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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Quantifying microsleep to help assess subjective sleepiness.

Allen J Blaivas¹, Rajeshri Patel, David Hom

¹Division of Pulmonary, Critical Care, and Sleep Medicine, VA New Jersey Health Care System, 385 Tremont Avenue, East Orange, NJ 07018, USA. allen.blaivas@va.gov

|January 24, 2007

Summary

Quantifying microsleep during the Multiple Sleep Latency Test (MSLT) did not significantly improve its correlation with subjective sleepiness scores. This study found that integrating microsleep metrics did not enhance diagnostic accuracy for sleepiness beyond the MSLT alone.

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Area of Science:

Sleep Medicine
Neuroscience
Clinical Diagnostics

Background:

Microsleep presence during Multiple Sleep Latency Test (MSLT) correlates with increased daytime sleepiness and accidents.
Current diagnostic tools lack methods to quantify microsleep for objective sleepiness measurement.
The study addresses the need for quantitative microsleep integration in sleepiness assessment.

Purpose of the Study:

To integrate quantitative microsleep data into the MSLT score.
To determine if this integration improves the correlation between objective MSLT measures and subjective sleepiness (Epworth Sleepiness Scale - ESS).
To evaluate a microsleep nap percentage (MNP) as a potential correlate of ESS.

Main Methods:

Reviewed charts of 54 patients with MSLT score > 5min and documented microsleep.
Developed an MSLT plus microsleep score (MSL-M) using microsleep as a surrogate for sleep onset.
Compared MSL-M and MNP correlations with ESS against the standard MSLT score using Spearman correlation.

Main Results:

The MSL-M showed a slightly improved, but not statistically significant, correlation with ESS compared to MSLT (r=0.106 vs. r=0.063).
Both MSLT and MSL-M demonstrated weak correlations with ESS.
The microsleep nap percentage (MNP) also showed a poor correlation with ESS (r=-0.099).

Conclusions:

Quantitatively integrating microsleep onset into the MSLT score did not significantly enhance its correlation with subjective sleepiness.
The simple presence of microsleep during MSLT offered a marginal improvement over MSLT alone, but quantification did not yield significant benefits.
Further research may be needed to develop effective quantitative measures of microsleep for sleepiness assessment.