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Disturbed sleep is one of the most common complaints following traumatic brain injury (TBI) and worsens morbidity and long-term sequelae. Further, sleep and TBI share neurophysiologic underpinnings with direct relevance to recovery from TBI. As such, disturbed sleep and clinical sleep disorders represent modifiable treatment targets to improve outcomes in TBI. This paper presents key findings from a national working group on sleep and TBI, with a specific focus on the testing and development of sleep-related therapeutic interventions for mild TBI (mTBI). First, mTBI and sleep physiology are briefly reviewed. Next, essential empirical and clinical questions and knowledge gaps are addressed. Finally, actionable recommendations are offered to guide active and efficient collaboration between academic, industry, and governmental stakeholders.

Posttraumatic stress disorder (PTSD) is a debilitating and common consequence of military service. PTSD is associated with increased incidence of mood disturbances (e.g., anxiety). Additionally, veterans with PTSD often have poor-quality sleep and poor emotion regulation ability. We sought to assess whether such sleep and emotion regulation deficits contribute to mood disturbances. In 144 veterans, using a double moderation model, we tested the relationship between PTSD and anxiety and examined whether sleep quality and emotion regulation interact to moderate this relationship. We found that PTSD predicts higher anxiety in veterans with poor and average sleep quality who utilize maladaptive emotion regulation strategies. However, there was no relationship between PTSD and anxiety in individuals with good sleep quality, regardless of emotion regulation. Similarly, there was no relationship between PTSD and anxiety in individuals with better emotion regulation, regardless of sleep quality. Results were unchanged when controlling for history of traumatic brain injury (TBI), despite the fact that those with both PTSD and TBI had the poorest emotion regulation overall. Taken together, these results suggest that good-quality sleep may be protective against poor emotion regulation in veterans with PTSD. Sleep may therefore be a target for therapeutic intervention in veterans with PTSD and heightened anxiety.

The military lifestyle often includes continuous operations whether in training or deployed environments. These stressful environments present unique challenges for service members attempting to achieve consolidated, restorative sleep. The significant mental and physical derangements caused by degraded metabolic, cardiovascular, skeletomuscular, and cognitive health often result from insufficient sleep and/or circadian misalignment. Insufficient sleep and resulting fatigue compromises personal safety, mission success, and even national security. In the long-term, chronic insufficient sleep and circadian rhythm disorders have been associated with other sleep disorders (e.g., insomnia, obstructive sleep apnea, and parasomnias). Other physiologic and psychologic diagnoses such as post-traumatic stress disorder, cardiovascular disease, and dementia have also been associated with chronic, insufficient sleep. Increased co-morbidity and mortality are compounded by traumatic brain injury resulting from blunt trauma, blast exposure, and highly physically demanding tasks under load. We present the current state of science in human and animal models specific to service members during- and post-military career. We focus on mission requirements of night shift work, sustained operations, and rapid re-entrainment to time zones. We then propose targeted pharmacological and non-pharmacological countermeasures to optimize performance that are mission- and symptom-specific. We recognize a critical gap in research involving service members, but provide tailored interventions for military health care providers based on the large body of research in health care and public service workers.

Background: It is known that sleep disturbance is associated with increased suicidal thinking. Moreover, completed suicides, when adjusted for the proportion of the populace that is awake at a given time, are more probable during the late night/early morning hours. Despite these concerns, no studies have examined the role of trait-like individual differences in vulnerability to suicidal ideation during sleep deprivation or insomnia. In two separate studies, we examined whether the trait of extraversion is predictive of changes in suicidal thinking following two nights of sleep deprivation and among individuals meeting criteria for insomnia. Methods: Study 1: Twenty-five healthy military personnel (20 males), ages 20-35 completed the NEO-PI-R Extraversion scale and the Suicidal Ideation (SUI) scale of the Personality Assessment Inventory (PAI). Participants completed 77 hours of continuous sleep deprivation. After 56 hours of sleep deprivation, participants completed the SUI scale a second time. We predicted change in SUI scores from baseline extraversion. Study 2: 2,061 adults aged 18-79 (900 males) were divided into two groups based on the clinical threshold (≥10) on the Insomnia Severity Index (ISI) and completed measures of extraversion and depression, including the suicide item of the Patient Health Questionnaire-9 (PHQ9). Results: Study 1: After controlling for caffeine group and changes in PAI Depression, Extraversion scores were used to predict changes in SUI scores using stepwise multiple linear regression. Higher Extraversion was significantly associated with increased non-clinical suicidal ideation following sleep loss, ??=.463, partial r=.512, p=.013. Study 2: After controlling for depression, the effect of insomnia on suicidal ideation was moderated by trait extraversion (p<.0001). Overall, the presence or absence of insomnia had little effect on individuals low in trait extraversion (i.e., introverts), but insomnia was associated with significantly higher suicidal ideation among high trait extraverted individuals. Conclusions: Higher trait extraversion was associated with increased vulnerability to suicidal ideation between rested baseline and total sleep deprivation and was associated with greater suicidal ideation among those meeting criteria for clinically severe insomnia. These findings point to a potential trait-like vulnerability factor that may further our understanding of sleep disruption in the phenomenology of suicide.

During multi-domain operations (MDO), soldiers need the physical supremacy, cognitive dominance, and emotional resilience to help defend and win our nation's wars. Optimal sleep has been shown to boost physical performance and cognitive processing. This manuscript will discuss how recent advances in sleep science strongly argue for the integration of sleep planning into military operations. Review article. We reviewed the current understanding of how sleep affects Soldier readiness, how sleep and pain are interrelated, and unique challenges to obtaining adequate sleep in military training environments. We then address solutions that can be implemented by leaders and individuals to manage warfighter fatigue and optimize unit performance. Since sleep is foundational to soldier health and readiness, improving warfighter fatigue management is a priority for leaders. To succeed in MDO, military personnel require physical supremacy, cognitive dominance, and emotional resilience to fight and win. Sleep science is a rapidly emerging field, and the clear implications for maximizing human performance argue strongly for more deliberate integration into military training and operations. Leaders that incorporate sleep and fatigue management into the planning and execution phases of operations will help facilitate mission priorities and prove a powerful force multiplier.

