In:
Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 29, No. 4 ( 2020-04-01), p. 769-776
Abstract:
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 self-efficacy 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. See all articles in this CEBP Focus section, “Modernizing Population Science.”
Type of Medium:
Online Resource
ISSN:
1055-9965
,
1538-7755
DOI:
10.1158/1055-9965.EPI-19-0907
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2020
detail.hit.zdb_id:
2036781-8
detail.hit.zdb_id:
1153420-5
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