Pathways, predictors and paradoxes of illbeing and wellbeing in older adults: Insights from a UK Biobank study

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.