Associations of digital self-efficacy and social network connectedness with loneliness among urban older adults in kerman, iran: a cross-sectional study

Digital technologies may help older adults maintain social connections, yet loneliness remains an important concern in later life. Digital self-efficacy may influence older adults’ confidence in using digital technologies and maintaining social connectedness. This study examined the associations of digital self-efficacy and social network connectedness with loneliness among urban older adults. This cross-sectional study included 300 urban older adults in Kerman, Iran. Data were collected using a researcher-developed Digital Self-Efficacy Questionnaire, the 6-item Lubben Social Network Scale (LSNS-6), and the UCLA Loneliness Scale. Data were analyzed using descriptive statistics, Mann–Whitney U, Kruskal–Wallis, Spearman’s rank-order correlation, and hierarchical multiple linear regression in SPSS version 17. Social network connectedness differed significantly by education level, residence status, occupation, and insurance coverage. Digital self-efficacy varied significantly by gender, number of children, education level, occupation, and disease status. Loneliness differed significantly by number of children, education level, occupation, and insurance coverage. Social network connectedness was positively correlated with digital self-efficacy (ρ = 0.283, p < 0.001) and negatively correlated with loneliness (ρ = −0.445, p < 0.001). Digital self-efficacy was also negatively correlated with loneliness (ρ = −0.481, p < 0.001). After adjustment for demographic and contextual characteristics, both variables were independently associated with loneliness. Adding them to the model increased explained variance by 22.4% (ΔR² = 0.224, p < 0.001), with the final model explaining 35.1% of the variance. Greater social network connectedness and higher digital self-efficacy were associated with lower loneliness among urban older adults. Longitudinal and intervention studies are needed to clarify temporal and causal relationships.