Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults.

Chronobiology International
Giulia RegaliaFrancesco Onorati

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 differen...Continue Reading

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