https://orcid.org/0000-0002-6964-888X
ABSTRACT
Variability in return-to-duty (RTD) decision-making following mild traumatic brain injury (mTBI) is a threat to troop readiness. Current RTD assessments lack military-specific tasks and quantitative outcomes to inform stakeholders of a service member’s (SM) capacity to successfully perform military duties. Augmented reality (AR), which places digital assets in a user’s physical environment, provides a technological vehicle to deliver military-relevant tasks to a SM to be used in the RTD decision-making process. In addition to delivering digital content, AR headsets provide biomechanical data that can be used to assess the integrity of the central nervous system in movement control following mTBI. The objective of this study was to quantify cognitive and motor performance on an AR rifle qualification test (RQT) in a group of neurologically healthy military SMs.
Data were collected from 111 healthy SMs who completed a basic (single-task) and complex (dual-task) RQT with a simulated M4 rifle. The complex scenario required the SM to perform the RQT while simultaneously answering arithmetic problems. Position data from the AR headset were used to capture postural sway, and the built-in microphone gathered responses to the arithmetic problems.
There were no differences in the number of targets hit, trigger pull reaction time, and transition time from kneeling to standing between the basic and complex scenarios. A significant worsening in postural sway following kneel-to-stand transition was observed in the complex scenario. The average reaction time to answer the arithmetic problems was nearly 2 times slower than the average reaction time to pull the trigger to a displayed target in the complex scenario.
The complex scenario provoked dual-task interference in SMs as evidenced by worsening postural sway and reaction time differences between the cognitive and motor tasks. An AR RQT provides objective and quantitative outcomes during a military-specific task. Greater precision in evaluating cognitive and motor performance during a military-relevant task has the potential to aid in the detection and management of SMs and their RTD following MTBI.
INTRODUCTION
Over the past two decades, approximately 450,000 traumatic brain injuries (TBIs) have been diagnosed in U.S. service members (SMs).1 Of those, 83.5% have been classified as mild TBIs (mTBIs).2 Symptoms of mTBI can persist for days to months,3 manifesting in impaired information processing, postural instability, and slowed movement execution. Considering the military’s emphasis on troop readiness for return to duty (RTD) following mTBI,4 it is critical to evaluate SMs on tasks that closely reflect activities performed in typical military service and provide quantitative, objective outcomes to better identify when an SM is capable of RTD.
The effects of mTBI are multifactorial, compromising cognitive and motor control processes,5 and the assessment of neuropsychological and motor function is recommended as part of a comprehensive mTBI evaluation.6 Post-mTBI evaluations have largely been developed for civilian athletes to determine the readiness to return to play and have historically consisted of motor and cognitive constructs being evaluated separately. These evaluations subsequently underwent minor changes and have been applied to SMs. Considering the drastically different performance requirements of the two groups, the adaptation of civilian return-to-play protocols to military personnel is a fundamental limitation in the management of military mTBI and is a direct threat to troop readiness.4 Independent evaluation of cognitive and motor function fails to inform a medical provider of a SM’s capacity to appropriately perform in training or combat environments or to interact with the members of their unit to accomplish a mission. Because military activities consist of cognitive and motor components, often completed simultaneously,7,8 utilizing an ecological dual-task model of assessment may aid in the detection and management of mTBI in SMs and thus enhance their capability to perform at a level of proficiency necessary for a successful mission.9,10 Failure to objectively measure cognitive and motor dysfunction under dual-task conditions may result in missed diagnosis, prolonged treatment, and incomplete clearance for full, unrestricted duty; all compromise troop readiness.
