Introduction
The expansion of internet access has significantly transformed health-seeking behaviour worldwide. Traditionally, individuals experiencing health anxiety engaged in repeated reassurance-seeking, bodily checking, and persistent doubt about medical explanations even after medical evaluation. These patterns were conceptualized within cognitive and behavioural frameworks long before digital technologies became central to daily life [1]. Individuals with heightened health anxiety tend to misinterpret normal bodily sensations as indicators of serious illness, resulting in recurring cycles of worry and temporary reassurance. With the rapid growth of online medical information, this vulnerability found a new and easily accessible outlet. The term cyberchondria was subsequently introduced to describe excessive or repetitive online searching for health-related information that paradoxically increases anxiety rather than relieving it [2].
Early cognitive models proposed that individuals prone to catastrophic interpretations selectively attend to threatening health information encountered during searches [3]. Search engines frequently prioritize severe or rare conditions even when users enter common symptoms, which may inadvertently amplify fear. Empirical work demonstrated that online searches can escalate benign symptom interpretations toward serious disease conclusions [4]. Studies further confirmed that ambiguous symptom searches often expose users to severe illness explanations, intensifying distress and reinforcing maladaptive beliefs [5]. This repetitive cycle of anxiety, searching, temporary reassurance, and renewed doubt resembles compulsive behavioural mechanisms described in anxiety-related disorders [6].
To quantify this construct, the Cyberchondria Severity Scale was developed and validated to assess excessiveness, distress, reassurance-seeking, and compulsive dimensions of online health searching [7]. The abbreviated CSS-12 has demonstrated sound psychometric properties and improved feasibility for epidemiological research without compromising reliability [8]. During the COVID-19 pandemic, reliance on digital platforms for symptom appraisal and health updates increased dramatically across populations [9]. Young adults, being highly digitally engaged, were especially likely to seek health information online during this period [10]. Increased screen time has also been linked with heightened anxiety symptoms and maladaptive coping behaviours [11].
In India, rapid smartphone penetration and affordable internet services have significantly expanded digital health engagement over the past decade [12]. Urban youth in metropolitan areas are exposed to sustained screen time, academic pressures, and continuous social media health messaging, which may increase vulnerability to anxiety-driven online searching. Despite these contextual risk factors, systematic community-based data on cyberchondria in Indian settings remain limited. Youth represent a high-risk group due to prolonged digital exposure and frequent reliance on online sources for health information [13]. A clearer assessment of prevalence and associated behavioural factors among Indian youth is therefore essential to inform digital literacy initiatives and preventive mental health strategies.
Materials and Methods
A community-based cross-sectional study was conducted among youth aged 18–25 years residing in Rangareddy district, Telangana, including the Hyderabad metropolitan region. The study was carried out over a defined data collection period using a structured questionnaire administered through an online survey platform (Google Forms) to ensure accessibility and ease of participation.
The required sample size was estimated using the formula for a single population proportion:
N=4pq/d2
where n is the required sample size, p is the expected prevalence of cyberchondria, q = 1 - p, and d is the allowable error. As reliable estimates of cyberchondria prevalence among youth in the study setting were limited, the prevalence was assumed to be 50% to obtain the maximum sample size. The allowable error was set at 5%. Using these parameters, the calculated sample size was approximately 400. Since this is online based data collection, using a non-response bias of around 30%, a total of 544 participants were included in the study.
A sampling frame was prepared using available youth registries and verified online community groups within Hyderabad and surrounding areas of Rangareddy district. Eligible participants were identified from these groups and invited electronically by sharing the survey link. Participation was voluntary, and anonymity and confidentiality were maintained throughout the study. Digital informed consent was obtained from all participants before completing the questionnaire.
The inclusion criteria comprised youth aged 18–25 years who had been residing in the study area for at least one year and who reported using the internet for health-related information. Participants were required to be able to read and understand English in order to complete the questionnaire. Individuals with a previously diagnosed psychiatric disorder currently under active treatment were excluded to minimize potential confounding effects on anxiety-related measures. Incomplete responses and duplicate submissions were screened and excluded prior to analysis to ensure data integrity. The study was reviewed and approved by the Institutional Ethics Committee, and all participants provided digital informed consent prior to participation.
