Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm (MASDA). MASDA uses a moving 24-hour-threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. MASDA was validated against sleep logs and polysomnography. Sleep-log validation was performed on 2 field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing 1 night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures via Bland-Altman plots and correlations. Compared with sleep logs, MASDA classified sleep with a median sensitivity of 80% (IQR = 75-86%) and specificity of 91% (87-92%). Mean onset and offset times were highly correlated (r = 0.86-0.91). Compared with polysomnography, MASDA reached a median sensitivity of 92% (85-100%), but low specificity of 33% (10-98%), owing to the low frequency of wake episodes in the nighttime polysomnographic recordings. MASDA overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically different from polysomnography in other sleep parameters. These results demonstrate the validity of MASDA to faithfully estimate sleep-wake patterns in field studies. With its good performance across day- and nighttime, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.
bioRxiv Subject Collection: Neuroscience