The rifle qualification test (RQT) is a yearly standard assessment performed by the Army and Marine Corps to assess situational awareness, safety with weapon manipulation, and core marksmanship competencies. Updated in 2019, as per the Army’s Training Circular 3-20.40, the RQT requires a SM to fire at 40 targets from 5 to 275 meters from the supported prone, unsupported prone, kneeling, and standing positions with 8 to 19 seconds between target presentations.11 The SM is assessed based on shot accuracy out of 40 possible points: 23 to 29 points for Marksman, 30 to 35 points for Sharpshooter, and 36 to 40 points for Expert.12 The historical precedence, military relevance, and well-defined standardization of the RQT make it ideally suited for the RTD assessment. The RQT, from a motor-control perspective, is complex, requiring postural transitions while maintaining weapon readiness. Simple accuracy grading (hit/miss) provides a cursory evaluation of overall performance and how the SM may perform in a more complex combat situation in which cognitive information must be processed while engaging targets. From an RTD perspective, a field-based RQT fails to assess and quantify the nuanced cognitive, motor, and dual-task performance that the test demands motor or executive functioning performance that occur throughout the test. Digitizing the RQT provides an opportunity to precisely quantify postural transition and characterize cognitive functions such as information processing, inhibitory responses, and complex reasoning. Preliminary reports of extracting reaction time from stimulus presentation to trigger pull using weapon-simulator technology support the feasibility of assessing information processing from the RQT.13 From a motor standpoint, the RQT offers an opportunity to evaluate transition times between static shooting postures along with the measures of postural sway. We are unaware of any attempts to measure postural sway during this task, despite the well-known and potentially sustained effects of mTBI on postural stability.14,15 When considering the richness of the RQT task from a cognitive, motor, and dual-task perspective, a digital version has the potential to aid in the detection and management of mTBI.
Head-mounted augmented reality (AR) devices, such as the Microsoft HoloLens 2 (HL2; Microsoft Corporation, Redmond, WA), embed digital content in the user’s view. The HL2 offers a platform to deliver dual-task paradigms during functional tasks16 and provide validated biomechanical outcomes of gait and postural control.16–18 Recent validation studies provide rationale to examine the potential of the HL2 to deliver a digital version of the RQT by demonstrating accuracy of biomechanical data within 5% of the gold standard 3D motion capture system in assessing gait. (please include citations 16 and 18 here) The Troop Readiness Evaluation with Augmented Reality Return-to-Duty (READY) platform, a digital version of the RQT, was recently developed to assess SM performance under military-specific tasks.19 The primary aim of this project was to quantify cognitive and motor performance on the digital RQT under single- and dual-task conditions in healthy SMs. The secondary aim was to characterize postural sway following the transition from kneeling to standing during the RQT conditions.
METHODS
SMs (n = 137) from Fort Moore, Columbus, GA, completed the RQT. The sample included enlisted SMs, including comissioned and non-comissioned officers, between the ages of 18 and 45. The exclusion criteria was the presence of a diagnosed mTBI within the previous 3 months. All procedures were approved by the Cleveland Clinic’s Institutional Review Board and the DoD Human Research Protection Office. Written informed consent was obtained from all participants before participation.
For this project, SMs completed the single- and dual-task RQT as part of the larger Troop READY platform.19
Troop READY Rifle Qualification Hardware
The Microsoft HL2 was used to display digital content, deliver auditory instructions to the SM, and record movement data and verbal responses. The HL2 is a head-worn AR device weighing 566 g, which provides a 52-degree diagonal field of view to the user allowing digital content to be displayed in the user’s natural environment. Embedded in the HL2 is an inertial measurement unit with an accelerometer and gyroscope. The HL2 tracks user position in 3D space. Visual content was presented to the SM at 60 Hz, and position data were sampled at 60 Hz.
The Haptech Defense Systems M4-Electronic Recoil System (ERS) rifle (Haptech Defense Systems, Inc., New Orleans, LA) was used to simulate a military-issued weapon. The M4-ERS is a near-specification replica of the weight, geometry, and functionality of the standard issue M4, thus having similar physical demands on the SM during the RQT.
The simulated M4-ERS rifle and HL2 were linked to the control computer via Wi-Fi. The HL2 displayed the digital shooting range, and sounds of the M4-ERS were provided through the HL2 speakers. When the M4-ERS trigger was pulled, the SM heard a shot fired through the speakers, and if the shot was successful, the downrange target would splinter.
Troop READY RQT
The Troop READY RQT was completed in a windowless 6 meter x 6 meter room with standard fluorescent lighting. SMs wore combat uniforms, sans gear, to complete the RQT. SMs viewed the digital shooting range, which included 12 islands scaled to appear 15 to 80 meters in front of them (Fig. 1). Similar to the traditional RQT, targets were presented at random locations, which required the SM to scan the digital environment. Unlike the traditional RQT, the prone firing position was omitted from the Troop READY RQT as it does not sufficiently challenge balance and postural control.
