| | Sleep following sport-related concussionsReceived 11 September 2007; received in revised form 22 November 2007; accepted 26 November 2007. Abstract ObjectivesSleep and vigilance disorders are among the most commonly reported symptoms following a concussion. The aim of the study was thus to investigate the effects of sport-related concussions on subjective and objective sleep quality. MethodsTen concussed athletes and 11 non-concussed athletes were included. Concussed athletes had a history of 4.6 ±2.1 concussions with at least one concussion during the last year. They were recorded for two consecutive nights in the laboratory and during a 10-min period of wakefulness. They completed questionnaires related to sleep quality and symptoms as well as neuropsychological tests and the CogSport computer battery. ResultsConcussed athletes reported more symptoms and worse sleep quality than control athletes, but no between-group differences were found on polysomnographic variables or on REM and NREM sleep quantitative EEG variables. However, concussed athletes showed significantly more delta activity and less alpha activity during wakefulness than did control athletes. ConclusionIn spite of the subjective complaints in sleep quality of concussed athletes, no change was observed in objective sleep characteristics. However, concussions were associated with an increase in delta and a reduction in alpha power in the waking EEG. Sport-related concussions are thus associated with wakefulness problems rather than sleep disturbances. 1. Introduction  A growing literature reports short- and long-term sleep and vigilance problems in patients who sustained traumatic brain injury (TBI) [1], [2], [3], [4], [5], [6], [7], [8]. In fact, it has been shown that 30 to 80% of patients with TBI complain of insomnia, longer sleep onset latency, difficulty to maintain sleep, fatigue, and daytime sleepiness. Moreover, these symptoms were found to be among the most common and most severe complaints after a TBI [9], [10]. Possible focal or diffuse lesions to the brainstem structures may explain sleep disturbances and fatigue after a TBI; however, the exact mechanisms involved in the etiology of these problems are still unknown. Surprisingly, more sleep problems are reported in patients with mild TBI than in patients with moderate or severe TBI [1], [4], [8]. In spite of the high incidence of sleep and vigilance problems following TBI, only a few studies have used objective measures to describe sleep and wake disturbances in these patients and even fewer have done it with patients with mild TBI, even if mild TBI represents as many as 70 to 90% of all TBIs [11]. Kaufmann and coworkers [12] studied the sleep characteristics of teenagers who had complaints about their sleep quality three years after a mild TBI. Their major finding was that teenagers with mild TBI showed poorer sleep efficiency than control subjects as measured with polysomnography and actigraphy. In another study carried out on eight teenagers who sustained a mild TBI, Parsons and coworkers [13] observed no anomaly in polysomnographic parameters at three times of recordings (72h, 6 weeks and 12 weeks post-trauma). However, significant power reduction of low EEG frequency activities (0.5–9.75Hz) during non-rapid eye movement (NREM) sleep was observed as the delay since the TBI increased. In a more recent study done by Schreiber and colleagues [14], mild TBI patients with chronic sleep complaints were evaluated in the sleep laboratory, and an increase in stage 2 NREM sleep and a decrease in REM sleep were found in these patients as well as significant daytime sleepiness in comparison with healthy control subjects. Athletes practicing contact sports, such as hockey and football, are at high risk to sustain multiple TBIs during their career. The incidence of concussions occurring in sports and their impact on brain functions are currently a major concern. Most sport-related concussions correspond to a mild TBI with a Glasgow Coma Scale score varying from 13 to 15 [15], normal structural neuroimaging results and infrequent loss of consciousness or post-traumatic amnesia [16], [17]. While the severity of injury is generally mild, it has been shown that a history of concussion increases six times the risk of sustaining another concussion [18], which can explain that 50% of football players have a history of multiple concussions [19], [20]. The short- and long-term impacts of multiple concussions in athletes are still a matter of debate [21], [22], [23]. As for patients who sustained a mild TBI in contexts other than sports, 35 to 70% of concussed athletes report sleep problems, fatigue, and vigilance disturbances after a concussion [17], [24], [25]. No study has yet investigated the impact of sport-related concussions on sleep using standardized sleep questionnaires and polysomnography. The study of sleep and vigilance in concussed athletes is of high importance, since their performance may be altered by sleep disturbances and/or vigilance decrement. The aim of the study was thus to subjectively and objectively measure sleep quality in concussed athletes. We used standardized sleep questionnaires, polysomnographic recording and we verified the integrity of the EEG activity during wakefulness, REM, and NREM sleep by quantitative EEG analysis (QEEG) in order to understand the nature of sleep complaints and vigilance problems in this population. 2. Methods 2.1. Subjects Ten athletes who sustained concussions were included in this study. They all experienced a concussion during the last year (see Table 1 for detailed clinical characteristics) and they all had a self-reported history of at least two sport-related concussions, which were validated during the clinical interview. Diagnosis with regard to the last concussion was made by the team physicians according to the recently proposed criteria [26], namely the presence of one or more of the following manifestations: confusion or disorientation, loss of consciousness for 30min or less, post-traumatic amnesia for less than 24h, and/or other transient neurological abnormalities such as focal signs, seizure, and intracranial lesion. All athletes had a Glasgow Coma Scale score between 13 and 15. The athletes were recruited independently of the nature of the reported symptoms. They practiced their sports in professional (n=3) or university and semi-professional (n=7) leagues, and were playing hockey (n=2), football (n=3), rugby (n=1), soccer (n=2), or were on a skate team (n=2). They were compared to a group of 11 athletes without a history of concussion, matched for age, gender, education level, and age at which they started playing organized sports (see Table 2 for demographic characteristics). They presented no history or sleep laboratory evidence of sleep disorders. Control athletes came from tennis (n=4), swimming (n=5) or volleyball (n=2) university teams. Athletes from contact sports were not included in the control group, since it has been shown that many of these athletes are not aware that they have already suffered a concussion [19]. Exclusion criteria were the presence of any neurological or psychiatric diseases (including depression), extremely early or late habitual bedtimes (before 9:00 PM or after 01:00 AM), and the use of drugs known to affect sleep or daytime sleepiness. Concussed and non-concussed athletes who had worked nightshifts or had traveled to another time zone in the last two months before the sleep recording were excluded. Each participant was informed of the research protocol and gave written informed consent before the beginning of the study. The protocol was approved by the Sacre-Coeur Hospital ethics committee. | | |  | Athletes with concussions | Sport | Number of concussions | Delay since last concussion (months) | LOC after the last concussion | PTA after the last concussion | |
|---|
| 1 | Soccer | 4 | 1 | No | Yes | | | 2 | Football | 3 | 3 | No | No | | | 3 | Hockey | 6 | 2 | Yes | Yes | | | 4 | Skate | 3 | 10 | No | No | | | 5 | Skate | 9 | 8 | No | No | | | 6 | Soccer | 5 | 2 | Yes | Yes | | | 7 | Football | 7 | 1 | No | Yes | | | 8 | Hockey | 2 | 11 | No | No | | | 9 | Rugby | 4 | 2 | No | No | | | 10 | Football | 3 | 4 | No | No | | | | |
| | | | Variables | Athletes with concussions | Control athletes | t value | p value | |
|---|
| Subject characteristics | | | Female; male (no.) | 3; 7 | 4; 7 | | | | | Age (yrs) | 24.3±6.1 | 22.6±2.4 | 0.84 | NS | | | Education (yrs) | 14.7±1.9 | 15.1±1.1 | −0.58 | NS | | | Age when started sport (yrs) | 8.9±5.2 | 9.4±4.2 | −0.23 | NS | | | | | | Symptoms | | | Post Concussion Symptom Scale | 22.2±13.8 | 6.6±4.8 | 3.54 | 0.002 | | | Pittsburgh Sleep Quality Index | 7.7±3.8 | 3.5±2.3 | 3.12 | 0.006 | | | Epworth Sleepiness Scale | 9.9±6.1 | 9.6±6.1 | 0.13 | NS | | | Beck Depression Inventory | 9.0±5.7 | 2.6±2.5 | 3.37 | 0.003 | | | | |
