Association of Biomarkers of Neuronal Injury and Inflammation With Insomnia Trajectories After Traumatic Brain Injury: A TRACK-TBI Study.

BACKGROUND AND OBJECTIVES: Insomnia affects about one-third of patients with traumatic brain injury and is associated with worsened outcomes after injury. We hypothesized that higher levels of plasma neuroinflammation biomarkers at the time of TBI would be associated with worse 12-month insomnia trajectories. METHODS: Participants were prospectively enrolled from 18 level-1 trauma centers participating in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury study from February 26, 2014, to August 8, 2018. Plasma glial fibrillary acidic protein (GFAP), high-sensitivity C-reactive protein (hsCRP), S100b, neuron-specific enolase (NSE), and ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1) were collected on days 1 (D1) and 14 (D14) after TBI. The insomnia severity index was collected at 2 weeks, 3, 6, and 12 months postinjury. Participants were classified into insomnia trajectory classes based on a latent class model. We assessed the association of biomarkers with insomnia trajectories, controlling for medical and psychological comorbidities and demographics. RESULTS: Two thousand twenty-two individuals with TBI were studied. Elevations in D1 hsCRP were associated with persistent insomnia (severe, odds ratio [OR] = 1.33 [1.11, 1.59], p = 0.002; mild, OR = 1.10 [1.02, 1.19], p = 0.011). Similarly, D14 hsCRP elevations were associated with persistent insomnia (severe, OR = 1.27 [1.02, 1.59], p = 0.03). Of interest, D1 GFAP was lower in persistent severe insomnia (median [Q1, Q3]: 154 [19, 445] pg/mL) compared with resolving mild (491 [154, 1,423], p < 0.001) and persistent mild (344 [79, 1,287], p < 0.001). D14 GFAP was similarly lower in persistent (11.8 [6.4, 19.4], p = 0.001) and resolving (13.9 [10.3, 20.7], p = 0.011) severe insomnia compared with resolving mild (20.6 [12.4, 39.6]. Accordingly, increases in D1 GFAP were associated with reduced likelihood of having persistent severe (OR = 0.76 [95% CI 0.63-0.92], p = 0.004) and persistent mild (OR = 0.88 [0.81, 0.96], p = 0.003) compared with mild resolving insomnia. No differences were found with other biomarkers. DISCUSSION: Elevated plasma hsCRP and, surprisingly, lower GFAP were associated with adverse insomnia trajectories after TBI. Results support future prospective studies to examine their utility in guiding insomnia care after TBI. Further work is needed to explore potential mechanistic connections between GFAP levels and the adverse insomnia trajectories.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

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%; χ21 = 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.

Patient Engagement and Provider Effectiveness of a Novel Sleep Telehealth Platform and Remote Monitoring Assessment in the US Military: Pilot Study Providing Evidence-Based Sleep Treatment Recommendations.

