Context

Comprehensive assessments are recommended to evaluate sport-related concussion (SRC). The degree to which the King-Devick (KD) test adds novel information to an SRC evaluation is unknown.

Objective

To describe relationships at baseline among the KD and other SRC assessments and explore whether the KD provides unique information to a multimodal baseline concussion assessment.

Design

Cross-sectional study.

Setting

Five National Collegiate Athletic Association institutions participating in the Concussion Assessment, Research and Education (CARE) Consortium.

Patients or Other Participants

National Collegiate Athletic Association student-athletes (N = 2258, age = 20 ± 1.5 years, 53.0% male, 68.9% white) in 11 men's and 13 women's sports.

Main Outcome Measure(s)

Participants completed baseline assessments on the KD and (1) the Symptom Inventory of the Sport Concussion Assessment Tool–3rd edition, (2) the Brief Symptom Inventory-18, (3) the Balance Error Scoring System, (4) the Standardized Assessment of Concussion (SAC), (5) the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) test battery, and (6) the Vestibular/Ocular Motor Screening tool during their first year in CARE. Correlation coefficients between the KD and the 6 other concussion assessments in isolation were determined. Assessments with ρ magnitude >0.1 were included in a multivariate linear regression analysis to evaluate their relative association with the KD.

Results

Scores for SAC concentration, ImPACT visual motor speed, and ImPACT reaction time were correlated with the KD (ρ = −0.216, −0.276, and 0.164, respectively) and were thus included in the regression model, which explained 16.8% of the variance in baseline KD time (P < .001, Cohen f2 = 0.20). Better SAC concentration score (β = −.174, P < .001), ImPACT visual motor speed (β = −.205, P < .001), and ImPACT reaction time (β = .056, P = .020) were associated with faster baseline KD performance, but the effect sizes were small.

Conclusions

Better performance on cognitive measures involving concentration, visual motor speed, and reaction time was weakly associated with better baseline KD performance. Symptoms, psychological distress, balance, and vestibular-oculomotor provocation were unrelated to KD performance at baseline. The findings indicate limited overlap at baseline among the CARE SRC assessments and the KD.

Key Points
  • The King-Devick (KD) test was only weakly associated with concentration, visual motor speed, and reaction time at baseline.

  • At baseline, limited overlap occurred between the KD and commonly used sport-related concussion assessments.

  • The KD may augment current multimodal baseline concussion assessments. However, future researchers should explore these relationships in the postinjury setting.

Sport-related concussions (SRCs) are common, with an estimated 1.6 to 3.8 million each year in the United States resulting from sports and recreational activities.1  Sport-related concussions typically result in clinical symptoms such as dizziness and headache and may lead to impaired balance,2  cognitive,3  and vestibular4  function. Current guidelines57  advocate for a comprehensive approach to evaluating patients with this injury, which may involve baseline assessments to measure preinjury-to-postinjury changes in symptoms and function. Decisions regarding which tests to include are based on institutional requirements, clinical standards of care, financial cost, and the time and available resources required to administer the assessments. As a best practice, the National Collegiate Athletic Association (NCAA)5  recommends that member institutions perform baseline concussion assessments containing (1) personal concussion and brain-injury history, (2) concussion-related symptom evaluation, (3) cognitive assessment, and (4) balance evaluation. Institutions may perform additional testing at their discretion. Ideally, an additional assessment should increase the ability to detect and manage SRC by evaluating different domains or modes of function, thereby providing unique information with limited overlap.

Vision-based measures, referred to as tests of oculomotor function, are emerging assessments for patients with concussion at some institutions.811  Examples of tools that assess oculomotor function are the King-Devick (KD) test (King-Devick Technologies, Inc, Oakbrook Terrace, IL),12  Vestibular/Ocular Motor Screening (VOMS) tool,13  and central and peripheral vision reaction times.14  These modalities evaluate various aspects of oculomotor function, such as smooth pursuit, saccadic eye movements, and convergence. The KD is a timed horizontal-saccadic eye-movement test that requires individuals to quickly read numbers aloud from either spiral-bound paper test cards or an electronic tablet application.12,15  Performance on the KD and similar tests may be influenced by attention, concentration, reaction time, and processing speed, and therefore, these domains may overlap with performance domains measured using other commonly used assessments, such as those recommended by the NCAA.5  A number of researchers1622  have demonstrated that slower KD time relative to baseline is sensitive in detecting SRC, but its relationship with other concussion tests among collegiate student-athletes is not well understood in either the baseline or postinjury setting.