2.2. Procedure All subjects underwent two consecutive nights of polysomnographic recording in the laboratory. The bedtime was at 11:00 PM and sleep offset was at 7:00 AM. The first night served to rule out sleep disorders and was an adaptation night. Polysomnography was performed using four EEG derivations (C3, C4, O1, O2) the first night and using 19 EEG derivations (FP1, FP2, Fz, F3, F4, F7, F8, Cz, C3, C4, Pz, P3, P4, O1, O2, T7, T8 P7, P8) the second night, referred to linked earlobes. A right and left electrooculogram and chin electromyogram were also recorded. A surface electromyogram of both, right and left, anterior tibialis muscles were measured to quantify periodic leg movements during sleep. The electrocardiogram was recorded using a standard D1 lead. During the first night, a thoracoabdominal plethysmograph and oral/nasal canula were used to monitor respiration, and a transcutaneous finger pulse oximeter was used to measure oxygen saturation. A 10-min waking EEG with eyes closed was recorded 30min following the sleep offset. To prevent drowsiness, athletes were asked to open their eyes every minute or when slow-rolling eye movements were noted. Following the second night of sleep, the CogSport computer battery (CogState Ltd., Melbourne, Australia) was performed [27]. This battery comprised tasks evaluating simple, choice or complex reaction times, divided attention, working memory, and associate learning. All athletes also underwent a short neuropsychological evaluation consisting in an adaptation of the National Football League battery [28]. Verbal tests were excluded from that battery since three of the concussed athletes were evaluated in their second language. The battery comprised the Color Trail Test, the Ruff Figural Fluency Test conditions 1, 3, and 5, the Symbol Digit Modality Test, and the Pennsylvania State University Cancellation task. In order to assess subjective symptoms, all athletes also filled out the Post Concussion Symptom Scale [28]. This questionnaire is a list of common symptoms that athletes have to rate for severity on a scale varying from zero to six. All athletes also completed the Pittsburgh Sleep Quality Index [29], the Epworth Sleepiness Scale [30], the Beck Depression Inventory [31], and a home-made questionnaire on sleep quality following polysomnographic recording. 2.3. Data analyses Sleep stages were scored according to the standard method [32] using epochs of 20s. Sleep efficiency was defined as time spent asleep over the entire duration of the sleep period (from sleep onset to the last awakening). Power spectral analysis was carried out for the EEG recorded during wakefulness, REM sleep, and NREM sleep. For waking EEG, 120s of artifact-free samples were selected for fast Fourier transform (FFT). During REM sleep, only samples in tonic REM sleep and between two bursts of rapid eye movements were selected. Since the spectral composition of the REM sleep varies across the night, the samples were selected equally throughout the night. QEEG was also done for NREM sleep periods (stages 2, 3, and 4) of the entire night and for each sleep cycle separately. NREM–REM sleep cycles were determined according to the criteria of Aeschbach and Borbely [33]. For NREM sleep, artifacts were detected both automatically and through visual inspection. FFTs were carried out on waking, REM, and NREM sleep EEG with a cosine filter on artifact-free mini-epochs of 4s with a spectral resolution of 0.25Hz. The data were analysed using a software package (Harmonie Stellate Systems ©, Montreal, Canada). The analysed frequency bands were: delta (0.5–4Hz), theta (4–8Hz), alpha (8–13Hz), beta 1 (13–22Hz), and beta 2 (22–32Hz). QEEG was performed on five different regions, for which EEG power was averaged on a group of electrodes: frontal (Fz, F3, and F4), central (Cz, C3, and C4), parietal (Pz, P3, and P4), occipital (O1 and O2) and temporal (T7, T8, P7, and P8). The absolute and the relative power for each frequency band and the ratio of slow to fast frequencies ((Delta+Theta)/(Alpha+Beta 1+Beta 2)) were calculated for each region and for each subject. 2.4. Statistical analyses Between-group differences on demographic variables, subjective symptoms, computerized tests, neuropsychological results, and sleep variables were assessed with Student t-tests or with two-tailed Fisher exact tests for categorical variables. Group differences in QEEG for wakefulness, REM, and NREM sleep were analyzed by two-way analyses of variance (ANOVAs) with one independent factor (Group: concussed athletes and control athletes) and one repeated factor (Region: frontal, central, parietal, occipital and temporal). Tukey HSD tests were used for post-hoc comparisons in the case of significant main effects. A Greenhouse–Geisser correction for sphericity was applied to all repeated measures. Pearson correlation coefficients were used to measure the relationships between EEG activity and other variables (demographic data, concussion characteristics, subjective symptoms, sleep characteristics) and they were considered significant at p<0.05. Nighttime course of delta activity (0.5–4Hz) was determined using a non-linear regression analysis calculated on all-night NREM sleep. The independent variable was the time of the cycle midpoint and was determined for each cycle of each athlete. The dependent variable was the mean delta activity value within each cycle and was expressed as the percentage of all-night NREM sleep delta activity. An exponential decay function was fitted to the data: Deltat=Delta∞+Delta0×e−rt, with t=time of the cycle midpoint, Deltat=Delta activity averaged per cycle, Delta∞=horizontal asymptote for t=∞, Delta0=intercept of the y axis minus asymptote, and r=slope of the decay [34], [35]. Group estimates of Delta∞, Delta0 and r were compared with F-tests [36]. 