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Effects of Napping During Shift Work on Sleepiness and Performance in Emergency Medical Services Personnel and Similar Shift Workers: A Systematic Review and Meta-Analysis

NA-PP-Napping During Shift Work

NA (ED)

Background: Scheduled napping during work shifts may be an effective way to mitigate fatigue-related risk. This study aimed to critically review and synthesize existing literature on the impact of scheduled naps on fatigue-related outcomes for EMS personnel and similar shift worker groups.


Methods: A systematic literature review was performed of the impact of a scheduled nap during shift work on EMS personnel or similar shift workers. The primary (critical) outcome of interest was EMS personnel safety. Secondary (important) outcomes were patient safety; personnel performance; acute states of fatigue, alertness, and sleepiness; indicators of sleep duration and/or quality; employee retention/turnover; indicators of long-term health; and cost to the system. Meta-analyses were performed to evaluate the impact of napping on a measure of personnel performance (the psychomotor vigilance test [PVT]) and measures of acute fatigue.


Results: Of 4,660 unique records identified, 13 experimental studies were determined relevant and summarized. The effect of napping on reaction time measured at the end of shift was small and non-significant (SMD 0.12, 95% CI -0.13 to 0.36; p = 0.34). Napping during work did not change reaction time from the beginning to the end of the shift (SMD -0.01, 95% CI -25.0 to 0.24; p = 0.96). Naps had a moderate, significant effect on sleepiness measured at the end of shift (SMD 0.40, 95% CI 0.09 to 0.72; p = 0.01). The difference in sleepiness from the start to the end of shift was moderate and statistically significant (SMD 0.41, 95% CI 0.09 to 0.72; p = 0.01).


Conclusions: Reviewed literature indicated that scheduled naps at work improved performance and decreased fatigue in shift workers. Further research is required to identify the optimal timing and duration of scheduled naps to maximize the beneficial outcomes.


Authors: Christian Martin-Gill, Laura K Barger, Charity G Moore, J Stephen Higgins, Ellen M Teasley, Patricia M Weiss, Joseph P Condle, Katharyn L Flickinger, Patrick J Coppler, Denisse J Sequeira, Ayushi A Divecha, Margaret E Matthews, Eddy S Lang, P Daniel Patterson

P. Daniel Patterson - pattersonpd@upmc.edu

Preliminary data gathering/ baseline

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