Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults. (4th March 2021)

• Record Type: Journal Article
• Authors: Regalia, Giulia; Gerboni, Giulia; Migliorini, Matteo; Lai, Matteo; Pham, Jonathan; Puri, Nirajan; Pavlova, Milena K.; Picard, Rosalind W.; Sarkis, Rani A.; Onorati, Francesco
• Is Part Of: Chronobiology international. Volume 38:Number 3(2021)
• Page Start: 400
• Page End: 414
• Publication Date: 2021-03-04
• Subjects: Actigraphy-based sleep tracking algorithms -- older adults -- polysomnography -- sleep quality -- wearables -- sleep detection; Chronobiology -- Periodicals; Biological rhythms -- Periodicals; Circadian rhythms -- Periodicals; 571.77
• DOI: 10.1080/07420528.2020.1835942
• Languages: English
• ISSNs: 0742-0528

Abstract

ABSTRACT: The purpose of the present work is to examine, on a clinically diverse population of older adults (N = 46) sleeping at home, the performance of two actigraphy-based sleep tracking algorithms (i.e., Actigraphy-based Sleep algorithm, ACT-S1 and Sadeh's algorithm) compared to manually scored electroencephalography-based PSG (PSG-EEG). ACT-S1 allows for a fully automatic identification of sleep period time (SPT) and within the identified sleep period, the sleep-wake classification. SPT detected by ACT-S1 did not differ statistically from using PSG-EEG (bias = −9.98 min; correlation 0.89). In sleep-wake classification on 30-s epochs within the identified sleep period, the new ACT-S1 presented similar or slightly higher accuracy (83–87%), precision (86–89%) and F1 score (90–92%), significantly higher specificity (39–40%), and significantly lower, but still high, sensitivity (96–97%) compared to Sadeh's algorithm, which achieved 99% sensitivity as the only measure better than ACT-S1's. Total sleep times (TST) estimated with ACT-S1 and Sadeh's algorithm were higher, but still highly correlated to PSG-EEG's TST. Sleep quality metrics of sleep period efficiency and wake-after-sleep-onset computed by ACT-S1 were not significantly different from PSG-EEG, while the same sleep quality metrics derived by Sadeh's algorithm differed significantly from PSG-EEG. Agreement between ACT-S1 and PSG-EEG reached was highest when analyzing the subset of subjects with least disrupted sleep (N = 28). These results provide evidence of promising performance of a full-automation of the sleep tracking procedure with ACT-S1 on older adults. Future longitudinal validations across specific medical conditions are needed. The algorithm's performance may further improve with integrating multi-sensor information.