Objective The aim of this study was to develop a predictive algorithm of “high-risk” periods for weight regain after weight loss. Methods Longitudinal mixed-effects models and random forest regression were used to select predictors and develop an algorithm to predict weight regain on a week-to-week basis, using weekly questionnaire and self-monitoring data (including daily e-scale data) collected over 40 weeks from 46 adults who lost ≥5% of baseline weight during an initial 12-week intervention (Study 1). The algorithm was evaluated in 22 adults who completed the same Study 1 intervention but lost <5% of baseline weight and in 30 adults recruited for a separate 30-week study (Study 2). Results The final algorithm retained the frequency of self-monitoring caloric intake and weight plus self-report ratings of hunger and the importance of weight-management goals compared with competing life demands. In the initial training data set, the algorithm predicted weight regain the following week with a sensitivity of 75.6% and a specificity of 45.8%; performance was similar (sensitivity: 81%–82%, specificity: 30%–33%) in testing data sets. Conclusions Weight regain can be predicted on a proximal, week-to-week level. Future work should investigate the clinical utility of adaptive interventions for weight-loss maintenance and develop more sophisticated predictive models of weight regain.

    Kathryn M. Ross, Lu You, Peihua Qiu, Meena N. Shankar, Taylor N. Swanson, Jaime Ruiz, Lisa Anthony, and Michael G. Perri. Predicting high-risk periods for weight regain following initial weight loss. Obesity 32, 1: 41-49. https://doi.org/10.1002/oby.23923