Sleep Patterns and Problems Among Army National Guard Soldiers

Sleep Patterns and Problems Among Army National Guard Soldiers Abstract Introduction Adequate sleep plays an integral role in the physical and mental health of individuals, while simultaneously influencing their cognitive and work performance. Having recognized this, the U.S. Army has focused efforts on improving soldiers’ healthy sleep behaviors. This study examines the extent to which mental health, alcohol use, and certain sleep hygiene behaviors predict sleep problems within an Army National Guard sample (N = 438). Materials and Methods This manuscript is part of a larger study approved through the Minneapolis Veterans Affairs Medical Center Institutional Review Board. Mailed surveys were sent to Minnesota Army National Guard soldiers collecting data on sleep hygiene behaviors, mental health symptoms (post-traumatic stress disorder and depression), and alcohol use. Predictors of sleep problems were evaluated with ordinary least squares multiple linear regression analyses, regressing Insomnia Severity Index total scores on demographic variables, post-traumatic stress disorder (PTSD), depression, alcohol use, sleep hygiene factors (routine and consumption activity; both derived from exploratory factor analysis), and technology use (multiple device use and use before bed). Results Overall, the majority of participants did not endorse high levels of sleep impairment, while 16.4% screened positive for moderate or even severe levels of clinical insomnia. Bivariate correlations demonstrated that sleep problems were correlated with PTSD symptoms (r = 0.41, p < 0.001), depression (r = 0.49, p < 0.001), Sleep Hygiene Routine (r = −0.34, p < 0.001), and more frequent use of multiple devices before bed (r = 0.15, p = 0.002). The overall regression model predicting sleep problems was significant (R2 = 0.35, adj R2 = 0.34, F[8,408] = 27.58, p < 0.001). Independent predictors of sleep problems included gender (B = 0.99, β = 0.09, t = 2.10, p = 0.036), PTSD (B = 0.89, β = 0.22, t = 4.86, p < 0.001), depression (B = 1.53, β = 0.20, t = 7.56, p < 0.001), and Sleep Hygiene Routine (B = −0.88, β = −0.23, t = −5.473, p < 0.001). Alcohol use, Sleep Hygiene Consumption, and technology use did not emerge as independent predictors. Conclusion Although most soldiers denied sleep problems, a sizeable minority met screening criteria for clinical insomnia. Greater numbers of sleep-related complaints were related to psychological distress including depressive and PTSD symptoms, while adherence to a bedtime routine (Sleep Hygiene Routine) showed an inverse relationship. Alcohol use and sleep hygiene consumption activities were not predictive of sleep problems, suggesting that different sleep hygiene behaviors have differential relationships with sleep problems. Screening and intervention for specific sleep problems may be helpful even very early in Army National Guard service members’ careers. Particular focus may be needed for those showing signs of emotional distress, such as PTSD or depression. Future research examining the impact of individual sleep hygiene components is warranted. INTRODUCTION A large body of empirical work has demonstrated that sleep has significant importance to the physical and psychological well-being of military personnel. Sleep is a necessary activity for the body and brain to function optimally. To date, sleep has been linked to several different physical health outcomes such as cardiovascular disease,1,2 inflammatory responses,3 and excess weight gain,4,5 to name a few. Regarding mental health, poor sleep has been further associated with depression,6–8 suicidal ideation,7 anxiety/stress,9 post-traumatic stress disorder (PTSD),8,10 and memory.11–13 In addition, inadequate sleep has an impact on one’s ability to work effectively.14 In some cases, these impacts are merely expressed through decreased cognitive function,11–13 which results in a loss of productivity.14 However, on military deployments service members are often subject to operational demands that require prolonged periods of inconsistent or insufficient sleep.7,15 Such sleep deficiencies can account for a substantial proportion of accidents and potentially dangerous behaviors16,17 (e.g., sleeping while on guard duty). In these cases, what may be simply an inconvenience in the civilian sector may have severe consequences in a combat-related environment. The military has acknowledged the importance of sleep and the role it plays in maintaining soldier readiness to execute military operations.18–20 There are currently active programs such as the Comprehensive Soldier Fitness initiative, designed to enhance sleep education in an effort to improve unit readiness and resilience.4 The military population is incredibly diverse, however, and the effectiveness of intervention strategies is likely to be constrained by the characteristics of service members and the environment and context in which they operate. Since the events leading to the Global War on Terror, approximately 850,000 National Guard soldiers have been mobilized and deployed throughout the world in support of military operations. Currently, the National Guard provides the Army with 39% of its operational forces and is responsible for managing 42% of its manned and unmanned aircraft.21 Furthermore, the director of the Army National Guard, Lieutenant General Timothy Kadavy, recently stated that mobilizations will increase and combat center rotations will double in 2018.22 The prominence and activity of National Guard and Reserve (NGR) Component troops brings with it questions about the specific environmental context and demands faced by these “citizen soldiers.” Unlike regular component service members, NGR personnel are required to make regular transition from civilian roles and functioning to military contexts. Simply to maintain unit readiness, National Guard soldiers interrupt their established, civilian routines for military duty on a monthly basis, at minimum. When mobilized for an extended period, soldiers must acclimate to specific sleep patterns dictated by the requirements of the mission and the environmental hazards in which those soldiers operate. Often, when returning home from an extended mobilization, soldiers are not subject to the same demands and are forced to again acclimate to previous sleep patterns. The unique needs and environmental demands faced by NGR personnel are demonstrated in part by elevated rates of mental health disorders, including PTSD, within the context of military deployments.23 It is, therefore, essential that levels of sleep impairment and predictors of that impairment within NGR populations be investigated so that intervention efforts can be evaluated and, if needed, tailored, for this critical component of today’s military. Study Rationale and Hypotheses The purpose of this study is to examine sleep patterns among Army National Guard (NG) soldiers and predictors of sleep problems within this population. This specific work is part of a larger study of sleep-related behaviors and interventions. Following prior findings in military and civilian samples, we hypothesized that sleep problems would be predicted by psychiatric distress (PTSD and depressive symptoms), poor sleep hygiene behaviors, and heightened alcohol use.6–10 Given that there are many distinct types of behaviors discussed as influencing