Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings. (7th May 2021)

• Record Type: Journal Article
• Authors: Loock, Ann‐Sophie; Khan Sullivan, Ameena; Reis, Catia; Paiva, Teresa; Ghotbi, Neda; Pilz, Luisa K.; Biller, Anna M.; Molenda, Carmen; Vuori‐Brodowski, Maria T.; Roenneberg, Till; Winnebeck, Eva C.
• Is Part Of: Journal of sleep research. Volume 30:Number 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-07
• Subjects: accuracy -- actigraphy -- automated sleep analysis -- PSG -- sleep diary -- sleep–wake rhythms; Sleep -- Periodicals; Sleep disorders -- Periodicals; 612.821
• DOI: 10.1111/jsr.13371
• Languages: English
• ISSNs: 0962-1105

Abstract

Abstract: 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. The Munich Actimetry Sleep Detection Algorithm uses a moving 24–h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep‐log validation was performed on two 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 one night of polysomnography. Epoch‐by‐epoch analyses were conducted and comparisons of sleep measures carried out via Bland‐Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR] = 75%–86%) and specificity of 91% (87%–92%). Mean onset and offset times were highly correlated ( r = .86–.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm 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 night‐time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep–wake patterns in field studies. With its good performance across daytime and night‐time, 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.