Neuroscience Research Australia, Randwick, NSW, 2031, Australia.
School of Psychology, University of New South Wales, Sydney, NSW, 2052, Australia.
Sci Rep. 2023 Apr 5;13(1):5565. doi: 10.1038/s41598-023-32588-3.
Various sociodemographic, psychosocial, cognitive, and life event factors are associated with mental wellbeing; however, it remains unclear which measures best explain variance in wellbeing in the context of related variables. This study uses data from 1017 healthy adults from the TWIN-E study of wellbeing to evaluate the sociodemographic, psychosocial, cognitive, and life event predictors of wellbeing using cross-sectional and repeated measures multiple regression models over one year. Sociodemographic (age, sex, education), psychosocial (personality, health behaviours, and lifestyle), emotion and cognitive processing, and life event (recent positive and negative life events) variables were considered. The results showed that while neuroticism, extraversion, conscientiousness, and cognitive reappraisal were the strongest predictors of wellbeing in the cross-sectional model, while extraversion, conscientiousness, exercise, and specific life events (work related and traumatic life events) were the strongest predictors of wellbeing in the repeated measures model. These results were confirmed using tenfold cross-validation procedures. Together, the results indicate that the variables that best explain differences in wellbeing between individuals at baseline can vary from the variables that predict change in wellbeing over time. This suggests that different variables may need to be targeted to improve population-level compared to individual-level wellbeing.
各种社会人口统计学、心理社会、认知和生活事件因素与心理健康有关；然而，在相关变量的背景下，哪些措施最能解释幸福感的差异仍不清楚。本研究使用来自 TWIN-E 幸福感研究的 1017 名健康成年人的数据，使用横断面和重复测量多元回归模型，在一年的时间内评估幸福感的社会人口统计学、心理社会、认知和生活事件预测因素。考虑了社会人口统计学（年龄、性别、教育）、心理社会（人格、健康行为和生活方式）、情绪和认知处理以及生活事件（近期积极和消极生活事件）变量。结果表明，神经质、外向性、尽责性和认知重评在横断面模型中是幸福感的最强预测因素，而外向性、尽责性、锻炼和特定生活事件（与工作相关和创伤性生活事件）是重复测量模型中幸福感的最强预测因素。使用十重交叉验证程序验证了这些结果。总之，这些结果表明，在基线时个体之间幸福感差异的最佳解释变量可能与随时间变化预测幸福感的变量不同。这表明，与个体层面相比，改善人群层面的幸福感可能需要针对不同的变量。