Our objective was to explore relationships among the KD and commonly used concussion assessments (ie, symptoms, psychological distress, balance, cognitive, and vestibular and oculomotor measures) at baseline in a large sample of collegiate athletes from the NCAA and the Department of Defense Concussion Assessment, Research and Education (CARE) Consortium. The presence or absence of relationships at baseline could help determine the uniqueness of information provided by the KD and potentially provide helpful insight in the postinjury setting.

METHODS

Sample

Baseline assessments (ie, data from uninjured athletes before the start of the competitive season) were obtained from the CARE Consortium. The consortium collected SRC-related measures including baseline and postinjury test results from student-athletes, cadets, and midshipmen at 29 NCAA institutions. Detailed information on the CARE methods has been described.23 

Participants

All participants provided written informed consent, as approved by the institutional review boards at their respective institutions, the lead study site, and the US Army Human Research Protection Office before their involvement in the CARE Consortium. Student-athletes from the 5 CARE institutions that administered the KD test during the 2014–2015, 2015–2016, and partial 2016–2017 academic years contributed information to this data set. Only baseline results from the student-athlete's first year of study participation were included.

Participating student-athletes also completed a clinical reporting form with self-reported information on demographics and medical history. This included information on age, race, sex, concussion history, academic history, neurodevelopmental and psychological history, and sport participation.

Measures

The following measures were performed at baseline. Their characteristics and the participants' exclusion criteria for this study, where applicable, are described.

King-Devick Test

The KD measures the time to complete a single-digit rapid number-naming task. The KD consists of 1 demonstration card and 3 test cards. The KD was performed using either spiral-bound paper test cards (n = 1567) or an electronic tablet application (n = 691). To perform the KD, each student-athlete read the numbers on each test card or tablet screen aloud from left to right. The combined time (in seconds) to read the numbers on the 3 test cards constituted the total KD time. Higher values (ie, slower time) on the KD reflect worse performance. Varied KD times among participants may result from differences in the ability to perform the saccadic eye movements required for the test, reading ability, concentration or attention to the task, or other factors. During the baseline administration, student-athletes were instructed to state the numbers as fast as possible without making errors. They were required to complete 2 trials of the test on the same modality (spiral-bound paper test cards or the tablet application). If an athlete made an error, he or she performed additional trials on the same modality until an error-free time was achieved. The fastest error-free time was retained as the athlete's baseline. A KD time that was 3 or more standard deviations slower than the mean was considered a statistical outlier and excluded from the analysis (n = 22).

Symptom Checklist

Baseline concussion symptoms were self-reported in graded fashion via the Symptom Inventory of the Sport Concussion Assessment Tool–3rd edition (SCAT3), a 22-item questionnaire that assesses cognitive, somatic, psychological, and sleep-related symptoms typically associated with SRC.24  The 22 symptoms are assessed on a 7-point Likert scale: 0 indicates the symptom is not currently present, 1 to 2 indicates the symptom is currently mild, 3 to 4 indicates the symptom is currently moderate, and 5 to 6 indicates the symptom is currently severe, for a possible symptom severity ranging from 0 to 132. The total number of symptoms (out of 22) and symptom severity (out of 132) were included as predictor variables in the analysis.

Psychological Distress

Psychological distress was assessed with the Brief Symptom Inventory-18 (BSI-18), an 18-item questionnaire that assesses symptoms of anxiety, depression, and somatization during the preceding 7 days.25  The BSI-18 contains 6 questions for each of the 3 domains assessed on a 5-point Likert scale: 0 indicates that the symptom has not been present, 1 indicates that the symptom has been present a little bit, 2 indicates that the symptom has been moderately present, 3 indicates that the symptom has been present quite a bit, and 4 indicates that the symptom has been extremely present, for a possible score of 0 to 24 in each domain. Subscores for each domain (anxiety, depression, and somatization) as well as the total score were included as predictor variables in the analysis.

