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Intraindividual Cognitive Variability Before and After Sports-Related Concussion

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Objective: Inconsistent performance is associated with cognitive dysfunction in a number of clinical populations. However, intraindividual cognitive variability in healthy individuals is poorly understood. Inconsistency poses a challenge to clinicians when interpreting change over time. This study examined intraindividual cognitive variability within a sample of college athletes tested at baseline and postconcussion. Method: Athletes (n = 71) and control participants (n = 42) were tested with a comprehensive neuropsychological battery at baseline and postconcussion (athletes) or one month later (controls). A subset of indices with high internal consistency was used to calculate overall performance and performance variability. A k-means cluster analysis of baseline and postconcussion performance variability examined heterogeneity within the sample. Results: In the athlete sample, performance variability was significantly greater than zero, and was negatively correlated with overall performance at both time points (p < .001). Wechsler Test of Adult Reading Full Scale IQ estimate was significantly correlated with overall performance (p < .01), but not with performance variability. Cluster analysis revealed low-variability (n = 46) and high-variability (n = 25) cluster groups. Whereas the low-variability cluster group exhibited a pattern of performance similar to that of control participants, membership in the high-variability cluster group was associated with postconcussion cognitive dysfunction. Conclusion: These findings suggest that normative cognitive performance in college athletes is characterized by significant intraindividual variation across tests. Cross-test intraindividual variability may impart clinically meaningful information, as higher levels of variability were related to poorer overall performance and postconcussion cognitive dysfunction.
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Intraindividual Cognitive Variability Before and After
Sports-Related Concussion
Amanda R. Rabinowitz
University of Pennsylvania School of Medicine
Peter A. Arnett
The Pennsylvania State University
Objective: Inconsistent performance is associated with cognitive dysfunction in a number of clinical
populations. However, intraindividual cognitive variability in healthy individuals is poorly understood.
Inconsistency poses a challenge to clinicians when interpreting change over time. This study examined
intraindividual cognitive variability within a sample of college athletes tested at baseline and postcon-
cussion. Method: Athletes (n 71) and control participants (n 42) were tested with a comprehensive
neuropsychological battery at baseline and postconcussion (athletes) or one month later (controls). A
subset of indices with high internal consistency was used to calculate overall performance and perfor-
mance variability. A k-means cluster analysis of baseline and postconcussion performance variability
examined heterogeneity within the sample. Results: In the athlete sample, performance variability was
significantly greater than zero, and was negatively correlated with overall performance at both time
points (p .001). Wechsler Test of Adult Reading Full Scale IQ estimate was significantly correlated
with overall performance (p .01), but not with performance variability. Cluster analysis revealed
low-variability (n 46) and high-variability (n 25) cluster groups. Whereas the low-variability cluster
group exhibited a pattern of performance similar to that of control participants, membership in the
high-variability cluster group was associated with postconcussion cognitive dysfunction. Conclusion:
These findings suggest that normative cognitive performance in college athletes is characterized by
significant intraindividual variation across tests. Cross-test intraindividual variability may impart clini-
cally meaningful information, as higher levels of variability were related to poorer overall performance
and postconcussion cognitive dysfunction.
Keywords: concussion, intraindividual variability, traumatic brain injury
Clinical and research protocols designed to assess sports-related
concussion and track its recovery have been implemented at the
professional, collegiate, and high school levels over the past 20
years. Barth and colleagues (1989) at the University of Virginia
were the first to develop the Sports as a Laboratory Assessment
Model as an attempt to better characterize the neuropsychological
impact of concussion and the trajectory of recovery. This was the
first program to systematically conduct preconcussion (baseline)
neuropsychological assessment on athletes, a method that allows
for the impact of the injury to be assessed directly (as change from
baseline performance) rather than simply inferred based on cutoff
scores or assumptions regarding premorbid functioning. Today,
baseline testing is becoming the gold standard in concussion
management as an objective measure of athletes’ premorbid cog-
nitive abilities provides an ideal comparison standard for postcon-
cussion assessment.
However, some authors have drawn attention to issues that
complicate straightforward pre–postinjury test comparisons, such
as motivation toward testing and psychometric issues (Bailey,
Echemendia, & Arnett, 2006; Ragan & Kang, 2007; Randolph,
McCrea, & Barr, 2005). Inconsistency in an individual’s cognitive
performance is another factor that poses a challenge to clinicians
when interpreting change across pre- and postinjury test sessions.
Intraindividual variability is a term that has been used to describe
moment-to-moment fluctuations in state, or inconsistencies (Ram,
Rabbitt, Stollery, & Nesselroade, 2005). Cognitive variability
could be related to true idiosyncratic strengths and weaknesses,
fluctuations in effort due to premorbid neurological vulnerabilities,
or fluctuations in effort due to suboptimal motivation. Distinguish-
ing between these potential sources of cognitive inconsistency is
crucial for valid assessment.
In the field of psychology, a science that is dominated by central
tendency, intraindividual variability has traditionally been charac-
terized as error, instability, and noise. However, within the neuo-
psychological literature, recent research has shown that fluctua-
tions in cognitive performance reflect more than random error and
measurement unreliability, and, in fact, are often negatively cor-
related with mean levels of performance (Hultsch & MacDonald,
2004; Hultsch, Strauss, Hunter, & MacDonald, 2008). Research
has linked increased variability with a number of clinical phenom-
ena including aging, depression and anxiety, traumatic brain in-
jury, attention-deficit/hyperactivity disorder, and schizophrenia
(see MacDonald, Li, & Bäckman, 2009, for review). Furthermore,
there is some evidence suggesting that variability may be, in fact,
Amanda R. Rabinowitz, Department of Neurosurgery, University of
Pennsylvania School of Medicine; Peter A. Arnett, Psychology Depart-
ment, The Pennsylvania State University.
Correspondence concerning this article should be addressed to Amanda
R. Rabinowitz, Department of Neurosurgery, University of Pennsylvania
School of Medicine, 370 Stemmler Hall, Philadelphia, PA 19104. E-mail:
rabinowitz.a@gmail.com
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Neuropsychology © 2013 American Psychological Association
2013, Vol. 27, No. 4, 481– 490 0894-4105/13/$12.00 DOI: 10.1037/a0033023
481
more sensitive than mean level of performance in detecting cog-
nitive decline. In a longitudinal study of cognitive aging, Lövdén,
Li, Shing, and Lindenberger (2007) found that changes in vari-
ability preceded measurable deficits in mean level of performance.
These findings suggest that intraindividual variability is a prom-
ising indicator of cognitive dysfunction. Theoretical proposals
have been put forth to explain the cognitive and neurobiological
mechanisms that may lead to inconsistency in performance in
order to elucidate the link between variability and dysfunction. At
the cognitive level, variability has been attributed to lapses in
attention (Bunce, Warr, & Cochrane, 1993) and failure to maintain
executive control (West, Murphy, Armilio, Craik, & Stuss, 2002).
At the neuroanatomical level, variability is thought to arise from
inefficiencies in central nervous system functioning. Central ner-
vous system inefficiencies may be related to disrupted neural
connectivity (Kelly, Uddin, Biswal, Castellanos, & Milham,
2008), reduced efficacy of neurotransmitter systems (Bäckman,
Nyberg, Lindenberger, Li, & Farde, 2006), structural damage to
gray matter (Sowell et al., 2003; Stuss, Murphy, Binns, & Alex-
ander, 2003), or loss of white-matter integrity (Anstey et al., 2007;
Britton, Meyer, & Benecke, 1991; Walhovd & Fjell, 2007).
Investigators examining intraindividual inconsistencies in cog-
nitive performance have operationalized performance variability in
a number of ways. Some work has used intraindividual standard
deviations taken from multiple trials within a single task (Ram et
al., 2005). Other studies have considered variability in perfor-
mance on the same task on separate occasions (Hultsch, MacDon-
ald, Hunter, Levy-Bencheton, & Strauss, 2000). A few investiga-
tors have taken a cross-domain approach to intraindividual
variability by examining within-person inconsistency in perfor-
mance across different neuropsychological tests administered as
part of a neuropsychological test battery (Holtzer, Verghese,
Wang, Hall, & Lipton, 2008; Kliegel & Sliwinski, 2004; Schretlen,
Munro, Anthony, & Pearlson, 2003).
