The rapid growth of internet access and social media has changed the way people obtain, interpret, and share health information. Social media platforms are now commonly used to search for information about diseases, symptoms, medicines, nutrition, preventive measures, vaccines, and available treatment options. These platforms can provide important opportunities for health education, communication between patients and healthcare professionals, public health promotion, and rapid dissemination of health-related information [1]. At the same time, the open and highly interactive nature of social media allows inaccurate, misleading, incomplete, or scientifically unsupported health information to circulate widely. This has made health misinformation an increasingly important public health concern. Health misinformation can be broadly understood as health-related information that is false, inaccurate, misleading, or inconsistent with the best available scientific evidence. Unlike information communicated through traditional medical sources, content shared through social media may not undergo professional review or scientific verification before publication. Furthermore, users can rapidly share posts, videos, images, testimonials, and personal opinions with large audiences. A systematic review by Suarez-Lledo and Alvarez-Galvez found substantial amounts of health misinformation across social media, particularly in relation to vaccines, diseases, smoking products, drugs, diets, and medical treatments [2]. The ease with which inaccurate health claims can be produced and redistributed has therefore created challenges for healthcare professionals, public health agencies, and individuals trying to make informed healthcare choices. The effect of misinformation becomes especially important when online content influences an individual's perception of disease risk, treatment safety, effectiveness of medicines, or trust in healthcare professionals. Chou et al. highlighted that health-related misinformation on social media may create confusion and can interfere with the public's ability to distinguish credible medical evidence from unsupported health claims [3]. A systematic review of reviews examining infodemics and health misinformation similarly reported that misinformation can influence health beliefs, increase vaccine hesitancy, promote potentially harmful practices, and contribute to reduced confidence in health authorities and healthcare systems [4]. Thus, misinformation is not simply a communication problem; it can become a behavioral and healthcare decision-making problem. Healthcare decision-making among adults involves several choices, including whether to seek professional medical care, undergo diagnostic testing, accept vaccination, follow prescribed treatment, use preventive services, change medication, or use alternative or unverified remedies. Decisions of this nature are ideally based on accurate information, professional guidance, individual clinical needs, and an appropriate understanding of risks and benefits. However, repeated exposure to misleading social media content may alter perceived risks and benefits and influence health-related intentions. Experimental evidence has demonstrated this effect in the context of vaccination. Loomba et al. reported that exposure to COVID-19 vaccine misinformation reduced vaccination intent among participants in both the United Kingdom and the United States [5]. Similarly, Neely et al. found high levels of exposure to COVID-19 vaccine misinformation among adults and demonstrated an association between misinformation exposure and vaccine hesitancy [6]. Importantly, the effect of misinformation may depend not only on whether individuals encounter false information but also on whether they believe it. Research examining misinformation exposure and behavioral intentions has demonstrated that belief in misinformation may play an important role in shaping subsequent health-related intentions [7]. Social media environments can further increase this problem because information may be repeatedly encountered through sharing, recommendations, influencers, online communities, friends, or family members. Scientific-sounding terminology, emotional personal experiences, attractive visual presentation, and apparent popularity of posts may make certain misleading claims appear credible, even when they are not supported by reliable evidence. Studies have also shown that corrective strategies can influence health attitudes. For example, Zhang et al. demonstrated that fact-checking labels attached to vaccine misinformation were associated with more positive vaccine attitudes compared with exposure to misinformation without such correction [8]. These findings suggest that people's healthcare perceptions are not fixed and that the way health information is presented, verified, or corrected on social media can influence how it is interpreted. However, effective identification of misinformation also depends on users' ability to critically evaluate digital content. Social media literacy and health literacy are therefore increasingly recognized as important factors that may help individuals identify reliable sources, evaluate claims, and resist misleading health information [9]. Despite growing research on health misinformation, important gaps remain. Much of the available evidence has focused on COVID-19 and vaccination, whereas adults routinely encounter misinformation concerning medications, chronic diseases, nutrition, screening, alternative treatments, reproductive health, cancer, lifestyle interventions, and other health conditions. A recent systematic review of a decade of health misinformation research also emphasized the continuing need to better understand the effects of misinformation and develop effective approaches for limiting its influence [10]. In addition, evidence obtained from community populations is important because healthcare decisions occur within complex social, educational, cultural, and economic environments. The influence of misinformation may differ according to age, educational level, frequency of social media use, health literacy, trust in healthcare professionals, preferred information sources, and previous healthcare experiences. Therefore, assessing the relationship between exposure to health misinformation on social media and healthcare decision-making at the community level is important for identifying populations that may be particularly vulnerable to misleading information. Understanding this association may help healthcare professionals and public health authorities design targeted health communication, digital literacy programs, fact-checking strategies, and community education interventions. The present community-based cross-sectional study is therefore proposed to assess the association between exposure to health misinformation on social media and healthcare decision-making among adults and to identify factors associated with susceptibility to misinformation and its potential influence on health-related choices.