Balance Error Scoring System

The Balance Error Scoring System (BESS) is a measure of postural stability that consists of 3 stances: double-legged stance, nondominant single-legged stance, and tandem stance.2  Stances are performed with the eyes closed for 20 seconds each on a firm surface and a foam surface, and errors are counted. The maximum error count is 10 for each trial, for a total error range of 0 to 60, with higher scores indicating poorer performance. The number of errors on the firm surface (0–30) and foam surface (0–30) as well as the total errors (0–60) were included as predictor variables in the analysis.

Standardized Assessment of Concussion

The Standardized Assessment of Concussion (SAC) tests cognitive functioning in 4 domains: orientation, immediate memory, concentration, and delayed memory.26  Scoring for each item is dichotomous: 1 point for a correct answer, 0 points for an incorrect answer or omitted response. Orientation (maximum = 5 points), immediate memory (maximum = 15 points), concentration (maximum = 5 points), and delayed memory (maximum = 5 points) are totaled for a score ranging from 0 to 30; lower scores indicate poorer performance. Domain scores (orientation, immediate memory, concentration, and delayed memory) and the total score were included in the analysis as predictor variables.

Immediate Post-Concussion Assessment and Cognitive Testing

Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT; ImPACT Applications, Inc, San Diego, CA) is a computerized neurocognitive test battery commonly used to evaluate SRC. It comprises 6 subtests,27  the results of which are used to calculate 4 composite scores: verbal memory, visual memory, visual motor speed, and reaction time. Higher composite scores for verbal memory, visual memory, and visual motor speed indicate better performance, whereas a lower composite score on reaction time indicates better performance. Automated data-integrity checks in the ImPACT program evaluate test performance validity. The CARE protocol indicates that 1 invalid assessment should be repeated (timing of the repeat subtest at the discretion of the institution), but a second invalid assessment is deemed to reflect the athlete's true performance. It is therefore possible that some invalid ImPACT scores were contained in the CARE data set. Because this data set consisted of composite scores only and not individual subtest results, we were unable to calculate validity as typically indicated by ImPACT. Thus, in an effort to minimize the inclusion of invalid ImPACT measures, each composite score was standardized as a z score metric, and the data of student-athletes with composite scores 3 or more standard deviations worse than the mean (n = 51) were excluded from the analysis. The other SRC assessments in this study do not have internal validity measures, and as a result, the data were not standardized in this manner.

Vestibular/Ocular Motor Screening

The VOMS was used to measure symptom provocation immediately after the vestibular and oculomotor function tasks.13  The VOMS assesses (1) smooth pursuits, (2) horizontal saccades, (3) vertical saccades, (4) near-point convergence (NPC), (5) horizontal vestibular-ocular reflex (VOR), (6) vertical VOR, and (7) visual motion sensitivity. Before the assessment, the student-athletes reported their current level of 4 symptoms: headache, dizziness, nausea, and fogginess on a scale of 0 (symptom is not present) to 10 (symptom is severely present). After the assessment, the student-athletes rated each of the symptoms again. Symptom change scores were calculated by subtracting the preassessment symptom rating from the postassessment symptom rating. Symptom change scores for each of the 7 domains (eg, smooth pursuits, horizontal saccades) were summed for each symptom to create an overall provocation score for each domain. In addition, NPC was assessed via self-report of double vision or observed exophoria and concurrent measurement of the distance (in centimeters) where this occurred from the tip of the participant's nose. Mean NPC values were calculated across 3 trials. The overall symptom provocation score for each domain and mean NPC value were used in the analysis.

Procedures

All sites administered the SCAT3 Symptom Inventory, BSI-18, BESS, SAC, ImPACT, and KD; 2 sites administered the VOMS. Baseline testing was administered by athletic trainers, trained research assistants, and team physicians per the CARE methods.23  The student-athletes' demographic characteristics are described in Table 1.

