Potential corner case cautions regarding publicly available implementations of the National Cancer Institute's nonwear/wear classification algorithm for accelerometer data.

Autor: Moore HE 4th; 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., Haydel KF; Stanford Solutions Science Lab, Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Stanford University, Stanford, CA, United States of America., Banda JA; Department of Health and Kinesiology, Purdue University, West Lafayette, IN, United States of America., Fiterau M; Mobilize Center and Department of Computer Science, Stanford University, Stanford, CA, United States of America., Desai M; Quantitative Science Unit, Stanford University, Stanford, CA, United States of America., Robinson TN; Stanford Solutions Science Lab, Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Stanford University, Stanford, CA, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2018 Dec 31; Vol. 13 (12), pp. e0210006. Date of Electronic Publication: 2018 Dec 31 (Print Publication: 2018).
DOI: 10.1371/journal.pone.0210006
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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