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Objective Sleep deprivation is common among both college students and athletes and has been correlated with negative health outcomes, including worse cognition. As such, the current study sought to examine the relationship between sleep difficulties and self-reported symptoms and objective neuropsychological performance at baseline and post-concussion in collegiate athletes. Method Seven hundred seventy-two collegiate athletes completed a comprehensive neuropsychological test battery at baseline and/or post-concussion. Athletes were separated into two groups based on the amount of sleep the night prior to testing. The sleep duration cutoffs for these group were empirically determined by sample mean and standard deviation ( M = 7.07, SD = 1.29). Results Compared with athletes getting sufficient sleep, those getting insufficient sleep the night prior to baseline reported significantly more overall symptoms and more symptoms from each of the five symptom clusters of the Post-Concussion Symptom Scale. However, there were no significant differences on objective performance indices. Secondly, there were no significant differences on any of the outcome measures, except for sleep symptoms and headache, between athletes getting insufficient sleep at baseline and those getting sufficient sleep post-concussion. Conclusion Overall, the effect of insufficient sleep at baseline can make an athlete appear similar to a concussed athlete with sufficient sleep. As such, athletes completing a baseline assessment following insufficient sleep could be underperforming cognitively and reporting elevated symptoms that would skew post-concussion comparisons. Therefore, there may need to be consideration of prior night’s sleep when determining whether a baseline can be used as a valid comparison.
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Sleep Deprived or Concussed? The Acute Impact of Self-Reported
Insufficient Sleep in College Athletes
Kaitlin E. Riegler* Erin T. Guty , Garrett A. Thomas and Peter A. Arnett
Department of Psychology, The Pennsylvania State University, University Park, PA 16802, USA
(RECEIVED January 20, 2020; FINAL REVISION May 15, 2020; ACCEPTED May 19, 2020; FIRST PUBLISHED ONLINE July 9, 2020)
Abstract
Objective: Sleep deprivation is common among both college students and athletes and has been correlated with
negative health outcomes, including worse cognition. As such, the current study sought to examine the relationship
between sleep difficulties and self-reported symptoms and objective neuropsychological performance at baseline and
post-concussion in collegiate athletes. Method: Seven hundred seventy-two collegiate athletes completed a
comprehensive neuropsychological test battery at baseline and/or post-concussion. Athletes were separated into two
groups based on the amount of sleep the night prior to testing. The sleep duration cutoffs for these group were
empirically determined by sample mean and standard deviation (M=7.07, SD =1.29). Results: Compared with athletes
getting sufficient sleep, those getting insufficient sleep the night prior to baseline reported significantly more overall
symptoms and more symptoms from each of the five symptom clusters of the Post-Concussion Symptom Scale.
However, there were no significant differences on objective performance indices. Secondly, there were no significant
differences on any of the outcome measures, except for sleep symptoms and headache, between athletes getting
insufficient sleep at baseline and those getting sufficient sleep post-concussion. Conclusion: Overall, the effect of
insufficient sleep at baseline can make an athlete appear similar to a concussed athlete with sufficient sleep. As such,
athletes completing a baseline assessment following insufficient sleep could be underperforming cognitively and
reporting elevated symptoms that would skew post-concussion comparisons. Therefore, there may need to be
consideration of prior nights sleep when determining whether a baseline can be used as a valid comparison.
Keywords: Cognition function, Post-concussion symptoms, Concussion, Mild, Sports injuries, Self report, Sports
INTRODUCTION
There is growing attention to the identification and treatment
of sport-related concussions (SRCs). According to the
National Collegiate Athletic Association (NCAA) Injury
Surveillance System, there has been a 50% increase in the
concussion rate reported from the 19881989 season to the
20032004 season (Hootman, Dick, & Agel, 2007).
Currently, NCAA institutions are required to have a concus-
sion management program in place that includes a cognitive
assessment, among other things (Parsons, 2014a, 2014b).
Given these guidelines, baseline testing has become standard
best practice for many sports concussion programs (Iverson,
2007; McCrory et al., 2017; Valovich McLeod, Fraser, &
Johnson, 2017). Ensuring validity of baseline testing is
important as it is only useful as a comparative measure if it
is an accurate reflection of normal functioning (Grindel,
Lovell, & Collins, 2001). However, several factors have been
shown to have an influence on these baseline assessments,
including sleep, emotional difficulties, history of attention
deficit hyperactivity disorder (ADHD), learning disorders
(LDs), and stress (Iverson et al., 2015a, 2007; Makdissi
et al., 2013; Putukian, Riegler, Amalfe, Bruce, &
Echemendia, 2018). Obtaining a baseline assessment that
reflects a non-impaired comparison for return-to-play deci-
sions can help to ensure that athletes are not returned to play
too soon following an injury. Prematurely returning to play
can be associated with an exacerbation of symptoms as well
as a higher likelihood of re-injury (Guskiewicz et al., 2003;
McCrory, 2001).
Sleep in Student Athletes
Sleep can influence baseline performance but has received
less research attention even though difficulties with sleep
are common among both college students and athletes.
*Correspondence and reprint requests to: Kaitlin Riegler, 372 Moore
Building, The Pennsylvania State University, University Park, PA 16802,
USA. E-mail: kriegler25@gmail.com
Journal of the International Neuropsychological Society (2021), 27,3546
Copyright © INS. Published by Cambridge University Press, 2020.
doi:10.1017/S135561772000065X
35
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Demands on collegiate student athletes, including academics,
practices, and travel for games, may result in disrupted sleep
patterns. A 2015 report found that 29% of student athletes
surveyed reported sleep difficulties and over 50% reported
that these sleep problems impacted their academic perfor-
mance (American College Health Association, 2015). A
recent systematic review highlighted that student athletes
report concussion-like symptoms in their everyday lives
(Iverson et al., 2015b). This constellation of symptoms can
be nonspecific, resulting from increases in emotional difficul-
ties, stress, exercise, and poor sleep quality (Iverson et al.,
2015b). In this sample of non-concussed high school athletes,
sleep symptoms were the most frequently endorsed symp-
toms with significant numbers of both males and females
reporting fatigue, sleeping less than usual, and trouble falling
asleep (Iverson et al., 2015b).
Sleep and Cognitive Performance
Sleep has a negative impact on cognitive performance across
a variety of patient populations. Several review papers on the
neurocognitive consequences of sleep deprivation have con-
sistently shown decreases in attention, executive functioning,
psychomotor speed, vigilance, and working memory (Goel,
Rao, Durmer, 2013; Simpson, Gibbs, & Matheson, 2017).
One meta-analysis found that individuals experiencing sleep
difficulties are at a higher risk for both cognitive decline and
Alzheimers disease (Bubu et al., 2017). Further, research has
demonstrated a doseresponse relationship between sleep
restriction and cognitive performance in the domains of sus-
tained attention, working memory, and cognitive throughput1
performance (Van Dongen, Maislin, Mullington, &
Dinges, 2003).
Research specifically on the relationship between sleep and
cognitive functioning in adolescents/college-aged individuals
has been more limited. Adults and children with sleep-
disordered breathing and other sleep disorders have shown
executive dysfunction (OBrien, 2011). Additionally, in chil-
dren and adolescents, poor sleep quality has been associated
with poor attention, working memory, impulse control, and
diminished executive functioning skills (Beebe, 2011). In
healthy individuals, sleep loss and sleep deprivation have been
associated with increased severity of symptom reporting and
worse performance on a task of visual memory, reaction time,
and visual motor speed compared to those getting normal sleep
(Stocker, Khan, Henry, & Germain, 2017). Taheri and col-
leagues (2012) examined the short-term, acute impact of total
sleep deprivation on cognitive functioning in collegiate male
athletes. Results revealed that reaction time was significantly
slower following sleep deprivation than it was at baseline, prior
to sleep deprivation (Taheri & Arabameri, 2012). However,
understanding the impact of less extreme forms of sleep disrup-
tion, such as sleeping less than normal, on cognitive
functioning, might be more widely applicable to a college
athlete population.
