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Identifying and validating the earliest acceptable prediction window for 14‑week weight loss outcome: a nationwide lifestyle intervention
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DOI：10.1186/s13690-026-02037-4.png)
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Abstract
En 中文
Early identification of individuals at high risk of weight loss failure may support risk stratification and timely intervention adjustment. However, evidence remains limited regarding the earliest time window at which weight loss failure can be predicted with acceptable performance in real-world walking interventions. Data from participants in the 2025 10,000 Steps Challenge were randomly split into training and test sets (7:3). Using landmark datasets from weeks 2–7, we identified the earliest prediction window achieving prespecified acceptable discrimination (AUC > 0.75). We then compared five prediction models and evaluated discrimination, calibration, and the incremental value of baseline covariates. A total of 12,773 participants were included, of whom 23.14% achieved weight loss ≥ 5% at 14 weeks. In the landmark analysis, the area under the curve increased from 0.646 at week 2 to 0.832 at week 7. Week 4 was the earliest landmark window achieving the prespecified acceptable discrimination (AUC = 0.752), after which predictive performance continued to improve with progressively smaller gains. All five models developed using week 4 data showed good discrimination and calibration in test set, with Brier scores ranging from 0.150 to 0.156, calibration intercepts close to 0, calibration slopes close to 1, and no statistically significant differences in AUC between models. Adding baseline covariates to percentage weight change at week 4 increased the AUC by only 0.006. Sensitivity analyses yielded consistent results. Week 4 was the earliest acceptable time window for predicting the risk of weight loss failure at 14 weeks, serving as a practical checkpoint to identify non‑responders and inform targeted intervention while sufficient time remains.
Keywords:
Weight loss
Early weight change
Prediction window
Lifestyle intervention
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