Integrating Non-Invasive Physical and Behavioral Indicators Into Routine Health Check-ups for Detecting Early...

As South Korea faces a rapidly aging population, older adults living alone are becoming increasingly vulnerable to depression. This study aimed to identify non-invasive and easily measurable physical and behavioral indicators that are associated with depression in this high-risk group. We proposed a novel screening framework-Depression Prediction based on Anthropometric and Clinical Signs-designed for integration into existing health check-ups to support early detection.

We conducted a cross-sectional study among 1,093 older adults living alone in South Korea. The Depression Prediction based on Anthropometric and Clinical Signs was developed by incorporating vital signs, anthropometric measures, physical function tests, and health behaviors. Multiple logistic regression analyses were used to examine the association between these indicators and depression, with subgroup analyses by sex.

Several indicators of Depression Prediction based on Anthropometric and Clinical Signs were significantly associated with depression. Larger head circumference and sensory impairments were associated with a higher risk of depression. In contrast, better neurological gait performance, more frequent meals, and longer exercise duration were associated with lower risk. Sex-specific patterns were identified, among which visual, olfactory, and gustatory dysfunctions were notable predictors. Among men, elevated respiratory rate and gustatory dysfunction were prominent. Receiver operating characteristic curve analysis showed fair discrimination for pooled (area under the curve = 0.74, cut-off = 0.304) and female (area under the curve = 0.73) models, whereas the male model demonstrated lower performance (area under the curve = 0.62).

Simple, non-invasive physical and behavioral measures may serve as effective indicators for identifying depression risk in older adults living alone. Incorporating these indicators into routine health check-ups could support early detection and referral, particularly for those who are unlikely to seek psychiatric care. The Depression Prediction based on Anthropometric and Clinical Signs model offers a feasible approach to enhance mental health screening in aging populations, although further validation and longitudinal studies are warranted.