The study instrument consisted of two sections. The first section collected sociodemographic information including age, gender, place of residence (urban or rural), and average daily screen time categorized in hours. The second section included the Cyberchondria Severity Scale–12 (CSS-12), a validated instrument used to assess the severity of cyberchondria. The CSS-12 consists of 12 items measured on a 5-point Likert scale ranging from 1 (never) to 5 (always). The total score ranges from 12 to 60, with higher scores indicating greater severity of cyberchondria. The scale assesses four domains: Excessiveness, Distress, Reassurance, and Compulsion, with each domain consisting of three items and a possible score range of 3 to 15. The instrument has demonstrated acceptable reliability and construct validity in young adult populations. Internal consistency of the CSS-12 in the present sample was high (Cronbach’s α = 0.89). Domain-wise reliability coefficients were 0.84 for Excessiveness, 0.81 for Distress, 0.79 for Reassurance, and 0.76 for Compulsion.
Data from the online survey were exported to Microsoft Excel for cleaning and coding, and the final dataset was analysed using IBM SPSS Statistics demo version 23. Continuous variables were summarized as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Normality was assessed using the Shapiro–Wilk test. Independent sample t-tests were used to compare mean CSS scores across gender categories, and one-way ANOVA was applied to evaluate differences across screen time groups, with post hoc tests where appropriate. Effect size was calculated using Eta squared (η²). A p-value < 0.05 was considered statistically significant at the 95% confidence level.
Results
A total of 544 participants were included in the analysis. As shown in Table 1, most participants had no or mild cyberchondria (64.9%), while 16.9% had moderate, 16.2% had high, and 2.0% had severe cyberchondria. The distribution of severity did not differ significantly across gender or residence. Although higher screen time categories showed slightly greater proportions of moderate-to-severe cyberchondria, the association was not statistically significant (p = 0.913).
| Variable | No/Mild n (%) | Moderate n (%) | High n (%) | Severe n (%) | p-value |
| Gender | 0.921 | ||||
| Male | 196 (64.9) | 52 (17.2) | 47 (15.6) | 7 (2.3) | |
| Female | 157 (64.9) | 40 (16.5) | 41 (16.9) | 4 (1.7) | |
| Residence | 0.901 | ||||
| Urban | 293 (65.0) | 76 (16.9) | 72 (16.0) | 10 (2.1) | |
| Rural | 60 (64.5) | 16 (17.2) | 16 (17.2) | 1 (1.1) | |
| Screen Time (hours/day) | 0.913 | ||||
| 1 hour (n=15) | 12 (80.0) | 2 (13.3) | 1 (6.7) | 0 (0.0) | |
| 2 hours (n=108) | 76 (70.4) | 18 (16.7) | 13 (12.0) | 1 (0.9) | |
| 3 hours (n=165) | 104 (63.0) | 28 (17.0) | 30 (18.2) | 3 (1.8) | |
| 4 hours (n=136) | 85 (62.5) | 24 (17.6) | 24 (17.6) | 3 (2.2) | |
| 5 hours (n=120) | 76 (63.3) | 20 (16.7) | 20 (16.7) | 4 (3.3) |
Domain-wise analysis demonstrated a significant gradient across severity categories (Table 2). Mean scores for excessiveness, distress, reassurance, and compulsion increased progressively from the no/mild to the severe category (p < 0.001).
| Severity Level | Excessiveness Mean ± SD | Distress Mean ± SD | Reassurance Mean ± SD | Compulsion Mean ± SD | p-value |
| No/Mild (n=353) | 6.42 ± 2.10 | 5.21 ± 2.03 | 5.10 ± 2.20 | 4.12 ± 1.85 | <0.001* |
| Moderate (n=92) | 8.65 ± 2.25 | 7.85 ± 2.12 | 7.62 ± 2.34 | 6.02 ± 2.11 | |
| High (n=88) | 10.21 ± 2.31 | 9.14 ± 2.45 | 9.02 ± 2.41 | 7.42 ± 2.30 | |
| Severe (n=11) | 11.36 ± 2.08 | 10.21 ± 2.33 | 10.44 ± 2.15 | 8.61 ± 2.12 |
*Statistically Significant
As shown in Table 3, younger participants had significantly higher domain scores compared with older participants (p = 0.003). Medical students also demonstrated significantly higher excessiveness, distress, reassurance, and compulsion scores than non-medical students (p = 0.001). Domain scores increased with higher daily screen exposure (p = 0.002).
