TY - JOUR
T1 - Self-efficacy and physical activity in overweight and obese adults participating in a worksite weight loss intervention
T2 - Multistate modeling of wearable device data
AU - Robertson, Michael C.
AU - Green, Charles E.
AU - Liao, Yue
AU - Durand, Casey P.
AU - Basen-Engquist, Karen M.
N1 - Funding Information:
M.C. Robertson was supported by the NCI of the NIH under award number F31 CA236433. Y. Liao was supported by a faculty fellowship from the Duncan Family Institute for Cancer Prevention and Risk Assessment at The University of TexasMD Anderson Cancer Center. This work was supported by the NIH through MD Anderson's Cancer Center Support Grant (NCI grant P30 CA016672), Assessment, Intervention and Measurement (AIM) Shared Resource. This research was made possible by the Center for Energy Balance in Cancer Prevention and Survivorship, and the Duncan Family Institute for Cancer Prevention and Risk Assessment at The University of Texas MD Anderson Cancer Center. Vibrant Lives is supported by the Pasadena Vibrant Community. The Pasadena Vibrant Community is an initiative of The University of Texas MD Anderson Cancer Center made possible by an investment from and collaboration with Shell Oil Company.
Funding Information:
M.C. Robertson was supported by the NCI of the NIH under award number F31 CA236433. Y. Liao was supported by a faculty fellowship from the Duncan Family Institute for Cancer Prevention and Risk Assessment at The University of Texas MD Anderson Cancer Center. This work was supported by the NIH through MD Anderson's Cancer Center Support Grant (NCI grant P30 CA016672), Assessment, Intervention and Measurement (AIM) Shared Resource. This research was made possible by the Center for Energy Balance in Cancer Prevention and Survivorship, and the Duncan Family Institute for Cancer Prevention and Risk Assessment at The University of Texas MD Anderson Cancer Center. Vibrant Lives is supported by the Pasadena Vibrant Community. The Pasadena Vibrant Community is an initiative of The University of Texas MD Anderson Cancer Center made possible by an investment from and collaboration with Shell Oil Company.
Publisher Copyright:
© 2020 American Association for Cancer Research.
PY - 2020
Y1 - 2020
N2 - Background: Physical activity is associated with a reduced risk of numerous types of cancer and plays an important role in maintaining a healthy weight. Wearable physical activity trackers may supplement behavioral intervention and enable researchers to study how determinants like self-efficacy predict physical activity patterns over time. Methods: We used multistate models to evaluate how selfefficacy predicted physical activity states among overweight and obese individuals participating in a 26-week weight loss program (N = 96). We specified five states to capture physical activity patterns: (i) active (i.e., meeting recommendations for 2 weeks), (ii) insufficiently active, (iii) nonvalid wear, (iv) favorable transition (i.e., improvement in physical activity over 2 weeks), and (v) unfavorable transition.We calculated HRs of transition probabilities by self-efficacy, body mass index, age, and time. Results: The average prevalence of individuals in the active, insufficiently active, and nonvalid wear states was 13%, 44%, and 16%, respectively. Low self-efficacy negatively predicted entering an active state [HR, 0.51; 95% confidence interval (CI), 0.29- 0.88]. Obesity negatively predicted making a favorable transition out of an insufficiently active state (HR, 0.61; 95% CI, 0.40-0.91). Older participants were less likely to transition to the nonvalid wear state (HR, 0.53; 95% CI, 0.30-0.93). Device nonwear increased in the second half of the intervention (HR, 1.73; 95% CI, 1.07-2.81). Conclusions: Self-efficacy is an important predictor for clinically relevant physical activity change in overweight and obese individuals. Multistate modeling is useful for analyzing longitudinal physical activity data. Impact: Multistate modeling can be used for statistical inference of covariates and allow for explicit modeling of nonvalid wear.
AB - Background: Physical activity is associated with a reduced risk of numerous types of cancer and plays an important role in maintaining a healthy weight. Wearable physical activity trackers may supplement behavioral intervention and enable researchers to study how determinants like self-efficacy predict physical activity patterns over time. Methods: We used multistate models to evaluate how selfefficacy predicted physical activity states among overweight and obese individuals participating in a 26-week weight loss program (N = 96). We specified five states to capture physical activity patterns: (i) active (i.e., meeting recommendations for 2 weeks), (ii) insufficiently active, (iii) nonvalid wear, (iv) favorable transition (i.e., improvement in physical activity over 2 weeks), and (v) unfavorable transition.We calculated HRs of transition probabilities by self-efficacy, body mass index, age, and time. Results: The average prevalence of individuals in the active, insufficiently active, and nonvalid wear states was 13%, 44%, and 16%, respectively. Low self-efficacy negatively predicted entering an active state [HR, 0.51; 95% confidence interval (CI), 0.29- 0.88]. Obesity negatively predicted making a favorable transition out of an insufficiently active state (HR, 0.61; 95% CI, 0.40-0.91). Older participants were less likely to transition to the nonvalid wear state (HR, 0.53; 95% CI, 0.30-0.93). Device nonwear increased in the second half of the intervention (HR, 1.73; 95% CI, 1.07-2.81). Conclusions: Self-efficacy is an important predictor for clinically relevant physical activity change in overweight and obese individuals. Multistate modeling is useful for analyzing longitudinal physical activity data. Impact: Multistate modeling can be used for statistical inference of covariates and allow for explicit modeling of nonvalid wear.
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U2 - 10.1158/1055-9965.EPI-19-0907
DO - 10.1158/1055-9965.EPI-19-0907
M3 - Article
C2 - 31871110
AN - SCOPUS:85082754354
SN - 1055-9965
VL - 29
SP - 769
EP - 776
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
IS - 4
ER -