Sleep and SRC
Sleep difficulties might have important implications both for
performance at baseline and following an SRC. Some
research has demonstrated that low sleep quality the night
prior to baseline testing is associated with subjective percep-
tion of overall difficulties, as measured by self-report symp-
tom measures, but not objective neurocognitive effects
(Mihalik et al., 2013; Silverberg, Berkner, Atkins, Zafonte,
& Iverson, 2016). Additionally, one sleep laboratory study
demonstrated that this was also the case when comparing con-
cussed to non-concussed athletes (Gosselin et al., 2008).
Concussed athletes reported more sleep symptoms and worse
sleep quality compared to control athletes; however, no
objective differences in sleep disturbance (measured by poly-
somnographic variables) or cognitive impairment were found
(Gosselin et al., 2008). One study has contradicted these find-
ings, showing some objective performance differences
between athletes with and without sleep difficulties at base-
line (McClure, Zuckerman, Kutscher, Gregory, & Solomon,
2013). Overall, there is clear evidence that subjective reports
of difficulties, as measured by overall symptom reporting, are
increased at baseline for athletes getting insufficient sleep,
while evidence of objective performance differences is
variable.
Sleep difficulties may also have an impact on post-
concussion performance. A study by Kostyun and colleagues
(2014) reported that short sleep duration (<7 hr) was associ-
ated with higher self-reported symptoms, but not with any
objective test performance on the ImPACT. In one study,
concussed athletes reporting symptoms from the sleep cluster
of the Post-Concussion Symptom Scale (PCSS) performed
significantly worse on a neurocognitive composite of
memory tests than concussed athletes not reporting sleeping
difficulties (Guty & Arnett, 2018). Another study by Sufrinko
and colleagues (2015) found that pre-injury sleep disturb-
ances were predictive of post-injury symptom total and worse
performance on visual memory and reaction time as com-
pared to the control group post-injury.
Gaps in the Literature
Presently, one of the most consistent findings on sleep and
baseline performance is that athletes getting less sleep prior
to baseline self-report significantly more symptomatology.
However, currently there is no clear understanding of what
types of symptoms are associated with less sleep. Previous
exploratory factor analysis has led to the identification of four
symptom clusters on the PCSS: affective, cognitive, physical/
somatic, and sleep (Kontos, Elbin, et al., 2012; Merritt,
Meyer, & Arnett, 2015). Identifying whether there are
differences in the PCSS total symptom score reported across
each of these symptom clusters could highlight what types of
1In this study, a serial addition/subtraction task was used to measure this index; this
task required 50 serial trials of mental addition/subtraction tasks. Performance is mea-
sured as the number of responses per minute.
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interventions would be most helpful for these athletes.
Further, previous research has generally used a theoretically
derived cutoff of 7 hr of sleep to categorize athletes with sleep
difficulties prior to a concussion. Yet, given the unique pat-
terns of sleep difficulties in both college athletes and students,
it might be beneficial to use an empirically derived value,
based on a normative sample, so difficulties that are experi-
enced over and above peers can be specifically examined.
Further, to our knowledge, no previous work has compared
athletes with insufficient sleep at baseline to concussed athletes
with sufficient sleep. SRC is known to have an impact on cog-
nitive functioning, such that concussed athletesperformance
is worse than non-concussed athletes on tests of attention and
concentration, verbal learning, verbal memory, divided atten-
tion, and global cognitive deficits (Belanger & Vanderploeg,
2005; Broglio & Puetz, 2008; Echemendia & Julian, 2001).
These areas overlap with the areas of functioning also thought
to be impaired due to poor sleep. Therefore, it is possible that
athletes getting insufficient sleep may be performing similarly
to athletes who have sustained a concussion, complicating the
picture when interpreting post-concussion findings.
Current Study
The current study sought to examine the relationship of sleep
difficulties with self-reported symptoms and objective neuro-
psychological test performance in two ways. First, we exam-
ined differences between athletes getting either sufficient or
insufficient sleep the night prior to a baseline on these out-
come measures. Next, we examined whether insufficient
sleep the night prior to a baseline assessment resulted in a
similar pattern of symptom reporting and neuropsychological
test performance as athletes sustaining a concussion. In the
current study, athletes were tested at baseline and/or post-
concussion on a comprehensive neuropsychological test
battery. Ratings of symptoms on the PCSS were assessed
for PCSS total symptom score, total symptom score in the
four symptom clusters (sleep, physical, cognitive, and affec-
tive), and headache. Performance on neurocognitive test
composites for memory and attention/processing speed were
evaluated.
Specific Aims and Hypotheses
Aim 1
To examine whether there are subjective differences in
self-reported symptoms, or objective performance on two
neurocognitive composites, between a group of insufficient
sleepers (5.78 hr) or sufficient sleepers (>7.07 hr) at
baseline.
Hypothesis 1
In line with previous research, we predict that, compared to
athletes with sufficient sleep at baseline, those with insuffi-
cient sleep will report higher PCSS total symptom scores
overall and for each symptom cluster. However, we predict
that the sufficient and insufficient groups will not signifi-
cantly differ on objective neuropsychological outcome
measures.
Aim 2
To examine differences between athletes getting insufficient
sleep the night prior to baseline assessment and athletes get-
ting sufficient sleep the night before a post-concussion
assessment on both symptom reports and performance on a
neurocognitive test battery.
Hypothesis 2
Athletes reporting insufficient sleep at baseline will not sig-
nificantly differ from athletes reporting sufficient sleep post-
concussion in terms of both self-reported symptoms and
objective test performance.
METHODS
Participants
This was a longitudinal prospective research study that is part
of the sports concussion program at our large Division 1 uni-
versity. Athletes who participated in the sports concussion
program between 2002 and 2018 were included in this study.
All participants were referred for concussion testing either
prior to their participation in collegiate athletics and/or after
sustaining an SRC. Referrals were made by an athletic trainer
or team physician. The evaluation at either time point
included a hybrid neuropsychological test battery, as well
as psychosocial questionnaires. For both aims, participants
were divided into two groups based on hours slept the night
prior to the neuropsychological evaluation (baseline or post-
concussion). These groups were empirically derived based on
the mean and standard deviation of hours slept the night prior
to the baseline in the sample of all participants who responded
to that question at baseline (n=772, M=7.07, SD =1.29).
The sufficient sleep group consisted of anyone getting more
hours of sleep than the mean in our sample (>7.07 hr) and the
insufficient sleep group consisted of anyone getting less than
or equal hours of sleep to one standard deviation below the
mean in our sample (5.78 hr).
For Aim 1, athletes were selected from the total sample of
athletes completing a baseline assessment (n=1,056).
Athletes were excluded if they were missing data for the
ImPACT question related to prior nights sleep (n=284).
Additionally, given how the two sleep groups were created,
any athlete sleeping between 5.77 and 7.06 hr the night prior
to a baseline was excluded from the analyses (n=163). This
resulted in a final sample of 608 athletes for Aim 1 of the
study (insufficient sleep =100, sufficient sleep =508). The
mean age of the participants was 18.52 years old
(SD =1.08) with a range from 17 to 22. There were no
Sufficent/insufficent sleep differences 37
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baseline differences between these two groups on Wechsler
Test of Adult Reading (WTAR) mean Full Scale IQ
(FSIQ) estimate, number of previous head injuries, history
of LD or ADHD.
