Self-Reported Sleep, Anxiety, and Cognitive Performance in a Sample of U.S. Military Active Duty and Veterans

Self-Reported Sleep, Anxiety, and Cognitive Performance in a Sample of U.S. Military Active Duty... Abstract Unhealthy sleep can interfere with U.S. military service members affective and cognitive functioning, and increase accident and injury risks. This study examined the relationship between U.S. active duty and veterans’ (n = 233) self-reported sleep (Pittsburgh Sleep Quality Index), anxiety (Zung Self-Rating Anxiety Scale), and cognitive performance (Automated Neuropsychological Assessment Metric). Statistical analyses included Pearson product moment correlations and multivariate analysis of variance, with Tukey-b post-hoc tests, with a p < 0.05 significance level. Higher education, abstinence from sleep aids, longer time in active duty service, and being on active duty were correlated with better sleep and lower anxiety. Greater sleep disturbance, poor sleep quality, and sleepiness-related daytime dysfunction were associated with greater anxiety and slower response times, and lower response accuracy. Statistically controlling for anxiety diminished the magnitude and significance of the correlations between sleep and cognitive performance, suggesting that reducing anxiety will improve sleep and diminish cognitive performance effects. These findings suggest the need for addressing both sleep and anxiety for those with diagnosed sleep disorders, as well as using a procedural systems approach to decrease anxiety during missions that demand outstanding cognitive performance. INTRODUCTION It is our view that appropriate sleep planning and management affords military units and commanders a near-term tactical advantage in terms of maintaining alertness, a midterm tactical advantage of decreasing susceptibility to sleep and behavioral health disorders, and a long-term strategic advantage with increased readiness and resiliency of their soldiers.1 Insufficient and poor sleep can have significant negative repercussions on physical and mental health.1–3 Sleep disorders, such as insomnia and obstructive sleep apnea are pervasive among both active duty service members4–7 and veterans.8,9 Although the cause of sleep problems are multifaceted, some evidence suggests that erratic and long work schedules10 and deployments2 are contributing factors to symptoms of poor sleep (such as repeated awakenings, tossing and turning, and more time spent in lighter sleep, in addition to diagnosed sleep problems such as insomnia and obstructive sleep apnea). In addition to numerous long-term health risks, such as hypertension,11 cardiovascular disease,12 and stroke,13 poor sleep is also associated with immediate effects such as impaired thinking9,14 and impaired performance,15–18 which can jeopardize the safety and welfare of both the individual and their fellow service members. Although the causal relationship between poor sleep and mental health is not well understood (i.e., whether one precedes the other),19,20 studies have shown that poor sleep is associated with psychological distress, including anxiety and depression.5,6,12,20–22 For example, Mysliwiec and colleagues6 found that 35% of service members with obstructive sleep apnea met the diagnostic criteria for anxiety or depression and 55% of those with insomnia met the criteria for anxiety or depression diagnoses. In another study, Capaldi and colleagues5 found similar rates of sleep apnea among active duty service members with co-morbid anxiety, depression, or post-traumatic stress disorder (PTSD). Comparable findings have been reported with veterans. For example, Ulmer and colleagues12 found that veterans with mental health diagnoses, including depression and PTSD, were more likely to report sleep difficulties (e.g., sleep disturbances, and daytime dysfunction) as compared with those that did not have these diagnoses. Cognitive impairments have also been linked with poor sleep. For example, studies with both active duty14 and veteran9 samples found greater subjective incidents of cognitive difficulties (e.g., forgetfulness, distractibility) among participants who reported poor sleep, compared with those who did not report poor sleep. Other studies have shown that participants with insomnia took significantly longer to respond to cognitive tasks when compared with healthy control subjects.15,16,23 These findings are disquieting because sleep-related cognitive impairments increase the risk for workplace accidents.24–26 Thus, sleep is an important component of service member’s readiness and performance. The above findings highlight the imperative for identifying and treating poor sleep among U.S Military service members, as well as developing policies for operational sleep management. Despite continued scientific advancements regarding service member’s sleep, performance, and readiness, there is still much to learn. Moreover, there are noteworthy shortcomings and discrepancies in prior research that merit further inquiry. For example, some findings5,6,21,22 were based on secondary or retrospective data, rather than direct report or observation. Also, evidence for sleep-related deficits in cognitive performance has been mixed. Whereas some studies have found evidence that poor sleep impairs performance,15,23 others have not found this to be true.17 Finally, it is not clear if sleep deficits, other than the ability to fall asleep or sleep deprivation, contribute to cognitive deficits in military service members.9,14,18 Accordingly, a closer examination of the relationship between self-reported sleep, psychological distress, and cognitive performance is warranted. The Goal of the Study The goal of this study was to examine a sample of non-patient, U.S. active duty and veteran service member’s self-reported sleep, anxiety, and cognitive performance. Our specific research questions were as follows: Is there a relationship between self-reported sleep and anxiety? Is there a relationship between self-reported sleep and cognitive performance? Does self-reported anxiety affect the relationship between sleep and cognitive performance? Regarding (1), it was anticipated that poor sleep would be associated with higher anxiety.6 Regarding (2), it was anticipated that poor sleep would be associated with longer response times and poor response accuracy on measures of cognitive performance.15,16,18,23 Regarding (3), given that prior research has shown that anxiety and sleep are closely related6 and that other research has shown that increased anxiety is associated with poor cognitive performance,27 it was anticipated that anxiety would be correlated with both self-reported sleep and cognitive performance. METHODS Study Population Under an Army Research Laboratory, Institutional Review Board approved protocol, research volunteers (n = 233) read and signed an informed consent form prior to participation. To meet study criteria, volunteers had to: Be at least 18 years of age or older Not have a current diagnosis of severe traumatic brain injury Not be experiencing hallucinations or delusions Be on U.S. military active duty status or be a U.S. military veteran The data reported here were collected as part of a pre-intervention data collection in a larger study on resilience and mindfulness. Care was taken to recruit volunteers from the general military and veteran populations. That is, this was not specifically a patient population, but a cross-section of service members and veterans. All volunteers were recruited from the catchment area of a large military medical facility and several adjacent military posts. The focus was on a representative sample of U.S. active duty service members and veterans. No clinical populations were targeted. Volunteers did not receive compensation for their participation. Questionnaires Demographic Questionnaire A self-report measure that included questions about volunteers’ background information was given to each volunteer. Information collected included age, race/ethnicity, gender, education, marital status, hours-of-sleep, sleep aid use, service status (active duty, veteran), time-in-service, and deployment (i.e., whether they had deployed or not). Pittsburgh Sleep Quality Index (PSQI) The PSQI is a 19-item, self-rated questionnaire. Item ratings are grouped into seven subscale scores (efficiency, latency, duration, disturbance, quality, medication to sleep, daytime dysfunction due to sleepiness) each weighed on a 0–3 scale.28 The sum of the weighted subscale scores represents a “global” score. The higher the global score, the greater ones’ sleep difficulties are (poor sleep). Previous research has shown that, although global PSQI scores greater than five reflect clinical levels of poor sleep quality, PSQI scores should not be interpreted as analogs to physiological measures of sleep,28 as they are not correlated with traditional measures of sleep (e.g., polysomnography).29 That is, the PSQI and polysomnography measure different sleep properties and both measures are valuable for assessing sleep.30 The PSQI has been shown to have acceptable test-retest reliability (r = 0.83),28 content, and convergent and discriminate validity.28,29,31 Zung Self-Rating Anxiety Scale (SAS) The SAS is a 20-item self-rating scale.32 Respondents are instructed to rate their frequency of experiencing a given feeling or emotion word using a scale of 1 to 5 where 1 = little or none of the time and 4 = most of the time. Summary scores were calculated for each volunteer. Higher scores indicate greater anxiety. The SAS has been shown to have acceptable split-half reliability (r = 0.71),32 content, convergent validity, and