Attention and Visual Tracking Degradation During Acute Sleep Deprivation in a Military Sample

Address correspondence and reprint requests to: Kristin J. Heaton, Ph.D., U.S. Army Research Institute of Environmental Medicine, 42 Kansas Street, Natick, MA 01760; [email protected].
 

Heaton KJ, Maule AL, Maruta J, Kryskow EM, Ghajar J. Attention and visual tracking degradation during acute sleep deprivation in a military sample. Aviat Space Environ Med 2014; 85:497–503.

Background: Fatigue due to sleep restriction places individuals at elevated risk for accidents, degraded health, and impaired physical and mental performance. Early detection of fatigue-related performance decrements is an important component of injury prevention and can help to ensure optimal performance and mission readiness. This study used a predictive visual tracking task and a computer-based measure of attention to characterize fatigue-related attention decrements in healthy Army personnel during acute sleep deprivation. Methods: Serving as subjects in this laboratory-based study were 87 male and female service members between the ages of 18 and 50 with no history of brain injury with loss of consciousness, substance abuse, or significant psychiatric or neurologic diagnoses. Subjects underwent 26 h of sleep deprivation, during which eye movement measures from a continuous circular visual tracking task and attention measures (reaction time, accuracy) from the Attention Network Test (ANT) were collected at baseline, 20 h awake, and between 24 to 26 h awake. Results: Increases in the variability of gaze positional errors (46–47%), as well as reaction time-based ANT measures (9–65%), were observed across 26 h of sleep deprivation. Accuracy of ANT responses declined across this same period (11%). Discussion: Performance measures of predictive visual tracking accurately reflect impaired attention due to acute sleep deprivation and provide a promising approach for assessing readiness in personnel serving in diverse occupational areas, including flight and ground support crews.

Keywords
 

MILITARY SERVICE members are frequently exposed to conditions that may contribute to disrupted sleep patterns. These conditions, which are often exacerbated during deployment, include extreme physical exertion, psychological or emotional stress, unpredictable or irregular work shifts, and extended mission durations. Within the aviation community, the link between fatigue and accident risk has long been established (1). Flight crews involved in long-haul missions (10) as well as those conducting short-haul operations (e.g., the low-cost air travel industry), where scheduling can be irregular and use of discretionary time for flight is commonplace (13), are at increased risk of fatigue-related accidents. Since the National Transportation Safety Board (NTSB) first cited fatigue as a probable cause in an aviation accident in 1993 (22), numerous fatigue-related accidents have been identified, including a Colgan Air accident in 2009 with 50 fatalities and an Air India crash in 2010 with 158 fatalities. It is estimated that nearly 15–20% of all aviation accidents are fatigue-related, involving flight and ground support crews (e.g., air traffic controllers, maintenance personnel, and fuel handlers) (1). Testimonies to the United States House of Representatives Subcommittee on Aviation by the National Aeronautical and Space Administration (NASA), the NTSB, and the Federal Aviation Administration regarding pilot fatigue and accident risk (8,21) underscore the importance of identifying and mitigating the effects of fatigue in flight crews and ground support personnel.

The impact of fatigue due to sleep loss on an individual’s health and performance can be profound. Sleep loss or restriction increases risk of accidents and injury (24), alters immune function and elevates risk for illness (15), and impairs cognition, including executive function (18,23), decision making (14), attention (17,18,21), working memory (5), and visuospatial perception (14). Attention appears to be especially sensitive to sleep loss, even in the acute phase (17,18,21), but the precise characterization of attention degradation is challenging. Rather than a unitary function, attention has been described as a multidimensional system consisting of distinct but interacting neural networks (25). In particular, one theory posits three distinct but interrelated attention constructs. These include an alerting component (vigilance, achieving and maintaining activation of cognitive activity), an orienting component (allocating attentional focus selectively to relevant elements in the sensory environment), and an executive control/conflict component (maintaining control over one’s behavior to achieve an intended goal and resolve conflict among competing alternative responses) (25). Research examining the effects of acute sleep deprivation have consistently shown reduced vigilance (alerting function) as measured by slower reaction times across a variety of tests (4,32), as well as impaired executive system function such as in decision making, response inhibition, planning, and error detection (14,23,31). These findings are supported by studies showing reductions in cerebral blood flow following a 26-h period of sustained wakefulness in regions related to alertness and attention, as well as other cognitive processes, particularly in the ventral regions of the prefrontal cortex and in the thalamus (5,30). Relatively few studies have examined the impact of sleep deprivation on the orienting function, and those that have report inconsistent results (4,33).

