Important Correlates of Purpose in Life in a Diverse Population-Based Cohort: A Machine Learning Approach

Am J Geriatr Psychiatry. 2023 Sep;31(9):691-703. doi: 10.1016/j.jagp.2023.03.003. Epub 2023 Mar 16.

Abstract

Background: Purpose-in-life (PiL) refers to the tendency to derive meaning and purpose from daily life experiences. Individuals with higher PiL were more likely to have better physical, mental, and cognitive health in prospective studies. Here, we aimed to identify important correlates of PiL among people of diverse backgrounds.

Methods: Participants were recruited by the population-based Health and Retirement Study and provided information on 34 different sociodemographic and psychosocial factors through psychometrically validated measures. To identify important correlates of PiL, we employed regularized regression implemented by Elastic Net on the entire cohort as well as among self-identified black participants only and white participants only, respectively.

Results: A total of 6,620 participants were included in this study, among whom 913 were black and 5,707 were white. We identified 12 and 23 important sociodemographic and psychosocial correlates of PiL among black and white participants, respectively. Notably, all the 12 correlates in black participants were also correlates among white participants. Interestingly, when we examined both black and white participants together, being black was associated with having higher PiL. The correlates with the largest effect on PiL that were shared among black and white participants were hopelessness, perceived constraint on personal control, and self-mastery.

Conclusion: Several sociodemographic and psychosocial factors most strongly associated with PiL were shared among black and white participants. Future studies should investigate whether interventions targeting correlates of PiL can lead to higher sense of life purpose in participants of diverse backgrounds.

Keywords: Correlates of purpose-in-life; diverse populations; machine learning.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Black People
  • Humans
  • Machine Learning*
  • Personal Satisfaction*
  • Psychology
  • Sociodemographic Factors
  • White People