Uncovering key factors in weight loss effectiveness through machine learning

• Published in: International Journal of Obesity Vol. 49; no. 6; pp. 1189 - 1199
• Main Authors: Yang, Hui-Wen, De la Peña-Armada, Rocío, Sun, Haoqi, Peng, Yu-Qi, Lo, Men-Tzung, Scheer, Frank A. J. L., Hu, Kun, Garaulet, Marta
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.06.2025
• Subjects: Adult; Body Mass Index; Body weight; Body weight loss; Boosting Machine Learning Algorithms; Cognitive Behavioral Therapy - methods; Diet; Dietary intake; Eating; Eating behavior; Effectiveness; Exercise; Female; Food intake; Humans; Learning algorithms; Machine Learning; Male; Middle Aged; Monitoring; Motivation; Obesity - psychology; Obesity - therapy; Physical activity; Treatment Outcome; Weight control; Weight loss; Weight Loss - physiology; Weight Reduction Programs
• ISSN: 0307-0565; 1476-5497; 1476-5497
• DOI: 10.1038/s41366-025-01766-w

One of the main challenges in weight loss is the dramatic interindividual variability in response to treatment. We aim to systematically identify factors relevant to weight loss effectiveness using machine learning (ML). We studied 1810 participants in the ONTIME program, which is based on cognitive-behavioral therapy for obesity (CBT-OB). We assessed 138 variables representing participants' characteristics, clinical history, metabolic status, dietary intake, physical activity, sleep habits, chronotype, emotional eating, and social and environmental barriers to losing weight. We used XGBoost (extreme gradient boosting) to predict treatment response and SHAP (SHapley Additive exPlanations) to identify the most relevant factors for weight loss effectiveness. The total weight loss was 8.45% of the initial weight, the rate of weight loss was 543 g/wk., and attrition was 33%. Treatment duration (mean ± SD: 14.33 ± 8.61 weeks) and initial BMI (28.9 ± 3.33) were crucial factors for all three outcomes. The lack of motivation emerged as the most significant barrier to total weight loss and also influenced the rate of weight loss and attrition. Participants who maintained their motivation lost 1.4% more of their initial body weight than those who lost motivation during treatment (P < 0.0001). The second and third critical factors for decreased total weight loss were lower "self-monitoring" and "eating habits during treatment" (particularly higher snacking). Higher physical activity was a key variable for the greater rate of weight loss. Machine learning analysis revealed key modifiable lifestyle factors during treatment, highlighting avenues for targeted interventions in future weight loss programs. Specifically, interventions should prioritize strategies to sustain motivation, address snacking behaviors, and enhance self-monitoring techniques. Further research is warranted to evaluate the efficacy of these strategies in improving weight loss outcomes. clinicaltrials.gov: NCT02829619.