Health misinformation exposure and psychological distress among women using social media: The roles of credibility uncertainty and loneliness

Health misinformation exposure and psychological distress among women using social media: The roles of credibility uncertainty and loneliness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health misinformation exposure and psychological distress among women using social media: The roles of credibility uncertainty and loneliness Nikesh Lagun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9157969/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Social media is a major source of health information, yet misinformation and difficulty judging information credibility may affect mental well-being. Women are highly engaged with mental health content online and experience higher levels of anxiety, depression, and loneliness, making this environment particularly relevant to women’s mental health. Methods We analysed 2024 Health Information National Trends Survey (HINTS 7) data, restricting the sample to adult women reporting social media use in the past 12 months. Exposures included perceived exposure to misleading health information and difficulty judging whether health information was true or false. Outcomes were psychological distress (PHQ-4) and loneliness. Survey-weighted linear regression models adjusted for sociodemographic and health-related covariates were used. Results Among women social media users, the mean PHQ-4 score was 2.14 (SE 0.07), and the mean loneliness score was 7.75 (SE 0.11). High difficulty judging information credibility was associated with higher psychological distress compared with low difficulty (β = 1.01, 95% CI 0.72–1.31). Associations between perceived misinformation exposure and distress were weaker and inconsistent. Loneliness was associated with higher distress across exposure levels, although interaction terms were not statistically significant. Peer-support engagement was associated with higher loneliness (β = 1.00, 95% CI 0.51–1.48) and modestly higher distress (β = 0.34, 95% CI 0.09–0.59). Conclusion Difficulty judging the credibility of health information on social media was more strongly associated with women’s psychological distress than perceived misinformation exposure alone, highlighting credibility uncertainty, mental health literacy, and social isolation as key considerations for women’s digital well-being and mental health policy. Social media health misinformation women’s mental health psychological distress loneliness health information literacy Figures Figure 1 Figure 2 Plain English Summary How social media health information may affect women’s mental health Social media is a common place where women look for health and mental health information, but the reliability of this information varies. This study used data from a large U.S. national survey conducted in 2024 and focused on adult women who reported using social media. About 35% of women reported being exposed to some misleading health information, while 6% reported exposure to a lot. Difficulty judging whether health information online was true or false was reported by 16% of women. Women who had high difficulty judging information credibility reported higher psychological distress, with PHQ-4 scores about 1 point higher than women with low difficulty. Exposure to misleading health information alone showed weaker and less consistent links with distress. Loneliness was strongly related to poorer mental health overall, regardless of social media experiences. Around 25% of women reported engaging in online peer support, and these women reported higher loneliness and slightly higher psychological distress. These findings suggest that women’s mental health may be shaped not only by exposure to health information on social media but also by how confident they feel in evaluating whether that information is trustworthy. 1. Introduction Social media platforms have become a dominant source of health information for the public, shaping how individuals encounter, interpret, and act on information related to mental and physical health. 14 – 16 Alongside expanded access to health content, concerns have intensified regarding the volume and visibility of misleading or inaccurate health information circulating on social media. 2 , 11 , 21 Mental health-related content has been identified as a particularly active and vulnerable domain, characterised by high engagement, uneven information quality, and substantial variability in credibility. 4 , 22 At the same time, population-level trends indicate rising prevalence of anxiety, depression, and loneliness, raising questions about how the contemporary digital information environment may intersect with mental well-being. 5 , 17 , 27 A growing body of literature has examined associations between social media use and mental health outcomes, including psychological distress, depressive symptoms, and perceived social isolation. 1 , 5 , 16 , 24 , 25 However, much of this evidence relies on measures of time spent, frequency of use, or platform engagement, which have yielded mixed and sometimes contradictory findings. 1 , 10 , 26 Experimental and observational studies suggest that social media use may be associated with both benefits and harms, depending on patterns of engagement, informational context, and individual vulnerability. 10 , 16 , 23 Recent reviews emphasise that exposure-based or duration-based metrics may be insufficient to capture the psychological relevance of social media experiences, particularly in relation to mental health. 1 , 2 Increasingly, researchers have called for greater attention to the quality and credibility of information encountered on social media rather than use alone. 2 , 3 , 11 , 18 Studies across diverse settings have documented widespread dissemination of mental health misinformation, including inaccurate portrayals of psychiatric conditions, self-diagnosis narratives, and misleading treatment claims. 4 , 9 , 21 , 22 Exposure to misinformation has been linked to confusion, maladaptive coping behaviours, and erosion of trust in health systems. 14 , 18 , 19 Emerging evidence further suggests that difficulty judging whether online health information is true or false may itself constitute a psychological burden, independent of exposure volume, contributing to distress through heightened uncertainty and cognitive strain. 2 , 3 , 20 However, population-level evidence simultaneously examining perceived exposure to misleading health information and credibility uncertainty remains limited. Women represent a particularly important population for examining these dynamics. Women report higher levels of health information seeking on social media, greater engagement with mental health-related content, and a disproportionate burden of anxiety, depression, and loneliness. 5 , 10 , 17 Gendered social roles and caregiving responsibilities may further shape how women encounter, evaluate, and respond to health information online. 15 , 16 Loneliness and social isolation, which have been consistently linked to social media use and mental health outcomes, may heighten vulnerability to distress in the context of uncertain or misleading information. 13 , 25 Prior research also underscores the importance of inclusive analyses that recognise heterogeneity among women, including differences related to sexual orientation and minority stress exposure, although such dimensions remain underexplored in population-based research. 20 At the same time, social media environments are not uniformly harmful. Prior work demonstrates that online peer interactions can foster social support, connection, and shared understanding, particularly among individuals navigating similar health conditions. 16 , 23 These peer-support processes may coexist with, or potentially counterbalance, the negative effects of misinformation exposure, underscoring the importance of examining harms and benefits within the same analytic framework. Using nationally representative data from the 2024 Health Information National Trends Survey (HINTS 7), this study examines associations between women’s psychological distress and key features of the social media health information environment, including perceived exposure to misleading health information and difficulty judging information credibility. We further assess whether loneliness modifies these associations and explore peer-support engagement as a potential countervailing factor. By focusing on information quality rather than use frequency, and by situating women’s mental health within a broader digital information context, this study aims to address critical gaps in population-level evidence and inform public health communication and digital wellbeing efforts. 2. Methods 2.1 Data source This study used data from the Health Information National Trends Survey 7 (HINTS 7), a nationally representative, cross-sectional survey administered by the U.S. National Cancer Institute. HINTS 7 was conducted between March and September 2024 using a self-administered mail survey with a push-to-web option. The survey employed a two-stage, stratified sampling design, in which residential addresses were sampled in the first stage, and one adult per household was selected using the next-birthday method in the second stage. Sampling strata were defined by minority concentration and rural-urban status to enhance representation of historically underrepresented populations. The target population for HINTS 7 was non-institutionalised adults aged 18 years and older residing in the United States. The final sample included 7,278 respondents, with survey weights provided to support population-level inference. HINTS is widely used for population health and health communication research, including studies of misinformation, trust, and mental health–related outcomes in digital information environments. 18 , 21 2.2 Study population The analytic sample was restricted to respondents who met three criteria. First, analyses were limited to individuals who self-reported female sex at birth, consistent with the study’s focus on women’s mental health. Second, respondents were required to report any social media use in the past 12 months, as measured by the frequency with which they visited social media sites. This restriction ensured that all respondents were eligible for the social media-specific exposure measures examined in the analysis. Third, respondents were required to have non-missing data on the primary outcome variables. Sexual orientation was retained as a key subgroup characteristic to support inclusive analyses of heterogeneity among women. All analyses accounted for the complex survey design and weighting structure of HINTS 7 to maintain national representativeness. 2.3 Measures 2.3.1 Psychological distress Psychological distress was assessed using the Patient Health Questionnaire-4 (PHQ-4), a brief screening instrument capturing symptoms of anxiety and depression over the past two weeks. The PHQ-4 consists of four items assessing loss of interest, depressed mood, nervousness, and excessive worry. Response options ranged from “not at all” to “nearly every day.” Items were summed to create a total score ranging from 0 to 12, with higher scores indicating greater psychological distress. The PHQ-4 was treated as a continuous outcome in primary analyses, with sensitivity analyses examining anxiety and depression subscales separately. 2.3.2 Loneliness and social isolation Loneliness and perceived social isolation were measured using a four-item scale assessing feelings of being left out, social disconnection, and lack of companionship. Each item was rated on a frequency scale ranging from “never” to “always.” Items were combined to create a composite loneliness score, with higher values indicating greater perceived loneliness. Loneliness was modelled as a continuous variable and, in moderation analyses, was also examined as a contextual vulnerability factor interacting with social media information exposures. Loneliness was included based on prior evidence linking social media experiences to social isolation and mental health outcomes. 13 , 25 2.3.3 Social media health misinformation exposure Three dimensions of the social media health information environment were examined. First, perceived exposure to misleading health information was measured by respondents’ assessment of how much of the health information they encountered on social media they believed to be false or misleading. Responses ranged from “none” to “a lot.” Second, credibility uncertainty was assessed using an item capturing difficulty judging whether health information on social media is true or false. Responses ranged from strong disagreement to strong agreement, with higher values indicating greater uncertainty. This measure reflects the interpretive burden of navigating ambiguous or conflicting health information online, which has been identified as a psychologically salient feature of contemporary misinformation environments. 2 , 19 Third, echo-chamber perception was measured by agreement with the statement that most people in one’s social media networks share similar views about health. This variable was included as a contextual characteristic of the information environment, reflecting perceived homogeneity of viewpoints rather than content accuracy per se. 19 Echo-chamber perception was examined descriptively and in exploratory models but was not a central exposure in the primary analyses. 