Table 2.

Case Scenarios for Deriving Psychosocial Phenotypes from Selected Meta Analyses

Analytic Approach	Data Source	Use Case Scenario	Pros	Cons
NLP-based algorithm on EHR records	a) EHR, n = 316,355 documents	Terms related to homelessness phenotype that provided direct evidence of actual (e.g. sleeping in park), at risk (e.g. doubled up) or needs (e.g. needs socks) related to homelessness was extracted from the EHR	Sheer volume and variety of available data through EHRs	i) Potential inaccuracies in data quality and accuracy
a) Gundlapalli (2013)				ii) Static data that fails to capture of the dynamic evolution of health status of an individual temporally
Meta-Analysis	a) 31 studies, n = 519,971	a) Phenotype for medication non-adherence included younger age, higher number of concurrent medications, orthopedic practitioner specialty and higher co-payment	i) Marked increases in statistical power;	i) Restriction of variables to those that were measured by instruments in the included studies, but fail to capture unmeasured covariates that can act of potential confounders
a) Cheen (2019): Prevalence and factors contributing to medication non-adherence	b) 759 studies, n = 533,445	b) Phenotype for improved glucose control included self-efficacy, coping, dietary adherence and medication adherence	ii) Greater heterogeneity in subject demographics;	ii) Pooling of data may introduce uncertainty due to potential sampling errors or unmeasured covariates (Lemstra, et al., 2016)
b) Brown (2016): Predictors of diabetes outcomes			iii) Ppportunity to test hypotheses not considered in the original studies; and	iii) Failure to capture temporal influence of psychosocial factors due to static data measurement. (Lueng, et al., 2015)
			iv) Increased efficiency in both time and money incorporating the vast historical information already available
Population-level rich questionnaires	a) RECORD questionnaire with 6460 participants aged 30-79 years living in the Paris region between 2011 and 2014	i) Phenotype for adverse weight profile - negative body image, underestimation of the impact of weight in quality of life, low weight-related self-efficacy, and weight-related external locus of control;	i) Combination of multiple dimensions of socioeconomic characteristics in current socioeconomic status, economic status in childhood, and education status in the residential neighborhood allowed to assess the overall impact of a family of psychosocial mechanisms on obesity simultaneously	i) Eligibility to complete the survey was restricted to employed individuals which could have excluded more socio-economically deprived individuals
a) Fuentes (2020)		ii) Phenotype for favorable weight profile-positive body image, high self-efficacy, and internal locus of control		ii) Recall bias of participants to answer life-course related questions
				iii) Large proportion of observations with missing values (32%).
Qualitative	a) Interviews with 18 students participating in a school-based behavior change interventions	i) Activated psychosocial phenotype - successful behavior-changers with strong internal supports	Rich, and nuanced details that identify psychosocial characteristics of varying responses to behavioral interventions	Timing of the interviews precludes prospective identification of psychosocial phenotypes to assess their influence on intervention results
a) Burgermaster (2018)		ii) Indifferent psychosocial phenotype - uninterested in behavior change and only did target behaviors if family insisted