sleep quality, this study also investigated the dimensionality of a sleep hygiene questionnaire containing commonly mentioned sleep-related behaviors to facilitate analyses. In addition, we examined the relationship between electronic usage (i.e., screen time) immediately before bed and amount of sleep and sleep problems. Given the increasing availability of handheld electronic devices, and the accompanying potential for increased engagement across multiple electronic devices prior to sleep (e.g., texting while watching TV or playing games), we also examined multiple device usage prior to sleep-onset as a potential predictor of sleep problems. MATERIALS AND METHODS Participants This sample is composed of 438 Minnesota Army National Guard soldiers who filled out and returned a mailed survey as part of a larger study. The soldiers were identified by expressing interest in future research while completing their enlistment contract. All soldiers have an enlistment date within the last three years. Of those soldiers, 295 (67.4%) identified as male, 134 (30.6%) female, 2 (0.5%) as “other,” and data on gender were missing for 7 (1.6%) participants. Ages ranged from 17 to 54 (M = 22.8, SD = 5.274). The majority of participants (325, or 74.2%) identified as White, 37 (8.4%) identified as African-American or Black, 27 (6.2%) identified as Asian or Pacific Islander, 2 (0.5%) identified as American Indian or Alaskan Native, and 39 (8.9%) as Other or Multiracial. Data on race were missing for 8 (1.8%) participants. Thirty-two participants identified as of Hispanic origin (7.3%). Regarding relationship status, 180 (41.1%) identified as single, 63 (14.4%) married, 72 (16.4%) were in a relationship and living their partner, and 116 (26.5%) were in a relationship, but not living together. Measures All participants completed questionnaires assessing sleep and sleep difficulties, mental health difficulties, sleep-related behaviors, and the use of technology before going to sleep. Insomnia Severity Index The Insomnia Severity Index (ISI) is a brief self-report instrument of people’s perception of their sleep.24 The measure includes both objective and subjective symptoms of insomnia, as well as their perceptions of negative consequences that may occur due to poor sleep. The ISI is made up of seven items that assess problems with sleep onset, sleep maintenance difficulties, level of satisfaction with sleep, level of noticeable impairment due to sleep problems, and level of distress due to the sleep problem. Items are rated on a 0–4 scale; total scores range from 0 to 28. A higher score is indicative of more sleep problems. Prior studies have demonstrated acceptable levels of internal consistency and validity through concurrence with other measures of sleep impairment.24,25 Established cutoffs for the ISI demonstrate that a score from 0 to 7 indicates no sleep problems, 8 to 14 indicates subthreshold insomnia, 15 to 21 indicates moderate clinical insomnia, and 22 to 28 suggests severe clinical insomnia. Internal consistency (Cronbach’s alpha) within the present sample was 0.86. Alcohol Use Disorders Identification Test-Consumption Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a three-item measure that is used to measure potential heavy and/or hazardous drinking.26 Items in the AUDIT-C address frequency of drinking in the last year, how much alcohol on average is consumed on a day where the individual does drink alcohol, and frequency of binge drinking episodes (6 or more drinks on one occasion). Total scores range from 0 to 12. A higher score suggests that alcohol abuse is likely a problem for that individual. A score of 3 or more is considered a positive screening for hazardous drinking in women, and a score of 4 or more is a positive screening for men. There is strong evidence for the use and interpretation of this instrument, specifically in younger, OIF/OEF veterans.27 Internal consistency (Cronbach’s alpha) within this sample was 0.84. Primary Care-Post-traumatic Stress Disorder Screen The Primary Care-Post-Traumatic Stress Disorder Screen is a four-item screening tool for PTSD that is commonly used in primary care settings and considered an acceptable screening method within the VA system.28 The screen addresses symptoms of re-experiencing a traumatic event, numbing, avoidance, and hyperarousal. The items are answered with either “Yes” or “No.” Total scores range from 0 to 4. A cutoff score of 3 demonstrated a strong sensitivity (0.78) and specificity (0.87) in VA general medical settings.28 Prior studies have demonstrated acceptable levels of internal consistency and validity through concurrence with other measures of PTSD.29,30 Internal consistency (Cronbach’s alpha) within the present sample was 0.80. Patient Health Questionnaire-2 The Patient Health Questionnaire-2 is a two-item depression screening tool. The items address depressed mood and anhedonia over the past 2 wk.31 Both items are scored from 0 (“not at all”) to 3 (“nearly every day”). The total score (the sum of both items) ranges from 0 to 6. A score of ≥3 or higher is considered a positive initial screening for depression. These items demonstrated a 0.76 pooled sensitivity and 0.86 pooled specificity in a recent meta-analysis.32 Prior studies have demonstrated acceptable levels of internal consistency and validity through concurrence with other measures of depression.32,33 Internal consistency (Cronbach’s alpha) within the present sample was 0.80. Sleep hygiene behaviors Common sleep hygiene behaviors were assessed with 11 items asking about the weekly frequency of behaviors associated with healthy sleep including routine and consistent sleep and wake times, avoidance of alcohol, caffeine, spicy food within 4 h of bedtime, and exercise routines.34 Each behavior was rated as occurring between 0 and 7 times per week. The 11 items were subject to a principal components factor analysis with promax factor rotation. Three factors produced eigenvalues greater than one, accounting for a total of 49.41% total item variance, and the scree plot confirmed a three-factor solution. The first factor (eigenvalue = 2.64, 24.03% total variance accounted for) involved items related to sleep routine including “go to bed the same time each night,” “get up at the same time each day,” “create a sleep environment that is dark, quiet, comfortable, and cool,” “reserve your bed for sleep or sex,” and “have a bedtime routine.” The second factor (eigenvalue = 1.69, 15.36% variance accounted for) contained items relating to consumption behaviors involving food, drink, or chemicals prior to bed including “avoid caffeine within 4 h of bedtime,” “avoid spicy foods within 4 h of bedtime,” “avoid alcohol within 4 h of bedtime,” and “avoid nicotine within 4 h of bedtime.” Two items with low commonalities loaded on a final factor (eigenvalue = 1.10, 10.01% variance accounted for): “exercise” and “get out of bed if you have been awake for more than 20 minutes”. As the resulting factor had only two items and a reliability of 0.19, these items were excluded from further analysis. Mean scores were calculated for the two remaining factors: Sleep Hygiene Routine and Sleep Hygiene Consumption. Screen Use Before Bed This section consisted of two questions regarding electronic device usage within 1 h of going to sleep. Multiple device usage was measured on a five-point Likert scale ranging from “never” to “all the time,” asking how often in a given week multiple devices (e.g., computers, cell phones, tablets, game consoles, television, or electronic reading devices) were used. Any device usage was measured by asking (with the same response format) how often any devices were used in the bedroom in the hour before going to bed. Procedures A standard survey protocol was followed in which potential participants were first sent a pre-notification postcard explaining the study. Two weeks later they were sent the survey along with a cover-letter detailing the study and a modest ($2) payment. Non-responders were sent, at 2-wk intervals, a reminder postcard, a second survey, and then a third survey (this time via priority mail).35 Of 2,063 surveys mailed out, 438 were returned, yielding a response rate of 21%. Data Analysis Rates of sleep problems and potential psychiatric distress were established with simple frequencies. Gender (male vs. female) and race (Caucasian vs. non) groups were compared on the ISI and its potential predictors using independent samples t-tests. Predictors of sleep problems were evaluated with ordinary least squares multiple linear regression analyses, regressing ISI total scores on demographic variables, PTSD, depression, alcohol use, sleep hygiene (routine and restricting activity), and technology use (multiple device use and use before bed). RESULTS According to the ISI, 365 participants (83.7%) screened for either no sleep impairment (n = 203) or subthreshold insomnia (n = 162). The remaining 71 (16.3%) screened positive for moderate (n = 61) or severe (n = 10) clinical insomnia. Regarding other psychiatric screens, 42 (9.6%) screened positive for depression, 79 (18.1%) for PTSD, and 221 (50.7%) for potentially problematic alcohol use. Male and female soldiers did not differ in terms of depression, Sleep Hygiene Routine, or frequency of multi-device use before bed. Means, standard deviations, and correlations are displayed in Table I. Sleep problems, as assessed by the ISI, were correlated with PTSD symptoms (r = 0.41, p < 0.001), depression (r = 0.49, p < 0.001), Sleep Hygiene Routine (r = −0.34, p < 0.001), and more frequent use of multiple devices before bed (r = 0.15, p = 0.002). Considering potential demographic covariates, male and female participants differed in terms of alcohol use (2.91; SD = 2.63 and 2.41, SD = 2.02, respectively; t = 2.14, df = 326.63, p = 0.033), PTSD symptoms (0.81, SD = 1.27 and 1.17, SD = 1.47, respectively; t = −2.56, df = 425, p = 0.011), Sleep Hygiene Restricted Activity (4.02, SD = 1.61 and 3.53, SD = 1.40, respectively; t = −2.53, df = 418, p = 0.012), use of electronics before bed (2.94, SD = 1.27 and 3.35, SD = 0.91, respectively; t = −3.79, df = 343.98, p < 0.001), and sleep impairment (8.13, SD = 5.26 and 9.44, SD = 5.70, respectively; t = 2.31, df = 425, p = 0.022). Non-Caucasian and Caucasian participants differed in terms of alcohol use (2.05, SD = 2.17 vs. 3.00, SD = 2.52; t = −3.57, df = 433, p < 0.001) Sleep Hygiene Activity Consumption (4.15, SD = 1.63 and 3.79, SD = 1.53, respectively; t = 2.10, df = 422, p = 0.037) and use of multiple devices before bed (2.46, SD = 1.31 and 2.07, SD = 1.33; t = 2.69, df = 435, p = 0.008). Table I. Means, Standard Deviations, and Correlations       Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00        Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, using any electronic device <4 h prior to sleep.*p < 0.05,**p < 0.01,***p < 0.001. Table I. Means, Standard Deviations, and Correlations       Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00        Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, using any electronic device <4 h prior to sleep.*p < 0.05,**p < 0.01,***p < 0.001. Given the relationship between gender and ISI scores, gender was included as a covariate when regressing ISI scores on alcohol use, PTSD, depression, sleep hygiene, and technology use. The overall model was significant (R2 = 0.35, adj R2 = 0.34, F[8,408] = 27.58, p < 0.001). Significant independent predictors included gender (B = 0.99, β = 0.09, t = 2.10, p = 0.036), PTSD (B = 0.89, β = 0.22, t = 4.86, p < 0.001), depression (B = 1.53, β = 0.35, t = 7.56, p < 0.001), and Sleep Hygiene Routine (B = −0.88, β = −0.23, t = −5.473, p < 0.001). Alcohol use, Sleep Hygiene Consumption, and technology use did not emerge as independent predictors (see Table II). Table II. Regressing Sleep Problems on Predictors   B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104    B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, Using any electronic device <4 h prior to sleep. Table II. Regressing Sleep Problems on Predictors   B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104    B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, Using any electronic device <4 h prior to sleep. Conclusion Discussion National Guard personnel represent a vital component of the nation’s armed forces with distinct roles, duties, and life circumstances. This study evaluated levels and predictors of sleep impairment in a sample of National Guard soldiers as an examination of needs and risk/protective factors within the population. Overall, the majority of the present sample did not endorse high levels of sleep impairment. However, a sizeable minority (16.4%) did screen positive for moderate or even severe levels of clinical insomnia. Greater numbers of sleep-related complaints were related particularly to psychological distress including depressive and PTSD symptoms. This is an expected finding, given prior work documenting the co-occurrence of sleep impairments with both disorders.6–8,10 Further, both depression and PTSD are defined in part by the symptom of impaired sleep in the Diagnostic and Statistics Manual of Mental Disorders.36 We also found, as expected, that sleep hygiene behaviors relating to regular scheduling of bedtime and awakening and adherence to a bedtime routine were negatively correlated with sleep problems. It is impossible to know, in the present sample, if disrupted sleep resulted from poorly structured sleep activities (such as chaotic bedtimes) or if sleep problems led to more disrupted sleep hygiene routines. The evaluation of these potential pathways will require future studies making use of longitudinal methodology so that temporal cause and effect can be evaluated. Recent questions have emerged regarding the role of electronic device usage in potentially interfering with sleep.37–40 The present findings, however, provide only limited support for this hypothesis. The use of multiple devices before sleep did not correlate with sleep problems, and the relationship between sleep problems and frequency of any device use before bed was small. Impaired sleep has been associated with performance decrements in several occupational domains.14,16,17 In addition, impaired or disrupted sleep has been linked to numerous physical and mental health problems.1–10 The present findings suggest that screening and intervention for sleep problems may be helpful even very early in Army National Guard service members’ careers. Particular focus may be needed for those showing signs of emotional distress such as PTSD or depression, aligning with the current literature on psychopathological comorbidities related to insomnia.41 Findings also suggest that a focus on teaching and encouraging consistent sleep scheduling habits may be