Assessment of children’s sleep using thigh-worn Axivity AX3 accelerometers

Abstract Background Accurate assessment of sleep is vital, but the gold standard, polysomnography, is costly and impractical for large-scale studies. An alternative is wearable accelerometers, which reduce participant burden and eliminate potential recall biases. This study aimed to develop and validate a method for estimating time in bed (TIB), total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) utilizing machine learning applied to accelerometry data. Methods Data on 309 nights from 134 children aged 4–17 years was used to develop a method utilizing two machine learning models applied to data from thigh-worn accelerometers to estimate sleep metrics. Inputs were collected simultaneously from the Zmachine Insight + and raw data from thigh-worn accelerometers, and validated using k-fold cross-validation. The method was then cross-validated against polysomnography in an independent sample of 136 children aged 8–16 years. Results The independent validation showed overestimations of 28.0 minutes for bedtime and 11.2 minutes for wake time, with ICC of 0.59 and 0.55. TIB and TST were overestimated by 13.8 and 3.4 minutes with ICC of 0.59 and 0.56, respectively. The correlation for estimating SE, SOL and WASO was weak with ICC of 0.21, 0.01 and 0.04, respectively. Conclusions This method demonstrated sufficient accuracy for assessing bedtime, wake time, TIB and TST at the group level when validated in an independent sample against polysomnography, although wide limits of agreement suggest limited precision for individual-level assessments. Low agreement for SE, SOL and WASO indicated insufficient accuracy of the method for these metrics.