Explaining Body Composition After Bariatric Surgery using Accelerometry

Obesity, a complex condition involving genetic, behavioral, socioeconomic, and environmental factors, poses significant health risks and contributes to increased morbidity and mortality. Bariatric surgery is an effective treatment for individuals with severe obesity, resulting in substantial weight loss. However, weight regain remains a significant challenge in the long-term after surgery. This study focuses on analyzing movement patterns of patients who have undergone bariatric surgery using wearable accelerometry to investigate the relationships between movement behaviors, body composition, and weight regain. An intelligent system employing machine learning techniques was utilized to predict total fat percentage and visceral fat. Results indicate that Multivariate Adaptive Regression Splines and Gradient Boosting models show promising performance in predicting fat percentage and visceral fat. Furthermore, the study reveals associations between age, sedentary behavior, post-surgery BMI, day-and night-time movement, and body composition following bariatric surgery. These findings contribute to a better understanding of factors influencing weight regain and may inform future interventions to promote long-term weight loss maintenance in bariatric surgery patients.