Identifying ActiGraph non-wear time in pregnant women with overweight or obesity

Abstract

Objectives: Non-wear time algorithms have not been validated in pregnant women with overweight/obesity (PW-OW/OB), potentially leading to misclassification of sedentary/activity data, and inaccurate estimates of how physical activity is associated with pregnancy outcomes. We examined: (1) validity/reliability of non-wear time algorithms in PW-OW/OB by comparing wear time from five algorithms to a self-report criterion and (2) whether these algorithms over- or underestimated sedentary behaviors.

Design: PW-OW/OB (N = 19) from the Healthy Mom Zone randomized controlled trial wore an ActiGraph GT3x + for 7 consecutive days between 8-12 weeks gestation.

Methods: Non-wear algorithms (i.e., consecutive strings of zero acceleration in 60-second epochs) were tested at 60, 90, 120, 150, and 180-min. The monitor registered sedentary minutes as activity counts 0-99. Women completed daily self-report logs to report wear time.

Results: Intraclass correlation coefficients for each algorithm were 0.96-0.97; Bland-Altman plots revealed no bias; mean absolute percent errors were <10%. Compared to self-report (M = 829.5, SD = 62.1), equivalency testing revealed algorithm wear times (min/day) were equivalent: 60- (M = 816.4, SD = 58.4), 90- (M = 827.5, SD = 61.4), 120- (M = 830.8, SD = 65.2), 150- (M = 833.8, SD = 64.6) and 180-min (M = 837.4, SD = 65.4). Repeated measures ANOVA showed 60- and 90-min algorithms may underestimate sedentary minutes compared to 150- and 180-min algorithms.

Conclusions: The 60, 90, 120, 150, and 180-min algorithms are valid and reliable for estimating wear time in PW-OW/OB. However, implementing algorithms with a higher threshold for consecutive zero counts (i.e., ≥150-min) can avoid the risk of misclassifying sedentary data.

Keywords: Accelerometer; Activity monitor; Non-wear algorithm; Physical activity; Pregnancy.

Conflict of interest statement

Declarations of Interest: None

Copyright © 2020 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Figures

Figure 1.

Bland-Altman plot between self-report (criterion) and ActiGraph algorithms for estimates of wear time (minutes). Note. Solid black line = mean of the difference between the methods; red dashed lines = upper and lower 95% confidence intervals of the mean difference. Diff = difference; alg = algorithm; SR = self-report.

Figure 1.

Bland-Altman plot between self-report (criterion) and ActiGraph algorithms for estimates of wear time (minutes). Note. Solid black line = mean of the difference between the methods; red dashed lines = upper and lower 95% confidence intervals of the mean difference. Diff = difference; alg = algorithm; SR = self-report.

Figure 2.

Equivalency testing for agreement in wear time between self-report and non-wear time algorithms. Note. Gray area indicates proposed equivalency zone (±10% of the average self-report wear time); solid black lines indicate 90% confidence intervals for estimated wear time from non-wear time algorithms.