A cross-test approach to variability considers performance on a
battery of neuropsychological tests, standardizes these tests with
reference to a common sample, and indexes the extent to which
these scores deviate from each other using a variance parameter,
such as standard deviation. For example, cross-test variability for
a neuropsychological battery including tests of verbal memory,
visual memory, processing speed, and attention—with partici-
pants’ scores of 98, 105, 111, and 102 in standard score units—
could be indexed by spread (13 standard score points), variance
(30 standard score points), or standard deviation (5.48 standard
score points).
A cross-test approach to intraindividual variability is appealing
because indices of cross-test variability can be derived from any
neuropsychological battery. However, to interpret inconsistencies
across a test battery as abnormal or meaningful, it must first be
established that normal performance is consistent. From a theoret-
ical perspective, there are reasons to posit that a given individual’s
test performance should have low variability under typical condi-
tions, granted that an individual is noninjured, healthy, and devel-
opmentally normal. The concept of general intelligence (or Spear-
man’s g) supports this assumption. Intraindividual consistency in
cognitive behavior, subserved by IQ, is an assumption underlying
the deficit measurement paradigm in clinical neuropsychological
assessment—a rubric for determining the presence of neuropsy-
chological impairment by comparing an individual’s test scores
with a comparison standard. This comparison standard may be
estimated based on population norms, premorbid test data, per-
sonal historical information such as academic performance or
occupation, or indirectly from the current test findings and obser-
vations. The expectation that premorbid cognitive performance
should be consistent justifies the use of a unitary comparison
standard.
However, many investigators have noted that environmental
factors and personal characteristics can lead to intraindividual
differences in cognitive skills. Research using a cross-test variabil-
ity approach has found evidence of marked performance variabil-
ity in healthy adults (Schretlen et al., 2003). Specialization of
interests and activities, socialization experiences, personal expec-
tations, educational limitations, or emotional disturbance could
lead to inconsistencies in performance across cognitive domains
(Halpern, 1997). Furthermore, research has demonstrated that, for
normal healthy individuals, performance in the impaired range on
at least one test index from a large neuropsychological battery is
psychometrically normal (Binder, Iverson, & Brooks, 2009). This
suggests that some level of cross-test variability is normative.
Furthermore, there are many exceptions to the generality that
cognitive behavior is consistent across situations. Few persons
consistently function at their maximum potential for a variety of
reasons, including illness, educational deficiencies, impulsivity,
test anxiety, or disinterest.
With these considerations in mind, the present study examined
cognitive inconsistency in college athletes tested at baseline and
postconcussion by examining the degree of intraindividual vari-
ability across cognitive tests administered as part of a sports
concussion assessment battery. Because noninjured athletes are
tested at baseline (before the start of team activities when they first
arrive at the university) and also postconcussion, these data pro-
vide an opportunity to address the following questions: (1) How
variable is cognitive performance in healthy noninjured persons?
(2) What is the effect of cerebral concussion on intraindividual
variability? (3) What factors are related to cross-domain cognitive
inconsistency before and after head injury?
Method
Participants
Participants in the present study were 71 college athletes par-
ticipating in a multisport program at a large state university that
assesses participants prior to and following sports-related concus-
sion, as well as 42 control participants (see Table 1 for character-
istics of the sample). Control participants were students from the
same university who participate in athletic activities at the intra-
mural level but do not participate in the concussion management
program. This program is designed according to the Sports as a
Laboratory Assessment Model (Barth et al., 1989) paradigm to
provide objective neuropsychological test data to team physicians
to inform return-to-play decisions. Eight athletic programs regu-
larly participate in baseline testing: football, men’s and women’s
lacrosse, men’s and women’s soccer, men’s and women’s basket-
ball, men’s ice hockey, and wrestling. Postconcussion testing is
provided for any athlete who sustains a head injury. Whenever
possible, postconcussion assessment takes place within 48 hr of
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482
RABINOWITZ AND ARNETT
injury. Only athletes who had undergone both baseline and post-
concussion assessment were included in the present analyses.
Measures
The test battery consists of a number of measures that assess
cognitive, physiological, and affective functioning, including the
Hopkins Verbal Learning Test—Revised (HVLT–R; Benedict,
Schretlen, Groninger, & Brandt, 1998), the Brief Visuospatial
Memory Test—Revised (BVMT–R; Benedict, 1997), the Symbol-
Digit Modalities Test (SDMT; Smith, 1982), the Digit Span Test
(Wechsler, 1997), the Pennsylvania State University (PSU) Can-
cellation Task (Echemendia & Julian, 2001), the Stroop Color-
Word Test (SCWT; Trenerry, Crosson, DeBoe, & Leber, 1989),
Controlled Oral Word Association Test (COWAT; Ruff, Light,
Parker, & Levin, 1996), Comprehensive Trail-Making Test
(CTMT; Reynolds, 2002), the Vigil Continuous Performance Task
(Cegalis & Bowlin, 1991), and the Wechsler Test of Adult Read-
ing (WTAR; Wechsler, 2001). For athletes who underwent multi-
ple evaluations (baseline testing and one or more postconcussion
evaluations), alternative forms of the HVLT–R, BVMT–R,
SDMT, and PSU Cancellation Task were used (see Benedict,
1997; Benedict et al., 1998; Smith, 1982, for alternative form
reliability information). In the case of the CTMT, Trails 2 and 4
were used as simple and complex trail-making trials at one time
point, and Trails 3 and 5 were administered at the other. With the
exception of the WTAR, all of these tests have been shown to be
sensitive to traumatic brain injury in prior work (Bailey, Echemen-
dia, & Arnett, 2005; Bohnen et al., 1992; Bruce & Echemendia,
2003; Ponsford & Kinsella, 1992; Vanderploeg, Curtiss, &
Belanger, 2005).
The Immediate Post-Concussion Assessment and Cognitive
Testing computerized battery (ImPACT; Lovell, Collins, Podell,
Powell, & Maroon, 2000) was also used. Although complete
validity data for the use of this test battery in sports-related
concussion have not yet been published, it assesses cognitive
domains typically affected following such injury. The ImPACT is
a computerized test battery that was designed to offer a time-
effective and standardized method for collecting data to assist in
concussion assessment and management. The battery consists of
three main parts: demographic data, neuropsychological tests, and
the Post-Concussion Symptom Scale (PCSS). Six neuropsycholog-
ical tests are included, designed to target attention, memory, pro-
cessing speed, and reaction time. From the six tests, five composite
scores are derived: verbal memory, visual memory, visuomotor
speed, reaction time, and impulse control. Studies in high school
and college athletes have demonstrated that ImPACT performance
is correlated with performance on similar paper-and-pencil neuro-
psychological tests (Iverson, Lovell, & Collins, 2005); further-
more, ImPACT performance is sensitive to the acute effects of
concussion (Schatz, Pardini, Lovell, Collins, & Podell, 2006).
The WTAR is a test of reading recognition that was designed for
premorbid IQ estimation. It was developed and conormed with the
Wechsler Adult Intelligence Scale—Third Edition and Wechsler
Memory Scale—Third Edition in both the United States and
United Kingdom using the same large, representative sample of
normally functioning adults. Using the normative data from the
conorming sample, WTAR scores can be converted to Full Scale
IQ (FSIQ) estimates. Research has demonstrated that the WTAR
has strong correlations (.70 –.80) with Wechsler Adult Intelligence
Scale—Third Edition FSIQ scores for a wide age range of WTAR
scores, and WTAR performance is relatively resistant to the effects
of traumatic brain injury (Wechsler, 2001). The Computerized
Assessment of Response Bias is a digit-recognition task that was
administered as a test of effort. Accuracy below 89% is indicative
of inadequate effort (Conder, Allen, & Cox, 1992). In addition to
the neuropsychological tests, the battery includes a self-report
measure of symptoms. The PCSS is a list of symptoms that are
commonly associated with concussion, including headache, nau-
sea, dizziness, trouble concentrating, and feeling “in a fog.” Ath-
letes rate the extent to which they are currently experiencing each
symptom on a scale from 0 (absence of the symptom)to6(extreme
distress from that symptom).