3. Results 3.1. Subjective symptoms Results obtained with the questionnaires are presented in Table 2. A significant group difference was observed for the Post Concussion Symptom Scale score; athletes with concussions reported more symptoms than control athletes. When each item was compared separately, significant group differences were observed for physical (dizziness, headaches, and feeling pressure in head, p<0.01), cognitive (difficulty concentrating, p<0.05) and neurobehavioral symptoms (difficulty falling asleep, irritability and sadness, p<0.05). Athletes with concussions also reported worse sleep quality on the Pittsburgh Sleep Quality Index in comparison with control subjects. Significant group differences were found for three items of the questionnaire: subjective sleep quality (p=0.01), sleep disturbances (p<0.05), and daytime dysfunctions (p<0.05). No group differences were observed for the reported bedtime and total sleep time, but a trend was found for a longer self-reported sleep onset latency in athletes with concussions in comparison with controls (49.7±54.6min and 17.5±10.8min, respectively, t(19)=1.92, p=0.06). No difference was found for the Epworth Sleepiness Scale, but a significant difference was obtained in the Beck Depression Index score; athletes with concussions reported more symptoms of depression than control athletes, without being clinically depressed. This significant difference was also found when questions related to sleep were excluded from the total score. 3.4. EEG spectral analysis Absolute spectral power showed high inter-subject variability in the concussed group and therefore, we decided to analyse only the relative spectral power for the EEG recorded during wakefulness, REM, and NREM sleep. Fig. 1 shows the mean EEG spectral power for all frequency bands in the concussed and control groups during REM sleep. A Group by Region ANOVA failed to show any between-group difference for any frequency band or any region during REM sleep. Similarly, no group differences were found for total NREM sleep (Fig. 2) or NREM sleep cycles separately. Moreover, the nighttime course of delta activity was similar in both groups; in fact, athletes with concussions showed a normal reduction in delta activity throughout the night given the between-group similarities in the Delta0, r and Delta∞ estimates of the non-linear regression analysis (Fig. 3). Three concussed athletes and two control athletes were excluded from the waking QEEG analysis because of an inability to stay awake and/or high number of ocular artifacts which led to less than 60s of artifact-free waking EEG in these subjects. A group effect was observed for the relative delta power during wakefulness (F1,14=12.7, p<0.01); concussed athletes showed more relative delta activity than did control athletes (see Fig. 4). A reduction in relative alpha power was also found in concussed athletes in comparison with controls (F1,14=8.8, p<0.05). These group effects were observed for all regions. When we examined individual relative delta power for the central region, we observed that delta was the prominent frequency band in 71.4% of concussed athletes and in only 22.2% of control athletes (a trend for group difference, p=0.07). Alpha was the most important frequency band in all other athletes. No group differences were found for theta, beta 1 and beta 2 frequency bands. A group effect was observed for the slow to fast frequencies ratio (F1,14=11.5, p<0.01) showing a higher ratio for concussed athletes than for control athletes. 