On the Unification of Common Actigraphic Data Scoring Algorithms

Actigraphy is a well-known, inexpensive method to investigate human movement patterns. Sleep and circadian rhythm studies are among the most popular applications of actigraphy. In this study, we investigate seven common sleep-wake scoring algorithms designed for actigraphic data, namely Cole-Kripke algorithm, two versions of Sadeh algorithm, Sazonov algorithm, Webster algorithm, UCSD algorithm and Scripps Clinic algorithm. We propose a unified mathematical framework describing five of them. One of the observed novelties is that five of these algorithms are in fact equivalent to low-pass FIR filters with very similar characteristics. We also provide explanations about the role of some factors defining these algorithms, as none were given by their Authors who followed empirical procedures. Proposed framework provides a robust mathematical description of discussed algorithms, which for the first time allows one to fully understand their operation and basics.

Validation of the Sleep-Wake Scoring of a New Wrist-Worn Sleep Monitoring Device

STUDY OBJECTIVES

To test the sleep-wake scoring reliability of a new wrist-worn sleep monitoring device.

METHODS

Twenty-seven adult good sleepers underwent 1 night of polysomnography (PSG) while wearing both the new device (myCadian [MC]; CurAegis Technologies, Rochester, New York, United States) and commercially available actigraphy (Actiwatch 2 [AW]; Philips Respironics, Murrysville, Pennsylvania, United States) on their nondominant wrist. PSG tests were manually stage scored. After excluding missing data, 20 participants had full-night data on all three devices with 17,734 total 30-second epochs. Using PSG as the gold standard, pooled epoch-by-epoch agreement for sleep and wake was calculated for each device using percent agreement and Cohen kappa statistic. Positive predictive values for both sleep and wake epochs, as well as sleep continuity statistics, were calculated.

RESULTS

Percent agreement with PSG-scored wake and sleep was 91.3% for MC (kappa = 0.67) and 87.7% for AW (kappa = 0.50). Positive predictive values for sleep epochs were 94.4% and 90.8% for MC and AW, respectively, and 74.5% and 65.6% for wake. Both devices underestimated wake and overestimated sleep compared to PSG. Descriptively, compared to PSG, sleep latency was higher with MC and wake after sleep onset higher with AW. Total sleep time and sleep efficiency were more similar across devices.

CONCLUSIONS

The kappa statistic for MC is consistent with a high level of agreement with PSG. Overall, the reliability of MC compared to PSG scoring was slightly more favorable than that of AW. Findings suggest that MC provides reliable sleep-wake scoring during a nocturnal sleep period for good sleepers.