Maxime Luiggi

Jean Griffet

level of play and sports played among a representative student population of French adolescents. A school-based study

Keywords: adolescent health, sports injury, athletic injuries, sports santé de l'adolescent, blessures sportives, blessures athlétiques, sports

Sport injury prevalence and risk by level of play and sports played among a representative population of French adolescents. A school-based study

Maxime Luiggi, Jean

Methode: Une collection de donnée a été réalisé dans des établissements scolaires Français auprès d'adolescents en 2015 et 2017 (n=1849; aged [START_REF] Thélot | Epidémiologie des accidents traumatiques en pratique sportive en France[END_REF][START_REF] Lefèvre | Facteurs sociodémographiques et pratiques associés aux accidents liés à la pratique physique et sportive[END_REF][START_REF] Wiese-Bjornstal | Psychology and socioculture affect injury risk, response, and recovery in high-intensity athletes: a consensus statement[END_REF][START_REF] Luiggi | From which level of competition in clubs are adolescents at greater risk of injury compared with outside-of-clubs athletes? A school-based study[END_REF][START_REF] Lystad | Epidemiology of injuries in competition taekwondo: A meta-analysis of observational studies[END_REF][START_REF] Frey | Neuf saisons de surveillance médicale de compétitions de judo[END_REF]. Seuls les sportifs ont été inclus (n=1366). Deux niveaux de pratique ont été formés. Le groupe de faible niveau comportait les adolescents pratiquant leur sport soit en dehors d'un club ou dans un club, au niveau de compétition départemental ou inférieur. Le groupe de haut niveau comportait les adolescents pratiquant leur sport dans un club au niveau de compétition régional ou plus. Les odds-ratios et leurs intervalles de confiance à 95% ajustés avec des variables sélectionnées en utilisant des analyses de régressions par étapes ont été calculés pour déterminer le risque de blessure des sportifs de plus haut niveau par rapport à ceux de plus faible niveau, par sport. Nous avons aussi calculé le risque de blessure pour chaque activité sportive comparé à l'échantillon total, par niveau de pratique.

Resultats: Au plus haut niveau de pratique, presque toutes les activités sportives présentaient une plus importante prévalence d'athlètes blessés qu'au plus faible niveau. L'augmentation du risque de blessure était le plus important en tennis, basket-ball, danse, athlétisme et volley-ball. En comparant les sports, au plus faible niveau, les pratiquants de handball, boxe, football et gymnastique avait un risque plus élevé de blessure que les autres pratiquants. Au plus haut niveau de pratique, seuls les joueurs de basket-ball étaient plus à risque. La danse est l'activité sportive la moins dangereuse en terme de survenue de blessures, quelque soit le niveau de pratique.

Introduction

Sports-related injuries limit, hinder and/or prevent sustained sport activity. These injuries may also be a health risks for adolescent [START_REF] Caine | Epidemiology of injury in child and adolescent sports: Injury rates, risk factors, and prevention[END_REF][START_REF] Maffulli | Sport injuries: a review of outcomes[END_REF]. In many countries (for e. g. Canada, United States of America, Sweden), the adolescents groups most vulnerable to injury have been identified [START_REF] Hootman | Epidemiology of Collegiate Injuries for 15 Sports: Summary and Recommendations for Injury Prevention Initiatives[END_REF][START_REF] Rose | Sociodemographic Predictors of Sport Injury in Adolescents[END_REF][START_REF] Emery | Sport participation, sport injury, risk factors and sport safety practices in Calgary and area junior high schools[END_REF]. The more (i) the total amount of practise (ii) the level of play and (iii) the frequency and the strength of impact (involved in team and contact sports) the higher the injury prevalence [START_REF] Emery | Risk factors for injury in child and adolescent sport: a systematic review of the literature[END_REF]. In contrast, age, sex and body mass index, 'BMI', are not consistently related to injury [START_REF] Richmond | Is body mass index a risk factor for sport injury in adolescents?[END_REF][START_REF] Jespersen | Total body fat percentage and body mass index and the association with lower extremity injuries in children: a 2.5-year longitudinal study[END_REF][START_REF] Yard | Injury patterns by body mass index in US high school athletes[END_REF][START_REF] Warsh | Are Overweight and Obese Youth at Increased Risk for Physical Activity Injuries?[END_REF][START_REF] Kemler | Is overweight a risk factor for sports injuries in children, adolescents, and young adults?[END_REF].

In France, national population-based epidemiologic studies of sports injury are rare. Their rarity points to « a lack of social concern » [START_REF] Thélot | Epidémiologie des accidents de sport et de loisirs chez les enfants et les adolescents[END_REF] (p. 21) to a phenomenon which accounted for one half of the non-road and non-work related accidents for 0-24 years olds in 1998.

They mainly measure the use of emergency units related to sex, age and the sport played [START_REF] Ricard | Description et incidence des recours aux urgences pour accidents de sport, en France. Enquête permanente sur les accidents de la vie courante, 2004-2005[END_REF][START_REF] Thélot | Epidémiologie des accidents traumatiques en pratique sportive en France[END_REF][START_REF] Lefèvre | Facteurs sociodémographiques et pratiques associés aux accidents liés à la pratique physique et sportive[END_REF] or the accidents reported by a global population without considering specifically adolescents [START_REF] Lefèvre | Facteurs sociodémographiques et pratiques associés aux accidents liés à la pratique physique et sportive[END_REF]. No data are available on the number, severity and nature of sport injuries among a representative population of adolescents. The international literature is very informative about this research subject but scientific advances, no matter how successful they may be in one context, cannot be fully transposed to other countries. As Wiese-Bjornstal said, the risk of injury could vary according to sport cultures, largely determined by sport specificities, countries and local particularities [START_REF] Wiese-Bjornstal | Psychology and socioculture affect injury risk, response, and recovery in high-intensity athletes: a consensus statement[END_REF].

Although French national population-based studies are lacking, however, regionally, a data collection was carried out among the school adolescent population in the south-eastern region of France. Initial published analysis [START_REF] Luiggi | From which level of competition in clubs are adolescents at greater risk of injury compared with outside-of-clubs athletes? A school-based study[END_REF] showed that age and sex were not related to injuries whereas hours of playing per week and collective sports were risks factors.