Early identification of fatigue-related performance decrements is an important component of injury prevention and can help to ensure optimal performance and mission readiness. However, measures that have demonstrated sensitivity to attention impairments following sleep loss/restriction may not differentiate the underlying etiology of such impairments and thus lack specificity. This presents particular challenges for screening and detection of conditions such as mild traumatic brain injury, which share a common symptom profile with fatigue due to sleep loss, including cognitive performance decrements (e.g., degraded attention, reaction time/RT, working memory, and executive system function) and changes in mood (e.g., irritability) (20). Additionally, implementation of these measures in field settings can be cumbersome and require trained examiners or clinicians to administer and interpret results. These limitations and the need for field-expedient, user-friendly tools to assess performance and readiness in service members from diverse occupational backgrounds, including flight crews, have led to a growing interest in novel metrics for assessment of degraded cognitive performance.

Physiology-based performance measures have drawn increasing interest as both independent and/or adjunct indicators of cognitive status. One such measure is a recently developed continuous predictive visual tracking task, designed to assess a construct of attention known as predictive timing (19). Unlike traditional attention assessment tools, this test specifically examines the dynamic interaction, i.e., synchronization, between the stimulus and action. Early work by Brezinova and Kendell (3) noted that visual tracking behavior in healthy subjects can be disturbed by fatigue. Humans use a combination of saccadic (quick repositioning) and smooth pursuit (continuous) eye movements to visually stabilize a moving object of interest on the fovea. Among its many functions, attention subserves the selection of visual information for processing (25) and, during visual tracking, these eye movements together reflect an overt expression of attention. Maintenance of visual tracking requires dynamic prediction of target velocity and trajectory, spatial working memory, and use of visual feedback to continuously adjust gaze position for accuracy (16). Numerous studies demonstrate that visual tracking is highly dependent on attention (2,16). Given that continuous and dynamic stabilization of the image of a moving object requires attention, quantification of visual tracking performance should provide a measure of attention system functioning.

Fatigue-related attention decrements represent a serious risk to human health and performance. The present study sought to characterize fatigue-related attention decrements in Army personnel within the first 26 h of sleep deprivation using a circular visual tracking paradigm. These results are then compared with attention performance as measured by the Attention Network Test (ANT) (7), a computer-based assessment specifically designed to delineate the efficiency of the alerting, orienting, and conflict components of attention.

 

The present study, using a prospective, repeat measurement design, was conducted at an active duty military facility located in New England as part of a clinical research award to the Brain Trauma Foundation, New York, NY.

Subjects

The protocol was reviewed and approved by the local Army Institutional Review Board. Written informed consent was obtained from all subjects prior to data collection. Subjects were U.S. Army soldiers (68 men, 19 women; mean age 21.8 ± 3.7 yr, range 18-33 yr), representing diverse military occupational specialties (including infantry, mechanics, logistics and supply, mortuary assistants, and communications specialists) who were recruited from an active duty military facility in New England via scheduled, in-person briefings. Selection criteria for participation excluded individuals with prior history of traumatic brain injury/concussion with loss of consciousness, substance abuse, known neurologic disorders, and known psychiatric conditions (including attention deficit disorder). Participation required normal (or corrected to normal) vision and was limited to men and women, 18 to 50 yr of age, who had completed at least 12 yr of education and were able to abstain from caffeine use for at least 26 h.