BACKGROUND: Sleep problems are common and costly in the US military. Yet, within the military health system, there is a gross shortage of trained specialist providers to address sleep problems. As a result, demand for sleep medicine care far exceeds the available supply. Telehealth including telemedicine, mobile health, and wearables represents promising approaches to increase access to high-quality and cost-effective care. OBJECTIVE: The purpose of this study was to evaluate patient engagement and provider perceived effectiveness of a novel sleep telehealth platform and remote monitoring assessment in the US military. The platform includes a desktop web portal, native mobile app, and integrated wearable sensors (ie, a commercial off-the-shelf sleep tracker [Fitbit]). The goal of the remote monitoring assessment was to provide evidence-based sleep treatment recommendations to patients and providers. METHODS: Patients with sleep problems were recruited from the Internal Medicine clinic at Walter Reed National Military Medical Center. Patients completed intensive remote monitoring assessments over 10 days (including a baseline intake questionnaire, daily sleep diaries, and 2 daily symptom surveys), and wore a Fitbit sleep tracker. Following the remote monitoring period, patients received assessment results and personalized sleep education in the mobile app. In parallel, providers received a provisional patient assessment report in an editable electronic document format. Patient engagement was assessed via behavioral adherence metrics that were determined a priori. Patients also completed a brief survey regarding ease of completion. Provider effectiveness was assessed via an anonymous survey. RESULTS: In total, 35 patients with sleep problems participated in the study. There were no dropouts. Results indicated a high level of engagement with the sleep telehealth platform, with all participants having completed the baseline remote assessment, reviewed their personalized sleep assessment report, and completed the satisfaction survey. Patients completed 95.1% of sleep diaries and 95.3% of symptom surveys over 10 days. Patients reported high levels of satisfaction with most aspects of the remote monitoring assessment. In total, 24 primary care providers also participated and completed the anonymous survey. The results indicate high levels of perceived effectiveness and identified important potential benefits from adopting a sleep telehealth approach throughout the US military health care system. CONCLUSIONS: Military patients with sleep problems and military primary care providers demonstrated high levels of engagement and satisfaction with a novel sleep telehealth platform and remote monitoring assessment. Sleep telehealth approaches represent a potential pathway to increase access to evidence-based sleep medicine care in the US military. Further evaluation is warranted.

Association Between Insomnia and Mental Health and Neurocognitive Outcomes Following Traumatic Brain Injury.

We previously described five trajectories of insomnia (each defined by a distinct pattern of insomnia severity over 12 months following traumatic brain injury [TBI]). Our objective in the present study was to estimate the association between insomnia trajectory status and trajectories of mental health and neurocognitive outcomes during the 12 months after TBI. In this study, participants included N = 2022 adults from the Federal Inter-agency Traumatic Brain Injury Repository database and Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. The following outcome measures were assessed serially at 2 weeks, and 3, 6, and 12 months post-injury: Insomnia Severity Index, Patient Health Questionnaire, Post-Traumatic Stress Disorder (PTSD) Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Patient Reported Outcomes Measurement Information System-Pain, and Quality of Life After Brain Injury-Overall Scale. Neurocognitive performance was assessed at 2 weeks, and 6 and 12 months using the Wechsler Adult Intelligence Scales Processing Speed Index and the Trails Making Test Parts A and B. Results indicated that greater insomnia severity was associated with greater abnormality in mental health, quality of life, and neuropsychological testing outcomes. The pattern of insomnia over time tracked the temporal pattern of all these outcomes for all but a very small number of participants. Notably, severe insomnia at 3 or 6 months post-TBI was a risk factor for poor recovery at 12 months post-injury. In conclusion, in this well-characterized sample of individuals with TBI, insomnia severity generally tracked severity of depression, pain, PTSD, quality of life, and neurocognitive outcomes over 12 months post-injury. More intensive sleep assessment is needed to elucidate the nature of these relationships and to help inform best strategies for intervention.

Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy.

STUDY OBJECTIVES: Although many military personnel with insomnia are treated with prescription medication, little reliable guidance exists to identify patients most likely to respond. As a first step toward personalized care for insomnia, we present results of a machine-learning model to predict response to insomnia medication. METHODS: The sample comprised n = 4,738 nondeployed US Army soldiers treated with insomnia medication and followed 6-12 weeks after initiating treatment. All patients had moderate-severe baseline scores on the Insomnia Severity Index (ISI) and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble machine-learning model was developed in a 70% training sample to predict clinically significant ISI improvement, defined as 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: 21.3% of patients had clinically significant ISI improvement. Model test sample area under the receiver operating characteristic curve (standard error) was 0.63 (0.02). Among the 30% of patients with the highest predicted probabilities of improvement, 32.5.% had clinically significant symptom improvement vs 16.6% in the 70% sample predicted to be least likely to improve (χ21 = 37.1, P < .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity. CONCLUSIONS: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment, but 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: development of a machine-learning model to predict response to pharmacotherapy. J Clin Sleep Med. 2023;19(8):1399-1410.

Association of Traumatic Brain Injury Severity and Self-Reported Neuropsychiatric Symptoms in Wounded Military Service Members.