Originally, the results showed that club sports did not uniformly expose adolescents to injury. Injury prevalence was similar among adolescents playing outside-clubs and those playing in clubs at the local level of competition or below. However, at the state (or regional) level, the risk of injury increased significantly. This study highlighted the general influence of the level of competition and a threshold effect in injury risk (number and severity) at the state level of competition. However, it is still unclear whether this effect is observable in each specific sport activity. Previous findings showed that increases in level of competition did not increase adolescent injury risk in all sports. For example, the risk of injury for experts and novice in taekwondo were similar [START_REF] Lystad | Epidemiology of injuries in competition taekwondo: A meta-analysis of observational studies[END_REF]. In judo, a study showed an increases in injury risk with the level of play [START_REF] Frey | Neuf saisons de surveillance médicale de compétitions de judo[END_REF] but results are different across the studied samples [START_REF] Pocecco | Injuries in judo: a systematic literature review including suggestions for prevention[END_REF]. The risk is also greater for high level practitioners in soccer [START_REF] Van Beijsterveldt | Differences in injury risk and characteristics between Dutch amateur and professional soccer players[END_REF], gymnastics [START_REF] Kolt | Epidemiology of injury in elite and subelite female gymnasts: a comparison of retrospective and prospective findings[END_REF][START_REF] Bak | Epidemiology of injuries in gymnastics[END_REF], mixedmartial arts [START_REF] Mcclain | Injury profile of mixed martial arts competitors[END_REF], baseball [START_REF] Chambless | Rate of injury in minor league baseball by level of play[END_REF], netball [START_REF] Hopper | A descriptive epidemiology of netball injuries during competition: a five year study[END_REF] and rugby football [START_REF] King | Match and training injuries in rugby league. A review of published studies[END_REF][START_REF] Roberts | Epidemiology of timeloss injuries in English community-level rugby union[END_REF]. However, these studies used different definitions of high-level and low-level athletes. Some studies compared adolescents playing in Olympic teams [START_REF] Kolt | Epidemiology of injury in elite and subelite female gymnasts: a comparison of retrospective and prospective findings[END_REF] or professional teams [START_REF] Van Beijsterveldt | Differences in injury risk and characteristics between Dutch amateur and professional soccer players[END_REF] with those playing at the national level or one division below, or in a recreational context. Others studies compared the injury risk at a national level of competition with those playing at a lower level [START_REF] Roberts | Epidemiology of timeloss injuries in English community-level rugby union[END_REF]. To our knowledge, no school-based studies have measured the injury risk using the state level and higher as a "high-level" population. It would be interesting for public health officials to know in which sports the injury risk is greater at these levels of competition compared to the lowest level of play. Results could help the development of sports injury prevention programs, as well as help physicians in sports medicine and parents to influence the commitment of adolescents in sport.

The main purpose of the present study was to conduct secondary analysis on this sample [START_REF] Luiggi | From which level of competition in clubs are adolescents at greater risk of injury compared with outside-of-clubs athletes? A school-based study[END_REF] to determine the injury prevalence by level of play (low/high) and by sport. Firstly, we aimed to identify sports in which a high-level of play (state level and higher) implied a greater injury risk. Secondly, we estimated the odds-ratio for injury by sports (i) at low-level and (ii) at high-level.

Methods

Participants and Procedure

Comparing with 2015 (n=1343), the sample size has increased in 2017 by 506 students who answered the same survey in the same schools as in the original survey. The final sample included 1424 high-school students and 425 first-year university students. Only adolescents who agreed to participate and returned signed parental informed consent forms, or those aged 18 years and over, answered the survey (overall response rate: 98.7%). Participants responded to the survey under the supervision of a physical education teacher and a researcher. The data collection was approved by the Aix-Marseille University Ethics Committee (n°2019-23-05-003) and authorized by the Superintendent of Schools.

Adolescents were asked to report sociodemographic, anthropomorphic variables (age, sex, height, weight), and informations related to their preferred sport participation (hours of play per week, context of participation and level of competition). For a detailed protocol, see Luiggi et al. [START_REF] Luiggi | From which level of competition in clubs are adolescents at greater risk of injury compared with outside-of-clubs athletes? A school-based study[END_REF]. In the present study, among all the participants, only those who declared (i) to play sport outside of mandatory physical education classes at school for at least one hour per week, (ii) what sport they preferred to practice, (iii) their level of competition, were included in the analysis (n=1366). This sample included 611 girls et 755 boys with a mean age of 17.0 (SD=1.7). 424 participants declared to not play sport at least one hour a week. 17 participants did not report their level of competition. 42 participants play sport but did not report sport they practice.

The body mass index 'BMI' was calculated by dividing weight (kg) by height squared (m) 2 .

Boys and girls were classified as 'not overweight', 'overweight' or 'obese' according to the International Obesity Task Force cut-offs for girls and boys and as a function of their age [START_REF] Cole | Establishing a standard definition for child overweight and obesity worldwide: international survey[END_REF]. This sample included respectively 2.3% of underweight and 6.0% of obese adolescents.

Independent Variables

Level of play (low/high)

According to the previous study conducted on the influence of level of competition on injury risk among this adolescent population in 2015, two groups were created. The first one included adolescents with a low injury risk (adolescents playing outside-clubs or within clubs below the departmental level of competition) (n=876). The second included adolescents with a high injury risk (adolescents playing in clubs above the state level of competition) (n=442). Adolescents who declared to play sport outside-clubs and take part in competition were excluded from the level of competition analysis (n=48).

Sports

A total of 83 different sports activities were reported by adolescents. We reduced the number of sport from 83 to 24. The least practiced sports (n<15) were combined into more general categories. For example, a "Boxing" category was created which included adolescents who declared to practice "Boxing", "Thaï boxing" or "French boxing". The 24 sports and sports categories identified were athletics, badminton, basket-ball, bodybuilding, boxing, climbing, cycling activities, dancing activities, fitness activities, gymnastics, handball, horse riding, judo, martial arts, rugby football, running activities, skiing activities, soccer, surfing/windsurfing, tennis, urban activities, and volley-ball. A category « Others » was also created. For the detailed sports activities included in each category, see additional file 1.

Outcomes

Adolescents were asked to report how many injuries they sustained during the past 12 months while playing their favorite sport. They were also asked to report the nature (sprain, overuse, tendon strain, muscle strain, fracture, dislocation) and the severity of injuries (minor, moderate, severe). Two outcomes were used. The first was (i) All Sport Injury 'ASI', irrespective of the need for medical attention or time loss from adolescents' activity. The second outcome used was (ii) injury treated by a medical practitioner -Medically Treated Injury 'MTI'.

Data Analysis

Data analyses were performed with R 3.5.0. First, anthropomorphic and sociodemographic characteristics were given by sports and levels of play. Second, we calculated injury prevalence for ASI, and for MTI, by level of play. Third, we calculated odds-ratios 'OR' for ASI and for MTI, and their 95% confidence interval '95% CI' by sports (injury odds of highlevel players / injury odds of low-level players). Fourth, we calculated ORs and 95% CI measuring ASI and MTI injury odds by sports compared to injury odds of all the other sports. These analyses were conducted twice. At the lowest level of play, then at the highest. As recommended by Peduzzi et al. [START_REF] Peduzzi | A simulation study of the number of events per variable in logistic regression analysis[END_REF], these analyses were performed only when the two compared groups have had a minimum size of 10. Otherwise, not applicable 'NA' was entered into the results table. Finally, additional file 4 details the nature and number of injuries by sports with ORs and 95% CI describing odds of specific injury (sprain, tendon strain, etc) by sports comparing with odds of the rest of sample.

Adjustment variables

For the step three, analysis were performed separately for each sport. Given the relative low number of participants in some sports (between 19 and 197), we decided to limit the number of adjustment variables using a stepwise regression with the Akaike Information Criterion (AIC) as the elimination criterion (for detailed procedures of variable selection see [START_REF] Heinze | Variable selection -A review and recommendations for the practicing statistician[END_REF]). This procedure helps to avoid over-adjustment bias -or unnecessary adjustment-of the models [START_REF] Cole | Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies[END_REF]. The package stepAIC in R was used. Firstly, for each model (by sport), we selected candidates variables based on univariate analysis. The variables were the sex, age, BMI and hours of play per week 'HP' of participants.