2.3.4 Peer support engagement Potential benefits of social media use were operationalised through peer-support engagement, measured by how frequently respondents interacted online with others who had similar health or medical issues. This item captures social connection and experiential information exchange rather than passive content exposure. Peer interaction on social media has been conceptualised as a potential source of social capital and emotional support, with implications for mental well-being. 16 , 23 In this study, peer-support engagement was examined as both an independent correlate of distress and loneliness and as a potential moderator of misinformation-related associations. 2.3.5 Covariates Analyses adjusted for a pre-specified set of sociodemographic and health-related covariates selected a priori to reduce confounding. Sociodemographic variables included age, race and ethnicity, educational attainment, household income, and marital status. Health-related covariates included self-rated general health and a history of having ever been diagnosed with depression or an anxiety disorder. These covariates were included because they are known to be associated with both social media experiences and mental health outcomes. 5 , 16 Sexual orientation was included as a key subgroup variable to explore heterogeneity in associations among women. Given sample size constraints, analyses by sexual orientation were treated as exploratory. 20 2.4 Statistical analysis All analyses incorporated HINTS 7 final person-level survey weights to produce nationally representative estimates. Variance estimation was conducted using jackknife replicate weights, consistent with HINTS analytic recommendations, to account for the complex sampling design. Analyses proceeded in a sequential modelling framework. First, weighted descriptive statistics were calculated to characterise the analytic sample. Second, survey-weighted linear regression models were estimated to examine associations between perceived misinformation exposure, credibility uncertainty, and psychological distress. Third, moderation analyses tested interactions between loneliness and key exposure variables to assess whether associations differed by level of social isolation. Fourth, heterogeneity by sexual orientation was examined using interaction terms and stratified models where cell sizes permitted. Peer-support engagement was examined both as an independent correlate of distress and loneliness and as a potential buffering factor in models including misinformation exposure and credibility uncertainty. Sensitivity analyses included alternative coding of key exposures, examination of anxiety and depression subscales, and stratification by history of depression or anxiety diagnosis. All statistical tests were two-sided, and estimates are reported with 95% confidence intervals. 2.5 Ethical considerations HINTS 7 is a publicly available, de-identified dataset. The study involved secondary analysis of existing survey data and did not involve direct interaction with human participants. As such, this research was exempt from institutional review board oversight. 3. Results 3.1 Sample characteristics The analytic sample comprised women who reported using social media in the past 12 months. After application of survey weights, the sample was nationally representative of adult women social media users in the United States. Weighted sociodemographic, health, and social media characteristics are presented in Table 1 . Participants were distributed across age groups, with the largest proportion aged ≥ 60 years (35.0%, SE = 1.5), followed by those aged 50–59 years (18.7%, SE = 1.3) and 18–29 years (16.9%, SE = 1.4). The majority identified as non-Hispanic White (71.1%, SE = 1.6), with representation from non-Hispanic Black (9.7%, SE = 1.1), Hispanic (10.9%, SE = 1.0), and other or multiracial groups (8.3%, SE = 0.9). Table 1 Weighted characteristics of women using social media in HINTS 7 (2024). Characteristics Weighted % or Mean (SE) Sociodemographic characteristics Age group (years) 18–29 16.9 (1.4) 30–39 15.1 (1.2) 40–49 14.3 (1.2) 50–59 18.7 (1.3) ≥ 60 35.0 (1.5) Race/ethnicity Non-Hispanic White 71.1 (1.6) Non-Hispanic Black 9.7 (1.1) Hispanic 10.9 (1.0) Other / multiracial 8.3 (0.9) Education Less than high school 4.0 (0.6) High school graduate 21.8 (1.5) Some college 36.8 (1.6) College graduate or higher 37.4 (1.6) Household income < $ 35,000 30.9 (1.5) $ 35,000– $ 74,999 32.0 (1.6) ≥ $ 75,000 37.1 (1.7) Marital status Married / partnered 59.2 (1.8) Not married 40.8 (1.8) Health characteristics Self-rated health Excellent / very good 55.0 (1.8) Good 32.2 (1.6) Fair / poor 12.8 (1.0) Prior diagnosis of depression or anxiety Yes 32.6 (1.5) No 67.4 (1.5) Social media health information environment Perceived misleading health information None / a little 59.5 (1.7) Some 34.8 (1.6) A lot 5.7 (0.7) Difficulty judging true vs false health information Low 50.4 (1.7) Moderate 33.4 (1.6) High 16.2 (1.2) Peer-support engagement Yes 25.1 (1.5) No 74.9 (1.5) Mental health outcomes PHQ-4 score, mean (SE) 2.14 (0.07) Loneliness score, mean (SE) 7.75 (0.11) Footnotes: Estimates are weighted to be nationally representative of U.S. women social media users. Percentages may not sum to 100 due to rounding. SE = standard error. Educational attainment was relatively high, with 37.4% (SE = 1.6) reporting a college degree or higher and 36.8% (SE = 1.6) reporting some college education. Household income was broadly distributed, with 37.1% (SE = 1.7) reporting incomes ≥ $ 75,000 and 30.9% (SE = 1.5) reporting incomes < $ 35,000. Approximately six in ten women were married or partnered (59.2%, SE = 1.8). Most respondents rated their health as excellent or very good (55.0%, SE = 1.8), while 12.8% (SE = 1.0) reported fair or poor health. Nearly one-third reported a prior diagnosis of depression or anxiety (32.6%, SE = 1.5). With respect to the social media health information environment, 59.5% (SE = 1.7) reported little or no exposure to misleading health information, 34.8% (SE = 1.6) reported some exposure, and 5.7% (SE = 0.7) reported a lot of exposure. Difficulty judging whether health information was true or false was reported as low by 50.4% (SE = 1.7), moderate by 33.4% (SE = 1.6), and high by 16.2% (SE = 1.2). Peer-support engagement was reported by 25.1% (SE = 1.5) of women. Mean psychological distress (PHQ-4) score was 2.14 (SE = 0.07), and the mean loneliness score was 7.75 (SE = 0.11). 3.2 Health misinformation exposure and psychological distress Associations between perceived misleading health information exposure and psychological distress are shown in Table 2 . In unadjusted models, women reporting some misleading health information exposure had lower PHQ-4 scores compared with those reporting little or no exposure (β = −0.40, 95% CI − 0.66 to − 0.13). Women reporting a lot of exposure had higher PHQ-4 scores, although confidence intervals included the null (β = 0.47, 95% CI − 0.04 to 0.98). Table 2 Associations of misleading health information exposure and credibility uncertainty with psychological distress among women (Outcome: PHQ-4 total score). Predictor Model 1: Unadjusted β (95% CI) Model 2: Sociodemographic-adjusted β (95% CI) Model 3: Fully adjusted β (95% CI) Some vs none/a little -0.40 (-0.66, -0.13) -0.39 (-0.62, -0.16) -0.24 (-0.48, -0.01) A lot vs none/a little 0.47 (-0.04, 0.98) 0.32 (-0.15, 0.79) 0.22 (-0.25, 0.68) Moderate vs low (credibility uncertainty) 0.59 (0.32, 0.85) 0.46 (0.22, 0.70) 0.27 (0.04, 0.50) High vs low (credibility uncertainty) 1.55 (1.21, 1.89) 1.25 (0.95, 1.56) 1.01 (0.72, 1.31) Model notes: Model 2 adjusted for age, race/ethnicity, education, income, and marital status. Model 3 additionally adjusted for self-rated health and prior depression/anxiety diagnosis. Footnotes: β coefficients represent the change in PHQ-4 score. All models are survey-weighted with jackknife replicate variance estimation. After adjustment for sociodemographic covariates, the inverse association for some exposure persisted (β = −0.39, 95% CI − 0.62 to − 0.16), while estimates for high exposure were attenuated (β = 0.32, 95% CI − 0.15 to 0.79). In fully adjusted models including health-related covariates, women reporting some exposure continued to exhibit lower psychological distress compared with those reporting little or no exposure (β = −0.24, 95% CI − 0.48 to − 0.01). Associations for high exposure remained positive but imprecise (β = 0.22, 95% CI − 0.25 to 0.68). Adjusted predicted PHQ-4 scores across levels of perceived misleading health information exposure are illustrated in Fig. 1 . 3.3 Credibility uncertainty and psychological distress Difficulty judging whether health information on social media was true or false was independently associated with psychological distress (Table 2 ). In unadjusted models, moderate credibility uncertainty was associated with higher PHQ-4 scores compared with low uncertainty (β = 0.59, 95% CI 0.32 to 0.85), while high uncertainty was associated with substantially higher distress (β = 1.55, 95% CI 1.21 to 1.89). These associations remained statistically significant after adjustment for sociodemographic characteristics (moderate vs low: β = 0.46, 95% CI 0.22 to 0.70; high vs low: β = 1.25, 95% CI 0.95 to 1.56) and after additional adjustment for self-rated health and prior depression or anxiety diagnosis (moderate vs low: β = 0.27, 95% CI 0.04 to 0.50; high vs low: β = 1.01, 95% CI 0.72 to 1.31). In models including both perceived misleading health information exposure and credibility uncertainty, credibility uncertainty remained associated with psychological distress, with larger effect estimates than those observed for misleading health information exposure. 3.4 Moderating role of loneliness Results of interaction analyses examining moderation by loneliness are presented in Table 3 . The interaction between perceived misleading health information exposure and loneliness was not statistically significant (β = 0.01, 95% CI − 0.02 to 0.04; p = 0.475). Similarly, the interaction between credibility uncertainty and loneliness was not statistically significant (β = −0.00, 95% CI − 0.05 to 0.04; p = 0.850). Table 3 Moderation of associations between social media health information factors and psychological distress by loneliness (Outcome: PHQ-4 total score). Interaction term β (95% CI) p-value Misleading health information × loneliness 0.01 (-0.02, 0.04) 0.475 Credibility uncertainty × loneliness -0.00 (-0.05, 0.04) 0.850 Footnotes: Loneliness modelled as a continuous variable. Models adjusted for sociodemographic and health covariates. Interaction terms were evaluated in fully adjusted models. Despite non-significant interaction terms, predicted values indicated consistently higher psychological distress among women with higher loneliness across all levels of misinformation exposure. Figure 2 displays adjusted predicted PHQ-4 scores by perceived misleading health information exposure stratified by low versus high loneliness. 3.5 Peer support engagement and mental health outcomes Associations between peer-support engagement and mental health outcomes are shown in Table 4 . In fully adjusted models, women who reported engaging with others with similar health or medical issues on social media had higher loneliness scores compared with those who did not report such engagement (β = 1.00, 95% CI 0.51 to 1.48; p < 0.001). Table 4 Associations of peer-support engagement with loneliness and psychological distress among women. Outcome β (95% CI) p-value Loneliness score 1.00 (0.51, 1.48) < 0.001 PHQ-4 total score 0.34 (0.09, 0.59) 0.008 Footnotes: Models adjusted for sociodemographic and health covariates. β coefficients represent the mean difference in outcome scores. Peer-support engagement was also associated with higher psychological distress scores, although the magnitude of association was smaller (β = 0.34, 95% CI 0.09 to 0.59; p = 0.008). 