Predicting Digital Health Technology Adoption Intentions Among Medical and Health Sciences Students: A Social Cognitive Theory-Based Cross-Sectional Study

Abstract Background Digital health technology adoption among medical and health sciences students is important for health education and promotion. This study used Social Cognitive Theory (SCT) to examine factors associated with behavioral intention toward digital health technology adoption. Methods Nine SCT-related constructs knowledge, self-efficacy, outcome expectations, outcome value, observational learning, social support, facilitators and barriers, reinforcement, and behavioral intention were assessed among 450 medical and health sciences students. Confirmatory factor analysis and structural equation modeling examined measurement properties and hypothesized relationships. Moderation analyses assessed age, gender, and prior digital health technology experience. Results The measurement model showed acceptable fit (χ²/df = 2.235, CFI = 0.926, RMSEA = 0.047, SRMR = 0.049), with Cronbach’s alpha ranging from 0.78 to 0.89. Self-efficacy showed the strongest association with behavioral intention (β = 0.34, p < .001), followed by outcome expectations (β = 0.28, p < .001) and social support (β = 0.19, p = .006). Observational learning was associated with self-efficacy and outcome expectations. The model explained 52% of behavioral intention variance. No significant moderation effects were observed. Conclusion SCT-related factors were associated with behavioral intention toward digital health technology adoption. Longitudinal studies are needed to assess subsequent technology use.