For Aim 2, athletes were included if they completed a
baseline and/or post-concussion assessment. Given the nature
of this sport concussion program, there were instances of ath-
letes completing a baseline assessment, but never experienc-
ing a concussion and therefore not completing any post-
concussion assessments. Alternately, there were also cases
where athletes were not referred to our program for baseline
testing but were referred if they did sustain a concussion;
therefore, they would only have the post-concussion assess-
ment(s). Thus, the sample of participants included for Aim 2
of the study consisted of any individual who completed a
baseline assessment and met criteria for insufficient sleep
and any individual who completed a post-concussion assess-
ment (days since injury: M=21.60, SD =44.90, Mdn =7.00,
range =1243) and met criteria for sufficient sleep. Any ath-
lete who was tested at both time points, and simultaneously
met both criteria, was removed from the analysis (n=7). The
mean age of participants included for analyses for Aim 2 was
18.83 years (SD =1.20) with a range from 17 to 22. There
was a total of 169 athletes (insufficient at baseline =93, suf-
ficient at post-concussion =76) included in the analyses for
Aim 2 of the study. There were no differences between these
two groups on WTAR mean FSIQ estimate, number of pre-
vious head injuries, history of LD or ADHD. The post-
concussion sample was significantly older than the baseline
sample. Athletes are referred for baseline prior to their first
year of play, thus athletes referred for concussion following
injury are inherently referred later in their careers (and thus
when they are older).
Demographic data are provided in Tables 1(Aim 1) and 2
(Aim 2). Injury characteristics for the post-concussion sample
are provided in Table 3. Figure 1provides a flowchart of
inclusion and exclusion criteria for each aim.
Procedures
Baseline and post-concussion testing were completed as part
of the sports concussion program at our university. This pro-
gram is based on the Sports as Laboratory Assessment
Model (SLAM)(Bailey, Barth, & Bender, 2009). Athletes
are referred by their athletic trainer or team physician at base-
line and/or post-concussion. Concussion was defined accord-
ing to the following criteria: an injury to the head resulting
from a trauma or biomechanical force wherein brain function
is disrupted as evidenced by any alteration in mental status
and/or post-concussion signs or symptoms at the time of
injury, posttraumatic amnesia lasting less than 24 hr, and/
or loss of consciousness lasting 30 min or less (Mild
Traumatic Brain Injury Committee, 1993; Ruff, Iverson,
Barth, Bush, & Broshek, 2009). The neuropsychological
test battery was administered by undergraduate research
assistants or graduate students who were supervised by a
Ph.D.-level clinical neuropsychologist. This study was
approved by the Universitys Institutional Review Board
and informed consent was collected from all participants.
Measures
The ImPACT test includes the PCSS which is a self-report of
types of symptoms and severity of symptoms, demographic
and other background questions, and neurocognitive testing
modules used to derive four neurocognitive composite
scores. The PCSS consists of 22 items which are rated on
a 7-point Likert scale ranging from 0 to 6 (0 =no symptoms,
6=severe symptoms). These 22 items can be further grouped
together into four common clusters(cognitive, physical,
affective, and sleep) and headache based on previous factor
analysis (Merritt, Meyer, & Arnett, 2015). The outcome mea-
sures for subjective, self-reported difficulties in this study
were the PCSS total symptom severity, the four symptom
cluster scores, and a headache rating. Additionally, hours
of sleep the night prior to the baseline or post-concussion
assessment was determined using a question on the
ImPACT test that asks participants how many hours did
you sleep last night?. Reponses to this question were used
to create the two sleep groups described above. Measuring
sleep with a single-item self-report measure is a cost-effective
and practical way to measure sleep that has been used in sev-
eral previous research studies examining sleep in athletes
(Girschik, Fritschi, Heyworth, & Waters, 2012; Kostyun,
Milewski, & Hafeez, 2015; McClure et al., 2013; Mihalik
et al., 2013). Self-report of a single nights sleep has been
shown to be moderately correlated (r=.47) with sleep mea-
sured using actigraphy and, when comparing self-report of
sleep from the night before, a community sample overesti-
mated sleep by only 18 min compared to polysomnography
(the gold standard measurement) (Girschik et al., 2012; Silva
et al., 2007).
The paper-and-pencil tests were: the Brief-Visuospatial
Memory Test-Revised (BVMT-R) (Benedict, 1997), the
Comprehensive Trail-Making Test (CTMT) (Reynolds,
2002), a modified version of the Digit Span Test
(Weschler, 1997), the Hopkins Verbal Learning Test-
Revised (HVLT-R) (Brandt & Benedict, 2001), the Penn
State University Cancellation Test (Echemendia & Julian,
2001), the Stroop Color-Word Test (Trenerry, Crosson,
DeBoe, & Leber, 1989), and the Symbol-Digit Modalities
Test (SDMT) (Smith, 1991). The computerized tests were
the ImPACT (Lovell, Collins, Podell, Powell, & Maroon,
2000) and the Vigil/W Continuous Performance Test
(Cegalis & Cegalis, 1994).
Two neurocognitive composites were created using the
indices from each test from the battery described above.
These two composites were the objective performance out-
come measures used in our study. The indices from the
ImPACT test are the Verbal Memory Composite, Visual
Memory Composite, Visual Motor Speed Composite, and
Reaction Time Composite. The three indices that were used
38 K. E. Riegler et al.
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from the Vigil/W Continuous Performance Test were Vigil-
Total Omissions, Vigil-Total Commissions, and Vigil-
Average Delay. For the paper-and-pencil neuropsychological
tests, the following indices were used: the HVLT-R Total
Immediate Recall and HVLT-R Delayed Recall, the
BVMT-R Total Immediate Recall and BVMT-R Delayed
Recall, the SDMT Total Score, the Stroop Word Time
(Stroop 1) and the Stroop Color-Word Time (Stroop 2), the
Penn State Cancellation Test Total Correct, the CTMT
Simpletime and the CTMT Executivetime, and Digits
Span Forward and Backward total digits.
Statistical Analyses
All analyses were conducted with the Statistical Package for
the Social Sciences (SPSS), Version 24.0 (IBM Corp., 2017).
Scores on all neuropsychological test measures were stand-
ardized to z-scores using published baseline norms from a large
sample of college athletes (Age: M=18.48, SD =1.01) from a
Division I university (Merritt et al., 2016). Norms from Merritt
et al. (2016) were used for all test measures except the
ImPACT test, since norms for this test were not provided in
the previous paper. Therefore, we created separate norms, from
a reasonably equivalent sample to calculate z-scores for the
Table 1. Participant demographic variables for sufficient sleep at baseline, insufficient sleep at baseline, and all athletes at baseline (Aim 1)
Sufficient sleep Insufficient sleep Total
Variables M SD M SD p M SD
Age (years) 18.54 1.10 18.51 .96 .83 18.53 1.08
WTAR (FSIQ) 103.41 6.04 103.31 6.04 .87 103.40 6.03
Variables N%N%pN%
Sex .16
Male 372 73.2 80 80.0 452 74.3
Female 136 26.8 20 20.0 156 25.7
History of Previous Concussions (#) .06
0 287 56.8 46 46.0 333 55.0
1 149 29.5 38 38.0 187 30.9
2þ69 13.7 16 16.0 85 14.1
History of Learning Disability .81
Yes 16 3.3 3 3.0 19 3.3
No 465 96.3 96 97.0 561 96.4
History of ADHD .97
Yes 27 5.6 6 6.1 33 5.7
No 451 93.2 92 92.9 543 93.1
Maybe 6 1.3 1 1.0
Ethnicity 477 78.5
Caucasian 411 80.9 66 66.0 94 15.5
African American 69 13.6 25 25.0 5 0.8
Hispanic American 3 0.6 2 2.0 4 0.7
Asian American 4 0.8 0 0.0 15 2.5
Biracial/multiracial 10 2.0 5 5.0 13 2.2
Other 11 2.2 2 2.0
Sport
Baseball 1 0.2 0 0.0 1 0.2
Crew 0 0.0 1 1.0 1 0.2
Football 100 19.7 39 39.0 139 22.9
Mens basketball 34 6.7 10 10.0 44 7.2
Mens ice hockey 49 9.6 4 4.0 53 8.7
Mens lacrosse 112 22.0 12 12.0 124 20.4
Mens soccer 56 11.0 11 11.0 67 11.0
Other 2 0.4 1 1.0 3 0.5
Rugby 1 0.2 0 0.0 1 0.2
Softball 1 0.2 1 1.0 2 0.3
Volleyball 0 0.0 1 1.0 1 0.2
Womens basketball 24 4.7 5 5.0 29 4.8
Womens ice hockey 3 0.6 0 0.0 3 0.5
Womens lacrosse 38 7.5 5 5.0 43 7.1
Womens coccer 67 13.2 8 8.0 75 12.3
Wrestling 20 3.9 2 2.0 22 3.6
Sufficent/insufficent sleep differences 39
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ImPACT test indices. A more detailed account of deriving
these z-scores has been described in previous work (Riegler,
Guty, & Arnett, 2019). z-scores for all tests were created such
that higher scores indicated better performance.