3.6 Sensitivity analyses Sensitivity analyses are summarised in Table 5 . Associations between credibility uncertainty and psychological distress were consistent across analytic specifications. High credibility uncertainty remained positively associated with both anxiety (β = 0.76, 95% CI 0.61 to 0.91) and depression subscales (β = 0.26, 95% CI 0.09 to 0.43). Table 5 Sensitivity analyses (Fully adjusted models). Analysis Misleading health information β (95% CI)* Credibility uncertainty β (95% CI)** Anxiety subscale 0.10 (-0.13, 0.34) 0.76 (0.61, 0.91) Depression subscale 0.11 (-0.13, 0.35) 0.26 (0.09, 0.43) Complete case on all PHQ-4 and loneliness items 0.24 (-0.28, 0.77) 1.00 (0.69, 1.31) No prior depression/anxiety diagnosis -0.06 (-0.35, 0.24) 0.81 (0.52, 1.10) Prior depression/anxiety diagnosis 0.46 (-0.20, 1.13) 1.06 (0.56, 1.56) *Misleading health information reported as A lot vs none/a little. **Credibility uncertainty reported as High vs low. Footnotes: All models are survey-weighted and adjusted for covariates. Patterns of association were compared across analytic specifications. Results were similar when analyses were restricted to complete cases (β = 1.00, 95% CI 0.69 to 1.31) and when stratified by prior depression or anxiety diagnosis. Among women without a prior diagnosis, credibility uncertainty remained associated with distress (β = 0.81, 95% CI 0.52 to 1.10), as did associations among women with a prior diagnosis (β = 1.06, 95% CI 0.56 to 1.56). Across sensitivity analyses, associations for misleading health information exposure (a lot vs none/a little) were smaller in magnitude and less consistent, with confidence intervals frequently including the null. 4. Discussion 4.1 Principal findings In this nationally representative sample of U.S. women who use social media, features of the social media health information environment were differentially associated with psychological distress. The most consistent association was observed for credibility uncertainty: women reporting moderate and high difficulty judging whether health information on social media was true or false had higher PHQ-4 scores in fully adjusted models, with the largest differences observed among those reporting high uncertainty. These findings directly address the primary research questions by indicating that credibility uncertainty is independently associated with psychological distress, even after accounting for sociodemographic characteristics, self-rated health, and prior depression or anxiety diagnosis. Associations for perceived misleading health information exposure were smaller and less consistent. Contrary to an expectation of monotonic harm, women reporting “some” exposure exhibited slightly lower distress than those reporting none or very little exposure, while those reporting “a lot” of exposure showed higher distress, although estimates were imprecise. Loneliness did not statistically moderate the association between misleading exposure or credibility uncertainty and distress; however, predicted values indicated a consistently higher distress burden among women with greater loneliness across all exposure levels. Peer-support engagement was associated with higher loneliness and modestly higher psychological distress. In cross-sectional data, this pattern suggests that engagement with online peers may cluster with greater psychosocial need rather than representing a uniformly protective factor. Taken together, these results indicate that distress among women social media users is more strongly linked to uncertainty in assessing health information credibility than to perceived exposure alone, while peer-support engagement appears to identify a subgroup experiencing elevated loneliness and distress. 4.2 Comparison with prior literature Prior research linking social media use to mental health has produced mixed findings, in part because many studies rely on time-based or frequency-based indicators that may not capture psychologically salient exposures or information quality. 1 , 10 , 16 , 26 Experimental and observational studies show that social media use can coincide with declines in subjective well-being and increases in distress under some conditions, 24,27 while also facilitating social connection and social capital through supportive networks. 23 , 26 Evidence linking social media use with perceived social isolation further underscores that effects are not uniform across users or contexts. 25 By focusing on information quality and credibility appraisal, the present findings extend this literature in a direction increasingly emphasised in misinformation and mental health research. 2 , 11 , 21 Reviews consistently document widespread mental health misinformation and persistent difficulty among users in distinguishing credible from misleading content. 2 , 4 , 22 Interventions that aim to improve well-being through reductions in social media use alone show heterogeneous effects, reinforcing the importance of mechanisms more proximal to distress than use frequency. 1 The strong association observed here for credibility uncertainty aligns with conceptual accounts that frame uncertainty and low confidence in evaluating mental health information as a psychological stressor. 2 , 3 , 8 It is also consistent with empirical work linking misinformation environments to confusion, maladaptive coping, and distress, particularly under conditions of high informational ambiguity. 7 , 20 The inverse association observed for “some” perceived exposure may reflect heterogeneity in how exposure is experienced. Rather than indicating benefit from misinformation per se, this pattern may capture awareness accompanied by critical appraisal or selective engagement with health content, distinguishing perceived exposure from vulnerability. Prior work emphasises that perceived exposure, trust, and appraisal are related but distinct constructs that may differentially relate to downstream outcomes. 18 , 19 Engagement with mental health content on social media has also been shown to include both validating recognition and problematic narratives, yielding complex associations with distress. 9 , 16 These findings reinforce that perceived exposure and credibility uncertainty are not interchangeable dimensions of the information environment. 4.3 Interpreting harms and benefits in the social media information environment The results support a conceptual distinction between misinformation exposure and credibility uncertainty as related but separable dimensions of the social media health information environment. While public discourse often emphasises the volume of misinformation, contemporary reviews suggest that the psychological burden may arise not only from exposure to misleading content but also from sustained difficulty discerning credibility, particularly in mental health contexts where claims are emotionally salient and frequently framed as personal experience. 2 , 4 , 22 Broader misinformation research similarly highlights dissemination dynamics that amplify ambiguity and reduce confidence in evaluating truth claims. 11 , 21 Credibility uncertainty may therefore function as a chronic stressor by increasing cognitive load, heightening vigilance, and generating persistent doubt about whether health-related decisions are informed or risky. Approaches centred on eHealth literacy and self-efficacy aim to reduce this uncertainty by strengthening confidence in evaluating information quality, providing a plausible pathway linking uncertainty to distress. 3 , 8 Trust-oriented frameworks further suggest that difficulty assessing credibility can erode confidence in health information ecosystems, potentially intensifying uncertainty and distress over time. 18 , 19 The peer-support findings illustrate the coexistence of harms and benefits within the same environments. 16 Social media can facilitate connection, community, and social capital, 23,26 particularly for individuals navigating health concerns. At the same time, peer-support engagement may identify individuals with higher baseline symptom burden or loneliness, especially when engagement is driven by unmet needs. In cross-sectional analyses, this can manifest as a positive association between peer engagement and distress, even if such engagement provides subjective benefit or buffers worsening trajectories. 10 , 16 , 17 4.4 Implications for women’s mental health and digital wellbeing The most actionable implication of these findings is that credibility uncertainty represents a population-relevant marker of psychological distress among women social media users. Public health strategies focused solely on debunking individual misinformation items may be insufficient if users remain uncertain about how to evaluate credibility across a continuous stream of health content. Interventions that strengthen mental health literacy, appraisal skills, and confidence in source evaluation may be more directly responsive to uncertainty-driven distress. 3 , 8 , 16 This approach aligns with professional guidance emphasising clinician engagement with misinformation and patient education to support informed decision-making. 8 Platform-level considerations remain relevant. Reviews of misinformation dissemination emphasise that platform design shapes exposure, visibility, and perceived credibility, particularly under conditions of rapid diffusion and high engagement. 11 , 19 , 21 Observational analyses of mental health content on platforms such as TikTok demonstrate substantial variability in information quality and limited reliability cues. 4 , 22 Multi-level strategies combining platform transparency, friction for misleading claims, and clearer signals of evidence quality may help reduce credibility uncertainty even when misinformation cannot be fully eliminated. 2 , 19 More broadly, evidence reviews and public health statements note that digital environments can both support and undermine mental well-being across the life course as social media becomes a routine source of health information. 1 , 14 , 17 Framing digital wellbeing partly as an information-environment issue, rather than exclusively as a screen-time problem, may better align interventions with mechanisms proximal to distress. 4.5 Equity and vulnerable subgroups Loneliness was strongly patterned in the sample and associated with substantially higher predicted distress across misinformation exposure levels. Although formal interaction terms were not statistically significant, the co-occurrence of loneliness and elevated distress reinforces loneliness as a key dimension of vulnerability in social media contexts, consistent with prior evidence linking social media use to perceived social isolation. 13 , 25 From an equity perspective, interventions aimed at improving credibility appraisal may be most effective when paired with strategies that address social isolation and strengthen both offline and online support pathways. The study also highlights the importance of heterogeneity among women. Prior research indicates that exposure to misleading mental health content and coping processes may differ across social contexts and identity-linked stressors. 20 Although sexual orientation was included to support inclusive analyses, subgroup analyses were exploratory and constrained by sample size, limiting statistical power to detect differential associations. Future research designed specifically to examine these dimensions is needed to avoid treating women as a homogeneous group and to inform tailored, identity-responsive interventions. 10 , 16 , 20 4.6 Strengths and limitations This study has several strengths. It used a large, population-based dataset with survey weights to generate nationally representative estimates for U.S. women social media users. It employed validated measures of psychological distress and loneliness and examined multiple dimensions of the social media health information environment rather than relying solely on use frequency. The use of prespecified covariates and sensitivity analyses strengthens confidence that the observed associations, particularly for credibility uncertainty, were not artefacts of a single analytic specification. Limitations should be acknowledged. First, the cross-sectional design precludes establishing temporality; psychological distress may influence perceptions of credibility or misinformation exposure, and reciprocal relationships are plausible. Second, all measures were self-reported and subject to recall or reporting bias. Third, the dataset does not provide platform-specific exposure measures or objective indicators of misinformation exposure, limiting inference about platform-level mechanisms. Fourth, the peer-support measure does not capture the quality or accuracy of peer interactions. Despite these limitations, the findings remain informative because they reflect population-level patterns in how women experience the social media health information environment and how these experiences co-occur with psychological distress. The consistency of associations for credibility uncertainty across models and sensitivity analyses supports its relevance as a potential target for future research and intervention. 5. Conclusion In this nationally representative analysis of U.S. women who use social media, psychological distress was most consistently associated with difficulty judging the credibility of health information, rather than with perceived exposure to misleading content alone. This distinction suggests that uncertainty in evaluating information may represent a more salient psychosocial stressor than exposure volume itself. Understanding women’s mental health within the social media information environment therefore requires attention not only to misinformation prevalence, but also to the interpretive burden created by ambiguous, conflicting, or difficult-to-evaluate health content. By simultaneously examining perceived misinformation exposure, credibility uncertainty, loneliness, and peer-support engagement, this study advances population-level evidence beyond use-based metrics and highlights mechanisms that are more proximal to mental well-being. The findings indicate that credibility uncertainty and loneliness cluster with higher distress, while peer-support engagement appears to identify women experiencing greater psychosocial need rather than functioning as a uniformly protective factor in cross-sectional data. These results have implications for public health and platform-level responses. Interventions that strengthen mental health literacy, enhance confidence in evaluating online health information, and reduce uncertainty across diverse content streams may be particularly relevant for supporting women’s digital wellbeing. Efforts to address social isolation alongside information quality may further improve effectiveness. Framing digital wellbeing as partly an information-environment challenge, rather than solely a matter of screen time or exposure reduction, may help align future research, policy, and intervention strategies with mechanisms most closely linked to psychological distress. 