beneficial in this population, further supporting broad based programs such as the Comprehensive Soldier Fitness initiative. In addition, a recent RAND report highlighted the importance of more individually focused evidence-based practices, such as Cognitive-Behavioral Therapy for Insomnia (CBT-I).41 Finally, innovative electronic interventions, such as tailored smartphone apps may be helpful in engaging younger National Guard service members in a more individualized fashion. The measure of sleep hygiene behaviors used in this study is new and could benefit from further psychometric analysis. If further studies confirm its validity and these initial findings, sleep hygiene behaviors may be particularly useful treatment targets as they are directly observable, modifiable, and can be addressed through numerous formats such as in person teaching, smartphone apps, or even innovative educational game play. The small relationships between sleep problems and technology use before bed suggest that intervention efforts may be better spend addressing the broad sleep routine rather than targeting specific technology use. With the complexity inherent to military operations, simply aiming for standardized rest periods to address soldier sleep hygiene may not always be feasible. A more tangible path toward improved sleep hygiene through the identification of barriers to sleep may prove to be a more fruitful one. Budding research is beginning to shed light on some of these barriers, such as time demands, unpredictable habits, technology use, and peoples’ ability to “switch off”.42,43 Furthermore, continued research is necessary to address topics such as causal relationships between specific barriers and participants’ willingness to change sleep behaviors, behavior intervention strategies, and barrier modification (e.g., leveraging already-utilized technology to educate or adjust sleep-related behavior). Limitations of this study include a single time-point correlational design, and population sampling limitations. Although significant relationships were identified, we are unable to confirm causal relationships between variables due to the passive observation, cross-sectional design of the model. Furthermore, only abbreviated, though validated, measures were utilized for many constructs due to a need to minimize participant burden at this early phase of the research project. Utilizing more robust, full-length versions of these instruments would allow for a more detailed and nuanced understanding of sleep-related variables in future research endeavors. Regarding sampling limitations, these results may not generalize accurately across groups due to the unique characteristics of the Minnesota National Guard, such as demographic makeup, proximity to active duty forces, and deployment rates. Notwithstanding these limitations, this study demonstrates the importance of attending to sleep and sleep behaviors within a National Guard context and identifies the role of mental health symptoms within this population. Acknowledgements This project was conducted with resources from the Minneapolis VA Healthcare System and funded in part through a Departments of Defense Small Business Innovation Research grant to Smart Information Flow Technologies (Award Number W81XWH-16-C-0032). 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Sleep  2010; 33: 1615– 1622. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2982731&tool=pmcentrez&rendertype=abstract. Google Scholar CrossRef Search ADS PubMed  16 Lopresti M.L., Anderson J.A., Saboe K.N., McGurk D.L., Balkin T.J., Sipos M.L.: The impact of insufficient sleep on combat mission performance. Mil Behav Health  2016; 4: 356– 63. doi:10.1080/21635781.2016.1181585. Google Scholar CrossRef Search ADS   17 Philip P., Åkerstedt T.: Transport and industrial safety, how are they affected by sleepiness and sleep restriction? Sleep Med Rev  2006; 10: 347– 56. doi:10.1016/j.smrv.2006.04.002. Google Scholar CrossRef Search ADS PubMed  18 Wesensten N.J., Balkin T.J.: The challenge of sleep management in military operations. US Army Med Dep J  2013; 109– 18. 19 Department of the Army: FM 6–22.5: Combat And Operational Stress Control . Washington, D.C., Government Printing Office, 2009. 20 Seelig A., Jacobson I., Donoho C., Trone D., Crum-Cianflone N., Balkin T.: Sleep and health resilience metrics in a large military cohort. Sleep  2016; 39: 1111– 20. doi:10.5665/sleep.5766. Google Scholar CrossRef Search ADS PubMed  21 2018 National Guard Bureau Posture Statment: Building a Force for the Future [PDF]. ( 2017). Available at http://www.nationalguard.mil/portals/31/Documents/PostureStatements/2018-National-Guard-Bureau-Posture-Statement.pdf; accessed December 22, 2017. 22 South T. ( 2017) Abrams: Guard, Reserve increasing op tempo. Army Times. Available at https://www.armytimes.com/news/your-army/2017/10/09/abrams-guard-reserve-increasing-op-tempo/; accessed December 12, 2017. 23 Milliken C.S., Auchterlonie J.L., Hoge C.W.: Longitudinal assessment of mental health problems among active and reserve component soldiers returning from the Iraq war. JAMA  2007; 298: 2141– 8. doi:10.1001/jama.298.18.2141. 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Google Scholar CrossRef Search ADS PubMed  31 Kroenke K., Spitzer R.: The Patient Health Questionnaire-2: validity of a two-item depression dcreener. Med Care  2003; 41: 1284– 92. http://www.jstor.org/stable/3768417. Google Scholar CrossRef Search ADS PubMed  32 Manea L., Gilbody S., Hewitt C., et al.  : Identifying depression with the PHQ-2: a diagnostic meta-analysis. J Affect Disord  2016; 203: 382– 95. doi:10.1016/j.jad.2016.06.003. Google Scholar CrossRef Search ADS PubMed  33 Arroll B., Goodyear-Smith F., Crengle S., et al.  : Validation of PHQ-2 and PHQ-9 to screen for major depression in the primary care population. Ann Fam Med  2010; 8: 348– 53. doi:10.1370/afm.1139. Google Scholar CrossRef Search ADS PubMed  34 Pro-Change Behavior Systems. ( 2015). Sleep Hygiene Behavior Scale [Measurement Instrument]. Unpublished instrument. Retrieved from www.prochange.com 35 Dillman DA.: Mail and Telephone Surveys: The Total Design Method . New York, Wiley, 1978. 36 American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders , 5th ed., Washington, DC, Author, 2013. 37 Exelmans L., Van Den Bulck J.: Bedtime mobile phone use and sleep in adults. Soc Sci Med  2016; 148: 93– 101. doi:10.1016/j.socscimed.2015.11.037. Google Scholar CrossRef Search ADS PubMed  38 Hale L., Guan S.: Screen time and sleep among school-aged children and adolescents: a systematic literature review. Sleep Med Rev  2015; 21: 50– 8. doi:10.1016/j.smrv.2014.07.007. Google Scholar CrossRef Search ADS PubMed  39 Heo J., Kim K., Fava M., et al.  : Effects of smartphone use with and without blue light at night in healthy adults: a randomized, double-blind, cross-over, placebo-controlled comparison. J Psychiatr Res  2017; 87: 61– 70. doi:10.1016/j.jpsychires.2016.12.010. 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Google Scholar CrossRef Search ADS PubMed  Author notes The views expressed here are those of the authors and not of the Department of Defense, Department of Veteran Affairs, or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Military Medicine Oxford University Press

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Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018.