Table 1

Demographic Characteristics of Student-Athletes Who Completed Baseline King-Devick Testing

Demographic Characteristics of Student-Athletes Who Completed Baseline King-Devick Testing
Demographic Characteristics of Student-Athletes Who Completed Baseline King-Devick Testing

Analysis

Assessment of the KD results revealed a normal distribution with minimal, positive skew (skewness = 0.432). A bivariate Spearman rank order correlation grid was used to evaluate the associations between baseline KD time and the nonnormally distributed predictor variables: (1) SCAT3 Symptom Inventory (total number of symptoms and symptom severity), (2) BSI-18 (anxiety, depression, somatization, and total score), (3) BESS (firm surface, foam surface, and total errors), (4) SAC (orientation, immediate memory, concentration, delayed memory, and total scores), (5) ImPACT (visual memory, verbal memory, visual motor speed, and reaction time values), and (6) VOMS (symptom provocation for each subtest—smooth pursuits, horizontal saccades, vertical saccades, NPC, horizontal VOR, vertical VOR, and visual motion sensitivity—as well as mean NPC). Relationships with a small or greater effect size (ρ > 0.1) were subsequently included in a multiple regression analysis to evaluate the relative contributions of each individual measure to baseline KD performance. Spearman ρ values were used to estimate effect size (0.1 = small, 0.3 = medium, 0.5 = large).28  For regression analyses, we examined P-P plots and scatterplots of standardized residuals to ensure that assumptions of homoscedasticity were met for each outcome variable. Polynomial terms were also explored for quadratic or cubic relationships. We controlled for sex, history of a learning disorder, history of attention-deficit/hyperactivity disorder, number of previous concussions, primary language other than English, and KD modality (testing using paper test cards versus electronic tablet application) because previous analyses29,30  have demonstrated significant influences of these factors on KD performance. The Cohen f2 was calculated by dividing the regression variance explained by 1 minus the variance explained (R2/1 − R2) and used to estimate effect size (0.02 = small, 0.15 = medium, 0.35 = large).28 

RESULTS

Student-athletes (N = 2344) completed baseline KD testing during their first year in the study. After the exclusion criteria were applied, the final sample consisted of 2258 participants (age = 20.0 ± 1.5 years; 53.0% male, 47.0% female) from 11 men's and 13 women's sports of varied collision, contact, and noncontact types. Of this final sample, 461 athletes from 5 men's and 8 women's sports completed the VOMS (mean age = 20.3 years; 52.3% male, 63.8% white).

Mean baseline KD time was 41.1 ± 7.0 seconds (range = 24.0–63.1 seconds). Performance statistics for all measures are provided in Table 2. The Spearman bivariate correlation statistics for associations between the predictor variables and baseline KD time are shown in Table 3. Faster KD completion time significantly correlated with better SAC concentration (ρ = −0.216, P < .001; small effect size), ImPACT visual motor speed (ρ = −0.267, P < .001; small effect size), and ImPACT reaction time (ρ = 0.164, P < .001; small effect size). No other correlations with ρ magnitude >0.1 were observed.

Table 2

Baseline Performance on Commonly Used Concussion Measures and the King-Devick Testa

Baseline Performance on Commonly Used Concussion Measures and the King-Devick Testa
Baseline Performance on Commonly Used Concussion Measures and the King-Devick Testa
Table 3

Spearman Bivariate Correlation Statistics for Associations Between Commonly Used Concussion Measures and the King-Devick Testa at Baseline

Spearman Bivariate Correlation Statistics for Associations Between Commonly Used Concussion Measures and the King-Devick Testa at Baseline
Spearman Bivariate Correlation Statistics for Associations Between Commonly Used Concussion Measures and the King-Devick Testa at Baseline

Scores for SAC concentration, ImPACT visual motor speed, and ImPACT reaction time were then used as predictors of KD performance in a linear multiple regression analysis while controlling for sex, learning disorder, attention-deficit/hyperactivity disorder, number of previous concussions, primary language other than English, and KD modality (Table 4). Assumptions of noncollinearity were met. The regression model explained 16.8% (P < .001, Cohen f2 = 0.20, medium effect size) of the variance in KD baseline times. In the full model, better SAC concentration (β = −.174; 95% CI = −0.197, −0.120; P < .001), ImPACT visual motor speed (β = −.205; 95% CI = −0.243, −0.151; P < .001), and ImPACT reaction time (β = .056; 95% CI = 0.011, 0.109; P = .020) were associated with better KD performance, but the individual effect sizes were small.