These four variables were selected as adjustment variables candidates because some previous findings showed an association between them and injury. However, associations remained inconsistent. Only variables with a p-value <= 0.15 were selected for the next procedure. Second, we performed full models including all variables previously selected.

Finally, we performed stepwise regressions. The adjusted ORs obtained from these final steps were used to estimate injury risk of adolescents playing at the highest level compared to those playing at the lowest.

For the step four, analyses were performed on the entire sample. Adjustment variables were the same for each model. They were selected based on univariate analysis between potential adjustment variables and each outcome. Only variables with p-value <= 0.15 were selected in the final models.

Selection of variables format

Age

No differences were observed in ASI or MTI prevalence between high-school and 1st year university students (ASI: respectively, 46.6% and 45.4%, Chi-square=0.062, p-value=0.804; MTI: respectively, 13.7% and 15.0%, Chi-square=0.293, p-value=0.588). These absences of effects suggested no threshold effect of age on the injury risk. Thus, further analysis were neither conducted separately between these two subgroups of students nor conducted using different age classes.

Body mass index

In addition, contrary with previous studies [START_REF] Richmond | Is body mass index a risk factor for sport injury in adolescents?[END_REF][START_REF] Jespersen | Total body fat percentage and body mass index and the association with lower extremity injuries in children: a 2.5-year longitudinal study[END_REF][START_REF] Yard | Injury patterns by body mass index in US high school athletes[END_REF], no effect of an overweight BMI status was observed for ASI (ORoverweight: 0.81, p-value=0.35; ORobese: 0.72, p-value=0.38) and MTI (ORoverweight: 1.21, p-value=0.53 ; ORobese: 0.87, p-value=0.80) compared to sports players with a normal weight. Thus, BMI variable was kept continuous in the further models.

Hours of Play per week

After the model's comparison using 'HP' as categorical or continuous, no significant changes in the models' adjustments were found. Thus, the 'HP' was used as continuous in the further models.

Results

Sociodemographic and anthropomorphic characteristics of the studied sample by sport and level of play are provided in Table 1. The most practiced sports were soccer (14.9%), dancing (12.7%), basket-ball (6.8%), tennis (6.1%) and boxing (5.8%). Some activities were almost exclusively practiced by boys (for example, soccer and rugby football) whilst others by girls (for example, dancing and horse riding).

With respect to injury, 46.5% of adolescents declared to have had an injury within the past 12 months. More precisely, 14.6% had sustained a medically treated injury and only 2% had been treated in hospital. Prevalence of injured athletes by sport and level of play are detailed in Table 2. In Table 2, are also provided the injury risk (ASI and MTI) for high level players compared to low level players. Figure 1 and 2 gave a visual overview of these results. In general, adolescents playing at the highest level were 1.8 (95% CI: 1.4-2.3) and 2.5 (95% CI: 1.8-3.5) times more likely to have had respectively, an ASI or a MTI. For ASI, the increasing risk was significant and pronounced in athletics (OR:10.0, 95% CI: 1.98-77.92), volley-ball (OR:11.0, 95% CI: 1.50-229.91), judo (OR:9.6, 95% CI: 1.58-86.91), basket-ball (OR:5.1, 95% CI: 2.08-13.33), and others martial arts (OR:4.8, 95% CI: 1.29-20.66). For MTI, the increasing risk was significant in tennis (OR:9.0, 95% CI: 1.42-94.50), basket-ball (OR:5.0, 95% CI: 1.63-18.94) and dancing (OR:3.7, 95% CI: 1.12-11.60). Although a greater prevalence of injured athletes was observed in the others sports, the odds-ratios were not significantly higher between high-level and low-level players.

In additional file 2, Table 1 and Table 2 are available results of all univariate analysis between predictors and injury, followed by models including only variables with a p-value <= 0.15, followed by results of final models using stepwise regression procedures. ASI and MTI ORs from these last models were those presented in the last paragraph.

Table 3 provides the odds of injury of each sport compared with the odds for all the other sports by level of play. Figure 3 and 4 gave a visual overview of ORs and 95% CI, for ASI and MTI, at the lowest level of play. At the low level of play, swimmers were 2.5 times less likely to have sustained an injury in the past 12 months (95% CI: 1.25-7.14). At the other extreme, those who preferred boxing, gymnastics and handball reported a higher risk of ASI.

In gymnastics, they were 4.0 times more likely to have had an ASI compared to all the other sports (95% CI: 1.27-14.78). A greater risk was also observed, for MTI, in handball (OR:3.2, 95% CI: 1.10-7.95) and soccer (OR:1.8, 95% CI: 1.03-3.14). At the highest level of play, injury risk was similar for all activities. However, as in low level of play, we observed that swimmers (OR:0.4, 95% CI: 0.15-0.88) and skiers (OR:0.3, 95% CI: 0.07-0.93) were significantly less at risk of ASI compared to all the other sports. Contrary with these activities, at the highest level of play, basket-ball players were the most at risk compared to all the other sports. They were at 2.4 greater risk of ASI (95% CI: 1.20-5.29). In additional file 3, Table 1 to 4 are available results of the full models including adjustment variables effects.

Finally and for information, Table 1 in additional file 4 presented the numbers and nature of injuries for each sport. Table 2 showed the ORs for each nature of injury by sport compared to all the other sports. To sum up, 1180 injuries have been reported. Sprain is the most common injury (38.8%), followed by muscle strain (18.3%), overuse symptoms (15.9%), tendon strain (14.8%), then fractures (8.6%) and dislocations (3.6%). Once again significant differences appeared between sports. With regards to injury severity, we observed that horse riders, rugby football players and soccer players were at greater risk of fractures (respectively, OR:2.5, 95% CI: 1.53-4.22; OR:2.2, 95% CI: 1.6-3.01; OR:1.3, 95% CI: 1.16-1.48). While dancing activities were significantly less at-risk (around 2 times less fractures reported). Regarding dislocations, they were more frequent in cycling (OR:3.0, 95% CI:

1.66-5.45), judo (OR:2.8, 95% CI: 1.3-6.05), martial arts (OR:2.6, 95% CI: 1.22-5.63), rugby football (OR:4.7, 95% CI: 3.25-6.92), skiing (OR:6.0, 95% CI: 1.73-20.94) and volley-ball (OR:3.0, 95% CI: 1.66-5.45).