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Abstract

Abstract Introduction Adequate sleep plays an integral role in the physical and mental health of individuals, while simultaneously influencing their cognitive and work performance. Having recognized this, the U.S. Army has focused efforts on improving soldiers’ healthy sleep behaviors. This study examines the extent to which mental health, alcohol use, and certain sleep hygiene behaviors predict sleep problems within an Army National Guard sample (N = 438). Materials and Methods This manuscript is part of a larger study approved through the Minneapolis Veterans Affairs Medical Center Institutional Review Board. Mailed surveys were sent to Minnesota Army National Guard soldiers collecting data on sleep hygiene behaviors, mental health symptoms (post-traumatic stress disorder and depression), and alcohol use. Predictors of sleep problems were evaluated with ordinary least squares multiple linear regression analyses, regressing Insomnia Severity Index total scores on demographic variables, post-traumatic stress disorder (PTSD), depression, alcohol use, sleep hygiene factors (routine and consumption activity; both derived from exploratory factor analysis), and technology use (multiple device use and use before bed). Results Overall, the majority of participants did not endorse high levels of sleep impairment, while 16.4% screened positive for moderate or even severe levels of clinical insomnia. Bivariate correlations demonstrated that sleep problems were correlated with PTSD symptoms (r = 0.41, p < 0.001), depression (r = 0.49, p < 0.001), Sleep Hygiene Routine (r = −0.34, p < 0.001), and more frequent use of multiple devices before bed (r = 0.15, p = 0.002). The overall regression model predicting sleep problems was significant (R2 = 0.35, adj R2 = 0.34, F[8,408] = 27.58, p < 0.001). Independent predictors of sleep problems included gender (B = 0.99, β = 0.09, t = 2.10, p = 0.036), PTSD (B = 0.89, β = 0.22, t = 4.86, p < 0.001), depression (B = 1.53, β = 0.20, t = 7.56, p < 0.001), and Sleep Hygiene Routine (B = −0.88, β = −0.23, t = −5.473, p < 0.001). Alcohol use, Sleep Hygiene Consumption, and technology use did not emerge as independent predictors. Conclusion Although most soldiers denied sleep problems, a sizeable minority met screening criteria for clinical insomnia. Greater numbers of sleep-related complaints were related to psychological distress including depressive and PTSD symptoms, while adherence to a bedtime routine (Sleep Hygiene Routine) showed an inverse relationship. Alcohol use and sleep hygiene consumption activities were not predictive of sleep problems, suggesting that different sleep hygiene behaviors have differential relationships with sleep problems. Screening and intervention for specific sleep problems may be helpful even very early in Army National Guard service members’ careers. Particular focus may be needed for those showing signs of emotional distress, such as PTSD or depression. Future research examining the impact of individual sleep hygiene components is warranted. INTRODUCTION A large body of empirical work has demonstrated that sleep has significant importance to the physical and psychological well-being of military personnel. Sleep is a necessary activity for the body and brain to function optimally. To date, sleep has been linked to several different physical health outcomes such as cardiovascular disease,1,2 inflammatory responses,3 and excess weight gain,4,5 to name a few. Regarding mental health, poor sleep has been further associated with depression,6–8 suicidal ideation,7 anxiety/stress,9 post-traumatic stress disorder (PTSD),8,10 and memory.11–13 In addition, inadequate sleep has an impact on one’s ability to work effectively.14 In some cases, these impacts are merely expressed through decreased cognitive function,11–13 which results in a loss of productivity.14 However, on military deployments service members are often subject to operational demands that require prolonged periods of inconsistent or insufficient sleep.7,15 Such sleep deficiencies can account for a substantial proportion of accidents and potentially dangerous behaviors16,17 (e.g., sleeping while on guard duty). In these cases, what may be simply an inconvenience in the civilian sector may have severe consequences in a combat-related environment. The military has acknowledged the importance of sleep and the role it plays in maintaining soldier readiness to execute military operations.18–20 There are currently active programs such as the Comprehensive Soldier Fitness initiative, designed to enhance sleep education in an effort to improve unit readiness and resilience.4 The military population is incredibly diverse, however, and the effectiveness of intervention strategies is likely to be constrained by the characteristics of service members and the environment and context in which they operate. Since the events leading to the Global War on Terror, approximately 850,000 National Guard soldiers have been mobilized and deployed throughout the world in support of military operations. Currently, the National Guard provides the Army with 39% of its operational forces and is responsible for managing 42% of its manned and unmanned aircraft.21 Furthermore, the director of the Army National Guard, Lieutenant General Timothy Kadavy, recently stated that mobilizations will increase and combat center rotations will double in 2018.22 The prominence and activity of National Guard and Reserve (NGR) Component troops brings with it questions about the specific environmental context and demands faced by these “citizen soldiers.” Unlike regular component service members, NGR personnel are required to make regular transition from civilian roles and functioning to military contexts. Simply to maintain unit readiness, National Guard soldiers interrupt their established, civilian routines for military duty on a monthly basis, at minimum. When mobilized for an extended period, soldiers must acclimate to specific sleep patterns dictated by the requirements of the mission and the environmental hazards in which those soldiers operate. Often, when returning home from an extended mobilization, soldiers are not subject to the same demands and are forced to again acclimate to previous sleep patterns. The unique needs and environmental demands faced by NGR personnel are demonstrated in part by elevated rates of mental health disorders, including PTSD, within the context of military deployments.23 It is, therefore, essential that levels of sleep impairment and predictors of that impairment within NGR populations be investigated so that intervention efforts can be evaluated and, if needed, tailored, for