Table 4

Linear Regression Statistics for Predictors of King-Devick Testa Performance

Linear Regression Statistics for Predictors of King-Devick Testa Performance
Linear Regression Statistics for Predictors of King-Devick Testa Performance

Translated clinically, each 1-point increase in SAC concentration was associated with a KD performance that was 1.2 seconds faster. Each 6.2-point increase in ImPACT visual motor speed was associated with a KD performance that was 1.4 seconds faster. Each 0.07-second decrease in ImPACT reaction time was associated with a KD performance that was 0.4 seconds faster.

DISCUSSION

This study revealed several significant yet small-effect associations between baseline KD performance and components of cognition (SAC concentration, ImPACT visual motor speed, and ImPACT reaction time) but no association between KD and reported symptoms, psychological distress, balance, or vestibular/oculomotor provocation screening in collegiate student-athletes. These relationships explained only 16.8% of the variance in baseline KD time, and the individual test relationships had small effect sizes. This suggests the remaining 83.2% of variance in baseline KD times was due to other unmeasured influences not directly assessed by the SRC tests we used. Our findings support the position that KD may measure a domain not addressed by these commonly used SRC assessments at baseline.

Previous researchers who studied fewer participants at different ages also reported that better scores on ImPACT visual motor speed20  and ImPACT reaction time31  were associated with faster baseline KD performance. The consistency of our result provides further evidence of overlap between KD and ImPACT speed measures at baseline. This association was not surprising as the KD is a timed horizontal-saccadic eye-movement and recognition task, a function necessary for efficient completion of the ImPACT test modules that load on the visual motor speed (eg, 3-letter distractor “countdown” task and shape ID) and reaction time (eg, symbol-matching response time) composite scores.

Our finding that better SAC concentration scores were associated with better KD performance has not been previously demonstrated. The SAC concentration test requires participants to manipulate strings of numbers and report them in backward order and to name the months of the year in reverse order. Although reporting digits backward is primarily a working-memory task, it requires attention, which is also required during the KD. However, these results differed from those of Galetta et al,32  who identified associations between better baseline KD times and better SAC immediate memory and SAC total scores but not SAC concentration in 27 professional ice hockey players. The larger sample size and inclusion of both sexes and collegiate student-athletes from multiple sports in our study may have contributed to the discrepancy.

Performance on the KD was not associated with any of the VOMS measures. Given that the KD is often described as a timed saccadic eye-movement test and that the VOMS includes horizontal and vertical saccades as 2 of its 7 subtests, this lack of a relationship may seem paradoxical. However, this result corroborates findings from Yorke et al,33  who noted no association between VOMS components and KD performance in nonconcussed adolescents, and suggests the 2 tests may measure different parameters of saccadic eye movement. The KD involves rapid naming of linearly displayed numbers with variable spacing from left to right, and ability is quantified by the time it takes to complete the task. In contrast, the saccades component of the VOMS involves rapid eye movement between 2 fixed points about 3 ft (0.9 m) apart held 3 ft from the eyes for 10 repetitions, with a subsequent rating of symptom provocation without quantifying the time required to complete the task. Moreover, the VOMS saccades component does not involve recognition or naming and focuses only on rapid eye movements and whether such movements trigger symptoms. This difference may explain the lack of association between performances on the KD and VOMS. A relationship might exist between the 2 saccadic assessments if the VOMS quantified the number of completed saccades in a set period of time, as suggested by previous authors,34  or if symptom provocation was assessed after KD administration.

