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

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.

KEYWORDS:

• Actigraphy-based sleep tracking algorithms
• older adults
• polysomnography
• sleep quality
• wearables
• sleep detection

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Acknowledgements

The authors would like to thank Chiara Caborni for her support in the management of the wristband data and PSG-EEG-derived sleep scores data used in this study.

Disclosure statement

All authors have seen and approved this manuscript. The study was supported by the A.J. Trustey Epilepsy Research Endowed Fund. R.W.P., G.G., F.O., G.R. and M.M. are employees of Empatica Inc., which manufactured the E4 devices used in this work and developed the new algorithm (ACT-S1) tested in this work. The remaining authors have no conflict of interest. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Additional information

Funding

The study was supported by the A.J. Trustey Epilepsy Research Endowed Fund.