rywith pre-participation risk factor screening: A pilot study

Jacobsson , Toomas Timpka , Juan-Manuel Alonso ,Jan Kowalski d, e, Sverker NilssonPedro Branco f, i

a Department of Clinical and Exercise Physiology, Sportsb Laboratory of Exercise Physiology (LPE EA 4338), Univc Medical Commission, French Athletics Federation (FFAd Department of Medical and Health Sciences, Faculty oe Swedish Athletics Association, Stockholm, Swedenf ns (IAAF

rthopedtions andmmission

ore than 12 h/weekfor in-competitiontes) were reported.reported (50/1000

a were the most re-

mising results withto extrinsic factors,

. All rights reserved.

mittee (IOC) have an ethical obligation to protect athletes' health(Alonso, Edouard, Fischetto, Adams, Depiesse, & Mountjoy, 2012;Edouard, Branco, & Alonso, 2014; Edouard, Depiesse, Branco, &

o, & Alonso, 2013;e rst step in the& Kemper, 1992),

elite international outdoor Athletics championships have reportedthat 10e14% of athletes sustain an injury and 7% develop an illness(Alonso, Junge, Renstrom, Engebretsen, Mountjoy, & Dvorak, 2009;Alonso, Tscholl, Engebretsen, Mountjoy, Dvorak, & Junge, 2010;Alonso et al., 2012; Edouard, Depiesse, Branco, et al., 2013;Feddermann, Junge, Edouard, & Alonso, 2014). Lower incidenceshave been observed during indoor championships (5e8% for in-juries and 3% for illnesses) (Edouard, Depiesse, Hertert, et al., 2013;

* Corresponding author. Service de Physiologie Clinique et de l'Exercice, Unite deMedecine du Sport, Ho^pital Bellevue, CHU de Saint-Etienne, 42 055 Saint-EtienneCedex 2, France. Tel.: 33 674 574 691; fax: 33 477 127 229.

Contents lists availab

Physical Thera

w

Physical Therapy in Sport xxx (2014) 1e9E-mail address: Pascal.Edouard42@gmail.com (P. Edouard).Athletics Federations (IAAF) and the International Olympic Com- epidemiological injury and illness (I&I) surveillance studies duringSports injury preventionPre-competition medical assessmentInjury/illness surveillanceEpidemiology

sustained at least one injury and/or illness during the championships. Training mpredisposed for sustaining an in-competition injury, and a recent health problemillness. Among the 577 registered athletes, 60 injuries (104/1000 registered athle31% of injuries were caused by the track, and 29% by overuse. 29 illnesses wereregistered athletes); upper respiratory tract infection and gastro-enteritis/diarrhoeported diagnoses.Conclusions: Pre-participation screening using athletes' self-report PHQ showed proregard to identication of individuals at risk. Indoor injury types could be attributedsuch as small track size, track inclination, and race tactics.

2014 Elsevier Ltd

1. Introduction

The European Athletics (EAA), the International Association of

Alonso, 2013; Edouard, Depiesse, Hertert, BrancJunge et al., 2008; Ljungqvist et al., 2009). As thsequence of prevention (van Mechelen, Hlobil,Keywords:pursued during the championships.Results: 74 athletes (58.3%) from the sample submitted a complete PHQ. 21 (28%) of these athletesInternational Association of Athletics Federatiog Sports Medicine Department, Aspetar, Qatar Oh Department of Functional Physiology Explorai European Athletics Medical & Anti Doping Co

a r t i c l e i n f o

Article history:Received 4 March 2014Received in revised form10 April 2014Accepted 26 May 2014http://dx.doi.org/10.1016/j.ptsp.2014.05.0031466-853X/ 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Edouard,risk factor screening: A pilot study, Physicald, e, David Karlsson d, Frederic Depiesse c, h, i,

Medicine Unity, University Hospital of Saint-Etienne, Faculty of Medicine, Saint-Etienne, Franceersity of Lyon, F-42023, Saint Etienne, France), Paris, Francef Health Sciences, Linkoping University, Linkoping, Sweden

), Monacoics and Sports Medicine Hospital, Doha, QatarSports Medicine, Larrey Hospital University Hospital of Toulouse, Toulouse, France, European Athletics Association (EAA), Lausanne, Switzerland

a b s t r a c t

Objectives: To explore the performance of retrospective health data collected from athletes before Ath-letics championships for the analysis of risk factors for in-competition injury and illness (I&I).Methods: For the 2013 European Athletics Indoor Championships, a self-report questionnaire (PHQ) wasdeveloped to record the health status of 127 athletes during the 4 weeks prior to the championship.Physician-based surveillance of in-competition I&I among all 577 athletes registered to compete wasPascal Edouard a, b, c, *, Jenny d, e d f, gOriginal research