Third-person view of a service member completing the standing portion of the rifle qualification test. Target and target islands are only visible by the wearer of the augmented reality headset. Inset contains graphics for pictorial representation only.
The RQT module consisted of basic and complex scenarios, each lasting 160 seconds. In the basic scenario, SMs were directed to assume a kneeling position, holding the M4-ERS. From the HL2, 10 holographic targets at various distances were individually presented in the digital field in random order. The targets remained in the field of view for 6 seconds with a 2-second interval between target presentations. Following the presentation of the tenth target, an auditory command instructed the SM to stand. During the remainder of the trial, an additional 10 targets were presented at random locations in identical intervals to the kneeling position. If hit, the target would splinter and fall.
The complex scenario increased the cognitive demands of the task. As the SM was performing the complex scenario, background radio chatter and moderately difficult subtraction and addition arithmetic questions embedded in the chatter (e.g., 19 + 27) were presented via the HL2. The purpose of the arithmetic questions was to provide a cognitive distraction, and the arithmetic accuracy was considered a tertiary outcome. The accuracy of arithmetic questions was evaluated by the HL2 voice-to-text software. SMs were not instructed to monitor the background radio chatter and responses to the chatter were not required. In total, both RQT scenarios required approximately 6 minutes to complete, including instruction time.
RQT Outcomes
Motor outcomes for basic and complex scenarios included number of targets hit, transition time from kneel to stand (s), sway path (cm) and area 95% ellipse (cm2) post-transition, and transition from kneel-to-stand sway path (cm) and area 95% ellipse (cm2). To identify the onset and completion of the kneel-to-stand transition, the height of each individual was estimated by the vertical position data from the HL2. Vertical velocity data were filtered with a second-order 3 Hz cutoff Butterworth filter. Transition onset was calculated as the last point before the average height in the vertical position where the vertical velocity was zero. Transition offset was calculated as the first point after the average height in the vertical position where the vertical velocity was zero.
The post-transition phase was defined as the 3-second window following the SM achieving their maximum vertical position. No targets were displayed in this post-transition window to ensure a non-obstructed assessment of post-transition postural sway.
Sway path was calculated as the horizontal displacement of the HL2 over set time interval. Sway area was calculated as the product of the medial–lateral and anterior–posterior displacements of the HL2 over a set interval. Previous work supports the validity of data from the HL2 and its predecessor, the HL1, in deriving commonly reported metrics of gait, turning, and complex military-related movements such as marching and squatting.16–18 Furthermore, head-mounted displays have been reported to provide valid measures of postural sway consistent with the center of pressure mapping during static balance assessment.20 In this project, the sway area metric was designed to capture head movement the in 3 seconds immediately following the transition from kneel to stand.
Cognitive outcomes included average trigger pull reaction time (ms), accuracy of arithmetic answers (No. correct/20), and average response time (ms) to the arithatic problems. Average trigger pull reaction time (ms) was calculated as the interval between the presentation of a target and initiation of trigger pull. Arithmetic answer reaction time (ms), a tertiary outcome, was calculated as the time interval between the presentation of the problem to the SM’s verbal response.
Statistical Analysis
Paired t-tests were used to evaluate differences in the motor and cognitive outcomes between the basic and complex RQT scenarios. A paired t-test was used to determine the difference between trigger pull reaction time and verbal response reaction time to the arithmetic problems for the complex RQT only.
All statistical tests were conducted using a 0.05 level of significance. All statistical analyses were conducted using RStudio 2022.07.1, R version 4.2.1.
RESULTS
Of the 137 data sets, 111 were included in the final analyses; demographics are provided in Table I. Data sets were excluded if 1) the SM did not adhere to task instructions (e.g., standing up during the kneeling portion of the RQT or 2) if the positional data had a poor signal-to-noise ratio. With the included 111 data sets, power exceeded 0.90.