3 , 8 , 16 , 17 Declarations Conflict of interest The author declares no conflicts of interest. Ethical approval Ethical approval was not required for this study. The analysis was conducted using secondary data from publicly available, fully anonymised datasets and involved no identifiable personal information or direct interaction with human participants. Funding statement The author received no external funding for the preparation of this manuscript. Consent to participate Not applicable. Consent for publication Not applicable. Acknowledgments None. Data availability The data analysed in this study are publicly available from the Health Information National Trends Survey (HINTS) website (https://hints.cancer.gov/data/download-data.aspx). The specific dataset used was HINTS 7 (2024). ORCID iD Nikesh Lagun: https://orcid.org/0009-0005-6372-4852 Author contributions Nikesh Lagun: Conceptualisation; Methodology; Software; Validation; Formal analysis; Investigation; Data curation; Writing – original draft; Writing – review & editing; Visualisation. References Plackett R, Blyth A, Schartau P (2023) The impact of social media use interventions on mental well-being: systematic review. J Med Internet Res 25:e44922 Starvaggi I, Dierckman C, Lorenzo-Luaces L (2024) Mental health misinformation on social media: Review and future directions. Curr Opin Psychol 56:101738 Hoffner CA, Salomi V, Apkhazishvili S, Edu S (2025 Oct) Challenging mental health misinformation on social media: The role of eHealth literacy, self-efficacy and presumed media influence. Comput Hum Behav 24:108844 Hudon A, Perry K, Plate AS, Doucet A, Ducharme L, Djona O, Testart Aguirre C, Evoy G (2025) Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos. J Med Internet Res 27:e64225 Ventriglio A, Ricci F, Torales J, Castaldelli-Maia JM, Bener A, Smith A, Liebrenz M (2024) Social media use and emerging mental health issues. Industrial Psychiatry J 33(Suppl 1):S261–S264 Hammad MA, Alqarni TM (2021) Psychosocial effects of social media on the Saudi society during the Coronavirus Disease 2019 pandemic: A cross-sectional study. PLoS ONE 16(3):e0248811 Strasser MA, Sumner PJ, Meyer D (2022) COVID-19 news consumption and distress in young people: A systematic review. J Affect Disord 300:481–491 Abrams Z (2024) Addressing misinformation about mental health with patients. American Psychological Association. https://www.apa.org/topics/journalism-facts/misinformation-mental-health accessed 23 January 2026) Armstrong S, Osuch E, Wammes M, Chevalier O, Kieffer S, Meddaoui M, Rice L (2025) Self-diagnosis in the age of social media: A pilot study of youth entering mental health treatment for mood and anxiety disorders. Acta Psychol 256:105015 Weir K (2023) Social media brings benefits and risks to teens. Here’s how psychology can help identify a path forward. Monit Psychol 54(6):46–53 Xu G, Qian M, Meng L (2025) Misinformation dissemination on social media: key research themes and evolutionary paths between 2013 and 2023. Humanit Social Sci Commun 12(1):1775 Arora S, Arora S, Kumar D, Agrawal V, Gupta V, Vasdev D (2025) Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach. arXiv preprint arXiv:2503.02333. Mar 4 Hughes K (2022) Social media use and loneliness during the COVID-19 pandemic. MSc Thesis, Georgia Southern University, USA World Health Organization (2022) Infodemics and misinformation negatively affect people’s health behaviours, new WHO review finds. https://www.who.int/europe/news/item/01-09-2022-infodemics-and-misinformation-negatively-affect-people-s-health-behaviours--new-who-review-finds accessed 23 January 2026) Paul B, Headley-Johnson SA The impact of social media on health behaviors, a systematic review. InHealthcare 2025 Oct 30 (13, 21, p. 2763) Naslund JA, Bondre A, Torous J, Aschbrenner KA (2020) Social media and mental health: benefits, risks, and opportunities for research and practice. J Technol Behav Sci 5(3):245–257 U.S. Department of Health and Human Services, Office of the Surgeon General Social media and youth mental health, https://www.hhs.gov/surgeongeneral/reports-and-publications/youth-mental-health/social-media/index.html (last reviewed 19 February 2025, accessed 23 January 2026). Stimpson JP, Park S, Adhikari EH, Nelson DB, Ortega AN (2025) Perceived Health Misinformation on Social Media and Public Trust in Health Care. Med Care 63(9):686–693 Shahbazi M, Bunker D (2024) Social media trust: Fighting misinformation in the time of crisis. Int J Inf Manag 77:102780 Nguyen TT, Nguyen DC, Nguyen HT, Do HT, Ngo T, Pham AB, Tran TQ, Hoang LP, Dang H, Boyer L, Fond G (2025) Exposure to fake news on social media, coping mechanisms, and mental health impact among Vietnamese adolescents and young adults. Sci Rep 15(1):35117 Rocha YM, De Moura GA, Desidério GA, De Oliveira CH, Lourenço FD, de Figueiredo Nicolete LD (2023) The impact of fake news on social media and its influence on health during the COVID-19 pandemic: A systematic review. J Public Health 31(7):1007–1016 Hall R, Keenan R (2025) More than half of top 100 mental health TikToks contain misinformation, study finds. The Guardian. https://www.theguardian.com/society/2025/may/31/more-than-half-of-top-100-mental-health-tiktoks-contain-misinformation-study-finds accessed 23 January 2026) Ellison NB, Steinfield C, Lampe C (2007) The benefits of Facebook friends: Social capital and college students’ use of online social network sites. J computer-mediated communication 12(4):1143–1168 Kross E, Verduyn P, Demiralp E, Park J, Lee DS, Lin N, Shablack H, Jonides J, Ybarra O (2013) Facebook use predicts declines in subjective well-being in young adults. PLoS ONE 8(8):e69841 Primack BA, Shensa A, Sidani JE, Whaite EO, yi Lin L, Rosen D, Colditz JB, Radovic A, Miller E (2017) Social media use and perceived social isolation among young adults in the US. Am J Prev Med 53(1):1–8 Valkenburg PM, Peter J (2007) Online communication and adolescent well-being: Testing the stimulation versus the displacement hypothesis. J computer-mediated communication 12(4):1169–1182 Twenge JM, Joiner TE, Rogers ML, Martin GN (2018) Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci 6(1):3–17 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9157969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608194176,"identity":"d16b9df7-c043-41b5-a520-3fc4f3b8619f","order_by":0,"name":"Nikesh Lagun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYBACPgglYcDAwMP4IKGCTQ7EPfAAjxY2JC3MBh/O8BmDtSQQ1sIA0sImObNFLrEBxMWrhf34NenCHRbG/PxnD0jzNpilzw87/BBoi52cbgMOLTw5ZdIzz0iYSc7ISzDm3ZGWu/F2mgFQS7Kx2QFcDstJk+Ztk7AxuMFjkMx75ljuxtkJIC0HErfh0sL/BqLF/vwZg8O8bf/TDWenf8CvRSL9GEiLmQFDjmHjzDa2BHnpHAK2SLxhtgZqMZa4kWPM8OEMm+EG6ZyCAwkGuP3Cz5/+8DZvW51hf/8Z8x/AqJSXn52++cOHCjs5XFqA0WGAyjcAqzTAohIO2B+g8uUb8KkeBaNgFIyCkQgA2p5cQBVdBDgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0005-6372-4852","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Nikesh","middleName":"","lastName":"Lagun","suffix":""}],"badges":[],"createdAt":"2026-03-18 10:13:50","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9157969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9157969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104943888,"identity":"1dd68a74-9000-47de-8c4f-1d7cfc688aa0","added_by":"auto","created_at":"2026-03-19 04:25:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28607,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted association between perceived misleading health information exposure and psychological distress (PHQ-4).\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9157969/v1/cc4bee48ec8ae39a7d0668e0.png"},{"id":104943889,"identity":"0e1e41e7-2351-4aee-ab5b-039bc2c6c1a5","added_by":"auto","created_at":"2026-03-19 04:25:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28555,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted psychological distress by misinformation exposure and credibility uncertainty, stratified by loneliness.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9157969/v1/811d311fe75e4e6d68490d8a.png"},{"id":104943892,"identity":"3f3011a6-a227-4656-b77c-98c8b0db1174","added_by":"auto","created_at":"2026-03-19 04:25:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1115818,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9157969/v1/875c01d1-c2c3-4e8e-817c-1baeb960df36.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHealth misinformation exposure and psychological distress among women using social media: The roles of credibility uncertainty and loneliness\u003c/p\u003e","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003e\u003cstrong\u003eHow social media health information may affect women\u0026rsquo;s mental health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial media is a common place where women look for health and mental health information, but the reliability of this information varies. This study used data from a large U.S. national survey conducted in 2024 and focused on adult women who reported using social media. About 35% of women reported being exposed to some misleading health information, while 6% reported exposure to a lot. Difficulty judging whether health information online was true or false was reported by 16% of women. Women who had high difficulty judging information credibility reported higher psychological distress, with PHQ-4 scores about 1 point higher than women with low difficulty. Exposure to misleading health information alone showed weaker and less consistent links with distress. Loneliness was strongly related to poorer mental health overall, regardless of social media experiences. Around 25% of women reported engaging in online peer support, and these women reported higher loneliness and slightly higher psychological distress. These findings suggest that women\u0026rsquo;s mental health may be shaped not only by exposure to health information on social media but also by how confident they feel in evaluating whether that information is trustworthy.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eSocial media platforms have become a dominant source of health information for the public, shaping how individuals encounter, interpret, and act on information related to mental and physical health.\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Alongside expanded access to health content, concerns have intensified regarding the volume and visibility of misleading or inaccurate health information circulating on social media.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Mental health-related content has been identified as a particularly active and vulnerable domain, characterised by high engagement, uneven information quality, and substantial variability in credibility.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e At the same time, population-level trends indicate rising prevalence of anxiety, depression, and loneliness, raising questions about how the contemporary digital information environment may intersect with mental well-being.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA growing body of literature has examined associations between social media use and mental health outcomes, including psychological distress, depressive symptoms, and perceived social isolation.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e However, much of this evidence relies on measures of time spent, frequency of use, or platform engagement, which have yielded mixed and sometimes contradictory findings.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Experimental and observational studies suggest that social media use may be associated with both benefits and harms, depending on patterns of engagement, informational context, and individual vulnerability.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Recent reviews emphasise that exposure-based or duration-based metrics may be insufficient to capture the psychological relevance of social media experiences, particularly in relation to mental health.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIncreasingly, researchers have called for greater attention to the quality and credibility of information encountered on social media rather than use alone.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Studies across diverse settings have documented widespread dissemination of mental health misinformation, including inaccurate portrayals of psychiatric conditions, self-diagnosis narratives, and misleading treatment claims.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Exposure to misinformation has been linked to confusion, maladaptive coping behaviours, and erosion of trust in health systems.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Emerging evidence further suggests that difficulty judging whether online health information is true or false may itself constitute a psychological burden, independent of exposure volume, contributing to distress through heightened uncertainty and cognitive strain.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e However, population-level evidence simultaneously examining perceived exposure to misleading health information and credibility uncertainty remains limited.\u003c/p\u003e \u003cp\u003eWomen represent a particularly important population for examining these dynamics. Women report higher levels of health information seeking on social media, greater engagement with mental health-related content, and a disproportionate burden of anxiety, depression, and loneliness.