Login / Signup

Role Value, Occupational Balance, and Quality of Life: A Cross-Sectional Study on Exploring the Urban Older People Perspective in South Korea.

Myoung-Ok ParkJi-Hyun Lee
Published in: International journal of environmental research and public health (2022)
The purpose of this study was to investigate the role value, occupational balance, and quality of life among urban older adults in South Korea. We recruited 90 urban older adults in Seoul, Gyeonggi-do and Chungcheong-do. Assessments used (1) Role Checklist, (2) Life Balance Inventory (LBI), and (3) WHO Quality of Life Scale abbreviated version (WHOQOL-BREF). Our results showed that the roles that were perceived as very valuable were as family members, housekeepers, and guardians (in descending order). The roles that were perceived as less valuable were students, volunteers, and organizational members (in descending order). The activities that individuals were actively pursuing were hygiene management, rest, and healthy eating (in descending order). By contrast, the activities that were not being actively pursued were composing (music, poetry), preparing for event planning, dancing, yoga, and taekwondo. The total score of the Role Checklist and WHOQOL-BREF total (r = 0.343, p < 0.01), K-LBI total and WHOQOL-BREF total (r = 0.386, p < 0.01), and role value total and K-LBI (r = 323, p < 0.01) showed a statistically significant correlation. As a result of the regression analysis, the sub-item of work balance that affected the quality of life was managing appearance (R 2 = 51.7, p < 0.001). These data showed that the role of urban older adults in Korea was mainly played within the family. The level of participation was low in the areas of instrumental daily life activities, work, leisure, and social participation. We propose that this population needs to be provided with opportunities for active aging through broader professional participation.
Keyphrases
  • physical activity
  • mental health
  • depressive symptoms
  • risk factors
  • social support
  • weight loss
  • big data
  • machine learning