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

被引:4
作者
Bizhanova, Zhadyra [1 ,3 ]
Sereika, Susan M. [1 ,2 ]
Brooks, Maria M. [1 ]
Rockette-wagner, Bonny [1 ]
Kariuki, Jacob K. [2 ]
Burke, Lora E. [1 ,2 ]
机构
[1] Univ Pittsburgh, Dept Epidemiol, Sch Publ Hlth, Pittsburgh, PA USA
[2] Univ Pittsburgh, Dept Hlth & Community Syst, Sch Nursing, Pittsburgh, PA USA
[3] 4 Bayard Rd,Apt 66, Pittsburgh, PA 15213 USA
基金
美国国家卫生研究院;
关键词
GOAL ATTAINMENT; ENGAGEMENT; GUIDELINES; WEARABLE ACTIVITY TRACKERS; FITBIT; MACHINE LEARNING METHODS; ACTIVITY BEHAVIOR; SELF-EFFICACY; TASK-FORCE; ADULTS; OBESITY; INTERVENTIONS; ASSOCIATION; OVERWEIGHT; PROGRAM; RISK;
D O I
10.1249/MSS.0000000000003114
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
Introduction/PurposeResearch 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.MethodsA 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 & GE;150 min & BULL;wk(-1) of moderate- and vigorous-intensity aerobic activities (MVPA) from Fitbit Charge 2 & TRADE; 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.ResultsThe sample (N = 502) was mostly female (80%), White (82%) with the average age of 45 & PLUSMN; 14.4 yr and body mass index of 33.7 & PLUSMN; 4.0 kg & BULL;m(-2). 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.ConclusionsTo 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.
引用
收藏
页码:856 / 864
页数:9
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