Procedure
All 71 athletes had baseline and postconcussion testing, includ-
ing the instruments described in detail above. Athletes may have
been tested a number of times postinjury, depending on the course
of their recovery and the team physician’s referral. The majority of
initial postconcussion assessments took place within 1 week of the
head injury and within 48 hr whenever possible (mean days
postconcussion 7.2, SD 10.8, min 0 days, max 210
days). For the purpose of the present study, only the initial post-
concussion evaluation was considered. Control participants were
administered the same test battery at two time points 1 month
apart. Evaluations were conducted by a PhD-level clinical neuro-
psychologist or a graduate or undergraduate assistant who had
been trained by a PhD-level clinical neuropsychologist. Testing
sessions took approximately 1.5 hr/evaluation and also required
approximately 30 min for paperwork, debriefing, and administra-
tion of other instruments not included in the present study. Written
informed consent was obtained from study participants. The study
Table 1
Characteristics of the Sample
Characteristic Athlete Control
Female, % 21 51
Caucasian, % 72 95
African American, % 25 1
ADHD diagnosis, % 5 0
LD, % 6 0
Mean (SD) age at baseline (years) 18.6 (0.8) 18.5 (0.8)
Previous head injuries, %
Denied 51 100
1340
2 or more 14 0
Sport, %
Football 38
Men’s ice hockey 14
Men’s lacrosse 13
Women’s soccer 10
Women’s lacrosse 7
Men’s basketball 3
Men’s soccer 4
Wrestling 4
Women’s basketball 3
Note. ADHD attention-deficit/hyperactivity disorder (self-report);
LD learning disorder (self-report).
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483
COGNITIVE VARIABILITY AND CONCUSSION
received approval from The Pennsylvania State University Insti-
tutional Review Board and was conducted in accordance with its
ethical guidelines.
Data Analysis
Indices of overall performance and performance variability.
Because the intent of the present study was to examine cross-test
variability as an individual difference variable, the index of vari-
ability should assess variability that is meaningful with respect to
the individual and eliminate, to the greatest extent possible, vari-
ability that is related to properties of the tests. For this purpose, a
principal components analysis was performed on the following test
indices as assessed at baseline: Vigil—Total Omissions, Vigil—
Total Commissions, Vigil—Average Delay, ImPACT Verbal
Memory composite, ImPACT Visual Memory composite, Im-
PACT Visuomotor Speed composite, ImPACT Reaction Time
composite, ImPACT Impulse Control composite, BVMT–R Total
Immediate Recall, BVMT–R Total Delayed Recall, HVLT–R To-
tal Immediate Recall, HVLT–R Total Delayed Recall, SDMT—
Total Correct, SDMT—Incidental Memory, Digit Span Forwards,
Digit Span Backwards, SCWT—Word Time, SCWT—Color-
Word Time, PSU Cancellation Test Total Correct, COWAT Total,
CTMT—Trial 1 Time, and CTMT—Trial 2 Time. A priori, it was
decided that all test indices with component loadings on the first
component greater than .4 would be retained for subsequent anal-
ysis and Cronbach’s alpha would be consulted to confirm the
internal consistency of the retained indices at the aggregate level.
These retained indices were then used to calculate measures of
overall performance and performance variability. First, all test
indices were put on the same metric by converting them to stan-
dard scores (SSs). SSs have a mean of 100 and a standard deviation
of 15. SS units have been chosen because these are the metric
employed by many commonly used neuropsychological tests, in-
cluding the Wechsler Adult Intelligence Scale FSIQ score. The
sample mean and sample standard deviation from the athletes
tested at baseline were used to calculate SSs. Test indices for
which higher scores indicate poorer performance (i.e., Stroop—
Color-Word Time) were calculated by subtracting the observed
score from the sample mean, so that for all SSs, higher scores
indicate better performance.
Overall performance at baseline was calculated by taking the
mean across all SS-converted retained test indices for that indi-
vidual at baseline. Similarly, overall performance postconcussion
was calculated by taking the mean across all SS-converted retained
test indices for that individual postconcussion. Performance vari-
ability at baseline was calculated by taking the standard deviation
across all SS-converted retained test indices for that individual at
baseline. Similarly, performance variability postconcussion was
calculated by taking the standard deviation across all SS-converted
retained test indices for that individual postconcussion.
To examine potential subgroups in the sample of 71 athletes, we
entered baseline and postconcussion performance variability indi-
ces into a k-means cluster analysis where k was set equal to 2.
Reliable decline. Reliability statistics were calculated based
on the control group. A 90% confidence interval was selected as
the criterion for determining reliable decline in performance post-
concussion. An index of total declined scores was calculated by
summing the total number of tests for which an individual dem-
onstrated reliable decline.
Results
In the athlete sample, 85% of concussions were Cantu Grade II
injuries, characterized by loss of consciousness less than 5 min in
duration or amnesia lasting between 30 min and 24 hr. To evaluate
a possible influence of days since concussion on likelihood of
exhibiting cognitive decline postconcussion, athletes who were
tested within 48 hr of their injury were compared with those who
were tested longer than 48 hr since their injury. These groups were
not significantly different in total number of scores to reliably
decline postinjury, t(69) ⫽⫺0.70, p .47.
A list of the tests included and their component loadings can be
found in Table 2. Cronbach’s alpha was calculated using the
retained indices to confirm consistency of these items at the
aggregate level. This analysis suggested that the retained items had
good internal consistency (␣⫽.82).
Based on those retained tests, indices of overall performance
and performance variability were calculated as described above.
Performance variability was significantly greater than zero at base-
line, M 12.05, t(70) 23.20, p .001, and postconcussion,
M 12.39, t(70) 21.30, p .001. To examine the relationship
between performance variability and other indices of cognitive
functioning, we examined bivariate correlations among perfor-
mance variability, overall performance, and WTAR FSIQ esti-
mate. At baseline, overall performance was negatively correlated
with performance variability (r ⫽⫺.44, p .001), indicating that
better performance was associated with less variability. This in-
verse relationship between performance variability and overall
performance was also significant postconcussion (r ⫽⫺.52, p
.001). WTAR FSIQ estimate was significantly correlated with
overall performance, but not performance variability at both time
points (see Table 3).
Paired-sample t tests revealed that overall performance and
performance variability did not differ across time points in the
athlete sample as a whole. For overall performance, the mean
difference between baseline and postconcussion assessments was
0.33 SS points higher on the postconcussion assessment, t(70)
0.30, p .77. Similarly, for performance variability, the mean
difference between time points was 0.33 SS points higher on the
Table 2
Retained Test Indices and Their Component 1 Loadings
Test index Loading
Comprehensive Trail-Making Trial 1 (Trail 2 or 3) .41
Comprehensive Trail-Making Trial 2 (Trail 4 or 5) .57
ImPACT Verbal Memory composite .41
ImPACT Visual Memory composite .64
ImPACT Visual Motor Speed composite .76
ImPACT Reaction Time composite .67
BVMT–R Delayed Recall .62
HVLT–R Delayed Recall .46
Symbol-Digit Modalities Test .54
Stroop Color-Word Time .63
Note. ImPACT Immediate Post-Concussion Assessment and Cognitive
Testing; BVMT–R Brief Visual Memory Test—Revised; HVLT–R
Hopkins Verbal Memory Test—Revised.
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484
RABINOWITZ AND ARNETT
postconcussion assessment, t(70) ⫽⫺0.53, p .60. In the control
sample, overall performance significantly increased across assess-
ments, whereas performance variability significantly decreased.
For overall performance, the mean difference between Time 1 and
Time 2 assessments was 3.0 SS points higher on the Time 2
assessment, t(41) ⫽⫺4.2, p .001; for performance variability,
the mean difference between time points was 1.5 SS points lower
on the Time 2 assessment, t(41) 2.1, p .04 (see Figure 1).
Cluster Analysis
Cluster analysis was also used to examine performance variabil-
ity in the athlete sample. Baseline and postconcussion performance
variability indices were entered into a k-means cluster analysis
where k was set equal to 2. This approach allowed for examination
of potential subgroups with distinct longitudinal variability pro-
files. Final cluster centers for Cluster 1 (n 46) were baseline
performance variability 10.4 SS points and postconcussion
performance variability 9.6 SS points. Cluster centers for Clus-
ter2(n 25) were baseline performance variability 15.3 SS
points and postconcussion performance variability 17.6 SS
points. These are referred to subsequently as the low-variability
cluster and high-variability cluster, respectively. A repeated mea-
sures analysis of variance (ANOVA) was conducted to examine a
potential cluster by time point interaction, which was confirmed,
F(1, 69) 5.77, p .05. The nature of the effect was such that the
low-variability cluster group demonstrated a slight decrease in
performance variability over time, whereas the high-variability
cluster group demonstrated an increase in performance variability
postconcussion.
