In this study, we systemically evaluated the automated algorithm most commonly used in population-based studies to classify accelerometer wear and nonwear time intervals and proposed improvements to the algorithm. The recommended elements in the new algorithm are: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero/nonzero counts, and 3) allowance of a 2-min interval of nonzero counts with up/downstream 30-min consecutive zero counts windows for artifactual movement detection. These improvements would mostly affect the misclassification of time intervals spent in sedentary behaviors that do not pass the wear/nonwear classification criteria for the low activity counts. Thus, studies in populations with a low active and high sedentary behavior PA patterns could likely benefit from these improvements.
In the current algorithm, classification of time intervals to wear/nonwear depends on three major criteria: 1) nonzero counts threshold, 2) time window for zero/nonzero counts, and 3) artifactual movement detection. In this study, to test the validity of nonzero counts threshold criterion, we examined counts threshold ranging from 100 to 0 counts/min since the 100-counts threshold is the default in the original version of the algorithm and specific for the accelerometer used in the analysis (31
). The number of misclassified nonwear time intervals decreased as the counts threshold decreased, resulting in the optimal threshold at zero count. Thus, we have chosen the zero-count threshold for further testing and potential algorithm improvement. We also evaluated time windows; the number of misclassified nonwear time intervals sharply decreased until the current default 60-min window and reached an optimal 90-min for both youth and adults. However, due to the implementation method in the current algorithm, the 90-min window could increase false detection of wear time intervals around midnight. Thus, the 60-min time window was used for comparison of the current and new algorithms.
The third criterion in the algorithm is a procedure for proper classification of nonzero counts potentially caused by artifactual monitor movements during nonwear periods, which may be caused by accidental movement of the monitor (e.g. nudged or touched while sitting on a table or nightstand). During validation, we found that the current algorithm misclassified nonwear/wear time intervals, especially in sedentary behaviors (<1.5 MET). A plausible explanation is that it is difficult to distinguish between artifactual movement and a sporadic movement during sedentary PA. To mitigate this misclassification, in addition to the 2-min interval (artifactual movement interval) in the current algorithm, we included a second criterion termed window-2 in the new algorithm.
We validated the current algorithm using the programming language R (23
) and compared it with the SAS program available from the NHANES (20
) website using the same data sets. We found that both algorithms generated the same result when the entire monitoring study period is classified without daily segments. During the validation, we also examined the effect of daily summation of wear/nonwear classification with the midnight time break used by the SAS program. This is a potential source of misclassification of wear/nonwear time periods in cases when the actual wear stops and nonwear starts after 11 pm. Thus, we suggest that the minute-to-minute output for the entire monitoring study period be classified into wear/nonwear intervals and then further categorized into daily segments. This approach is implemented in the new algorithm.
To make the new algorithm applicable for specific studies and other types of accelerometer, the parameters in (i.e. windows 1 & 2 and artifactual movement interval) can be defined by users depending on their experimental needs. In addition, our R program is readily applicable for data collected with various (e.g. 1-second) epochs and can be provided to interested investigators upon request.
A potential clinical consequence of wear time underestimation by the algorithm could be miscalculation of time spent in sedentary, light, moderate and vigorous PA categories, expressed as a proportion of the total wear time, which is very often used to quantify PA in population-based studies. In the context of the current study, we estimated that the observed mean of 50-min misclassification of wear time as nonwear during waking period could lead to approximately 8% underestimation of time spent in sedentary behaviors. Subsequently, the calculated percentage of time spent in light and/or moderate intensity activities would be overestimated. In addition, misclassification of wear/nonwear time could cause bias in the prediction of PA-related EE. Although the estimated EE daily bias might be relatively small, the differences could be substantially higher when extrapolated over longer periods such as week, month, or year in epidemiological and cohort studies. Assuming that 30% of total energy is spent for PA, we estimated that the difference in misclassification of wearing time during sedentary activities as rest (~60 kcal/day) would create an approximately 20 kcal/day gap between the two assessments. For an average person this would equate to ~7,000 kcal/year overestimation of energy spent on PA, the amount often linked to the current obesity epidemic and related health consequences (13
Thus, even modest increases in the accuracy of wear/nonwear time classification have the potential to improve our understanding of the relationships between PA, PA-related EE, and health outcomes. The proposed algorithm improvements might be especially important in cohort studies in which the baseline PA assessment is often linked to longitudinal health risks and disease outcomes (7
Our study has several strengths. First, the room calorimeter allowed us to validate wear/nonwear intervals and assess clinical importance of the improvements by measuring PA-related EE. Second, we used a relatively large group of males and females with a wide range of ages (10 to 67 years old) and BMI (16 −52 kg/m2). Finally, since our R program uses modifiable arguments, it could be easily modified for each study’s needs and adopted to other accelerometers (e.g. RT3, Actical, Actiwatch).
We also recognize that our methodology has some limitations. First, the standardization of activity bouts performed in the room calorimeter may not accurately represent individuals’ habitual daily PA patterns. This could include variations in sleep patterns and longer periods of sedentary PA that may be misclassified, causing overestimation of nonwear time intervals. Second, although we confirmed that adding criteria for artifactual movement detection improved the correct classification of artifactual movement, the proposed criteria should be validated in other studies with larger number of participants in various free-living settings. Third, data were collected for a single 24-h period. Longer observation period (e.g. total 36-h) would provide additional data from wear day-time activities. Finally, we did not find substantial differences in the criteria for the new algorithm between men and women or adults and youth. While it is possible that differences may emerge in larger studies, or in studies of older adults, our results do not suggest a compelling need for gender or age-specific algorithms. More work is needed to verify and/or optimize the algorithm in studies of older adults.
In conclusion, we found that the classification of Actigraph nonwear and wear time intervals could be improved by modifying the currently used algorithm. The improvements include eliminating nonzero counts threshold during a nonwear time interval along with 90-min window for consecutive zero/nonzero counts, and handling artifactual movement detection using an additional component. Application of the improved algorithm in population-based studies may lead to a better prediction of time spent in PA and especially sedentary behaviors.