JMIR Publications

ABSTRACT

Background:

For decades the measurement of sleep and wake has relied upon watch-based Actigraphy as an alternative to expensive, obtrusive, clinical monitoring. To date we have relied upon a handful of algorithms to score actigraphy data as sleep or wake. However, these algorithms have almost exclusively been tested and validated with small samples of young healthy individuals.

Objective:

. To address this issue, this study established the accuracy and agreement of conventional and traditional actigraphy algorithms against polysomnography the clinical standard using the diverse MESA sleep dataset.

Methods:

We assessed 5 well established algorithms including Cole-Kripke, UCSD scoring, Kripke 2010, Philips-Respironics, and Sadeh with and without rescoring across 1440 individuals (Mage=69.36+/-8.97) from the MESA sleep dataset. We conducted epoch-by-epoch comparison assessing accuracy, confusion matrix analyses, Receiver Operator Characteristic Curves, Area Under the Curve, and Bland Altman analyses for agreement. As a secondary objective we examined algorithm and polysomnography agreement for key sleep metrics including total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO).

Results:

Primary results indicated all algorithms demonstrated accuracy between 78%-80% with the highest accuracy by the Kripke 2010 (80%) algorithm and closely by Cole-Kripke (80%) and Philips-Respironics (80-79%) algorithms. Further, all algorithms demonstrated significant mean difference across sleep metrics.

Conclusions:

The findings of this study establish that these traditional actigraphy algorithms can, with high accuracy detect sleep and wake in large diverse population samples, including older adults, or populations at risk of health conditions. However, these algorithms may carry difficulty for precise assessment of sleep metrics especially in cases of sleep disorders or irregular sleep.