To further examine the effect of concussion on neuropsycho-
logical performance and symptom reporting in the low- and high-
variability cluster groups, we ran separate repeated measures
ANOVAs using overall performance and PCSS score as dependent
variables. Results indicated a significant main effect of cluster
group on overall performance, F(1, 69) 21.18, p .001, as well
as a significant cluster group by time point interaction, F(1, 69)
6.38, p .05. The nature of this effect was such that the low-
variability cluster group demonstrated a slight increase in overall
performance from baseline to postconcussion, whereas the high-
variability cluster group demonstrated a decrease in overall per-
formance. Figures 1 and 2 illustrate the cluster by time point
interaction; Time 1 and Time 2 means for the control group are
also depicted for context. With regard to PCSS score, there were
no significant main effect of time point and no significant time
Table 3
Correlations
Variable BL OP BL PV PC OP PC PV
Baseline overall performance (BL OP)
Baseline performance variability (BL PV) .44
ⴱⴱ
———
Postconcussion overall performance (PC OP) .61
ⴱⴱ
.30
——
Postconcussion performance variability (PC PV) .27 .36
.52
ⴱⴱ
WTAR FSIQ estimate .38
ⴱⴱ
.03 .46
ⴱⴱ
.14
Note. Overall performance is the mean of standard score-transformed indices from the following measures:
Comprehensive Trail-Making Test, Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT
Verbal Memory, Visual Memory, Visual Motor Speed, and Reaction Time composites), Brief Visual Memory
Test—Revised Delayed Recall, Hopkins Verbal Memory Test—Revised Delayed Recall, Symbol-Digit Modal-
ities Test, and Stroop Color-Word Time. Performance variability is the standard deviation of the indices listed
above. WTAR FSIQ Wechsler Test of Adult Reading Full Scale IQ.
Significant at the .05 level.
ⴱⴱ
Significant at the .01 level.
Figure 1. Cluster by time point interaction for performance variability.
Intraindividual variability is in standard score (SS) units. SSs have a mean
of 100 and a standard deviation of 15. Repeated measures analysis of
variance, F(1, 69) 5.77, p .05. Post hoc paired-samples t test revealed
a trend toward postconcussion increase in performance variability from
baseline for the high-variability cluster group: t(24) 1.96, p .06.
Figure 2. Cluster by time point interaction for overall performance. Mean
performance is in standard score (SS) units. SSs have a mean of 100 and
a standard deviation of 15. Repeated measures analysis of variance,
F(1, 69) 6.38, p .05. Post hoc paired-samples t test revealed signif-
icant postconcussion improvement from baseline in overall performance
for the low variability cluster group: t(45) 2.14, p .05.
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485
COGNITIVE VARIABILITY AND CONCUSSION
point by cluster group interaction. There was a trend toward a main
effect of cluster group on PCSS score, F(1, 69) 3.25, p .08.
Post hoc paired-sample t tests were conducted to further exam-
ine the significant cluster group by time point interactions. This
analysis revealed that the low-variability cluster group exhibited a
significant increase in overall performance postconcussion,
t(45) 2.14, p .05, but no significant change in performance
variability, t(45) ⫽⫺1.12, p .27. In contrast, the high-
variability cluster group exhibited a trend toward a significant
increase in performance variability, t(24) 1.96, p .06, but no
significant change in overall performance postconcussion, t(24)
1.44, p .16.
The high-variability and low-variability cluster groups were
not significantly different from each other with respect to
self-reported previous head injuries (p .97), WTAR FSIQ
estimate (p .16), or diagnosis of attention-deficit/hyperactiv-
ity disorder or learning disability (p .14, and p .29,
respectively). Scores for individual test indices at both time
points by group are provided in Table 4.
Reliable Decline
To examine the potential clinical significance of these findings,
we calculated reliable decline on one or more, two or more, and
three or more test indices. Individuals in the high-variability clus-
ter group were more likely than individuals in the low-variability
cluster or control groups to exhibit reliable decline on one or more
test,
2
(2) 8.5, p .05, two or more tests,
2
(2) 14.6, p
.005, and three or more tests,
2
(2) 20.5, p .001 (see Table 5
and Figure 3).
Discussion
The purpose of the present study was to examine intraindividual
variability across cognitive tests in college athletes tested pre- and
postinjury. These findings suggest that normative cognitive per-
formance in college athletes is characterized by significant intra-
individual variation across tests on the order of nearly 1 standard
deviation (12 SS points). Greater performance variability was
unrelated to an estimate of intellectual functioning (WTAR FSIQ
estimate), but was significantly related to lower mean level of
performance on the test battery.
In the athlete sample as a whole, there was no effect of concus-
sion on either performance variability or overall performance. This
is a counterintuitive finding for which there are multiple explana-
tions. It is likely that injury effects on specific tests are obscured
in analyses considering the summary indices. However, the cluster
analysis strongly suggests that the null finding in the overall
sample is at least, in part, due to heterogeneity in the neurocogni-
tive effects of concussion. Despite the absence of an injury effect
in the athlete sample overall, cluster membership predicted pre-
and postinjury change in overall performance and performance
variability. Furthermore, compared with the low-variability cluster
and control groups, the high-variability cluster group was signif-
icantly more likely to display reliable declines on individual tests.
Roughly two thirds of the sample exhibited cognitive performance
that was characterized by relatively low variability. Membership in
this low-variability cluster group predicted a decrease in perfor-
mance variability and an increase in overall performance postcon-
cussion. In contrast, roughly one third of the sample exhibited
cognitive performance that was characterized by relatively high
variability. Membership in the high-variability cluster group pre-
dicted an increase in performance variability and a decrease in
overall performance postconcussion.
Control participants who were administered the same test bat-
tery at two time points 1 month apart demonstrated a pattern of
performance similar to athletes in the low-variability cluster
group—that is, controls showed a decrease in performance vari-
ability and an increase in overall performance from Time 1 to
Time 2. It is likely that this pattern of performance reflects a
practice effect—that is, control participants and low-variability
athletes demonstrated the ability to benefit from prior exposure to
the test battery. By contrast, the high-variability cluster group did
not demonstrate a benefit from practice.
There are at least two likely explanations for this finding. It
could be that high-variability athletes were poorly motivated at
baseline, and hence, failed to benefit from prior exposure to the
test battery. Alternatively, this group of athletes may be experi-
encing cognitive dysfunction as a result of the recent head injury,
and their increased performance inconsistency may represent the
neurocognitive consequence of their concussion.
We examined the likelihood of exhibiting reliable decline on the
test battery to evaluate the clinical utility of these findings, and
also to help distinguish between the two explanations for the
high-variability cluster group’s failure to exhibit a practice effect:
(1) poor motivation versus (2) concussion-related cognitive dys-
function. Lower incidence of reliable decline in the high-
variability athletes would support the notion that these athletes
were poorly motivated at baseline; hence, their baseline perfor-
mance represented an underestimate their true premorbid abilities.
Conversely, higher incidence of reliable decline from baseline
performance would support the hypothesis that these athletes ex-
perienced concussion-related cognitive dysfunction. This latter
explanation was supported by our results: Athletes in the high-
variability cluster group were more likely than the other groups to
exhibit reliable decline from their baseline performance. We note
that these two explanations are not mutually exclusive; it is, of
course, possible that high-variability athletes were both poorly
motivated at baseline and also experiencing neurocognitive se-
quelae of their concussion. However, the fact that these athletes
exhibited reliable decline from their baseline performance suggests
that intraindividual variability could impart clinically meaningful
information regarding which athletes are likely to exhibit
concussion-related cognitive dysfunction.
Taken together, these results suggest that there is a relationship
between cross-test intraindividual variability and mean level of
performance such that as performance improves, cross-test vari-
ability decreases. This relationship between performance and vari-
ability on a cross-domain level is consistent with previous work
demonstrating a link between cross-test intraindividual variability
and cognitive decline (Holtzer et al., 2008; Kliegel & Sliwinski,
2004), as well as a larger body of research that has reported an
inverse relationship between mean level of performance and in-
traindividual inconsistency within a single task (Hultsch & Mac-
Donald, 2004; Hultsch et al., 2008). The replication of this finding
at the cross-domain level has potential clinical relevance because
cross-test variability can be easily calculated in typical neuropsy-
chological batteries administered for clinical purposes.