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Gendered social roles and caregiving responsibilities may further shape how women encounter, evaluate, and respond to health information online.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Loneliness and social isolation, which have been consistently linked to social media use and mental health outcomes, may heighten vulnerability to distress in the context of uncertain or misleading information.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Prior research also underscores the importance of inclusive analyses that recognise heterogeneity among women, including differences related to sexual orientation and minority stress exposure, although such dimensions remain underexplored in population-based research.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAt the same time, social media environments are not uniformly harmful. Prior work demonstrates that online peer interactions can foster social support, connection, and shared understanding, particularly among individuals navigating similar health conditions.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e These peer-support processes may coexist with, or potentially counterbalance, the negative effects of misinformation exposure, underscoring the importance of examining harms and benefits within the same analytic framework.\u003c/p\u003e \u003cp\u003eUsing nationally representative data from the 2024 Health Information National Trends Survey (HINTS 7), this study examines associations between women\u0026rsquo;s psychological distress and key features of the social media health information environment, including perceived exposure to misleading health information and difficulty judging information credibility. We further assess whether loneliness modifies these associations and explore peer-support engagement as a potential countervailing factor. By focusing on information quality rather than use frequency, and by situating women\u0026rsquo;s mental health within a broader digital information context, this study aims to address critical gaps in population-level evidence and inform public health communication and digital wellbeing efforts.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThis study used data from the Health Information National Trends Survey 7 (HINTS 7), a nationally representative, cross-sectional survey administered by the U.S. National Cancer Institute. HINTS 7 was conducted between March and September 2024 using a self-administered mail survey with a push-to-web option. The survey employed a two-stage, stratified sampling design, in which residential addresses were sampled in the first stage, and one adult per household was selected using the next-birthday method in the second stage. Sampling strata were defined by minority concentration and rural-urban status to enhance representation of historically underrepresented populations.\u003c/p\u003e \u003cp\u003eThe target population for HINTS 7 was non-institutionalised adults aged 18 years and older residing in the United States. The final sample included 7,278 respondents, with survey weights provided to support population-level inference. HINTS is widely used for population health and health communication research, including studies of misinformation, trust, and mental health\u0026ndash;related outcomes in digital information environments.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study population\u003c/h2\u003e \u003cp\u003eThe analytic sample was restricted to respondents who met three criteria. First, analyses were limited to individuals who self-reported female sex at birth, consistent with the study\u0026rsquo;s focus on women\u0026rsquo;s mental health. Second, respondents were required to report any social media use in the past 12 months, as measured by the frequency with which they visited social media sites. This restriction ensured that all respondents were eligible for the social media-specific exposure measures examined in the analysis. Third, respondents were required to have non-missing data on the primary outcome variables.\u003c/p\u003e \u003cp\u003eSexual orientation was retained as a key subgroup characteristic to support inclusive analyses of heterogeneity among women. All analyses accounted for the complex survey design and weighting structure of HINTS 7 to maintain national representativeness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Psychological distress\u003c/h2\u003e \u003cp\u003ePsychological distress was assessed using the Patient Health Questionnaire-4 (PHQ-4), a brief screening instrument capturing symptoms of anxiety and depression over the past two weeks. The PHQ-4 consists of four items assessing loss of interest, depressed mood, nervousness, and excessive worry. Response options ranged from \u0026ldquo;not at all\u0026rdquo; to \u0026ldquo;nearly every day.\u0026rdquo; Items were summed to create a total score ranging from 0 to 12, with higher scores indicating greater psychological distress. The PHQ-4 was treated as a continuous outcome in primary analyses, with sensitivity analyses examining anxiety and depression subscales separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Loneliness and social isolation\u003c/h2\u003e \u003cp\u003eLoneliness and perceived social isolation were measured using a four-item scale assessing feelings of being left out, social disconnection, and lack of companionship. Each item was rated on a frequency scale ranging from \u0026ldquo;never\u0026rdquo; to \u0026ldquo;always.\u0026rdquo; Items were combined to create a composite loneliness score, with higher values indicating greater perceived loneliness. Loneliness was modelled as a continuous variable and, in moderation analyses, was also examined as a contextual vulnerability factor interacting with social media information exposures.\u003c/p\u003e \u003cp\u003eLoneliness was included based on prior evidence linking social media experiences to social isolation and mental health outcomes.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Social media health misinformation exposure\u003c/h2\u003e \u003cp\u003eThree dimensions of the social media health information environment were examined.\u003c/p\u003e \u003cp\u003eFirst, perceived exposure to misleading health information was measured by respondents\u0026rsquo; assessment of how much of the health information they encountered on social media they believed to be false or misleading. Responses ranged from \u0026ldquo;none\u0026rdquo; to \u0026ldquo;a lot.\u0026rdquo;\u003c/p\u003e \u003cp\u003eSecond, credibility uncertainty was assessed using an item capturing difficulty judging whether health information on social media is true or false. Responses ranged from strong disagreement to strong agreement, with higher values indicating greater uncertainty. This measure reflects the interpretive burden of navigating ambiguous or conflicting health information online, which has been identified as a psychologically salient feature of contemporary misinformation environments.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThird, echo-chamber perception was measured by agreement with the statement that most people in one\u0026rsquo;s social media networks share similar views about health. This variable was included as a contextual characteristic of the information environment, reflecting perceived homogeneity of viewpoints rather than content accuracy per se.\u003csup\u003e19\u003c/sup\u003e Echo-chamber perception was examined descriptively and in exploratory models but was not a central exposure in the primary analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Peer support engagement\u003c/h2\u003e \u003cp\u003ePotential benefits of social media use were operationalised through peer-support engagement, measured by how frequently respondents interacted online with others who had similar health or medical issues. This item captures social connection and experiential information exchange rather than passive content exposure. Peer interaction on social media has been conceptualised as a potential source of social capital and emotional support, with implications for mental well-being.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e In this study, peer-support engagement was examined as both an independent correlate of distress and loneliness and as a potential moderator of misinformation-related associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Covariates\u003c/h2\u003e \u003cp\u003eAnalyses adjusted for a pre-specified set of sociodemographic and health-related covariates selected a priori to reduce confounding. Sociodemographic variables included age, race and ethnicity, educational attainment, household income, and marital status. Health-related covariates included self-rated general health and a history of having ever been diagnosed with depression or an anxiety disorder. These covariates were included because they are known to be associated with both social media experiences and mental health outcomes.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSexual orientation was included as a key subgroup variable to explore heterogeneity in associations among women. Given sample size constraints, analyses by sexual orientation were treated as exploratory.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses incorporated HINTS 7 final person-level survey weights to produce nationally representative estimates. Variance estimation was conducted using jackknife replicate weights, consistent with HINTS analytic recommendations, to account for the complex sampling design.\u003c/p\u003e \u003cp\u003eAnalyses proceeded in a sequential modelling framework. First, weighted descriptive statistics were calculated to characterise the analytic sample. Second, survey-weighted linear regression models were estimated to examine associations between perceived misinformation exposure, credibility uncertainty, and psychological distress. Third, moderation analyses tested interactions between loneliness and key exposure variables to assess whether associations differed by level of social isolation. Fourth, heterogeneity by sexual orientation was examined using interaction terms and stratified models where cell sizes permitted.\u003c/p\u003e \u003cp\u003ePeer-support engagement was examined both as an independent correlate of distress and loneliness and as a potential buffering factor in models including misinformation exposure and credibility uncertainty. Sensitivity analyses included alternative coding of key exposures, examination of anxiety and depression subscales, and stratification by history of depression or anxiety diagnosis. All statistical tests were two-sided, and estimates are reported with 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ethical considerations\u003c/h2\u003e \u003cp\u003eHINTS 7 is a publicly available, de-identified dataset. The study involved secondary analysis of existing survey data and did not involve direct interaction with human participants. As such, this research was exempt from institutional review board oversight.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eThe analytic sample comprised women who reported using social media in the past 12 months. After application of survey weights, the sample was nationally representative of adult women social media users in the United States.\u003c/p\u003e \u003cp\u003eWeighted sociodemographic, health, and social media characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were distributed across age groups, with the largest proportion aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (35.0%, SE\u0026thinsp;=\u0026thinsp;1.5), followed by those aged 50\u0026ndash;59 years (18.7%, SE\u0026thinsp;=\u0026thinsp;1.3) and 18\u0026ndash;29 years (16.9%, SE\u0026thinsp;=\u0026thinsp;1.4). The majority identified as non-Hispanic White (71.1%, SE\u0026thinsp;=\u0026thinsp;1.6), with representation from non-Hispanic Black (9.7%, SE\u0026thinsp;=\u0026thinsp;1.1), Hispanic (10.9%, SE\u0026thinsp;=\u0026thinsp;1.0), and other or multiracial groups (8.3%, SE\u0026thinsp;=\u0026thinsp;0.9).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted characteristics of women using social media in HINTS 7 (2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted % or Mean (SE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.9 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.3 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.7 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.0 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.7 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.9 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther / multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.8 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.8 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e35,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e74,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.0 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u003cspan\u003e$\u003c/span\u003e75,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried / partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.2 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.8 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent / very good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.0 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair / poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.8 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior diagnosis of depression or anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial media health information environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived misleading health information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone / a little\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.5 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.8 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA lot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.7 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulty judging true vs false health information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.4 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.2 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeer-support engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-4 score, mean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.14 (0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness score, mean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.75 (0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eFootnotes: Estimates are weighted to be nationally representative of U.S. women social media users. Percentages may not sum to 100 due to rounding. SE\u0026thinsp;=\u0026thinsp;standard error.