Accelerometry for sleep assessment in children: Criterium validity of different algorithms in wrist‐ and ankle‐worn devices

Polysomnography, the gold-standard for measuring sleep, is costly, intrusive and usually limited to 1 night. Actigraphy offers a more affordable, less intrusive method over multiple nights. However, little research validates ActiGraph accelerometers against polysomnography, especially in children. This study evaluated the validity of different algorithms and compared wrist versus ankle accelerometer placements for estimating sleep in children aged 1-12 years. Twenty-nine children undergoing overnight type 1 polysomnography wore ActiGraph accelerometers. Six algorithms were evaluated against polysomnography using Pearson correlations, intraclass correlation, paired t-tests and Bland-Altman plots. Agreement was classified as poor (intraclass correlation coefficient < 0.4), fair (0.4 < intraclass correlation coefficient < 0.6), good (0.6 < intraclass correlation coefficient < 0.75) or excellent (intraclass correlation coefficient > 0.75). Total sleep time was the primary outcome. For wrist-worn devices, the Sadeh (Actilife) and Cole-Kripke (Actilife and GGIR) algorithms showed excellent agreement with polysomnography (intraclass correlation coefficient = 0.80-0.85), while vanHees showed good agreement (intraclass correlation coefficient = 0.67) and Galland showed fair agreement (intraclass correlation coefficient = 0.46). The Cole-Kripke algorithm did not significantly differ from polysomnography total sleep time, whereas others underestimated total sleep time. For ankle-worn devices, Sadeh (Actilife), Cole-Kripke (Actilife) and vanHees algorithms demonstrated excellent agreement (intraclass correlation coefficient = 0.75-0.82). No significant differences were found between wrist and ankle placements for certain algorithms. The findings support accelerometry as a valid tool for sleep assessment in children, recommending that algorithm selection be tailored to specific study requirements.