Total symptoms reported or severity of symptoms on the SCAT3 Symptom Inventory were not significantly associated with KD time at baseline. This was not surprising at baseline, when most participants were asymptomatic. Symptoms such as blurred vision, sensitivity to light, feeling slowed down, difficulty concentrating, and confusion are part of this inventory, and their presence might affect an athlete's KD performance. Future investigators could focus on individual or clusters of symptoms that may be associated with KD performance postinjury.

None of the domain or total BSI-18 scores were related. It is feasible that psychological distress could lead to changes in performance on the KD (eg, depression or anxiety causing poor concentration or attention and a worse KD score), but in our sample, the means for each BSI-18 domain were <1 out of a possible maximum score of 24. This low baseline rate of psychological distress may have limited the ability to detect associations between psychological distress and KD performance. Exploring the relationship of reported psychological distress and KD postinjury, when BSI-18 scores are typically increased, may be useful.

Finally, KD time and postural stability as measured by the BESS were not related. This finding differs from the result of a recent study35  that showed a relationship between balance as measured by the Sensory Organization Test and cognitive measures such as reaction time and executive function. Although the BESS and KD both require concentration and attention, in a baseline sample of healthy participants, the effects of these factors may be less apparent than in patients postinjury. As the KD requires visual input and the BESS is intentionally performed with the participant's eyes closed, this lack of association at baseline is less surprising.

Our study was limited in several aspects. It involved only NCAA student-athletes, which may lessen its generalizability to individuals of other ages and at other competition levels. Given the large numbers of athletes on different sports teams and at different institutions, testing procedures (eg, test order, training of test administrators, and test setting) may have varied within and between sites, which could have affected the results. Sampling bias could have affected the VOMS findings, as this test was performed at only 2 of the 5 sites. Also, prior exposure of the athletes to each assessment before their first year of participation in the CARE Consortium was unknown, so potential differences in practice effects among the assessments were not taken into account. Finally, we looked only at baseline measures, when variability in performances may be reduced. Whether these relationships, or lack of relationships, are also present postinjury, when performance variability is likely greater, is unknown. Further investigation of this topic was not possible from this early, baseline-only CARE data set, but such postinjury data may be available in the future, and this remains an area for further research.

CONCLUSIONS

Our results suggest that better performance on baseline cognitive measures involving concentration, visual motor speed, and reaction time commonly used in concussion management was weakly associated with faster KD performance, but the effect sizes for these relationships were small. Concussion-like symptoms, psychological distress, postural stability, and vestibular-ocular provocation measures were unrelated to KD performance. Together, these findings indicate limited overlap among these commonly used baseline concussion assessments and KD performance and that the KD may potentially augment current multimodal baseline concussion assessments. However, future authors should explore potential relationships in the postinjury setting.

ACKNOWLEDGMENTS

This publication was made possible, in part, by support from the Grand Alliance CARE Consortium, funded by the NCAA and the Department of Defense. The US Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick, MD 21702-5014 is the awarding and administering acquisition office. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Psychological Health and Traumatic Brain Injury Program under award W81XWH-14-2-0151. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense (Defense Health Program funds). This research was also supported in part by a grant to Dr Kontos at the University of Pittsburgh from the National Institute on Deafness and Other Communication Disorders (1K01DC012332-01A1).

