Abstract
Recent interest in sedentary behavior and technological advances expanded use of watch-size accelerometers for continuous monitoring of physical activity (PA) over extended periods (e.g., 24 h/day for 1 week) in studies conducted in natural living environment. This approach necessitates the development of new methods separating bedtime rest and activity periods from the accelerometer recordings. The goal of this study was to develop a decision tree with acceptable accuracy for separating bedtime rest from activity in youth using accelerometer placed on waist or wrist. Minute-by-minute accelerometry data were collected from 81 youth (10–18 years old, 47 females) during a monitored 24-h stay in a whole-room indirect calorimeter equipped with a force platform covering the floor to detect movement. Receiver Operating Characteristic (ROC) curve analysis was used to determine the accelerometer cut points for rest and activity. To examine the classification differences, the accelerometer bedtime rest and activity classified by the algorithm in the development group (n = 41) were compared with actual bedtime rest and activity classification obtained from the room calorimeter-measured metabolic rate and movement data. The selected optimal bedtime rest cut points were 20 and 250 counts/min for the waist- and the wrist-worn accelerometer, respectively. The selected optimal activity cut points were 500 and 3,000 counts/min for waist and wrist-worn accelerometers, respectively. Bedtime rest and activity were correctly classified by the algorithm in the validation group (n = 40) by both waist- (sensitivity: 0.983, specificity: 0.946, area under ROC curve: 0. 872) and wrist-worn (0.999, 0.980 and 0.943) accelerometers. The decision tree classified bedtime rest correctly with higher accuracy than commonly used automated algorithm for both waist- and wrist-warn accelerometer (all p<0.001). We concluded that cut points developed and validated for waist- and wrist-worn uniaxial accelerometer have a good power for accurate separation of time spent in bedtime rest from activity in youth.
Highlights
Accelerometry has been frequently used for the measurement of time spent in activities performed at various intensities and for the prediction of physical activity (PA) related energy expenditure [1,2,3,4]
The bedtime rest cut points from 20 to 30 counts/min in combination with activity cut points 400 or 500 counts/min resulted in the median area under each cut point (AUC).0.8 and the medians of sensitivity and specificity .0.88 (Table 2)
The bedtime rest cut points from 250 to 300 counts/min in combination with activity cut point 3,000 counts/min resulted in the median AUC.0.89 and the medians of sensitivity and specificity .0.94 (Table 2)
Summary
Accelerometry has been frequently used for the measurement of time spent in activities performed at various intensities and for the prediction of physical activity (PA) related energy expenditure [1,2,3,4]. Accelerometer applications range from clinical interventional trials [5,6] to epidemiological studies [7,8,9] Recent technological advances such as watch size devices with high data storage capacity allows capturing PA for extended monitoring period (e.g. 24 hours per day for 7 days). This increasingly popular ‘‘24/ 7’’approach can lead to more detailed assessments of individual’s PA amount and patterns, which is relevant because of the rapidly growing interest in sedentary behavior and sleep patterns and their relationship to health in children and adolescents [10,11,12,13]. Validity and limitations of these methods were described elsewhere [17]
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