Sleep and high-risk behavior in military service members: a mega-analysis of four diverse U.S. Army units.

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 nonmilitary populations due to a number of occupational, cultural, and psychosocial factors (e.g. continuous operations, stress, and 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 = 2,296, 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 [h]) 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 military-wide policy changes regarding sleep and HRB.

Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

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/duty-related 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.

Objective changes in activity levels following sleep extension as measured by wrist actigraphy.

OBJECTIVE/BACKGROUND: It is widely established that insufficient sleep can lead to adverse health outcomes. Paradoxically, epidemiologic research suggests that individuals who report habitual nightly sleep greater than 9 h also are at risk for adverse health outcomes. Further, studies have shown that long sleepers have decreased activity levels, which may partially explain the relationship between long sleep duration and mortality. The influence of sleep extension (longer time in bed) on levels of daily activity has not yet been established. The current study examined whether a week of sleep extension altered activity levels within the subsequent daily waking active and sleep period in order to determine whether increased time in bed indeed is related to decreased activity levels. METHODS: A total of 26 healthy volunteers wore wrist accelerometer devices (Actiwatch 2.0, Philips) in order to objectively measure sleep and activity for six days during their normal schedules and for six days during a sleep extension (10 h time in bed) intervention. RESULTS: There were no significant or clinically-relevant differences in 24-h activity or activity during the active or sleep period between baseline and sleep extension conditions. There were no main or interaction effects of day and condition when daily activity counts were compared between baseline and sleep extension conditions for the 24 h period (Day: F(5, 21) = 1.92, p = 0.12; Condition: F(1,25) = 2.93, p = 0.09; Day by Condition: F(5,21) = 0.32, p = 0.83), Active Waking Period (Day: F(5,25) = 1.53, p = 0.18; Condition: F(1,25) = 0.26, p = 0.61; Day by Condition: F(5,21) = 0.55, p = 0.74) or Nightly Sleep (Day: F(5,21) = 0.86, p = 0.51; Condition: F(1,25) = 1.78, p = 0.19; Day by Condition: F(5,21) = 0.79, p = 0.56) periods. In contrast, there was a main effect of condition when examining sleep duration by day between conditions (Day: F(5,21) = 1.60, p = 0.16; Condition: F(1,25) = 167.31, p < 0.001; Day by Condition: F(5,21) = 2.31, p = 0.07), such that sleep duration was longer during the sleep extension condition. DISCUSSION: Sleep duration increased during six days of a sleep extension protocol but activity levels remained similar to their baseline (normal) sleep schedule. The current findings suggest that extending time in bed alone does not alter waking activity counts in young healthy adults. The link between extended sleep and adverse health outcomes may be attributable to other phenotypic factors, or other biological correlates of extended sleep and poor health.