Discussion

This study aimed to investigate the level of play effects in sport injury risk among a French adolescent population representative of the third biggest area of France. Results showed that prevalence of injured athletes increased notably at the highest level of play -state level of competition or higher-compared with the lower level. Meanwhile, the increases in risk were not significant in many sports. We observed that injury risk for ASI was significantly higher with the increases in level for 5 sports out of 24. Results also never showed a significantly lower injury risk -ASI or MTI-at the highest level compared with the lowest, except in gymnastic. This surprising effect in gymnastic was like those of Kolt and Kirkby [START_REF] Kolt | Epidemiology of injury in elite and subelite female gymnasts: a comparison of retrospective and prospective findings[END_REF] who observed that elite gymnasts were at a lower risk than sub-elites when controlling for the hours of play. Concerning MTI, tennis, basket-ball, and dance practitioners presented a significant higher risk at the highest level of play. For ASI, a greater risk also appeared in athletics, basket-ball, volley-ball, judo and others martial arts activities. Mixed-martial arts [START_REF] Mcclain | Injury profile of mixed martial arts competitors[END_REF] practiced at the highest level have already been shown to put adolescents at greater risk of injury than when played at low level. Concerning rugby football, previous studies have shown that players at the highest level were at greater risk than those at the lowest level [START_REF] King | Match and training injuries in rugby league. A review of published studies[END_REF][START_REF] Roberts | Epidemiology of timeloss injuries in English community-level rugby union[END_REF]. Our study shows that when controlling for hours of play per week, the ASI risk was similar between high-level and low-level players. However, the risk of moderate to severe injury was greater and highlight the high-intensity of contact presents in rugby football at the highest level of play [START_REF] Preatoni | The influence of playing level on the biomechanical demands experienced by rugby union forwards during machine scrummaging[END_REF].

Others results from our study should be compared with studies measuring how the level of play influences the risk of injury for different sports. To our knowledge, such studies do not exist for all sports. It would be interesting to develop systematic studies measuring this influence in order to inform public health officials and sports federations regarding to the hypothetical increasing health risks of competition in each sport. This need is even more important considering that improving knowledge about injury in adolescent elite sport is a major issue [START_REF] Steffen | More data needed on injury risk among youth elite athletes[END_REF]. However, at present, sports injury studies have been mainly performed among professional, semi-professional or athletes playing at Olympic level. It could be interesting to determine at which level of competition adolescent athletes are at greater risk of injury, in each sport activity. Such findings would be especially interesting with focusing on injuries with long-term implications [START_REF] Maffulli | Sport injuries: a review of outcomes[END_REF]. It would help to initiate appropriate injury prevention programs [START_REF] Caine | Epidemiology of injury in child and adolescent sports: Injury rates, risk factors, and prevention[END_REF][START_REF] Abernethy | Strategies to prevent injury in adolescent sport: a systematic review[END_REF][START_REF] Soomro | The efficacy of injury prevention programs in adolescent team sports: a meta-analysis[END_REF], surveillance [START_REF] Ekegren | Sports injury surveillance systems: a review of methods and data quality[END_REF] and research.

Regarding the odds of injury by sports at each level of play, we observed that at the highest level, injury prevalence and risk were similar between sports. Only volley-ball and basketball players were at a significantly greater risk. Meanwhile, and consistent with previous findings, non-contact sports such as dance and swimming activities were the less dangerous sports. Surprisingly, at this level of play, skiers were also at less risk than other sports players. That contradicts a previous study that showed the high prevalence of severe injury in winter sports [START_REF] Thélot | Epidémiologie des accidents traumatiques en pratique sportive en France[END_REF]. This result may be explained by the area of our surveys.

Adolescents lived in the department of Bouches-du-Rhône and in this area ski resort are at approximatively 2 to 3 hours of driving. It is likely that their amount of participation is lower than one of adolescents living near mountains. That could explain their low injury risk observed in the present study. At the lowest level of play, we also observed that non-contact sports activities were health protective, whilst contact sports exposed adolescents to injury.

For example, swimming and badminton were sport activities with significantly less proportions of adolescents sustaining at least one injury in the past 12 months. On the other hand, and still consistent with previous findings, we observed that handball [START_REF] Åman | Incidence and severity of reported acute sports injuries in 35 sports using insurance registry data: incidence of acute sports injuries[END_REF], gymnastics [START_REF] Emery | Sport participation, sport injury, risk factors and sport safety practices in Calgary and area junior high schools[END_REF] and boxing activities presented significantly higher proportions of injured adolescents.

These sports are commonly classified as contact sports and these activities have already been identified to put adolescents at greater risk of injury. This observation was confirmed by the nature of injuries by sports. Fractures and dislocations were clearly more frequent in contact sports. Significant differences were observed in rugby, martial arts, skiing, soccer and horse riding compared with the rest of the sample. Once again, appropriate interventions on adolescent participation such as proper self-protection equipment [START_REF] Daneshvar | Helmet and mouth guards: the role of personal equipment in preventing sport-related[END_REF], or changes in rules, could reduce such risks.

Limitations

Firstly, these results must be interpreted with caution. The present study offered a relatively low statistical power in some activities (low sample size) which could have been a source of type II (false negative) errors. In addition, the relative important numbers of statistical analyses conducted may have increased the type I (false positive) errors. In addition, sample size was too small to perform complete analyses for all sports. For example, the martial arts category included different combat sports which may have different injury risk [START_REF] Lystad | Epidemiology of injuries in competition taekwondo: A meta-analysis of observational studies[END_REF][START_REF] Pocecco | Injuries in judo: a systematic literature review including suggestions for prevention[END_REF][START_REF] Mcclain | Injury profile of mixed martial arts competitors[END_REF].

It would be useful to increase sample size in order (i) to reduce likelihood of type II errors and (ii) to assess precisely the injury risk in each specific sport activity across levels of competition.

Secondly, in this sample we excluded all adolescents that reported to not play sport at least one hour a week. Some adolescents who play sport but that are currently injured may have been excluded of the sample due to our selection criteria. Thus, prevalence of injury may have been underestimated in some sports. Further studies should consider this issue and develop a survey that could consider currently injured sports players.

Thirdly, following prior analyses that showed no increased risk for overweight/obese adolescents compared to those with a normal weight, we decided to enter BMI variable in a linear form. This choice assumes a linear effect of BMI whilst previous findings showed a threshold effect [START_REF] Richmond | Is body mass index a risk factor for sport injury in adolescents?[END_REF][START_REF] Warsh | Are Overweight and Obese Youth at Increased Risk for Physical Activity Injuries?[END_REF]. Even if models' sensibility analysis showed no differences between BMI in a linear form or non-linear, it is likely that model adequation would be different sport by sport. Further studies could investigate systematically BMI effects in linear and nonlinear form to deepen understanding his role in injury risk by sports participation of adolescents.

