Wisdom and Life Purpose as Predictors of Mental Well-Being Among Middle-Aged to Older Adults: Cross-Sectional Study

AI Summary

Meaning and purpose, wisdom, and quality of life significantly predict mental well-being in adults aged 50–102, explaining 71% of variance.
Artificial neural network predicted mental well-being with 82.1% training and 73% test accuracy; sensitivity analysis identified meaning and purpose as most influential.
Findings inform social psychology theory and guide policymakers and care providers to design interventions promoting positive ageing and mental well-being among older adults.

JMIR Ment Health. 2026 May 19;13:e91716. doi: 10.2196/91716.

ABSTRACT

BACKGROUND: Positive aging, a concept found in positive psychology, serves as the theoretical foundation for this study. To age positively, one must manage hidden or unrecognized challenges, show flexibility in behavior and thought, adopt a positive outlook on problems involving regression, and make decisions that promote one’s well-being.

OBJECTIVE: This study examined the role of wisdom and life purpose in the mental well-being of middle-aged and older adults. More specifically, we tested 4 hypotheses: wisdom would exhibit a positive correlation with mental well-being, quality of life would exhibit a positive correlation with mental well-being, meaning and purpose would exhibit a positive correlation with mental well-being, and freedom would exhibit a positive correlation with mental well-being.

METHODS: The research used a multianalytical methodology combining covariance-based structural equation modeling and artificial neural network techniques to analyze data from 377 individuals aged 50 to 102 years.

RESULTS: Results from the covariance-based structural equation modeling indicate that meaning and purpose, wisdom, and quality of life were significantly associated with the mental well-being, accounting for 71% of the explained variance. Additionally, the artificial network analysis yielded exact forecasts of mental well-being. The artificial network model achieved an accuracy of 82.1% and 73% on the training and test sets, respectively, for predicting mental well-being. Sensitivity analysis revealed that meaning and purpose were the most critical factors in explaining participants’ mental well-being.

CONCLUSIONS: These findings have prominent theoretical implications for social psychology researchers and practical consequences for authorities involved in the care of older adults, who can use the results to develop strategic plans and take necessary actions.

PMID:42155053 | DOI:10.2196/91716

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