Principal components analyses (PCA) were used to iden-
tify and compute composite scores for conceptually related
test indices (attention/processing speed and memory tests).
For the PCA of attention/processing speed tests, the follow-
ing indices were included: Vigil Average Delay, SDMT
Total, Stroop 1 and 2 Time, Penn State University (PSU)
Cancellation, CTMT Simple, CTMT Executive, Digits
Forward, and Digits Backward. Of the 13 tests entered into
the analysis, 9 of the variables loaded above .40 and were thus
retained for the final attention/processing speed composite.
The indices eliminated included: Vigil Commissions (.26),
Vigil Omissions (.35), ImPACT Visual Motor Speed
Composite (.33), and ImPACT Reaction Time Composite
(.08). A comparable PCA was conducted with the following
memory indices: ImPACT Verbal Memory Composite,
ImPACT Visual Memory Composite, BVMT-R Total
Immediate and Delayed Recall, and HVLT-R Total
Immediate and Delayed Recall. All of the variables loaded
above .40 and were retained for the final memory composite.
The loadings for the retained tests for each composite can be
found in Table 4.
RESULTS
Aim 1
Independent samples t-tests were conducted to examine
differences between the sufficient and insufficient sleep
groups at baseline on self-report symptoms and two neuro-
cognitive composites. Results revealed that, compared with
Table 2. Participant demographics for Aim 2
Insufficient
sleep at
baseline
Sufficient
sleep post-
concussion
Variables M SD M SD p
Age (years) 18.51 .93 19.25 1.37 <.001
WTAR (FSIQ) 103.16 6.08 104.41 6.72 .24
Days since concussion ––21.60 44.90
Variables N%N % p
Sex .10
Male 74 79.6 52 68.4
Female 19 20.4 24 31.6
History of Previous
Concussions (#)
.14
0 45 48.4 34 49.3
1 33 35.5 15 21.7
2þ15 16.2 20 29.0
History of Learning
Disability
.91
Yes 3 3.3 2 2.9
No 89 96.7 66 97.1
History of ADHD .69
Yes 6 6.5 7 10.1
No 85 92.4 61 88.4
Maybe 1 1.1 1 1.4
Ethnicity
Caucasian 62 66.7 60 80.0
African American 22 23.7 9 12.0
Hispanic American 2 2.2 0 0.0
Asian American 0 0.0 3 4.0
Biracial/multiracial 5 5.4 2 2.7
Other 2 2.2 1 1.3
Sport
Baseball 0 0.0 2 2.7
Cheerleading 0 0.0 1 1.3
Crew 1 1.1 0 0.0
Golf 0 0.0 2 2.7
Football 35 37.6 17 22.7
Mens basketball 9 9.7 5 2.7
Mens ice hockey 4 4.3 4 5.3
Mens lacrosse 12 12.9 7 9.3
Mens soccer 11 11.8 1 1.3
Other 1 1.1 0 0.0
Rugby 0 0.0 10 13.3
Softball 1 1.1 3 4.0
Swimming and diving 0 0.0 1 1.3
Track and field 0 0.0 1 1.3
Volleyball 1 1.1 1 1.3
Womens basketball 5 5.4 2 2.7
Womens ice hockey 0 0.0 3 4.0
Womens lacrosse 4 4.3 3 4.0
Womens soccer 8 8.6 5 6.7
Wrestling 1 1.1 5 6.7
Table 3. Post-concussion sample (sufficient sleepers) injury
characteristics
Injury characteristics N%
Loss of consciousness
Yes 13 17.1
No 51 67.1
Missing 12 15.8
Retrograde amnesia
Yes 12 18.2
No 54 81.8
Missing 10 13.2
Anterograde amnesia
Yes 26 34.2
No 38 58.5
Missing 11 14.5
Mechanism of injury
Contact with another player or self 37 48.7
Contact with playing surface 8 10.5
Contact with another player or self and
playing surface
5 6.6
Hit in head by object 8 10.4
Fight 2 2.6
Other 4 5.2
Unknown/missing 12 15.8
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those athletes getting sufficient sleep, those getting insufficient
sleep the night prior to baseline assessment reported signifi-
cantly more overall symptoms, t(109.26) =4.07, p<.001,
d=.57; sleep cluster symptoms, t(111.79) =5.13, p<.001,
d=.65; physical cluster symptoms, t(103.10) =2.98, p=.004,
d=.40; cognitive cluster symptoms, t(112.23) =3.16, p=.002,
d=.40; and headache, t(120.41) =2.87, p=.005, d=.34. No
significant effect was found for the affective symptoms cluster,
t(120.07) =1.78, p=.08, d=.20. There were no significant
differences between the sufficient and insufficient sleep groups
on either of the neurocognitive composites: memory composite,
t(104.26) =.68, p=.50, d=.10, and processing speed/attention
composite, t(601) =.12, p=.91, d=.10. These results are
showninFigure2and Table 5.
Aim 2
Independent samples t-tests were conducted to examine
differences between the insufficient sleep group at baseline
and the sufficient sleep group post-concussion on self-
reported symptoms and two neurocognitive composites.
There were no significant differences between the groups
on the following outcome measures: overall symptoms,
t(167) =.03, p=.98, d=.004; affective symptom cluster,
t(167) =.88, p=.38, d=.14; cognitive symptom cluster,
t(127.78) =1.71, p=.10, d=.26; physical symptom
cluster, t(167) =.71, p=.48, d=.11; memory composite,
t(106) =.48, p=.63, d=.10; or attention/processing speed
composite, t(106) =.06, p=.95, d=.01. The groups did
Fig. 1. Flowchart of participants included in analyses.
Sufficent/insufficent sleep differences 41
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significantly differ on the sleep symptom cluster,
t(167) =2.22, p=.03, d=.35, and headache, t(138.22) =
2.44, p=.02, d=.392. This difference was such that
the post-concussion group reported a higher severity of head-
ache and the insufficient sleep at baseline group reported
more sleep difficulties. These results are shown in Figure 33.
DISCUSSION
Contributions from the Current Study
Previous research has provided mixed results related to the
impact of poor sleep on baseline neuropsychological assess-
ment. Some researchers have found that, while athletes sub-
jectively are experiencing more overall symptoms as a result
of poor sleep, this has no impact on cognitive performance,
while others have found that poor sleep impacts both subjec-
tive report of difficulties and objective performance deficits.