this critical component of today’s military. Study Rationale and Hypotheses The purpose of this study is to examine sleep patterns among Army National Guard (NG) soldiers and predictors of sleep problems within this population. This specific work is part of a larger study of sleep-related behaviors and interventions. Following prior findings in military and civilian samples, we hypothesized that sleep problems would be predicted by psychiatric distress (PTSD and depressive symptoms), poor sleep hygiene behaviors, and heightened alcohol use.6–10 Given that there are many distinct types of behaviors discussed as influencing sleep quality, this study also investigated the dimensionality of a sleep hygiene questionnaire containing commonly mentioned sleep-related behaviors to facilitate analyses. In addition, we examined the relationship between electronic usage (i.e., screen time) immediately before bed and amount of sleep and sleep problems. Given the increasing availability of handheld electronic devices, and the accompanying potential for increased engagement across multiple electronic devices prior to sleep (e.g., texting while watching TV or playing games), we also examined multiple device usage prior to sleep-onset as a potential predictor of sleep problems. MATERIALS AND METHODS Participants This sample is composed of 438 Minnesota Army National Guard soldiers who filled out and returned a mailed survey as part of a larger study. The soldiers were identified by expressing interest in future research while completing their enlistment contract. All soldiers have an enlistment date within the last three years. Of those soldiers, 295 (67.4%) identified as male, 134 (30.6%) female, 2 (0.5%) as “other,” and data on gender were missing for 7 (1.6%) participants. Ages ranged from 17 to 54 (M = 22.8, SD = 5.274). The majority of participants (325, or 74.2%) identified as White, 37 (8.4%) identified as African-American or Black, 27 (6.2%) identified as Asian or Pacific Islander, 2 (0.5%) identified as American Indian or Alaskan Native, and 39 (8.9%) as Other or Multiracial. Data on race were missing for 8 (1.8%) participants. Thirty-two participants identified as of Hispanic origin (7.3%). Regarding relationship status, 180 (41.1%) identified as single, 63 (14.4%) married, 72 (16.4%) were in a relationship and living their partner, and 116 (26.5%) were in a relationship, but not living together. Measures All participants completed questionnaires assessing sleep and sleep difficulties, mental health difficulties, sleep-related behaviors, and the use of technology before going to sleep. Insomnia Severity Index The Insomnia Severity Index (ISI) is a brief self-report instrument of people’s perception of their sleep.24 The measure includes both objective and subjective symptoms of insomnia, as well as their perceptions of negative consequences that may occur due to poor sleep. The ISI is made up of seven items that assess problems with sleep onset, sleep maintenance difficulties, level of satisfaction with sleep, level of noticeable impairment due to sleep problems, and level of distress due to the sleep problem. Items are rated on a 0–4 scale; total scores range from 0 to 28. A higher score is indicative of more sleep problems. Prior studies have demonstrated acceptable levels of internal consistency and validity through concurrence with other measures of sleep impairment.24,25 Established cutoffs for the ISI demonstrate that a score from 0 to 7 indicates no sleep problems, 8 to 14 indicates subthreshold insomnia, 15 to 21 indicates moderate clinical insomnia, and 22 to 28 suggests severe clinical insomnia. Internal consistency (Cronbach’s alpha) within the present sample was 0.86. Alcohol Use Disorders Identification Test-Consumption Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a three-item measure that is used to measure potential heavy and/or hazardous drinking.26 Items in the AUDIT-C address frequency of drinking in the last year, how much alcohol on average is consumed on a day where the individual does drink alcohol, and frequency of binge drinking episodes (6 or more drinks on one occasion). Total scores range from 0 to 12. A higher score suggests that alcohol abuse is likely a problem for that individual. A score of 3 or more is considered a positive screening for hazardous drinking in women, and a score of 4 or more is a positive screening for men. There is strong evidence for the use and interpretation of this instrument, specifically in younger, OIF/OEF veterans.27 Internal consistency (Cronbach’s alpha) within this sample was 0.84. Primary Care-Post-traumatic Stress Disorder Screen The Primary Care-Post-Traumatic Stress Disorder Screen is a four-item screening tool for PTSD that is commonly used in primary care settings and considered an acceptable screening method within the VA system.28 The screen addresses symptoms of re-experiencing a traumatic event, numbing, avoidance, and hyperarousal. The items are answered with either “Yes” or “No.” Total scores range from 0 to 4. A cutoff score of 3 demonstrated a strong sensitivity (0.78) and specificity (0.87) in VA general medical settings.28 Prior studies have demonstrated acceptable levels of internal consistency and validity through concurrence with other measures of PTSD.29,30 Internal consistency (Cronbach’s alpha) within the present sample was 0.80. Patient Health Questionnaire-2 The Patient Health Questionnaire-2 is a two-item depression screening tool. The items address depressed mood and anhedonia over the past 2 wk.31 Both items are scored from 0 (“not at all”) to 3 (“nearly every day”). The total score (the sum of both items) ranges from 0 to 6. A score of ≥3 or higher is considered a positive initial screening for depression. These items demonstrated a 0.76 pooled sensitivity and 0.86 pooled specificity in a recent meta-analysis.32 Prior studies have demonstrated acceptable levels of internal consistency and validity through concurrence with other measures of depression.32,33 Internal consistency (Cronbach’s alpha) within the present sample was 0.80. Sleep hygiene behaviors Common sleep hygiene behaviors were assessed with 11 items asking about the weekly frequency of behaviors associated with healthy sleep including routine and consistent sleep and wake times, avoidance of alcohol, caffeine, spicy food within 4 h of bedtime, and exercise routines.34 Each behavior was rated as occurring between 0 and 7 times per week. The 11 items were subject to a principal components factor analysis with promax factor rotation. Three factors produced eigenvalues greater than one, accounting for a total of 49.41% total item variance, and the scree plot confirmed a three-factor solution. The