Participant Demographics
| n = 111 | |
|---|---|
| Age, years | |
| Mean (SD) | 29.9 (6.07) |
| Race | |
| Asian | 4 (3.6%) |
| Black or African American | 17 (15.3%) |
| More than one race | 5 (4.5%) |
| White | 78 (70.3%) |
| American Indian/Alaska Native | 1 (0.9%) |
| Native Hawaiian or Other Pacific Islander | 2 (1.8%) |
| Other | 2 (1.8%) |
| Unknown or prefer not to answer | 2 (1.8%) |
| Ethnicity | |
| Hispanic or Latino | 13 (11.8%) |
| Not Hispanic or Latino | 94 (84.7%) |
| No response | 4 (3.5%) |
| Gender | |
| Female | 10 (9.0%) |
| Male | 101 (91.0%) |
| Years of education | |
| Mean (SD) | 13.7 (2.19) |
| Number of combat deployments | |
| Mean (SD) | 1.36 (1.53) |
| n = 111 | |
|---|---|
| Age, years | |
| Mean (SD) | 29.9 (6.07) |
| Race | |
| Asian | 4 (3.6%) |
| Black or African American | 17 (15.3%) |
| More than one race | 5 (4.5%) |
| White | 78 (70.3%) |
| American Indian/Alaska Native | 1 (0.9%) |
| Native Hawaiian or Other Pacific Islander | 2 (1.8%) |
| Other | 2 (1.8%) |
| Unknown or prefer not to answer | 2 (1.8%) |
| Ethnicity | |
| Hispanic or Latino | 13 (11.8%) |
| Not Hispanic or Latino | 94 (84.7%) |
| No response | 4 (3.5%) |
| Gender | |
| Female | 10 (9.0%) |
| Male | 101 (91.0%) |
| Years of education | |
| Mean (SD) | 13.7 (2.19) |
| Number of combat deployments | |
| Mean (SD) | 1.36 (1.53) |
Data are presented as n (%) or mean (SD).
Participant Demographics
| n = 111 | |
|---|---|
| Age, years | |
| Mean (SD) | 29.9 (6.07) |
| Race | |
| Asian | 4 (3.6%) |
| Black or African American | 17 (15.3%) |
| More than one race | 5 (4.5%) |
| White | 78 (70.3%) |
| American Indian/Alaska Native | 1 (0.9%) |
| Native Hawaiian or Other Pacific Islander | 2 (1.8%) |
| Other | 2 (1.8%) |
| Unknown or prefer not to answer | 2 (1.8%) |
| Ethnicity | |
| Hispanic or Latino | 13 (11.8%) |
| Not Hispanic or Latino | 94 (84.7%) |
| No response | 4 (3.5%) |
| Gender | |
| Female | 10 (9.0%) |
| Male | 101 (91.0%) |
| Years of education | |
| Mean (SD) | 13.7 (2.19) |
| Number of combat deployments | |
| Mean (SD) | 1.36 (1.53) |
| n = 111 | |
|---|---|
| Age, years | |
| Mean (SD) | 29.9 (6.07) |
| Race | |
| Asian | 4 (3.6%) |
| Black or African American | 17 (15.3%) |
| More than one race | 5 (4.5%) |
| White | 78 (70.3%) |
| American Indian/Alaska Native | 1 (0.9%) |
| Native Hawaiian or Other Pacific Islander | 2 (1.8%) |
| Other | 2 (1.8%) |
| Unknown or prefer not to answer | 2 (1.8%) |
| Ethnicity | |
| Hispanic or Latino | 13 (11.8%) |
| Not Hispanic or Latino | 94 (84.7%) |
| No response | 4 (3.5%) |
| Gender | |
| Female | 10 (9.0%) |
| Male | 101 (91.0%) |
| Years of education | |
| Mean (SD) | 13.7 (2.19) |
| Number of combat deployments | |
| Mean (SD) | 1.36 (1.53) |
Data are presented as n (%) or mean (SD).
