Predictive Model of Digital Health Anxiety Based on Online Symptom Searching, Intolerance of Uncertainty, Rumination, and Health Literacy

Abstract

Introduction and Aim: The widespread use of the internet for health-related information seeking has created new psychological challenges, including digital health anxiety, characterized by excessive health concerns and repetitive online symptom searching. The present study aimed to develop a predictive model of digital health anxiety based on online symptom searching, intolerance of uncertainty, rumination, and health literacy among adults in Tehran.

Methodology: This descriptive-correlational study employed structural equation modeling. The statistical population consisted of adults residing in Tehran in 2024 who used the internet to search for health-related information. A total of 450 participants were selected through multistage cluster sampling, and data from 432 individuals were included in the final analysis after excluding incomplete questionnaires. Research instruments included the Digital Health Anxiety Scale, Online Symptom Searching Questionnaire, Intolerance of Uncertainty Scale, Ruminative Responses Scale, and the European Health Literacy Questionnaire. Data were analyzed using SPSS version 28 and AMOS version 26.

Findings: The results indicated a significant positive relationship between online symptom searching and digital health anxiety (r=0.68, p<0.01). Rumination was also positively associated with digital health anxiety (r=0.61, p<0.01). In contrast, intolerance of uncertainty (r=-0.54, p<0.01) and health literacy (r=-0.42, p<0.01) showed significant negative relationships with digital health anxiety. Multiple regression analysis revealed that the predictor variables collectively explained 63% of the variance in digital health anxiety (R²=0.63). Online symptom searching (β=0.43) emerged as the strongest predictor, followed by rumination (β=0.32), intolerance of uncertainty (β=-0.27), and health literacy (β=-0.15). Structural equation modeling further demonstrated satisfactory model fit indices, supporting the proposed predictive framework.

Conclusion: The findings suggest that digital health anxiety is influenced by a combination of behavioral, cognitive, and informational factors. Excessive online symptom searching and rumination increase vulnerability to digital health anxiety, whereas higher tolerance for uncertainty and greater health literacy serve protective roles. Interventions focused on improving health literacy, promoting responsible online health information seeking, reducing rumination, and enhancing tolerance of uncertainty may contribute to the prevention and reduction of digital health anxiety.