Fourthly, adolescents were asked to self-report numbers and severity of injury they have had in the past 12 months. This methodology is common in retrospective studies in sport injury [START_REF] Rose | Sociodemographic Predictors of Sport Injury in Adolescents[END_REF][START_REF] Emery | Sport participation, sport injury, risk factors and sport safety practices in Calgary and area junior high schools[END_REF][START_REF] Emery | Risk factors for injury in child and adolescent sport: a systematic review of the literature[END_REF][START_REF] Richmond | Is body mass index a risk factor for sport injury in adolescents?[END_REF][START_REF] Jespersen | Total body fat percentage and body mass index and the association with lower extremity injuries in children: a 2.5-year longitudinal study[END_REF][START_REF] Yard | Injury patterns by body mass index in US high school athletes[END_REF][START_REF] Warsh | Are Overweight and Obese Youth at Increased Risk for Physical Activity Injuries?[END_REF][START_REF] Kemler | Is overweight a risk factor for sports injuries in children, adolescents, and young adults?[END_REF]. Severity is measured through the type of medical attending that have been provided. However, seeking medical attention could have been influenced by adolescents or parents' perceptions of the severity of the injury, as well, as the sport specific risk culture [START_REF] Wiese-Bjornstal | Psychology and socioculture affect injury risk, response, and recovery in high-intensity athletes: a consensus statement[END_REF]. It has been shown that some activities emphasize the importance of pain and injury. In results, some athletes continue to play despite pain or injury. These influences could have both either minimized or accentuated the objective severity of the injury. Not in terms of medical attention but in terms of physical damages. Further studies in adolescent sport injury would be more precise with investigating severity of injury with the help of a medical staff. Fifthly, our results should be considered as specific to the study location. They do not necessarily relate to activities more often practiced elsewhere in France, such as skiing or sailing. It could be interesting to develop national studies measuring adolescent sport injuries. Such information would be useful to develop more appropriate injury prevention programs.

Finally, the 12-month retrospective report is a potential bias for this study. Although it offers a representative view of the injury phenomenon, this study design suffers from injury recall bias [40]. Further studies should measure the influence of level of competition on injury risk with longitudinal methodology.

Conclusions

Our study offers a picture of adolescents injury risk and prevalence in sports in the third biggest department of France (Bouches-du-Rhône). Sport injury prevention programs should be developed across all sports activities when played at the state level of competition and higher. This increasing risk was especially high in tennis and basket-ball. Finally, contact sports present a high injury risk even at the lowest level of play: a continued attention on the adolescent injury risk should be done toward them. In contrast, for physicians, non-contact sports could be efficiently promoted for adolescents with health fragilities, especially those at greater risk of injury due to their intrinsic characteristics (see [START_REF] Caine | Epidemiology of injury in child and adolescent sports: Injury rates, risk factors, and prevention[END_REF]). Others activities due to their higher injury risk should be promoted with cautious. The Committee of Sports

  Sport injury prevalence and risk by level of play and sports played among a representative population of French adolescents. A school-based study Prévalence et risque de blessure en sport par niveau de pratique et sport pratiqué auprès d'une population représentative d'adolescents Français. Une étude menée en milieu scolaire FRENCH ABSTRACT Position du problème: La pratique sportive comporte à la fois des bénéfices et des risques corporels. En France, les données concernant les blessures subies en sport par les adolescents sont rares. L'objectif principal de cet article est d'étudier la prévalence et les risques de blessures par niveau de pratique et sport dans le sud-est de la France.

  Medicine and Fitness [41] provided important decision elements regarding high-intensity and contact sports activities. concussions. Clin Sports Med; 30: 145-63.Lystad RP. Epidemiology of injuries in fullcontact combat sports. Australasion Epidemiologist 2015; 22: 14-8. 40. Johansen B, Wedderkopp N. Comparison between data obtained through real-time data capture by SMS and a retrospective telephone interview. Chiropr Osteopat 2010; 18: 10. 41. Rice SG, Council of Sports Medicine and Fitness. Medical conditions affecting sports participation. Pediatrics 2008; 121: 841-8.

Table 1

 1 Anthropomorphics and sociodemographic characteristics of the studied sample by sport and level of play

		n	Age	Low-level of play Girls (%) Height Weight	n	High-level of play Age Girls (%) Height Weight	All
	ATHLETICS	12 16.3	66.7%	166.5	54.8	21 17.8	34.8%	175.0 63.5
	BADMINTON	26 16.5	38.5%	169.1	59.1	5	17.8	33.3%	177.0 66.2
	BASKETBALL	45 16.9	28.9%	174.4	61.6	45 16.6	23.9%	175.7 66.1
	BODYBUILDING	54 17.7	22.2%	175.5	67.3	1	18.0	0.0%	178.0 53.0
	BOXING	61 16.7	34.4%	171.8	62.7	15 16.7	25.0%	172.6 62.4
	CLIMBING	14 16.6	78.6%	163.1	56.1	3	17.3	50.0%	163.3 55.7
	CYCLING	26 17.6	15.4%	175.9	64.8	10 18.6	18.2%	171.4 63.0
	DANCING	135 16.4	97.0%	163.7	53.6	32 16.7	93.9%	162.9 49.9
	FITNESS	25 17.1	80.0%	168.7	58.6	1	19.0	50.0%	169.0 72.0
	GYMNASTICS	14 17.1	71.4%	164.1	57.2	20 16.8	86.4%	163.0 52.7
	HANDBALL	23 18.2	65.2%	171.4	60.7	31 17.7	45.2%	174.7 65.7
	HORSE RIDING	36 17.2	100.0% 165.6	53.7	15 16.9 100.0% 167.7 55.3
	JUDO	9	17.2	22.2%	173.9	61.2	15 17.1	26.7%	170.9 61.7
	MARTIAL ARTS	30 16.6	30.0%	172.7	61.9	15 16.9	13.3%	175.2 65.1
	OTHERS	28 16.6	32.1%	171.1	61.7	25 17.2	21.9%	175.7 69.7
	RUGBY FOOTBALL	19 17.5	5.3%	179.9	77.8	30 17.2	9.4%	177.4 76.0
	RUNNING	37 17.5	62.2%	170.8	59.4	9	18.1	33.3%	171.9 59.8
	SKIING	13 18.2	76.9%	170.0	64.8	12 18.4	58.3%	170.8 61.2
	SOCCER	126 17.0	9.5%	175.6	66.6	71 16.8	10.8%	172.0 64.3
	SURFING/WINDSURFING 16 17.6	43.8%	171.9	57.7	6	17.5	50.0%	171.3 63.0
	SWIMMING	38 16.8	57.9%	172.9	60.1	24 17.0	45.8%	172.6 61.0
	TENNIS	59 17.1	35.6%	171.9	61.5	21 17.5	18.2%	177.0 67.0
	URBAN ACTIVITIES	16 15.4	12.5%	168.3	56.8	3	17.0	40.0%	171.7 58.3
	VOLLEYBALL	14 17.4	57.1%	170.4	60.7	12 17.2	53.8%	170.0 58.7
	ALL	876 17.0	47.6%	170.9	61.0	442 17.2	36.3%	172.3 63.2
	Note. Low level of play: outside-club, no competition; club, local level or below. High-level of play: club, state level and higher