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEducational attainment was relatively high, with 37.4% (SE\u0026thinsp;=\u0026thinsp;1.6) reporting a college degree or higher and 36.8% (SE\u0026thinsp;=\u0026thinsp;1.6) reporting some college education. Household income was broadly distributed, with 37.1% (SE\u0026thinsp;=\u0026thinsp;1.7) reporting incomes \u0026ge;\u003cspan\u003e$\u003c/span\u003e75,000 and 30.9% (SE\u0026thinsp;=\u0026thinsp;1.5) reporting incomes \u0026lt;\u003cspan\u003e$\u003c/span\u003e35,000. Approximately six in ten women were married or partnered (59.2%, SE\u0026thinsp;=\u0026thinsp;1.8).\u003c/p\u003e \u003cp\u003eMost respondents rated their health as excellent or very good (55.0%, SE\u0026thinsp;=\u0026thinsp;1.8), while 12.8% (SE\u0026thinsp;=\u0026thinsp;1.0) reported fair or poor health. Nearly one-third reported a prior diagnosis of depression or anxiety (32.6%, SE\u0026thinsp;=\u0026thinsp;1.5).\u003c/p\u003e \u003cp\u003eWith respect to the social media health information environment, 59.5% (SE\u0026thinsp;=\u0026thinsp;1.7) reported little or no exposure to misleading health information, 34.8% (SE\u0026thinsp;=\u0026thinsp;1.6) reported some exposure, and 5.7% (SE\u0026thinsp;=\u0026thinsp;0.7) reported a lot of exposure. Difficulty judging whether health information was true or false was reported as low by 50.4% (SE\u0026thinsp;=\u0026thinsp;1.7), moderate by 33.4% (SE\u0026thinsp;=\u0026thinsp;1.6), and high by 16.2% (SE\u0026thinsp;=\u0026thinsp;1.2). Peer-support engagement was reported by 25.1% (SE\u0026thinsp;=\u0026thinsp;1.5) of women.\u003c/p\u003e \u003cp\u003eMean psychological distress (PHQ-4) score was 2.14 (SE\u0026thinsp;=\u0026thinsp;0.07), and the mean loneliness score was 7.75 (SE\u0026thinsp;=\u0026thinsp;0.11).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Health misinformation exposure and psychological distress\u003c/h2\u003e \u003cp\u003eAssociations between perceived misleading health information exposure and psychological distress are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In unadjusted models, women reporting some misleading health information exposure had lower PHQ-4 scores compared with those reporting little or no exposure (β = \u0026minus;0.40, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.66 to \u0026minus;\u0026thinsp;0.13). Women reporting a lot of exposure had higher PHQ-4 scores, although confidence intervals included the null (β\u0026thinsp;=\u0026thinsp;0.47, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.04 to 0.98).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of misleading health information exposure and credibility uncertainty with psychological distress among women (Outcome: PHQ-4 total score).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1: Unadjusted β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2: Sociodemographic-adjusted β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3: Fully adjusted β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome vs none/a little\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.40 (-0.66, -0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39 (-0.62, -0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.24 (-0.48, -0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA lot vs none/a little\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (-0.04, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (-0.15, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22 (-0.25, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate vs low (credibility uncertainty)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.32, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.22, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27 (0.04, 0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh vs low (credibility uncertainty)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.21, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.95, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.72, 1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eModel notes: Model 2 adjusted for age, race/ethnicity, education, income, and marital status. Model 3 additionally adjusted for self-rated health and prior depression/anxiety diagnosis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFootnotes: β coefficients represent the change in PHQ-4 score. All models are survey-weighted with jackknife replicate variance estimation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment for sociodemographic covariates, the inverse association for some exposure persisted (β = \u0026minus;0.39, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.62 to \u0026minus;\u0026thinsp;0.16), while estimates for high exposure were attenuated (β\u0026thinsp;=\u0026thinsp;0.32, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.15 to 0.79). In fully adjusted models including health-related covariates, women reporting some exposure continued to exhibit lower psychological distress compared with those reporting little or no exposure (β = \u0026minus;0.24, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.48 to \u0026minus;\u0026thinsp;0.01). Associations for high exposure remained positive but imprecise (β\u0026thinsp;=\u0026thinsp;0.22, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.25 to 0.68).\u003c/p\u003e \u003cp\u003eAdjusted predicted PHQ-4 scores across levels of perceived misleading health information exposure are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Credibility uncertainty and psychological distress\u003c/h2\u003e \u003cp\u003eDifficulty judging whether health information on social media was true or false was independently associated with psychological distress (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In unadjusted models, moderate credibility uncertainty was associated with higher PHQ-4 scores compared with low uncertainty (β\u0026thinsp;=\u0026thinsp;0.59, 95% CI 0.32 to 0.85), while high uncertainty was associated with substantially higher distress (β\u0026thinsp;=\u0026thinsp;1.55, 95% CI 1.21 to 1.89).\u003c/p\u003e \u003cp\u003eThese associations remained statistically significant after adjustment for sociodemographic characteristics (moderate vs low: β\u0026thinsp;=\u0026thinsp;0.46, 95% CI 0.22 to 0.70; high vs low: β\u0026thinsp;=\u0026thinsp;1.25, 95% CI 0.95 to 1.56) and after additional adjustment for self-rated health and prior depression or anxiety diagnosis (moderate vs low: β\u0026thinsp;=\u0026thinsp;0.27, 95% CI 0.04 to 0.50; high vs low: β\u0026thinsp;=\u0026thinsp;1.01, 95% CI 0.72 to 1.31).\u003c/p\u003e \u003cp\u003eIn models including both perceived misleading health information exposure and credibility uncertainty, credibility uncertainty remained associated with psychological distress, with larger effect estimates than those observed for misleading health information exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Moderating role of loneliness\u003c/h2\u003e \u003cp\u003eResults of interaction analyses examining moderation by loneliness are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The interaction between perceived misleading health information exposure and loneliness was not statistically significant (β\u0026thinsp;=\u0026thinsp;0.01, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.02 to 0.04; p\u0026thinsp;=\u0026thinsp;0.475). Similarly, the interaction between credibility uncertainty and loneliness was not statistically significant (β = \u0026minus;0.00, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.05 to 0.04; p\u0026thinsp;=\u0026thinsp;0.850).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModeration of associations between social media health information factors and psychological distress by loneliness (Outcome: PHQ-4 total score).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisleading health information \u0026times; loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.01 (-0.02, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredibility uncertainty \u0026times; loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-0.00 (-0.05, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eFootnotes: Loneliness modelled as a continuous variable. Models adjusted for sociodemographic and health covariates. Interaction terms were evaluated in fully adjusted models.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDespite non-significant interaction terms, predicted values indicated consistently higher psychological distress among women with higher loneliness across all levels of misinformation exposure. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays adjusted predicted PHQ-4 scores by perceived misleading health information exposure stratified by low versus high loneliness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Peer support engagement and mental health outcomes\u003c/h2\u003e \u003cp\u003eAssociations between peer-support engagement and mental health outcomes are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In fully adjusted models, women who reported engaging with others with similar health or medical issues on social media had higher loneliness scores compared with those who did not report such engagement (β\u0026thinsp;=\u0026thinsp;1.00, 95% CI 0.51 to 1.48; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of peer-support engagement with loneliness and psychological distress among women.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.51, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-4 total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.09, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eFootnotes: Models adjusted for sociodemographic and health covariates. β coefficients represent the mean difference in outcome scores.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePeer-support engagement was also associated with higher psychological distress scores, although the magnitude of association was smaller (β\u0026thinsp;=\u0026thinsp;0.34, 95% CI 0.09 to 0.59; p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Associations between credibility uncertainty and psychological distress were consistent across analytic specifications. High credibility uncertainty remained positively associated with both anxiety (β\u0026thinsp;=\u0026thinsp;0.76, 95% CI 0.61 to 0.91) and depression subscales (β\u0026thinsp;=\u0026thinsp;0.26, 95% CI 0.09 to 0.43).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analyses (Fully adjusted models).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMisleading health information β (95% CI)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCredibility uncertainty β (95% CI)**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.10 (-0.13, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.61, 0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.11 (-0.13, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26 (0.09, 0.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete case on all PHQ-4 and loneliness items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.24 (-0.28, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.69, 1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo prior depression/anxiety diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-0.06 (-0.35, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.52, 1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior depression/anxiety diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.46 (-0.20, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.56, 1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Misleading health information reported as A lot vs none/a little.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e**Credibility uncertainty reported as High vs low.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFootnotes: All models are survey-weighted and adjusted for covariates. Patterns of association were compared across analytic specifications.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eResults were similar when analyses were restricted to complete cases (β\u0026thinsp;=\u0026thinsp;1.00, 95% CI 0.69 to 1.31) and when stratified by prior depression or anxiety diagnosis. Among women without a prior diagnosis, credibility uncertainty remained associated with distress (β\u0026thinsp;=\u0026thinsp;0.81, 95% CI 0.52 to 1.10), as did associations among women with a prior diagnosis (β\u0026thinsp;=\u0026thinsp;1.06, 95% CI 0.56 to 1.56).