| Excessiveness Mean ± SD | Distress Mean ± SD | Reassurance Mean ± SD | Compulsion Mean ± SD | p-value | |
| Age Group (years) | 0.003* | ||||
| 18–20 (n=210) | 8.74 ± 2.82 | 7.65 ± 2.71 | 7.52 ± 2.90 | 6.12 ± 2.35 | |
| 21–23 (n=214) | 8.02 ± 2.73 | 6.88 ± 2.81 | 6.91 ± 2.95 | 5.55 ± 2.44 | |
| 24–25 (n=120) | 7.31 ± 2.60 | 6.20 ± 2.72 | 6.28 ± 2.86 | 5.02 ± 2.30 | |
| Field Study | 0.001* | ||||
| Medical Students (n=214) | 8.91 ± 2.74 | 7.82 ± 2.68 | 7.65 ± 2.93 | 6.18 ± 2.40 | |
| Non-Medical Students (n=330) | 7.64 ± 2.69 | 6.52 ± 2.77 | 6.57 ± 2.88 | 5.35 ± 2.36 | |
| Screen Time | 0.002* | ||||
| 1 hour (n=15) | 5.92 ± 2.11 | 4.98 ± 2.03 | 5.12 ± 2.16 | 3.71 ± 1.84 | |
| 2 hours (n=108) | 7.36 ± 2.54 | 6.28 ± 2.63 | 6.41 ± 2.78 | 5.02 ± 2.15 | |
| 3 hours (n=165) | 8.09 ± 2.71 | 7.05 ± 2.74 | 7.01 ± 2.93 | 5.63 ± 2.32 | |
| 4 hours (n=136) | 8.28 ± 2.79 | 7.16 ± 2.83 | 7.08 ± 3.01 | 5.74 ± 2.41 | |
| 5 hours (n=120) | 8.47 ± 2.92 | 7.33 ± 2.90 | 7.22 ± 3.12 | 5.89 ± 2.46 |
*Statistically Significant
Multiple linear regression analysis (Table 4) showed that higher screen time and being a medical student were independent predictors of increased excessiveness and distress scores, while increasing age was associated with lower domain scores. Gender was not significantly associated with the domain scores.
| Predictor | Excessiveness β | Distress β | Reassurance β | Compulsion β | p-value |
| Age | -0.18 | -0.16 | -0.14 | -0.10 | 0.002* |
| Female Gender | 0.09 | 0.07 | 0.03 | -0.05 | 0.118 |
| Medical Student | 0.21 | 0.19 | 0.15 | 0.11 | 0.001* |
| Screen Time (hours/day) | 0.24 | 0.20 | 0.18 | 0.12 | <0.001* |
*Statistically Significant
In multivariable logistic regression analysis (Table 5), medical students (AOR = 1.76; 95% CI: 1.22–2.54) and participants with ≥4 hours of daily screen time (AOR = 1.94; 95% CI: 1.35–2.78) had significantly higher odds of moderate-to-severe cyberchondria. Increasing age was associated with lower odds of cyberchondria severity (AOR = 0.89; 95% CI: 0.81–0.97).
| Predictor | Adjusted Odds Ratio (AOR) | 95% CI | p-value |
| Age (per year increase) | 0.89 | 0.81 – 0.97 | 0.009* |
| Female gender | 1.14 | 0.82 – 1.63 | 0.324 |
| Medical student | 1.76 | 1.22 – 2.54 | 0.002* |
| Screen time ≥4 hours/day | 1.94 | 1.35 – 2.78 | <0.001* |
*Statistically Significant
Discussion
The present study found a 35.1% prevalence of cyberchondria among youth in Hyderabad, indicating that more than one in three young adults experience clinically meaningful levels of anxiety associated with online health searching. This prevalence is comparable to findings from European university populations reporting rates between 30 percent and 40 percent, suggesting that cyberchondria is not confined to Western settings but represents a broader phenomenon among digitally engaged youth [14]. Similar prevalence patterns have been observed in student cohorts where frequent online symptom checking was associated with heightened distress and reassurance seeking [15]. Indian online surveys conducted during the COVID-19 pandemic also reported elevated health anxiety linked to internet-based information seeking, particularly in urban populations with high digital access [16]. The convergence of findings across diverse cultural contexts suggests that cyberchondria may be strongly influenced by global patterns of digital information consumption rather than purely sociocultural variables.