After consent procedures were completed, prospective subjects underwent a structured screening interview conducted by a member of the research staff. This screening included the assessment for symptoms of attention deficit disorder (Conners Adult ADHD Rating Scale – Self-Report: Short Version; CAARS-S:S; Pearson, San Antonio, TX), posttraumatic stress disorder [Post Traumatic Stress Disorder (PTSD) Checklist – Civilian Version; PCL-C; National Center for PTSD, U.S. Department of Veterans Affairs], depression (Center for Epidemiologic Studies Depression Scale; CES-D) (26), and mild brain injury/concussion (Brain Injury Screening Questionnaire; BISQ) (11). Individuals were excluded from participation if they screened positive for ADD/ADHD symptomatology (t-score > 70 on the CAARS-S:S, or self-report of prior ADD/ADHD diagnosis) or brain injury (positive or “possible” brain injury rating on BISQ). Participant scores on the PCL-C and CES-D were included in subsequent analyses as covariates, if indicated.

Materials

Predictive visual tracking: A full description of the visual tracking protocol and analyses was reported by Maruta et al. (19) and is briefly summarized here. The visual tracking protocol was carried out using a commercially available infrared visual-tracking system (EyeLink CL, SR Research, Ontario, Canada). Visual acuity was verified prior to testing using a Snellen chart. Subjects were seated comfortably with their heads stabilized using a head/chin rest during testing.

The semiautomated test sequence lasted approximately 5 min. During each test sequence, subjects received standardized instructions for test completion, calibration, and practice through both audio and visual formats. The test stimulus was a small target on a computer screen that moved along a circular trajectory of a 10° radius in visual angle six times at 0.4 Hz, and the participant was instructed to follow the target movement. Eye movements were recorded during two identical test runs.

Eye movement data were analyzed using a custom Matlab program (The MathWorks, Natick, MA). In the present study, primary visual tracking outcomes of interest were variability of gaze error along directions perpendicular and parallel to the target trajectory and mean phase error (MPE). Gaze error variability was measured with the SD of radial and tangential errors (SDRE and SDTE, respectively). These parameters were chosen as the primary outcomes of interest because precision and accuracy of continuous and dynamic predictive visual tracking relies upon one’s ability to accurately predict where a target will appear (spatial prediction) and when it will appear (temporal prediction). Sleep loss/restriction has been shown to increase variability in performance across numerous tasks, including those assessing reaction time (simple and choice/decision RT), computational skills, sustained attention, and response inhibition (9,14,27). MPE is a measure of the overall temporal accuracy of visual tracking and is calculated as the average angular difference between the gaze and the target relative to the origin of the circular target trajectory (negative phase error = gaze trailing target or phase lag). SDRE and SDTE provide a measure of spatial and temporal precision of the gaze with respect to the moving target. Together, these measures provide an indication of gaze-target synchronization sensitive to lapses in attention (19,20).

Attention Network Test: The ANT (7) is a computer-based assessment of attention using RT measures and various cue and stimulus combinations. Subjects are asked to indicate the direction in which a central arrow is pointing. In some trials, the central arrow is presented alone and, in others, it is flanked by two arrows to either side that point in the same (congruent) or opposite (incongruent) directions. Cues providing temporal and spatial information pertaining to the target may also be presented.

In this study, the ANT variables of interest were: alerting effect (median RT of no-cue trials minus median RT of double cue trials); orienting effect (median RT of central cue trials minus median RT of spatial cue trials); executive control/conflict effect (EC/C; median RT of all incongruent flanker trials minus median RT of all congruent flanker trials); mean RT (average of raw response times for accurate responses across cue conditions); and accuracy (averaged across cue conditions). In the case of the alerting and orienting effects, a larger value may be inferred as either the ability to reduce the response time by taking advantage of cues or dependence on the cue presence to make a quick response. In the case of the EC/C effect, a larger value is inferred as a less efficient ability to resolve conflicting information.

Sleep questionnaire: At the start of the 26-h sleep deprivation period, subjects were asked to complete a 12-item questionnaire assessing their current level of wakefulness, amount of sleep achieved the preceding night, quality of sleep achieved, and use of sleep aids (e.g., prescription or over-the-counter medications or homeopathic supplements).

