Conditional pathways to loneliness in older adults with vision impairment: a decision-tree analysis

The prevalence of vision impairment continues to increase globally due to population growth and aging. Among older adults with vision impairment, loneliness is common. Prior studies assessing loneliness risk have relied on regression-based approaches, which may not capture complex, heterogeneous conditional pathways. We apply a theoretically guided machine learning approach to examine loneliness risk among older adults with age-related macular degeneration (AMD).

Using baseline data from 210 adults with AMD (Age_mean = 80.1 years) enrolled in a randomized controlled trial, we applied a decision tree method using measures of legal blindness, emotional responses to vision loss, social life satisfaction, anxiety, depression, optimism, future planning, and sociodemographic factors to investigate heterogeneous pathways to loneliness.

Decision tree models demonstrated stable discrimination (AUC_train = .88, AUC_test = .78, DeLong 95% confidence interval: 0.68-0.88) and robust classification performance across thresholds. Social life satisfaction emerged as the primary branching condition differentiating loneliness outcomes. Distinct pathways suggested that high social life satisfaction was strongly associated with lower loneliness; optimism served as a compensatory resource when social satisfaction was lower. Additional pathways highlighted the effects of vision loss severity, anxiety, and emotional responses to vision loss on loneliness. Depressive symptoms differentiated loneliness outcomes within a lower-anxiety branch, suggesting depression may overshadow protective factors.

These findings suggest that loneliness arises through heterogeneous psychological pathways rather than single risk factors. By identifying conditional configurations of psychosocial and health-related factors, interpretable decision-tree models may inform more targeted screening, evaluation, and intervention strategies.