Important Correlates of Purpose in Life Identified Through a Machine Learning Approach

A wealth of evidence has linked purpose in life (PiL) to better mental and physical health and healthy aging. Here, the authors aimed to determine important correlates of PiL using a machine learning approach.

Participants were recruited from retirement communities by the Rush Memory and Aging Project and assessed for childhood experience, adulthood sociodemographic factors (e.g., education, income, marital status), lifestyle and health behavior (e.g., cognitively stimulating activities, exercise, social activities, social network size), psychological factors (e.g., depression, loneliness, perceived discrimination, perceived social support), personality traits (e.g., PiL, harm avoidance), and medical conditions. Elastic Net was implemented to identify important correlates of PiL.

A total of 1,839 participants were included in our analysis. Among the 23 variables provided to Elastic Net, 10 were identified as important correlates of PiL. In order of decreasing effect size, factors associated with lower PiL were loneliness, harm avoidance, older age, and depressive symptoms, while those associated with greater PiL were perceived social support, more social activities, more years of education, higher income, intact late-life cognitive performance, and more middle-age cognitive activities.

Our findings identify potentially important modifiable factors as targets for intervention strategies to enhance PiL.