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486
RABINOWITZ AND ARNETT
Table 4
Scores for Individual Test Indices by Group
Test
High variability
(n 25)
Low variability
(n 46)
Control
(n 42)
BL/T1
between groups
PC/T2
between groups
BL
M (SD)
PC
M (SD)
Within
group
BL
M (SD)
PC
M (SD)
Within
group
T1
M (SD)
T2
M (SD)
Within
group
t(24) pt(45) pt(41) pF(2, 109) pF(2, 110) p
CTMT
Trial 1 Z-score
(Trail 2 or 3) 0.56 (1.68) 0.21 (0.21) 0.8 .43 0.26 (0.68) 0.46 (0.16) 2.3 .02 0.23 (1.18) 0.23 (0.16) 4.4 .01 4.8 .01 4.5 .01
Trial 2 Z-score
(Trail 4 or 5) 0.26 (1.09) 0.25 (0.18) 0.3 .74 0.06 (0.81) 0.40 (0.14) 2.5 .01 0.12 (0.83) 0.36 (0.14) 4.0 .01 1.1 .33 6.3 .01
ImPACT
Verbal Memory composite 79.8 (9.3) 74.4 (2.0) 2.8 .01 85.6 (10.9) 86.0 (1.5) 0.2 .88 88.4 (9.0) 88.3 (1.5) 0.1 .95 5.3 .01 17.3 .01
Visual Memory composite 70.9 (12.4) 69.0 (2.7) 0.7 .47 77.8 (12.6) 79.1 (2.0) 1.0 .31 75.6 (16.2) 78.7 (2.0) 1.4 .17 1.4 .25 6.5 .01
Visual Motor Speed
composite 35.1 (9.9) 34.4 (1.3) 0.5 .62 36.6 (6.6) 37.7 (1.0 1.4 .17 38.3 (6.9) 38.5 (1.0) 0.0 .99 1.4 .25 3.0 .05
Reaction Time composite 0.59 (0.09) 0.61 (0.02) 0.3 .80 0.59 (0.06) 0.58 (0.01) 0.7 .48 0.59 (0.09) 0.59 (0.02) 0.2 .88 0.1 .94 0.6 .58
BVMT–R Delayed Recall raw
score 9.5 (2.6) 8.8 (0.3) 1.0 .31 10.8 (1.4) 10.8 (0.3) 0.1 .90 11.2 (0.9) 11.4 (0.3) 1.2 .22 9.1 .01 19.2 .01
HVLT–R Delayed Recall raw
score 8.8 (2.4) 7.6 (0.4) 2.1 .05 9.7 (1.5) 9.7 (0.3) 0.2 .87 9.7 (1.6) 9.9 (0.3) 0.4 .72 2.2 .11 14.2 .01
SDMT raw score 57.1 (10.8) 54.5 (2.5) 1.5 .16 61.3 (9.6) 63.6 (1.9) 1.9 .06 62.8 (11.2) 68.9 (1.9) 5.5 .01 2.3 .10 12.0 .01
Stroop Color-Word Time 111.3 (18.7) 119.4 (5.2) 0.4 .69 111.3 (21.3) 105.3 (4.0) 3.6 .01 102.3 (17.7) 98.6 (4.0) 1.6 .11 3.7 .03 4.6 .01
CARB % failure 0.0 12.0 6.5 0.0 0.0 2.4
Note.BL Baseline; T1 Time 1; PC postconcussion; T2 Time 2; CTMT Comprehensive Trail-Making Test; ImPACT Immediate Post-Concussion Assessment and Cognitive Testing;
BVMT–R Brief Visuospatial Memory Test—Revised; HVLT–R Hopkins Verbal Learning Test—Revised; SDMT Symbol-Digit Modalities Test; CARB Computerized Assessment of
Response Bias. Within-group t tests compare means for BL and PC or for T1 and T2. Between-groups analyses of variance compare means across three groups: concussed athletes in the high-variability
cluster group, concussed athletes in the low-variability cluster group, and controls.
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487
COGNITIVE VARIABILITY AND CONCUSSION
Importantly, although performance inconsistency was signifi-
cantly correlated with mean level of performance, it was not
significantly correlated with an estimate of intellectual function-
ing. This suggests that the association between performance in-
consistency and performance level may be independent of IQ;
however, a more thorough IQ assessment (rather than a
performance-based estimate like the WTAR) would be necessary
to test this hypothesis. Furthermore, all participants in this study
were college students at the same institution. Hence, there were no
gross educational discrepancies among these participants. These
observations suggest that intraindividual variability may be index-
ing an important cognitive process that cannot be fully explained
by IQ or education. It is possible that this process is related to
lapses in attention (Bunce et al., 1993), failure to maintain exec-
utive control (West et al., 2002), or inefficiencies in central ner-
vous system functioning (Holtzer et al., 2008).
Although there was no effect of concussion on cognitive per-
formance in the sample as a whole, cluster membership based on
performance variability at baseline was related to response to
injury, with the high-variability cluster group demonstrating signs
of concussion-related neurocognitive dysfunction. There are a
number of possible explanations for this finding. Intraindividual
variability in the high-variability cluster group may indicate a level
of premorbid vulnerability to injury. That is, this group may be
more likely to experience neurological decline as a result of mild
traumatic brain injury. The high- and low-variability cluster groups
were not significantly different from each other with regard to
self-report of previous head injury, attention-deficit/hyperactivity
disorder diagnosis, or learning disability diagnosis. These results
suggest that any group differences in vulnerability to injury may
not be related to these particular premorbid conditions; however,
inaccuracies in self-report may hamper the validity of these vari-
ables.
As discussed previously, athletes’ approach to testing could be
another possible explanation for the difference between clusters. It
may be the case that higher levels of intraindividual variability are
related to poor effort toward testing. At baseline, athletes may
undervalue the importance of putting forth adequate effort. They
may fail to see the significance of baseline concussion testing or
regard it as an imposition. It is also possible that some athletes may
purposefully underperform at baseline to decrease their chances of
being evaluated as impaired postconcussion. By contrast, return-
to-play decisions provide a powerful incentive for athletes to
exhibit maximal effort toward testing postconcussion—perhaps
even greater effort than patients typically seen in other neuropsy-
chological assessment settings. This notion has been supported in
work by Bailey et al. (2006) demonstrating that some athletes
exhibit markedly improved cognitive performance in response to
concussion.
Alternatively, the low-variability cluster group may be highly
motivated at baseline. In this group, practice effects may mask any
neurocognitive impairment related to mild and uncomplicated con-
cussions. This notion is supported by the fact that participants in
the control group demonstrated improved performance suggestive
of practice effects, whereas the low-variability athletes exhibited
stable performance. Prior research has demonstrated that, within a
single task, intraindividual variability in reaction time decreases
with repeated exposure to the task, suggesting that decreased
inconsistency is a hallmark of practice (Ram et al., 2005). We note,
however, that the results of formal effort testing do not support this
hypothesis, as three athletes in the low-variability cluster group
and no athletes in the high-variability cluster group failed formal
effort testing (see Table 4). However, this does not preclude the
possibility that more subtle motivational forces, perhaps not de-
tectible with standard symptom validity indicators, may be at play.
There are limitations to the present study. First, with 71 partic-
ipants in the athlete group, this study may have been underpowered
to detect small-to-moderate effects that may be meaningful, par-
ticularly with regard to group differences between the high-
Table 5
Number of Cases Exhibiting Reliable Decline on One or More, Two or More, and Three or More Test Indices
Test
Group
2
(2)
p
High variability Low variability Control
Decline Stable Decline Stable Decline Stable
1 or more 16 (64%) 9 (36%) 15 (33%) 31 (67%) 13 (31%) 29 (69%) 8.5 .014
2 or more 12 (48%) 13 (52%) 8 (17%) 38 (83%) 4 (10%) 38 (90%) 14.6 .001
3 or more 9 (36%) 16 (64%) 4 (9%) 42 (91%) 0 (0%) 42 (100%) 20.5 .001
Note. Reliable change statistics were calculated based on control group scores. A 90% confidence interval was selected as the criterion for determining
reliable decline in performance postconcussion. An index of total declined scores was calculated by summing the total number of tests for which an
individual demonstrated reliable decline.