REFERENCES

REFERENCES
1
Langlois
JA
,
Rutland-Brown
W
,
Wald
MM
.
The epidemiology and impact of traumatic brain injury: a brief overview
.
J Head Trauma Rehabil
.
2006
;
21
(
5
):
375
378
.
2
Guskiewicz
KM
.
Postural stability assessment following concussion: one piece of the puzzle
.
Clin J Sport Med
.
2001
;
11
(
3
):
182
189
.
3
Barth
JT
,
Alves
WM
,
Ryan
TV
, et al.
Mild head injury in sports: neuropsychological sequelae and recovery of function
.
In
:
Levin
HS
,
Eisenberg
HM
,
Benton
AL
,
eds
.
Mild Head Injury
.
New York, NY
:
Oxford University Press;
1989
:
257
275
.
4
Hoffer
ME
,
Gottshall
KR
,
Moore
R
,
Balough
BJ
,
Wester
D.
Characterizing and treating dizziness after mild head trauma
.
Otol Neurotol
.
2004
;
25
(
2
):
135
138
.
5
National Collegiate Athletic Association Sports Science Institute
.
Interassociation Consensus: Diagnosis and Management of Sport-Related Concussion. Best Practices
.
Indianapolis, IN
:
National Collegiate Athletic Association;
2017
:
1
20
.
6
McCrory
P
,
Meeuwisse
W
,
Dvorak
J
, et al.
Consensus statement on concussion in sport—the 5th International Conference on Concussion in Sport held in Berlin, October 2016
.
Br J Sports Med
.
2017
;
51
(
11
):
838
847
.
7
Harmon
KG
,
Clugston
JR
,
Dec
K
, et al.
American Medical Society for Sports Medicine position statement on concussion in sport
.
Br J Sports Med
.
2019
;
53
(
4
):
213
225
.
8
Ventura
RE
,
Balcer
LJ
,
Galetta
SL
.
The neuro-ophthalmology of head trauma
.
Lancet Neurol
.
2014
;
13
(
10
):
1006
1016
.
9
Ventura
RE
,
Balcer
LJ
,
Galetta
SL
.
The concussion toolbox: the role of vision in the assessment of concussion
.
Semin Neurol
.
2015
;
35
(
5
):
599
606
.
10
Ventura
RE
,
Balcer
LJ
,
Galetta
SL
,
Rucker
JC
.
Ocular motor assessment in concussion: current status and future directions
.
J Neurol Sci
.
2016
;
361
:
79
86
.
11
Sussman
ES
,
Ho
AL
,
Pendharkar
AV
,
Ghajar
J.
Clinical evaluation of concussion: the evolving role of oculomotor assessments
.
Neurosurg Focus
.
2016
;
40
(
4
):
E7
.
12
Devick
S.
King-Devick Test
.
Oakbrook Terrace, IL
:
King-Devick Technologies, Inc;
2010
.
13
Mucha
A
,
Collins
MW
,
Elbin
RJ
, et al.
A brief Vestibular/Ocular Motor Screening (VOMS) assessment to evaluate concussions: preliminary findings
.
Am J Sports Med
.
2014
;
42
(
10
):
2479
2486
.
14
Clark
JF
,
Ellis
JK
,
Burns
TM
,
Childress
JM
,
Divine
JG
.
Analysis of central and peripheral vision reaction times in patients with postconcussion visual dysfunction
.
Clin J Sport Med
.
2017
;
27
(
5
):
457
461
.
15
Patricios
J
,
Fuller
GW
,
Ellenbogen
R
, et al.
What are the critical elements of sideline screening that can be used to establish the diagnosis of concussion? A systematic review
.
Br J Sports Med
.
2017
;
51
(
11
):
888
894
.
16
Galetta
KM
,
Barrett
J
,
Allen
M
, et al.
The King-Devick test as a determinant of head trauma and concussion in boxers and MMA fighters
.
Neurology
.
2011
;
76
(
17
):
1456
1462
.
17
Galetta
KM
,
Brandes
LE
,
Maki
K
, et al.
The King–Devick test and sports-related concussion: study of a rapid visual screening tool in a collegiate cohort
.
J Neurol Sci
.
2011
;
309
(
1–2
):
34
39
.
18
King
D
,
Clark
T
,
Gissane
C.
Use of a rapid visual screening tool for the assessment of concussion in amateur rugby league: a pilot study
.
J Neurol Sci
.
2012
;
320
(
1–2
):
16
21
.
19
King
D
,
Brughelli
M
,
Hume
P
