Validation of an automated sleep detection algorithm using data from multiple accelerometer brands

Abstract

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5 [7.2] years, body mass index 25.5 [3.7] kg/m 2 ) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1-night PSG assessment. Sleep estimates (sleep period time window [SPT-window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open-source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer-derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT-window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08).The mean bias between all accelerometer-derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT-window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

Sleep and day-to-day PTSD symptom variability: an ecological momentary assessment and actigraphy monitored study in trauma-exposed young adults

“…Further, the watch generally underestimated sleep efficiency and overestimated nocturnal awakenings compared to the sleep diary, which is in line with previous studies (Jenkins et al, 2022 ; Yap et al, 2020 ). While the algorithm used here has been found to provide equivalent sleep estimates to PSG, specificity to detect wakefulness in the sleep window is low as previously reported (Plekhanova et al, 2022 ). This means that sleep indices that include wakefulness, such as sleep efficiency, number of awakenings, or WASO have to be discussed cautiously.…”

“…The data was then analysed using the GGIR algorithm in R (van Hees et al, 2019 , see below). The algorithm has been found to have high accuracy compared to PSG (84%) and high sensitivity to detect sleep (93%), but lower specificity to detect wakefulness (20%; Plekhanova et al, 2022 ).…”

Is rest-activity rhythm prospectively associated with all-cause mortality in older people regardless of sleep and physical activity level? The ‘Como Vai?’ Cohort study

“…After that, all minutes inside this window are classified as sleep or wake (sleep = changes in z-angle below 3º) [20]. This sleep detection procedure was previously used on the research community [21][22][23].…”