Table 2

 2 Injury prevalence and odds-ratios describing the relationship between level of play and injury adjusted for variables in

	column ADJASI and ADJMTI
		Low-level of play ASI MTI	High-level of play ASI MTI	ORASI	OR (High-level / Low-level) 95% CI ORMTI	95% CI	ADJASI ADJMTI
	ATHLETICS	16.7% (2)	0.0% (0)	66.7% (14)	19.0% (4)	10.0** 1.98-77.92	NA		NA	N
	BADMINTON	23.1% (6)	3.8% (1)	60.0% (3)	40.0% (2)	NA	NA	NA		NA
	BASKETBALL	40.0% (18)	8.9% (4)	75.6% (34)	33.3% (15) 5.1*** 2.08-13.33 5.0** 1.63-18.94	N	N
	BODYBUILDING	37.0% (20)	9.3% (5)	100.0% (1)	0.0% (0)	NA	NA	NA		NA
	BOXING	55.7% (34)	3.3% (2)	66.7% (10)	13.3% (2)	1.5	0.47-5.58	3.0 0.30-29.03	B	HP
	CLIMBING	57.1% (8)	7.1% (1)	33.3% (1)	0.0% (0)	NA	NA	NA		NA
	CYCLING	50.0% (13)	15.4% (4)	70.0% (7)	30.0% (3)	0.3	0.01-3.39	2.4 0.39-13.48	HP	N
	DANCING	35.6% (48)	6.7% (9)	50.0% (16)	18.8% (6)	1.2	0.49-2.80	3.7* 1.12-11.60 B;HP	A
	FITNESS	36.0% (9)	16.0% (4)	0.0% (0)	0.0% (0)	NA	NA	NA		NA
	GYMNASTICS	64.3% (9)	21.4% (3)	70.0% (14)	30.0% (6)	1.3	0.29-5.63	0.0	0.00-0.70	N A;HP
	HANDBALL	60.9% (14)	26.1% (6)	64.5% (20)	25.8% (8)	0.7	0.18-2.43	0.8	0.20-2.94	HP	HP
	HORSE RIDING	38.9% (14)	11.1% (4)	60.0% (9)	26.7% (4)	1.7	0.45-6.39	2.9 0.60-14.33	HP	N
	JUDO	22.2% (2)	11.1% (1)	73.3% (11)	33.3% (5)	9.6* 1.58-86.91	4.0 0.50-85.06	N	N
	MARTIAL ARTS	36.7% (11)	6.7% (2)	73.3% (11)	20.0% (3)	4.8* 1.29-20.66	3.5 0.52-29.27	N	N
	OTHERS	28.6% (8)	3.6% (1)	60.0% (15)	8.0% (2)	3.8* 1.23-12.32	1.1 0.06-28.24	N	B
	RUGBY FOOTBALL	42.1% (8)	21.1% (4)	56.7% (17)	43.3% (13)	0.9	0.21-3.68	2.0 0.43-11.40	HP	HP
	RUNNING	29.7% (11)	5.4% (2)	22.2% (2)	11.1% (1)	NA	NA	NA		NA
	SKIING	23.1% (3)	7.7% (1)	33.3% (4)	0.0% (0)	1.7 0.29-10.66	NA		NA
	SOCCER	48.4% (61)	15.9% (20)	56.3% (40)	26.8% (19)	1.1	0.57-2.01	1.6	0.74-3.40	HP	HP
	SURFING/WINDSURFING	25.0% (4)	0.0% (0)	33.3% (2)	33.3% (2)	NA	NA	NA		NA
	SWIMMING	18.4% (7)	7.9% (3)	41.7% (10)	16.7% (4)	1.0	0.18-4.91	0.2	0.01-2.87	HP A;HP
	TENNIS	30.5% (18)	3.4% (2)	61.9% (13)	33.3% (7)	2.2	0.69-6.93	9.0* 1.42-94.50	HP	HP
	URBAN ACTIVITIES	68.8% (11)	18.8% (3)	100.0% (3)	33.3% (1)	NA	NA	NA		NA
	VOLLEYBALL	50.0% (7)	14.3% (2)	91.7% (11)	25.0% (3)	11.0* 1.50-229.91	2.0 0.28-17.74	N
	ALL	39.5% (346) 9.6% (84) 60.6% (268) 24.9% (110) 1.8***	1.4-2.3 2.5***		1.8-3.5 A;HP B;HP

Note. ORASI: Odds-ratio for all sports injury; ORMTI: Odds-ratio for medically treated injury; ADJASI: Adjustment variables for ASI; ADJMTI: Adjustment variables for MTI. A: Age; B: Body Mass Index; HP: Hours of Play per Week; S: Sexe; N: No adjustment variables kept. Reading: At the low-level of play, 40.0% of basket-ball players reported to have sustain at least one injury in the past 12 months. Bold values indicate a significate higher injury risk -ASI or MTI-for high-level players compared with low-level players.

Table 3

 3 Odds-ratio and 95% confidence interval describing the injury odds of each sport compared to all the other sports by level of play Sport categories according to sport declared by adolescents

		Low-level of play	High-level of play
		ASI a	MTI b	ASI b	MTI c
	ATHLETICS	0.3 (0.05-1.18)	NA	1.2 (0.49-3.27)	0.6 (0.18-1.78)
	BADMINTON	0.4 (0.14-1.02)	0.4 (0.02-1.79)	NA	NA
	BASKETBALL	0.9 (0.46-1.61)	0.9 (0.25-2.19) 2.4* (1.20-5.29)	1.6 (0.81-3.20)
	BODYBUILDING	0.8 (0.45-1.48)	0.9 (0.30-2.12)	NA	NA
	BOXING	1.9* (1.13-3.32)	0.3 (0.05-0.99)	1.4 (0.47-4.46)	0.5 (0.07-1.81)
	CLIMBING	2.2 (0.75-6.82)	0.8 (0.04-4.05)	NA	NA
	CYCLING	1.4 (0.61-3.15)	1.5 (0.42-4.16)	1.2 (0.32-5.93)	0.9 (0.19-3.53)
	DANCING	0.9 (0.58-1.38)	0.7 (0.33-1.42)	0.7 (0.32-1.41)	1.0 (0.36-2.51)
	FITNESS	0.9 (0.36-2.03)	1.9 (0.53-5.14)	NA	NA
	GYMNASTICS	4.0* (1.27-14.78) 3.2 (0.70-10.72)	1.5 (0.60-4.42)	1.6 (0.54-4.19)
	HANDBALL	2.5* (1.06-6.26) 3.2* (1.10-7.95)	1.1 (0.54-2.56)	1.0 (0.42-2.38)
	HORSE RIDING	1.2 (0.55-2.36)	1.3 (0.37-3.37)	1.0 (0.36-3.11)	1.3 (0.36-4.10)
	JUDO	0.5 (0.07-2.08)	NA	1.9 (0.64-7.05)	1.7 (0.50-4.84)
	MARTIAL ARTS	1.0 (0.43-2.07)	0.7 (0.12-2.57)	1.8 (0.59-6.48)	0.7 (0.15-2.31)
	OTHERS	0.6 (0.26-1.48)	0.4 (0.02-1.82)	1.0 (0.43-2.30) 0.2* (0.03-0.72)
	RUGBY FOOTBALL	1.1 (0.37-2.86)	2.1 (0.47-6.56)	0.9 (0.41-1.94)	2.1 (0.88-4.77)
	RUNNING	0.8 (0.36-1.59)	0.6 (0.10-2.07)	NA	NA
	SKIING	0.4 (0.07-1.52)	NA 0.3* (0.07-0.93)	NA
	SOCCER	1.3 (0.87-1.98) 1.8* (1.03-3.14)	0.8 (0.48-1.36)	1.2 (0.63-2.11)
	SURFING/WINDSURFING	0.6 (0.16-1.77)	NA	NA	1.3 (0.17-6.80)
	SWIMMING	0.4* (0.14-0.80)	0.9 (0.22-2.67) 0.4* (0.15-0.88)	0.5 (0.14-1.44)
	TENNIS	0.7 (0.38-1.22)	0.3 (0.06-1.13)	1.1 (0.46-2.88)	1.6 (0.60-4.07)
	URBAN ACTIVITIES	2.2 (0.76-7.33)	1.7 (0.38-5.69)	NA	NA
	VOLLEYBALL	1.8 (0.58-5.59)	1.7 (0.26-6.51) 7.4 (1.42-135.89)	1.1 (0.23-3.74)

