Background. Concussions affect nearly 3 million people a year and are the leading cause of traumatic brain injury–related emergency department visits among youth. Evidence shows neuromotor regions are sensitive to concussive events and that motor symptoms may be the earliest clinical manifestations of neurodegenerative traumatic brain injuries. However, little is known about the effects repeated concussions play on motor learning. Namely, how does concussion acuity (time since injury) affect different behavioral and neurophysiological components of motor learning? Methods. Using a 3-pronged approach, we assessed (1) behavioral measures of motor learning, (2) neurophysiological measures underlying retention of motor learning known as occlusion, and (3) quantitative survey data capturing affective symptoms of each participant. Collegiate student athletes were stratified across 3 groups depending on their concussion history: (1) NonCon, no history of concussion; (2) Chronic, chronic-state of concussion (>1 year postinjury), or (3) Acute, acute state of concussion (<2 weeks postinjury). Results. We found that athletes in both the acute and chronic state of injury following a concussion had impaired retention and aberrant occlusion in motor skill learning as compared with athletes with no history of concussion. Moreover, higher numbers of previous concussions (regardless of the time since injury) correlated with more severe behavioral and neurophysiological motor impairments by specifically hindering neurophysiological mechanisms of learning to affect behavior. Conclusions. These results indicate the presence of motor learning impairment that is evident within 2 weeks postinjury. Our findings have significant implications for the prognosis of concussion and emphasize the need for prevention, but can also direct more relevant rehabilitation treatment targets.
Experimental sleep restriction and deprivation lead to risky decision-making. Further, in naturalistic settings, short sleep duration and poor sleep quality have been linked to real-world high-risk behaviors (HRB), such as reckless driving or substance use. Military populations, in general, tend to sleep less and have poorer sleep quality than non-military populations due to a number of occupational, cultural, and psychosocial factors (e.g., continuous operations, stress, trauma). Consequently, it is possible that insufficient sleep in this population is linked to HRB. To investigate this question, we combined data from four diverse United States Army samples and conducted a mega-analysis by aggregating raw, individual-level data (n = 2296, age 24.7 ± 5.3). A negative binomial regression and a logistic regression were used to determine whether subjective sleep quality (Pittsburgh Sleep Quality Index [PSQI], Insomnia Severity Index [ISI] and duration [hours]) predicted instances of military-specific HRB and the commission of any HRB (yes/no), respectively. Poor sleep quality slightly elevated the risk for committing HRBs (PSQI Exp(B): 1.12 and ISI Exp(B): 1.07), and longer duration reduced the risk for HRBs to a greater extent (Exp(B): 0.78), even when controlling for a number of relevant demographic factors. Longer sleep duration also predicted a decreased risk for commission of any HRB behaviors (Exp(B): 0.71). These findings demonstrate that sleep quality and duration (the latter factor, in particular) could be targets for reducing excessive HRB in military populations. These findings could therefore lead to unit-wide or even military-wide policy changes regarding sleep and HRB.
Burke, TM, Lisman, PJ, Maguire, K, Skeiky, L, Choynowski, JJ, CapaldiII, VF, Wilder, JN, Brager, AJ, and Dobrosielski, DA. Examination of sleep and injury among college football athletes. J Strength Cond Res 34(3): 609–616, 2020—The purpose of this study was to characterize subjective sleep metrics in collegiate football players at the start of the season, determine the relationship between preseason subjective sleep measures and in-season objective sleep characteristics, and examine the association between subjective and objective sleep metrics and incidence of time-loss injury during the competitive season. Ninety-four Division I football players completed 5 validated sleep-related questionnaires to assess sleep quality, insomnia severity, daytime sleepiness, sleep apnea risk, and circadian preference before the start of the season. Clinical thresholds for sleep questionnaires were used to determine risk of sleep disorders. Continuous wrist actigraphy was collected throughout the season to generalize sleep behaviors. Time-loss injury incidence data were recorded and used for analysis. Results indicated that 67.4% (60 of 89) of athletes scored above clinical threshold in at least 1 questionnaire to indicate sleep disorder risk. At the start of the season, players subjectively reported an average sleep duration of 7:16 ± 1:18 hours:minutes, which was in contrast to the 6:04 ± 0:41 hours:minutes measured through actigraphy during the season. Logistic regression models adjusted for age and body mass index revealed no significant associations between injury and subjective (odds ratio [OR] = 1.00; 95% confidence interval [CI] = 0.99–1.01) and objective (OR = 1.01; 95% CI = 0.99–1.02) sleep duration or measures attained from sleep questionnaires (ORs ranged from 1.01 to 2.87). Sleep metrics (quantity and quality) were not associated with increased risk of injury in this cohort of collegiate football players.
