Journal of Science and Medicine in Sport
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1 Journal of Science and Medicine in Sport 16 (2013) Contents lists available at ScienceDirect Journal of Science and Medicine in Sport journal homepage: Original research Monitoring of sport participation and injury risk in young athletes Laurent Malisoux a, Anne Frisch a,b, Axel Urhausen a,b, Romain Seil a,b, Daniel Theisen a, a Sports Medicine Research Laboratory, Public Research Centre for Health, Luxembourg b Sports Clinic, Centre Hospitalier de Luxembourg, Luxembourg article info abstract Article history: Received 27 September 2012 Received in revised form 15 January 2013 Accepted 23 January 2013 Keywords: Sports injury prevention Youth sport Sport categories Risk factors Prospective study Training load Objectives: Careful modulation of training characteristics in high-level sports optimizes performance and avoids inappropriate workloads and associated sports injury risk. The aims of this study were to compare sport participation characteristics in different youth sport categories and to investigate their relationship with injury. Design: Prospective cohort follow-up. Methods: Young (12 19 years) high-level athletes (n = 154) from a regional sport school were followed during 41 weeks regarding sport participation characteristics and traumatic and overuse sports injuries (time-loss definition). All data were self-recorded by the athletes in an electronic system TIPPS (Training and Injury Prevention Platform for Sports) and subject to a systematic data quality control. Volume and intensity (self-rated perceived exertion) of each sport session were used to compute weekly load, monotony and strain. Sport categories were defined as team, racket, and individual sports. Results: All sport participation characteristics were dependent on sport category (p < 0.05). intensity, load and strain were dependent on age (p < 0.05). Racket and individual sports were associated with lower injury risk (HR = 0.37 and 0.34, p = and p < 0.001, respectively) compared to team sports. Average sport participation characteristics were not related to injury according to the survival analysis. However, intensity during the week prior to injury was significantly higher (p < 0.01) compared to that of the 4 preceding weeks. Conclusions: This study investigated for the first time the relationship between sport participation pattern and injury risk in young athletes. The monitoring method was sensitive to variations according to pertinent variables and might help identify athletes with increased sports injury risk Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. 1. Introduction Improvement in athletic performance is highly correlated to the training load of the athlete 1,2 and to the alternation between periods of intense and light training. 3 5 Indeed, sport participation characteristics in high-level sports require careful modulation of both training volume and intensity, in the short term and the long term, to help athletes reach their highest performance level. 6 Monitoring of training load is fundamental for trainers to fine-tune the athlete s daily program. 7 Although periodization of training should be quantifiable, there is no single indicator or methodology applicable to different training types. Endurance athletes generally use volume as the main outcome, but this does not account for training intensity. Similarly, the latter aspect also deserves attention in sports characterized by high demands in strength and power. A feasible and well-accepted method is the recording of selfreported rating of the overall workout intensity, called session Corresponding author. address: daniel.theisen@crp-sante.lu (D. Theisen). rating of perceived exertion (RPE), using the Borg scale. 8 Training load is subsequently determined by multiplying session intensity by its duration, expressed in arbitrary units. This method was originally proposed by Foster and co-workers 2,8,9 who concluded that it was a valid method to quantify sport participation in a wide variety of exercise types. Significant correlations have recently been reported between this approach and objective measurements of training intensity in individual high-intensity disciplines, 10,11 in endurance sports 12 and in team sports This method could be helpful to monitor sport participation characteristics (e.g. volume and intensity of training and competition) in the field of competitive youth sport. Indeed, many disciplines are characterized by increasingly early specialization, combined with an ever growing training load. 16,17 As a consequence, young athletes encounter many sports-related injuries when practicing at a high level. As previously suggested, inappropriate training load and scheduling could be risk factors for sports injuries. Applying the session RPE method in youth sport could therefore provide an opportunity to analyze training load more precisely and to study the relationship between specific characteristics of sport participation and sports injuries /$ see front matter 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
2 L. Malisoux et al. / Journal of Science and Medicine in Sport 16 (2013) Therefore, the primary aim of this study was to compare, for the first time, sport participation characteristics of different sport categories in youth sport as evaluated by self-reported rating of overall working intensity and training volume. We hypothesized that sport participation characteristics would be different in team, racket and individual sports. A secondary hypothesis was that sport participation characteristics would be related to injuries. 2. Methods This prospective cohort study focused on 269 young elite athletes (12 19 years) enrolled in a regional sport school and followed for 41 weeks (from the 20th of September until the 1st of July). The sport school provides a typical high school curriculum but proposes an adapted time schedule permitting two daily training sessions, some of which are organized within the school program. All pupils were athletes enrolled in the national training center of their respective sport federation and practiced at the highest national level. Some of them had been selected for international championships. Fifteen different sport disciplines were represented and grouped into 3 sport categories: team sports (basketball, handball, football, and volleyball), racket sports (badminton, tennis, and table tennis), and individual sports (athletics, canoe-kayak, cycling, gymnastics, judo, karate, swimming and triathlon). The latter classification is based on sport characteristics and specific injury risk. 19 To participate in the study, a written informed consent had to be provided by the parents or the athlete if 18 years or older. The study protocol had been previously approved by the National Ethics Committee of Research. Athlete inclusion criteria were: (1) be enrolled in the sport school during the full school season, (2) be a member of the federal training center during the entire period of the follow-up and (3) comply with the data collection procedure (see below). An internet-based electronic surveillance system (TIPPS, Training and Injury Prevention Platform for Sports) was implemented at the sport school to allow for easy recording of daily information by the athletes via personal access codes. Personal data recorded at baseline included age, sex, sport practiced and injury history over the past 12 months. Throughout the follow-up, the athletes uploaded volume, subjectively perceived intensity and context for each sport session on their TIPPS account. Subjectively perceived training intensity was evaluated on a 4-level scale consisting of 4 smiley-icons representing light, moderate, intense and very intense sessions. Sports injuries, defined as a physical complaint resulting from a match or training that forces the athlete to interrupt or modify his/her usual training plan for at least one training unit (time-loss definition), were recorded by the athletes via a dedicated questionnaire on their TIPPS account, and classified based on the latest consensus on sports injury surveillance studies Athletes were instructed to fill out their diary on a daily basis. appointments were made with every school class to improve study compliance. An athlete was considered compliant (3rd inclusion criteria, see above) if he/she had completed his/her personal sports diary for at least 80% of the observation period and doing so with a relative delay of no longer than 2 weeks to avoid recall bias. All uploaded data relative to sport participation were corroborated with regard to the schedule of the sport school and the respective sport federation. When uncertain information was identified, a direct verification was made with the athlete or, if necessary, the school staff and trainers to ensure the highest quality of data. Recorded information on sports injuries was systematically cross-checked and verified regarding completeness by a member of the investigating team, present daily on site. Active assistance was provided by the physical therapists responsible for the training of injured athletes at the sport school and the national trainers, which lead to a complete dataset for sports injuries, regardless of athlete compliance to data uploading. Sport participation characteristics were determined following the methodology presented by Foster. 9 Total volume of sport practice (exposure time) was expressed in minutes. To be able to apply the session RPE methodology previously presented 8,9 to our data set, we attributed the following, corresponding values from the Borg CR-10 scale (arbitrary unit) to each smiley icon: light = 2, moderate = 4, intense = 7 and very intense = 9. The product of session intensity thus determined and session volume was defined as session load. The sessions of each day were added-up to provide daily training load and, similarly, the sessions of each week were summated to obtain weekly training load. Mean daily load and the standard deviation (SD) were calculated for each week. The monotony of each week was computed by dividing the mean daily load by the standard deviation of the training load of that week. The weekly strain was determined as the product of weekly training load and monotony. sport participation characteristics were averaged over the period from the beginning of the follow-up until the first injury or the end of the observation period (= period of interest). The effect of sport category on sport participation characteristics was tested using analyses of covariance (ANCOVA), controlling for sex and age. Injury incidence was defined as the number of sports injuries per 1000 h of exposure (training and competition). Two different approaches were used to study the relationship between sport participation characteristics and injury occurrence. Firstly, a Cox proportional hazards regression (backward LR method) was used to identify injury risk factors amongst the average characteristics of sport participation over the period of interest. Only weekly volume and intensity of sport participation were used in the model, since the other variables are calculated based on the preceding ones. Exposure volume to sports (hours) until injury occurrence (event of interest) or the end of the observation period was used as the outcome variable. Secondly, the short-term changes of sport participation characteristics of injured athletes during the period preceding the injury were investigated by comparing the data of the week just prior to injury (W 1 ) with the mean values of sport participation characteristics of the preceding 4 weeks (W 2 W 5 ), using a repeated measures analysis of variance. Values are presented as mean ± SD. Significance was accepted for p < Results From the 269 young athletes enrolled at the sport school, 154 athletes (57%) met our inclusion criteria. Compliant and the noncompliant subgroups were similar regarding the proportion of injured athletes (66.3 and 68.8%, respectively), as well as sex distribution and sport categories represented. All data presented hereafter are from the subsample of compliant athletes. On average, athletes were 14.1 years old, males accounted for 64.9%, and the proportion of athletes engaged in team, racket and individual sports were 45.5, 16.9 and 37.7%, respectively. Fig. 1 illustrates the weekly load of sport participation according to sport category. load showed periodic variations throughout the observation period with easily identifiable decreases during holidays (weeks 7, 14 15, 22, and 36). Additionally, sport categories were characterized by largely distinct weekly loads. Table 1 provides the mean (±SD) values of sport participation characteristics according to sport category, sex or age category. It should be noted, however, that age was used as a continuous variable in the ANCOVA model. Significant differences between sport categories were observed for all variables. Additionally, weekly
3 506 L. Malisoux et al. / Journal of Science and Medicine in Sport 16 (2013) Table 2 Differences in sport participation characteristics (mean ± SD) between the week prior to a traumatic or overuse injury (N = 155; W 1) and the mean of the previous 4 weeks (W 2 W 5). W 1 W 2 W 5 p-value frequency (sessions/week) 6.4 ± ± volume (min) 571 ± ± Intensity (a.u.) 3.5 ± ± load (a.u.) 2714 ± ± monotony (unitless) 1.10 ± ± strain (a.u.) 3236 ± ± a.u.: arbitrary unit. Fig. 1. load (training and competitions) of the young elite athletes according to their sport category over the period of interest (from beginning of the follow-up until the first injury or the end of the observation period). intensity, load and strain were dependent on age. No difference was observed between boys and girls. Throughout the observation period, a total of 181 injuries were recorded, involving 102 athletes. Of all injuries recorded, 63 (34.8%) were classified as traumatic contact injuries, 76 (42.0%) as traumatic non-contact injuries, and 42 (23.2%) as overuse injuries. General incidence was evaluated at 2.81 injuries/1000 h. The influence of personal characteristics (sex, age, previous injury and sport category) and those related to sport participation (weekly volume and intensity) was tested in a Cox proportional hazards regression model. Age and sport category were controlled for, since these variables significantly influenced sport participation characteristics (Table 1). Sport category was the only significant risk factor (p < 0.001), racket and individual sports being associated with a lower injury risk compared to team sports (HR = 0.37 and 0.34, p = and p < 0.001, respectively). In other words, athletes engaged in racket or individual sports had a 63% and 66% lower risk of getting injured, respectively. None of the other variables had a significant impact on injury risk. Only 155 injuries could be included in the analysis of the short-term changes (W 1 vs. W 2 W 5 ) of sport participation characteristics prior to injury, since some of the 181 occurred within the first 5 weeks of the observation period. The number of weekly sessions tended to be more elevated during the week just prior to the injury, but the difference was not significance (p = 0.057). Intensity was found to be significantly higher during the week prior to injury when compared to the 4 preceding weeks (Table 2). Similar results were obtained when testing separately for traumatic contact injuries (3.6 ± 1.4 at W 1 vs. 3.0 ± 1.1 at W 2 W 5, p = 0.002) and non-contact injuries, i.e. traumatic non-contact and overuse injuries (3.5 ± 1.6 at W 1 vs. 3.1 ± 1.2 at W 2 W -5, p = 0.046). 4. Discussion The most important finding of this study was that the youth sport categories investigated here were defined by largely distinct sport participation characteristics. This observation confirms our main hypothesis which stipulated that sport participation patterns would be different in team, racket and individual sports. Moreover, the methodology applied here allowed for identification of variations of weekly load over the sport season (Fig. 1). Additionally, differences of several sport participation characteristics related to age could be clearly highlighted. Therefore, this approach demonstrated its sensitivity in describing the sport participation pattern within a large panel of sport disciplines and appears to be an appropriate tool to follow training load in youth sport. The method was originally proposed by Foster and subsequently validated to quantify exercise training in adult endurance sports, 8,9 high-intensity sports, 10,11 team, 13 15,23 and racket sports. 24 Nevertheless, to the authors knowledge, this study is the first to compare training load in different youth sport disciplines using the same methodology. Overall, athletes engaged in racket sports reported the highest volume and the highest intensity, while those engaged in team sports exhibit the lowest weekly load. Additionally, subjective intensity and training load increased progressively with age (Table 1). However, no difference was found when comparing young male and female athletes, demonstrating similar training load exposure for a given age and sport category. This observation is in accordance with previous work comparing training volume in girls and boys. 19 Table 1 Sport participation characteristics (mean ± SD) of 154 young elite athletes according to their sport category, sex and age. N frequency (sessions/w) volume (min) Intensity (a.u.) load (a.u.) monotony (unitless) strain (a.u.) Sport category Team sports ± ± ± ± ± ± 1380 Racket sports ± ± ± ± ± ± 3218 Individual sports ± ± ± ± ± ± 1855 p-value <0.001 <0.001 < <0.001 Sex Male ± ± ± ± ± ± 2251 Female ± ± ± ± ± ± 2365 p-value Age a years ± ± ± ± ± ± years ± ± ± ± ± ± years ± ± ± ± ± ± 2920 p-value a The results for age are presented according to 3 categories, but age was used as a covariable in the ANCOVA model; a.u.: arbitrary unit.
4 L. Malisoux et al. / Journal of Science and Medicine in Sport 16 (2013) The approach used here may not only be convenient when monitoring training load in youth sport, but it might also potentially help to prevent sports injuries in this population. Indeed, injury incidence was previously reported to be related to sport category. 19,25 A retrospective analysis in young high-level athletes revealed differences in the injury risk between sport categories for males and females. 19 A recent prospective follow-up demonstrated that young athletes from team sports had a 2 times higher risk of sustaining a traumatic or overuse injury compared to individual sports. 25 Unsurprisingly, sport category was revealed as a risk factor by the Cox survival analysis in the present study. The risk of getting injured was respectively 63% and 66% lower for athletes engaged in racket sports and individual sports compared to team sports. Therefore, sport category is the prime factor to consider when investigating working load and injury incidence. It should be highlighted here that team sports have the lowest working load but the highest injury risk. On the other hand, team sports are characterized by a relatively high number of competitions (15.2 ± 6.7 competitions per 100 days) compared to individual sports (7.6 ± 5.9 competitions per 100 days), 25 a context which is related to a greater injury risk. 26 Taken together, these observations point out the need to focus on these disciplines to prevent sports injuries, in particular by systematically monitoring training load and competition frequency. In spite of the foregoing considerations, our secondary hypothesis that sport participation characteristics would be related to injuries was only partly confirmed, in that none of those variables tested in our regression analysis were significant. One reason could be that our approach is rather global, in the sense that sport participation characteristics were determined as average values over the period of interest, while sports injuries may be more related to the changes of weekly load and behavior. Indeed, short-term changes of sport participation characteristics did reveal a higher weekly intensities, as well as a trend for an increased number of weekly sessions prior to injury (Table 2). Although this result does not necessarily indicate that an increase of intensity is coupled with a higher injury risk, health problems have been previously associated with a short-term behavior change. Foster 9 showed that 84% of illnesses observed in serious competitive athletes could be explained by a spike in training load, defined as the product of volume and intensity. In a study on semi-professional rugby league players, Gabbett 27 found that incidence of training injuries was correlated with the weekly intensity, duration and load of training, in spite of a periodized training program. These studies are different from ours insofar that they were performed on adult athletes and, above all, within a single sport discipline for each of them. Thus, further studies should be directed toward analyzing the effect of sport participation characteristics on injuries on a per sport basis. Some limitations of this study may partly explain why no clearer associations were found between sport participation patterns and injuries. Firstly, athletes assessed the intensity of their workouts by rating their perceived exertion on a 4-level scale to guarantee a good understanding and an easier use by these young athletes. While the data processing applied here to assess weekly load was similar to the previously described methodology, 8,9 the sensitivity of our scale is certainly lower. Therefore, it is possible that a scale with a greater sensitivity would have allowed a better discrimination between injured and non-injured athletes regarding sport participation patterns. The second limitation of this study is the low compliance of the young athletes with our data collection procedures. Our strict control algorithm regarding data completeness and reporting delay increases the validity of our analyses, but may have introduced a selection bias. However, given that similar characteristics were found between compliant and non-compliant athletes, even with respect to the proportion of injured athletes, our sample is likely representative of the regional sport school population. Finally, our study group was characterized by a great heterogeneity, with as much as 15 different sport disciplines represented. Unfortunately, a per sport analysis of the relationship between sport participation characteristics and injuries was not possible here, considering our small sample size. The large number of different sport disciplines included in this study led us to group them into 3 main sport categories, giving rise to a less sensitive analysis. Therefore, this study should throw the basis for further investigations on the relationship between sport participation behavior within a specific sport discipline and injury risk. 5. Conclusion This study investigated, for the first time, the relationship between sport participation pattern and injury risk in youth sport. The method applied here to describe sport participation behavior was sensitive to display the weekly load variations over the whole sport season, to discriminate between different sport categories, and to highlight age-related differences. Team sports had the lowest training load but the highest injury risk. Apart from short-term modifications in intensity, no characteristics related to sporting activities were associated with injury risk. Further research is required to investigate this relationship on a per sport basis. 6. Practical implications The monitoring of both volume and intensity of sport participation is important to optimize the training schedule in youth sports. Intensity can be evaluated by the session rating of perceived exertion. The methodology applied in this study allows for more accurate determination of true training load and is sensitive to distinguish between different sport categories and identify seasonal variations. This methodology has the potential to identify athletes who are at an increased risk of sports injuries. Large and sudden increases in mean weekly intensity should be avoided. Acknowledgments The present study was financially supported by the National Research Fund (AFR ref ) via a post-doc grant for the first author, the National Ministry of Sport, and the National Olympic Committee. References 1. Laursen PB. Training for intense exercise performance: high-intensity or highvolume training? Scand J Med Sci Sports 2010; 20 Suppl 1: Foster C, Daines E, Hector L et al. Athletic performance in relation to training load. Wis Med J 1996; 95(6): Mujika I. The influence of training characteristics and tapering on the adaptation in highly trained individuals: a review. Int J Sports Med 1998; 19(7): Issurin VB. New horizons for the methodology and physiology of training periodization. Sports Med 2010; 40(3): Garcia-Pallares J, Garcia-Fernandez M, Sanchez-Medina L et al. Performance changes in world-class kayakers following two different training periodization models. Eur J Appl Physiol 2010; 110(1): Smith DJ. A framework for understanding the training process leading to elite performance. Sports Med 2003; 33(15): Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med 2009; 39(9): Foster C, Florhaug JA, Franklin J et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15(1): Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc 1998; 30(7):
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