{"doi":"10.1002/oby.23923","title":"Predicting high‐risk periods for weight regain following initial weight loss","abstract":"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.","journal":"Obesity","year":2023,"id":382677,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9495,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":787689,"name":"Lu You","orcid":"0000-0002-9400-2060","position":1,"is_corresponding":false},{"id":400011,"name":"Peihua Qiu","orcid":"0000-0003-4439-9466","position":2,"is_corresponding":false},{"id":1149153,"name":"Meena N. Shankar","orcid":"0009-0005-3792-966X","position":3,"is_corresponding":false},{"id":422623,"name":"Taylor N. Swanson","orcid":"0000-0001-7161-4980","position":4,"is_corresponding":false},{"id":805738,"name":"Jaime Ruiz","orcid":"0000-0002-9139-6172","position":5,"is_corresponding":false},{"id":805737,"name":"Lisa Anthony","orcid":"0000-0002-9617-2952","position":6,"is_corresponding":false},{"id":761810,"name":"Michael G. Perri","orcid":"0000-0003-3651-1542","position":7,"is_corresponding":false},{"id":379210,"name":"Kathryn M. Ross","orcid":"0000-0002-3628-766X","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T01:17:20.969491Z","pmid":"37919882","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}