Using Machine Learning to Identify Predictors of Heterogeneous Intervention Effects in Childhood Obesity Prevention

Obesity prevention interventions in children often produce small or null effects. However, ignoring heterogeneous responses may widen pre-existing inequalities. This secondary analysis explored baseline predictors of differential effects on BMI z-score, Fat mass (%), stress, and sleep outcomes in obesity-susceptible, healthy-weight children (n = 543). A modified LASSO regression was applied to baseline characteristics, including physical activity and socio-demographics. Few predictors were retained. For BMI z-score, weekly chores and parental divorce were the strongest predictors: children who did chores had a slightly larger increase in BMI z-score in the intervention group compared with controls (MD = 0.15, 95% CI: −0.03, 0.33), while children with divorced parents showed a smaller increase (MD = −0.19, 95% CI: −0.69, 0.31). These results align with evidence that low-intensity activity has limited impact on obesity outcomes and that children with compounded vulnerability may respond differently to tailored interventions. Even when overall effects are small, machine learning approaches can identify potential predictors of heterogeneous intervention effects, supporting the design of future targeted interventions aimed at reducing inequalities.