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PLoS One. 2016 Mar 3;11(3):e0150534. doi: 10.1371/journal.pone.0150534. eCollection 2016.

Effects of Varying Epoch Lengths, Wear Time Algorithms, and Activity Cut-Points on Estimates of Child Sedentary Behavior and Physical Activity from Accelerometer Data.

Author information

1
Stanford Solutions Science Lab, Department of Pediatrics and Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, United States of America.
2
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America.
3
Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California, United States of America.
4
Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Palo Alto, California, United States of America.

Abstract

OBJECTIVE:

To examine the effects of accelerometer epoch lengths, wear time (WT) algorithms, and activity cut-points on estimates of WT, sedentary behavior (SB), and physical activity (PA).

METHODS:

268 7-11 year-olds with BMI ≥ 85th percentile for age and sex wore accelerometers on their right hips for 4-7 days. Data were processed and analyzed at epoch lengths of 1-, 5-, 10-, 15-, 30-, and 60-seconds. For each epoch length, WT minutes/day was determined using three common WT algorithms, and minutes/day and percent time spent in SB, light (LPA), moderate (MPA), and vigorous (VPA) PA were determined using five common activity cut-points. ANOVA tested differences in WT, SB, LPA, MPA, VPA, and MVPA when using the different epoch lengths, WT algorithms, and activity cut-points.

RESULTS:

WT minutes/day varied significantly by epoch length when using the NHANES WT algorithm (p < .0001), but did not vary significantly by epoch length when using the ≥ 20 minute consecutive zero or Choi WT algorithms. Minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA varied significantly by epoch length for all sets of activity cut-points tested with all three WT algorithms (all p < .0001). Across all epoch lengths, minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA also varied significantly across all sets of activity cut-points with all three WT algorithms (all p < .0001).

CONCLUSIONS:

The common practice of converting WT algorithms and activity cut-point definitions to match different epoch lengths may introduce significant errors. Estimates of SB and PA from studies that process and analyze data using different epoch lengths, WT algorithms, and/or activity cut-points are not comparable, potentially leading to very different results, interpretations, and conclusions, misleading research and public policy.

PMID:
26938240
PMCID:
PMC4777377
DOI:
10.1371/journal.pone.0150534
[Indexed for MEDLINE]
Free PMC Article

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