The current study explored this issue using a more rigorous
method to operationalize insufficientcompared to suffi-
cientsleep. Rather than using a theoretically derived cutoff
of 7 hr, we grouped our sample into sufficient and insufficient
sleepers based on the mean and standard deviation of a large
normative sample of college athletes at baseline. Examining
differences between these two groups revealed that, com-
pared with sufficient sleepers at baseline, insufficient sleepers
reported significantly more overall symptoms and more
symptoms from each of the five symptom clusters of the
PCSS but did not demonstrate any significant differences
on objective test performance. Further, our study explored
whether athletes getting insufficient sleep the night prior to
a baseline were significantly different on symptom report
and cognitive testing than athletes with sufficient sleep before
their post-concussion assessment. The results showed that
these groups of athletes were not significantly different in
either their symptom reporting of cognitive, physical, affec-
tive, total symptom score, or cognitive test performance. The
only differences between groups were that insufficient sleep-
ers at baseline reported more sleep symptoms than sufficient
sleepers post-concussion and sufficient sleepers post-concus-
sion reported worse headache than insufficient sleepers at
baseline. The fact that the insufficient sleep group reported
more sleep symptoms is expected, given that the nature of
the grouping is based on sleep therefore, unsurprisingly insuf-
ficient sleepers report more subjective sleep difficulties.
Further, headache is a hallmark symptom of concussion,
and previous research has shown that it is one of the most
common symptoms following concussion (Merritt,
Rabinowitz, & Arnett, 2015). Thus, it is to be expected that
following concussion athletes are reporting a greater severity
of headache. In sum, the effect of insufficient sleep at baseline
made athletes appear similar to a concussed athlete with suf-
ficient sleep.
There are a few potential explanations for why athletes
who do not get enough sleep prior to testing might appear
concussed from self-report and objective testing. Athletes
who report insufficient sleep may also be characterized by
a more long-standing pattern of poor sleep, and both short-
term sleep deprivation and long-term chronic sleep issues
have been demonstrated to impact cognitive functioning in
a variety of literatures (Bucks, Olaithe, & Eastwood, 2013;
Lim & Dinges, 2012; Lo, Groeger, Cheng, Dijk, & Chee,
2016). Additionally, non-concussed athletes with insufficient
sleep reported similar levels of post-concussion symptoms as
concussed athletes. Post-concussion symptoms are hetero-
geneous and nonspecific to concussion, and an individual
can experience dizziness, mental fogginess, irritability, and
mood symptoms for a variety of reasons. Additionally, issues
with sleep are comorbid with mood disorders, chronic head-
ache, and self-reported cognitive problems (Baglioni et al.,
2016; Benca, William, Thisted, & Gillin, 1992; Dosi,
Figura, Ferri, & Bruni, 2015; Vaessen, Overeem, &
Sitskoorn, 2015).
These findings have important implications for both
assessment and treatment for athletes both at baseline and
post-concussion. The results from this study indicate that
an athlete who was administered a baseline assessment after
an insufficient night of sleep could underperform cognitively
and report elevated symptoms that would skew any compari-
son post-concussion. An athlete could be identified as
returned to baseline functioning, but such a determination
could be driven solely by the lower cognitive performance
and higher symptom scores at baseline (due to insufficient
sleep), rather than the return to the athletes true normative
Table 4. Retained tests from PCA for each composite score and their
loadings
Test index Loading
Attention/processing speed composite
Vigil average delay .47
SDMT total score .57
PSU cancellation .45
CTMT Simple.56
CTMT Executive.53
Digits forward .47
Digits backward .42
Stroop 1 time .61
Stroop 2 time .65
Memory composite
ImPACT verbal memory .64
ImPACT visual memory .59
BVMT-R delayed recall .69
BVMT-R total immediate recall .60
HVLT-R delayed recall .74
HVLT-R total immediate recall .58
2Degrees of freedom for some outcomes (headache and cognitive symptom cluster)
are lower because the Levenes test for Equality of Variances was significant and the
reported degrees of freedom is the lower df estimate.
3Follow-up analyses were conducted removing athletes who were evaluated >14
days post-concussion to test the hypothesis that athletes in the post-concussion group
who were tested further out from time of injury were significantly contributing to these
results. After removing these athletes, results remained the same. Therefore, the original
sample was retained for all major analyses.
42 K. E. Riegler et al.
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Table 5. Mean performance at baseline on individual test indices and composites
Sufficient Insufficient
Measure M SD M SD t-test pd
Attention/processing speed composite .02 .54 .03 .60 .12 .91 .10
Memory composite .01 .71 .15 1.94 .68 .50 .10
PCSS total symptom score 4.44 7.12 10.29 14.01 4.07 <.001 .57
Affective symptom cluster 1.16 2.59 1.83 3.60 1.78 .08 .21
Sleep symptom cluster 1.56 2.70 4.08 4.76 5.13 <.001 .65
Physical symptom cluster .37 1.29 1.57 3.99 2.98 .004 .40
Cognitive symptom cluster .82 2.01 1.96 3.50 3.16 .002 .40
Headache .35 .85 .70 1.17 2.87 .005 .34
M=mean, SD =standard deviation, * indicates p<.05. PCSS =Post-Concussion Symptom Scale. All cognitive indices are have been standardized to z-score;
PCSS indices are raw scores.
Fig. 2. Results from Aim 1 of the study for objective and subjective differences between sufficient and insufficient sleepers.
Note. * denotes p<.05 and ** denotes p<.001. Error bars represent standard deviations.
Fig. 3. Results from Aim 2 of the study for objective and subjective differences between insufficient sleepers at baseline compared to suffi-
cient sleepers post-concussion.
Note. * denotes p<.05. Error bars represent standard deviations.
Sufficent/insufficent sleep differences 43
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functioning. There may need to be consideration of prior
nights sleep when determining whether a baseline is a valid
comparison and whether it should be administered to athletes
who endorse insufficient sleep. Future work could explore
whether potential performance and symptom adjustments
based on sleep prior to testing should be considered when
assessing change from baseline to post-concussion.
The results of our study also show that poor sleep is related
to both symptom reporting and cognitive performance at
baseline and highlights the importance of assessing sleep at
baseline. There are several validated sleep questionnaires that
capture duration, quality, and patterns of sleep, and such mea-
sures are brief and easy to administer. Some common sleep
questionnaires such as the Pittsburgh Sleep Quality Index,
the Sleep Hygiene Index, and the Epworth Sleepiness
Scale assess different aspects of sleep patterns and daily
fatigue and have been validated in nonclinical populations
(Buysse et al., 1991; Driller, Mah, & Halson, 2018;
Mastin, Bryson, & Corwyn, 2006). Such instruments could
be incorporated as part of baseline assessments to flag ath-
letes who may need sleep-related interventions.
Previous research on post-concussion risk factors and out-
comes has often focused on non-modifiable athlete character-
istics such as diagnosis of ADHD or LD and history of prior
concussions. Sleep is a modifiable aspect of behavior that is
treatable both through pharmacological and behavioral inter-
vention. Behavioral treatment for sleep disturbances may be
more desirable for athletes given the side effects of pharma-
cological interventions and the impact that this could have
on performance. Additionally, evidence suggests that behav-
ioral interventions for sleep issues, specifically Cognitive
Behavioral Therapy for Insomnia (CBT-I) has equivalent
short-term effects as pharmacological interventions and supe-
rior long-term effects (Riemann & Perlis, 2009). There is also
evidence that pre-injury sleep debt and reduced neurocogni-
tive reserve as a result of chronic sleep difficulties may lead to
exacerbated deficits following concussion (Sufrinko 2015).
Targeted sleep interventions could then be viewed as part
of a holistic approach to athlete health and coordinated care.
Such treatment would not only be helpful as a preventive
measure against potential worse outcomes following concus-
sive injuries but also provide benefits to athletic performance,
cognitive functioning, emotional and physical health, and
overall well-being.
