ArticleAnnals of behavioral medicine : a publication of the Society of Behavioral Medicine2022
Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning.
Article in Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Precision nutrition-based interventions for the management of obesity in children and adolescents up to the age of 19 years.The Cochrane database of systematic reviews · 2025Pooled it
- Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support.Scientific reports · 2026Article
- Developing psychosocial phenotypes to understand engagement with digital health technologies for heart failure.NPJ digital medicine · 2026Article
- Biopsychosocial profiles following ACL reconstruction: A latent class analysis of barriers to return-to-activity.Physical therapy in sport : official journal of the Association of Chartered Physiotherapists in Sports Medicine · 2025Article
- Modeling Patients' Progression through Health-Related Social Needs.Applied clinical informatics · 2025Article
- Performance of 4 Methods to Assess Health-Related Social Needs.JAMA network open · 2025Article
- Segmenting Preventive Health Behavior: Gender Disparities and Psychosocial Predictors in a Culturally Diverse Italian Region.European journal of investigation in health, psychology and education · 2025Article
- Development and validation of computable social phenotypes for health-related social needs.JAMIA open · 2025Article
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- Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework.Frontiers in public health · 2024Article
- Precision prevention in worksite health-A scoping review on research trends and gaps.PloS one · 2024Article
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- Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods.Applied clinical informatics · 2023Article
- A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda.Journal of the Indian Institute of Science · 2023Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
methodsIn 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).
resultsWe 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.
conclusionsBy 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.