Table II provides summary data and statistics from the basic and complex RQT scenarios. Overall, shot accuracy and transition time were not significantly different between scenarios. Differences were seen in transition sway path, post-transition sway path, and post-transition area 95% ellipse. Figure 2 provides an example of a SM’s performance on the basic and complex scenarios. Shooting and cognitive performance with insets exemplify the increase in postural sway following the transition from kneel to stand as measured by the area 95% ellipse. Sway path of the transition phase was impacted by the secondary task in the complex scenario, with 43.0 to 46.9 cm in the basic and complex scenarios, respectively (P < .05). Post-transition sway path, defined as the SM’s distance traveled in the horizontal plane in the 3-second window following the termination of the transition phase, (31.9-37.0 cm) was significantly larger under the complex scenario (P < .05). Area 95% ellipse in the post-transition window was also greater (104.9-146.5 cm2) in the complex, compared to the basic scenario (P < .05).
Rifle Qualification Test Motor and Cognitive Outcomes
| Basic RQT | Complex RQT | P-value | Hedges’ g | |
|---|---|---|---|---|
| Shot accuracy (No. of hits/20) | 19.40 (1.06) | 19.61 (0.79) | .09 | 0.23 |
| Transition time (s) | 1.54 (0.40) | 1.61 (0.41) | .15 | 0.16 |
| Transition—kneel-to-stand sway path (cm) | 43.04 (15.87) | 46.92 (15.03) | .02* | 0.25 |
| Transition—kneel-to-stand area 95% (cm2) | 339.17 (283.40) | 370.33 (270.00) | .28 | 0.11 |
| Post-transition sway path (cm) | 31.85 (11.92) | 36.98 (14.45) | <.001* | 0.39 |
| Post-transition area 95% (cm2) | 104.90 (96.31) | 146.52 (110.58) | <.001* | 0.40 |
| Trigger pull reaction time (ms) | 1730 (310) | 1719 (329) | .46 | −0.05 |
| Basic RQT | Complex RQT | P-value | Hedges’ g | |
|---|---|---|---|---|
| Shot accuracy (No. of hits/20) | 19.40 (1.06) | 19.61 (0.79) | .09 | 0.23 |
| Transition time (s) | 1.54 (0.40) | 1.61 (0.41) | .15 | 0.16 |
| Transition—kneel-to-stand sway path (cm) | 43.04 (15.87) | 46.92 (15.03) | .02* | 0.25 |
| Transition—kneel-to-stand area 95% (cm2) | 339.17 (283.40) | 370.33 (270.00) | .28 | 0.11 |
| Post-transition sway path (cm) | 31.85 (11.92) | 36.98 (14.45) | <.001* | 0.39 |
| Post-transition area 95% (cm2) | 104.90 (96.31) | 146.52 (110.58) | <.001* | 0.40 |
| Trigger pull reaction time (ms) | 1730 (310) | 1719 (329) | .46 | −0.05 |
Outcomes are presented as mean (SE). * indicate statistical significance at P < .05.
Rifle Qualification Test Motor and Cognitive Outcomes
| Basic RQT | Complex RQT | P-value | Hedges’ g | |
|---|---|---|---|---|
| Shot accuracy (No. of hits/20) | 19.40 (1.06) | 19.61 (0.79) | .09 | 0.23 |
| Transition time (s) | 1.54 (0.40) | 1.61 (0.41) | .15 | 0.16 |
| Transition—kneel-to-stand sway path (cm) | 43.04 (15.87) | 46.92 (15.03) | .02* | 0.25 |
| Transition—kneel-to-stand area 95% (cm2) | 339.17 (283.40) | 370.33 (270.00) | .28 | 0.11 |
| Post-transition sway path (cm) | 31.85 (11.92) | 36.98 (14.45) | <.001* | 0.39 |
| Post-transition area 95% (cm2) | 104.90 (96.31) | 146.52 (110.58) | <.001* | 0.40 |
| Trigger pull reaction time (ms) | 1730 (310) | 1719 (329) | .46 | −0.05 |
| Basic RQT | Complex RQT | P-value | Hedges’ g | |
|---|---|---|---|---|
| Shot accuracy (No. of hits/20) | 19.40 (1.06) | 19.61 (0.79) | .09 | 0.23 |
| Transition time (s) | 1.54 (0.40) | 1.61 (0.41) | .15 | 0.16 |
| Transition—kneel-to-stand sway path (cm) | 43.04 (15.87) | 46.92 (15.03) | .02* | 0.25 |
| Transition—kneel-to-stand area 95% (cm2) | 339.17 (283.40) | 370.33 (270.00) | .28 | 0.11 |
| Post-transition sway path (cm) | 31.85 (11.92) | 36.98 (14.45) | <.001* | 0.39 |
| Post-transition area 95% (cm2) | 104.90 (96.31) | 146.52 (110.58) | <.001* | 0.40 |
| Trigger pull reaction time (ms) | 1730 (310) | 1719 (329) | .46 | −0.05 |
Outcomes are presented as mean (SE). * indicate statistical significance at P < .05.