first factor (eigenvalue = 2.64, 24.03% total variance accounted for) involved items related to sleep routine including “go to bed the same time each night,” “get up at the same time each day,” “create a sleep environment that is dark, quiet, comfortable, and cool,” “reserve your bed for sleep or sex,” and “have a bedtime routine.” The second factor (eigenvalue = 1.69, 15.36% variance accounted for) contained items relating to consumption behaviors involving food, drink, or chemicals prior to bed including “avoid caffeine within 4 h of bedtime,” “avoid spicy foods within 4 h of bedtime,” “avoid alcohol within 4 h of bedtime,” and “avoid nicotine within 4 h of bedtime.” Two items with low commonalities loaded on a final factor (eigenvalue = 1.10, 10.01% variance accounted for): “exercise” and “get out of bed if you have been awake for more than 20 minutes”. As the resulting factor had only two items and a reliability of 0.19, these items were excluded from further analysis. Mean scores were calculated for the two remaining factors: Sleep Hygiene Routine and Sleep Hygiene Consumption. Screen Use Before Bed This section consisted of two questions regarding electronic device usage within 1 h of going to sleep. Multiple device usage was measured on a five-point Likert scale ranging from “never” to “all the time,” asking how often in a given week multiple devices (e.g., computers, cell phones, tablets, game consoles, television, or electronic reading devices) were used. Any device usage was measured by asking (with the same response format) how often any devices were used in the bedroom in the hour before going to bed. Procedures A standard survey protocol was followed in which potential participants were first sent a pre-notification postcard explaining the study. Two weeks later they were sent the survey along with a cover-letter detailing the study and a modest ($2) payment. Non-responders were sent, at 2-wk intervals, a reminder postcard, a second survey, and then a third survey (this time via priority mail).35 Of 2,063 surveys mailed out, 438 were returned, yielding a response rate of 21%. Data Analysis Rates of sleep problems and potential psychiatric distress were established with simple frequencies. Gender (male vs. female) and race (Caucasian vs. non) groups were compared on the ISI and its potential predictors using independent samples t-tests. Predictors of sleep problems were evaluated with ordinary least squares multiple linear regression analyses, regressing ISI total scores on demographic variables, PTSD, depression, alcohol use, sleep hygiene (routine and restricting activity), and technology use (multiple device use and use before bed). RESULTS According to the ISI, 365 participants (83.7%) screened for either no sleep impairment (n = 203) or subthreshold insomnia (n = 162). The remaining 71 (16.3%) screened positive for moderate (n = 61) or severe (n = 10) clinical insomnia. Regarding other psychiatric screens, 42 (9.6%) screened positive for depression, 79 (18.1%) for PTSD, and 221 (50.7%) for potentially problematic alcohol use. Male and female soldiers did not differ in terms of depression, Sleep Hygiene Routine, or frequency of multi-device use before bed. Means, standard deviations, and correlations are displayed in Table I. Sleep problems, as assessed by the ISI, were correlated with PTSD symptoms (r = 0.41, p < 0.001), depression (r = 0.49, p < 0.001), Sleep Hygiene Routine (r = −0.34, p < 0.001), and more frequent use of multiple devices before bed (r = 0.15, p = 0.002). Considering potential demographic covariates, male and female participants differed in terms of alcohol use (2.91; SD = 2.63 and 2.41, SD = 2.02, respectively; t = 2.14, df = 326.63, p = 0.033), PTSD symptoms (0.81, SD = 1.27 and 1.17, SD = 1.47, respectively; t = −2.56, df = 425, p = 0.011), Sleep Hygiene Restricted Activity (4.02, SD = 1.61 and 3.53, SD = 1.40, respectively; t = −2.53, df = 418, p = 0.012), use of electronics before bed (2.94, SD = 1.27 and 3.35, SD = 0.91, respectively; t = −3.79, df = 343.98, p < 0.001), and sleep impairment (8.13, SD = 5.26 and 9.44, SD = 5.70, respectively; t = 2.31, df = 425, p = 0.022). Non-Caucasian and Caucasian participants differed in terms of alcohol use (2.05, SD = 2.17 vs. 3.00, SD = 2.52; t = −3.57, df = 433, p < 0.001) Sleep Hygiene Activity Consumption (4.15, SD = 1.63 and 3.79, SD = 1.53, respectively; t = 2.10, df = 422, p = 0.037) and use of multiple devices before bed (2.46, SD = 1.31 and 2.07, SD = 1.33; t = 2.69, df = 435, p = 0.008). Table I. Means, Standard Deviations, and Correlations       Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00        Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, using any electronic device <4 h prior to sleep.*p < 0.05,**p < 0.01,***p < 0.001. Table I. Means, Standard Deviations, and Correlations       Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00        Correlations  Mean  SD  ISI  PTSD  Depression  Alcohol  SH-Routine  SH-Cons.  Multi-Device  Any Device  ISI  8.52  5.48  1.00  0.41***  0.49***  0.02  −0.34***  −0.09  0.15*  0.05  PTSD  0.93  1.35    1.00  0.46***  0.00  −0.15**  −0.12*  0.07  0.04  Depression  0.88  1.24    1.00  0.02  −0.21***  −0.10*  0.12*  0.10*  Alcohol  2.76  2.48    1.00  −0.03  −0.26***  0.00  0.04  SH–routine  5.53  1.43    1.00  0.21***  −0.16**  −0.13**  SH–cons.  5.76  1.92    1.00  −0.17  0.05  Multi-device  2.15  1.33    1.00  0.26***  Any device  3.04  1.19    1.00  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, using any electronic device <4 h prior to sleep.*p < 0.05,**p < 0.01,***p < 0.001. Given the relationship between gender and ISI scores, gender was included as a covariate when regressing ISI scores on alcohol use, PTSD, depression, sleep hygiene, and technology use. The overall model was significant (R2 = 0.35, adj R2 = 0.34, F[8,408] = 27.58, p < 0.001). Significant independent predictors included gender (B = 0.99, β = 0.09, t = 2.10, p = 0.036), PTSD (B = 0.89, β = 0.22, t = 4.86, p < 0.001), depression (B = 1.53, β = 0.35, t = 7.56, p < 0.001), and Sleep Hygiene Routine (B = −0.88, β = −0.23, t = −5.473, p < 0.001). Alcohol use, Sleep Hygiene Consumption, and technology use did not emerge as independent predictors (see Table II). Table II. Regressing Sleep Problems on Predictors   B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104    B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, Using any electronic device <4 h prior to sleep. Table II. Regressing Sleep Problems on Predictors   B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104    B  Beta  t  p  (Constant)  9.56    6.58  0.000  Gender  0.99  0.09  2.10  0.036  Alcohol  0.05  0.02  0.54  0.588  PTSD  0.89  