Figure 3. The percentage of cases to exhibit reliable decline by group.
Reliable change statistics were calculated based on control group scores. A
90% confidence interval was selected as the criterion for determining
reliable decline in performance postconcussion. An index of total declined
scores was calculated by summing the total number of tests for which an
individual demonstrated reliable decline.
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RABINOWITZ AND ARNETT
variability and low-variability cluster groups. Furthermore, self-
reports of important clinical variables such as history of premorbid
conditions, previous concussions, and postconcussion symptoms
are subject to biases. A more objective examination of the rela-
tionship between these variables and intraindividual variability is
warranted. Finally, the present study is exploratory in nature.
Intraindividual variability in cognitive performance is a relatively
new area of research, and cross-domain inconsistency has not thus
far been evaluated for its potential clinical and theoretical rele-
vance in concussion.
The aim of the present study was twofold: (1) to describe
cross-test intraindividual variability in a sample of college athletes
before and after sports-related head injury, and (2) to begin to
explore potential correlates of this cognitive inconsistency. Future
studies evaluating some of the specific hypotheses discussed above
(i.e., cognitive inconsistency as an index of previous head trauma
and cognitive inconsistency as an index of suboptimal motivation)
are necessary to determine the potential theoretical significance
and clinical utility of cross-test intraindividual variability in sports-
related concussion assessment.
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Received August 17, 2012
Revision received January 4, 2013
Accepted April 9, 2013
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... A more recent method of assessing IIV, neuropsychological dispersion, involves measuring the variability among standardized neuropsychological test scores obtained within a single evaluation for a given individual. In previous studies, neuropsychological dispersion scores have been derived by calculating the standard deviation across standardized neuropsychological test scores (Hill et al., 2013;Merritt et al., 2018;Rabinowitz & Arnett, 2013;Thaler et al., 2015) with an alternative measure of dispersion, the intra-individual maximum deviation score (MD; Merritt et al., 2018), calculated by subtracting the smallest standardized score from the largest standardized score for each participant. Given that we lack normative or cut-scores to signify "normal" versus "pathological" dispersion measures, researchers have explored how dispersion scores relate to indicators of functioning. ...
... Greater dispersion scores were associated with lower overall mean performance across the same tests in TBI samples (Hill et al., 2013;Merritt et al., 2018), but not in healthy adults (Tanner-Eggen et al., 2015), suggesting that dispersion may be a marker of overall cognitive functioning only in cognitively compromised populations. Also, studies suggest that higher dispersion may reflect underlying medical illness in older adults (Thaler et al., 2015) and signify vulnerability to impairment after concussion in college athletes (Rabinowitz & Arnett, 2013). Further, in older adults, higher dispersion has been associated with greater self-reported memory problems and poorer objective memory scores (Thaler et al., 2015), suggesting that neuropsychological dispersion may be related to specific neuropsychological processes. ...
... Neuropsychological dispersion scores were derived using methods outlined in previous studies (Hill et al., 2013;Merritt et al., 2018;Rabinowitz & Arnett, 2013;Thaler et al., 2015). For calculation of dispersion scores, standardized scores for tests of executive functioning and processing speed were used. ...
Article
Introduction: Previous studies of neuropsychological performance in electrical injury (EI) patients have produced evidence of deficits in various cognitive domains, but studies have yet to investigate relationships among performance in cognitive domains post-EI. This study examined whether dispersion among neuropsychological test scores was associated with injury parameters and neuropsychological performance in EI patients. Additionally, we examined whether dispersion, processing speed and/or executive abilities explain variance in episodic verbal and visual memory performance among EI patients. Method: Data from 52 post-acute EI patients undergoing outpatient evaluation with objectively-verified valid neuropsychological test performance were examined. Tests included measures of verbal and visual memory, processing speed, and executive functioning. Dispersion was calculated from executive functioning and processing speed scores. Results: Dispersion was not related to mean performance or injury characteristics, but was significantly negatively correlated with performance on a test of processing speed, suggesting that increased dispersion is associated with reduced cognitive efficiency post-EI. Delayed visual memory was related to both dispersion scores and processing speed. Stepwise regression equations predicting delayed memory determined that processing speed most significantly predicted delayed visual memory, even after controlling for immediate visual memory. No significant relationships emerged between verbal memory and non-memory neuropsychological scores. Conclusions: This is the first study to examine neuropsychological dispersion and relationships among domains of cognitive functioning in EI. Current results suggested that neuropsychological dispersion is not a marker of general functioning or severity of injury in EI patients, but may represent more specific processing speed abilities. Processing speed predicts delayed visual memory performance in EI patients, which should be considered in interpreting test scores during evaluations.
... Intraindividual variability (IIV) has long been assessed as a metric of an individual's functioning across behavioural, physiological and neuropsychological domains. [10][11][12] Inconsistency is one type of IIV, operationalised as the variability of an individual's performance on a single task across multiple assessment visits. Dispersion is another type of IIV, operationalised as the variability in an individual's performance across multiple tasks at one assessment visit. ...
... Dispersion is another type of IIV, operationalised as the variability in an individual's performance across multiple tasks at one assessment visit. 13 Increased dispersion across cognitive tasks has been linked with attention-deficit/hyperactivity disorder (ADHD), 14 postconcussive CD, 10 cognitive decline in adults with dementia 15 16 and contradictory study results for CD in ageing adults. 11 17 Interest in dispersion in the context of cognition is driven by theoretical assumptions that dispersion reflects compromised neurological mechanisms that may be attributed to disrupted neural networks, altered functional connectivity and executive dysfunction or impaired cognitive control. ...
Article
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Objective Dispersion, or variability in an individual’s performance across multiple tasks at a single assessment visit, has been associated with cognitive dysfunction (CD) in many neurodegenerative and neurodevelopmental disorders. We aimed to compute a dispersion score using neuropsychological battery (NB) tests and determine its association with CD in patients with SLE. Methods CD was defined as a z-score of ≤−1.5 on ≥2 domains of the NB. To compute a type of dispersion score known as the intraindividual SD (ISD), the SD of age-adjusted and sex-adjusted z-scores was calculated for each visit in each patient. To estimate the association between ISD and cognitive status (CD and non-CD), we used multilevel logistic regression, adjusting for clinically important covariates. Results A total of 301 adult patients with SLE completed the NB at baseline, 187 of whom were reassessed at 6 months and 189 at 12 months. CD was observed in 35.2% of patients at baseline, 27.8% at 6 months and 28.0% at 12 months. Prior to covariate adjustment, the mean ISD for non-CD was 1.10±0.31 compared with 1.50±0.70 for CD. After adjusting for ethnicity, education, employment, socioeconomic status and anxiety/depression, there was a statistically significant association between ISD and CD (OR for one-unit increase in ISD: 13.56, 95% CI 4.80 to 38.31; OR for 1/10th-unit increase in ISD: 1.30, 95% CI 1.17 to 1.44). Findings were valid across multiple sensitivity analyses. Conclusion This is the first study to show that patients with SLE who were classified as having CD by the NB had more variability across the NB tests (ie, higher ISD score) compared with those who were not classified as having CD.
... Persistent post-concussive symptoms (experienced more than three months post-injury) are often estimated to occur in 10-15% of concussions, but estimates vary from 1.4-29.3%, depending on the diagnostic criteria used (Rabinowitz & Arnett, 2013;Rose, Fischer, & Heyer, 2015;Sterr, Herron, Hayward, & Montaldi, 2006). The most common cognitive deficits observed more than one year post-injury are in attention, memory, and processing speed (Dean & Sterr, 2013;Konrad et al., 2011;Sterr et al., 2006). ...
... recognition, r = .39 (Benedict, Schretlen, Groninger, & Brandt, 1998;Collins et al., 1999;McCrea et al., 2003;Rabinowitz & Arnett, 2013). ...