Note. At a high-level of play, rugby players were 2.3 more likely to report a medically treated injury than the rest of sample. Bold values indicate a significate higher injury risk -ASI or MTI-for a specific sport compared to all the other sports. *p<0.05; **p<0.01; ***p<0.001. a : Adjusted for age, sex and hours of play per week; b : Adjusted for hours of play per week; c : Adjusted for age, body mass index and hours of player per week.

Additional File 1.

4% (23) 9.5% (19) 27.4% (55) 10.9% (22)

  Bold values indicate a significant higher injury nature risk compared to all the other sports, according to ORs and 95% CI available in additional file 2, table2

	Additional file 4.
							3.5% (7)	100.0% (201)
	SURFING/WINDSURFING 30.0% (6)	10.0% (2)	25.0% (5)	25.0% (5)	10.0% (2) 0.0% (0)	100.0% (20)
	SWIMMING	24.2% (8)	24.2% (8)	27.3% (9)	21.2% (7)	3.0% (1)	0.0% (0)	100.0% (33)
	TENNIS	47.3% (26)	10.9% (6)	27.3% (15)	9.1% (5)	3.6% (2)	1.8% (1)	100.0% (55)
	URBAN ACTIVITIES	33.3% (3)	11.1% (1)	0.0% (0)	44.4% (4)	11.1% (1) 0.0% (0)	100.0% (9)
	VOLLEYBALL	37.5% (15)	12.5% (5)	22.5% (9)	15.0% (6)	2.5% (1)	10.0% (4) 100.0% (40)
	All	38.8% (458) 15.9% (188) 14.8% (175)	18.3% (216) 8.6% (101) 3.6% (42) 100.0% (1180)

Note.

Table 2

 2 ORs and 95% CI describing the relationship between injury nature and sport played

		Sprain	Overuse	Tendon strain	Muscle strain	Fracture	Dislocation
	ATHLETICS	0.3 (0.22-0.49)	1.1 (0.73-1.64)	1.2 (0.79-1.79)	3.0 (2.33-3.80)	1.0 (0.48-2.09)	0.4 (0.01-21.39)
	BADMINTON	0.8 (0.43-1.43)	2.6 (1.45-4.81)	0.9 (0.28-2.77)	0.7 (0.22-2.15)	0.4 (0.01-20.79) 1.9 (0.23-16.59)
	BASKETBALL	1.8 (1.70-1.92)	0.6 (0.56-0.75)	0.8 (0.66-0.87)	0.6 (0.51-0.68)	1.1 (0.95-1.35)	0.8 (0.44-1.33)
	BODYBUILDING	0.7 (0.51-0.87)	1.2 (0.86-1.76)	1.6 (1.14-2.19)	0.9 (0.58-1.29)	0.9 (0.45-1.96)	1.5 (0.52-4.58)
	BOXING	1.4 (1.28-1.63)	1.7 (1.46-2.04)	0.5 (0.38-0.79)	0.5 (0.36-0.70)	1.0 (0.70-1.49)	0.2 (0-10.55)
	CLIMBING	0.2 (0.02-2.13)	3.2 (1.10-9.12)	3.4 (1.20-9.93)	0.6 (0.07-6.06)	0.7 (0.01-43.7)	1.7 (0.02-113.04)
	CYCLING	0.6 (0.46-0.77)	1.1 (0.79-1.59)	1.4 (1.04-1.98)	0.8 (0.53-1.17)	1.2 (0.67-2.09)	3.0 (1.66-5.45)
	DANCING	0.6 (0.51-0.64)	1.7 (1.51-1.94)	1.5 (1.26-1.68)	1.4 (1.28-1.64)	0.4 (0.18-0.71)	0.3 (0.04-2.22)
	FITNESS	0.8 (0.43-1.43)	1.3 (0.58-3.02)	2.1 (1.06-4.13)	0.7 (0.22-2.15)	0.8 (0.09-6.37)	0.9 (0.01-53.78)
	GYMNASTICS	1.0 (0.79-1.23)	1.2 (0.81-1.64)	1 (0.70-1.56)	1.3 (1.00-1.80)	0.6 (0.20-1.66)	0.3 (0.01-19.09)
	HANDBALL	1.6 (1.44-1.67)	0.9 (0.74-1.00)	0.9 (0.75-1.02)	0.8 (0.73-0.95)	0.8 (0.57-1.00)	0.2 (0.03-1.71)
	HORSE RIDING	0.6 (0.39-0.86)	1.3 (0.76-2.07)	1.7 (1.11-2.67)	0.6 (0.28-1.23)	2.5 (1.53-4.22)	0.5 (0.01-29.36)
	JUDO	1.6 (1.23-2.03)	0.2 (0.02-1.30)	1.1 (0.66-1.71)	0.6 (0.36-1.13)	1.1 (0.53-2.32)	2.8 (1.30-6.05)
	MARTIAL ARTS	0.5 (0.35-0.67)	1.9 (1.40-2.59)	1.2 (0.82-1.85)	0.6 (0.34-1.05)	1.4 (0.80-2.54)	2.6 (1.22-5.63)
	OTHERS	0.8 (0.65-1.04)	1.0 (0.66-1.48)	0.7 (0.39-1.18)	0.8 (0.56-1.25)	2.4 (1.68-3.47)	1.5 (0.51-4.45)
	RUGBY FOOTBALL	1.4 (1.17-1.65)	0.5 (0.28-0.85)	0.1 (0-3.24)	0.7 (0.44-0.96)	2.2 (1.6-3.01)	4.7 (3.25-6.92)
	RUNNING	1.1 (0.61-1.82)	3.5 (2.02-6.14)	0.2 (0-11.11)	1.1 (0.49-2.55)	0.4 (0.01-20.79) 0.9 (0.01-53.78)
	SKIING	0.6 (0.24-1.46)	0.5 (0.06-4.61)	1.6 (0.47-5.34)	1.0 (0.30-3.32)	1.1 (0.12-9.42)	6.0 (1.73-20.94)
	SOCCER	0.9 (0.89-0.99)	0.7

(0.61-0.76) 0.6 (0.53-0.68) 1.7 (1.58-1.79) 1.3 (1.16-1.48)

  Note. Injured rugby football players were 2.2 times more likely to report a fracture than the entire sample; Bold values indicate a significant higher injury nature risk compared to all the other sports

