Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: A latent profile analysis
Author(s)
Author(s)
Liu, C.
Luo, Q.
Luo, D.
Zhou, Y.
Feng, X.
Wang, Z.
Xiao, J.
Bi, Q.
Date Issued
2023
Publisher
Springer
Journal
BMC Geriatrics
Volume
23
Issue
1
Abstract
Background
Recently developments in the field of positive psychology have provided new perspectives for understanding the connection between individual variation in Quality of life (QoL) and positive aspects of human potential, strengths, and resources, commanding increasing attention. This study aimed to examine self-reported quality of life (QoL) profiles and the association of QoL profiles with positive psychosocial characteristics in Chinese older adults.
Methods
A convenient sample of 354 older adults in nursing homes was recruited from Guangdong Province, China, between November 2020 and January 2021. Latent Profile Analysis (LPA) was conducted to explore QoL profiles using the four WHOQOL-BREF domains as input variables. Multinomial logistic regression was performed to explore the association between latent profiles and predictors.
Results
LPA identified three latent QoL profiles: “low QoL with poor psychological health” (18.1%), “moderate QoL” (46.0%) and “high QoL” (35.9%). Frequency of weekly activity, optimism, gratitude, and social support were associated with the increased likelihood of belonging to the moderate-to-high QoL classes. Furthermore, Class 2 (moderate QoL group, reference) was compared with Class3 (high QoL group), higher frequency of weekly physical activity and spending more time on physical activity exhibited higher odds of belonging to high QoL class.
Conclusion
Using the domains of the WHOQOL-BREF scale, the QoL profiles Chinese older adults can be identified. We found that psychosocial variables and demographic characteristic, including lower level of optimism and gratitude, lack of social support, low frequency of physical activity, and shorter activity duration time, heighten the risk for lower levels of QoL. Identifying classification may help focus on those at elevated risk for poor QoL and for developing tailored QoL improvement programs.
SFU Affiliated Publication
Yes
Availability at SFU Library
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