The impact of traumatic brain injury (TBI) severity and loss of consciousness (LOC) on the development of neuropsychiatric symptoms was studied in injured service members (SMs; n = 1278) evacuated from combat settings between 2003 and 2012. TBI diagnoses of mild TBI (mTBI) or moderate-to-severe TBI (MS-TBI) along with LOC status were identified using International Classification of Diseases, Ninth Revision (ICD-9) codes and the Defense and Veterans Brain Injury Center Standard Surveillance Case Definition for TBI. Self-reported psychiatric symptoms were evaluated for post-traumatic stress disorder (PTSD) with the PTSD Checklist, Civilian Version for PTSD, the Patient Health Questionnaire-9 for major depressive disorder (MDD), and the Patient Health Questionnaire-15 for somatic symptom disorder (SSD) in two time periods post-injury: Assessment Period 1 (AP1, 0.0-2.5 months) and Assessment Period 2 (AP2, 3-12 months). mTBI, but not MS-TBI, was associated with increased neuropsychiatric symptoms: PTSD in AP1 and AP2; MDD in AP1; and SSD in AP2. A subgroup analysis of mTBI with and without LOC revealed that mTBI with LOC, but not mTBI without LOC, was associated with increased symptoms as compared to non-TBI: PTSD in AP1 and AP2; MDD in AP1; and SSD in AP1 and AP2. Moreover, mTBI with LOC was associated with increased MDD symptoms in AP2, and SSD symptoms in AP1 and AP2, compared to mTBI without LOC. These findings reinforce the need for the accurate characterization of TBI severity and a multi-disciplinary approach to address the devastating impacts of TBI in injured SMs.

Cross-Cutting Lessons Learned During the COVID-19 Pandemic-the Walter Reed Army Institute of Research Experience.

INTRODUCTION: At the start of the coronavirus disease 2019 (COVID-19) pandemic, Walter Reed Army Institute of Research (WRAIR) mobilized to rapidly conduct medical research to detect, prevent, and treat the disease in order to minimize the impact of the pandemic on the health and readiness of U.S. Forces. WRAIR's major efforts included the development of the Department of Defense (DoD) COVID-19 vaccine candidate, researching novel drug therapies and monoclonal antibodies, refining and scaling-up diagnostic capabilities, evaluating the impact of viral diversity, assessing the behavioral health of Soldiers, supporting U.S. DoD operational forces overseas, and providing myriad assistance to allied nations. WRAIR personnel have also filled key roles within the whole of government response to the pandemic. WRAIR had to overcome major pandemic-related operational challenges in order to quickly execute a multimillion-dollar portfolio of COVID-19 research. Consequently, the organization learned lessons that could benefit other leaders of medical research organizations preparing for the next pandemic. MATERIALS AND METHODS: We identified lessons learned using a qualitative thematic analysis of 76 observation/recommendation pairs from across the organization. These lessons learned were organized under the Army's four pillars of readiness (staffing, training, equipping, and leadership development). To this framework, we added organizing and leading to best capture our experiences within the context of pandemic response. RESULTS: The major lessons learned for organizing were: (1) the pandemic created a need to rapidly pivot to new scientific priorities; (2) necessary health and safety precautions disrupted the flow of normal science and put programs at risk of missing milestones; (3) relationships with partners and allies facilitated medical diplomacy and advancement of U.S. national military and economic goals; and (4) a successful response required interoperability within and across multiple organizations. For equipping: (1) existing infrastructure lacked sufficient capacity and technical capability to allow immediate countermeasure development; (2) critical supply chains were strained; and (3) critical information system function and capacity were suddenly insufficient under maximum remote work. For staffing and training: (1) successful telework required rapid shifts in management, engagement, and accountability methods; and (2) organizational policies and processes had to adapt quickly to support remote staffing. For leading and leadership development (1) engaged, hopeful, and empathetic leadership made a difference; and (2) the workforce benefitted from concerted leadership communication that created a shared understanding of shifting priorities as well as new processes and procedures. CONCLUSIONS: An effective pandemic response requires comprehensive institutional preparedness that facilitates flexibility and surge capacity. The single most important action leaders of medical research organizations can take to prepare for the next pandemic is to develop a quick-reaction force that would activate under prespecified criteria to manage reprioritization of all science and support activities to address pandemic response priorities at the velocity of relevance.