LIMITATIONS
One of this studys main limitations is that sleep is assessed
with self-report of a single nights sleep. Such an index only
addresses sleep quantity and does not capture other helpful
subjective measures such as sleep quality and timing, fatigue,
or diagnosed sleep disorders. However, self-report of a single
nights sleep is still a valid measure of sleep as it is compa-
rable to sleep measured with actigraphy and polysomnogra-
phy and has been used in previous research to characterize
sleep (Girschik et al., 2012; Kostyun et al., 2015; McClure
et al., 2013; Silva et al., 2007). Therefore, it is not clear from
the current results whether such issues or longer-term disrup-
tions in sleep may be related to elevated symptom reporting or
deficits in cognitive performance at baseline. The inclusion
of more thorough measures of sleep quality, patterns, and
fatigue would further elucidate the relationships between
sleep disturbances and baseline impairments. Additionally,
future work could further explore how such deficits at base-
line may be related to worse outcomes following concussion.
Future work should also explore how more objective mea-
sures of sleep quality, such as sleep testing, would provide
additional evidence for a direct relationship between sleep,
baseline performance, and outcomes following concussion.
CONCLUSIONS
The present study demonstrates that sleep is related to
elevated symptom reporting and deficits in cognitive perfor-
mance at baseline in such a way that non-injured athletes look
similar to concussed athletes. These findings highlight the
importance of assessing sleep at baseline and how interven-
tions focused on sleep should be integrated into care plans for
athletes. Future research should further examine the validity
of using baseline assessments as a comparison when the ath-
lete has had insufficient sleep the night prior and whether
adjustments should be made based on such information.
Additionally, sleep is a modifiable behavior and is a prime
candidate for early identification and treatment in athletes
pre-injury. Not only would such procedures be beneficial
to non-injured athletes, but such preventative measures could
be additionally helpful for athletes who have sustained a
concussion. Empirically validated treatments for sleep dis-
turbances, such as CBT-I, are effective and can be imple-
mented as part of an athletes overall coordinated care. Our
study demonstrates the importance of assessing for sleep
issues as part of an athletes baseline and post-concussion
care so that athletes who may be experiencing sleep difficul-
ties can be referred for more rigorous assessment of sleep and
specialized treatment. Further, our study demonstrates a
potential need to adjust how baseline assessments are utilized
when an athlete has been assessed with insufficient sleep.
ACKNOWLEDGMENTS
The authors do not have a financial investment in this study or
the outcome of the data to report.
CONFLICT OF INTEREST
The authors have nothing to disclose.
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... Concussion-like symptom reporting is relatively common in youth in their daily lives (23) and it is not known how common it is for youth to meet various definitions of "persistent symptoms" in the absence of recent concussion. To inform both research and clinical practice, definitions of persistent symptoms should be examined, stratified by gender, in uninjured youth with pre-existing conditions such as ADHD, learning disorders, and prior mental health problems, as well as youth who might be experiencing situational symptoms relating to psychological distress (25) or insufficient sleep (26,27). The purpose of this study is to compare 14 different operational definitions for persistent symptoms in a sample of approximately 50,000 uninjured children and adolescents. ...
... questionnaire, mild physical, cognitive, or emotional symptoms in their daily lives as part of their personality or dispositional temperament, and these symptoms are reasonably stable but can fluctuate. Youth can also experience (and endorse) these symptoms as a result of situational life stressors (25), insufficient sleep (26,27), or both. Clinicians treating children and adolescents who sustained a concussion and may be experiencing a prolonged recovery are encouraged to carefully consider the potential contribution of temperamental and situational factors that might be related to symptom reporting. ...
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Researchers operationalize persistent post-concussion symptoms in children and adolescents using varied definitions. Many pre-existing conditions, personal characteristics, and current health issues can affect symptom endorsement rates in the absence of, or in combination with, a recent concussion, and the use of varied definitions can lead to differences in conclusions about persistent symptoms and recovery across studies. This study examined how endorsement rates varied by 14 different operational definitions of persistent post-concussion symptoms for uninjured boys and girls with and without pre-existing or current health problems. This cross-sectional study included a large sample (age range: 11–18) of girls ( n = 21,923) and boys ( n = 26,556) without a recent concussion who completed the Post-Concussion Symptom Scale at preseason baseline. Endorsements rates varied substantially by definition, health history, and current health issues. The most lenient definition (i.e., a single mild symptom) was endorsed by most participants (54.5% of boys/65.3% of girls). A large portion of participants with pre-existing mental health problems (42.7% of boys/51.5% of girls), current moderate psychological distress (70.9% of boys/72.4% of girls), and insufficient sleep prior to testing (33.4% of boys/47.6% of girls) endorsed symptoms consistent with mild ICD-10 postconcussional syndrome; whereas participants with no current or prior health problems rarely met this definition (1.6% of boys/1.6% of girls). The results illustrate the tremendous variability in the case definitions of persistent symptoms and the importance of harmonizing definitions across future studies.
... Headache was also included as a separate factor because previous work has shown that headaches are one of the most common and persistent symptoms associated with sports-related concussion (Heyer et al., 2016;Kontos et al., 2013;Womble et al., 2019) and may predict prolonged recovery following concussion (Iverson et al., 2017). This inclusion of headache as a separate factor, in addition to clusters of Cognitive, Physical, Affective, and Sleep symptoms, is supported by previous research (Guty & Arnett, 2018;Merritt et al., 2015;Riegler et al., 2021). These symptom clusters and their associated PCSS items are described in Table 1. ...
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Objectives The current study explored how affective disturbances, particularly concomitant anxiety and depressive symptoms, impact baseline symptom self-reporting on the Post-Concussion Symptoms Scale (PCSS) in college athletes. Methods Athletes were separated into four groups (Healthy Control (HC) ( n = 581), Depression Only ( n = 136), Anxiety Only ( n = 54), Concomitant Depression/Anxiety ( n = 62)) based on their anxiety and depression scores. Groups were compared on Total PCSS Score as well as 5 PCSS Symptom Cluster scores (Cognitive, Physical, Affective, Sleep, and Headache). Results The three affective groups reported significantly greater symptomatology than HCs, with the Concomitant group showing the highest symptomatology scores across all clusters. The depressive symptoms only group also reported significantly elevated symptomatology, compared to HCs, on every symptom cluster except headache. The anxiety symptoms only group differed from HCs on only the cognitive symptoms cluster. Additionally, the Concomitant group reported significantly increased PCSS symptomatology, in terms of total scores and all 5 symptom clusters, compared to the depressive symptoms only and anxiety symptoms only groups. Conclusions Our findings suggest that athletes experiencing concomitant depressive/anxiety symptoms report significantly greater levels of symptomatology across all 5 PCSS symptom clusters compared to HCs. Further, results suggest that athletes experiencing concomitant affective disturbance tend to report greater symptomatology than those with only one affective disturbance. These findings are important because, despite the absence of concussion, the concomitant group demonstrated significantly elevated symptomatology at baseline. Thus, future comparisons with post-concussion data should account for this increased symptomatology, as test results may be skewed by affective disturbances at baseline.
... There has also been a significant amount of research examining the effect of sleep dysfunction and fatigue on cognitive functioning more broadly, and this has also been explored to a lesser degree in postconcussion samples. Most research has focused on the effects of sleep on assessment of cognitive functioning at baseline in nonconcussed athlete samples, and has demonstrated the negative effect of lack of sleep or sleep symptoms on cognitive performance (Riegler, Guty, Thomas, & Arnett, 2020;Stocker, Khan, Henry, & Germain, 2017). Some research has demonstrated a relationship between self-reported sleep symptoms and cognitive impairment (Kostyun, Milewski, & Hafeez, 2015). ...
Article
Objective: The present study explored the relationship between specific types of postconcussion symptoms and cognitive outcomes in student-athletes with chronic concussion symptoms. Method: Forty student-athletes with chronic concussion symptoms were given a battery of neuropsychological tests and rated themselves on a variety of postconcussion symptoms, which included the following factors derived from prior work: Physical, Sleep, Cognitive, Affective, and Headache. Cognitive outcomes included performance on composites for the memory and attention/executive functioning speed tests, respectively. The following covariates were also explored: Sex, depression symptoms, number of previous concussions, and time since injury. Results: Headache was the only individual symptom factor that significantly (p < .05) predicted worse attention/executive functioning performance. None of the symptom factors were significantly related to memory performance over and above the variable of time since injury, such that longer time since injury was related to worse memory performance. Conclusion: Comparable to work examining symptom predictors of cognitive outcomes in acutely concussed samples, headache predicted worse attention/executive functioning performance. Additionally, we found that the longer athletes had been symptomatic since injury, the "worse" their memory functioning. Understanding how headache and the length of time an individual is symptomatic are related to cognitive outcomes can help inform treatment and recommendations for athletes with prolonged symptom recovery.
