Quantitative Science Unit, Stanford University, Stanford, CA, United States of America.
Stanford Solutions Science Lab, Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Stanford University, Stanford, CA, United States of America.
PLoS One. 2018 Dec 31;13(12):e0210006. doi: 10.1371/journal.pone.0210006. eCollection 2018.
The National Cancer Institute's (NCI) wear time classification algorithm uses a rule based on the occurrence of physical activity data counts-a cumulative measure of movement, influenced by both magnitude and duration of acceleration-to differentiate between when a physical activity monitoring (PAM) device (ActiGraph accelerometer) is being worn by a participant (wear) from when it is not (nonwear). It was applied to PAM data generated from the 2003-2004 National Health and Nutrition Examination Survey (NHANES 2003-2004). We discuss two corner case conditions that can produce unexpected, and perhaps unintended results when the algorithm is applied. We show, using simulated data of two special cases, how this algorithm classifies a 24-hour period with only 72 total counts as 100% wear in one case, and classifies a 24-hour period with 96,000 counts as 0.1% wear in another. The prevalence of like scenarios in the NHANES 2003-2004 PAM dataset is presented with corresponding summary statistics for varying degrees of the algorithm's nonwear classification threshold (T). The number of participants with valid days, defined as 10 or more hours classified as wear time in a 24-hour day, increased while the mean counts-per-minute (CPM) decreased as the threshold for excluding non-wear was reduced from the allowed 4,000 counts in an hour. The number of participants with four or more valid days increased 2.29% (n = 113) and mean CPM dropped 2.45% (9.5 CPM) when adjusting the nonwear classification threshold to 50 counts an hour. Applying the most liberal criteria, only excluding hours as nonwear which contained 1 count or less, resulted in a 397 more participants (7.83% increase) and 26.5 fewer CPM (6.98% decrease) in NHANES 2003-2004 participants with four or more valid days. The algorithm should be used with caution due to the potential influence of these corner cases.
美国国家癌症研究所（NCI）的佩戴时间分类算法使用基于体力活动数据计数发生的规则-运动的累积量，受加速度幅度和持续时间的影响-将参与者佩戴体力活动监测（PAM）设备（ActiGraph 加速度计）时与未佩戴时（不佩戴）区分开来。它应用于 2003-2004 年全国健康与营养调查（NHANES 2003-2004）生成的 PAM 数据。我们讨论了两种特殊情况，当应用该算法时，这两种情况可能会产生意外的，也许是意外的结果。我们使用两种特殊情况的模拟数据表明，在一种情况下，该算法将仅具有 72 个总计数的 24 小时时间段分类为 100％佩戴，而在另一种情况下，将具有 96000 个计数的 24 小时时间段分类为 0.1％佩戴。在 NHANES 2003-2004 PAM 数据集的类似情况下提出了算法的非佩戴分类阈值（T）的变化程度的相应摘要统计信息。具有有效天数（定义为 24 小时内分类为佩戴时间的 10 小时或更长时间）的参与者数量增加，而每分钟计数（CPM）的平均值却降低了，因为排除非佩戴的阈值从每小时允许的 4000 个计数减少了。当将非佩戴分类阈值调整为每小时 50 个计数时，具有四个或更多有效天数的参与者数量增加了 2.29％（n = 113），平均 CPM 下降了 2.45％（9.5 CPM）。应用最宽松的标准，仅排除包含 1 个计数或更少计数的小时数，导致 NHANES 2003-2004 中具有四个或更多有效天数的参与者增加了 397 人（增加 7.83％），CPM 减少了 26.5（减少 6.98％）。由于这些特殊情况的潜在影响，该算法应谨慎使用。