Article
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Concussion causes varying degrees of brain damage in athletes, and the neuropsychological consequences of concussion or incomplete recovery can impede skill acquisition. This study examined the neuropsychological recovery from concussion in athletes at moderate elevation, 80% of whom did not seek treatment for their concussion. We collected data on concussions sustained at or around 1966 m among university athletes. American football players at New Mexico Highlands University (N = 13) were administered a 40-minute neuropsychological battery to examine domains affected by concussion such as attention, memory, information processing speed, executive functioning, and depressive symptoms at two time points, before and after the 2016 football season. In total, there were 5 concussed athletes (assessed m = 45.6 days post-injury) and 8 non-concussed control athletes. A repeated-measures ANOVA showed a significant group-by-time interaction for depression, F = 6.335 (p = .029), with concussed athletes showing significant increases in depressive symptoms. Repeated-measures ANCOVAs (controlling for depressive symptoms) of the four athletes who did not seek treatment showed significant group-by-time interactions, with concussed athletes experiencing significant slowing in processing speed F = 26.51 (p = .001) and declines in verbal learning, F = 6.54 (p = .034). Additionally, two athletes (one who sustained a concussion and one who did not) were re-administered the battery mid-season, within 7 days post-injury; the concussed athlete experienced acute deficits in most domains and demonstrated incomplete recovery on measures of depression, verbal learning, and switching. These results indicate that untreated concussions sustained at moderate elevation may not fully recover within the frequently cited 10-day window, and suggest the need for future research into the role of both concussion treatment and elevation in concussion recovery prognosis.
... Post-concussion IIV changes have previously been explored in adults, [22][23][24][25][26][27][28][29][30][31] yet no studies were found that explore IIV following concussion in youth. Overall, results from adult studies are mixed; some studies show higher variability in participants following concussion, [22][23][24][25][26] while others report nonsignificant differences. ...
... Overall, results from adult studies are mixed; some studies show higher variability in participants following concussion, [22][23][24][25][26] while others report nonsignificant differences. [27][28][29][30] As expected, there are many differences between the studies; including study design and time of testing post-concussion, which may impact the ability to detect differences after injury. ...
Article
Full-text available
Background Concussion represents a growing concern in sports participation for adults and youth alike. Studies exploring the neurocognitive sequelae of concussion, such as speed of processing typically compare mean reaction time scores to a control group. Intraindividual variability measures the consistency of reaction times between trials and has been previously explored in adults post-concussion. Some adult studies show increased variability following injury. Developmentally youth show higher intraindividual variability than adults, which may put them at higher risk of increased intraindividual variability change post-concussion. Exploring intraindividual variability may provide additional insight into fluctuating performance reported following injury. Despite preliminary findings of slowed reaction time in youth, a pre-/post-concussion comparison of intraindividual variability of reaction time has not been explored. Objective To describe and compare pre- and post-concussion measures of processing speed and intraindividual variability in youth. Methods A pre-/post-concussion design was used to compare mean reaction time and the coefficient of variation before and after sports-related concussion in 18 youth athletes aged 10–14 years using verbal and nonverbal working memory tasks. Pre-/post-concussion reaction time and coefficient of variation were compared using t-tests. Results The coefficient of variation for nonverbal working memory was significantly higher following concussion, but no changes in average reaction time were found. Conclusions Preliminary findings suggest that average response times are unchanged following concussion, but the fluctuation across response times is more variable during a nonverbal working memory task in youth. Increased variability in speed of reaction times could have implications for safe return to sports and reduced academic performance.
... What seems to be lacking is an exploration of variability in collective game patterns within the same competitive level, as well as intra-team variability in individual patterns for players of the same positional status. (Gryc et al., 2015;Rabinowitz & Arnett, 2013). We proposed a conceptual framework to categorize different types of game patterns variability in team sports (a) inter-team variability across sex/gender; (b) inter-team variability across competitive levels; (c) inter-team variability within the same competitive level; (d) inter-and intra-team variability due to positional status; and (e) intra-team variability within the same positional status (FIGURE 1). ...
Article
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The purpose of this narrative review was to evaluate the game patterns variability in team sports, that is, how game patterns differ across and within different performance levels. We synthesized match analysis-derived knowledge addressing variability in game patterns and conceptually organized the findings into an umbrella framework to help researchers better situate future investigations and highlight gaps that should be addressed by the literature. We structured the study of game pattern variability under five dimensions: (a) inter-team variability based on sex/gender; (b) inter-team variability due to competitive level; (c) inter-team variability within the same competitive level; (d) inter- and intra-team variability due to positional status; and (e) intra-team variability within the same positional status. Relevant gender-based differences in game patterns are identified, but researchers often publish data on men and women separately, inhibiting more direct comparisons. Game pattern variability is present across different competitive levels and, more interestingly, also within the same competitive level, allowing different performance models to coexist at the highest levels of competition. There is variability emerging from the different positional status of the players, but also intra-team variability within the same positional status. Overall, the study of game patterns variability can be divided into (at least) five different, but complementary dimensions, each affording valuable knowledge to improve coaches’ understanding of the game dynamics.
... For Chi-square analyses, athletes were separated into two groups based on the criteria described above. Dichotomizing groups and conducting Chi-square analyses in this way have been utilized in several previously published studies regarding neurocognitive performance (Guty & Arnett, 2018;Merritt et al., 2018;Rabinowitz & Arnett, 2013;Riegler et al., 2019b). Additionally, this approach has shown clinical utility in that it allows for a more nuanced understanding of an athlete's individual presentation compared to the sole reliance upon mean performance scores, which can sometimes obscure important clinical findings. ...
Article
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Objectives: The current study aims to examine the prevalence rates and the relationship of symptoms of depression, anxiety, and comorbid depression/anxiety with neurocognitive performance in college athletes at baseline. We hypothesized a priori that the mood disturbance groups would perform worse than healthy controls, with the comorbid group performing worst overall. Methods: Eight hundred and thirty-one (M = 620, F = 211) collegiate athletes completed a comprehensive neuropsychological test battery at baseline which included self-report measures of anxiety and depression. Athletes were separated into four groups [Healthy Control (HC) (n = 578), Depressive Symptoms Only (n = 137), Anxiety Symptoms Only (n = 54), and Comorbid Depressive/Anxiety Symptoms (n = 62)] based on their anxiety and depression scores. Athletes' neurocognitive functioning was analyzed via Z score composites of Attention/Processing Speed and Memory. Results: One-way analysis of variance revealed that, compared to HC athletes, the comorbid group performed significantly worse on measures of Attention/Processing Speed but not Memory. However, those in the depressive symptoms only and anxiety symptoms only groups were not significantly different from one another or the HC group on neurocognitive outcomes. Chi-square analyses revealed that a significantly greater proportion of athletes in all three affective groups were neurocognitively impaired compared to the HC group. Conclusions: These results demonstrate that collegiate athletes with comorbid depressive/anxiety symptoms should be identified, as their poorer cognitive performance at baseline could complicate post-concussion interpretation. Thus, assessing for mood disturbance at baseline is essential to obtain an accurate measurement of baseline functioning. Further, given the negative health outcomes associated with affective symptomatology, especially comorbidities, it is important to provide care as appropriate.
... Two IIV indices were derived for each of the composites created above (memory and attention/ processing speed). Previous work has used the variability metric of MDS (Heyanka, Holster, & Golden, 2013;Rabinowitz & Arnett, 2013). This metric subtracts the lowest score from the highest score for each individual participant. ...
Article
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Objective People with Multiple Sclerosis (PwMS) and healthy controls (HCs) were evaluated on cognitive variability indices and we examined the relationship between fatigue and cognitive variability between these groups. Intraindividual variability (IIV) on a neuropsychological test battery was hypothesized to mediate the group differences expected in fatigue. Method Fifty-nine PwMS and 51 HCs completed a psychosocial interview and battery of neuropsychological tests and questionnaires during a 1-day visit. Fatigue in this study was measured with the Fatigue Impact Scale (FIS), a self-report multidimensional measure of fatigue. IIV was operationalized using two different measures, a maximum discrepancy score (MDS) and intraindividual standard deviation (ISD), in two cognitive domains, memory and attention/processing speed. Two mediation analyses with group (PwMS or HCs) as the independent variable, variability composite (memory or attention/processing speed) measures as the mediators, total residual fatigue (after accounting for age) as the outcome, and depression as a covariate were conducted. The Baron and Kenny approach to testing mediation and the PROCESS macro for testing the strength of the indirect effect were used. Results Results of a mediation analysis using 5000 bootstrap samples indicated that IIV in domains of both attention/processing speed and memory significantly mediated the effect of patient status on total residual fatigue. Conclusion IIV is an objective performance measure that is related to differences in fatigue impact between PwMS and HCs. PwMS experience more variability across tests of attention/processing speed and memory and this experience of variable performance may increase the impact of fatigue.