Background: The Walter Reed Army Institute of Research (WRAIR) Operational Research Kit-Actigraphy (WORK-A) is a set of unique practice parameters and actigraphy-derived measures for the analysis of operational military sleep patterns. The WORK-A draws on best practices from the literature and comprises 15 additional descriptive variables. Here, we demonstrate the WORK-A with a sample of United States Army Reserve Officers' Training Corps (ROTC) cadets (n = 286) during a month-long capstone pre-commissioning training exercise. Methods: The sleep of ROTC cadets (n = 286) was measured by Philips Actiwatch devices during the 31-day training exercise. The preliminary effectiveness of the WORK-A was tested by comparing differences in sleep measures collected by Actiwatches as calculated by Philips Actiware software against WORK-A-determined sleep measures and self-report sleep collected from a subset of ROTC cadets (n = 140). Results: Actiware sleep summary statistics were significantly different from WORK-A measures and self-report sleep (all P ≤ 0.001). Bedtimes and waketimes as determined by WORK-A major sleep intervals showed the best agreement with self-report bedtime (22:21 ± 1:30 vs. 22:13 ± 0:40, P = 0.21) and waketime (04:30 ± 2:17 vs. 04:31 ± 0: 47, P = 0.68). Though still significantly different, the discrepancy was smaller between the WORK-A measure of time in bed (TIB) for major sleep intervals (352 ± 29 min) and self-report nightly sleep duration (337 ± 57 min, P = 0.006) than that between the WORK-A major TIB and Actiware TIB (177 ± 42, P ≤ 0.001). Conclusions: Default actigraphy methods are not the most accurate methods for characterizing soldier sleep, but reliable methods for characterizing operational sleep patterns is a necessary first step in developing strategies to improve soldier readiness. The WORK-A addresses this knowledge gap by providing practice parameters and a robust variety of measures with which to profile sleep behavior in service members.
Background: The impact of sleep disorders on active-duty soldiers' medical readiness is not currently quantified. Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members. The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S. military healthcare records from fiscal year 2018 (FY2018). Methods: Medical diagnosis information and deployability profiles (e-Profiles) were queried for all active-duty U.S. Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018. Nondeployability was predicted from medical reasons for having an e-Profile (categorized as sleep, behavioral health, musculoskeletal, cardiometabolic, injury, or accident) using binomial logistic regression. Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability. Results: Out of 582,031 soldiers, 48.4% (n = 281,738) had a sleep-related diagnosis in their healthcare records, 9.7% (n = 56,247) of soldiers had e-Profiles, and 1.9% (n = 10,885) had a sleep e-Profile. Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident (pOR (prevalence odds ratio) =4.7, 95% CI 2.63-8.39, P ≤ 0.001) or work/duty-related injury (pOR = 1.6, 95% CI 1.32-1.94, P ≤ 0.001). The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile (pOR = 4.25, 95% CI 3.75-4.81, P ≤ 0.001) or work/dutyrelated injury (pOR = 2.62, 95% CI 1.63-4.21, P ≤ 0.001). Conclusion: Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018, but their sleep problems are largely not profiled as limitations to medical readiness. Musculoskeletal issues and physical injury predict nondeployability, and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues. Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.
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