discriminant validity.32,33 Cognitive Measures Cognitive tasks were administered via the Automated Neuropsychological Assessment Metric (ANAM34) software. All tasks were completed on a government owned, personal computer in a controlled laboratory setting. Volunteers completed three subtests in the ANAM suite: (1) the running memory continuous performance test (CPT), (2) the simple reaction time (SRT) test, and (3) the switching test (SWT) For each test, response times (in milliseconds) and throughput scores (the number of correct responses per unit of available time) were collected. The ANAM software has been shown to have good test-retest reliability.35 Running Memory Continuous Performance Test For the CPT, a single digit number appeared on the computer screen followed by either the same or a different number. The volunteer’s task was to decide if the second number was the same or different from the first number. If the second number matched the first, the volunteer pressed the right mouse button, and if the number was different the user pressed the left mouse button. The CPT was designed to measure attention, concentration, and working memory performance. Simple Reaction Time Test For the SRT, an asterisk (*) was presented on the screen at varying intervals. The user’s task was to press the right mouse button as quickly as possible each time the symbol appeared. The SRT was designed to measure visual-motor processing speed, simple motor speed, and attention. Switching Test The SWT requires users to alternate between two concurrently presented tasks. For each trial, users are presented with a three-digit math equation (e.g., “5 + 4 − 2”) on the right side of the screen and an animated character (manikin) holding a sphere on the left side of the screen. A red arrow directs the user’s attention to either the math equation or the manikin. If the arrow points to the math equation, the user’s task is to indicate on the keyboard, if the value of the equation is less than or greater than five (the “I” key = greater than five and the “J” key = less than five). If the arrow points to the manikin, the user’s task is to indicate, using the keyboard, if the manikin is holding the sphere in the left or right hand (the “W” key = left hand, the “D” key = right hand). For each trial, the manikin shifts positions, so that it may be facing towards the viewer, away from the viewer, or to the side). The SWT task was designed to measure divided attention, mental flexibility, and executive function. Statistical Analyses Data analyses were conducted with the IBM SPSS Statistics for Windows (Version 22, Armonk, NY: IBM Corp, Released 2013). Frequencies and Chi-square tests were used to analyze the demographic data. Bivariate correlational analyses were conducted using Pearson Product correlations. Multivariate analysis of variance (MANOVA) and Tukey-b post-hoc tests were used to analyze scores on the PSQI. Analysis of variance (ANOVA) and Tukey-b post-hoc tests were used to analyze scores on the SAS. MANOVA were used to analyze mean RT’s and throughput scores on the ANAM tasks. A partial correlation was used to examine the effect of controlling for scores on the SAS on the relationship between scores on the PSQI, mean RT’s and throughput scores on the ANAM tasks. A significance level of 0.05 was used for all analyses. RESULTS Demographics The majority of volunteers were male (53.2%), Caucasian (52.8%), married (57.5%), college educated (60.1%), veterans (65.7%), and had deployed (60.1%) (Table I). Volunteers reported an average age of 48.06 years (±11.96) and an average time-in-service of 15.26 years (±8.64). Self-reported age was significantly lower for active duty volunteers (M = 39.71 years, ±8.69) as compared with veteran volunteers (M = 52.45 years, ±11.07) (F(1,232) = 80.15, p < 0.001). Volunteers reported an average of 5.99 hours (±1.62) of sleep on weeknights and 6.75 hours (±1.96) of sleep on weekend nights. The percentage of volunteers that reported using a sleep aid on weeknights and weekends were 34.3% (n = 80) and 28.3% (n = 66), respectively. TABLE I. Frequencies and Percentages of Volunteer Demographics Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 TABLE I. Frequencies and Percentages of Volunteer Demographics Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 PSQI Results Means and standard errors for volunteers’ PSQI subscale scores are presented in Figure 1. The average global PSQI for all volunteers was 7.57 (±5.29), which was comparable to scores reported in previous studies with service members.12 Subscale scores ranged between 0.00 and 3.00 and total scores ranged between 0.00 and 21.00. Volunteer’s age, gender, race, and marital status had no significant effect on PSQI scores (p’s > 0.05), however education had a significant effect on PSQI subscale and global scores, (F(32, 896) = 1.66, p < 0.01, ηp2 = 0.06). Follow-up univariate tests showed that scores on sleep duration, disturbance, quality, medication use, and daytime dysfunction subscales differed significantly between the education levels (p’s <0.05). Means, standard deviations, and post-hoc comparisons are presented in Table II. Also, volunteers that reported using sleep aids on weeknights and weekends had significantly higher scores on all PSQI subscales and the global score (p’s < 0.01), except the sleep duration subscale (p > 0.05). TABLE II. Mean, Standard Deviations, and Multiple Comparisons for the Effects of Education on PSQI Subscale Scores Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Note. Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. Superscript values indicate significant between-group differences on subscale scores. For example, volunteers with a bachelor’s degree had significantly lower scores on the disturbance subscale (1.64) as compared with volunteer’s that had a high school diploma or GED (2.08) or some college/AA degree (1.92). For significant comparisons, p’s < 0.05. TABLE II. Mean, Standard Deviations, and Multiple Comparisons for the Effects of Education on PSQI Subscale Scores Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Note. Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. Superscript values indicate significant between-group differences on subscale scores. For example, volunteers with a bachelor’s degree had significantly lower scores on the disturbance subscale (1.64) as compared with volunteer’s that had a high school diploma or GED (2.08) or some college/AA degree (1.92). For significant comparisons, p’s < 0.05. FIGURE 1. View largeDownload slide Means and standard errors of volunteers PSQI subscale scores. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. FIGURE 1. View largeDownload slide Means and standard errors of volunteers PSQI subscale scores. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. Self-reported time-in-service was significantly negatively correlated with scores on the following PSQI subscales: sleep efficiency (r(233) = −0.13, p < 0.05), sleep quality (r(233) = −0.14, p < 0.05), daytime dysfunction due to sleepiness (r = −0.23, p < 0.01), and global PSQI (r(233) =−0.21, p < 0.01). Service status had a significant effect on PSQI subscale and global scores (F(8,224) = 3.57, p < 0.01, ηp2 = 0.11). Follow-up univariate tests showed that scores on the sleep latency, disturbance, medication, and daytime dysfunction subscales, as well as global PSQI, differed significantly between active duty and veteran volunteers (p’s < 0.05). Relative to active duty volunteers, veterans had significantly higher scores on the sleep latency, disturbance, medications, and daytime dysfunction subscales (Fig. 2) and global PSQI (p < 0.001). Deployment history had no significant effect on the subscale and global PSQI scores, p’s > 0.05. FIGURE 2. View largeDownload slide Means and standard errors for active duty and veterans significantly different PSQI subscale scores. Note: Lat, latency; Dist, disturbance; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. *p < 0.05, **p < 0.01. FIGURE 2. View largeDownload slide Means and standard errors for active duty and veterans significantly different PSQI subscale scores. Note: Lat, latency; Dist, disturbance; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. *p < 0.05, **p < 0.01. SAS Results The average SAS score for all volunteers was 37.74 (±9.43), which was lower than reported scores in studies with clinical populations.30,31 Volunteer’s age, gender, race, and marital status had no significant effects on SAS scores (p’s > 0.05), however education did (F(4,232) = 8.42, p < 0.0001, ηp2 = 0.13). Volunteers that reported having some college/AA degrees had significantly higher SAS scores (M = 41.35, ±8.65) than those that reported MA/PhD degrees (M = 34.03, ±8.97) or other advanced degrees (M = 31.36, ±6.32) (p’s < 0.001). Individuals that reported using sleep aids on weeknights and weekends had significantly higher scores on the SAS (M = 41.83, ±8.29 and M = 41.49 ± 8.66) as compared with those that did not self-identify as using sleep aids (M = 35.55, ±9.26 and M = 36.20, ±9.30) (p’s < 0.01). None of the other comparisons were statistically significant (p’s > 0.05). Volunteers time-in-service was significantly negatively correlated with scores on the SAS (r(233) = −0.27, p < 0.01). Veteran volunteers had significantly higher SAS scores (M = 39.24, ±9.37) compared with active duty volunteers (M = 34.86, ±8.90) (F(1, 232) = 11.87 p < 0.001, ηp2 = 0.05). Deployment history had no significant effect on SAS scores (p > 0.05). ANAM Cognitive Measures Results Table III displays the means and standard deviations of volunteers ANAM task performance. RT’s and throughput scores