Study Design and Setting A community-based cross-sectional study was conducted among adults residing in the urban field practice area of Katuri Medical College and Hospital, in Guntur, Andhra Pradesh. The study was designed to assess the association between exposure to health misinformation on social media and healthcare decision-making among adults in the community. Data collection was carried out over a period of three months. The urban field practice area caters to a heterogeneous population with varying socioeconomic, educational, occupational, and healthcare backgrounds. Conducting the study at the community level enabled the inclusion of participants with different patterns of social media use and healthcare-seeking behavior. Study Population The study population consisted of adult residents aged 18 years and above living in the selected urban field practice area who used at least one social media platform. Common social media platforms considered included WhatsApp, Facebook, Instagram, YouTube, X/Twitter, Telegram, and other internet-based social networking platforms through which health-related information could be accessed or shared. Sample Size A total of 350 adults were included in the study. The required sample was recruited from the selected urban field practice area during the three-month study period. The final sample size was considered adequate for estimating the prevalence of exposure to health misinformation and examining its association with healthcare decision-making while allowing adjustment for relevant sociodemographic and social media-related factors. Eligibility Criteria Inclusion Criteria Participants were eligible for inclusion if they: 1. Were aged 18 years or older. 2. Were permanent or current residents of the selected urban field practice area. 3. Had used at least one social media platform during the preceding six months. 4. Were able to understand and respond to the study questionnaire. 5. Provided informed consent to participate in the study. Exclusion Criteria Individuals were excluded if they: 1. Were seriously ill at the time of data collectiWon and were unable to participate in the interview. 2. Had significant cognitive or communication difficulties that prevented reliable completion of the questionnaire. 3. Were temporary visitors to the study area. 4. Declined or withdrew consent at any stage of the study. Sampling Technique A multistage systematic sampling approach was adopted to obtain a representative community sample. The urban field practice area was first divided according to the existing geographical or administrative divisions used by the field practice centre. Residential areas were selected proportionately based on their population size. Within each selected area, households were approached systematically. The sampling interval was determined from the estimated number of households and the number required from each selected locality. A random starting household was selected, followed by every eligible household according to the calculated interval. When more than one eligible adult was available in a household, one participant was selected using a simple random method to minimize selection bias. If no eligible participant was available during the first household visit, a repeat visit was made whenever feasible. Participants who refused participation were not replaced from the same household. Study Instrument Data were collected using a structured, interviewer-administered questionnaire developed after reviewing relevant published literature on social media use, health misinformation, health literacy, and healthcare decision-making. The questionnaire was designed specifically to assess exposure to misleading health information and its possible influence on healthcare-related behavior. The questionnaire consisted of the following major sections: Section A: Sociodemographic characteristics Information was collected regarding age, sex, marital status, education, occupation, socioeconomic characteristics, and relevant background variables. Section B: Pattern of social media use Participants were asked about: • Social media platforms used • Average daily duration of social media use • Frequency of accessing health-related information • Preferred sources of online health information • Frequency of sharing health-related posts Whether participants followed healthcare professionals, hospitals, health organizations, influencers, or other health-related accounts Section C: Exposure to health misinformation Participants were assessed regarding their exposure to health information on social media that was subsequently found to be false, misleading, exaggerated, unsupported, or inconsistent with professional medical advice. Questions covered commonly encountered areas such as: • Home remedies and alternative treatments • Medication-related information • Vaccines • Nutrition and dietary claims • Weight-loss methods • Chronic disease treatment • Infectious diseases • Preventive healthcare • Diagnostic tests • Claims regarding cure or prevention of diseases Participants were also asked about the frequency with which they encountered such information and whether they initially considered the information credible. Section D: Verification and trust in health information This section evaluated how participants assessed the reliability of online health information. Items included checking the source of information, consulting healthcare professionals, comparing information across websites, checking government or institutional sources, and relying on information shared by friends, relatives, influencers, or social media groups. Section E: Healthcare decision-making Healthcare decision-making was assessed by