Identifying Predictors of Adherence to the Physical Activity Goal: A Secondary Analysis of the SMARTER Weight Loss Trial

Abstract

Introduction/Purpose: Research is needed to inform tailoring supportive strategies for promoting physical activity (PA) in the context of behavioral treatment of obesity. We aimed to identify baseline participant characteristics and short-term intervention response predictors associated with adherence to the study-defined PA goal in a mobile health (mHealth) weight loss trial. Methods: A secondary analysis was conducted of a 12-month weight loss trial (SMARTER) that randomized 502 adults with overweight or obesity to either self-monitoring of diet, PA, and weight with tailored feedback messages (n = 251) or self-monitoring alone (n = 251). The primary outcome was average adherence to the PA goal of ≥150 min•wk −1 of moderate-and vigorous-intensity aerobic activities (MVPA) from Fitbit Charge 2™ trackers over 52 wk. Twenty-five explanatory variables were considered. Machine learning methods and linear regression were used to identify predictors of adherence to the PA goal. Results: The sample (N = 502) was mostly female (80%), White (82%) with the average age of 45 ± 14.4 yr and body mass index of 33.7 ± 4.0 kg•m À2 . Machine learning methods identified PA goal adherence for the first week as the most important predictor of long-term PA goal adherence. In the parsimonious linear regression model, higher PA goal adherence for the first week, greater PA FB messages opened, older age, being male, higher education, being single and not having obstructive sleep apnea were associated with higher long-term PA goal adherence. Conclusions: To our knowledge, this is the first study using machine learning approaches to identify predictors of long-term PA goal adherence in a mHealth weight loss trial. Future studies focusing on facilitators or barriers to PA among young and middle-age adults and women with low PA goal adherence are warranted.

A Narrative Review of Non-Pharmacological Strategies for Managing Sarcopenia in Older Adults with Cardiovascular and Metabolic Diseases

“…To achieve long-term adherence, incorporating technology is increasingly recommended; in mHealth and eHealth, for example, personalised apps for tablets have proven effective in promoting adherence to PA and diet interventions in older adults [259,269]. Furthermore, an mHealth randomised controlled trial used FitBit trackers to predict 12-month PA engagement and found that the best predictor of engagement was adherence in week one, which was achieved by 66.5% of the participants [270]. Notwithstanding a sample dominated by programme-ready, overweight-obese females (80%), replicating these findings would support reframing the powerful message of 'every contact counts' into 'first week contacts count most'.…”