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Background The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors. Purpose To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics. Methods In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2). Results We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design. Conclusions By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

annals of behavioral medicine

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning