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

Year: 2025

Type: article

Abstract: 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.

Source: Data

DOI: 10.3390/data10120196

Authors: Elizabeth Mannion, Kristine Bihrmann, Nanna Julie Olsen, Berit Lilienthal Heitmann, Christian Ritz

Institutions: University of Southern Denmark, Frederiksberg Hospital, Bispebjerg Hospital, University of Copenhagen

Language: English

Topic: Obesity, Physical Activity, Diet

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,930