Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks.

ABSTRACT: Obesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesity prevention to improve health. Leveraging baseline data from a unique clinical cohort comprising 333 postmenopausal overweight or obese breast cancer survivors participating in a weight-loss trial, we applied Bayesian networks, a machine learning approach, to infer interrelationships between lifestyle factors (e.g., sleep, physical activity), body mass index (BMI), and health outcomes (biomarkers and self-reported quality of life metrics). We used bootstrap resampling to assess network stabili

SUBMITTER: Xu S 

PROVIDER: S-EPMC6122792 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature