Relationships among Risk Factors for Concussion in Minor Ice Hockey

Journal of ASTM International, Vol. 6, No. 6 Paper ID JAI101878 Available online at www.astm.org Jeff Cubos, 1 Joseph Baker, 2 Brent Faught, 3 Jim McAuliffe, 4 Michelle L. Keightley, 5 Moira McPherson, 6 Alison Macpherson, 7 Nick Reed, 8 Catrin Duggan, 9 Tim Taha, 10 and William J. Montelpare 11 Relationships among Risk Factors for Concussion in Minor Ice Hockey ABSTRACT: There is increasing concern among parents, coaches, and officials about injury risk in youth ice hockey, particularly in light of recent evidence suggesting that incidence of serious injury is considerably under reported. However, an adequate method for ascertaining injury risk for concussion does not yet exist. The purpose of this study was to examine the relationships among variables measuring exposure and head impact forces in a group of representative level bantam aged hockey players. Across an entire hockey season, trained research assistants attended games and recorded the duration of time spent on the ice for each player i.e., exposure time and total number of body contacts using time-on-task software designed specifically for this study. A body contact included any intentional or incidental contact between two players. Collectively, these variables provide a simple, easily administered measure of head injury risk for researchers collecting data in this area. However, their relationship to actual brain trauma is unknown. To this end, head acceleration data were also collected using helmet-based accelerometers that provide measures of linear accelerations experienced by each player. These data were collected by telemetry methods and represent data that are likely very useful for injury researchers but not without sufficient costs. Results demonstrated low associations among the data sources. A method based on combining data sources through an examination of their potential relationships is proposed to maximize the potential to identify at-risk youth in minor hockey. KEYWORDS: youth, brain-injury, health Introduction The risk of injury in any sport is a function of a the nature of the sport, b the rules that govern behavior, c the environmental conditions and equipment used in the sport, and d the physical and physiological characteristics of the athletes participating. There is increasing concern among parents, coaches, and officials about injury risk in youth ice hockey, particularly in light of recent evidence suggesting that incidence of serious injuries such as concussions are considerably under reported 1. Recent advancements in concussion research have provided valuable insight into the causes and effects of sport-related brain injury 2 ; epidemiologic data indicate the majority of injuries in the sport of ice hockey are caused by collisions either intentional or unintentional with other players 3 5 and sustained by those on the receiving end of these collisions. In particular, head and neck injuries are often reported as resulting from body checking 6, and perhaps more alarming, head injuries can account for up to 24 % of all injuries sustained 4. Hagel et al. 4 reported increased rates of head and neck injuries among those in a Manuscript received May 8, 2008; accepted for publication April 15, 2009; published online May 2009. 1 School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada. 2 School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada. Corresponding author, e-mail: bakerj@yorku.ca 3 Department of Community Health Sciences, Brock University, St. Catharines, Toronto, Ontario, Canada. 4 Department of Physical and Health Education, Nipissing University, North Bay, Toronto, Ontario, Canada. 5 Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada. 6 School of Kinesiology, Lakehead University, Thunder Bay, Ontario, Canada. 7 School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada. 8 Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada. 9 Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada. 10 Faculty of Physical Education and Health, University of Toronto, Toronto, Ontario, Canada. 11 School of Kinesiology, Lakehead University, Thunder Bay, Ontario, Canada. Copyright 2009 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.

2 JOURNAL OF ASTM INTERNATIONAL body-checking group compared to those of the same age in a non-body-checking group and data suggest that children participating in associations that allow body checking are at increased risk of head injury 4,5,7. Thus, many authors advocate for the elimination of physical contact in minor hockey 7,8 and argue that the inclusion of body checking at young ages lengthens the period of exposure to intentional physical contact. Additionally, as children experience puberty and adolescent growth spurts, differences in size are evident with the largest variation in size at the bantam level 13 14 years old. Differences as high as 53 kg in bodyweight and 55 cm in height have been reported 9,10, and Peewee and Bantam aged players have been shown to be at an increased risk of injury 6 relative to other age groups. Central to the debate regarding the risk of head injury in ice hockey lies the need for standardization of surveillance measures and methods 11 since current estimates of injury are influenced by the surveillance methods used for data collection. A critical determinant of a player s development is time spent in activities that best capture the types of skills necessary for proficiency at the elite, adult level; however, most studies have not tracked the time at risk for individual players. Most commonly, athlete exposure has been used to estimate time at risk and has typically been defined in team sports as participation in organized and scheduled activities games or practices, therefore representing any opportunity for an athlete to be injured. In ice-hockey research, however, this has become a general epidemiological concept of person time at risk through estimates for the entire team, playing the entire game, at full strength, with five skaters on the ice. Thus, exact player exposure during games can only be over estimated and not directly measured. In contrast, tracking athletes on an individual basis may provide a more accurate means to measure athlete exposure. Similarly, studies based solely on administrative records such as injury reports and hospital records, as well as reports from team medical staff may not account for all head injuries and concussions sustained. Of particular concern has been the under-reporting of concussions, including the under-reporting by players to team staff. Other measures of head injury risk have been examined through more advanced means of surveillance. Since concussions may be caused by blows that result in impulsive forces transmitted to the head 12, recent research has been directed toward examining sustained head accelerations. Studies in football have used combinations of video data and dummy remodeling to estimate head acceleration measurements 13,14 ; however, these have been performed using indirect means, thus rendering it difficult to extrapolate results and make recommendations for youth ice hockey. Direct surveillance methods such as the Head Impact Telemetry HIT system that prospectively record and collect information without relying on symptomatic reports from individual players therefore become the protocol of choice for evaluating the risk of injury in sports. Consequently, contact sports such as ice hockey provide a valuable environment for examining relationships between exposure and head injury risk. The purpose of this study was to examine the relationships among measures of exposure time and exposure to physical contact with head impact forces in a group of representative level bantam aged ice-hockey players. Methods This study was a prospective examination of male representative level ice-hockey players registered on an AAA bantam level 13 14 years of age hockey team in the province of Ontario and was approved by the Human Participants Review Sub-Committee at York University. Players N=13 were recruited and followed for a period of 12 months eight forward and five defense. As mentioned previously, this particular demographic bantam-aged players at the representative level is at the highest risk of injury in minor hockey. Only participants who were healthy, asymptomatic, and not previously suffering from injury especially from spinal cord, brain, or other neurotrauma were invited to participate in this study. Data from games played between December 29, 2006 and March 15, 2007 were collected and analyzed. A total of four tournaments, six regular seasons, and seven playoff games were followed. Each game consisted of three 15-min periods with stop time in which the official game clock was stopped with each whistle blown, indicating a stoppage in live play. The exception to this rule was if either team was leading by five goals in the third period. Also known as running time, these situations result in the game clock continuing to run regardless of whether or not the game was considered live. A total of two games were not necessarily

CUBOS ET AL. ON MEASURING CONCUSSIVE RISK 3 equal to 45 min of total live play in length. Several outcome measures were considered in determining head injury risk. These are outlined below. Head Impact Forces Head impact forces were measured using the HIT System Simbex, Lebanon, NH, previously used in various sports that may be considered to place athletes at risk of head injuries 15 20. All players, with the exception of goaltenders, wore Canadian Standards Association CSA approved ice-hockey helmets equipped with accelerometers that measured head acceleration due to impact. Upon physical contact, helmets transmitted data via radio frequency to a sideline receiver and notebook computer system. HIT then recorded and time-stamped these measures for all players and provided real-time data relative to head acceleration m/s 2 or g, Head Injury Criterion value, Gadd Severity Index value, and impact location for future analysis 15 20. The frequency of these impacts was also recorded. Impacts recording less than 10g were subsequently removed from the analysis to account for incidental contact such as accidental stick and puck contacts, as well as congratulatory head patting. Since average linear accelerations for noninjured players have been reported to be between 30 and 60g, and 60g appears to be the 90th percentile of all impacts, particular attention was paid to the frequency and consequences of contacts within these ranges 15,17,20. Further, contacts with peak linear accelerations greater than 98g may have a predictive role in head injuries 14 and therefore, the frequency and consequences of contacts above this threshold were also examined. Exposure The exposure variables examined in this study were the number of seconds actually exposed in live game situations and the number of shifts in which a player participated, each considered a unique contributor to our understanding of exposure. In addition, the number of observed physical contacts rendered and received, both intentional and unintentional, provided measures of exposure to physical contact. Data exposure time and number of contacts were collected each game by two trained observers using a computing utility designed for the purpose of collecting data on multiple variables and previously used to collect time-on-task data on Atom-aged minor hockey players 21,22. The observers were trained by the senior researchers J.B. and W.J.M. who developed this utility, and a training period in data collection was performed between October and December 2007 to familiarize the research assistants with the use of the recording instrument. In total, six live games of full duration within this time period were used for mock collection. Data were not collected during team practices as the majority of concussions sustained in ice hockey occur during live game play 9. Throughout each game, the recorders clicked the corresponding start button each time a player stepped on the ice for live game play. If a player left the ice, or the whistle was blown to stop play, the recorder clicked the corresponding stop button. In addition, each time a player engaged in physical contact, the corresponding contact button was clicked. Figure 1 displays the pen-based data collection screen of this computing utility. Statistical Analyses Statistical analyses were performed using SPSS for MACINTOSH, Version 16.0. Univariate analyses provided measures of central tendency for all players for the length of time on the ice in live game situations, the number of shifts played per game, and the number of physical contacts engaged in per game. Peak and mean linear acceleration forces sustained were examined for differing trends across players. Bivariate correlational analyses were used to determine the relationships among exposure time and exposure to physical contact with measures of head trauma. Further, bivariate correlational analyses assessed the associations between all risk outcomes. The significance level for bivariate statistical procedures was set at p 0.05. Results Table 1 illustrates the descriptive statistics for exposure data per player. Included in this table are the average time on the ice per game in seconds, the average time per shift in seconds, the total live time on

4 JOURNAL OF ASTM INTERNATIONAL FIG. 1 The pen-based data collection screen. the ice during the collection period in seconds, the total number of physical contacts engaged in, and the average number of contacts per game. The frequency distributions of all exposure variables are illustrated in Fig. 2. The team average time on the ice during live game play, per game, was just greater than 15 min 897.62 s. The team average time per shift was 34.69 s. The team average of number of physical contacts per game, intentional or unintentional, was 5.62 contacts. Overall, the total number of physical contacts recorded for the team was 1,137 contacts while the total number of head impacts recorded, as measured by the HIT system, was 1,902 contacts. Above the threshold of 10g to account for incidental contact, a total of 1,147 head impacts were recorded. Table 2 illustrates the descriptive statistics for outcome data per player. Included in this table are the average linear acceleration, mean linear acceleration of values greater than 10g, median linear acceleration of values greater than 10g, the total number of linear accelerations falling between 30g and 60g, and the total number of linear accelerations greater than 60g. Figure 3 illustrates the frequency distribution of all recorded linear acceleration head impact forces. Of the total 1,137 contacts recorded, the average linear acceleration for the 13 players wearing the accelerometer-equipped helmets was 15.81g. Above the minimum threshold of 10g, the mean linear acceleration increased to 21.80g. The median linear acceleration above the minimum threshold of 10g was 17.54g. When examining the ranges considered normal for noninjured players, the average number of impacts falling between 30g and 60g was 12.54 per player. Conversely, the mean number of impacts recording greater than 60g was 2.38 per player. The overall peak linear acceleration recorded was 191.46g. A total of eight impacts recording greater than 98g were sus- TABLE 1 Player descriptive statistics of exposure data Player Time on ice/game s Time per shift s Total time on ice s Total number of contacts Number of contacts per game 1 986.12 32.81 16,764 124 7 2 952.08 32.83 12,377 62 5 3 655.69 32.18 10,491 106 7 4 635.88 31.50 10,174 69 4 5 733.00 33.86 12,461 77 5 6 938.07 44.52 13,133 67 5 7 845.75 32.61 13,532 125 8 8 1,110.57 35.82 15,548 91 6 9 959.36 31.60 13,431 89 6 10 993.00 33.56 16,881 50 3 11 918.06 33.42 15,607 85 5 12 991.38 40.99 15,862 97 6 13 950.06 35.26 16,151 95 6 Avg. 897.62 34.69 14,031.69 87.46 5.62

CUBOS ET AL. ON MEASURING CONCUSSIVE RISK 5 FIG. 2 Frequency distributions of exposure outcomes by player. TABLE 2 Player descriptive statistics of outcome data Mean linear acceleration 10g Median linear acceleration 10g Number of linear accelerations between 30 and 60g Number of linear accelerations 60g Linear acceleration Player g 1 18.33 27.38 19.48 11 3 2 12.98 17.91 15.11 2 1 3 13.79 18.84 14.93 8 2 4 18.94 23.78 18.38 11 5 5 14.06 19.66 16.55 12 1 6 16.75 22.61 17.55 19 7 7 16.69 24.03 18.91 36 5 8 16.38 23.22 19.12 19 2 9 14.87 20.69 18.27 6 1 10 17.20 22.48 20.31 8 0 11 18.82 23.63 17.82 10 3 12 13.49 19.15 16.47 9 0 13 13.25 20.00 15.08 12 1 Avg. 15.81 21.80 17.54 12.54 2.38

6 JOURNAL OF ASTM INTERNATIONAL FIG. 3 Frequency of all head impacts recorded by HIT system. tained, though no significant relationships were found between our exposure variables and the frequency of these impacts. These impacts 98g were distributed among three players: Player #1 2, Player #4 3, and Player #6 3. Among the bivariate correlational analyses, no statistically significant relationships were found between exposure time, exposure to physical contact, and head impact frequencies and forces Table 3. However, examining the relationships between the frequency of head impacts as measured directly by the HIT system and both the frequency of impacts between 30g and 60g, and above 60g, strong predictive relationships were revealed 0.83, p 0.01 and 0.77, p 0.05, respectively; Table 4. Surprisingly, no significant relationship was found between exposure to physical contact as tracked directly using the time-on-task software and the frequency of exposure to head impact above the minimum threshold of 10g Table 5. TABLE 3 Relationship between exposure time and exposure to physical contact with head impact forces [Pearson correlation r ] Exposure variables Number of linear accelerations between 30 and 60g Number of linear accelerations 60g Time on ice/game s 0.00 0.28 Time per shift s 0.16 0.25 Total time on ice s 0.13 0.38 Total number of contacts 0.47 0.12 Number of contacts per game 0.47 0.61 TABLE 4 Relationship between exposure to head contact and head impact forces [Pearson correlation r ] Exposure variable Number of linear accelerations between 30 and 60g Number of linear accelerations 60g Number of head impacts 0.83 a 0.77 b a p 0.01. b p 0.05. TABLE 5 Relationship between exposure to physical contact and exposure to head impact [Pearson correlation r ] Exposure variable Total number of contacts Number of contacts per game Number of head impacts 10g 0.31 p=0.30 0.27 p=0.38

CUBOS ET AL. ON MEASURING CONCUSSIVE RISK 7 Discussion While sports injury epidemiologists generally believe that risks of injury in sport are a function of participant exposure, few have actually measured exposure variables with precision. To date, injury incidence in the form of rates and proportions have been the most common and effective way to record, track, and compare epidemiological data in sport 23 ; however, studying exposure in this fashion has generally resulted in false assumptions i.e., overestimation in player participation 22. This study utilized time-on-task software 21,22 to track actual exposure time and physical contact of each participant. Rather than using athlete exposure and assuming that each participant is at risk for the entire duration of the event, we tracked individual exposure. As a result, we were able to examine the relationships among previously established predictors of injury risk in ice hockey. These variables included measures ranging from exposure time i.e., total time on the ice to linear acceleration of the head as measured using helmet accelerometers. When looking at the descriptive statistics of the outcome data per player, it was evident that the linear acceleration values were well below averages revealed in previous studies. Even when taking into account incidental contact, both the mean and median linear acceleration values fell well below the 90th percentile of all impacts 60g and considerably below the peak linear acceleration value that may have a predictive role in head injuries 98g. Specifically, nearly 38 % of linear accelerations sustained were less than 10g in magnitude, although the highest impact recorded was 191g. In previous studies, Duma et al. 17 reported a concussion sustained via a head acceleration value of 80g while McCaffrey et al. 19 suggested that sustaining an impact greater than 90g does not result in acute observable balance and neurocognitive deficits within 24 h of sustaining the impact. Further, Schnebel et al. 20 revealed college players more frequently sustained impacts greater than an established threshold of 60g than high school players. Still, neither the amount of ice hockey played nor the number of physical contacts predicted sustaining a potentially harmful impact of greater than 60g. This is particularly true for those three players who collectively sustained the eight impacts greater than 98g. Therefore, exposure to live game play may not predict head impact forces and, therefore, may not predict risk of injury in bantam-aged hockey players. Nevertheless, including multiple predictors of varying degrees, and tracking them individually, adds considerable depth to profiles of head injury risk and their examination. The only significant relationships revealed were between the frequency of head impacts as recorded by the HIT system and both the frequency of impacts between 30 and 60g, and above 60g, suggesting that the greater the number of impacts a player receives the greater their likelihood of receiving an impact above 30g. Further examinations of those impacts greater than 60g, and more specifically 98g, may provide useful information in concussion research. Indeed, this can only be performed by tracking and studying athletes on an individual basis. Interestingly, the number of body contacts visually reported by the data collection team was not associated with either of these outcomes i.e., impacts between 30g and 60g, and above 60g. In addition, the total number of physical contacts recorded differed greatly from the total number of head impacts recorded by the HIT system. Although frequencies were similar when removing all impacts recording less than 10g, no significant relationship was found between exposure to physical contact and the frequency of exposure to head impact above the minimum threshold of 10g. These results suggest that some impacts sustained by the body and reported visually are not transmitted up to the head and recorded by HIT and vice versa. Thus, a possible explanation for this finding is that engaging in physical contact or body checking may not predict the risk of head injury in ice hockey. In contrast, specific checking to the head may indeed prove predictive in a relationship with head impact forces and, subsequently, head injuries in ice hockey. Therefore, the estimated risk of sustaining a head injury associated with receiving a check to the head must be examined both at the amateur level and especially at the professional level where checking of this nature is considered legal. Alternatively, it must be considered that in a dynamic, fastpaced data collection environment, the HIT system is more precise in data collection than the visual method used by the researchers. As stated earlier, there is increasing concern regarding the under-reporting of concussions, by both players and team personnel. Surveillance methods, particularly those performed by third party researchers, that prospectively collect and record pertinent information without relying on symptomatic reports provide a practical means for examining one s risk of injury. The use of relatively simple measures such as observing the duration of time spent on the ice for each player, and total number of body contacts using

