Correlates of quality of life, happiness and life satisfaction among European adults older than 50 years: A machine-learning approach

Background and objectives: Previous research has documented the role of different categories of psychosocial factors (i.e., sociodemographic factors, personality, subjective life circumstances, activity, physical health, and childhood circumstances) in predicting subjective well-being and quality of life among older adults. No previous study has simultaneously modeled a large number of these psychosocial factors using a well-powered sample and machine learning algorithms to predict quality of life, happiness, and life satisfaction among older adults. The aim of this paper was to investigate the correlates of quality of life, happiness, and life satisfaction among European adults older than 50 years using machine learning techniques. Research design and methods: Data drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 7 were used. Participants were 62,500 persons aged 50 years and over living in 26 Continental EU Member States, Switzerland, and Israel. Multiple machine learning regression approaches were used. Results: The algorithms captured 53%, 33%, and 18% of the variance of quality of life, life satisfaction, and happiness, respectively. The most important categories of correlates of quality of life and life satisfaction were physical health and subjective life circumstances. Sociodemographic factors (mostly country of residence) and psychological variables were the most important categories of correlates of happiness. Discussion and implications: This study highlights subjective poverty, self-perceived health, country of residence, subjective survival probability, and personality factors (especially neuroticism) as important correlates of quality of life, happiness, and life satisfaction. These findings provide evidence-based recommendations for practice and/or policy implications.