To investigate the effects of caffeine on psychomotor vigilance and sleepiness during sleep restriction and following subsequent recovery sleep. Participants were N = 48 healthy good sleepers. All participants underwent five nights of sleep satiation (time-in-bed [TIB]: 10 hours), followed by five nights of sleep restriction (TIB: 5 hours), and three nights of recovery sleep (TIB: 8 hours) in a sleep laboratory. Caffeine (200 mg) or placebo was administered in the form of chewing gum at 08:00 am and 12:00 pm each day during the sleep restriction phase. Participants completed hourly 10-minute psychomotor vigilance tests and a modified Maintenance of Wakefulness Test approximately every 4 hours during the sleep restriction and recovery phases. Caffeine maintained objective alertness compared to placebo across the first 3 days of sleep restriction, but this effect was no longer evident by the fourth day. A similar pattern of results was found for Maintenance of Wakefulness Test sleep latencies, such that those in the caffeine group (compared to placebo) did not show maintenance of wakefulness relative to baseline after the second night of restriction. Compared to placebo, participants in the caffeine condition displayed slower return to baseline in alertness and wakefulness across the recovery sleep period. Finally, the caffeine group showed greater N3 sleep duration during recovery. Caffeine appears to have limited efficacy for maintaining alertness and wakefulness across 5 days of sleep restriction. Perhaps more importantly, there may be recovery costs associated with caffeine use following conditions of prolonged sleep loss.

Post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common injuries among returning combat veterans from the wars in Iraq and Afghanistan. Although these combat injuries have been associated with increased sleep disruption, little is known about the nature and specificity of sleep problems within these common injury categories. A retrospective chart review of 69 consecutive referrals to the Waiter Reed Army Medical Center sleep clinic was conducted. All cases were active duty soldiers who had recently returned from combat deployment in Iraq or Afghanistan. Data from polysomnographically (PSG) recorded sleep stages, sleepiness scales, and documented medical diagnoses were extracted from medical records. Sleep data were compared across diagnoses of PTSD, TBI, and other clinical conditions. As expected, clinical sleep disturbances, including rates of obstructive sleep apnea, excessive awakenings, daytime sleepiness, and hypoxia, were high for the sample as a whole. However, no differences across diagnostic groups were found. Differences were observed, however, on PSG measures of sleep quality, suggesting more frequent arousals from sleep among patients with PTSD and greater slow wave sleep among those with TBI. Except for REM latency, medication status had virtually no effect on sleep variables. Among recently redeployed combat veterans, clinically significant sleep disturbances and problems with sleep-disordered breathing are common but nonspecific findings across primary diagnoses of PTSD, TBI, major depression, and anxiety disorder, whereas more subtle differences in sleep architecture and arousals as measured by overnight PSG recordings were modestly, but significantly, effective at distinguishing among the diagnostic groups.

Study Objectives: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response. Methods: Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate–severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6–12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. Results: 19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ 2 1 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation. Conclusions: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value. Citation: Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: A machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med . 2024;20(6):921–931.

Insomnia is common after traumatic brain injury (TBI) and contributes to morbidity and long-term sequelae. To identify unique trajectories of insomnia in the 12 months after TBI. In this prospective cohort study, latent class mixed models (LCMMs) were used to model insomnia trajectories over time and to classify participants into distinct profile groups. Data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, a longitudinal, multisite, observational study, were uploaded to the Federal Interagency Traumatic Brain Injury Repository (FITBIR) database. Participants were enrolled at 1 of 18 participating level I trauma centers and enrolled within 24 hours of TBI injury. Additional data were obtained directly from the TRACK-TBI investigators that will be uploaded to FITBIR in the future. Data were collected from February 26, 2014, to August 8, 2018, and analyzed from July 1, 2020, to November 15, 2021. Traumatic brain injury. Insomnia Severity Index assessed serially at 2 weeks and 3, 6, and 12 months thereafter. The final sample included 2022 participants (1377 [68.1%] men; mean [SD] age, 40.1 [17.2] years) from the FITBIR database and the TRACK-TBI study. The data were best fit by a 5-class LCMM. Of these participants, 1245 (61.6%) reported persistent mild insomnia symptoms (class 1); 627 (31.0%) initially reported mild insomnia symptoms that resolved over time (class 2); 91 (4.5%) reported persistent severe insomnia symptoms (class 3); 44 (2.2%) initially reported severe insomnia symptoms that resolved by 12 months (class 4); and 15 (0.7%) initially reported no insomnia symptoms but had severe symptoms by 12 months (class 5). In a multinomial logistic regression model, several factors significantly associated with insomnia trajectory class membership were identified, including female sex (odds ratio [OR], 1.65 [95% CI, 1.02-2.66]), Black race (OR, 2.36 [95% CI, 1.39-4.01]), history of psychiatric illness (OR, 2.21 [95% CI, 1.35-3.60]), and findings consistent with intracranial injury on computed tomography (OR, 0.36 [95% CI, 0.20-0.65]) when comparing class 3 with class 1. These results suggest important heterogeneity in the course of insomnia after TBI in adults. More work is needed to identify outcomes associated with these insomnia trajectory class subgroups and to identify optimal subgroup-specific treatment approaches.