Algorithms for Using an Activity-based Accelerometer for Identification of Infant Sleep-wake States During Nap Studies

Objective: To determine the accuracy of using different algorithms on the output from an Actical accelerometer, a device normally used to measure physical activity, to distinguish sleep from wake states.

Methods: Thirty-one infants aged 10-22 weeks wore the accelerometer on the shin for a daytime nap recording in tandem with polysomnography. Sleep-wake epochs were identified using four computations/algorithms: the zero-threshold computation, two common algorithms used for wrist-based devices (Sadeh and Cole), and a new algorithm developed for this study (count-scaled). Accuracy was examined in direct epoch comparison with polysomnography using 15-, 30- and 60-s sampling epochs.

Results: Overall agreements (accuracy) for sleep-wake states were >80% for all computations. The count-scaled algorithm sampling 15-s epochs gave the highest accuracy, with sensitivity (sleep agreement) at 86% and specificity (awake agreement) at 85%. Other computations yielded higher sensitivity at the expense of specificity. Another way to assess the accuracy of identification of sleep-wake states was to compare sleep parameter outputs. All computations and sampling epochs were significantly correlated with total sleep time (r=0.76-0.88), sleep latency (r=0.70-0.93), sleep efficiency (r=0.76-0.87), and wake time after sleep onset (r=0.41-0.53). The number of awakenings after sleep onset was overestimated by accelerometry.

Conclusions: The Actical accelerometer, designed to measure physical activity, can reliably identify sleep in infants during napping, with the count-scaled algorithm showing some advantages over other methods for accurate identification of sleep-wake epochs.