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Abstract：
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.
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.
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.
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.
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.
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.
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.
摘要：
背景：积极心理学领域的最新发展为理解生活质量（QoL）的个体差异与人类潜能的积极方面之间的联系提供了新的视角，优势,和资源,引起越来越多的关注。这项研究旨在检查中国老年人的自我报告的生活质量（QoL）概况以及QoL概况与积极心理社会特征的关系。
方法：从广东省招募了354名养老院老年人的方便样本，中国,2020年11月至2021年1月。使用四个WHOQOL-BREF域作为输入变量，进行潜在谱分析（LPA）以探索QoL概况。进行了多项逻辑回归以探索潜在概况与预测因素之间的关联。
结果：LPA确定了三个潜在的QoL配置文件：“低QoL与心理健康差”（18.1％），“中等QoL”（46.0%）和“高QoL”（35.9%）。每周活动的频率，乐观,感恩,和社会支持与属于中高QoL类别的可能性增加相关。此外，2级(中等QoL组，参考)与Class3(高QoL组)进行比较，每周体育锻炼的频率更高，花费更多的时间在体育锻炼上，属于高QoL等级的可能性更高。
结论：使用WHOQOL-BREF量表的域，可以识别中国老年人的QoL概况。我们发现心理社会变量和人口统计学特征，包括较低水平的乐观和感激，缺乏社会支持,身体活动频率低，和较短的活动持续时间，增加较低水平QoL的风险。识别分类可能有助于关注QoL较差风险较高的人群，并制定量身定制的QoL改善计划。
方法：从广东省招募了354名养老院老年人的方便样本，中国,2020年11月至2021年1月。使用四个WHOQOL-BREF域作为输入变量，进行潜在谱分析（LPA）以探索QoL概况。进行了多项逻辑回归以探索潜在概况与预测因素之间的关联。
结果：LPA确定了三个潜在的QoL配置文件：“低QoL与心理健康差”（18.1％），“中等QoL”（46.0%）和“高QoL”（35.9%）。每周活动的频率，乐观,感恩,和社会支持与属于中高QoL类别的可能性增加相关。此外，2级(中等QoL组，参考)与Class3(高QoL组)进行比较，每周体育锻炼的频率更高，花费更多的时间在体育锻炼上，属于高QoL等级的可能性更高。
结论：使用WHOQOL-BREF量表的域，可以识别中国老年人的QoL概况。我们发现心理社会变量和人口统计学特征，包括较低水平的乐观和感激，缺乏社会支持,身体活动频率低，和较短的活动持续时间，增加较低水平QoL的风险。识别分类可能有助于关注QoL较差风险较高的人群，并制定量身定制的QoL改善计划。