Article
Objective: We investigated the degree to which the association between history of concussion with psychological distress and general symptom severity is independent of several factors commonly associated with elevated symptom severity. We also examined whether symptom severity endorsement was associated with concussion injury specifically or response to injury in general. Setting: Academic medical center. Participants: Collegiate athletes (N = 106; age: M = 21.37 ± 1.69 years; 33 female) were enrolled on the basis of strict medical/psychiatric exclusion criteria. Design: Cross-sectional single-visit study. Comprehensive assessment, including semistructured interviews to retrospectively diagnose the number of previous concussions, was completed. Single-predictor and stepwise regression models were fit to examine the predictive value of prior concussion and orthopedic injuries on symptom severity, both individually and controlling for confounding factors. Main outcome measures: Psychological distress was operationalized as Brief Symptom Inventory-18 Global Severity Index (BSI-GSI) ratings; concussion-related symptom severity was measured using the Sport Concussion Assessment Tool. Results: Controlling for baseline factors associated with the symptom outcomes (agreeableness, neuroticism, negative emotionality, and sleep quality), concussion history was significantly associated with psychological distress (B = 1.25 [0.55]; P = .025, ΔR2 = 0.034) and concussion-like symptom severity (B = 0.22 [0.08]; P = .005, ΔR2 = 0.064) and accounted for a statistically significant amount of unique variance in symptom outcomes. Orthopedic injury history was not individually predictive of psychological distress (B = -0.06 [0.53]; P = .905) or general symptom severity (B = 0.06 [0.08]; P = .427) and did not explain the relationship between concussion history and symptom outcomes. Conclusions: Concussion history is associated with subtle elevations in symptom severity in collegiate-aged athletes; this relationship is independent of medical, lifestyle (ie, sleep), and personality factors. Furthermore, this relationship is associated with brain injury (ie, concussion) and is not a general response to injury history.
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Objectives: To evaluate the relationship between preinjury risk factors (RFs) and subsequent occurrence of concussion and examine whether preinjury RFs or postinjury assessments predict clinical recovery in collegiate athletes. Methods: Risk factors (sex, sport, and self-report history of concussion, migraine, attention-deficit disorder, learning disability, depression, and anxiety) and Sport Concussion Assessment Tool (SCAT), depression/anxiety screenings, and neuropsychological testing were obtained before the season. For athletes who sustained concussion, RFs, postinjury SCAT, neuropsychological assessment, and clinical recovery were assessed. Results: We assessed 1152 athletes (69% male) at baseline and 145 (75% male) after subsequent concussion diagnosis. Only sport type (Wald = 40.29, P = 0.007) and concussion history (Wald = 9.91, P = 0.007) accounted for unique variance in subsequent concussion. Of athletes followed until full recovery, mean days until symptom-free (DUSF) was 9.84 ± 11.11 days (n = 138, median = 5 days, range = 1-86) and mean days until full return to play (DUFRTP) was 20.21 ± 19.17 (n = 98, median = 20.21, range = 4-150). None of the RFs or baseline testing measures were associated with DUSF or DUFRTP (P's > 0.05). After injury, athletes who reported more total symptoms (rs = 0.31, P < 0.001) and higher symptom severity (rs = 0.33, P < 0.001) exhibited longer DUSF. Days until symptom-free correlated with DUFRTP (rs = 0.75, P < 0.001). Among athletes assessed within 2 days after injury, DUSF was associated with Immediate Postconcussion Assessment and Cognitive Test visual motor (rs = -0.31, P = 0.004), reaction time (rs = 0.40, P < 0.001), and symptom score (rs = 0.54, P < 0.001). Conclusions: Only sport type and concussion history predicted subsequent occurrence of concussion, and none of the RFs or baseline measures predicted clinical recovery. Immediate postinjury assessments, including symptom number and severity, and select clinical measures predicted longer clinical recovery.
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Introduction Existing sleep questionnaires to assess sleep behaviors may not be sensitive in determining the unique sleep challenges faced by elite athletes. The purpose of the current study was to develop and validate the Athlete Sleep Behavior Questionnaire (ASBQ) to be used as a practical tool for support staff working with elite athletes. Methods 564 participants (242 athletes, 322 non-athletes) completed the 18-item ASBQ and three previously validated questionnaires; the Sleep Hygiene Index (SHI), the Epworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality Index (PSQI). A cohort of the studied population performed the ASBQ twice in one week to assess test-retest reliability, and also performed sleep monitoring via wrist-actigraphy. Results Comparison of the ASBQ with existing sleep questionnaires resulted in moderate to large correlations (r=0.32 - 0.69). There was a significant difference between athletes and non-athletes for the ASBQ global score (44±6 vs. 41±6, respectively, p<0.01) and for the PSQI, but not for the SHI or the ESS. The reliability of the ASBQ was acceptable (ICC=0.87) when re-tested within 7 days. There was a moderate relationship between ASBQ and total sleep time (r=-0.42). Conclusion The ASBQ is a valid and reliable tool that can differentiate the sleep practices between athletes and non-athletes, and offers a practical instrument for practitioners and/or researchers wanting to evaluate the sleep behaviors of elite athletes. The ASBQ may provide information on areas where improvements to individual athletes’ sleep habits could be made.
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Objective A systematic review of factors that might be associated with, or influence, clinical recovery from sport-related concussion. Clinical recovery was defined functionally as a return to normal activities, including school and sports, following injury. Design Systematic review. Data sources PubMed, PsycINFO, MEDLINE, CINAHL, Cochrane Library, EMBASE, SPORTDiscus, Scopus and Web of Science. Eligibility criteria for selecting studies Studies published by June of 2016 that addressed clinical recovery from concussion. Results A total of 7617 articles were identified using the search strategy, and 101 articles were included. There are major methodological differences across the studies. Many different clinical outcomes were measured, such as symptoms, cognition, balance, return to school and return to sports, although symptom outcomes were the most frequently measured. The most consistent predictor of slower recovery from concussion is the severity of a person’s acute and subacute symptoms. The development of subacute problems with headaches or depression is likely a risk factor for persistent symptoms lasting greater than a month. Those with a preinjury history of mental health problems appear to be at greater risk for having persistent symptoms. Those with attention deficit hyperactivity disorder (ADHD) or learning disabilities do not appear to be at substantially greater risk. There is some evidence that the teenage years, particularly high school, might be the most vulnerable time period for having persistent symptoms—with greater risk for girls than boys. Conclusion The literature on clinical recovery from sport-related concussion has grown dramatically, is mostly mixed, but some factors have emerged as being related to outcome.
