Trial reportThe American journal of clinical nutrition2015
Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study).
Trial report in The American journal of clinical nutrition, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Weight Loss after Left Gastric Artery Embolization: A Systematic Review and Meta-Analysis.Journal of vascular and interventional radiology : JVIR · 2019Pooled it
- Baseline characteristics associated with different BMI trajectories in weight loss trials: a case for better targeting of interventions.European journal of clinical nutrition · 2016Pooled it
- Sleep Disturbance and Changes in Energy Intake and Body Composition During Weight Loss in the POUNDS Lost Trial.Diabetes · 2022Trial
- Trial
- Changes in Gut Microbiota-Related Metabolites and Long-term Successful Weight Loss in Response to Weight-Loss Diets: The POUNDS Lost Trial.Diabetes care · 2018Trial
- Efficacy of SmartLoss, a smartphone-based weight loss intervention: results from a randomized controlled trial.Obesity (Silver Spring, Md.) · 2015Trial
- Machine learning algorithms for predicting glycemic control and weight loss outcomes in GLP-1 receptor agonist users.Frontiers in artificial intelligence · 2026Article
- A risk prediction model for medication safety: assessing cardiopulmonary adverse outcomes in 11,252 patients treated with ulinastatin.Frontiers in pharmacology · 2026Article
- Exploring Gender Differences in the Effects of Diet and Physical Activity on Metabolic Parameters.Nutrients · 2025Observational
- Review
- Development and validation of a weight-loss predictor to assist weight loss management.Scientific reports · 2023Article
- Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.The Journal of surgical research · 2023Article
- Lifestyle treatment in women with polycystic ovary syndrome: predictors of weight loss and dropout.Brain and behavior · 2022Article
- The Behavioral Intervention with Technology for E-Weight Loss Study (BITES): Incorporating Energy Balance Models and the Bite Counter into an Online Behavioral Weight Loss Program.Journal of technology in behavioral science · 2021Article
- Genetic variants for personalised management of very low carbohydrate ketogenic diets.BMJ nutrition, prevention & health · 2020Review
- Energy Balance as a Moderator of Neurologic Disease Risk and Progression.Neurotoxicity research · 2020Article
- Lessons Learned from the POUNDS Lost Study: Genetic, Metabolic, and Behavioral Factors Affecting Changes in Body Weight, Body Composition, and Cardiometabolic Risk.Current obesity reports · 2019Review
- Predictive Mathematical Models of Weight Loss.Current diabetes reports · 2019Review
- Resistance Training Reduces Skeletal Muscle Work Efficiency in Weight-Reduced and Non-Weight-Reduced Subjects.Obesity (Silver Spring, Md.) · 2018Article
- A review of machine learning in obesity.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2018Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundCurrently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds.
objectiveThe objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1.
designData consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve's capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated.
resultsOptimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50).
conclusionsThe newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention.
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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 OpenQuestion graph.