3.5. Relationships between subjective complaints, polysomnographic variables, and EEG spectral power In concussed athletes, a significant relationship was found between the Pittsburgh Sleep Quality Index score and the relative delta power during waking EEG in the frontal and central regions (r=0.83 and 0.91, p<0.05) showing that athletes with worse subjective sleep quality had higher relative delta power during the daytime. Moreover, concussed athletes with worse scores on this sleep quality questionnaire showed lower REM sleep efficiency (r=−0.82, p<0.01). No correlation was found between the reported sleep latency on the Pittsburgh Sleep Quality Index and the sleep latency measured by polysomnography. A relationship was observed between the symptoms reported on the Post Concussion Symptom Scale and the relative delta power for waking EEG in the parietal region (r=0.89, p<0.01); concussed athletes who reported more symptoms had a higher relative delta power. No correlation was observed between the concussion characteristics (delay since last concussion and number of concussions) and scores on any questionnaire, polysomnographic variable or EEG spectral power. Relatively short REM sleep latencies were observed in both groups of athletes and, since this polysomnographic characteristic is known to be associated with depression, we measured the relationship between REM sleep latency and Beck Depression Inventory score. No significant correlation was found in our subjects. Finally, no correlation was found between the four questionnaires, except for a relationship between the Pittsburgh Sleep Quality Index and Post Concussion Symptom Scale in concussed athletes (r=0.75, p=0.01). 4. Discussion The aim of the present study was to measure vigilance and sleep characteristics after concussions in athletes by using standardized questionnaires, polysomnographic recording, and QEEG. The main finding was that no difference in sleep architecture was found between concussed and control athletes; additionally, no difference was found in either NREM or REM sleep QEEG. However, concussed athletes showed significant increased delta and reduced alpha activities in the waking QEEG and reported poor sleep quality and daytime impairments. 4.1. Subjective sleep complaints and objective measures In spite of the poor sleep quality reported on the Pittsburgh Sleep Quality Index, concussed athletes showed normal sleep stage distribution and good objective sleep efficiency, which was greater than 91% in all concussed athletes. Surprisingly, we observed a mean sleep latency of 15.8min with polysomnography in concussed athletes, even though they reported to have a sleep latency of approximately 50min at home and 35min in the sleep laboratory. Overall, their sleep parameters measured by the polysomnographic recording were extremely similar to those obtained in control subjects, but their subjective perception of sleep quality was worse. The discrepancy between subjective and objective measures of sleep quality has been previously observed in patients with mild to severe TBI with poor sleep complaints [4] and is well known in patients with insomnia [37], [38]. According to these previous studies, sleep quality misperception in insomnia can be partly attributed to central nervous system hyperarousal as expressed by high levels of beta and gamma activities during sleep. In our athletes, no increase in high frequency EEG activity was found during wakefulness or sleep; thus, the central nervous system hyperarousal seen in insomniac patients cannot explain the trend in sleep quality misperception in our subjects. Athletes with concussions had a higher number of symptoms on the Post Concussion Symptom Scale, and they likewise reported more symptoms related to depression in the Beck Depression Inventory without being clinically depressed; this higher score on this questionnaire persisted even when questions related to sleep were excluded. In a recent study, patients with TBI with elevated scores on the depression scale had more subjective sleep complaints [5]. In other studies, it has been suggested that depression may increase the number and/or intensity of reported symptoms [39], [40]. In our concussed athletes, the depression score was not associated with either scores observed in the two other subjective symptom scales (Pittsburgh Sleep Quality Index and Post Concussion Symptom Scale) or with any polysomnographic or QEEG data. 4.2. Waking EEG anomalies In the waking QEEG, we observed an increase in the relative delta power and a decrease in relative alpha power in athletes with a concussion. In most of them, the prominent frequency band of their waking EEG was delta, whereas the alpha band was the predominant one in most control athletes. This increase in delta activity and decrease in alpha activity have already been reported in patients with mild TBI presenting with a Post Concussion Syndrome [41], [42]. In a previous study, source analysis (LORETA) was used to identify generators of this abnormal QEEG profile and the results suggested that, rather than having a single common generator or a general cortical slowing in mild TBI patients, focal cortical generators, which differed in localization between patients, were associated with these EEG changes [41]. This increased waking delta activity is