Validation of accelerometer wear and nonwear time classification algorithm

Abstract

Introduction: the use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for the validation of subjective PA self-reports. A vital step in PA measurement is the classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm.

Purpose: the purpose of this study was to validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter.

Methods: we conducted a validation study of a wear or nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target.

Results: the recommended elements in the new algorithm are as follows: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero or nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the upstream or downstream 30-min consecutive zero-count window for detection of artifactual movements. Compared with the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P values < 0.001).

Conclusions: the accelerometer wear or nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.

Figure 1

The frequency distribution of nonwear time intervals misclassified by the current algorithm (with the default time window 60-min) during waking period for adults (A) and youth (B). The thresholds with 100 (current default), 50, 25 and 0 counts/min are presented. The percents of subjects having at least one misclassified time interval with lengths of ≥ 60-min are also presented.

Figure 2

The frequency distribution of nonwear time intervals misclassified by the new algorithm without the 3^rd component during waking period for adults (A) and youth (B). The time windows with 20-, 60- (dashed lines), and 90-min (solid lines) are presented.

Figure 3

Representative data for two adults (A) and two youth (B) during a 24-h stay in the whole-room indirect calorimeter are presented along with nonwear time intervals misclassified by the current and new algorithms using a 60-min time window. Plots for the measured EE (gray lines) and the raw counts (black lines) were overlaid in a normalized scale from 0 to 1. The horizontal dotted line represents 1.5 metabolic equivalents (MET) and the dashed boxes shows the detected wear time intervals. The horizontal short solid lines along with the values above show the length of the misclassified nonwear time intervals. The waking period is also presented.