							1 (0.70-1.37)
	SURFING/WINDSURFING 0.7 (0.42-1.08)	0.6 (0.19-1.76)	1.9 (1.12-3.27)	1.5 (0.87-2.54)	1.2 (0.39-3.6)	0.7 (0.01-39.04)
	SWIMMING	0.5 (0.36-0.70)	1.7 (1.21-2.36)	2.2 (1.58-2.94)	1.2 (0.83-1.73)	0.3 (0.04-2.57)	0.4 (0.01-22.76)
	TENNIS	1.4 (1.22-1.64)	0.6 (0.44-0.94)	2.2 (1.78-2.61)	0.4 (0.29-0.69)	0.4 (0.14-1.14)	0.5 (0.06-3.88)
	URBAN ACTIVITIES	0.8 (0.29-2.11)	0.7 (0.07-6.06)	0.3 (0.01-20.2)	3.6 (1.46-8.72)	1.3 (0.14-12.37) 1.5 (0.02-97.78)
	VOLLEYBALL	0.9 (0.76-1.17)	0.8 (0.48-1.19)	1.7 (1.24-2.24)	0.8 (0.53-1.17)	0.3 (0.04-2.09)	3.0 (1.66-5.45)

Note. a reference category: Girls. Bold values indicate a significate different injury risk. *p<0.05; **p<0.01; ***p<0.001.

Acknowledgements

French adolescents sports injury by level of play and sports We wish to thanks all the schools, teachers and students who took part in the surveys. We also thank the superintendent of schools for having approved this study. We thank Dr.

Thelma Coyle and Ms. Penelope Tilsley for proofreading the manuscript.


R Duvigneau

S Pezzano

M Stauffert

A NURBS-BASED DISCONTINUOUS GALERKIN METHOD FOR CAD COMPLIANT FLOW SIMULATIONS

In this work, we explain how a classical nodal Discontinuous Galerkin (DG) method for conservation laws can be modified to be geometrically exact with respect to CAD (Computer-Aided Design) data. The proposed approach relies on the use of rational Bézier elements, that can exactly match geometries defined by NURBS (Non-Uniform Rational B-Splines) after some basic transformations. It has been found convenient to use the same basis to describe the solution, yielding a so-called isogeometric formulation. The resulting method exhibits optimal convergence rates and facilitates couplings with geometry, e.g. for local refinement, shape sensitivity analysis, or moving computational domains. Illustrations are provided for two-dimensional compressible Euler and Navier-Stokes equations.

NURBS-BASED DG METHOD

We consider a two-dimensional conservation law, that can be written in the conservative form as follows:

∂ W ∂t + ∇ • F = 0, (1)
where W are the conservative flow variables and F = (F x (W), F y (W)) is the vector of fluxes, composed of inviscid fluxes for Euler equations and including both inviscid and viscous fluxes for Navier-Stokes equations. A DG method [START_REF] Hesthaven | Nodal Discontinuous Galerkin Methods[END_REF] is derived from the weak formulation, by multiplying Eq. ( 1) by an arbitrary test function φ and integrating by parts over an arbitrary element Ω j :

Ω j ∂ W ∂t φ dΩ - Ω j F(W) • ∇φ dΩ + ∂ Ω j F(W) • n φ dΓ = 0. ( 2
)
Since the solution is a priori discontinuous at the interfaces, the normal flux is evaluated by a numerical flux function F (W + , W -, n), defined according to the values of the solution that prevail at each side of the interface and the local unit vector n directed outwards.

In order to match exactly a boundary defined by a NURBS curve, a straightforward appproach consists in choosing a rational Bézier patch as element Ω j . Indeed, any NURBS curve can be splitted in a set of rational Bézier curves, by applying a classical CAD transformation known as Bézier extraction [START_REF] Piegl | The NURBS book[END_REF]. Such a patch is defined by a tensor product of rational Bernstein basis functions, associated to control points and weights:

x(ξ , η) = p+1 ∑ i 1 =1 p+1 ∑ i 2 =1 R p i 1 i 2 (ξ , η)X i 1 i 2 , (3)
R p i 1 i 2 (ξ , η) = w i 1 i 2 N p i 1 (ξ ) N p i 2 (η) ∑ n 1 j 1 =1 ∑ n 2 j 2 =1 w j 1 j 2 N p j 1 (ξ )N p j 2 (η) , (4)
where x = (x, y) are the physical coordinates in the patch and

(X i 1 i 2 ) i 1 =1,••• ,n 1 i 2 =1,••• ,n 2 is the lattice of control points. R p i 1 i 2 (ξ , η)
represents the basis function of index i 1 i 2 , defined thanks to the Bernstein polynomials N p i 1 and N p i 2 of degree p in parametric coordinates (ξ , η). The weights

(ω i 1 i 2 ) i 1 =1,••• ,n 1 i 2 =1,••• ,n 2 ,
associated to each control point, are necessary to represent conic curves.

By using the basis functions R p i 1 i 2 to represent the solution fields over each element (isogeometric framework [START_REF] Cottrell | Isogeometric analysis : towards integration of CAD and FEA[END_REF]), and employing R p k 1 k 2 as test function in Eq. ( 2), one obtains:

p+1 ∑ i 1 =1 p+1 ∑ i 2 =1 ∂ W i 1 i 2 ∂t Ω j R p i 1 i 2 R p k 1 k 2 |J Ω | d Ω = Ω j F(W) • ∇R p k 1 k 2 |J Ω | d Ω - ∂ Ω j F (W + , W -, n) R p k 1 k 2 |J Γ | d Γ, ( 5
)
where integrals are expressed in the parametric domain Ω j . This formulation is implemented by using Gauss quadratures and explicit Runge-Kutta methods for time integration. Numerical tests using analytical solutions exhibit optimal convergence rates [START_REF] Duvigneau | Isogeometric analysis for compressible flows using a Discontinuous Galerkin method[END_REF].

ILLUSTRATING RESULTS

As example, we consider the inviscid transonic flow in a nozzle, whose geometry is defined as a cubic NURBS curve. A very coarse discretization of the computational domain is first constructed using a set of 8 × 4 rational Bézier patches, which allow to represent exactly the nozzle geometry. An adaptive refinement strategy is then implemented, controled by a measure of the interface jumps as error indicator. Thanks to the representation used, the refinement process is achieved without alteration of the geometry, because rational Bézier patches can be identically subdivided [START_REF] Piegl | The NURBS book[END_REF]. Here, HLL method is employed to evaluate the inviscid fluxes while shock capturing is achieved using subcell artificial viscosity [START_REF] Persson | Sub-cell shock capturing for Discontinuous Galerkin methods[END_REF] and LDG method [START_REF] Hesthaven | Nodal Discontinuous Galerkin Methods[END_REF] for the resulting viscous fluxes. As illustrated in Fig. [START_REF] Hesthaven | Nodal Discontinuous Galerkin Methods[END_REF], coarse elements are maintained in regular regions, matching however the nozzle geometry exactly, whereas refinement allows a sharp capture of the shock.

FIGURE 1 :

 1 FIGURE 1: transonic flow in a nozzle using cubic NURBS elements.