The mean CSS score of 27.89 observed in this study aligns with values reported in validation studies of the CSS-12 among young adults and university students [8]. Comparable mean scores have been documented in psychometric assessments conducted in community samples, indicating that the severity distribution in the present study reflects typical patterns seen in non-clinical youth populations [17]. Among the domains, excessiveness emerged as the highest scoring component, highlighting repetitive and prolonged searching behaviours as a central feature. This finding is consistent with prior work identifying repeated checking and difficulty disengaging from health-related searches as core characteristics of cyberchondria [18]. Excessive searching may initially be motivated by curiosity or reassurance but gradually evolves into a habitual pattern driven by uncertainty intolerance and fear of missing critical information.
Gender was not significantly associated with total CSS scores in the present study, which mirrors findings from Turkish samples where overall severity did not differ substantially between males and females [19]. Similar results have been reported in German cohorts, where gender differences were minimal after adjusting for general anxiety levels [20]. However, females in this study demonstrated significantly higher excessiveness scores. Behavioural research suggests that women are more likely to engage in active health information seeking and preventive health behaviours, which may explain greater frequency of online searches without necessarily increasing overall severity [21]. This distinction underscores that while overall cyberchondria severity may not differ markedly by gender, specific behavioural dimensions such as search frequency and engagement may vary.
Screen time demonstrated a statistically significant association with cyberchondria severity. Participants reporting four or more hours of daily screen exposure had higher CSS scores compared to those with lower screen time. This observation supports earlier research linking prolonged internet exposure with anxiety amplification and compulsive reassurance-seeking patterns [22]. Repeated exposure to health-related content, particularly during periods of uncertainty, can reinforce catastrophic interpretations and maintain anxiety cycles. Longitudinal studies have also indicated that excessive digital engagement predicts persistence of health-related worry over time [23]. Furthermore, algorithm-driven content delivery systems may amplify exposure to alarming or sensationalized medical information, thereby increasing perceived threat even when objective risk is low [24]. The association observed in the present study reinforces the need to consider digital usage patterns as an important behavioural correlate of cyberchondria [25].
No significant difference in cyberchondria was observed between urban and rural participants in the present study. This suggests that cyberchondria may not be strongly influenced by place of residence. With the widespread availability of smartphones and internet access across both urban and rural settings, exposure to online health information has become increasingly similar. Consequently, factors such as patterns of internet use, individual health anxiety, and digital health literacy may play a more important role in influencing cyberchondria among young adults than geographic location alone.
Conclusion
Cyberchondria affects over one-third of youth in Hyderabad and is significantly associated with increased screen time. Excessive online searching for health information appears to be the predominant behavioural component contributing to this pattern. Frequent exposure to large amounts of online health content, including inaccurate or conflicting information, may increase health-related anxiety and encourage repeated reassurance-seeking. These findings highlight the importance of improving digital health literacy among young adults. Structured awareness programs that promote critical evaluation of online health information and responsible health information–seeking behaviours may help reduce anxiety amplification and support healthier internet use among youth.
Declarations
Ethical Clearance
The study was conducted following ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Ethics Committee of PMRIMS, Chevella. (Approval no: IEC/PMRIMS/074) Informed written consent was obtained from all participants. Confidentiality and anonymity were maintained throughout the study.
Contribution Details
Josya Sagili Lakshmi contributed to the concept, design, literature search, clinical studies, and manuscript preparation. Vishnu Vardhan Yenuganti was responsible for the definition of intellectual content, data analysis, statistical analysis. Priyanka Adireddy Bukke handled manuscript editing. Goutham Kankanam handled the experimental studies, data acquisition, and manuscript review.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to restrictions (their containing information that could compromise the privacy of research participants).
Declaration on Use of AI
The authors declare that they have not used any generative AI/AI-assisted technologies in the writing process.
Sources of support
Nil
Conflicting Interest
None declared
Acknowledgement
None