0.22  4.86  <0.001  Depression  1.53  0.35  7.56  <0.001  SH-Routine  −0.88  −0.23  −5.47  <0.001  SH-Consumption  0.11  0.04  0.90  0.371  Multi-Device  0.24  0.06  1.39  0.164  Any Device  −0.32  −0.07  −1.63  0.104  Note: SH–Routine, Sleep Hygiene Routine; SH–Cons., Sleep Hygiene Consumption; Multi-Device, Using Multiple Devices <4 h prior to sleep; Any Device, Using any electronic device <4 h prior to sleep. Conclusion Discussion National Guard personnel represent a vital component of the nation’s armed forces with distinct roles, duties, and life circumstances. This study evaluated levels and predictors of sleep impairment in a sample of National Guard soldiers as an examination of needs and risk/protective factors within the population. Overall, the majority of the present sample did not endorse high levels of sleep impairment. However, a sizeable minority (16.4%) did screen positive for moderate or even severe levels of clinical insomnia. Greater numbers of sleep-related complaints were related particularly to psychological distress including depressive and PTSD symptoms. This is an expected finding, given prior work documenting the co-occurrence of sleep impairments with both disorders.6–8,10 Further, both depression and PTSD are defined in part by the symptom of impaired sleep in the Diagnostic and Statistics Manual of Mental Disorders.36 We also found, as expected, that sleep hygiene behaviors relating to regular scheduling of bedtime and awakening and adherence to a bedtime routine were negatively correlated with sleep problems. It is impossible to know, in the present sample, if disrupted sleep resulted from poorly structured sleep activities (such as chaotic bedtimes) or if sleep problems led to more disrupted sleep hygiene routines. The evaluation of these potential pathways will require future studies making use of longitudinal methodology so that temporal cause and effect can be evaluated. Recent questions have emerged regarding the role of electronic device usage in potentially interfering with sleep.37–40 The present findings, however, provide only limited support for this hypothesis. The use of multiple devices before sleep did not correlate with sleep problems, and the relationship between sleep problems and frequency of any device use before bed was small. Impaired sleep has been associated with performance decrements in several occupational domains.14,16,17 In addition, impaired or disrupted sleep has been linked to numerous physical and mental health problems.1–10 The present findings suggest that screening and intervention for sleep problems may be helpful even very early in Army National Guard service members’ careers. Particular focus may be needed for those showing signs of emotional distress such as PTSD or depression, aligning with the current literature on psychopathological comorbidities related to insomnia.41 Findings also suggest that a focus on teaching and encouraging consistent sleep scheduling habits may be beneficial in this population, further supporting broad based programs such as the Comprehensive Soldier Fitness initiative. In addition, a recent RAND report highlighted the importance of more individually focused evidence-based practices, such as Cognitive-Behavioral Therapy for Insomnia (CBT-I).41 Finally, innovative electronic interventions, such as tailored smartphone apps may be helpful in engaging younger National Guard service members in a more individualized fashion. The measure of sleep hygiene behaviors used in this study is new and could benefit from further psychometric analysis. If further studies confirm its validity and these initial findings, sleep hygiene behaviors may be particularly useful treatment targets as they are directly observable, modifiable, and can be addressed through numerous formats such as in person teaching, smartphone apps, or even innovative educational game play. The small relationships between sleep problems and technology use before bed suggest that intervention efforts may be better spend addressing the broad sleep routine rather than targeting specific technology use. With the complexity inherent to military operations, simply aiming for standardized rest periods to address soldier sleep hygiene may not always be feasible. A more tangible path toward improved sleep hygiene through the identification of barriers to sleep may prove to be a more fruitful one. Budding research is beginning to shed light on some of these barriers, such as time demands, unpredictable habits, technology use, and peoples’ ability to “switch off”.42,43 Furthermore, continued research is necessary to address topics such as causal relationships between specific barriers and participants’ willingness to change sleep behaviors, behavior intervention strategies, and barrier modification (e.g., leveraging already-utilized technology to educate or adjust sleep-related behavior). Limitations of this study include a single time-point correlational design, and population sampling limitations. Although significant relationships were identified, we are unable to confirm causal relationships between variables due to the passive observation, cross-sectional design of the model. Furthermore, only abbreviated, though validated, measures were utilized for many constructs due to a need to minimize participant burden at this early phase of the research project. Utilizing more robust, full-length versions of these instruments would allow for a more detailed and nuanced understanding of sleep-related variables in future research endeavors. Regarding sampling limitations, these results may not generalize accurately across groups due to the unique characteristics of the Minnesota National Guard, such as demographic makeup, proximity to active duty forces, and deployment rates. Notwithstanding these limitations, this study demonstrates the importance of attending to sleep and sleep behaviors within a National Guard context and identifies the role of mental health symptoms within this population. Acknowledgements This project was conducted with resources from the Minneapolis VA Healthcare System and funded in part through a Departments of Defense Small Business Innovation Research grant to Smart Information Flow Technologies (Award Number W81XWH-16-C-0032). 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Google Scholar CrossRef Search ADS PubMed  Author notes The views expressed here are those of the authors and not of the Department of Defense, Department of Veteran Affairs, or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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Military MedicineOxford University Press

Published: May 18, 2018

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