monitoring objectives, workflow adaptations, and response pathways. Robust patients may benefit primarily from optimization, self-management support, and guideline-directed therapy titration; prefrail patients from early detection of deterioration and functional decline; frail patients from proxy-supported reporting, nurse-led triage, and rapid-access cardiogeriatric reassessment; and palliative patients from simplified symptom-guided monitoring focused on comfort, hospitalization avoidance, and caregiver support. This framework reframes remote monitoring as a stratified clinical process rather than a purely technological intervention. By aligning digital strategies with frailty status, functional capacity, and care goals, frailty-adaptive remote monitoring may improve clinical relevance, promote digital health equity, and support more sustainable models of care for older adults with heart failure.

Barriers and Facilitators to the 3 Sides of Extended Reality-Rehabilitation Adoption: Scoping Review

2026-05-20T16:15:05-04:00

Background: Rehabilitation using extended reality (XR) technologies can be used to address the growing shortage of health care staff, but the adoption level remains low. The current literature has given some first insights into what drives patients and clinicians to (not yet) adopt XR-rehabilitation tools. However, it does not sufficiently take into account that these tools will only be widely adopted if 3 types of actors collectively commit to it: developers must develop a tool, clinicians must prescribe it, and patients must use it. Because the preferences of these 3 actors may not always align, we aim to provide the first multi-actor insight into adoption. Objective: This research aims (1) to determine what drives patients, clinicians, and developers to adopt or develop XR-rehabilitation tools and (2) to determine if and how these drivers align or misalign. Methods: We searched PubMed, Embase, SCOPUS, and Web of Science with four search term categories: (1) types of rehabilitation care, (2) XR technologies, (3) adoption constructs, and (4) behavioral drivers. Using these search terms, we identified 1164 results, of which we included 64 in our review. All relevant empirical results within these papers were structured using the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. Results: After exploring the adoption drivers of patients, clinicians, and developers, we identified 3 potential misalignments among these actors. The first possible misalignment is that clinicians may have much higher standards for a tool’s medical efficacy. Because of this, they refuse to prescribe a medically less effective tool that would have matched the experience needs of patients and developers. The second possible misalignment is that clinicians value their work experience, while this is not a relevant factor for patients. Because using XR-rehabilitation tools can negatively impact a clinician’s work experience, they may decide not to use a tool that patients and developers would have liked to use and develop. The third possible misalignment is that the patients’ and clinicians’ limited ability or willingness to pay may hinder the developer’s economies of scale. Developers currently face high development costs, which they can recover by letting patients or clinicians pay for the tool. But these actors are not always able or willing to do so. As a result, developers may struggle to gain profitability, which limits the supply of XR tools. Conclusions: Our scoping review provides initial evidence that differences in the behavioral drivers of patients, clinicians, and developers may lead to misalignments that hinder the adoption of XR-based rehabilitation tools. Scholars can use this review to further investigate potential misalignments between relevant stakeholders and how to resolve them. We encourage developers and regulatory institutes to collaboratively investigate the feasibility of new revenue models and product offerings to increase the adoption of XR-rehabilitation tools.

Development of a Validated Lay Checklist (Info Without Side Effects) for Assessing Health Information on Websites: Mixed Methods Study