hard to explain in the context of normal sleep, as it was the case in our concussed athletes. One can argue that these athletes may have a slow rate of sleep delta dissipation and that this residual delta activity has infiltrated the waking EEG activity. However, this is unlikely, since concussed athletes showed a NREM sleep delta activity dissipation similar to that of controls throughout the night. Although alpha is normally the prominent frequency band in the waking EEG, a predominant delta activity and a reduced alpha activity have recently been reported by Ferrara and coworkers [43] in healthy subjects 10min after sleep offset, in a study measuring sleep inertia. The authors suggested that this EEG activity pattern may reflect the intrusion of sleep EEG into the waking state. In the present study, waking EEGs were performed 30min following bedtime offset, but it has been shown that complete sleep inertia and drowsiness dissipation may, in some cases, take up to 75min even in normal controls [44], [45]. It is thus possible that concussed athletes have slower sleep inertia dissipation, which could explain why they report a feeling of fatigue and daytime impairments. As a significant correlation was found between the total score on the Pittsburgh Sleep Quality Index and the relative delta power for the waking EEG, studies with multiple daytime waking EEG will be needed in order to test this hypothesis. 4.3. Daytime cognitive functioning With regard to cognitive functions, concussed athletes also reported more daytime dysfunctions than controls. However, except for a slowing in one test (Symbol Digit Modality Test), all neuropsychological tests were normal in both groups of athletes, including performance on the CogSport computer battery. Therefore, despite their subjective cognitive complaints, concussed athletes were adequate on all tests. One could argue that neuropsychological tests may not be sensitive enough to detect subtle cognitive changes perceived by athletes in their everyday university student life. In fact, recent event-related potential studies have shown that concussed athletes display neurophysiological anomalies during attention tasks [24], [46], [47], [48], even when they obtained normal neuropsychological results and reported no symptom [24]. Neuropsychological and computerized evaluations are extremely sensitive to short-time cognitive deteriorations following a concussion, but the waking EEG spectral analysis and event-related potentials seem to be more sensitive to detect the subtle long-term impact of concussions. 4.4. Limitations of the study Although careful statistical steps were taken to ensure the validity of our results, our sample size was relatively small. However, the subjects included in this pilot study were extremely homogenous in terms of age, education, lifestyle, and mechanism of injury, which leads to very small variability in the sleep parameters in both concussed and control groups. Moreover, in spite of the relatively small sample, clear group differences emerged from almost all questionnaires and, inversely, very similar results were observed in both groups for objective measures, except for the waking quantitative EEG. Thus, this pilot study confirms that self-reported symptoms are not sufficient to determine the impact of sport-related concussions, and objective measures are henceforth needed to better understand the impact of these concussions on brain functions. Studies with larger samples, using subjective reports, vigilance measures and polysomnographic recording, are still needed to clarify the vigilance and sleep profiles of non-athlete patients who suffered a mild TBI. Acknowledgments This research was supported by the Canadian Institutes of Health Research (J.M. and N.G.), by the Fonds pour la Recherche en Santé du Québec (M.L. and N.G.) and by the Natural Sciences and Engineering Research Council of Canada (V.M.). 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a Research Center on Sleep and Biological Rhythms, Sacre-Cœur Hospital, 5400 boul. Gouin Ouest, Montreal, Que., Canada H4J 1C5 b Research Center on Neuropsychology and Cognition, University of Montreal, Montreal, Canada c Medicine Faculty, University of Montreal, Montreal, Canada d Centre for Neuroscience, University of Melbourne, Melbourne, Australia e CogState Ltd., Melbourne, Australia  Corresponding author. Address: Research Center on Sleep and Biological Rhythms, Sacre-Cœur Hospital, 5400 boul. Gouin Ouest, Montreal, Que., Canada H4J 1C5. Tel.: +1 514 338 2693; fax: +1 514 338 2531.
PII: S1389-9457(07)00435-2 doi:10.1016/j.sleep.2007.11.023 © 2007 Elsevier B.V. All rights reserved. | |
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