ArticleObesity (Silver Spring, Md.)2024
Predicting high-risk periods for weight regain following initial weight loss.
Article in Obesity (Silver Spring, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Adaptive vs Monthly Support for Weight-Loss Maintenance: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Design of Project STAR: A randomized controlled trial evaluating the impact of an adaptive intervention on long-term weight-loss maintenance.Contemporary clinical trials · 2024Article
- MyTrack+: Human-centered design of an mHealth app to support long-term weight loss maintenance.Frontiers in digital health · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe aim of this study was to develop a predictive algorithm of "high-risk" periods for weight regain after weight loss.
methodsLongitudinal 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).
resultsThe 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.
conclusionsWeight 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.
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