,
Gissane
C.
Concussions in amateur rugby union identified with the use of a rapid visual screening tool
.
J Neurol Sci
.
2013
;
326
(
1–2
):
59
63
.
20
Marinides
Z
,
Galetta
KM
,
Andrews
CN
, et al.
Vision testing is additive to the sideline assessment of sports-related concussion
.
Neurol Clin Pract
.
2015
;
5
(
1
):
25
34
.
21
Leong
DF
,
Balcer
LJ
,
Galetta
SL
,
Evans
G
,
Gimre
M
,
Watt
D.
The King–Devick test for sideline concussion screening in collegiate football
.
J Optom
.
2015
;
8
(
2
):
131
139
.
22
Galetta
KM
,
Morganroth
J
,
Moehringer
N
, et al.
Adding vision to concussion testing: a prospective study of sideline testing in youth and collegiate athletes
.
J Neuroophthalmol
.
2015
;
35
(
3
):
235
241
.
23
Broglio
SP
,
McCrea
M
,
McAllister
T
, et al.
A national study on the effects of concussion in collegiate athletes and US military service academy members: The NCAA-DoD Concussion Assessment, Research and Education (CARE) Consortium. Structure and methods
.
Sports Med
.
2017
;
47
(
7
):
1437
1451
.
24
McCrory
P
,
Meeuwisse
WH
,
Aubry
M
, et al.
Consensus statement on concussion in sport: the 4th International Conference on Concussion in Sport held in Zurich, November 2012
.
Br J Sports Med
.
2013
;
47
(
5
):
250
258
.
25
Derogatis
L.
Brief Symptom Inventory (BSI)-18: Administration, Scoring and Procedures Manual
.
Minneapolis, MN
:
NCS Pearson, Inc;
2001
.
26
McCrea
M
,
Kelly
J
,
Randolph
C.
The Standardized Assessment of Concussion: Manual for Administration, Scoring and Interpretation
.
Vienna, VA
:
Brain Injury Association;
1997
.
27
Lovell
M
,
Collins
M
,
Podell
K
,
Powell
J
,
Maroon
J.
ImPACT: Immediate Post-Concussion Assessment and Cognitive Testing
.
Pittsburgh, PA
:
NeuroHealth Systems, LLC
;
2000
.
28
Cohen
J.
Statistical Power Analysis for the Behavioral Sciences. 2nd ed
.
Hillsdale, NJ
:
L. Erlbaum Associates;
1988
.
29
Chrisman
SPD
,
Harmon
KG
,
Schmidt
JD
, et al.
Impact of factors that affect reading skill level on King-Devick baseline performance time
.
Ann Biomed Eng.
2019
;
47
(
10
):
2122
2127
.
30
Clugston
JR
,
Chrisman
SPD
,
Houck
ZM
, et al.
King-Devick Test time varies by testing modality
[published online ahead of print October 23, 2018]
.
Clin J Sport Med.
doi:.
31
Vernau
BT
,
Grady
MF
,
Goodman
A
, et al.
Oculomotor and neurocognitive assessment of youth ice hockey players: baseline associations and observations after concussion
.
Dev Neuropsychol
.
2015
;
40
(
1
):
7
11
.
32
Galetta
MS
,
Galetta
KM
,
McCrossin
J
, et al.
Saccades and memory: baseline associations of the King–Devick and SCAT2 SAC tests in professional ice hockey players
.
J Neurol Sci
.
2013
;
328
(
1–2
):
28
31
.
33
Yorke
AM
,
Smith
L
,
Babcock
M
,
Alsalaheen
B.
Validity and reliability of the Vestibular/Ocular Motor Screening and associations with common concussion screening tools
.
Sports Health
.
2017
;
9
(
2
):
174
180
.
34
Asken
B
,
Mihalik
J
,
Schmidt
JD
,
Littleton
AC
,
Guskiewicz
KM
,
Hopfinger
JB
.
Visual performance measures and functional implications in healthy participants: a sports concussion perspective
.
Athl Train Sports Health Care
.
2016
;
8
(
4
):
145
153
.
35
Vander Vegt
CB
,
Register-Mihalik
JK
,
Ford
CB
,
Rodrigo
CJ
,
Guskiewicz
KM
,
Mihalik
JP
.
Baseline concussion clinical measures are related to sensory organization and balance
.
Med Sci Sports Exerc
.
2019
;
51
(
2
):
264
270
.