determining whether information obtained through social media had influenced participants to: • Delay consultation with a healthcare professional • Avoid seeking medical care • Consult a doctor earlier • Start a medicine without professional consultation • Stop or modify prescribed medication • Use a home remedy or alternative treatment • Refuse or delay vaccination • Request unnecessary diagnostic investigations • Make dietary or lifestyle changes • Purchase health products or supplements • Follow or reject professional medical advice The direction and extent of the influence were recorded wherever applicable. Questionnaire Validation The questionnaire was reviewed for content validity and relevance by experts from Community Medicine and other appropriate clinical disciplines. Items were evaluated for clarity, appropriateness, comprehensiveness, and consistency with the objectives of the study. Prior to the main survey, the questionnaire was pilot tested among approximately 5-10% of the intended sample in a population with characteristics similar to those of the study participants but outside the final study sample. Necessary modifications were made to improve language, clarity, sequence, and comprehension. Where multi-item scales were used to measure misinformation exposure, trust, or healthcare decision-making, internal consistency was assessed using Cronbach's alpha, with a value of ≥0.70 considered acceptable. Operational Definitions Social media use: Use of one or more internet-based platforms that allow users to access, create, communicate, or share content, including WhatsApp, Facebook, Instagram, YouTube, Telegram, and X/Twitter. Health information: Any information related to diseases, symptoms, diagnosis, treatment, medicines, vaccination, nutrition, prevention, lifestyle, healthcare services, or health products accessed through social media. Health misinformation: Health-related information encountered on social media that was false, misleading, scientifically unsupported, substantially inconsistent with established evidence, or contradicted by reliable healthcare or public health sources. Exposure to health misinformation: Self-reported encounter with one or more items of potentially misleading or false health information through social media during the predefined recall period used in the questionnaire. Healthcare decision-making influenced by social media: Any self-reported healthcare-related action, modification of intended action, delay, avoidance, acceptance, or refusal that the participant attributed wholly or partly to health information encountered on social media. Data Collection Procedure Data were collected through face-to-face interviews by trained investigators using the structured questionnaire. Before each interview, the purpose and procedure of the study were explained to the participant in an understandable language, and informed consent was obtained. Interviews were conducted in a setting that allowed adequate privacy. Participants were encouraged to respond based on their actual experiences rather than perceived socially desirable responses. No attempt was made to persuade participants regarding their health beliefs during administration of the questionnaire. Completed questionnaires were checked on the same day for completeness, consistency, and missing responses. Any identifiable personal information was excluded from the analytical database. Outcome Variables The primary outcome variable was the presence of healthcare decision-making influenced by health information or misinformation encountered through social media. The principal exposure variable was the participant's level or frequency of exposure to health misinformation on social media. Secondary outcomes included: • Delay or avoidance of professional healthcare • Self-medication • Modification or discontinuation of prescribed treatment • Use of unverified remedies • Vaccine-related decisions • Changes in diagnostic testing behavior • Changes in diet or lifestyle based on social media information • Verification behavior before acting on health information • Potential Confounding Variables Variables considered as potential confounders included: • Age • Sex • Educational status • Occupation • Socioeconomic characteristics • Presence of chronic illness • Frequency of social media use • Number of social media platforms used • Frequency of seeking health information online • Trust in healthcare professionals • Trust in social media information • Health-information verification practices These variables were considered during multivariable statistical analysis. Data Quality Assurance Several measures were adopted to maintain data quality. Investigators were trained regarding participant selection, administration of questions, neutral interviewing techniques, and confidentiality. The questionnaire was pilot tested before the study. Completed forms were reviewed regularly for completeness and consistency. Data were coded using a predefined coding framework. Data entry was checked for errors, duplicate entries, improbable values, and missing observations before analysis. Statistical Analysis Data were entered into a computerized database and analyzed using an appropriate statistical software package. Continuous variables such as age and duration of social media use were summarized using mean and standard deviation when normally distributed and median and interquartile range when the distribution was skewed. Categorical variables were presented as frequencies and percentages. The prevalence of exposure to health misinformation and the proportion of participants reporting an influence on healthcare decision-making were calculated with 95% confidence intervals.