Extending in-competition Athletics inju

journal homepage: wP., et al., Extending in-compeTherapy in Sport (2014), httpand illness surveillance

le at ScienceDirect

py in Sport

w.elsevier .com/ptsptition Athletics injury and illness surveillance with pre-participation://dx.doi.org/10.1016/j.ptsp.2014.05.003

heraFeddermann et al., 2014). However, these latter results must beinterpreted with caution due to the low number of covered athletes(Edouard, Depiesse, Hertert, et al., 2013; Feddermann et al., 2014).

For the second step in the sequence of prevention, the currentI&I surveillance system has to be improved by collecting pre-participation data on risk factors. In football (soccer), a pre-competition medical assessment (PCMA) is performed by teamphysicians during the three months before the FIFA championshipsto identify players at risk of injuries or cardiac problems (Dvorak,Grimm, Schmied, & Junge, 2009; Dvorak, Grimm, Schmied, &Junge, 2012). In an effort to adapt pre-participation screeningmethods to the heterogeneity of Athletics (Edouard et al., 2014;Jacobsson, Timpka, Ekberg, Kowalski, Nilsson, & Renstrom, 2010),we hypothesised that self-reporting of an athlete's training prac-tices and health status in the month preceding the championshipsusing a pre-participation health questionnaire (PHQ) would bemore relevant.

Thus, the aim of this study was to explore the performance ofretrospective health data collected from athletes before Athleticschampionships in the analysis of risk factors for in-competition I&I.The incidence and characteristics of I&I sustained during thechampionship were analysed as a baseline.

2. Methods

2.1. Study design and data collection procedures

The study was performed during the European Athletics IndoorChampionships (28 February to 3 March 2013) held at Goteborg,Sweden. It used a nested cohort design involving a sub-cohort ofathletes (n 127) invited to participate in a pilot study of pre-participation risk factors and an I&I surveillance study involvingthe total cohort of athletes registered at the event (n 577).

1) Pre-participation risk factor study: This part of the study wasbased on a sub-cohort of athletes belonging to 6 national teamswhose team physicians accepted to inform and activelyencourage their athletes to return a pre-participation healthquestionnaire (PHQ). At the start of the championships, theseathletes were asked to provide individual pre-participation in-formation (personal, training, and health status during themonth preceding the championships) using a PHQ (Fig. 1). Allnew I&I during the championships were recorded using the I&Isurveillance system. The athletes' gender, date of birth and na-tionality were used to combine pre-participation and I&I sur-veillance data.

2) I&I surveillance study: The recording of all new I&I occurringduring the championships among all the athletes registered(total cohort of athletes) was made by national medical teams(physicians and/or physiotherapists) and/or by the local organ-ising committee (LOC) following the methodology previouslyused during international Athletics championships for moreinformation about implementation, denitions and reportforms see previous publications (Alonso et al., 2009, 2010, 2012;Edouard, Depiesse, Branco, et al., 2013; Edouard, Depiesse,Hertert, et al., 2013; Junge et al., 2008).

All the national medical teams and LOC were informed aboutthe study two months before the championships by e-mail, and theday before the championships during an instructional meeting.

2.2. Denitions

A pre-participation health problem was for the present study

P. Edouard et al. / Physical T2dened as any pain, injury and/or illness reported by an athlete to

Please cite this article in press as: Edouard, P., et al., Extending in-comperisk factor screening: A pilot study, Physical Therapy in Sport (2014), httphave occurred during the month prior to the championships. Anew health problem was any new injury and/or new illnessrecorded by their national medical team and/or LOC during thechampionships.

2.3. Development of the pre-participation health questionnaire

The PHQ was developed by a group of experts consisting ofscientists and practitioners (n 9) with backgrounds in sportsmedicine and physiotherapy, epidemiology, biostatistics and Ath-letics coaching. The PHQ was developed to collect relevant infor-mation directly from the athletes regarding their morphologicalstatus, training conditions, and pre-competition I&I (Alonso et al.,2012; Edouard, Depiesse, Branco, et al., 2013). The overall goalwas to identify possible in-championship I&I risk factors bycomparing data from this questionnaire with those from I&I sur-veillance. In practice, the questionnaire needed to be easy to un-derstand and quick to complete, enabling its use during majorchampionships with a maximum response rate.