Representative RQT data from one SM completing the basic (panel A) and complex (panel B) RQT. Green traces in the top panels represent the vertical position and the blue trace represents the movement velocity of the SM. The position trace is represented by the upward shift in vertical position at the 80 second mark of the trial. In the basic scenario, the SM demonstrated 100% accuracy in hitting the targets denoted by the green dots. With the addition of the cognitive task in the complex scenario, target accuracy decreased to 95% as shown by the red dot denoting a missed target. A clear task prioritization was present as arithmetic answers were less accurate compared to target shooting accuracy. Performance in the SM’s post-transition postural sway worsened from 77.25 cm2 during the basic scenario (panel C) to 217.28 cm2 during the complex scenario (panel D), illustrating the interference of a secondary cognitive task on motor performance in this individual. RQT, rifle quantification test; SM, service member.
Trigger pull reaction time was not different between scenarios (1730 vs. 1719 ms). In the complex scenario, cognitive reaction time (3307 ms) was significantly longer than trigger pull reaction time (1719 ms) (P < .001; Hedges’ g = 1.81). The average number of correct responses to the arithmetic problems was 6.77, or 35% accuracy, during the complex scenario.
DISCUSSION
The Troop READY RQT was developed as an assessment of cognitive, motor, and dual-task function during the performance of a military-relevant task, with the ultimate goal of aiding in the detection of mTBI and management of RTD post-mTBI by providing detailed quantitative outcomes. Data from this project indicate that the Troop READY RQT is a viable approach to precisely quantify motor (e.g., postural transitions), cognitive (e.g., information processing), and dual-task function in SMs during a military-relevant task.
Digitizing the RQT to assess troop readiness for RTD is a viable option over the traditional RQT given the time, personnel, and facility requirements to operate a live fire RQT.11 In addition to the logistical hurdles to completing a live-fire RQT, assessing troop readiness with live fire could be potentially hazardous when assessing the mTBI population, as it has been well documented that post-concussive syndrome affects multiple physiological systems and psychological factors that can last for months following injury.3 Several digital target systems have been created, but limitations exist, including tethered weapons reducing the SMs’ freedom of movement,21 digital content displayed from projectors,22 and a lack of biomechanical quantification.13 The Troop READY RQT assessment provides shot accuracy, biomechanical, and decision-making performance outcomes that can be evaluated under controlled, low-risk conditions while providing SMs with an ecological, military-specific task.
Before utilizing this technology with individuals with mTBI, establishing normative shooting, biomechanical, and decision-making performance metrics was necessary. Data from this project provide initial normative performance metrics for the basic and complex RQTs in healthy SMs. In this cohort of neurologically healthy SMs, the average number of targets hit was high (>95%) in both scenarios, and reaction time to pull the trigger following target presentation was not affected by the addidition of a cognitive task. The lack of dual-task interference on these variables should not be surprising as shooting accuracy and reaction time are considered part of the Mission Essential Task,23 or a list of tasks the SM must accomplish in combat. Based on the accuracy and reaction time to the arithmetic task, it appears that SMs generally prioritized the shooting-related outcomes, as reaction time to pull the trigger was nearly two times faster than the reaction time to provide an answer to the arithmetic problems. A plausible explanation for the delayed reaction time in the cognitive task is the psychological refractory period,24 where the second presented stimulus results in a longer reaction time because of attention capacity limitations associated with dual tasks. As such, this response is expected in this population of healthy SMs.
