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

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Title: Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning.
Authors: Burgermaster, Marissa (AUTHOR), Rodriguez, Victor A (AUTHOR)
Source: Annals of Behavioral Medicine. Dec2022, Vol. 56 Issue 12, p1258-1271. 14p.
Subjects: Social determinants of health, Machine learning, Bayesian field theory, Behavioral assessment, Psychological factors, Variability (Psychometrics)
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
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Abstract:<bold>Background: </bold>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.<bold>Purpose: </bold>To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics.<bold>Methods: </bold>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).<bold>Results: </bold>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.<bold>Conclusions: </bold>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. [ABSTRACT FROM AUTHOR]
ISSN:08836612
DOI:10.1093/abm/kaac012