were comparable to available normative data.33 TABLE III. Means and Standard Deviations of Volunteers ANAM Task Scores, Mean Response Times, and Throughput ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 Note. CPT, running memory continuous performance task; SRT, simple response task; SWT, switching task. TABLE III. Means and Standard Deviations of Volunteers ANAM Task Scores, Mean Response Times, and Throughput ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 Note. CPT, running memory continuous performance task; SRT, simple response task; SWT, switching task. ANAM Task RT Results Although volunteers age was significantly positively correlated with mean RT on the CPT (r(232) = 0.26, p < 0.01) and with SRT (r(232) = 0.15, p < 0.05), it was not significantly correlated with mean RT on the SWT (p > 0.05). Volunteer’s gender, race, education, and marital status had no significant effects on the mean RT’s (p’s > 0.05). Self-reported weeknight and weekend sleep medication usage was not significantly correlated with RT for the three ANAM tasks (p’s > 0.05). Self-reported time-in-service was not significantly correlated with mean RT for any of the ANAM tasks (p’s > 0.05). Service status had a significant effect on volunteers mean RT’s (F(3,224) = 4.30, p < 0.01, ηp2 = 0.05). Descriptive statistics and follow-up univariate test results show that RTs were slower among veterans (p < 0.05) (Table IV). Deployment history had a significant effect on volunteers mean RT’s (F(3,224) = 3.98, p < 0.01, ηp2 = 0.05). Volunteers that reported deploying had significantly faster mean RT’s on the CPT and SWT tasks (respectively, M = 631.30, ±95.84 and M = 3,136.73, ±885.32), as compared with volunteers that reported no prior deployment (respectively, M = 660.22, ±101.24 and M = 3,440.47, ±955.67) (p’s < 0.05). Mean RT’s on the SRT did not differ significantly between those that had and had not deployed (p > 0.05). TABLE IV. Means (msec’s), Standard Deviations, and Univariate F-Test Output for Active Duty and Veteran Volunteers RT’s and Throughput Scores on the ANAM Tasks Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 TABLE IV. Means (msec’s), Standard Deviations, and Univariate F-Test Output for Active Duty and Veteran Volunteers RT’s and Throughput Scores on the ANAM Tasks Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 ANAM Task Throughput Scores Results Although volunteers age was significantly negatively correlated with CPT throughput scores (r = −0.23, p < 0.0001) and SRT throughput scores (r(232) = −0.17, p < 0.0001), it was not significantly correlated with SWT throughput scores (p > 0.05). Gender, race, education, and marital status had no significant effect on throughput scores (p’s > 0.05). Self-reported weeknight and weekend sleep medication usage was not significantly correlated with throughput for the three ANAM tasks (p’s > 0.05). Self-reported time-in-service was not significantly correlated with throughput scores on the ANAM tasks (p’s > 0.05). Although deployment history had no significant effect on throughput scores (p’s > 0.05), service status did (F(3, 224) = 4.12, p < 0.01, ηp2 = 0.05). Active duty service members had higher throughput scores than veterans on the CPT and SRT (p < 0.05). Descriptive statistics and follow-up univariate test results are presented in Table IV. Relationship Between PSQI and SAS As shown in Table V, all PSQI subscale scores and the global score were significantly positively correlated with total score on the SAS (p < 0.05). TABLE V. Pearson Product Correlations Between Subscale and Total PSQI Scores and Total Scores on the SAS PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** **p < 0.01. TABLE V. Pearson Product Correlations Between Subscale and Total PSQI Scores and Total Scores on the SAS PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** **p < 0.01. Relationship Between PSQI, SAS, and ANAM Mean RT on the CPT was significantly positively correlated with scores on both the sleep quality and daytime dysfunction PSQI subscales, as well as scores on the SAS (p < 0.05, Table VI). For the SRT, mean RT was significantly positively correlated with scores on the sleep disturbance, quality, daytime dysfunction PSQI subscales, as well as scores on the SAS (p < 0.01). Throughput scores were significantly negatively correlated with scores on the sleep disturbance, quality, and daytime dysfunction PSQI subscales, as well as scores on the SAS (p < 0.01). TABLE VI. Pearson Product Correlations Between ANAM Task Performance, Scores on the PSQI, and Scores on the SAS PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 Note. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime to dysfunction due to sleepiness. **p < 0.01, *p < 0.05. TABLE VI. Pearson Product Correlations Between ANAM Task Performance, Scores on the PSQI, and Scores on the SAS PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 Note. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime to dysfunction due to sleepiness. **p < 0.01, *p < 0.05. The SWT mean RT (but not throughput) was significantly negatively correlated with scores on the daytime dysfunction PSQI subscale (p < 0.05), but not with the SAS (p > 0.05). To control for anxiety, partial correlations were conducted between scores on the ANAM CPT, SRT, and SWT with scores on the PSQI disturbance, quality, and daytime dysfunction due to sleepiness subscales (Table VII). Scores on these subscales were selected because they demonstrated significant zero-order correlations with scores on the ANAM tasks, while others did not. As seen in Table VII, eight of the nine significant findings on the neurocognitive ANAM tasks were no longer significant. TABLE VII. Partial Correlation Coefficients (rp) Between ANAM Task Performance (PT, SRT, and the SWT) and Scores on the PSQI Controlling for scores on the SAS PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 Note. Bolded scores were significant prior to controlling for SAS. Dist, disturbance; Qual, quality; Daydys, daytime dysfunction due to sleepiness; RT, reaction time. *p < 0.05. TABLE VII. Partial Correlation Coefficients (rp) Between ANAM Task Performance (PT, SRT, and the SWT) and Scores on the PSQI Controlling for scores on the SAS PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 Note. Bolded scores were significant prior to controlling for SAS. Dist, disturbance; Qual, quality; Daydys, daytime dysfunction due to sleepiness; RT, reaction time. *p < 0.05. DISCUSSION The goal of the present study was to examine the relationship between self-reported sleep, anxiety, and cognitive performance in active duty and veteran U.S. Military service members. The following three paragraphs discuss the sleep, anxiety, and cognitive performance results as they relate to demographics. The PSQI scores of more than half of the volunteers fell at or above the accepted cut-off for a sleep disorder. This finding supports research demonstrating the high prevalence of sleep disorders among military service members,9,36 with 75% of those who have deployed reporting difficulties with sleep. Although increased age is associated with a higher prevalence of sleep disorders (e.g., OSA),11 in the present sample age was not significantly correlated with scores on the PSQI.29 Higher levels of education, abstaining from sleep aids, longer time-in-service, and active duty military status were associated with better self-reported sleep. These findings support prior research that reported a positive correlation between self-reported sleep aid use and PSQI scores37 and a negative correlation between short sleep duration and education levels.38 However, that longer time-in-service and active duty status were associated with better sleep has not been reported (although outcomes from other studies have shown that lower ranking service members tend to experience greater sleep difficulties than higher ranking members).39 Nevertheless, taking into account the significant age discrepancy between the active duty and veteran volunteers, this finding is consistent with reports that have shown that advanced age was accompanied by increased difficulty with sleep.40,41 Higher levels of education, abstaining from sleep aids, longer time-in-service, and active duty military status were associated with lower anxiety. This finding is consistent with the reports that education was significantly negatively correlated with both anxiety and depression.42 One explanation for this finding is that greater education may buffer against the negative effects of anxiety.42 Longer time spent on active duty and active duty vs. veteran status have not been identified in the literature as associated with lower anxiety. Certainly, anxiety is associated with military service and duties in harms’ way, and those who have deployed report greater anxiety than those who have not.43 However, service members who remain on active duty for a career or a longer period, have been selected for promotion and retention. Therefore, it may be that the selection process inadvertently includes individuals with sufficient coping skills and resilience. Another possibility is that those in active leadership positions, or those who are veterans of higher rank and potentially higher retirement or disability pay, may have a greater sense of control over their futures and therefore experience less anxiety or stress about their future.44 For the cognitive tasks, age was associated with slower response times and poor task accuracy, which supports research demonstrating slower reaction times and degrading cognitive performance among older individuals.45,46 Yet, despite the effects of age on CPT and SRT performance, age