\u003c/p\u003e \u003cp\u003eAcross sensitivity analyses, associations for misleading health information exposure (a lot vs none/a little) were smaller in magnitude and less consistent, with confidence intervals frequently including the null.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal findings\u003c/h2\u003e \u003cp\u003eIn this nationally representative sample of U.S. women who use social media, features of the social media health information environment were differentially associated with psychological distress. The most consistent association was observed for credibility uncertainty: women reporting moderate and high difficulty judging whether health information on social media was true or false had higher PHQ-4 scores in fully adjusted models, with the largest differences observed among those reporting high uncertainty. These findings directly address the primary research questions by indicating that credibility uncertainty is independently associated with psychological distress, even after accounting for sociodemographic characteristics, self-rated health, and prior depression or anxiety diagnosis.\u003c/p\u003e \u003cp\u003eAssociations for perceived misleading health information exposure were smaller and less consistent. Contrary to an expectation of monotonic harm, women reporting \u0026ldquo;some\u0026rdquo; exposure exhibited slightly lower distress than those reporting none or very little exposure, while those reporting \u0026ldquo;a lot\u0026rdquo; of exposure showed higher distress, although estimates were imprecise. Loneliness did not statistically moderate the association between misleading exposure or credibility uncertainty and distress; however, predicted values indicated a consistently higher distress burden among women with greater loneliness across all exposure levels.\u003c/p\u003e \u003cp\u003ePeer-support engagement was associated with higher loneliness and modestly higher psychological distress. In cross-sectional data, this pattern suggests that engagement with online peers may cluster with greater psychosocial need rather than representing a uniformly protective factor. Taken together, these results indicate that distress among women social media users is more strongly linked to uncertainty in assessing health information credibility than to perceived exposure alone, while peer-support engagement appears to identify a subgroup experiencing elevated loneliness and distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison with prior literature\u003c/h2\u003e \u003cp\u003ePrior research linking social media use to mental health has produced mixed findings, in part because many studies rely on time-based or frequency-based indicators that may not capture psychologically salient exposures or information quality.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Experimental and observational studies show that social media use can coincide with declines in subjective well-being and increases in distress under some conditions,\u003csup\u003e24,27\u003c/sup\u003e while also facilitating social connection and social capital through supportive networks.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Evidence linking social media use with perceived social isolation further underscores that effects are not uniform across users or contexts.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBy focusing on information quality and credibility appraisal, the present findings extend this literature in a direction increasingly emphasised in misinformation and mental health research.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Reviews consistently document widespread mental health misinformation and persistent difficulty among users in distinguishing credible from misleading content.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Interventions that aim to improve well-being through reductions in social media use alone show heterogeneous effects, reinforcing the importance of mechanisms more proximal to distress than use frequency.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The strong association observed here for credibility uncertainty aligns with conceptual accounts that frame uncertainty and low confidence in evaluating mental health information as a psychological stressor.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e It is also consistent with empirical work linking misinformation environments to confusion, maladaptive coping, and distress, particularly under conditions of high informational ambiguity.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe inverse association observed for \u0026ldquo;some\u0026rdquo; perceived exposure may reflect heterogeneity in how exposure is experienced. Rather than indicating benefit from misinformation per se, this pattern may capture awareness accompanied by critical appraisal or selective engagement with health content, distinguishing perceived exposure from vulnerability. Prior work emphasises that perceived exposure, trust, and appraisal are related but distinct constructs that may differentially relate to downstream outcomes.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Engagement with mental health content on social media has also been shown to include both validating recognition and problematic narratives, yielding complex associations with distress.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e These findings reinforce that perceived exposure and credibility uncertainty are not interchangeable dimensions of the information environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Interpreting harms and benefits in the social media information environment\u003c/h2\u003e \u003cp\u003eThe results support a conceptual distinction between misinformation exposure and credibility uncertainty as related but separable dimensions of the social media health information environment. While public discourse often emphasises the volume of misinformation, contemporary reviews suggest that the psychological burden may arise not only from exposure to misleading content but also from sustained difficulty discerning credibility, particularly in mental health contexts where claims are emotionally salient and frequently framed as personal experience.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Broader misinformation research similarly highlights dissemination dynamics that amplify ambiguity and reduce confidence in evaluating truth claims.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCredibility uncertainty may therefore function as a chronic stressor by increasing cognitive load, heightening vigilance, and generating persistent doubt about whether health-related decisions are informed or risky. Approaches centred on eHealth literacy and self-efficacy aim to reduce this uncertainty by strengthening confidence in evaluating information quality, providing a plausible pathway linking uncertainty to distress.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Trust-oriented frameworks further suggest that difficulty assessing credibility can erode confidence in health information ecosystems, potentially intensifying uncertainty and distress over time.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe peer-support findings illustrate the coexistence of harms and benefits within the same environments.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Social media can facilitate connection, community, and social capital,\u003csup\u003e23,26\u003c/sup\u003e particularly for individuals navigating health concerns. At the same time, peer-support engagement may identify individuals with higher baseline symptom burden or loneliness, especially when engagement is driven by unmet needs. In cross-sectional analyses, this can manifest as a positive association between peer engagement and distress, even if such engagement provides subjective benefit or buffers worsening trajectories.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications for women\u0026rsquo;s mental health and digital wellbeing\u003c/h2\u003e \u003cp\u003eThe most actionable implication of these findings is that credibility uncertainty represents a population-relevant marker of psychological distress among women social media users. Public health strategies focused solely on debunking individual misinformation items may be insufficient if users remain uncertain about how to evaluate credibility across a continuous stream of health content. Interventions that strengthen mental health literacy, appraisal skills, and confidence in source evaluation may be more directly responsive to uncertainty-driven distress.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e This approach aligns with professional guidance emphasising clinician engagement with misinformation and patient education to support informed decision-making.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePlatform-level considerations remain relevant. Reviews of misinformation dissemination emphasise that platform design shapes exposure, visibility, and perceived credibility, particularly under conditions of rapid diffusion and high engagement.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Observational analyses of mental health content on platforms such as TikTok demonstrate substantial variability in information quality and limited reliability cues.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Multi-level strategies combining platform transparency, friction for misleading claims, and clearer signals of evidence quality may help reduce credibility uncertainty even when misinformation cannot be fully eliminated.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMore broadly, evidence reviews and public health statements note that digital environments can both support and undermine mental well-being across the life course as social media becomes a routine source of health information.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Framing digital wellbeing partly as an information-environment issue, rather than exclusively as a screen-time problem, may better align interventions with mechanisms proximal to distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Equity and vulnerable subgroups\u003c/h2\u003e \u003cp\u003eLoneliness was strongly patterned in the sample and associated with substantially higher predicted distress across misinformation exposure levels. Although formal interaction terms were not statistically significant, the co-occurrence of loneliness and elevated distress reinforces loneliness as a key dimension of vulnerability in social media contexts, consistent with prior evidence linking social media use to perceived social isolation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e From an equity perspective, interventions aimed at improving credibility appraisal may be most effective when paired with strategies that address social isolation and strengthen both offline and online support pathways.\u003c/p\u003e \u003cp\u003eThe study also highlights the importance of heterogeneity among women. Prior research indicates that exposure to misleading mental health content and coping processes may differ across social contexts and identity-linked stressors.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Although sexual orientation was included to support inclusive analyses, subgroup analyses were exploratory and constrained by sample size, limiting statistical power to detect differential associations. Future research designed specifically to examine these dimensions is needed to avoid treating women as a homogeneous group and to inform tailored, identity-responsive interventions.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths. It used a large, population-based dataset with survey weights to generate nationally representative estimates for U.S. women social media users. It employed validated measures of psychological distress and loneliness and examined multiple dimensions of the social media health information environment rather than relying solely on use frequency. The use of prespecified covariates and sensitivity analyses strengthens confidence that the observed associations, particularly for credibility uncertainty, were not artefacts of a single analytic specification.\u003c/p\u003e \u003cp\u003eLimitations should be acknowledged. First, the cross-sectional design precludes establishing temporality; psychological distress may influence perceptions of credibility or misinformation exposure, and reciprocal relationships are plausible. Second, all measures were self-reported and subject to recall or reporting bias. Third, the dataset does not provide platform-specific exposure measures or objective indicators of misinformation exposure, limiting inference about platform-level mechanisms. Fourth, the peer-support measure does not capture the quality or accuracy of peer interactions.\u003c/p\u003e \u003cp\u003eDespite these limitations, the findings remain informative because they reflect population-level patterns in how women experience the social media health information environment and how these experiences co-occur with psychological distress. The consistency of associations for credibility uncertainty across models and sensitivity analyses supports its relevance as a potential target for future research and intervention.