0280 Unlocking the Potential of Consumer Wearables for Predicting Sleep in Children: A Device-Agnostic Machine Learning Approach

Abstract Introduction Consumer wearables use accelerometry and heart rate to predict sleep, the same data signals used by research-grade devices to classify sleep/wake patterns. However, metrics predicted by consumer wearables are based on black box algorithms limiting their use in research. The objective of this study was to develop algorithms for predicting sleep in children based on the raw accelerometry and heart rate data from a popular consumer wearable device, therefore bypassing the onboard black box algorithms. Methods 38 children (M=8.5 years, SD=2.4, 42% black, 61% male) underwent overnight laboratory-based polysomnography (PSG) while wearing an Apple Watch Series 7. Heart rate and accelerometry data were collected via the Apple Watch application program interface. Features extracted included (1) age, (2) heart rate, (3) y-axis offset angle, (4) y-axis angle relative to x-axis (y-angle), (5) x-axis fast Fourier transformation (FFT) 4Hz, (6) x-axis FFT 9Hz, (7) vector magnitude (VM) FFT 9Hz, (8) VM FFT 14Hz, (9) mean power dispersion (MPD), (10) bandpass filter followed by Euclidean norm/vector magnitude (BFEN), (11) dominant signal power at 0.6–2.5Hz (PMAXBAND), and (12) activity counts. Four machine learning models: logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and neural network (NN) predicted sleep or wake. Model performance was evaluated by F1 score, precision, sensitivity, and specificity. Feature importance was evaluated using the RF model. Results LR achieved the best performance according to F1 score (RF: 85.04, LR: 86.50, KNN: 82.52, NN: 85.13). LR also had the highest precision (RF:88.33, LR:88.94, KNN: 86.77, NN:87.95), and nearly the highest sensitivity (RF:84.52, LR:86.11, KNN:84.73, NN:86.55) and specificity (RF:75.72, LR:75.00, KNN:54.01, NN:60.57). BFEN was the most important feature, followed by MPD, age, PMAXBAND, VM FFT 14Hz, x-axis FFT 9Hz, heart rate, y-axis offset angle, y-angle, activity count, VM FFT 9Hz, x-axis FFT 4Hz. Conclusion Raw accelerometry data combined with heart rate data from the Apple Watch Series 7 was able to detect sleep and wake in children. Leveraging the raw accelerometry data is a viable alternative to relying on black-box algorithms for sleep classification in consumer wearable devices. Support (if any)