was not significantly correlated with performance on the SWT tasks, showing that age was related to attention, working memory, and visual-motor processing speed, but not to divided attention and mental flexibility. Active duty volunteers demonstrated significantly faster mean RT’s and better response accuracy as compared with veterans on the CPT and SRT, which may also reflect age-related differences in the populations. Volunteers that reported deploying demonstrated faster reaction times on two of the three tasks (CPT and SWT). This is perhaps the first study to find that deployment history was associated with faster response times and appears to indicate higher levels of attention in personnel who have deployed. Certainly, this is true for those with PTSD,47 as well as anecdotally reported by those who have recently returned from deployment. In fact, some experience hyperarousal, which may also be associated with faster reaction times. On the one hand, the faster response times findings for volunteers that deployed raises the possibility they may have relied on speed-accuracy trade-off performance strategy. On the other hand, throughput scores were not significantly different for volunteers that had/had not deployed. However, it should be noted that throughput is less sensitive to speed-accuracy tradeoffs as compared with response accuracy (e.g., correct/incorrect responses) or response time alone.48 Sleep and Anxiety Both demographic and service-related demographics were associated with self-reported sleep and anxiety. Whereas higher education, longer time-in-service, and active duty service status were associated with better daytime functioning and lower anxiety, higher education and active duty service status were also associated with lower reported sleep disturbance and less reliance on medication to sleep. Consistent with prior research,36,38 higher levels of anxiety were associated with greater sleep difficulties. In sum, sleep and anxiety are related to one another and both were impacted by individual differences (education), military service status, and time-in-service. Sleep and Cognitive Performance Greater self-reported sleep disturbance, sleep quality, daytime dysfunction due to sleepiness, and anxiety were associated with slower RTs and poor response accuracy. The relationship between sleep and cognitive performance is well documented, and sleep duration, the timing of when one sleeps, and recent sleep history are known to be predictive of reaction times and cognitive performance.49 In this study, controlling for anxiety significantly diminished the relationship between sleep and performance on the cognitive tasks. While this finding is not reflected in the literature, reductions in sleep disturbances have been found to reduce depression in military personnel.50 In addition, improving sleep quality has yielded reductions in both depression and post-traumatic arousal symptoms common to military personnel,51 and some evidence suggests that programs designed to treat sleep disorders have resulted in reduced self-reported anxiety.52 Although findings from research with civilian samples indicate that treatment of anxiety disorders is associated with improvement in sleep,53 other studies have shown that treatment of primary insomnia is associated with improvement in psychological symptoms.54 Sleep, Anxiety, and Cognitive Performance Greater sleep-related daytime dysfunction was associated with longer RTs on all three of the cognitive measures (CPT, SRT, and SWT) and on correct responses per unit of time for the SRT. These findings suggest that visual-motor processing speed, simple motor speed, attention and concentration, and more complex tasks of divided attention, mental flexibility, and executive functioning are negatively influenced by sleep-induced daytime dysfunction. In addition, these results are consistent with outcomes from prior research that have shown an association between sleep deprivation and slower reaction time,18,55 as well as research that has shown that greater subjectively assessed daytime sleepiness was associated with poorer performance on psychomotor vigilance tasks.56,57 Increased anxiety was associated with longer RT’s on both the CPT and SRT, as well as diminished throughput on the SRT. One possible explanation for this finding is that anxiety may interfere with the encoding, processing, and storage of information in working memory.27 However, anxiety scores were not significantly correlated with performance on the SWT, which suggests that the effects of anxiety on cognitive performance may be task-dependent, that is, more pronounced on tasks that require visual-motor processing, simple motor speed, and attention, rather than more complex tasks requiring divided attention and executive functioning. Controlling for anxiety eliminated 8 of the 9 (89%) of the significant degradations in cognitive performance when both anxiety and sleep were considered. Only accuracy per unit of time for the SRT remained significant. Future research could examine the effects of anxiety and poor sleep on other cognitive activities. While this finding has not been mentioned in prior literature, it has implications for the management of service members engaged in actions that impact sleep. Given the observed pattern of correlations in this study, it is plausible that sleep affects the association between anxiety and cognitive performance. However, for this study we focused on the impact of anxiety on sleep and cognitive performance, as we believe it may be easier to impact anxiety through interventions such as mindfulness and stress reduction programs particularly prior to and during deployment, compared with increasing improving and sleep. In fact, by reducing anxiety, sleep and cognitive performance should improve. Using a systems approach,58 it would appear that reducing the effects of sleep disturbance on cognitive performance entails minimizing acute anxiety. For example, when engaging in a mission that is likely to impact sleep, commanders might employ the following techniques for reducing troop anxiety: reducing uncertainty through transparency of mission expectations and requirements eliminating conflicting information by establishing a strong chain of command that meets regularly and is in agreement developing a consistent, reliable culture that includes high-performance standards, as well as personal concern for one another integrating methods for reducing stress and promoting performance during normal duty days, as well as during pre-deployment activities, and role-modeling such behaviors. These might include mindfulness meditation, active coping, healthy lifestyle education, breathing techniques, yoga, or other mind-body techniques. Limitations One limitation of the current study was the small overall sample size as well as the unequal sample sizes of the active duty and veteran volunteers, as this limits the statistical generalizability of the results. A second limitation was that data on volunteers’ sleep were based on self-report, rather than medical evaluation (e.g., polysomnography). Although this may limit the clinical utility of the findings, it should be noted that this was not a clinical or diagnostic study, but a study directed at a cross- section of active duty and veteran personnel. Noting a diagnosis of a sleep disorder and the resulting disorder (e.g., insomnia) among volunteers would have provided additional information, however this was not part of the original study design. Previous Presentations Presented as a poster at the 2017 Military Health System Research Symposium (Abstract number: MHSRS-17-1422). Funding This study was supported by a grant from the U.S. Army Study Program Management Office (ASPMO). This supplement was sponsored by the Office of the Secretary of Defense for Health Affairs. Acknowledgments We acknowledge the men and women who serve and have served in the U.S. Military services and particularly to those who chose to volunteer their time to participate in this study. We also thank our colleagues, Gary Boykin, Angela Jeter, Jessica Villarreal, Cory Overby, and Leah Enders for their integral assistance with this research. References 1 Mysliwiec V , Walter RJ , Collen J , Wesensten N : Military sleep management: an operational imperative . US Army Med Dep J 2016 ; 128 – 34 . PMID:27215880 . 2 Troxel WM , Shih RA , Pedersen E , et al. : Sleep in the Military: Promoting Healthy Sleep Among U.S. Service Members . Santa Monica, CA , RAND Corporation , 2015 . 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Google Scholar Crossref Search ADS PubMed 58 Marras WS , Hancock PA : Putting mind and body back together: a human-systems approach to the integration of the physical and cognitive dimensions of task design and operations . Appl Ergon 2014 ; 45 : 55 – 60 . Google Scholar Crossref Search ADS PubMed Author notes The views expressed in this paper are those of the authors and do not necessarily represent the official position or policy of the U.S. Government, the Department of Defense, or the Department of the Army. Published by Oxford University Press on behalf of Association of Military Surgeons of the United States 2019. 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

Self-Reported Sleep, Anxiety, and Cognitive Performance in a Sample of U.S. Military Active Duty and Veterans

Military Medicine, Volume 184 (Supplement_1) – Mar 1, 2019

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Oxford University Press
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Published by Oxford University Press on behalf of Association of Military Surgeons of the United States 2019.