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this nationally representative analysis of U.S. women who use social media, psychological distress was most consistently associated with difficulty judging the credibility of health information, rather than with perceived exposure to misleading content alone. This distinction suggests that uncertainty in evaluating information may represent a more salient psychosocial stressor than exposure volume itself. Understanding women\u0026rsquo;s mental health within the social media information environment therefore requires attention not only to misinformation prevalence, but also to the interpretive burden created by ambiguous, conflicting, or difficult-to-evaluate health content.\u003c/p\u003e \u003cp\u003eBy simultaneously examining perceived misinformation exposure, credibility uncertainty, loneliness, and peer-support engagement, this study advances population-level evidence beyond use-based metrics and highlights mechanisms that are more proximal to mental well-being. The findings indicate that credibility uncertainty and loneliness cluster with higher distress, while peer-support engagement appears to identify women experiencing greater psychosocial need rather than functioning as a uniformly protective factor in cross-sectional data.\u003c/p\u003e \u003cp\u003eThese results have implications for public health and platform-level responses. Interventions that strengthen mental health literacy, enhance confidence in evaluating online health information, and reduce uncertainty across diverse content streams may be particularly relevant for supporting women\u0026rsquo;s digital wellbeing. Efforts to address social isolation alongside information quality may further improve effectiveness. Framing digital wellbeing as partly an information-environment challenge, rather than solely a matter of screen time or exposure reduction, may help align future research, policy, and intervention strategies with mechanisms most closely linked to psychological distress.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study. The analysis was conducted using secondary data from publicly available, fully anonymised datasets and involved no identifiable personal information or direct interaction with human participants.\u003c/p\u003e\n\u003cp\u003eFunding statement\u003c/p\u003e\n\u003cp\u003eThe author received no external funding for the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data analysed in this study are publicly available from the Health Information National Trends Survey (HINTS) website (https://hints.cancer.gov/data/download-data.aspx). The specific dataset used was HINTS 7 (2024).\u003c/p\u003e\n\u003cp\u003eORCID iD\u003c/p\u003e\n\u003cp\u003eNikesh Lagun: https://orcid.org/0009-0005-6372-4852\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eNikesh Lagun: Conceptualisation; Methodology; Software; Validation; Formal analysis; Investigation; Data curation; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing; Visualisation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePlackett R, Blyth A, Schartau P (2023) The impact of social media use interventions on mental well-being: systematic review. J Med Internet Res 25:e44922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStarvaggi I, Dierckman C, Lorenzo-Luaces L (2024) Mental health misinformation on social media: Review and future directions. Curr Opin Psychol 56:101738\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffner CA, Salomi V, Apkhazishvili S, Edu S (2025 Oct) Challenging mental health misinformation on social media: The role of eHealth literacy, self-efficacy and presumed media influence. Comput Hum Behav 24:108844\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudon A, Perry K, Plate AS, Doucet A, Ducharme L, Djona O, Testart Aguirre C, Evoy G (2025) Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos. J Med Internet Res 27:e64225\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVentriglio A, Ricci F, Torales J, Castaldelli-Maia JM, Bener A, Smith A, Liebrenz M (2024) Social media use and emerging mental health issues. Industrial Psychiatry J 33(Suppl 1):S261\u0026ndash;S264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammad MA, Alqarni TM (2021) Psychosocial effects of social media on the Saudi society during the Coronavirus Disease 2019 pandemic: A cross-sectional study. PLoS ONE 16(3):e0248811\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrasser MA, Sumner PJ, Meyer D (2022) COVID-19 news consumption and distress in young people: A systematic review. J Affect Disord 300:481\u0026ndash;491\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbrams Z (2024) Addressing misinformation about mental health with patients. American Psychological Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.apa.org/topics/journalism-facts/misinformation-mental-health\u003c/span\u003e\u003cspan address=\"https://www.apa.org/topics/journalism-facts/misinformation-mental-health\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed 23 January 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong S, Osuch E, Wammes M, Chevalier O, Kieffer S, Meddaoui M, Rice L (2025) Self-diagnosis in the age of social media: A pilot study of youth entering mental health treatment for mood and anxiety disorders. Acta Psychol 256:105015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir K (2023) Social media brings benefits and risks to teens. Here\u0026rsquo;s how psychology can help identify a path forward. Monit Psychol 54(6):46\u0026ndash;53\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu G, Qian M, Meng L (2025) Misinformation dissemination on social media: key research themes and evolutionary paths between 2013 and 2023. Humanit Social Sci Commun 12(1):1775\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArora S, Arora S, Kumar D, Agrawal V, Gupta V, Vasdev D (2025) Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach. arXiv preprint arXiv:2503.02333. Mar 4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes K (2022) Social media use and loneliness during the COVID-19 pandemic. MSc Thesis, Georgia Southern University, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2022) Infodemics and misinformation negatively affect people\u0026rsquo;s health behaviours, new WHO review finds. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/europe/news/item/01-09-2022-infodemics-and-misinformation-negatively-affect-people-s-health-behaviours--new-who-review-finds\u003c/span\u003e\u003cspan address=\"https://www.who.int/europe/news/item/01-09-2022-infodemics-and-misinformation-negatively-affect-people-s-health-behaviours--new-who-review-finds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed 23 January 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul B, Headley-Johnson SA The impact of social media on health behaviors, a systematic review. InHealthcare 2025 Oct 30 (13, 21, p. 2763)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaslund JA, Bondre A, Torous J, Aschbrenner KA (2020) Social media and mental health: benefits, risks, and opportunities for research and practice. J Technol Behav Sci 5(3):245\u0026ndash;257\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Health and Human Services, Office of the Surgeon General Social media and youth mental health, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hhs.gov/surgeongeneral/reports-and-publications/youth-mental-health/social-media/index.html\u003c/span\u003e\u003cspan address=\"https://www.hhs.gov/surgeongeneral/reports-and-publications/youth-mental-health/social-media/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (last reviewed 19 February 2025, accessed 23 January 2026).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStimpson JP, Park S, Adhikari EH, Nelson DB, Ortega AN (2025) Perceived Health Misinformation on Social Media and Public Trust in Health Care. Med Care 63(9):686\u0026ndash;693\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbazi M, Bunker D (2024) Social media trust: Fighting misinformation in the time of crisis. Int J Inf Manag 77:102780\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen TT, Nguyen DC, Nguyen HT, Do HT, Ngo T, Pham AB, Tran TQ, Hoang LP, Dang H, Boyer L, Fond G (2025) Exposure to fake news on social media, coping mechanisms, and mental health impact among Vietnamese adolescents and young adults. Sci Rep 15(1):35117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha YM, De Moura GA, Desid\u0026eacute;rio GA, De Oliveira CH, Louren\u0026ccedil;o FD, de Figueiredo Nicolete LD (2023) The impact of fake news on social media and its influence on health during the COVID-19 pandemic: A systematic review. J Public Health 31(7):1007\u0026ndash;1016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall R, Keenan R (2025) More than half of top 100 mental health TikToks contain misinformation, study finds. The Guardian. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.theguardian.com/society/2025/may/31/more-than-half-of-top-100-mental-health-tiktoks-contain-misinformation-study-finds\u003c/span\u003e\u003cspan address=\"https://www.theguardian.com/society/2025/may/31/more-than-half-of-top-100-mental-health-tiktoks-contain-misinformation-study-finds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed 23 January 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllison NB, Steinfield C, Lampe C (2007) The benefits of Facebook friends: Social capital and college students\u0026rsquo; use of online social network sites. J computer-mediated communication 12(4):1143\u0026ndash;1168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKross E, Verduyn P, Demiralp E, Park J, Lee DS, Lin N, Shablack H, Jonides J, Ybarra O (2013) Facebook use predicts declines in subjective well-being in young adults. PLoS ONE 8(8):e69841\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrimack BA, Shensa A, Sidani JE, Whaite EO, yi Lin L, Rosen D, Colditz JB, Radovic A, Miller E (2017) Social media use and perceived social isolation among young adults in the US. Am J Prev Med 53(1):1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValkenburg PM, Peter J (2007) Online communication and adolescent well-being: Testing the stimulation versus the displacement hypothesis. J computer-mediated communication 12(4):1169\u0026ndash;1182\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTwenge JM, Joiner TE, Rogers ML, Martin GN (2018) Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci 6(1):3\u0026ndash;17\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social media, health misinformation, women’s mental health, psychological distress, loneliness, health information literacy","lastPublishedDoi":"10.21203/rs.3.rs-9157969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9157969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSocial media is a major source of health information, yet misinformation and difficulty judging information credibility may affect mental well-being. Women are highly engaged with mental health content online and experience higher levels of anxiety, depression, and loneliness, making this environment particularly relevant to women\u0026rsquo;s mental health.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed 2024 Health Information National Trends Survey (HINTS 7) data, restricting the sample to adult women reporting social media use in the past 12 months. Exposures included perceived exposure to misleading health information and difficulty judging whether health information was true or false. Outcomes were psychological distress (PHQ-4) and loneliness. Survey-weighted linear regression models adjusted for sociodemographic and health-related covariates were used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong women social media users, the mean PHQ-4 score was 2.14 (SE 0.07), and the mean loneliness score was 7.75 (SE 0.11). High difficulty judging information credibility was associated with higher psychological distress compared with low difficulty (β\u0026thinsp;=\u0026thinsp;1.01, 95% CI 0.72\u0026ndash;1.31). Associations between perceived misinformation exposure and distress were weaker and inconsistent. Loneliness was associated with higher distress across exposure levels, although interaction terms were not statistically significant. Peer-support engagement was associated with higher loneliness (β\u0026thinsp;=\u0026thinsp;1.00, 95% CI 0.51\u0026ndash;1.48) and modestly higher distress (β\u0026thinsp;=\u0026thinsp;0.34, 95% CI 0.09\u0026ndash;0.59).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDifficulty judging the credibility of health information on social media was more strongly associated with women\u0026rsquo;s psychological distress than perceived misinformation exposure alone, highlighting credibility uncertainty, mental health literacy, and social isolation as key considerations for women\u0026rsquo;s digital well-being and mental health policy.\u003c/p\u003e","manuscriptTitle":"Health misinformation exposure and psychological distress among women using social media: The roles of credibility uncertainty and loneliness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 04:25:31","doi":"10.21203/rs.3.rs-9157969/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"310a3f9c-f6cb-4797-8872-c378427048bd","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T04:25:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 04:25:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9157969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9157969","identity":"rs-9157969","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}