... TBIs. Another study investigated cognitive dispersion in a sample of college athletes who were evaluated at baseline and following sports-related concussion (Rabinowitz & Arnett, 2013). Study authors showed an inverse relationship between dispersion and mean cognitive performance at both baseline and post-concussion. ...
Article
Objective: We examined whether intraindividual variability (IIV) across tests of executive functions (EF-IIV) is elevated in Veterans with a history of mild traumatic brain injury (mTBI) relative to military controls (MCs) without a history of mTBI. We also explored relationships among EF-IIV, white matter microstructure, and posttraumatic stress disorder (PTSD) symptoms. Method: A total of 77 Veterans (mTBI = 43, MCs = 34) completed neuropsychological testing, diffusion tensor imaging (DTI), and PTSD symptom ratings. EF-IIV was calculated as the standard deviation across six tests of EF, along with an EF-Mean composite. DSI Studio connectometry analysis identified white matter tracts significantly associated with EF-IIV according to generalized fractional anisotropy (GFA). Results: After adjusting for EF-Mean and PTSD symptoms, the mTBI group showed significantly higher EF-IIV than MCs. Groups did not differ on EF-Mean after adjusting for PTSD symptoms. Across groups, PTSD symptoms significantly negatively correlated with EF-Mean, but not with EF-IIV. EF-IIV significantly negatively correlated with GFA in multiple white matter pathways connecting frontal and more posterior regions. Conclusions: Veterans with mTBI demonstrated significantly greater IIV across EF tests compared to MCs, even after adjusting for mean group differences on those measures as well as PTSD severity. Findings suggest that, in contrast to analyses that explore effects of mean performance across tests, discrepancy analyses may capture unique variance in neuropsychological performance and more sensitively capture cognitive disruption in Veterans with mTBI histories. Importantly, findings show that EF-IIV is negatively associated with the microstructure of white matter pathways interconnecting cortical regions that mediate executive function and attentional processes.
Article
Objective Persons with multiple sclerosis (PwMS) are at increased risk for cognitive dysfunction. Considering the impact and potential ramifications of cognitive dysfunction, it is important that cognition is routinely assessed in PwMS. Thus, it is also important to identify a screener that is accurate and sensitive to MS-related cognitive difficulties, which can inform decisions for more resource-intensive neuropsychological testing. However, research focused on available self-report screeners has been mixed, such as with the Multiple Sclerosis Neuropsychological Screening Questionnaire (MSNQ). This study aims to clarify the relationship between subjective and objective assessment of cognitive functioning in MS by examining domain-specific performance and intraindividual variability (IIV). Methods 87 PwMS (F = 65, M = 22) completed a comprehensive neuropsychological battery which included self- and informant-report measures of neurocognitive functioning. Scores were examined in relation to mean performance on five domains of cognitive functioning and two measures of IIV. Results The MSNQ-Self was inversely associated with executive function, verbal memory, and visual memory; it was not associated with IIV. The MSNQ-Informant was inversely associated with executive function and verbal memory, and positively associated with one measure of IIV. The MSNQ-Self showed a correlation of moderate effect size with depression ( r = .39) while the MSNQ-Informant did not. Conclusions Results suggest that the MSNQ-Self and MSNQ-Informant show similar utility. Our findings also suggest that domains of executive function and memory may be most salient, thus more reflected in subjective reports of cognitive functioning. Future work should further examine the impact of mood disturbance with cognitive performance and IIV.
Article
Researchers and scientists in practical sports psychology are involved in the sports psychology practice process. Current models of training appear unsatisfied to assist trainees in psychology to learn the necessary humanistic skills for the requirement of athlete-centered services. This article aims to include an example of the value of Deep Neural Network Assisted Reflective Approaches (DNARA) as an alternative to clinical training, which may enable practitioners to manage themselves better in action. It addresses the essence of professional understanding; To describe reflection and present common examples of a reflective method in the “education professions” during the creation of reflective practice. It discusses how reflective exercise can support a clinician’s professional and personal growth within the field of sport psychology and illustrate how reflective practice may improve. Finally, there is a discussion about appropriate platforms for the distribution of insightful content. DNARA method achieves the highest classification accuracy of 94.12%, and error rate is reduced to 0.40, and DNARA method is more efficient for student health concepts.
Article
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The Hopkins Verbal Learning Test (HVLT) is a brief verbal learning and memory test with six alternate forms. The HVLT is ideal in situations calling for repeated neuropsychological examinations, but it lacks a delayed recall trial which is essential for the assessment of abnormal forgetting. We present a revised version of the HVLT which includes a delayed recall trial, and therefore delays the yes/no recognition trial. The equivalence of test forms was examined in two separate studies using between-groups and within-subjects research designs. In both studies, the six forms of the revised HVLT (HVLT-R) were found to be equivalent with respect to the recall trials, but there were some modest differences in recognition. Recommendations for the use of the HVLT-R in serial neuropsychological examinations are provided, as well as normative data tables from a sample of 541 subjects, spanning ages 17 to 88 years.
Book
With an ever increasing population of aging people in the western world, it is more crucial than ever that we try to understand how and why cognitive competence breaks down with advancing age why do some people follow normal patterns of cognitive change, while others follow a path of progressive decline, with neurodegenerative diseases such as Alzheimer's. What can be done to prevent cognitive decline or - to avoid neurodegenerative diseases? The answers, if they come, will not emerge from research within one discipline, but from work being done across a range of scientific and medical specialities. This book delves into the subjects of cognitive aging, neuroscience, pharmacology, health, genetics, sensory biology, and epidemiology. This book is about new frontiers rather than past research and accomplishments. Recently cognitive aging research has taken several new directions, linking with, and benefiting from, rapid technological and theoretical advances in these neighbouring disciplines. This book provides unique interdisciplinary coverage of the topic. © Roger A. Dixon, Lars Bäckman, and Lars Göran-Nilsson 2004. All rights reserved.
Article
Sex differences in intelligence is among the most politically volatile topics in contemporary psychology. Although no single finding has unanimous support, conclusions from multiple studies suggest that females, on average, score higher on tasks that require rapid access to and use of phonological and semantic information in long-term memory, production and comprehension of complex prose, fine motor skills, and perceptual speed. Males, on average, score higher on tasks that require transformations in visual–spatial working memory, motor skills involved in aiming, spatiotemporal responding, and fluid reasoning, especially in abstract mathematical and scientific domains. Males, however, are also overrepresented in the low-ability end of several distributions, including mental retardation, attention disorders, dyslexia, stuttering, and delayed speech. A psychobiosocial model that is based on the inextricable links between the biological bases of intelligence and environmental events is proposed as an alternative to nature–nurture dichotomies. Societal implications and applications to teaching and learning are suggested.
Chapter
This chapter makes a case for considering intraindividual differences in performance across tasks (dispersion) and intraindividual variability across occasions (inconsistency), in addition to mean level of performance, in characterizing groups and developing theoretical accounts of group differences and developmental trajectories. It reviews evidence that measures of intraindividual differences in variability tend to be stable over time and domains. © Roger A. Dixon, Lars Bäckman, and Lars Göran-Nilsson 2004. All rights reserved.
Article
Our focus in this chapter will be on variability in cognition within persons across occasions, or intraindividual variability. There are different ways to conceptualize intraindividual variability, and therefore it is useful to narrow our focus a bit more. Two key features that have been used to differentiate changes within persons over time include the permanence of the change and the timescale over which the change occurs (Cattell, 1957; Fisk & Rice, 1955; Li, Huxhold, & Schmiedek, 2004a; Nesselroade, 1991; Wohlwill, 1973). There are three major parts to the chapter. First, we begin with an expanded examination of different conceptualizations of intraindividual variability and the potential relevance of the phenomenon for understanding aging and cognition. In the second part, we review several strands of existing research, including work describing between-person differences in intraindividual variability as a function of age and neurological status, and the association of intraindividual variability with cognitive functioning, functional competence, and mortality. In the final part, we examine a number of unresolved issues in this area of research including appropriate statistical indicators of intraindividual variability, whether intraindividual variability provides unique information above and beyond what is carried by measures of central tendency, and potential mechanisms that might cause changes in intraindividual variability over the life course. (PsycINFO Database Record (c) 2012 APA, all rights reserved)