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0026-4075
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1930-613X
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10.1093/milmed/usy323
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Abstract

Abstract Unhealthy sleep can interfere with U.S. military service members affective and cognitive functioning, and increase accident and injury risks. This study examined the relationship between U.S. active duty and veterans’ (n = 233) self-reported sleep (Pittsburgh Sleep Quality Index), anxiety (Zung Self-Rating Anxiety Scale), and cognitive performance (Automated Neuropsychological Assessment Metric). Statistical analyses included Pearson product moment correlations and multivariate analysis of variance, with Tukey-b post-hoc tests, with a p < 0.05 significance level. Higher education, abstinence from sleep aids, longer time in active duty service, and being on active duty were correlated with better sleep and lower anxiety. Greater sleep disturbance, poor sleep quality, and sleepiness-related daytime dysfunction were associated with greater anxiety and slower response times, and lower response accuracy. Statistically controlling for anxiety diminished the magnitude and significance of the correlations between sleep and cognitive performance, suggesting that reducing anxiety will improve sleep and diminish cognitive performance effects. These findings suggest the need for addressing both sleep and anxiety for those with diagnosed sleep disorders, as well as using a procedural systems approach to decrease anxiety during missions that demand outstanding cognitive performance. INTRODUCTION It is our view that appropriate sleep planning and management affords military units and commanders a near-term tactical advantage in terms of maintaining alertness, a midterm tactical advantage of decreasing susceptibility to sleep and behavioral health disorders, and a long-term strategic advantage with increased readiness and resiliency of their soldiers.1 Insufficient and poor sleep can have significant negative repercussions on physical and mental health.1–3 Sleep disorders, such as insomnia and obstructive sleep apnea are pervasive among both active duty service members4–7 and veterans.8,9 Although the cause of sleep problems are multifaceted, some evidence suggests that erratic and long work schedules10 and deployments2 are contributing factors to symptoms of poor sleep (such as repeated awakenings, tossing and turning, and more time spent in lighter sleep, in addition to diagnosed sleep problems such as insomnia and obstructive sleep apnea). In addition to numerous long-term health risks, such as hypertension,11 cardiovascular disease,12 and stroke,13 poor sleep is also associated with immediate effects such as impaired thinking9,14 and impaired performance,15–18 which can jeopardize the safety and welfare of both the individual and their fellow service members. Although the causal relationship between poor sleep and mental health is not well understood (i.e., whether one precedes the other),19,20 studies have shown that poor sleep is associated with psychological distress, including anxiety and depression.5,6,12,20–22 For example, Mysliwiec and colleagues6 found that 35% of service members with obstructive sleep apnea met the diagnostic criteria for anxiety or depression and 55% of those with insomnia met the criteria for anxiety or depression diagnoses. In another study, Capaldi and colleagues5 found similar rates of sleep apnea among active duty service members with co-morbid anxiety, depression, or post-traumatic stress disorder (PTSD). Comparable findings have been reported with veterans. For example, Ulmer and colleagues12 found that veterans with mental health diagnoses, including depression and PTSD, were more likely to report sleep difficulties (e.g., sleep disturbances, and daytime dysfunction) as compared with those that did not have these diagnoses. Cognitive impairments have also been linked with poor sleep. For example, studies with both active duty14 and veteran9 samples found greater subjective incidents of cognitive difficulties (e.g., forgetfulness, distractibility) among participants who reported poor sleep, compared with those who did not report poor sleep. Other studies have shown that participants with insomnia took significantly longer to respond to cognitive tasks when compared with healthy control subjects.15,16,23 These findings are disquieting because sleep-related cognitive impairments increase the risk for workplace accidents.24–26 Thus, sleep is an important component of service member’s readiness and performance. The above findings highlight the imperative for identifying and treating poor sleep among U.S Military service members, as well as developing policies for operational sleep management. Despite continued scientific advancements regarding service member’s sleep, performance, and readiness, there is still much to learn. Moreover, there are noteworthy shortcomings and discrepancies in prior research that merit further inquiry. For example, some findings5,6,21,22 were based on secondary or retrospective data, rather than direct report or observation. Also, evidence for sleep-related deficits in cognitive performance has been mixed. Whereas some studies have found evidence that poor sleep impairs performance,15,23 others have not found this to be true.17 Finally, it is not clear if sleep deficits, other than the ability to fall asleep or sleep deprivation, contribute to cognitive deficits in military service members.9,14,18 Accordingly, a closer examination of the relationship between self-reported sleep, psychological distress, and cognitive performance is warranted. The Goal of the Study The goal of this study was to examine a sample of non-patient, U.S. active duty and veteran service member’s self-reported sleep, anxiety, and cognitive performance. Our specific research questions were as follows: Is there a relationship between self-reported sleep and anxiety? Is there a relationship between self-reported sleep and cognitive performance? Does self-reported anxiety affect the relationship between sleep and cognitive performance? Regarding (1), it was anticipated that poor sleep would be associated with higher anxiety.6 Regarding (2), it was anticipated that poor sleep would be associated with longer response times and poor response accuracy on measures of cognitive performance.15,16,18,23 Regarding (3), given that prior research has shown that anxiety and sleep are closely related6 and that other research has shown that increased anxiety is associated with poor cognitive performance,27 it was anticipated that anxiety would be correlated with both self-reported sleep and cognitive performance. METHODS Study Population Under an Army Research Laboratory, Institutional Review Board approved protocol, research volunteers (n = 233) read and signed an informed consent form prior to participation. To meet study criteria, volunteers had to: Be at least 18 years of age or older Not have a current diagnosis of severe traumatic brain injury Not be experiencing hallucinations or delusions Be on U.S. military active duty status or be a U.S. military veteran The data reported here were collected as part of a pre-intervention data collection in a larger study on resilience and mindfulness. Care was taken to recruit volunteers from the general military and veteran populations. That is, this was not specifically a patient population, but a cross-section of service members and veterans. All volunteers were recruited from the catchment area of a large military medical facility and several adjacent military posts. The focus was on a representative sample of U.S. active duty service members and veterans. No clinical populations were targeted. Volunteers did not receive compensation for their participation. Questionnaires Demographic Questionnaire A self-report measure that included questions about volunteers’ background information was given to each volunteer. Information collected included age, race/ethnicity, gender, education, marital status, hours-of-sleep, sleep aid use, service status (active duty, veteran), time-in-service, and deployment (i.e., whether they had deployed or not). Pittsburgh Sleep Quality Index (PSQI) The PSQI is a 19-item, self-rated questionnaire. Item ratings are grouped into seven subscale scores (efficiency, latency, duration, disturbance, quality, medication to sleep, daytime dysfunction due to sleepiness) each weighed on a 0–3 scale.28 The sum of the weighted subscale scores represents a “global” score. The higher the global score, the greater ones’ sleep difficulties are (poor sleep). Previous research has shown that, although global PSQI scores greater than five reflect clinical levels of poor sleep quality, PSQI scores should not be interpreted as analogs to physiological measures of sleep,28 as they are not correlated with traditional measures of sleep (e.g., polysomnography).29 That is, the PSQI and polysomnography measure different sleep properties and both measures are valuable for assessing sleep.30 The PSQI has been shown to have acceptable test-retest reliability (r = 0.83),28 content, and convergent and discriminate validity.28,29,31 Zung Self-Rating Anxiety Scale (SAS) The SAS is a 20-item self-rating scale.32 Respondents are instructed to rate their frequency of experiencing a given feeling or emotion word using a scale of 1 to 5 where 1 = little or none of the time and 4 = most of the time. Summary scores were calculated for each volunteer. Higher scores indicate greater anxiety. The SAS has been shown to have acceptable split-half reliability (r = 0.71),32 content, convergent validity, and discriminant validity.32,33 Cognitive Measures Cognitive tasks were administered via the Automated Neuropsychological Assessment Metric (ANAM34) software. All tasks were completed on a government owned, personal computer in a controlled laboratory setting. Volunteers completed three subtests in the ANAM suite: (1) the