Michael K Lemke1, Yorghos Apostolopoulos2, Adam Hege3, Sharon Newnam4, Sevil Sönmez5. 1. Complexity & Computational Population Health Group, Department of Kinesiology and Health Science, Stephen F. Austin State University, P.O. Box 13015, Nacogdoches, TX, 75962, USA. Electronic address: lemkem@sfasu.edu. 2. Complexity & Computational Population Health Group, Department of Health & Kinesiology, Texas A&M University, 4243 TAMU, College Station, TX, 77843-4243, USA. Electronic address: yaposto@hlkn.tamu.edu. 3. Department of Health & Exercise Science, Appalachian State University, 111 Rivers Street, Boone, NC, 28608, USA. Electronic address: hegeba@appstate.edu. 4. Accident Research Centre, Monash University, 21 Alliance Lane, Clayton, VIC, 3800, Australia. Electronic address: Sharon.Newnam@monash.edu. 5. Rosen College of Hospitality Management, University of Central Florida, 9907 Universal Blvd., Orlando, FL, 32819, USA. Electronic address: Sevil.Sonmez@ucf.edu.
Abstract
INTRODUCTION: Long-haul truck drivers experience poor sleep health and heightened accident rates, and undiagnosed sleep disorders contribute to these negative outcomes. Subjective sleep disorder screening tools may aid in detecting drivers' sleep disorders. This study sought to evaluate the value of subjective screening methods for detecting latent sleep disorders and identifying truck drivers at-risk for poor sleep health and safety-relevant performance. MATERIALS AND METHODS: Using cross-sectional data from 260 long-haul truck drivers, we: 1) used factor analysis to identify possible latent sleep disorders; 2) explored the construct validity of extracted sleep disorder factors by determining their associations with established sleep disorder risk factors and symptoms; and 3) explored the predictive validity of resulting sleep disorder factors by determining their associations with sleep health and safety-relevant performance. RESULTS: Five latent sleep disorder factors were extracted: 1) circadian rhythm sleep disorders; 2) sleep-related breathing disorders; 3) parasomnias; 4) insomnias; 5) and sleep-related movement disorders. Patterns of associations between these factors generally corresponded with known risk factors and symptoms. One or more of the extracted latent sleep disorder factors were significantly associated with all the sleep health and safety outcomes. DISCUSSION: Using subjective sleep problems to detect latent sleep disorders among long-haul truck drivers may be a timely and effective way to screen this highly mobile occupational segment. This approach should constitute one component of comprehensive efforts to diagnose and treat sleep disorders among commercial transport operators.
INTRODUCTION: Long-haul truck drivers experience poor sleep health and heightened accident rates, and undiagnosed sleep disorders contribute to these negative outcomes. Subjective sleep disorder screening tools may aid in detecting drivers' sleep disorders. This study sought to evaluate the value of subjective screening methods for detecting latent sleep disorders and identifying truck drivers at-risk for poor sleep health and safety-relevant performance. MATERIALS AND METHODS: Using cross-sectional data from 260 long-haul truck drivers, we: 1) used factor analysis to identify possible latent sleep disorders; 2) explored the construct validity of extracted sleep disorder factors by determining their associations with established sleep disorder risk factors and symptoms; and 3) explored the predictive validity of resulting sleep disorder factors by determining their associations with sleep health and safety-relevant performance. RESULTS: Five latent sleep disorder factors were extracted: 1) circadian rhythm sleep disorders; 2) sleep-related breathing disorders; 3) parasomnias; 4) insomnias; 5) and sleep-related movement disorders. Patterns of associations between these factors generally corresponded with known risk factors and symptoms. One or more of the extracted latent sleep disorder factors were significantly associated with all the sleep health and safety outcomes. DISCUSSION: Using subjective sleep problems to detect latent sleep disorders among long-haul truck drivers may be a timely and effective way to screen this highly mobile occupational segment. This approach should constitute one component of comprehensive efforts to diagnose and treat sleep disorders among commercial transport operators.
Authors: Adam Hege; Michael K Lemke; Yorghos Apostolopoulos; Brian Whitaker; Sevil Sönmez Journal: Int J Environ Res Public Health Date: 2019-03-19 Impact factor: 3.390