Procedure

The study measures were taken at three time points: 06:00-09:00 at the start of the 26-h sleep deprivation period (T1), 02:00-04:00 (T2, approximately 20 h awake), and 06:00-09:00 (T3, approximately 24-26 h awake). T2 represents a nadir in the circadian phase, while T3 represents the maximum cumulative period without sleep. Other tasks, including dynamic visual acuity testing and other neurocognitive measures, were carried out during the 26-h study period as part of a larger ongoing study of the effects of fatigue on physiologic and neurobehavioral performance in military service members; their results are, or will be, reported elsewhere (28).

A summary of activities throughout the 26-h sleep deprivation period is provided in Table I. Upon arrival for initial (T1) testing, subjects were briefed about study procedures and were allowed time to ask questions. Subjects then completed the testing protocol in the following order: sleep questionnaire, visual tracking protocol, brief break (5-10 min), ANT, and other measures (see below). The total time of T1 testing was 120-180 min. This test protocol, with the exception of the sleep questionnaire, was repeated at T2 and T3. No subject had more than one previous exposure to the visual tracking paradigm or the ANT prior to T1. Throughout the remainder of the sleep deprivation period, subjects were encouraged to continue with normal daily activities, including normal military duties, trainings and briefings, and low-impact recreation, according to individual routine. Meals were taken as per usual schedule and a variety of nutritious snacks and beverages were provided; caffeine containing products were not permitted throughout the study period. One or more members of the research team accompanied subjects throughout the study period to ensure wakefulness, safety, and compliance to study procedures. Following completion of T3 testing, subjects were escorted to their barracks where they were allowed to recover for 24 h before resuming normal duties.

Statistical Analysis

Data analyses were completed using SPSS 19.0 (SPSS Inc., 2010, Chicago, IL). Descriptive analyses were initially conducted to assess sample characteristics and distribution of scores across measures. Outliers, identified as scores falling more than 3 SDs above or below the mean, were truncated to the 3-SD level. Distributions of scores for each measure were examined for skewness and kurtosis.

The relative impact of age, education, ethnicity, and gender on study outcome measures were examined using analysis of variance. A repeated measures analysis of variance (ANOVA) using Type III sums of squares was used to assess changes in visual tracking and ANT outcomes across the 26-h sleep deprivation period. Post hoc comparisons across the three discrete testing points (T2-T1, T3-T2, and T3-T1) were then completed for those variables demonstrating significance on ANOVAs by calculating a t-statistic using Type III mean square error. The relationships between visual tracking measures and ANT outcomes were assessed via Spearman correlations. The alpha level was set at 0.05 and a Bonferroni adjustment was made to account for multiple comparisons when appropriate. Calculated P-values that were smaller than 10−4 are expressed as < 0.0001; otherwise exact P-values rounded to the third decimal place are provided.

 

A total of 97 subjects were enrolled in this study. Two subjects were withdrawn after screening positive for having a history of head injury with loss of consciousness (BISQ), two subjects were withdrawn due to illness unrelated to the study procedures, and six subjects withdrew due to changes in military duty requirements. None of the subjects screened positive for ADD/ADHD. A total of 87 subjects were included in the final sample. Subjects reported an average of 12.6 yr (SD = 1.2 yr) of formal education and 8.7 mo (SD = 2.7 mo) of active duty service in the Army, and identified their racial/ethnic background as 54% Caucasian (N = 47), 24.1% African American (N = 21), 17.2 Hispanic/Latino (N = 15), and 4.6% as “other” (N = 4). On average, they scored within normal limits compared to the general (nonclinical) population on the PCL-C (mean = 21.1, SD = 5.5) and CES-D (mean = 5.2, SD = 4.7).

Identifying and truncating statistical outliers at the 3-SD value limit impacted scores on 1.5% of the visual tracking parameters and 1.1% of the ANT measures at T1, 3.1% of the visual tracking parameters and 1.4% of the ANT measures at T2, and 2.3% of the visual tracking parameters and 1.4% of the ANT measures at T3. Attempts were made to reduce the skewness of score distributions on the study measures with a log transformation. However, analyses conducted using log transformed values produced similar outcomes to those conducted using the raw values. Thus, analyses in this report were conducted using raw values.