running memory continuous performance test (CPT), (2) the simple reaction time (SRT) test, and (3) the switching test (SWT) For each test, response times (in milliseconds) and throughput scores (the number of correct responses per unit of available time) were collected. The ANAM software has been shown to have good test-retest reliability.35 Running Memory Continuous Performance Test For the CPT, a single digit number appeared on the computer screen followed by either the same or a different number. The volunteer’s task was to decide if the second number was the same or different from the first number. If the second number matched the first, the volunteer pressed the right mouse button, and if the number was different the user pressed the left mouse button. The CPT was designed to measure attention, concentration, and working memory performance. Simple Reaction Time Test For the SRT, an asterisk (*) was presented on the screen at varying intervals. The user’s task was to press the right mouse button as quickly as possible each time the symbol appeared. The SRT was designed to measure visual-motor processing speed, simple motor speed, and attention. Switching Test The SWT requires users to alternate between two concurrently presented tasks. For each trial, users are presented with a three-digit math equation (e.g., “5 + 4 − 2”) on the right side of the screen and an animated character (manikin) holding a sphere on the left side of the screen. A red arrow directs the user’s attention to either the math equation or the manikin. If the arrow points to the math equation, the user’s task is to indicate on the keyboard, if the value of the equation is less than or greater than five (the “I” key = greater than five and the “J” key = less than five). If the arrow points to the manikin, the user’s task is to indicate, using the keyboard, if the manikin is holding the sphere in the left or right hand (the “W” key = left hand, the “D” key = right hand). For each trial, the manikin shifts positions, so that it may be facing towards the viewer, away from the viewer, or to the side). The SWT task was designed to measure divided attention, mental flexibility, and executive function. Statistical Analyses Data analyses were conducted with the IBM SPSS Statistics for Windows (Version 22, Armonk, NY: IBM Corp, Released 2013). Frequencies and Chi-square tests were used to analyze the demographic data. Bivariate correlational analyses were conducted using Pearson Product correlations. Multivariate analysis of variance (MANOVA) and Tukey-b post-hoc tests were used to analyze scores on the PSQI. Analysis of variance (ANOVA) and Tukey-b post-hoc tests were used to analyze scores on the SAS. MANOVA were used to analyze mean RT’s and throughput scores on the ANAM tasks. A partial correlation was used to examine the effect of controlling for scores on the SAS on the relationship between scores on the PSQI, mean RT’s and throughput scores on the ANAM tasks. A significance level of 0.05 was used for all analyses. RESULTS Demographics The majority of volunteers were male (53.2%), Caucasian (52.8%), married (57.5%), college educated (60.1%), veterans (65.7%), and had deployed (60.1%) (Table I). Volunteers reported an average age of 48.06 years (±11.96) and an average time-in-service of 15.26 years (±8.64). Self-reported age was significantly lower for active duty volunteers (M = 39.71 years, ±8.69) as compared with veteran volunteers (M = 52.45 years, ±11.07) (F(1,232) = 80.15, p < 0.001). Volunteers reported an average of 5.99 hours (±1.62) of sleep on weeknights and 6.75 hours (±1.96) of sleep on weekend nights. The percentage of volunteers that reported using a sleep aid on weeknights and weekends were 34.3% (n = 80) and 28.3% (n = 66), respectively. TABLE I. Frequencies and Percentages of Volunteer Demographics Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 TABLE I. Frequencies and Percentages of Volunteer Demographics Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 Demographic # % Gender  Male 124 53.2  Female 109 46.8 Race  African-American 60 25.8  Native American 4 1.7  Caucasian 123 52.8  Hispanic 41 17.6  Asian 4 1.7  Other 1 0.4 Education  High school/GED 13 5.6  Some college/AA 80 34.3  Bachelors 56 24.0  MA/PhD 70 30.0  Other 14 6.0 Marital status  Married 134 57.5  Divorced 47 20.2  Widowed 3 1.3  Single/separated 41 17.6  Partnered w/significant other 8 3.4 Military status  Active duty 80 34.3  Veterans 153 65.7 Deployment history  Did not deploy 93 39.9  Deployed 140 60.1 PSQI Results Means and standard errors for volunteers’ PSQI subscale scores are presented in Figure 1. The average global PSQI for all volunteers was 7.57 (±5.29), which was comparable to scores reported in previous studies with service members.12 Subscale scores ranged between 0.00 and 3.00 and total scores ranged between 0.00 and 21.00. Volunteer’s age, gender, race, and marital status had no significant effect on PSQI scores (p’s > 0.05), however education had a significant effect on PSQI subscale and global scores, (F(32, 896) = 1.66, p < 0.01, ηp2 = 0.06). Follow-up univariate tests showed that scores on sleep duration, disturbance, quality, medication use, and daytime dysfunction subscales differed significantly between the education levels (p’s <0.05). Means, standard deviations, and post-hoc comparisons are presented in Table II. Also, volunteers that reported using sleep aids on weeknights and weekends had significantly higher scores on all PSQI subscales and the global score (p’s < 0.01), except the sleep duration subscale (p > 0.05). TABLE II. Mean, Standard Deviations, and Multiple Comparisons for the Effects of Education on PSQI Subscale Scores Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Note. Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. Superscript values indicate significant between-group differences on subscale scores. For example, volunteers with a bachelor’s degree had significantly lower scores on the disturbance subscale (1.64) as compared with volunteer’s that had a high school diploma or GED (2.08) or some college/AA degree (1.92). For significant comparisons, p’s < 0.05. TABLE II. Mean, Standard Deviations, and Multiple Comparisons for the Effects of Education on PSQI Subscale Scores Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Education PSQI Dur Dist Qual Meds Daydys M SD M SD M SD M SD M SD (1) High school/GED 0.77 0.93 2.083,4,5 0.49 1.46 0.66 1.465 1.51 1.695 0.75 (2) Some college/AA 1.145 1.17 1.923,4 0.67 1.763,4 0.78 1.264 1.40 1.614 0.86 (3) Bachelors 1.274,5 1.02 1.641,2 0.70 1.462 0.81 1.055 1.33 1.465 0.85 (4) MA/PhD 0.893 0.93 1.571,2 0.69 1.202 0.73 0.712 1.19 1.212 0.92 (5) Other 0.362,3 0.50 1.431 0.51 1.14 0.53 0.211,3 0.80 1.001,3 0.55 Note. Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. Superscript values indicate significant between-group differences on subscale scores. For example, volunteers with a bachelor’s degree had significantly lower scores on the disturbance subscale (1.64) as compared with volunteer’s that had a high school diploma or GED (2.08) or some college/AA degree (1.92). For significant comparisons, p’s < 0.05. FIGURE 1. View largeDownload slide Means and standard errors of volunteers PSQI subscale scores. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. FIGURE 1. View largeDownload slide Means and standard errors of volunteers PSQI subscale scores. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. Self-reported time-in-service was significantly negatively correlated with scores on the following PSQI subscales: sleep efficiency (r(233) = −0.13, p < 0.05), sleep quality (r(233) = −0.14, p < 0.05), daytime dysfunction due to sleepiness (r = −0.23, p < 0.01), and global PSQI (r(233) =−0.21, p < 0.01). Service status had a significant effect on PSQI subscale and global scores (F(8,224) = 3.57, p < 0.01, ηp2 = 0.11). Follow-up univariate tests showed that scores on the sleep latency, disturbance, medication, and daytime dysfunction subscales, as well as global PSQI, differed significantly between active duty and veteran volunteers (p’s < 0.05). Relative to active duty volunteers, veterans had significantly higher scores on the sleep latency, disturbance, medications, and daytime dysfunction subscales (Fig. 2) and global PSQI (p < 0.001). Deployment history had no significant effect on the subscale and global PSQI scores, p’s > 0.05. FIGURE 2. View largeDownload slide Means and standard errors for active duty and veterans significantly different PSQI subscale scores. Note: Lat, latency; Dist, disturbance; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. *p < 0.05, **p < 0.01. FIGURE 2. View largeDownload slide Means and standard errors for active duty and veterans significantly different PSQI subscale scores. Note: Lat, latency; Dist, disturbance; Meds, medication to sleep; Daydys, daytime dysfunction due to sleepiness. *p < 0.05, **p < 0.01. SAS Results The average SAS score for all volunteers was 37.74 (±9.43), which was lower than reported scores in studies with clinical populations.30,31 Volunteer’s age, gender, race, and marital status had no significant effects on SAS scores (p’s > 0.05), however education did (F(4,232) = 8.42, p < 0.0001, ηp2 = 0.13). Volunteers that reported having some college/AA degrees had significantly higher SAS scores (M = 41.35, ±8.65) than those that reported MA/PhD degrees (M = 34.03, ±8.97) or other advanced degrees (M = 31.36, ±6.32) (p’s < 0.001). Individuals that reported using sleep aids on weeknights and weekends had significantly higher scores on the SAS (M = 41.83, ±8.29 and M = 41.49 ± 8.66) as compared with those that did not self-identify as using sleep aids (M = 35.55, ±9.26 and M = 36.20, ±9.30) (p’s < 0.01). None of the other comparisons were statistically significant (p’s > 0.05). Volunteers time-in-service was significantly negatively correlated with scores on the SAS (r(233) = −0.27, p < 0.01). Veteran volunteers had significantly higher SAS scores (M = 39.24, ±9.37) compared with active duty volunteers (M = 34.86, ±8.90) (F(1, 232) = 11.87 p < 0.001, ηp2 = 0.05). Deployment history had no significant effect on SAS scores (p > 0.05). ANAM Cognitive Measures Results Table III displays the means and standard deviations of volunteers ANAM task performance. RT’s and throughput scores were comparable to available normative data.33 TABLE III. Means and Standard Deviations of Volunteers ANAM Task Scores, Mean Response Times, and Throughput ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 Note. CPT, running memory continuous performance task; SRT, simple response task; SWT, switching task. TABLE III. Means and Standard Deviations of Volunteers ANAM Task Scores, Mean Response Times, and Throughput ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 ANAM Mean SD CPT  Mean RT (Msec’s) 642.31 98.19  Throughput 73.27 26.17 SRT  Mean RT (Msec’s) 352.81 152.62  Throughput 181.55 39.08 SWT  Mean RT (Msec’s) 3,259.29 924.42  Throughput 13.87 6.07 Note. CPT, running memory continuous performance task; SRT, simple response task; SWT, switching task. ANAM Task RT Results Although volunteers age was significantly positively correlated with mean RT on the CPT (r(232) = 0.26, p < 0.01) and with SRT (r(232) = 0.15, p < 0.05), it was not significantly correlated with mean RT on the SWT (p > 0.05). Volunteer’s gender, race, education, and marital status had no significant effects on the mean RT’s (p’s > 0.05). Self-reported weeknight and weekend sleep medication usage was not significantly correlated with RT for the three ANAM tasks (p’s > 0.05). Self-reported time-in-service was not significantly correlated with mean RT for any of the ANAM tasks (p’s > 0.05). Service status had a significant effect on volunteers mean RT’s (F(3,224) = 4.30, p < 0.01, ηp2 = 0.05). Descriptive statistics and follow-up univariate test results show that RTs were slower among veterans (p < 0.05) (Table IV). Deployment history had a significant effect on volunteers mean RT’s (F(3,224) = 3.98, p < 0.01, ηp2 = 0.05). Volunteers that reported deploying had significantly faster mean RT’s on the CPT and SWT tasks (respectively, M = 631.30, ±95.84 and M = 3,136.73, ±885.32), as compared with volunteers that reported no prior deployment (respectively, M = 660.22, ±101.24 and M = 3,440.47, ±955.67) (p’s < 0.05). Mean RT’s on the SRT did not differ significantly between those that had and had not deployed (p > 0.05). TABLE IV. Means (msec’s), Standard Deviations, and Univariate F-Test Output for Active Duty and Veteran Volunteers RT’s and Throughput Scores on the ANAM Tasks Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 TABLE IV. Means (msec’s), Standard Deviations, and Univariate F-Test Output for Active Duty and Veteran Volunteers RT’s and Throughput Scores on the ANAM Tasks Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 Active duty Veterans ANAM task M SD M SD F p ηp2 RT’s  CPT 619.70 101.00 655.07 95.85 6.73 0.01 0.03  SRT 323.50 71.89 367.20 179.61 4.26 0.04 0.02  SWT 3,071.56 880.04 3,356.90 934.69 4.97 0.03 0.02 Throughput scores  CPT 79.69 25.96 70.21 25.89 6.87 0.01 0.03  SRT 189.73 37.98 177.53 38.78 5.15 0.02 0.02  SWT 14.86 6.01 13.36 6.06 3.13 0.08 0.01 ANAM Task Throughput Scores Results Although volunteers age was significantly negatively correlated with CPT throughput scores (r = −0.23, p < 0.0001) and SRT throughput scores (r(232) = −0.17, p < 0.0001), it was not significantly correlated with SWT throughput scores (p > 0.05). Gender, race, education, and marital status had no significant effect on throughput scores (p’s > 0.05). Self-reported weeknight and weekend sleep medication usage was not significantly correlated with throughput for the three ANAM tasks (p’s > 0.05). Self-reported time-in-service was not significantly correlated with throughput scores on the ANAM tasks (p’s > 0.05). Although deployment history had no significant effect on throughput scores (p’s > 0.05), service status did (F(3, 224) = 4.12, p < 0.01, ηp2 = 0.05). Active duty service members had higher throughput scores than veterans on the CPT and SRT (p < 0.05). Descriptive statistics and follow-up univariate test results are presented in Table IV. Relationship Between PSQI and SAS As shown in Table V, all PSQI subscale scores and the global score were significantly positively correlated with total score on the SAS (p < 0.05). TABLE V. Pearson Product Correlations Between Subscale and Total PSQI Scores and Total Scores on the SAS PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** **p < 0.01. TABLE V. Pearson Product Correlations Between Subscale and Total PSQI Scores and Total Scores on the SAS PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** PSQI SAS Efficiency 0.17** Latency 0.42** Duration 0.35** Disturbance 0.61** Quality 0.63** Medication 0.31** Daytime dysfunction 0.62** Global PSQI 0.47** **p < 0.01. Relationship Between PSQI, SAS, and ANAM Mean RT on the CPT was significantly positively correlated with scores on both the sleep quality and daytime dysfunction PSQI subscales, as well as scores on the SAS (p < 0.05, Table VI). For the SRT, mean RT was significantly positively correlated with scores on the sleep disturbance, quality, daytime dysfunction PSQI subscales, as well as scores on the SAS (p < 0.01). Throughput scores were significantly negatively correlated with scores on the sleep disturbance, quality, and daytime dysfunction PSQI subscales, as well as scores on the SAS (p < 0.01). TABLE VI. Pearson Product Correlations Between ANAM Task Performance, Scores on the PSQI, and Scores on the SAS PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 Note. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime to dysfunction due to sleepiness. **p < 0.01, *p < 0.05. TABLE VI. Pearson Product Correlations Between ANAM Task Performance, Scores on the PSQI, and Scores on the SAS PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 PSQI SAS ANAM Eff Lat Dur Dist Qual Meds Daydys Global Total CPT  Mean RT 0.10 −0.05 0.09 0.09 0.14* 0.13 0.19** 0.08 0.15*  Throughput 0.01 0.00 −0.01 −0.05 −0.08 −0.09 −0.04 0.00 −0.07 SRT  Mean RT −0.01 0.09 0.11 0.19** 0.18** 0.02 0.19** 0.11 0.18**  Throughput −0.04 −0.07 −0.11 −0.19** −0.23** −0.03 −0.20** −0.08 −0.19** SWT  Mean RT −0.06 −0.02 0.00 0.11 0.00 0.03 0.16* 0.00 0.13  Throughput 0.10 0.06 −0.02 −0.03 −0.01 0.00 −0.10 0.04 −0.01 Note. Eff, efficiency; Lat, latency; Dur, duration; Dist, disturbance; Qual, quality; Meds, medication to sleep; Daydys, daytime to dysfunction due to sleepiness. **p < 0.01, *p < 0.05. The SWT mean RT (but not throughput) was significantly negatively correlated with scores on the daytime dysfunction PSQI subscale (p < 0.05), but not with the SAS (p > 0.05). To control for anxiety, partial correlations were conducted between scores on the ANAM CPT, SRT, and SWT with scores on the PSQI disturbance, quality, and daytime dysfunction due to sleepiness subscales (Table VII). Scores on these subscales were selected because they demonstrated significant zero-order correlations with scores on the ANAM tasks, while others did not. As seen in Table VII, eight of the nine significant findings on the neurocognitive ANAM tasks were no longer significant. TABLE VII. Partial Correlation Coefficients (rp) Between ANAM Task Performance (PT, SRT, and the SWT) and Scores on the PSQI Controlling for scores on the SAS PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 Note. Bolded scores were significant prior to controlling for SAS. Dist, disturbance; Qual, quality; Daydys, daytime dysfunction due to sleepiness; RT, reaction time. *p < 0.05. TABLE VII. Partial Correlation Coefficients (rp) Between ANAM Task Performance (PT, SRT, and the SWT) and Scores on the PSQI Controlling for scores on the SAS PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 PSQI ANAM Dist Qual Daydys CPT  Mean RT 0.00 0.06 0.13  Throughput −0.01 −0.05 0.00 SRT  Mean RT 0.11 0.09 0.10  Throughput −0.09 −0.15* −0.11 SWT  Mean RT 0.04 −0.10 0.11  Throughput −0.04 −0.01 −0.13 Note. Bolded scores were significant prior to controlling for SAS. Dist, disturbance; Qual, quality; Daydys, daytime dysfunction due to sleepiness; RT, reaction time. *p < 0.05. DISCUSSION The goal of the present study was to examine the relationship between self-reported sleep, anxiety, and cognitive performance in active duty and veteran U.S. Military service members. The following three paragraphs discuss the sleep, anxiety, and cognitive performance results as they relate to demographics. The PSQI scores of more than half of the volunteers fell at or above the accepted cut-off for a sleep disorder. This finding supports research demonstrating the high prevalence of sleep disorders among military service members,9,36 with 75% of those who have deployed reporting difficulties with sleep. Although increased age is associated with a higher prevalence of sleep disorders (e.g., OSA),11 in the present sample age was not significantly correlated with scores on the PSQI.29 Higher levels of education, abstaining from sleep aids, longer time-in-service, and active duty military status were associated with better self-reported sleep. These findings support prior research that reported a positive correlation between self-reported sleep aid use and PSQI scores37 and a negative correlation between short sleep duration and education levels.38 However, that longer time-in-service and active duty status were associated with better sleep has not been reported (although outcomes from other studies have shown that lower ranking service members tend to experience greater sleep difficulties than higher ranking members).39 Nevertheless, taking into account the significant age discrepancy between the active duty and veteran volunteers, this finding is consistent with reports that have shown that advanced age was accompanied by increased difficulty with sleep.40,41 Higher levels of education, abstaining from sleep aids, longer time-in-service, and active duty military status were associated with lower anxiety. This finding is consistent with the reports that