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The 2017 Concussion in Sport Group (CISG) consensus statement is designed to build on the principles outlined in the previous statements1–4 and to develop further conceptual understanding of sport-related concussion (SRC) using an expert consensus-based approach. This document is developed for physicians and healthcare providers who are involved in athlete care, whether at a recreational, elite or professional level. While agreement exists on the principal messages conveyed by this document, the authors acknowledge that the science of SRC is evolving and therefore individual management and return-to-play decisions remain in the realm of clinical judgement. This consensus document reflects the current state of knowledge and will need to be modified as new knowledge develops. It provides an overview of issues that may be of importance to healthcare providers involved in the management of SRC. This paper should be read in conjunction with the systematic reviews and methodology paper that accompany it. First and foremost, this document is intended to guide clinical practice; however, the authors feel that it can also help form the agenda for future research relevant to SRC by identifying knowledge gaps. A series of specific clinical questions were developed as part of the consensus process for the Berlin 2016 meeting. Each consensus question was the subject of a specific formal systematic review, which is published concurrently with this summary statement. Readers are directed to these background papers in conjunction with this summary statement as they provide the context for the issues and include the scope of published research, search strategy and citations reviewed for each question. This 2017 consensus statement also summarises each topic and recommendations in the context of all five CISG meetings (that is, 2001, 2004, 2008, 2012 as well as 2016). Approximately 60 000 published articles were screened by the expert panels for the Berlin …
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Study objectives: Mounting evidence implicates disturbed sleep or lack of sleep as one of the risk factors for Alzheimer's disease (AD), but the extent of the risk is uncertain. We conducted a broad systematic review and meta-analysis to quantify the effect of sleep problems/disorders on cognitive impairment and AD. Methods: Original published literature assessing any association of sleep problems or disorders with cognitive impairment or AD was identified by searching PubMed, Embase, Web of Science, and the Cochrane library. Effect estimates of individual studies were pooled and relative risks (RR) and 95% confidence intervals (CI) were calculated using random effects models. We also estimated the population attributable risk. Results: Twenty-seven observational studies (n = 69216 participants) that provided 52 RR estimates were included in the meta-analysis. Individuals with sleep problems had a 1.55 (95% CI: 1.25-1.93), 1.65 (95% CI: 1.45-1.86), and 3.78 (95% CI: 2.27-6.30) times higher risk of AD, cognitive impairment, and preclinical AD than individuals without sleep problems, respectively. The overall meta-analysis revealed that individuals with sleep problems had a 1.68 (95% CI: 1.51-1.87) times higher risk for the combined outcome of cognitive impairment and/or AD. Approximately 15% of AD in the population may be attributed to sleep problems. Conclusion: This meta-analysis confirmed the association between sleep and cognitive impairment or AD and, for the first time, consolidated the evidence to provide an "average" magnitude of effect. As sleep problems are of a growing concern in the population, these findings are of interest for potential prevention of AD.
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STUDY OBJECTIVES: Mounting evidence implicates disturbed sleep or lack of sleep as one of the risk factors for Alzheimer's disease (AD) but the extent of the risk is uncertain. We conducted a broad systematic review and meta-analysis to quantify the effect of sleep problems/disorders on cognitive impairment and AD. METHODS: Original published literature assessing any association of sleep problems or disorders with cognitive impairment or AD was identified by searching PubMed, Embase, Web of Science, and the Cochrane library. Effect estimates of individual studies were pooled and relative risks (RR) and 95% confidence intervals (CI) were calculated using random effects models. We also estimated the population attributable risk (PAR). RESULTS: Twenty-seven observational studies (n = 69,216 participants) that provided 52 RR estimates were included in the meta-analysis. Individuals with sleep problems had a 1.55 (95% CI: 1.25-1.93), 1.65 (95% CI: 1.45-1.86) and 3.78 (95% CI: 2.27-6.30) times higher risk for AD, cognitive impairment and preclinical AD than individuals without sleep problems respectively. The overall meta-analysis revealed that individuals with sleep problems had a 1.68 (95% CI: 1.51-1.87) times higher risk for the combined outcome of cognitive impairment and/or AD. Approximately 15% of AD in the population may be attributed to sleep problems. CONCLUSION: This meta-analysis confirmed the association between sleep and cognitive impairment or AD and, for the first time, consolidated the evidence to provide an "average" magnitude of effect. As sleep problems are of a growing concern in the population, these findings are of interest for potential prevention of AD.
Article
Objective: To examine neuropsychological test differences following concussion between collegiate athletes screening positive and negative for depression. Method: Participants included 113 (91 male) college athletes, who were assessed at baseline and following diagnosis of sport-related concussion (SRC). The Beck Depression Inventory-Fast Screen was used as a screener for depression. Athletes were categorized as either depressed (≥4) or nondepressed (<4) following injury and compared on composites for memory and attention-processing speed. Groups were also compared on reliable change index scores from baseline, as well as on proportion of impaired scores. Results: Depressed athletes performed significantly worse than did nondepressed athletes on the Memory Composite (p = .04, d = .51) but not on the Attention-Processing Speed Composite score (p = .15, d = .46). Chi-square tests indicated that, compared with nondepressed athletes, a significantly higher number of depressed athletes showed reliable decreases on the following test indices: Verbal Memory Composite of the Immediate Post-Concussion Assessment and Cognitive Testing (p = .03, φ = .21), Brief Visuospatial Memory Test-Revised Total (p = .02, φ = .22), and Hopkins Verbal Learning Test-Revised Total (p = .05, φ = .19). Chi-square test indicated that, compared with nondepressed athletes, a significantly higher proportion of depressed athletes met criteria for impairment (p = .02, φ = .23). Conclusion: Whether examining the data at the intraindividual or group level, there are memory deficits associated with the combination of an SRC and depression. The results highlight the importance of screening for depression to provide a more complete picture of an individual's functioning postconcussion. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Article
Objective: This study examined the effects of total and partial sleep deprivation on subjective symptoms and objective neurocognitive performance, as measured by the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) in a sample of healthy adults. Method: One-hundred and two, right-handed, healthy participants (between ages 18 and 30 years old) completed three consecutive nights in the sleep laboratory with concurrent continuous polysomnography monitoring. Night 1 served as a baseline night. Prior to Night 2, they were randomly assigned to one of three sleep conditions: undisrupted normal sleep (N = 34), sleep restriction (50% of habitual sleep, N = 37), or total sleep deprivation (N = 31). Participants slept undisturbed on Night 3. ImPACT was administered on three separate occasions. Results: Sleep loss was associated with increased severity of subjectively reported affective, cognitive, physical, and sleep symptoms. Although objective neurocognitive task scores derived from the ImPACT battery did not corroborate subjective complaints, sleep loss was associated with significant differences on tasks of visual memory, reaction time, and visual motor speed over time. Conclusions: While self-report measures suggested marked impairments following sleep loss, deficits in neurocognitive performance were observed only on three domains measured with ImPACT. ImPACT may capture subtle changes in neurocognitive performance following sleep loss; however, independent and larger validation studies are needed to determine its sensitivity to acute sleep loss and recovery sleep. Neurocognitive screening batteries may be useful for detecting the effects of more severe or chronic sleep loss under high-stress conditions that mimic high-risk occupations.
Article
Objective: The use of normative data is a hallmark of the neuropsychological assessment process. Within the context of sports-related concussion, utilizing normative data is especially essential when individualized baseline data are unavailable for comparison. The primary purpose of this study was to establish normative data for a comprehensive neuropsychological test battery used in the assessment of sports-related concussion. A secondary aim was to provide normative data for pertinent demographic variables relevant to the assessment of college athletes, including sex, previous head injuries (PHI), and history of attention deficit hyperactivity disorder (ADHD)/learning disability (LD). Method: Participants included male and female college athletes (N = 794) who were involved in a concussion management program at an NCAA Division I university between 2002 and 2015. Athletes were administered a comprehensive neuropsychological test battery at baseline designed to assess the following cognitive domains: learning and memory, attention and concentration, processing speed, and executive functioning. The test battery primarily comprises paper-and-pencil measures. Results: Normative data are presented for the overall athlete sample. Additional sub-norms are then provided for specified demographic populations (i.e., sex, PHIs, and history of ADHD/LD). Findings indicate that there are mild cognitive differences between men and women, as well as between those athletes with and without a history of ADHD/LD. Given these findings, additional norms are provided for men and women with and without a history of ADHD/LD. Conclusions: In the absence of baseline testing, the normative data presented here can be used clinically to assess athletes' cognitive functioning post-concussion.