A comparison of sleep metrics from mid-thigh and low-back accelerometers to wrist based data using open-source algorithms

IntroductionWearable accelerometers are a valuable tool for monitoring sleep, sedentary behaviour, and physical activity patterns within 24h time-use in free-living environments. While wrist-worn accelerometers are favoured for monitoring sleep, they do not accurately distinguish between sitting and lying positions (Narayanan et al., 2020). This study aims to determine whether back or thigh-mounted accelerometers yield sleep metrics comparable to wrist-worn devices using an open-source algorithm originally validated for the wrist. MethodsData from 20 healthy sleepers were collected using Axivity AX3 accelerometers. Participants wore accelerometers on their right thigh, low-back, and wrist for one night of sleep in their own bed. Sleep metrics were calculated using the van Hees algorithm through the GGIR package in R. The primary outcomes were: Total Sleep Time (TST), Wake After Sleep Onset (WASO), Awakenings (AWK), Sleep Efficiency (SE), Sleep Interval (SI) and Sleep Onset Timestamp (SOT). Within-subject ANOVA with Tukeys post hoc, Pearson correlation coefficients, Bland-Altman plots, and Cohens d were used to assess the comparability of sleep metrics between the body placements. ResultsData analysis included all 20 participants. Mid-thigh accelerometers demonstrated a strong linear relationship with wrist accelerometers across all metrics (r = 0.86-0.98). Bland-Altman plots demonstrated a narrow 95% confidence interval suggesting that wrist and mid-thigh metrics are in good agreement, except for AWK which is slightly underestimated by the mid-thigh device. Conversely, low-back accelerometers demonstrated moderate linear relationship with the wrist (r = 0.63-0.98) and the Bland-Altman results showed wide limits of agreement with significant overestimations of TST, SE, SI and underestimations of WASO, AWK, SOT. Cohens d demonstrated small differences between mid-thigh and wrist devices, except for AWK (d= 0.42). Low-back values for WASO, SE, and AWK showed moderate differences. ConclusionsThis analysis demonstrates that the mid-thigh accelerometer yields comparable sleep metrics to wrist-worn devices when processed with the van Hees algorithm.