Bayesian regression model estimates.

This study presents the first UK Biobank analysis to concurrently model subjective wellbeing and illbeing within a unified biopsychosocial framework, offering a novel, data-rich perspective on psychological functioning in later life. While wellbeing and illbeing are often studied in isolation, there is growing recognition that their determinants may differ in kind and form. We address this gap by examining how biological, psychological, and social factors dynamically shape both outcomes in a large community-dwelling sample. Drawing on data from 8,047 participants (mean age = 64.8 years; 46.7% male; 90.7% White British), we constructed a theory-informed partial least squares structural equation model (PLS-SEM) linking heart rate variability (HRV), meaning-oriented behaviour (MOB), resilience, social connectedness, and lifetime adversity to wellbeing and illbeing. Model robustness was supported through 10,000-sample bootstrapping and split-half replication. Network centrality analysis (NCA) was used to identify key drivers, and Bayesian regression was applied to test non-linear functional forms for each path, validated using a held-out test dataset. MOB emerged as the strongest direct predictor of both increased wellbeing and reduced illbeing. HRV influenced wellbeing indirectly via psychosocial mediators. Adversity had the largest total effect on illbeing but no direct effect on wellbeing. Together, predictors accounted for ~52% of variance in both outcomes. Bayesian models revealed exponential, cubic, and logarithmic forms, indicating that conditions optimising wellbeing are not merely the inverse of those reducing illbeing. These findings offer a detailed mapping of non-linear biopsychosocial pathways in older adults and challenge the assumption that wellbeing and illbeing lie on a single continuum. The study provides a robust empirical foundation for developing process-based, context-sensitive mental health interventions. Longitudinal and more demographically diverse studies are now needed to test causal directions and broader generalisability.

本研究首次基于英国生物样本库（UK Biobank）数据，在统一的生物心理社会框架下同时构建主观幸福感与不幸福感的预测模型，为晚年心理功能研究提供了新颖且富含数据支撑的视角。既往研究多将幸福感与不幸福感分开单独探讨，但学界日益认识到二者的决定因素在本质与形式上均存在差异。本研究弥补这一研究空白，在大型社区常住人群样本中考察生物、心理与社会因素如何动态影响两类结局。本研究纳入8047名参与者（平均年龄64.8岁；男性占比46.7%；英国白人占比90.7%），基于理论构建偏最小二乘结构方程模型（PLS-SEM），将心率变异性（HRV）、意义导向行为（MOB）、心理韧性、社会联结及终生逆境与幸福感和不幸福感关联起来。模型稳健性通过10000次Bootstrap抽样与折半重复验证得以确认。研究采用网络中心性分析（NCA）识别核心影响因素，并运用贝叶斯回归检验各路径的非线性函数形式，通过预留测试集完成验证。结果显示，意义导向行为是提升幸福感与降低不幸福感的最强直接预测因子；心率变异性通过社会心理中介变量对幸福感产生间接影响；终生逆境对不幸福感的总效应最大，但对幸福感无直接影响。所有预测因子共解释两类结局约52%的变异量。贝叶斯模型揭示了指数、三次与对数函数形式，表明优化幸福感的条件并非仅仅是降低不幸福感条件的反向对应。本研究详细刻画了老年人群的非线性生物心理社会通路，并对“幸福感与不幸福感处于单一连续体”的传统假设提出了挑战。本研究为开发基于过程、贴合情境的心理健康干预手段提供了坚实的实证基础。未来仍需开展纵向研究以及人口学特征更具多样性的研究，以检验因果方向并拓展研究结果的普适性。