education was significantly negatively correlated with both anxiety and depression.42 One explanation for this finding is that greater education may buffer against the negative effects of anxiety.42 Longer time spent on active duty and active duty vs. veteran status have not been identified in the literature as associated with lower anxiety. Certainly, anxiety is associated with military service and duties in harms’ way, and those who have deployed report greater anxiety than those who have not.43 However, service members who remain on active duty for a career or a longer period, have been selected for promotion and retention. Therefore, it may be that the selection process inadvertently includes individuals with sufficient coping skills and resilience. Another possibility is that those in active leadership positions, or those who are veterans of higher rank and potentially higher retirement or disability pay, may have a greater sense of control over their futures and therefore experience less anxiety or stress about their future.44 For the cognitive tasks, age was associated with slower response times and poor task accuracy, which supports research demonstrating slower reaction times and degrading cognitive performance among older individuals.45,46 Yet, despite the effects of age on CPT and SRT performance, age was not significantly correlated with performance on the SWT tasks, showing that age was related to attention, working memory, and visual-motor processing speed, but not to divided attention and mental flexibility. Active duty volunteers demonstrated significantly faster mean RT’s and better response accuracy as compared with veterans on the CPT and SRT, which may also reflect age-related differences in the populations. Volunteers that reported deploying demonstrated faster reaction times on two of the three tasks (CPT and SWT). This is perhaps the first study to find that deployment history was associated with faster response times and appears to indicate higher levels of attention in personnel who have deployed. Certainly, this is true for those with PTSD,47 as well as anecdotally reported by those who have recently returned from deployment. In fact, some experience hyperarousal, which may also be associated with faster reaction times. On the one hand, the faster response times findings for volunteers that deployed raises the possibility they may have relied on speed-accuracy trade-off performance strategy. On the other hand, throughput scores were not significantly different for volunteers that had/had not deployed. However, it should be noted that throughput is less sensitive to speed-accuracy tradeoffs as compared with response accuracy (e.g., correct/incorrect responses) or response time alone.48 Sleep and Anxiety Both demographic and service-related demographics were associated with self-reported sleep and anxiety. Whereas higher education, longer time-in-service, and active duty service status were associated with better daytime functioning and lower anxiety, higher education and active duty service status were also associated with lower reported sleep disturbance and less reliance on medication to sleep. Consistent with prior research,36,38 higher levels of anxiety were associated with greater sleep difficulties. In sum, sleep and anxiety are related to one another and both were impacted by individual differences (education), military service status, and time-in-service. Sleep and Cognitive Performance Greater self-reported sleep disturbance, sleep quality, daytime dysfunction due to sleepiness, and anxiety were associated with slower RTs and poor response accuracy. The relationship between sleep and cognitive performance is well documented, and sleep duration, the timing of when one sleeps, and recent sleep history are known to be predictive of reaction times and cognitive performance.49 In this study, controlling for anxiety significantly diminished the relationship between sleep and performance on the cognitive tasks. While this finding is not reflected in the literature, reductions in sleep disturbances have been found to reduce depression in military personnel.50 In addition, improving sleep quality has yielded reductions in both depression and post-traumatic arousal symptoms common to military personnel,51 and some evidence suggests that programs designed to treat sleep disorders have resulted in reduced self-reported anxiety.52 Although findings from research with civilian samples indicate that treatment of anxiety disorders is associated with improvement in sleep,53 other studies have shown that treatment of primary insomnia is associated with improvement in psychological symptoms.54 Sleep, Anxiety, and Cognitive Performance Greater sleep-related daytime dysfunction was associated with longer RTs on all three of the cognitive measures (CPT, SRT, and SWT) and on correct responses per unit of time for the SRT. These findings suggest that visual-motor processing speed, simple motor speed, attention and concentration, and more complex tasks of divided attention, mental flexibility, and executive functioning are negatively influenced by sleep-induced daytime dysfunction. In addition, these results are consistent with outcomes from prior research that have shown an association between sleep deprivation and slower reaction time,18,55 as well as research that has shown that greater subjectively assessed daytime sleepiness was associated with poorer performance on psychomotor vigilance tasks.56,57 Increased anxiety was associated with longer RT’s on both the CPT and SRT, as well as diminished throughput on the SRT. One possible explanation for this finding is that anxiety may interfere with the encoding, processing, and storage of information in working memory.27 However, anxiety scores were not significantly correlated with performance on the SWT, which suggests that the effects of anxiety on cognitive performance may be task-dependent, that is, more pronounced on tasks that require visual-motor processing, simple motor speed, and attention, rather than more complex tasks requiring divided attention and executive functioning. Controlling for anxiety eliminated 8 of the 9 (89%) of the significant degradations in cognitive performance when both anxiety and sleep were considered. Only accuracy per unit of time for the SRT remained significant. Future research could examine the effects of anxiety and poor sleep on other cognitive activities. While this finding has not been mentioned in prior literature, it has implications for the management of service members engaged in actions that impact sleep. Given the observed pattern of correlations in this study, it is plausible that sleep affects the association between anxiety and cognitive performance. However, for this study we focused on the impact of anxiety on sleep and cognitive performance, as we believe it may be easier to impact anxiety through interventions such as mindfulness and stress reduction programs particularly prior to and during deployment, compared with increasing improving and sleep. In fact, by reducing anxiety, sleep and cognitive performance should improve. Using a systems approach,58 it would appear that reducing the effects of sleep disturbance on cognitive performance entails minimizing acute anxiety. For example, when engaging in a mission that is likely to impact sleep, commanders might employ the following techniques for reducing troop anxiety: reducing uncertainty through transparency of mission expectations and requirements eliminating conflicting information by establishing a strong chain of command that meets regularly and is in agreement developing a consistent, reliable culture that includes high-performance standards, as well as personal concern for one another integrating methods for reducing stress and promoting performance during normal duty days, as well as during pre-deployment activities, and role-modeling such behaviors. These might include mindfulness meditation, active coping, healthy lifestyle education, breathing techniques, yoga, or other mind-body techniques. Limitations One limitation of the current study was the small overall sample size as well as the unequal sample sizes of the active duty and veteran volunteers, as this limits the statistical generalizability of the results. A second limitation was that data on volunteers’ sleep were based on self-report, rather than medical evaluation (e.g., polysomnography). Although this may limit the clinical utility of the findings, it should be noted that this was not a clinical or diagnostic study, but a study directed at a cross- section of active duty and veteran personnel. Noting a diagnosis of a sleep disorder and the resulting disorder (e.g., insomnia) among volunteers would have provided additional information, however this was not part of the original study design. Previous Presentations Presented as a poster at the 2017 Military Health System Research Symposium (Abstract number: MHSRS-17-1422). Funding This study was supported by a grant from the U.S. Army Study Program Management Office (ASPMO). This supplement was sponsored by the Office of the Secretary of Defense for Health Affairs. Acknowledgments We acknowledge the men and women who serve and have served in the U.S. Military services and particularly to those who chose to volunteer their time to participate in this study. We also thank our colleagues, Gary Boykin, Angela Jeter, Jessica Villarreal, Cory Overby, and Leah Enders for their integral assistance with this research. References 1 Mysliwiec V , Walter RJ , Collen J , Wesensten N : Military sleep management: an operational imperative . US Army Med Dep J 2016 ; 128 – 34 . PMID:27215880 . 2 Troxel WM , Shih RA , Pedersen E , et al. : Sleep in the Military: Promoting Healthy Sleep Among U.S. Service Members . Santa Monica, CA , RAND Corporation , 2015 . 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Journal

Military MedicineOxford University Press

Published: Mar 1, 2019

References

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