During telephone meetings, the authors developed a list ofquestions based on previous ndings fromAthletics studies (Alonsoet al., 2012; Edouard, Depiesse, Branco, et al., 2013; Edouard,Depiesse, Hertert, et al., 2013; Jacobsson et al., 2013) regardingthe athlete's characteristics (gender, age, country, height, weight,discipline, time spent in training) and health status (fatigue, pain,injury and illness). It was agreed that the health status part of thequestionnaire should focus only on the month immediately pre-ceding the championships, as this would avoid recall bias. Self-reported data were chosen because such athletes are not alwaysregularly followed-up by ofcial or team physicians, and alsobecause this would better reect the heterogeneous nature ofAthletics disciplines (Jacobsson et al., 2010). The questions wereformulated in line with a questionnaire used for monitoring over-use sports injuries, especially regarding the term problem(Clarsen, Myklebust, & Bahr, 2013).

The PHQ was deliberately designed to be short in order toimprove the response rate. The design of the documentation formalso aimed at simplicity, clearness, and data accuracy. Checkboxeswere used, and spaces for free text were provided for describing thediagnosis with the help of the tutorial and team physicians, ifneeded. Iterative corrections/comments were made by e-mail cor-respondence between the authors until there were no furtherchanges. After the nal iteration, all authors were asked to conrmtheir agreement with the nal PHQ. The PHQwas created in Englishbut also translated into Spanish and French by native speakers(JMA, PE). The PHQ (Fig. 1) was given to athletes in paper formateither in their welcome bag or by their team physicians. They wereasked to complete the questionnaire either alone or with help oftheir team physicians if necessary, and to return it to their teamphysician or the designated desk at the warm-up area.

2.4. Condentiality and ethical approval

The athletes' gender, date of birth and nationality were usedonly to avoid duplicate reporting from team and LOC physicians andto link I&I surveillance data to that collected during the pre-participation survey. The athletes were informed of the purposeand procedures of the study via an information letter in theirwelcome bags. All athletes were free to refuse that their data beused. All I&I reports were stored in a locked ling cabinet and wererendered anonymous after the championships. Condentiality of allinformation was ensured so that no individual athlete or nationalteam could be identied. Ethical approval was obtained from theSaint-Etienne University-Hospital Ethical Committee (Institutional

py in Sport xxx (2014) 1e9Review Board Information: IORG0004981).

tition Athletics injury and illness surveillance with pre-participation://dx.doi.org/10.1016/j.ptsp.2014.05.003

Fig. 1. The pre-participation health questionnaire for athletes was preceded by a letter of information sent to the athletes, presenting the study, and its objectives, the condentialityof data and the rights of athletes, and was followed by a tutorial to help athletes describe their potential health problems. The tutorial was similar to the I&I description in the I&Ireport forms with the addition of pictorials for injured body parts.

P. Edouard et al. / Physical Therapy in Sport xxx (2014) 1e9 3

Please cite this article in press as: Edouard, P., et al., Extending in-competition Athletics injury and illness surveillance with pre-participationrisk factor screening: A pilot study, Physical Therapy in Sport (2014), http://dx.doi.org/10.1016/j.ptsp.2014.05.003

2.5. Data analysis

All data were presented using frequency and percentages forcategorical data, and mean and standard deviations for continuousvariables.

For the pre-participation risk factor study, the response rate andcompleteness of PHQs were calculated and the athletes' charac-teristics and health problems were analysed using descriptive sta-tistics. An explorative analysis was thenmade to develop a decisionalgorithm aimed at identifying athletes at risk. The explorativeanalysis was initiated by calculating the relative risk (RR) and 95%condence interval (95%CI) of sustaining a new injury or newillness or new health problem during the championships for vari-ables recorded in the questionnaire (gender, age, mean trainingtime per week, fatigue, pre-participation health problems, pain,injuries, and illnesses during the month preceding the champion-ship). For age, mean training time per week and fatigue, the cut-offswere xed at the median (25 years, 12 h of training per week, and37 on the Visual Analog Scale, respectively). Multivariate analysiswas then performed by stepwise regression and logistic regressionto include all these variables, with new injuries, new illnesses, andnew health problems as endpoints in three different models; 0.3signicance level was needed for entry into the model, and 0.2 foroutput, using SAS 9.2 (SAS Institute Inc., Cary, NC, USA). As a po-tential decision support in the pre-participation health screeningprocess, athletes' I&I risk assessment algorithms (or decision treesor classication and regression trees) were also generated andevaluated using MatLab (The MathWorks Inc., Natik, MA, USA)(Rokach &Maimon, 2008). Sensitivity and specicity were used as

measures for evaluating the discriminatory performance of thealgorithm.

