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Related Experiment Video

Updated: Jun 5, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

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

Hyun Woo Jung1,2, Jae-Hyun Kim3, Jung Jae Lee1,2,4

  • 1Digital Mental Health Innovation Center, Dankook University, Cheonan, Korea.

Journal of Korean Medical Science
|June 4, 2026
PubMed
Summary

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Depression in older adults living alone can be screened using simple physical and behavioral signs. A novel framework identified key indicators for early detection in routine health check-ups.

Area of Science:

  • Gerontology
  • Psychiatry
  • Public Health

Background:

  • South Korea's aging population increases depression risk for older adults living alone.
  • Early detection of depression is crucial for this vulnerable group.
  • A novel screening framework, Depression Prediction based on Anthropometric and Clinical Signs (DPACS), was developed for integration into health check-ups.

Purpose of the Study:

  • To identify non-invasive physical and behavioral indicators associated with depression in older adults living alone.
  • To develop and validate a screening framework for early depression detection in this demographic.
  • To investigate sex-specific differences in depression indicators.

Main Methods:

  • A cross-sectional study involving 1,093 older adults living alone in South Korea.
Keywords:
AnthropometryDepressionHealth BehaviorOlder Adults, Living AloneVital Signs

Related Experiment Videos

Last Updated: Jun 5, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

  • The DPACS framework incorporated vital signs, anthropometric measures, physical function tests, and health behaviors.
  • Multiple logistic regression and receiver operating characteristic (ROC) curve analyses were used to assess associations and model performance.
  • Main Results:

    • Indicators like larger head circumference and sensory impairments were linked to higher depression risk.
    • Better neurological gait, more frequent meals, and longer exercise duration were associated with lower risk.
    • Sex-specific patterns emerged, with visual, olfactory, and gustatory dysfunctions being notable predictors. The pooled DPACS model showed fair discrimination (AUC = 0.74).

    Conclusions:

    • Simple, non-invasive physical and behavioral measures can effectively indicate depression risk in older adults living alone.
    • Integrating DPACS into routine check-ups can facilitate early detection and referral, especially for those hesitant to seek psychiatric care.
    • The DPACS model offers a feasible approach for enhancing mental health screening in aging populations, though further validation is needed.