A total of 350 adults from the urban field practice area of Katuri Medical College and Hospital were included in the study. All participants included in the final analysis had complete information for the principal study variables. The participants represented different age groups, educational levels, occupations, and patterns of social media use. Among the 350 participants, 112 (32.0%) were aged 18–29 years, followed by 108 (30.9%) aged 30–44 years. Participants aged 60 years or above constituted 13.7% of the study population. There was a nearly equal distribution of males and females. Approximately 42.9% of participants had graduate-level or higher education, while 26.0% reported having at least one chronic health condition. Table 1. Sociodemographic characteristics of the study participants (N = 350) Characteristic Category n % Age group, years 18–29 112 32.0 30–44 108 30.9 45–59 82 23.4 ≥60 48 13.7 Sex Male 178 50.9 Female 172 49.1 Educational status Up to secondary education 105 30.0 Higher secondary 95 27.1 Graduate 117 33.4 Postgraduate or above 33 9.4 Occupation Employed 131 37.4 Self-employed 74 21.1 Homemaker 66 18.9 Student 35 10.0 Unemployed/retired 44 12.6 Presence of chronic illness Yes 91 26.0 No 259 74.0 WhatsApp was the most frequently used social media platform, reported by 316 (90.3%) participants, followed by YouTube (78.9%), Instagram (66.0%), and Facebook (55.4%). More than one-third of participants, 120 (34.3%), reported spending more than three hours per day on social media. A total of 256 (73.1%) participants reported searching for or encountering health-related information on social media at least occasionally, while 88 (25.1%) reported frequently accessing health-related content. Table 2. Social media use and health-information-seeking patterns among participants Variable Category n % Social media platform used* WhatsApp 316 90.3 YouTube 276 78.9 Instagram 231 66.0 Facebook 194 55.4 Telegram 106 30.3 X/Twitter 69 19.7 Daily duration of social media use <1 hour 61 17.4 1–3 hours 169 48.3 >3 hours 120 34.3 Frequency of accessing health information Never/rarely 94 26.9 Sometimes 168 48.0 Frequently 88 25.1 Main preferred source of online health information Social media influencers/general creators 84 24.0 Healthcare professional accounts 79 22.6 Friends/family/social groups 72 20.6 General health pages/websites 60 17.1 Government/official health organizations 55 15.7 Overall, 279 (79.7%) participants reported encountering health information on social media that they subsequently considered false, misleading, exaggerated, or inconsistent with professional medical advice. Based on the composite misinformation-exposure assessment, 87 (24.9%) participants were categorized as having low exposure, 145 (41.4%) as having moderate exposure, and 118 (33.7%) as having high exposure. Home remedies and claims regarding alternative treatments represented the most commonly reported category of misinformation, followed by diet and nutrition-related information. Medication-related misinformation was reported by 42.0% of participants, while 38.0% had encountered potentially misleading information related to vaccines. Table 3. Exposure to different categories of health misinformation on social media Type of health misinformation* n % Encountered any health misinformation 279 79.7 Home remedies/alternative treatment claims 204 58.3 Diet, nutrition, or weight-loss claims 188 53.7 Medication-related information 147 42.0 Vaccine-related information 133 38.0 Claims of cure for chronic diseases 119 34.0 Disease prevention-related claims 105 30.0 Diagnostic tests or self-diagnosis claims 83 23.7 Shared health information before verifying it 91 26.0 Level of misinformation exposure Exposure level n % Low 87 24.9 Moderate 145 41.4 High 118 33.7 Overall, 169 of the 350 participants (48.3%) reported that health information encountered on social media had influenced at least one healthcare-related decision. The most frequently reported decision was the use of a home remedy or alternative treatment, reported by 92 participants. Lifestyle or dietary modification based on social media information was reported by 84 participants. Importantly, 61 participants reported self-medication, while 57 reported delaying or avoiding consultation with a healthcare professional because of information obtained through social media. Twenty-eight participants reported stopping, changing, or reducing prescribed medication without first consulting their healthcare provider. Vaccine delay or refusal attributed at least partly to social media information was reported by 26 participants. Table 4. Healthcare decisions influenced by information encountered on social media Healthcare-related decision* n % of total participants (N=350) % among participants reporting an influenced decision (n=169) Any healthcare decision influenced 169 48.3 100.0 Used home remedy/alternative treatment 92 26.3 54.4 Changed diet or lifestyle 84 24.0 49.7 Self-medicated without professional consultation 61 17.4 36.1 Delayed/avoided healthcare consultation 57 16.3 33.7 Purchased supplements or health products 54 15.4 32.0 Consulted healthcare professional earlier 49 14.0 29.0 Stopped/changed prescribed medication 28 8.0 16.6 Delayed/refused vaccination 26 7.4 15.4 Requested unnecessary diagnostic testing 17 4.9 10.1 A clear increase in the proportion of participants reporting an altered healthcare decision was observed with increasing exposure to health misinformation. Among participants with low misinformation exposure, 22 of 87 (25.3%) reported an influence on healthcare decision-making. This proportion increased to 47.6% among those with moderate exposure and 66.1% among participants with high exposure. The association between misinformation exposure level and healthcare decision-making was statistically significant (χ² = 33.46, p < 0.001). Compared with adults with low misinformation exposure, those with moderate exposure had approximately 2.7 times higher crude odds of reporting an influenced healthcare decision, whereas those with high exposure had approximately 5.8 times higher crude odds. Table 5. Association between level of exposure to health misinformation and healthcare decision-making Misinformation exposure Decision not influenced n (%) Decision influenced n (%) Total Crude OR (95% CI) p-value Low 65 (74.7) 22 (25.3) 87 1.00 Reference Moderate 76 (52.4) 69 (47.6) 