Prospective Associations of Military Discharge Characterization with Post-active Duty Suicide Attempts and Homelessness: Results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

INTRODUCTION: Active duty service members transitioning to civilian life can experience significant readjustment stressors. Over the past two decades of the United States' longest sustained conflict, reducing transitioning veterans' suicidal behavior and homelessness became national priorities. However, it remains a significant challenge to identify which service members are at greatest risk of these post-active duty outcomes. Discharge characterization, which indicates the quality of an individual's military service and affects eligibility for benefits and services at the Department of Veterans Affairs, is a potentially important indicator of risk. MATERIALS AND METHODS: This study used data from two self-report panel surveys of the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) (LS1: 2016-2018, n = 14,508; and LS2: 2018-2019, n = 12,156), which were administered to respondents who previously participated while on active duty in one of the three Army STARRS baseline self-report surveys (2011-2014): the New Soldier Study (NSS), a survey of soldiers entering basic training; All Army Study, a survey of active duty soldiers around the world; and the Pre-Post Deployment Study, a survey of soldiers before and after combat deployment. Human Subjects Committees of the participating institutions approved all recruitment, informed consent, and data collection protocols. We used modified Poisson regression models to prospectively examine the association of discharge characterization (honorable, general, "bad paper" [other than honorable, bad conduct, dishonorable], and uncharacterized [due to separation within the first 180 days of service]) with suicide attempt (subsample of n = 4334 observations) and homelessness (subsample of n = 6837 observations) among those no longer on active duty (i.e., separated or deactivated). Analyses controlled for other suicide attempt and homelessness risk factors using standardized risk indices that were previously developed using the LS survey data. RESULTS: Twelve-month prevalence rates of self-reported suicide attempts and homelessness in the total pooled LS sample were 1.0% and 2.9%, respectively. While not associated with suicide attempt risk, discharge characterization was associated with homelessness after controlling for other risk factors. Compared to soldiers with an honorable discharge, those with a bad paper discharge had an increased risk of homelessness in the total sample (relative risk [RR] = 4.4 [95% CI = 2.3-8.4]), as well as within subsamples defined by which baseline survey respondents completed (NSS vs. All Army Study/Pre-Post Deployment Study), whether respondents had been separated (vs. deactivated), and how much time had elapsed since respondents were last on active duty. CONCLUSIONS: There is a robust association between receiving a bad paper discharge and post-separation/deactivation homelessness. Policies that enhance transition assistance and access to mental healthcare for high-risk soldiers may aid in reducing post-separation/deactivation homelessness among those who do not receive an honorable discharge.

Central disorders of hypersomnolence: diagnostic discrepancies between military and civilian sleep centers.

STUDY OBJECTIVES: The majority of active-duty service members obtain insufficient sleep, which can influence diagnostic evaluations for sleep disorders, including disorders of hypersomnolence. An incorrect diagnosis of hypersomnia may be career ending for military service or lead to inappropriate medical care. This study was conducted to assess the rates at which narcolepsy (Nc) and idiopathic hypersomnia (IH) are diagnosed by military vs civilian sleep disorders centers. METHODS: This retrospective study utilized claims data from the Military Health System Data Repository. The analyses compared diagnostic rates of military personnel by provider type-either civilian provider or military provider-from January 1, 2016 to December 31, 2019. Three diagnostic categories for Nc and IH: Nc or IH, Nc only, and IH only, were assessed with multivariate logistic regression models. RESULTS: We found that among service members evaluated for a sleep disorder, the odds ratios of a positive diagnosis at a civilian facility vs a military facility for Nc or IH was 2.1, for Nc only was 2.1, and IH only was 2.0 over the 4-year period. CONCLUSIONS: Civilian sleep specialists were twice as likely to diagnose central disorders of hypersomnolence compared to military specialists. Raising awareness about this discrepancy is critical given the occupational and patient care-related implications of misdiagnoses. CITATION: Thomas CL, Vattikuti S, Shaha D, et al. Central disorders of hypersomnolence: diagnostic discrepancies between military and civilian sleep centers. J Clin Sleep Med. 2022;18(10):2433-2441.

