Observational studyInternational journal of obesity (2005)2025
Uncovering key factors in weight loss effectiveness through machine learning.
Observational study in International journal of obesity (2005), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02829619 (Meal Timing, Genetics and Weight Loss in a Mediterranean Population), which is not on this map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Meal Timing, Genetics and Weight Loss in a Mediterranean Population
Who cites it
1 citing paper in PubMed.
- Key Determinants of Weight Loss Trajectory Across Different Periods of a Behavioral Weight Loss Intervention.Obesity (Silver Spring, Md.) · 2026Trial
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesOne 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). SUBJECTS/
methodsWe 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.
resultsThe 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.
conclusionsMachine 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.
trial registrationclinicaltrials.gov: NCT02829619.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.