Subjects were medically screened and cleared for any conditions that would prohibit their participation in research prior to the start of this study. Although not specifically queried, none of the subjects reported a history of disordered sleeping. All subjects adhered to a similar work schedule, beginning with morning formation at 06:30 and ending at 16:30. Sleep-wake cycles were per individual preference. In the 24 h prior to the start of this study, subjects reported sleeping an average of 6.69 h (SD = 1.7; ranging from 3 to 12.25 h) and none reported use of medication or homeopathic aid to assist achieving sleep. When queried about caffeine intake in the 24 h prior to the start of testing procedures, the majority of subjects reported consuming no caffeinated beverages (71.3%), while 23% (N = 20) reported consuming 1-2 caffeine-containing beverages and only 5 (5.7%) reported drinking 3-4 caffeinated beverages. None of the subjects reported or were observed to experience adverse reaction to abstinence from caffeine (e.g., severe headache) during the 26-h study period. The majority of subjects (85%, N = 74) indicated feeling moderately to completely well-rested at the start of testing.

SDRE and SDTE visual tracking parameters demonstrated a significant degradation in performance over time [SDRE: F(2, 172) = 25.22, P < 0.0001, and SDTE: F(2, 172) = 21.14, P < 0.0001] (Table II, Fig. 1). Post hoc pairwise comparisons using a Bonferroni adjusted alpha level of 0.017 (0.05/3) examining changes between discrete time points (T2 vs. T1, T3 vs. T2, and T3 vs. T1) generally showed a significant and monotonic increase in gaze error variability (both radial and tangential) across the 26-h study period (Table II). The largest observed changes occurred between T3 and T1 for both SDRE (mean difference = 0.28°, P < 0.0001) and SDTE (mean difference = 0.36°, P < 0.0001). A measure of predictive temporal gaze accuracy (MPE) did not change significantly from a rested state to 26 h without sleep.

Fig.1.

SD of radial and tangential error across sleep deprivation period. SDRE = standard deviation of radial error. SDTE = standard deviation of tangential error. SDRE and SDTE are measured in degrees of visual angle.

 

Out of the five ANT indices, all except orienting effect showed an overall significant change in performance across the sleep deprivation period using an adjusted alpha of 0.01 (0.05/5) (Table III). Accuracy decreased [F(2, 172) = 53.59, P < 0.0001] and overall mean RT increased across the 26-h study period [F(2, 172) = 40.39, P < 0.0001]. Pairwise comparisons involving three comparisons (T2-T1, T3-T2, T3-T1) and using an adjusted alpha of 0.017 (0.05/3) indicated that average response times (mean RT T3-T1 mean difference = 24.31 ms, P < 0.0001) and both the alerting and EC/C effects generally increased across the sleep deprivation period while response accuracy (ACC T3-T1 mean difference = −11.02%, P < 0.0001) declined significantly with each measurement over this time period.

Correlations (Spearman) between the visual tracking and ANT measures were used to provide a measure of interrelationship among different constructs of attention. Of the 15 pairs of visual tracking and ANT parameters, none of the baseline (T1) measurements showed significant correlations using an adjusted alpha of 0.0033 (0.05/15).

Concurrent validation of the visual tracking paradigm as a measure of attention was examined using correlations among changes in visual tracking and ANT indices across the sleep deprivation period (T3-T1). The eight indices that showed sleep deprivation-related changes were chosen for comparisons. Using an adjusted alpha of 0.006 (0.05/8), a significant correlation was observed between SDRE of visual tracking and ANT ACC (r = 0.301, P = 0.005), showing a small interdependence between decreases in gaze stability and response accuracy.

 

Continuous gaze-target synchronization during visual tracking is dependent upon attention (2,16,19). In this study, we examined the relationships among dynamic visuo-motor synchronization, other measures of attention, and acute total sleep deprivation of 26 h. Consistent with prior work indicating that decrements in vigilance cause increased variability (9,14,27), we found that sleep deprivation produced a significant decrease in the precision of gaze stabilization (SDRE, SDTE) during a predictive visual tracking task. This finding is also consistent with degradation of gaze positional control during periods of drowsiness (29). On the other hand, the overall temporal relationship between the gaze and target (MPE) was not significantly impacted, also consistent with previous work (6).