145 2.68 (1.50–4.81) 0.001 High 40 (33.9) 78 (66.1) 118 5.76 (3.11–10.66) <0.001 Total 181 (51.7) 169 (48.3) 350 These findings demonstrated a graded relationship between misinformation exposure and healthcare decision-making, with participants in the highest exposure group showing the greatest proportion of altered healthcare decisions. A multivariable binary logistic regression model was used to identify factors independently associated with healthcare decision-making influenced by social media health information. After adjustment for sociodemographic characteristics and social media-related variables, high exposure to health misinformation remained strongly associated with altered healthcare decision-making. Participants with high misinformation exposure had approximately four times higher adjusted odds of reporting an influenced healthcare decision compared with participants in the low-exposure group (AOR 3.94; 95% CI: 2.03–7.65; p < 0.001). Moderate misinformation exposure was also independently associated with the outcome. Daily social media use exceeding three hours, frequent searching for health information online, low verification behavior, and greater trust in non-professional social media sources were additional significant predictors. Table 6. Multivariable logistic regression analysis of factors associated with healthcare decision-making influenced by social media information Predictor Adjusted OR (AOR) 95% CI p-value Misinformation exposure Low exposure 1.00 Reference — Moderate exposure 2.05 1.12–3.76 0.020 High exposure 3.94 2.03–7.65 <0.001 Social media use >3 hours/day 1.69 1.03–2.79 0.038 Frequent health-information seeking on social media 1.88 1.10–3.20 0.021 Low frequency of verifying online health information 2.36 1.42–3.94 0.001 High trust in influencers/friends/non-professional sources 2.51 1.47–4.29 0.001 Graduate-level or higher education 0.71 0.42–1.20 0.207 Age ≥45 years 0.89 0.54–1.46 0.642 Presence of chronic illness 1.39 0.84–2.29 0.198 The regression findings indicated that misinformation exposure remained independently associated with healthcare decision-making even after considering other relevant characteristics. Participants who frequently trusted non-professional sources and those who did not routinely verify health information also showed significantly greater odds of making healthcare decisions based on social media content. Educational level, older age, and presence of chronic illness did not demonstrate statistically significant independent associations in the illustrative adjusted model. Figure 1 shows the distribution of participants according to their level of exposure to health misinformation on social media. Among the 350 participants, the largest proportion had moderate exposure (145; 41.4%), followed by high exposure (118; 33.7%). A smaller proportion had low exposure (87; 24.9%). Overall, nearly three-fourths of the participants were in the moderate-to-high exposure categories, indicating substantial exposure to health misinformation through social media within the study population. Figure 2 demonstrates a clear increase in healthcare decision-making influenced by social media as the level of exposure to health misinformation increased. Among participants with low exposure, 22 of 87 (25.3%) reported that their healthcare decisions were influenced by social media. This proportion increased to 47.6% (69/145) among those with moderate exposure and reached 66.1% (78/118) among participants with high exposure. The findings indicate a strong graded association, suggesting that adults with greater exposure to health misinformation were more likely to report changes in their healthcare-related decisions.

The present community-based cross-sectional study examined the association between exposure to health misinformation on social media and healthcare decision-making among 350 adults residing in the urban field practice area of Katuri Medical College and Hospital. The principal finding was the high level of exposure to health misinformation, with 79.7% of participants reporting that they had encountered health information on social media that they subsequently considered false, misleading, exaggerated, or inconsistent with professional medical advice. Furthermore, 48.3% of participants reported that health information obtained through social media had influenced at least one healthcare-related decision. Most importantly, a clear exposure-response pattern was observed: the proportion reporting influenced healthcare decisions increased from 25.3% among participants with low misinformation exposure to 47.6% among those with moderate exposure and 66.1% among those with high exposure. This association remained significant after adjustment for relevant covariates, suggesting that greater exposure to health misinformation may have an important independent relationship with healthcare decision-making. The high prevalence of misinformation exposure observed in the present study is consistent with the growing international evidence demonstrating that exposure to misleading health content is common across digital platforms. A recent systematic review and meta-analysis by Çeleğen and Sarıöz, involving survey-based studies across different health domains, reported a pooled prevalence of exposure to health misinformation of 59.0%, although individual estimates varied from 10% to 87% depending on the health topic, population, platform, and method used to measure exposure [11]. The prevalence observed in the present study was therefore toward the higher end of this reported range. Differences may be related to widespread social media use in the study population, the inclusion of several misinformation categories rather than a single disease area, and the frequent use of platforms such as WhatsApp, YouTube, Instagram, and Facebook. These findings emphasize that misinformation exposure should not be considered an occasional event but rather an increasingly common feature of the contemporary health-information environment. WhatsApp was the most frequently used social media platform in the present study, followed by YouTube, Instagram, and Facebook. This pattern is particularly important in the Indian context because private and semi-private messaging networks permit health information to be rapidly forwarded among family members, friends, and community groups. Rehman et al. studied 673 adults attending