Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation.

Jaime K Devine,Rachel R Markwald,Steven R Hursh,Lindsay P Schwartz,Evan D Chinoy

doi:10.3390/s21010076

Abstract

Traditional measures of sleep or commercial wearables may not be ideal for use in operational environments. The Zulu watch is a commercial sleep-tracking device designed to collect longitudinal sleep data in real-world environments. Laboratory testing is the initial step towards validating a device for real-world sleep evaluation; therefore, the Zulu watch was tested against the gold-standard polysomnography (PSG) and actigraphy. Eight healthy, young adult participants wore a Zulu watch and Actiwatch simultaneously over a 3-day laboratory PSG sleep study. The accuracy, sensitivity, and specificity of epoch-by-epoch data were tested against PSG and actigraphy. Sleep summary statistics were compared using paired samples t-tests, intraclass correlation coefficients, and Bland–Altman plots. Compared with either PSG or actigraphy, both the accuracy and sensitivity for Zulu watch sleep-wake determination were >90%, while the specificity was low (~26% vs. PSG, ~33% vs. actigraphy). The accuracy for sleep scoring vs. PSG was ~87% for interrupted sleep, ~52% for light sleep, and ~49% for deep sleep. The Zulu watch showed mixed results but performed well in determining total sleep time, sleep efficiency, sleep onset, and final awakening in healthy adults compared with PSG or actigraphy. The next step will be to test the Zulu watch’s ability to evaluate sleep in industrial operations.

Highlights

Sleep impacts almost every domain of human function: from mood to job performance to physical health and mortality [1–3]
The Zulu watch was sensitive for detecting deep sleep compared with interrupted sleep, light sleep, or wake
Our findings indicate that a device with single-sensor input and a sampling rate longer than 60 s can detect sleep onset and final awakening with >80% agreement, and can differentiate sleep versus wake with >90% accuracy compared to PSG or research-grade actigraphy

Summary

Introduction

Sleep impacts almost every domain of human function: from mood to job performance to physical health and mortality [1–3]. Whether the goal of a monitoring system in the 4.0 Era is to improve personal well-being or to overhaul industry infrastructure, controlling for sleep as an intervening variable can only improve the model. Sleep researchers have been tracking sleep using wrist-worn actigraphy since the late 1970s [4]; recently, commercial wrist-worn wearable devices have begun to feature sleep-tracking technology as well. The Sleep Research Society recently published a white paper outlining the barriers and opportunities for using wearables in sleep and circadian science [5]. This paper identified poor validation of wearable technology as the primary barrier inhibiting the use of wearables in sleep and circadian science. Device validation is important for industrial or clinical research applications

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Results

Conclusion