For the I&I surveillance study, the response rate and coverageand the I&I incidence calculations and comparisons were analysedusing the t-test for comparisons of continuous data and the Chi2-test was used for categorical data, in accordance with previous I&Isurveillance studies (Alonso et al., 2012; Edouard, Depiesse, Branco,et al., 2013; Edouard, Depiesse, Hertert, et al., 2013), and using a listof athletes provided by the EAA and the internet database (http://www.goteborg2013.com).

All other data were processed using Excel. All tests were two-sided and p < 0.05 was regarded as statistically signicant.

3. Results

3.1. Pre-participation risk factors study

The response rate to the PHQ was 60.6% (n 77) in the sub-cohort of 127 athletes from 6 participating national teams(within-team response ranged from 45.2% to 100%). Data for riskfactor analyses were complete for 74 PHQs (96.1% of the returnedPHQs) (Table 1). These 74 athletes did not differ from all theregistered athletes (n 577) regarding age, gender, countries anddisciplines. Twenty-one athletes participating in the pilot studysuffered at least one new health problem during the champion-ships: 13 athletes were injured, 11 fell ill, and 3 experienced bothinjury and illness. The univariate analysis showed that athleteshaving trainedmore than 12 h per week were at increased risk for anew injury, and athletes who presented a pre-participation health

Table 1Pre-participation (or pre-competition) risk factor study: Pre-participation health questionnaire results for the 74 athletes who returned their PHQ with complete data for in-championship risk factor analyses.

haraor 1sed)bef

6); M

P. Edouard et al. / Physical Therapy in Sport xxx (2014) 1e94Athletes' characteristics (mean (SD))Age (years)Height (cm)Weight (kg)BMI (kg m2)Mean training time per week in the last month (h)Fatigue (Visual Analog Scale in %)

During the last month, have you had any difculties participating innormal training and competition due to health problems?Full participation without health problemsFull participation, but with health problemsReduced participation due to health problemsCannot participate due to health problems

No pre-participation health problems beforePre-participation health problems beforeb:PainInjuryIllness

For athletes who presented health problems: To what extenthas/have the health problem(s) affected your performanceduring training and/or competition?No reductionTo a minor extentTo a moderate extentTo a major extentCannot participate at all

Among the 77 returned PHQ, the completeness rate was 98.3% for the main athlete's cage and country were missing for one athlete, height for 5, weight for 1 and training freported in one PHQ, but data regarding the details of health problemswere not analymean training time, fatigue, pain, injury, illness and health problem during the month77 PHQ. The 74 complete PHQ (96.1%) were analysed.Repartition of athletes who returned PHQ by discipline: Sprints (n 21), Hurdles (nCombined events (n 8).

a Data on height and IMC were missing for two female and three male athletes.b Among the 40 athletes with pre-participation health problems, 7 reported associatio

Please cite this article in press as: Edouard, P., et al., Extending in-comperisk factor screening: A pilot study, Physical Therapy in Sport (2014), httpTotal (n 74) Female (n 27) Male (n 47)Mean (SD) Mean (SD) Mean (SD)25.3 (4.2) 24.7 (4.0) 26.4 (4.4)

179.0 (9.6)a 183.8 (7.5)a 170.6 (6.7)a

70.1 (14.7) 77.1 (13.6) 58.2 (6.6)21.7 (2.9)a 22.7 (3.1)a 20.1 (1.5)a

13.8 (5.6) 14.0 (6.3) 13.6 (4.3)41.1 (23.4) 37.1 (22.8) 47.9 (23.4)

n (%) n (%) n (%)

34 (45.9) 12 (44.4) 22 (46.8)23 (31.1) 9 (33.3) 14 (29.8)16 (21.6) 6 (22.2) 10 (21.3)1 (1.4) 0 (0.0) 1 (2.1)

34 (45.9) 12 (44.4) 22 (46.8)40 (54.1) 15 (55.6) 25 (53.2)22 (55.0) 9 (60.0) 13 (52.0)18 (45.0) 9 (60.0) 9 (36.0)13 (32.5) 5 (33.3) 8 (32.0)n (%) n (%) n (%)