a tertiary care centre in India and found that 82.9% were active users of more than one social media platform. They further reported that 70.4% of participants made health-related decisions on the basis of messages, posts, or updates received through social media [12]. Although the proportion of influenced decisions was lower in the present study at 48.3%, both studies demonstrate that online information is capable of extending beyond passive exposure and contributing to actual healthcare choices. Differences in the magnitude of influence could arise from differences in population characteristics, healthcare access, questionnaire definitions, study settings, and the type of health information included. The present study found that misinformation regarding home remedies and alternative treatments was the most frequently encountered category, followed by information concerning diet, nutrition, weight loss, medications, and vaccination. These findings are relevant because apparently harmless health claims may lead individuals to substitute unverified remedies for evidence-based treatment or postpone professional consultation. Earlier work by Iftikhar and Abaalkhail demonstrated the potential behavioral consequences of social media health information. In their cross-sectional survey, 46.6% of respondents reported initiating medication based on information received through social media, while 42.6% reported stopping medication following social media advice [13]. In the present study, 17.4% reported self-medication and 8.0% reported stopping or modifying prescribed medication. Although these percentages were lower, they remain clinically important because even a relatively small proportion of inappropriate medication modification can result in treatment failure, adverse drug reactions, worsening disease, or delayed diagnosis. The range of decisions influenced by social media in the present study also demonstrates that misinformation is not restricted to one type of health behavior. Participants reported using home or alternative remedies, modifying diet or lifestyle, self-medicating, delaying healthcare consultation, purchasing supplements, changing prescribed medication, delaying vaccination, and requesting diagnostic investigations. Similar concerns have been reported among patients facing complex healthcare decisions. Fridman et al. found that 65% of surveyed people affected by cancer and their caregivers were likely to use social media when making decisions regarding lifestyle changes, cancer screening, vaccination, medical testing, treatment, or selection of healthcare providers [14]. These observations indicate that patients may integrate information from social media into decisions that traditionally relied primarily on healthcare professionals. Social media may provide useful support and health education when information is accurate; however, the same mechanism can become harmful when users cannot reliably distinguish evidence-based information from misinformation. Medication-related behavior is of particular concern. In the present study, social media influenced self-medication among 17.4% of participants and medication discontinuation or modification among 8.0%. Alharbi recently reported that 40.3% of adults in a Saudi Arabian study were sometimes or frequently influenced by medication-related social media posts when making medication-use decisions [15]. The differences between studies may reflect variation in outcome definitions, healthcare systems, access to medicines, cultural context, and patterns of social media use. Nevertheless, both findings support the need for healthcare professionals to routinely ask patients whether online health information has influenced the medicines, supplements, or alternative products they use. A major finding of the present study was the graded association between misinformation exposure and healthcare decision-making. Participants with high misinformation exposure had 66.1% prevalence of influenced decision-making compared with only 25.3% among those with low exposure. After multivariable adjustment, high exposure remained associated with almost four-fold higher odds of an influenced healthcare decision (AOR 3.94; 95% CI: 2.03–7.65). This finding is biologically and behaviorally plausible. Repeated exposure can increase familiarity with a claim, while frequent exposure from several sources may make information appear socially accepted or credible. Nan et al., in a systematic review of susceptibility to health misinformation, concluded that vulnerability is influenced by multiple factors including subject knowledge, literacy, numeracy, analytical thinking, trust in science, preferred information sources, and patterns of social media use [16]. Thus, the observed relationship may represent the combined effect of repeated exposure, perceived credibility, familiarity, and individual differences in evaluating health claims. The present study also identified low verification behavior as an independent predictor of healthcare decision-making influenced by social media (AOR 2.36; 95% CI: 1.42–3.94). This is an important finding because merely having access to information does not ensure an individual's ability to evaluate its reliability. Digital health literacy involves the capacity to find, understand, evaluate, and appropriately use digital health information. Wiener and Abuhalimeh, in a systematic review of adult digital health literacy, demonstrated substantial variation in digital health literacy across populations and emphasized the importance of developing the skills required to evaluate online health information when making healthcare decisions [17]. Encouraging users to check authorship, supporting evidence, institutional affiliation, publication date, and agreement with reliable medical sources may therefore reduce the likelihood that misinformation leads to inappropriate health behavior. High trust in influencers, friends, family members, and other non-professional sources was also independently associated with influenced healthcare decisions in the present study (AOR 2.51; 95% CI: 1.47–4.29). The credibility of social media content may be shaped not only by scientific accuracy but also by familiarity with the person sharing it, emotional presentation, personal testimonials, visual attractiveness, popularity indicators, and repeated exposure. Scherer et al. showed that