8 JOURNAL OF ASTM INTERNATIONAL time-on-task software also known as the practical data, allows for easily calculated measures of head injury risk. On the other hand, collecting head acceleration data from helmet-based accelerometers utilizing telemetry methods also known as the impractical data bring additional depth to a player s risk profile. The lack of relationship between these established predictors suggests that each provides unique information about a player s risk profile. These measures, although not widely used in clinical or research settings, have clear application in these domains. Linear acceleration and body contact data could provide invaluable information to clinicians regarding the physical forces anteceding concussion and/or other impact injuries. Furthermore, researchers could use these measures to provide more comprehensive models of head injury risk, which would inform evidence-based interventions to reduce these risks. Unfortunately, due to associated logistical and financial costs such as equipping each team member with a custom-made helmet, telemetry methods may be impractical for use at the minor level. All the same, it is imperative that coaches/trainers collect as much data as possible, ideally on a player by player basis, in addition to symptomatic medical reports since this approach may be more likely to identify at-risk youth in minor hockey. Although this research adds to our understanding of head injury risk in ice hockey, there were some limitations to the study design. All subjects were from a single team and, therefore, may have resulted in selection bias. As this team placed within the top third of their division, it is assumed that they are not representative of the target population of interest. That is, relative to other individuals in the same league, these players are more skilled and therefore may be better able to dictate the speed, style, and type of game played. Still, previous research in football revealed that skilled players sustained impacts greater than the established threshold 60g for high school and 98g for college with more frequency than linemen 20. In addition, while the research assistants utilized a computing utility specifically developed for quantifying time-on-task data and were trained by its developers, information bias may be present as exposure data were recorded in a fast-paced environment. Future studies replicating these results and confirming the reliability of this tool are certainly warranted. Moreover, the small number of players likely limited the usefulness of statistical comparisons and while visual inspection of the data distributions suggests no meaningful relationship among these outcomes further research with larger samples is warranted. Lastly, since actual injuries and reported symptoms were not included in the analyses, actual risk of injury can only be assumed and, although admittedly difficult, prospective examination of these predictors in players who sustain injury would provide a more meaningful evaluation of the relationships between these predictors of risk. It should be noted, however, that during one of the games in which the researchers were not present, one of the players reported symptoms of, and was diagnosed as suffering from, a concussion. As such, this player was not wearing his HIT-equipped helmet and, therefore, was unable to provide us with the head acceleration measurement that contributed to his symptoms. Nevertheless, his recorded exposure measures did not significantly differ from his teammates with respect to the relationship with recorded head impact forces. Concluding Remarks Although many benefits come from playing sports, the potential risks should not be ignored, most notably, the risk of head, neck, and spinal injury. Contact sports such as ice hockey provide a valuable environment for examining relationships between exposure and risk. Utilizing surveillance instruments specifically designed to measure individual exposure and head impact forces, this study demonstrated that time on the ice did not significantly predict frequency of physical contact or head impact and, therefore, may not effectively predict risk of injury in bantam-aged hockey players in our sample. Further, exposure to physical contact was not associated with frequency of head impacts over 10g. Regardless, individual athlete exposures were prospectively recorded and provide a preliminary assessment of the usefulness of using a direct method of surveillance and potentially more accurate means of examining head injury risk as measured by head impact forces. It is proposed that further studies using similar methodologies be performed, ideally investigating players of varying skill levels and abilities.

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