Patterns of Everyday Thought as Correlates of Psychological Well-Being and Possible Targets for Personalized Mental Health Treatment

A broad aim of the precision mental healthcare (PMH) movement is to increase the efficacy of treatment. While showing promise, which factors are influential in predicting positive outcomes is still unclear. One possibility is an individual’s subjective experience – especially when using longitudinal, multidimensional data from naturalistic settings. The Mind Window app was used to gather data about nine thought characteristics from 2,222 participants (ages 18-89). Participants were clustered by their ‘thought-patterns’ via machine learning, and groups were further examined with respect to multiple traits (using Bayesian ANOVA); correlations between thought characteristics; and natural language descriptors. One, PMH-relevant, trait that groups differed on was psychological well-being. Although some groups showed similar well-being, their thought patterns differed in unique ways – implying multiple paths toward a common endpoint. It is proposed that knowledge of these individual differences can help identify personalized needs in mental healthcare and bolster the efficacy of PMH.