4 (10.3) 2 (14.3) 2 (8.0)18 (46.2) 6 (42.9) 12 (48.0)13 (33.3) 4 (28.6) 9 (36.0)3 (7.7) 2 (14.3) 1 (4.0)1 (2.6) 0 (0.0) 1 (4.0)

cteristics (gender, age, country, height, weight, discipline, time spent to training; the); and 99.4% for health status (fatigue and previous health problems; fatigue was not. For the 8 variables used in the in-championship I&I risk factor analyses (gender, age,ore the championship), the completeness rate was 99.5%; only 3 were missing of the

iddle distances (n 16), Long distances (n 5), Jumps (n 16), Throws (n 2), andn of pain & injury, 1 I&I, 2 pain & illness, and 2 pain & I&I; one is missing.

tition Athletics injury and illness surveillance with pre-participation://dx.doi.org/10.1016/j.ptsp.2014.05.003

problem were more likely to suffer a new illness (Table 2). Themultivariate analyses showed that training more than 12 h perweek was a risk factor for new injury, and that a pre-participationhealth problem predisposed for new illnesses; both these factorswere associated with the risk of new health problems during thechampionships (Table 3). The I&I risk assessment algorithms arepresented in Fig. 2.

3.2. Injury and illness surveillance study

All the 29 medical teams present during the championship(61.7% of the 47 national teams) participated in this study covering528 athletes (91.5% of 577 registered) with I&I report form response

p 0.30) and illness (Chi 2.83; p 0.09) incidence proportions.

Ljungqvist et al., 2009) and to perform PCMA or PHE before Ath-letics championships (Dvorak et al., 2009; Ljungqvist et al., 2009).

A previous injury is known to represent a risk factor for furtherinjury (Jacobsson et al., 2013; Malliaropoulos, Isinkaye, Tsitas, &Maffulli, 2011; Malliaropoulos, Ntessalen, Papacostas, Longo, &

the championship presented as Relative Risks (95%CI) calculated by univariate analysis.complete data on the following 8 variables.

New illness during Championships New health problem during Championships

RR (95%CI lowereupper) RR (95%CI lowereupper) RR (95%CI lowereupper)

0.7 (0.2e2.0) 0.9 (0.4e2.0)2.1 (0.7e6.4) 1.3 (0.6e2.7)1.5 (0.5e4.9) 2.2 (1.0e5.1)*0.6 (0.2e1.8) 1.1 (0.5e2.3)1.4 (0.4e4.2) 1.5 (0.7e3.0)2.6 (0.9e7.5) 1.9 (0.9e3.9)1.0 (0.3e4.3) 0.8 (0.3e2.3)8.5 (1.1e63.1)* 2.7 (1.1e6.7)*

Table 3Risk factors for sustaining a new injury (Model 1), a new illness (Model 2) or a newhealth problem (Model 3) during the championship presented as Relative Risks (95%CI) calculated by multivariate analysis.

Variables (exposed/unexposed) p RR (range of 95%IC)

Model 1: For a new injury during Championships:Training in hours per week (>12:12) 0.02 6.3 (1.3e31.3)*Fatigue (>37%:37%) 0.17Model 2: For a new illness during Championships:Pre-participation health problem before

Championships (Yes:No)0.02 15.9 (1.7e142.9)*

Fatigue (>37%:37%) 0.07Age 0.16Model 3: For a new health problem during Championships:Pre-participation health problem before 0.007 5.6 (1.6e19.6)*

P. Edouard et al. / Physical Therapy in Sport xxx (2014) 1e9 5Gender (M:F) 0.9 (0.3e2.5)Age (25:12:12) 4.9 (1.2e20.7)*Fatigue (>37%:37%) 2.3 (0.8e6.7)Pain before Championships (Yes:No) 1.5 (0.5e4.0)Injury before Championships (Yes:No) 1.9 (0.7e5.2)Illness before Championships (Yes:No) 0.8 (0.2e3.3)Pre-participation health problem

before Championships (Yes:No)1.9 (0.6e5.7)A total of 60 injuries were reported, representing an incidence of104.0 injuries per 1000 registered athletes (95% Condence Interval(CI): 79.1e128.9), including 24 time-loss injuries (40%) (Table 4).Themost common diagnosis was hamstring strain (11.7% of injuriesand 20.8% of time-loss injuries). The most frequent types of injurywere skin lacerations (n 21; 35%), followed by strains (n 9; 15%)and muscle cramps (n 8; 13.3%). The most commonly reportedcause of injury was eld play conditions (or track conditions)(n 18; 31%), followed by overuse (n 17; 29.3%), non-contacttrauma (n 6; 10.3%), and contact with another athlete (n 5;8.6%). Injury and time-loss injury risk during nals was signicantlyhigher than during qualifying rounds (Chi2 7.3 and 8.3; p 0.01).The proportion of injuries (Chi2 54.3; p < 0.001) and time-lossinjuries (Chi2 44.1; p < 0.001) differed signicantly betweendisciplines: the risk was higher in combined events (Table 4).

