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 intak

SUBMITTER: Ross KM 

PROVIDER: S-EPMC10872625 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature