Banda Jorge A, Haydel K Farish, Davila Tania, Desai Manisha, Bryson Susan, Haskell William L, Matheson Donna, Robinson Thomas N
Stanford Solutions Science Lab, Department of Pediatrics and Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, United States of America.
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS One. 2016 Mar 3;11(3):e0150534. doi: 10.1371/journal.pone.0150534. eCollection 2016.
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).
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.
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).
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.
研究加速度计的时段长度、佩戴时间(WT)算法以及活动切点对WT、久坐行为(SB)和身体活动(PA)估计值的影响。
268名年龄和性别BMI≥第85百分位数的7至11岁儿童在右髋部佩戴加速度计4至7天。数据在1秒、5秒、10秒、15秒、30秒和60秒的时段长度下进行处理和分析。对于每个时段长度,使用三种常见的WT算法确定每天的WT分钟数,并使用五个常见的活动切点确定每天的分钟数以及在SB、轻度(LPA)、中度(MPA)和剧烈(VPA)PA中花费的时间百分比。方差分析测试了使用不同的时段长度、WT算法和活动切点时WT、SB、LPA、MPA、VPA和MVPA的差异。
使用美国国家健康与营养检查调查(NHANES)的WT算法时,每天的WT分钟数因时段长度而有显著差异(p <.0001),但使用≥20分钟连续零算法或Choi WT算法时,每天的WT分钟数不因时段长度而有显著差异。对于所有用三种WT算法测试的活动切点组,每天的分钟数以及在SB、LPA、MPA、VPA和MVPA中花费的时间百分比因时段长度而有显著差异(所有p <.0001)。在所有时段长度中,使用三种WT算法时,每天的分钟数以及在SB、LPA、MPA、VPA和MVPA中花费的时间百分比在所有活动切点组中也有显著差异(所有p <.0001)。
将WT算法和活动切点定义转换以匹配不同时段长度的常见做法可能会引入重大误差。使用不同的时段长度、WT算法和/或活动切点处理和分析数据的研究中,SB和PA的估计值不可比,这可能导致非常不同的结果、解释和结论,误导研究和公共政策。