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Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis
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 a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644486/ https://www.ncbi.nlm.nih.gov/pubmed/37964191 http://dx.doi.org/10.1186/s12877-023-04456-2 |
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author | Liu, Chunqin Luo, Qing Luo, Dongyi Zhou, Ying Feng, Xue Wang, Zihan Xiao, Jiajian Bi, Qiulin Smith, Graeme Drummond |
author_facet | Liu, Chunqin Luo, Qing Luo, Dongyi Zhou, Ying Feng, Xue Wang, Zihan Xiao, Jiajian Bi, Qiulin Smith, Graeme Drummond |
author_sort | Liu, Chunqin |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04456-2. |
format | Online Article Text |
id | pubmed-10644486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106444862023-11-14 Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis Liu, Chunqin Luo, Qing Luo, Dongyi Zhou, Ying Feng, Xue Wang, Zihan Xiao, Jiajian Bi, Qiulin Smith, Graeme Drummond BMC Geriatr Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04456-2. BioMed Central 2023-11-14 /pmc/articles/PMC10644486/ /pubmed/37964191 http://dx.doi.org/10.1186/s12877-023-04456-2 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Chunqin Luo, Qing Luo, Dongyi Zhou, Ying Feng, Xue Wang, Zihan Xiao, Jiajian Bi, Qiulin Smith, Graeme Drummond Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis |
title | Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis |
title_full | Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis |
title_fullStr | Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis |
title_full_unstemmed | Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis |
title_short | Quality of life profiles and its association with predictors amongst Chinese older adults in nursing homes: a latent profile analysis |
title_sort | quality of life profiles and its association with predictors amongst chinese older adults in nursing homes: a latent profile analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644486/ https://www.ncbi.nlm.nih.gov/pubmed/37964191 http://dx.doi.org/10.1186/s12877-023-04456-2 |
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