Trajectories of Insomnia in Adults After Traumatic Brain Injury.

Importance: Insomnia is common after traumatic brain injury (TBI) and contributes to morbidity and long-term sequelae. Objective: To identify unique trajectories of insomnia in the 12 months after TBI. Design, Setting, and Participants: 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. Exposures: Traumatic brain injury. Main Outcomes and Measures: Insomnia Severity Index assessed serially at 2 weeks and 3, 6, and 12 months thereafter. Results: 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. Conclusions and Relevance: 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.

Active Duty Service Members, Primary Managers, and Administrators' Perspectives on a Novel Sleep Telehealth Management Platform in the U.S. Military Healthcare System.

INTRODUCTION: Sleep disorders are common in the military, and there is a gross shortage of sleep specialists in the military health system. The purposes of the present study were to (1) understand perceptions and expectations surrounding sleep telehealth approaches and (2) solicit feedback to optimize and refine a proposed novel sleep telehealth management platform. To accomplish these objectives, we investigated the perceptions, expectations, and preferences of active duty service members (ADSMs) with sleep disorders, primary care managers (PCMs), and administrative stakeholders regarding sleep telehealth management. MATERIALS AND METHODS: Using convenience sampling, we conducted five focus groups with 26 ADSMs and 11 individual interviews with PCMs from two military treatment facilities in the U.S National Capital Region and 11 individual interviews with administrative sleep stakeholders (9 military and 2 civilian). RESULTS: Active duty service members, PCMs, and administrative stakeholders provided insight regarding expectations for sleep telehealth as well as suggestions to optimize the novel sleep telehealth platform. In terms of outcomes, ADSMs expected sleep telehealth to improve sleep and convenience. Primary care managers expected improved sleep and other comorbidities, enhanced operational readiness, and reduced mortalities among their patients. Administrators expected increased access to care, optimized utilization of health services, realized cost savings, reduced accidents and errors, and improved military performance. In terms of the platform, for ADSMs, desired characteristics included delivery of timely clinical reports, improved patient-provider communication, and enhanced continuity of care. For PCMs and administrators,an ideal sleep telehealth solution will improve the diagnosis and triage of sleep patients, save PCM time, be easy to use, and integrate with the electronic health record system. CONCLUSION: The proposed sleep telehealth platform appealed to nearly all participants as a significant force multiplier to enhance sleep disorder management in the military. Stakeholders offered valuable recommendations to optimize the platform to ensure its successful real-world implementation.

Engaging Stakeholders to Optimize Sleep Disorders' Management in the U.S. Military: A Qualitative Analysis.

