Novel ranking of protective and risk factors for adolescent adiposity in US females

Abstract

Objective: Rank the importance of potentially modifiable psychosocial, dietary and environmental risk and protective factors for female adolescent obesity in order to target and inform public health prevention efforts. Utilizing the largest dataset available that captures the onset of the adolescent obesity surge in the USA, the study provides a more robust understanding of paediatric obesity risk factors.

Methods: Data were obtained from an observational, longitudinal study conducted between 1989 and 2001, the NHLBI Growth and Health Study. This study includes girls aged 9-19 years from three urban US locations, with Black and White girls generally represented equally. Data were analysed using multiple regression, random forest and propensity score matching to determine the strongest adiposity risk and protective factors during ages 9-12 predicting adiposity at age 19 with multiple methods to maximize the ability to identify possible public health interventions. Multiple linear regression and random forest analysis identified the strongest associations among 288 risk and protective factors selected from the study's literature review. For the 190 factors associated with follow-up adiposity from the data, propensity score matching was used to control for confounding of these factors.

Results: Findings suggest that highest priority interventional targets across the domains surveyed are lowering specific nutrients; eating meals with others or during activities without skipping; parents fixing evening snacks; improving perceptions of non-extremes as the healthy weight; improving self-worth, physical activity and social competence; and limiting any negative impact of dieting relatives. Similar associations were observed for Black and White girls.

Conclusion: The clinical implications of these findings allow health practitioners to target behavioural change efforts and address social and environmental factors that have demonstrated higher prioritization value for early obesity interventional efforts for adolescents.

Keywords: Adiposity; Machine learning; Obesity; Predictive analytics.

Figure 1

Random forest variable importance of adiposity predictors

Figure 2

Population attributable risk plot – stratified by variable category