Rest and Activity Patterns of U.S. Army Aviators in Routine and Operational Training Environments

Abstract

Fatigue continues to be a leading cause of military aviation mishaps. Several factors, including reversed shift missions, can negatively affect sleep patterns and increase the risk of sleep-related fatigue. The purpose of this descriptive study was to document the rest and activity patterns of U.S. Army aviators in operational training and garrison environments using wristworn actigraphy devices. Results from this study indicate that a substantial proportion of participants in the training environment, even after accounting for small sleep bouts during the day, averaged less than the recommended eight hours of sleep across the recording week. Approximately half of the participants in Garrison received less than eight hours of sleep. Sleep efficiency was relatively high in both groups. Moreover, an informal comparison of light exposure metrics revealed that participants in training were exposed to more light than those is garrison. The results highlight the importance of continued research on aviator sleep in operational missions.

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Document Details

Document Type
Technical Report
Publication Date
Aug 06, 2018
Accession Number
AD1074178

Entities

People

  • Amanda Hayes
  • Amanda M. Kelley
  • Colby Mathews
  • David Boudreaux
  • Ian P. Curry
  • Jim Chiaramonte
  • Kathryn A. Feltman
  • Kyle A. Bernhardt

Organizations

  • United States Army Aeromedical Research Lab

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accidents
  • Biomedical Research
  • Combat Readiness
  • Descriptive Analytics
  • Diseases And Disorders
  • Human Behavior
  • Human Factors Engineering
  • Information Operations
  • Instructions
  • Military Applications
  • Military Aviation
  • Military Operations
  • Pain
  • Physical Activity
  • Public Health
  • Statistics
  • Training

Fields of Study

  • Psychology

Readers

  • Circadian Sleep-Wake Regulation and Chronobiology
  • Military Training and Readiness Simulation
  • Regression Analysis.