INTRODUCTION: Sleep disorders' are highly prevalent among U.S. active duty service members (ADSMs) and present well-documented challenges to military health, safety, and performance. In addition to increased need for sleep medicine services, a major barrier to effective sleep management has been a lack of alignment among patients, health providers, and economic-decision-makers. To address this gap in knowledge, the purpose of the present study was to engage diverse stakeholders vested in improving sleep disorders' management in the military. MATERIALS AND METHODS: We elicited feedback from ADSMs with sleep disorders (five focus group discussion, n = 26) and primary care managers (PCMs) (11 individual semi-structured interview) in two military treatment facilities (MTFs) in the National Capitol Region, in addition to national level military and civilian administrative stakeholders (11 individual semi-structured interview) about their experiences with sleep disorders' management in U.S. MTFs, including facilitators and barriers for reaching a definitive sleep diagnosis, convenience and effectiveness of sleep treatments, and key desired outcomes from interventions designed to address effectively sleep disorders in the U.S. military health care system (MHS). Recordings from focus groups and semi-structured interviews were transcribed verbatim and analyzed using QSR International's NVivo 12 software using inductive thematic analysis. The study was approved by Walter Reed National Military Medical Center Department of Research Programs. RESULTS: Active duty service members with sleep disorders often fail to recognize their need for professional sleep management. Whereas PCMs identified themselves as first-line providers for sleep disorders in the military, patients lacked confidence that PCMs can make accurate diagnoses and deliver effective sleep treatments. Active duty service members cited needs for expeditious treatment, educational support and care coordination, and support for obtaining sleep treatments during deployment. Challenges that PCMs identified for effective management include insufficient time during routine care visits, delays in scheduling testing procedures, and limited number of sleep specialists. Primary care managers suggested offering evidence-based telehealth tools and enhanced care coordination between PCMs and specialists; standardized medical education, materials, and tools; patient preparation before appointments; self-administered patient education; and including behavioral sleep specialists as part of the sleep management team. For administrative stakeholders, key outcomes of enhanced sleep management included (1) improved resource allocation and cost savings, and (2) improved ADSM safety, productivity, and combat effectiveness. CONCLUSION: Current military sleep management practices are neither satisfactory nor maximally effective. Our findings suggest that solving the military sleep problem will require sustained effort and ongoing collaboration from ADSM patients, providers, and health systems leaders. Important potential roles for telehealth and technology were identified. Future research should seek to enhance implementation of sleep management best practices to improve outcomes for patients, providers, MHS, and the military as a whole.

How sleep can help maximize human potential: The role of leaders.

OBJECTIVE: 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. DESIGN: Review article. METHODS: 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. RESULTS: Since sleep is foundational to soldier health and readiness, improving warfighter fatigue management is a priority for leaders. CONCLUSION: 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.

Transitioning from daytime to nighttime operations in military training has a temporary negative impact on dynamic balance and jump performance in U.S. Army Rangers.

OBJECTIVES: Explore the impact transitioning from daytime to nighttime operations has on performance in U.S. Army Rangers. METHODS: Fifty-four male Rangers (age 26.1±4.0 years) completed the Y-Balance Test (YBT), a vertical jump assessment, and a grip strength test at three time points. Baseline testing occurred while the Rangers were on daytime operations; post-test occurred after the first night into the nighttime operation training (after full night of sleep loss), and follow-up testing occurred six days later (end of nighttime training). RESULTS: On the YBT, performance was significantly worse at post-test compared to baseline during right posteromedial reach (104.1±7.2cm vs 106.5±6.7cm, p=.014), left posteromedial reach (105.4±7.5cm vs 108.5±6.6cm, p=.003), right composite score (274.8±19.3cm vs 279.7±18.1cm, p=.043), left composite score (277.9±18.1cm vs 283.3±16.7cm, p=.016), and leg asymmetry was significantly worse in the posterolateral direction (4.8±4.0cm vs 3.7±3.1cm, p=.030) and the anterior direction (5.0±4.0cm vs 3.6±2.6cm, p=.040). The average vertical jump height was significantly lower at post-test compared to baseline (20.6±3.4 in vs 21.8±3.0 in, p=.004). Baseline performance on YBT and vertical jump did not differ from follow-up. CONCLUSIONS: Army Rangers experienced an immediate, but temporary, drop in dynamic balance and vertical jump performance when transitioning from daytime to nighttime operations. When feasible, Rangers should consider adjusting their sleep cycles prior to anticipating nighttime operations in order to maintain their performance levels. Investigating strategies that may limit impairments during this transition is warranted.

Diagnosed or prescribed only? A national analysis of initial evaluation and management of insomnia among older adult Medicare beneficiaries.

