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Updated: May 22, 2026
Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood
Published on: October 2, 2019
Algorithms for using an activity-based accelerometer for identification of infant sleep-wake states during nap
Barbara C Galland1, Gavin J Kennedy, Edwin A Mitchell
1Department of Women's and Children's Health, University of Otago, Dunedin, New Zealand. barbara.galland@otago.ac.nz
Insights
The Actical accelerometer can accurately identify infant sleep states using the count-scaled algorithm. This method reliably distinguishes sleep from wakefulness during naps in infants.
Area of Science:
- Biomedical Engineering
- Sleep Medicine
- Pediatric Research
Background:
- Actical accelerometers are typically used for physical activity monitoring.
- Accurate sleep assessment in infants is crucial for developmental monitoring.
- Distinguishing sleep from wake states using accelerometry requires validated algorithms.
Purpose of the Study:
- To evaluate the accuracy of different algorithms applied to Actical accelerometer data for differentiating infant sleep and wake states.
- To compare the performance of established and novel algorithms for sleep-wake classification in infants.
Main Methods:
- Thirty-one infants (10-22 weeks) wore an Actical accelerometer on their shin during daytime naps.
- Sleep-wake states were determined using polysomnography as the gold standard.
- Four algorithms (zero-threshold, Sadeh, Cole, count-scaled) were tested with 15-, 30-, and 60-second epochs.
Main Results:
- All algorithms achieved >80% overall agreement for sleep-wake states.
- The count-scaled algorithm with 15-second epochs demonstrated the highest accuracy (86% sensitivity, 85% specificity).
- Correlations were found between accelerometry outputs and key sleep parameters (total sleep time, sleep latency, sleep efficiency, wake time after sleep onset).
Conclusions:
- The Actical accelerometer reliably identifies infant sleep during naps.
- The count-scaled algorithm offers improved accuracy for sleep-wake epoch identification in infants compared to other tested methods.
- Accelerometry provides a viable tool for sleep research in infants, though awakenings may be overestimated.
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.