While shooting performance was not altered by the addition of a cognitive task, postural sway was increased during the complex scenario following the transition from kneeling to standing. These data, measured by the area 95% ellipse, indicate dual-task interference on postural sway during the complex scenario. In healthy individuals, there is an inconsistent effect of dual tasking on postural sway as it has been demonstrated that postural sway can improve or worsen with a secondary cognitive task,25 and according to a recent review, the control of postural stability largely depends on a combination of the complexity of each task.26 In the complex RQT scenario, it is likely that the challenge of the cognitive task was high given that the average percentage of correct responses to the mathematical problems was ∼35%. Although not foreign to the SMs, standing from a kneeling position with a near-replica service weapon poses biomechanical challenges that many studies assessing postural stability in young, healthy participants do not address.27,28 To our knowledge, there are few studies that have examined dual-task postural stability during and following movement transitions, and of those studies only pediatric and neurodegenerative diseases were included.29,30 Therefore, it is possible that the combination of a complex motor and a complex cognitive task resulted in increased postural sway in this cohort of SMs. Considering the known deficits in postural control following mTBI,31,32 we anticipate that the RQT would be appropriate to aid in RTD decision-making.
The study is not without limitations. The SMs participating in the study did not complete a single-task arithmetic trial, which limits the analysis of the dual-task cost to both the motor and cognitive systems. Despite the lack of a single-task cognitive trial, previous studies have used paradigms lacking a single-task cognitive trial and demonstrated the dual-task effect on motor performance.33,34 During the post-transition 3-second window, all efforts were taken to minimize voluntary, scanning head movements by not displaying targets; however, it is not possible to definitively parse sway from voluntary head scanning movements. Despite this limitation, postural sway measured by head-worn devices have been shown to correlate well with more traditional measures of postural control during static standing.20
CONCLUSION
The results of this study provide an initial understanding of normative SM performance and biomechanical data of military SMs when completing the Troop READY RQT. The Troop READY RQT was effective in provoking motor and cognitive dual-task interference in healthy SMs, providing support to assess the platform in individuals with mTBI. Given the duration required to administer and abundance of performance and biomechanical data provided by the Troop READY RQT, it has the potential to be integrated into the progressive RTD guidelines following further assessment of SMs with mTBI.35 AR technology offers a new avenue to increase the accessibility, improve standardization, lower the cost of military training, and bypass the limitations in physical environments in which traditional assessments can be conducted.
ACKNOWLEDGMENTS
The authors would like to thank the men and women of the U.S. Military stationed at Fort Moore who participated in the study. The authors would also like to thank Amanda L. Penko, Morgan McGrath, Colin Waltz, Kathryn Scelina, MacKenzie Dunlap, and A. Elizabeth Jansen for their time and commitment to the data collection at Fort Moore.
CLINICAL TRIAL REGISTRATION
None declared.
INSTITUTIONAL REVIEW BOARD APPROVAL
All participants were human subjects and underwent the informed consent process approved by the Cleveland Clinic’s Institutional Review Board (Cleveland, OH).
INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC)
Not applicable.
INDIVIDUAL AUTHOR CONTRIBUTION STATEMENT
Project design: J.L.A. and A.B.R. Data collection: R.D.K. and J.L.A. Data analyses: K.M.O., K.H., E.M.Z., and R.D.K. Data Interpretation: R.D.K., A.B.R., and J.L.A. Initial manuscript draft: R.D.K., A.B.R., and J.L.A. Manuscript editing: K.M.O., K.H., and E.M.Z.
INSTITUTIONAL CLEARANCE
Does not apply.
FUNDING
This work was supported by a grant from the DoD (W81XWH1910685) and Edward and Barbara Bell Family Chair.
CONFLICT OF INTEREST STATEMENT
J.L.A. and A.B.R. have authored intellectual property associated with the Troop READY platform.
DATA AVAILABILITY
Data will be made available upon request from the corresponding author.
REFERENCES
Author notes
Data and information from this manuscript were presented as a poster at the 2022 Military Health System Research Symposium (MHSRS-22-05950), 2023 American Academy of Neurology Conference, and 2023 Military Health System Research Symposium (MHSRS-23-09140).
The views expressed in this article are those of the authors and do not necessarily represent the official position or policy of the U.S. Government or the DoD.