2026-05-20T14:45:14-04:00

Background: The internet has become a major source of health information; yet, the quality of health information on websites varies considerably. Users’ ability to evaluate either the factual accuracy or the trustworthiness of health information on websites is limited, as around half of the European people have limited health literacy. Existing checklists and tools are either prepared for research purposes or to be used by health care professionals. They do not account for the lay user perspective, since they are too long and complicated to be used by laypersons, or were developed for printed health information only. Objective: The aim of the study was to develop and validate a checklist that enables laypersons to evaluate the trustworthiness of health information on websites without requiring prior training. Methods: We used a multistage mixed methods approach including (1) a comprehensive literature review to identify existing tools and quality criteria, (2) an expert Delphi study with 6 specialists in patient communication and health information, (3) 2 rounds of cognitive interviews with 19 lay users, (4) application testing on 15 selected web pages with information about health interventions with 20 additional lay users, (5) a determination of the factual correctness of 100 web pages with health information by assessing the difference between the claimed and factual strength of the evidence on these web pages, and (6) validation testing by research team members on these 100 web pages using a Bayesian logistic regression model to analyze the predictive validity. In the final step, we integrated all quantitative and qualitative results to select the final checklist items. Results: From an initial pool of 1740 items extracted from 73 documents, we systematically reduced the list through multiple evaluation and testing rounds. To ensure the checklist is user-friendly, we involved a diverse group of potential users. The final product, the Info Without Side Effects (iWISE) checklist, contains seven items that assess key aspects of health information trustworthiness, including the absence of advertising, balanced presentation of information, the limited use of professional jargon, origination from an independent organization, citation of sources, mention of scientific validation, and the presence of a publication date. The checklist demonstrated the ability to distinguish between evidence-based and nonevidence-based health information web pages in the German language: the validation testing showed that when all the items were marked with yes, there was a nearly 100% probability that the health information was also factually correct. Conclusions: The iWISE checklist represents a user-friendly, validated tool for evaluating the trustworthiness of health information about interventions on websites. With only 7 items, it is easy to remember and could significantly improve critical health literacy. Future research should test its reliability for social media posts and health information videos.

Women’s Engagement With Different Internet-Enabled Technologies to Access Digital Menopause Information: Mixed Methods, Multiphase Sequential Study

2026-05-20T14:30:13-04:00

Background: Information on menopause can come from a variety of sources, from friends and family to health care professionals, but increasingly, digital information has become a significant source. Digital information can be accessed online, through websites, social media, podcasts, and online groups or forums. The extent to which digital information on menopause is accessed and consumed can vary widely depending on individual circumstances. Existing literature has focused on investigating a single technology at a time in terms of digital menopause information, rather than exploring a wider ecosystem that people may use. As a result, existing evidence is perhaps limited in the understanding of the role these internet-enabled digital technologies play holistically, specifically for digital menopause information. Objective: This study explored how women engage with different internet-enabled technologies to access digital menopause information and to understand how the technologies relate to feelings of competence, autonomy, and relatedness. Using a feminist new materialism lens, participants’ experiences of different technologies are considered in terms of the affordances of the technology, the associated affective factors, and the agential capacities created. Methods: Sixteen women aged between 40 and 62 years, who were going through the menopause, took part in a multiphase, mixed methods study. Initially, participants completed an online survey to capture demographics, their perceived knowledge of menopause, and information regarding their existing digital engagement. This was followed by an entry interview to establish the context of their experiences. Participants then completed 3 menopause information gathering tasks using websites, podcasts, and online groups or forums. After each task, participants took part in an interview or completed an online survey to report and reflect on their experiences. Following this, 4 focus groups were run to gather a broader understanding of the role of technology in information seeking. Results: Websites afforded the greatest accessibility to digital menopause information, with participants reporting higher levels of competence and autonomy, primarily due to familiarity. Podcasts were the most novel of the 3 internet-enabled technologies for participants and also led to greater levels of competence and autonomy, particularly in comparison to the online groups or forums. Most participants found online groups or forums to be overwhelming and difficult to navigate. Across the 3 technologies, affective factors varied, markedly regarding levels of trust in information, but several key sources were relied upon. Engaging with internet-enabled technology for menopause information opened opportunities for participants to access peer experiences, which helped to normalize, validate, and understand their menopause experiences. Conclusions: It is important to consider how individuals engage with several different internet-enabled technologies for menopause information rather than investigating one at a time. This study highlights the nuances across websites, podcasts, and online groups or forums in terms of familiarity, accessibility, trust, and lived experience.