Study Objectives: To describe initial insomnia-related encounters among a national sample of Medicare beneficiaries, and to identify older adults at risk for potentially inappropriate prescription insomnia medication usage. Methods: Our data source was a random 5% sample of Medicare administrative claims data (2006-2013). Insomnia was operationalized as International Classification of Disease, Ninth Revision, Clinical Modification diagnostic codes. Insomnia medications included FDA-approved insomnia-related medication classes and drugs. Logistic regression was employed to identify predictors of being "prescribed only" (i.e., being prescribed an insomnia medication without a corresponding insomnia diagnosis). Results: A total of N = 60 362 beneficiaries received either an insomnia diagnosis or a prescription for an insomnia medication as their first sleep-related encounter during the study period. Of these, 55.1% (n = 33 245) were prescribed only, whereas 44.9% (n = 27 117) received a concurrent insomnia diagnosis. In a fully adjusted regression model, younger age (odds ratio (OR) 0.98; 95% confidence interval (CI) 0.98, 0.99), male sex (OR 1.15; 95% CI 1.11, 1.20), and several comorbid conditions (i.e., dementia [OR 1.21; 95% CI 1.15, 1.27] and anemia [OR 1.17; 95% CI 1.13, 1.22]) were positively associated with being prescribed only. Conversely, black individuals (OR 0.83; 95% CI 0.78, 0.89) and those of "other" race (OR 0.89; 95% CI 0.84, 0.94) were less likely to be prescribed only. Individuals who received care from a board-certified sleep medicine provider (BCSMP) were less likely to be prescribed only (OR 0.27; 95% CI 0.16, 0.46). Conclusions: Fewer than half of Medicare beneficiaries prescribed insomnia medications ever received a formal sleep-related diagnosis.

Self-Reported Sleep Need, Subjective Resilience, and Cognitive Performance Following Sleep Loss and Recovery Sleep.

OBJECTIVE: Individuals vary in response to sleep loss: some individuals are "vulnerable" and demonstrate cognitive decrements following insufficient sleep, while others are "resistant" and maintain baseline cognitive capability. Physiological markers (e.g., genetic polymorphisms) have been identified that can predict relative vulnerability. However, a quick, cost-effective, and feasible subjective predictor tool has not been developed. The objective of the present study was to determine whether two factors-"subjective sleep need" and "subjective resilience"-predict cognitive performance following sleep deprivation. METHODS: Twenty-seven healthy, sleep-satiated young adults participated. These individuals were screened for sleep disorders, comorbidities, and erratic sleep schedules. Prior to 40 hours of in-laboratory total sleep deprivation, participants were questioned on their subjective sleep need and completed a validated resilience scale. During and after sleep deprivation, participants completed a 5-minute psychomotor vigilance test every 2 hours. RESULTS: Both subjective resilience and subjective sleep need individually failed to predict performance during sleep loss. However, these two measures interacted to predict performance. Individuals with low resilience and low sleep need had poorer cognitive performance during sleep loss. However, in individuals with medium or high resilience, psychomotor vigilance test performance was not predicted by subjective sleep need. Higher resilience may be protective against sleep loss-related neurobehavioral impairments in the context of subjective sleep need. CONCLUSIONS: Following sleep loss (and recovery sleep), trait resilient individuals may outperform those with lower resiliency on real-world tasks that require continuous attention. Future studies should determine whether the present findings generalize to other, operationally relevant tasks and additional cognitive domains.

TNFα G308A genotype, resilience to sleep deprivation, and the effect of caffeine on psychomotor vigilance performance in a randomized, double-blind, placebo-controlled, crossover study.

The TNFα G308A gene polymorphism has been reported to influence performance impairment during total sleep deprivation (TSD). We investigated this effect in a randomized, double-blind, crossover laboratory study of repeated exposure to 48 h TSD with caffeine administration at different doses. In a retrospective analysis, we replicated the finding that the A allele of TNFα G308A, found in 4 of 12 study participants, confers resilience to performance impairment during TSD. There was no evidence of an interaction of TNFα genotype with the beneficial effect of caffeine (200 or 300 mg) on performance during TSD, suggesting distinct underlying mechanisms.