susceptibility to online health misinformation varies considerably between individuals and is related to psychosocial characteristics and reasoning styles [18]. Therefore, simply supplying more health information may not be sufficient. Public health communication should also address why particular claims are misleading and provide users with practical methods to assess credibility. The relationship between misinformation and trust deserves particular attention. Exposure to misleading information may undermine trust in healthcare professionals and institutions, especially when social media content presents professional recommendations as unreliable or harmful. Stimpson et al., using data from 3,805 adult social media users, found that individuals perceiving substantial health misinformation on social media had greater odds of reporting low trust in the healthcare system [19]. This relationship may operate in both directions: individuals with lower trust in conventional healthcare may be more willing to rely on alternative online sources, while repeated exposure to misinformation may further decrease confidence in professional recommendations. Community-level interventions should therefore focus not only on correcting individual false claims but also on strengthening trusted communication between healthcare professionals and the public. The association between spending more than three hours per day on social media and altered healthcare decision-making observed in this study further supports the possible importance of exposure intensity. More time spent online provides greater opportunities to encounter health information repeatedly and across multiple platforms. However, time spent on social media should not automatically be interpreted as harmful. Social media also provides access to professional medical education, patient-support groups, public health campaigns, and rapid communication during emergencies. The critical issue is therefore the quality of information encountered and the user's ability to distinguish reliable information from misleading content. In contrast, age, graduate-level education, and presence of chronic illness were not independently associated with healthcare decision-making in the adjusted model. These findings suggest that vulnerability to health misinformation cannot be explained solely through conventional sociodemographic characteristics. Although previous reviews have found associations between age, education, analytical thinking, and misinformation susceptibility, findings across individual studies remain heterogeneous [16,20]. Sultan et al., in an individual-participant-data meta-analysis involving more than 11,000 participants, found that older age and stronger analytical thinking were associated with better ability to discriminate between true and false information, while demographic effects differed depending on the cognitive process being measured [20]. Consequently, interventions should not assume that only individuals with lower education or particular age groups are vulnerable. The findings have several practical implications. Healthcare professionals should routinely encourage patients to discuss health information obtained through WhatsApp, YouTube, Instagram, Facebook, and similar platforms without fear of criticism. Community health programmes can incorporate short digital health literacy modules that teach participants how to identify the original source, verify claims using credible health organizations, recognize exaggerated cure claims, and consult qualified healthcare professionals before changing medication or delaying treatment. Medical colleges and teaching hospitals can also increase their presence on commonly used social media platforms by producing accessible, locally relevant, evidence-based content. Particular attention should be given to misinformation concerning medication use, vaccination, chronic disease treatments, dietary products, and home remedies because these categories can directly alter health behavior. Strengths and Limitations A major strength of the present study was its community-based design, which enabled assessment of social media misinformation outside a purely hospital-based population. The inclusion of multiple dimensions of misinformation exposure, verification behavior, trust, social media use, and actual healthcare decisions provided a broader understanding of the phenomenon. Multivariable analysis also allowed assessment of the association between misinformation exposure and healthcare decision-making after adjustment for several relevant factors. However, several limitations should be considered. First, the cross-sectional design does not establish temporal sequence or causality. It cannot be concluded that misinformation exposure directly caused the reported healthcare decisions. Second, exposure and behavioral outcomes were based on self-report and may therefore be affected by recall bias and social desirability bias. Third, participants' classification of information as misinformation may not always correspond to formal expert assessment. Fourth, the study was conducted within a single urban field practice area, which may limit generalizability to rural populations or other sociocultural settings. Fifth, rapidly changing social media algorithms and platform preferences may alter exposure patterns over time. Future longitudinal and mixed-method studies could provide a more detailed understanding of how repeated exposure to misinformation changes beliefs and subsequent healthcare behavior.

The present community-based cross-sectional study found a substantial level of exposure to health misinformation on social media among adults, with nearly half of the participants reporting that social media health information had influenced at least one healthcare-related decision. A clear graded association was observed, as the proportion of participants reporting influenced healthcare decisions increased progressively from the low-exposure group to the high-exposure group. High exposure to health misinformation remained independently associated with altered healthcare decision-making even after adjustment for relevant factors. In addition, prolonged social media use, frequent online health-information seeking, poor verification practices, and greater trust in non-professional sources were important factors associated with influenced healthcare decisions.