We used the ANT to provide an additional measure of the attention construct. The weak correlations to visual tracking metrics at baseline suggest that the two tests measure largely separate but interacting attention constructs. As the period of sleep loss increased, subjects demonstrated longer response times and decreased accuracy on the ANT. A significant increase in the EC/C effect indicated decreased efficiency for resolving conflicting information with an increasing sleep deficit. This change was directionally opposite of that noted by Ishigami and Klein (12), who reported a reduced EC/C effect with as many as 10 repeated administrations of the ANT, thus demonstrating learning effects that can be expected under normal conditions. In contrast, practice effects are negligible for the visual tracking test (19). We speculate that the effect of fatigue due to sleep loss either obscured or eliminated any effects practice may have had on a participant’s performance. These findings replicate, at least in part, those reported by Martella et al. (18) and Roca et al. (27). However, in contrast to these previous studies, we found a significant increase in the alerting effect as well. Consistent with prior studies (4,32), the observed increased RT facilitation due to a warning cue (enhanced alerting effect) most likely reflects an increase in overall response latencies due to sleep loss, rather than an increase in the participant’s ability to take advantage of an alerting cue.

Correlations between the indices of the visual tracking test and ANT were weak, suggesting that the attention constructs assessed with these tests were largely independent. However, changes in the ANT accuracy index were correlated with changes in gaze stability over the sleep deprivation period, lending further support to the premise that predictive visual tracking reflects the overall integrity of the attention system.

It should be noted that subjects in this study were predominantly young, Caucasian men in excellent physical condition and in the early stages of their Army career. None of our subjects had a history of deployment or other combat-related activity. As a result, generalizing our current findings to both the broader population of military service members and civilians, including those within the aviation community, should be made with caution. The subtle nature of the findings reported here may be a reflection of the relatively healthy and robust nature of the present sample. In addition, the possible impact of such factors as caffeine withdrawal and individual sleep-wake patterns on the observed pattern of results cannot be fully determined within the context of the current study design.

Previous work has shown that impaired predictive timing can produce poor visual tracking using the same highly predictable circular visual tracking paradigm as used in the present investigation (20). In these studies, physical injury to brain regions supporting attention processes impaired visual tracking performance. The current study provides further evidence linking impairments in attentional processes, in the present case due to an acute, reversible stressor (sleep deprivation), to dynamic visuo-motor synchronization performance. These findings support the utility of this highly predictable visual tracking paradigm as an accurate and efficient measure of fatigue in healthy individuals. The implication that a simple oculomotor assessment may be used as a fitness-for-duty test is relevant to a range of fields, including military, aviation, and aeromedicine, where fatigue due to sustained operations and variations in operational pacing is a common cause for accidents and related injuries or fatalities. Further work is needed to validate such application.

 

The authors gratefully acknowledge Ms. Abigail Laufer and Ms. Kelly Rudolph for providing technical assistance with the preparation of this manuscript.

This work was supported by Congressionally Directed Medical Research Programs (CDMRP) through an Advanced Technology Award (W81XWH-08-2-0177) to Jamshid Ghajar and by a U.S. Army Medical Research and Materiel Command award (W81XWH-08-1-0021; Project PI: SP Proctor, Site PI: KJ Heaton) to the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. Jamshid Ghajar holds United States patent 7,384,399.

The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the Army or the Department of Defense.

Authors and affiliations: Kristin J. Heaton, M.S., Ph.D., U.S. Army Research Institute of Environmental Medicine, Natick, MA; Alexis L. Maule, B.A., M.P.H., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Jun Maruta, M.A., Ph.D., and Jamshid Ghajar, M.D., Ph.D., Brain Trauma Foundation, New York, NY; and Elisabeth M. Kryskow, B.A., U.S. Army Research Institute of Environmental Medicine, Natick, MA.

 
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