Estimating the sleep period time window based on a hip- worn accelerometer collected in children and adults

Abstract Accurately identifying the sleep period time (SPT) window during daily life is important to understanding habitual sleep patterns and overall health. While actigraphy placement differs across studies, the majority of SPT algorithms are developed for wrist data. Open-source algorithms enhance reproducibility and transparency. This study sought to optimise and evaluate two open-source algorithms, HDCZA and HorAngle, for hip-worn accelerometer data. A total of 109 children and 194 adults wore wrist and hip accelerometers for six nights along sleep diaries. An established reference integrating wrist and diary information provided the benchmark. We tuned HDCZA and HorAngle parameters via Bayesian optimisation on a 60% training split and tested them on the remaining 40%. For HDCZA, mean differences for sleep onset and wake-up were − 3 and 4 minutes (limits of agreement [LoA]: − 221,215 and − 185,194; root-mean square error [RMSE] = 111 and 97), whereas HorAngle yielded 0 and − 4 minutes (LoA: − 199,199 and − 223,214; RMSE = 111 and 112). Regarding SPT duration, HDCZA showed a mean difference of 7 minutes (LoA: − 252,266; RMSE = 132), and HorAngle displayed − 4 minutes (LoA: − 254,246; RMSE = 128), with no statistically significant differences in SPT detected (P = 0.774; P = 0.237). Both algorithms showed moderate agreement with the reference in ranking sleep duration (κ ≈ 0.56–0.58). Differences were unrelated to age or sex but linked to non-wear time. Both open-source algorithms demonstrated value for estimating the SPT window from hip data. While HDCZA requires no additional sensor-specific parameters, HorAngle depends on accurate axis identification.