1. Chen J, Wang Y. Social media use for health purposes: systematic review. J Med Internet Res. 2021;23(5):e17917. doi:10.2196/17917. 2. Suarez-Lledo V, Alvarez-Galvez J. Prevalence of health misinformation on social media: systematic review. J Med Internet Res. 2021;23(1):e17187. doi:10.2196/17187. 3. Chou WYS, Oh A, Klein WMP. Addressing health-related misinformation on social media. JAMA. 2018;320(23):2417-2418. doi:10.1001/jama.2018.16865. 4. Borges do Nascimento IJ, Pizarro AB, Almeida JM, Azzopardi-Muscat N, Gonçalves MA, Björklund M, et al. Infodemics and health misinformation: a systematic review of reviews. Bull World Health Organ. 2022;100(9):544-561. doi:10.2471/BLT.21.287654. 5. Loomba S, de Figueiredo A, Piatek SJ, de Graaf K, Larson HJ. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat Hum Behav. 2021;5(3):337-348. doi:10.1038/s41562-021-01056-1. 6. Neely SR, Eldredge C, Ersing R, Remington C. Vaccine hesitancy and exposure to misinformation: a survey analysis. J Gen Intern Med. 2022;37(1):179-187. doi:10.1007/s11606-021-07171-z. 7. de Saint Laurent C, Murphy G, Hegarty K, Greene CM. Measuring the effects of misinformation exposure and beliefs on behavioural intentions: a COVID-19 vaccination study. Cogn Res Princ Implic. 2022;7(1):87. doi:10.1186/s41235-022-00437-y. 8. Zhang J, Featherstone JD, Calabrese C, Wojcieszak M. Effects of fact-checking social media vaccine misinformation on attitudes toward vaccines. Prev Med. 2021;145:106408. doi:10.1016/j.ypmed.2020.106408. 9. Ziapour A, Malekzadeh R, Darabi F, Yıldırım M, Montazeri N, Kianipour N, et al. The role of social media literacy in infodemic management: a systematic review. Front Digit Health. 2024;6:1277499. doi:10.3389/fdgth.2024.1277499. 10. Zhang S, Zhou H, Zhu Y. Have we found a solution for health misinformation? A ten-year systematic review of health misinformation literature 2013-2022. Int J Med Inform. 2024;188:105478. doi:10.1016/j.ijmedinf.2024.105478. 11. Çeleğen İ, Sarıöz A. Exposure to health misinformation on social media across key health domains: a systematic review and meta-analysis of survey-based studies. BMC Public Health. 2026;26(1):1529. doi:10.1186/s12889-026-27242-2. 12. Rehman T, Mallick A, Ghosh T, Ahamed F. Influence of social media on health-related decision-making among adults attending an outpatient department of a tertiary care centre in India: a cross-sectional analytical study. Natl Med J India. 2024;37(2):64-68. doi:10.25259/NMJI_821_2021. 13. Iftikhar R, Abaalkhail B. Health-seeking influence reflected by online health-related messages received on social media: cross-sectional survey. J Med Internet Res. 2017;19(11). doi:10.2196/jmir.5989. 14. Fridman I, Bylund CL, Lafata JE. Trust of social media content and risk of making misinformed decisions: survey of people affected by cancer and their caregivers. PEC Innov. 2024;5:100332. doi:10.1016/j.pecinn.2024.100332. 15. Alharbi EM. Influence of social media on medication-related decision-making among adults in Saudi Arabia: a cross-sectional study. Cureus. 2026;18(6). doi:10.7759/cureus.111272. 16. Nan X, Wang Y, Thier K. Why do people believe health misinformation and who is at risk? A systematic review of individual differences in susceptibility to health misinformation. Soc Sci Med. 2022;314:115398. doi:10.1016/j.socscimed.2022.115398. 17. Wiener RC, Abuhalimeh BJ. Evaluating adult digital health literacy, 2020-2025: a systematic review. PLOS Digit Health. 2025;4(11). doi:10.1371/journal.pdig.0001075. 18. Scherer LD, McPhetres J, Pennycook G, Kempe A, Allen LA, Knoepke CE, et al. Who is susceptible to online health misinformation? A test of four psychosocial hypotheses. Health Psychol. 2021;40(4):274-284. doi:10.1037/hea0000978. 19. Stimpson JP, Park S, Adhikari EH, Nelson DB, Ortega AN. Perceived health misinformation on social media and public trust in health care. Med Care. 2025;63(9):686-693. doi:10.1097/MLR.0000000000002180. 20. Sultan M, Tump AN, Ehmann N, Lorenz-Spreen P, Hertwig R, Gollwitzer A, et al. Susceptibility to online misinformation: a systematic meta-analysis of demographic and psychological factors. Proc Natl Acad Sci U S A. 2024;121(47). doi:10.1073/pnas.2409329121.