A total of 29 illnesses were reported, equivalent to an incidenceof 50.3 illnesses per 1000 registered athletes (95%CI: 32.4e68.1)and 12.6 illnesses per 1000 athlete days (95%CI: 8.0e17.1), including7 time-loss illnesses (24.1%; 12.1 illnesses per 1000 registeredathletes (95%CI: 3.2e21.1) and 3 time-loss illnesses per 1000athlete days (95%CI: 0.8e5.3)) (Table 4). Upper respiratory tractinfection was the most commonly reported diagnosis (27.6% of ill-nesses and 57.1% of time-loss illnesses), followed by gastro-enteritis/diarrhoea (24.1% of illnesses and 42.9% of time-loss ill-nesses); no dehydration and no episodes of cardio-vascular relatedcollapse and syncope were recorded.

4. Discussion

The main ndings of this study were that 1) the self-report PHQused by the athletes allowed the development of I&I risk

Table 2Risk factors for sustaining a new injury, a new illness or a new health problem duringAnalyses were performed on the sample of 74 athletes who returned the PHQ with

Variables (exposed/unexposed) New injury during Championshipsrates of 100%. No athlete refused that his/her data be used for sci-entic research. There were no statistically signicant differencesbetween athletes with (n 528) and without (n 49, covered onlyby LOC) accompanying medical teams in injury (Chi2 1.05;

2*Signicant RR (P < 0.05); 95%CI, 95% condence interval.

Please cite this article in press as: Edouard, P., et al., Extending in-comperisk factor screening: A pilot study, Physical Therapy in Sport (2014), httpassessment algorithms for pre-participation screening of athletes,and 2) indoor injury types could, to a large extent, be attributed toextrinsic factors, such as small track size, track inclination, andtactical races.

4.1. Pre-participation risk factors study

Although the PHQ was understandable and feasible for athletes(completeness rate of 98.3e99.4%), our results reported a moderateresponse rate (60.6%). This could be explained by methodologicalfactors: the PHQ was available in only 3 languages, the paper formwas not an optimal tool, PHQ could have been missing from somewelcome bags, athletes did not fully understand the study's aim,athletes did not ndwhere to return their questionnaire. Moreover,it could also be due to psychological factors: athletes did not wantto reect upon pre-participation health problems just before takingpart in the competition. In order to improve participation in futurestudies knowledge of these barriers needs to be claried. It could berelevant to develop a web-based system (such as the electronichealth record system proposed by IOC (Engebretsen et al., 2013))whereby the PHQ could be sent to athletes and/or medical teamsprior to the championships and to involve the medical teams in thestudy encouraging them to actively participate in completing thePHQ. Moreover, although no cardiac problems were recorded, itcould also be useful to include cardiac assessment such as in PCMAand periodic health evaluation (PHE) (Dvorak et al., 2009;

Championships (Yes:No)Training in hours per week (>12:12) 0.05 3.2 (1.0e10.3)*Illness before Championships (Yes:No) 0.13

*Signicant RR (P < 0.05); 95%CI, 95% condence interval. The variables presented inthe table are those accepted by the model with p < 0.2.tition Athletics injury and illness surveillance with pre-participation://dx.doi.org/10.1016/j.ptsp.2014.05.003

Fig. 2. The decision algorithm (or decision tree) describes (and can help to determine) the risk of an athlete who starts to compete sustaining a new injury (A), new illness (B) ornew health problem (C) during the championship according to his/her pre-participation training and health status. The evaluation of the predictive performance of the decisionalgorithms displayed poor sensitivity for predicting new injury (0.29), while the sensitivity for predicting new illness was moderate (0.80) and low for new health problems (0.55).Specicities in the moderate range were observed for predicting new injury (0.73), new illness (0.84), and any new health problem (0.73).

P. Edouard et al. / Physical Therapy in Sport xxx (2014) 1e96

Please cite this article in press as: Edouard, P., et al., Extending in-competition Athletics injury and illness surveillance with pre-participationrisk factor screening: A pilot study, Physical Therapy in Sport (2014), http://dx.doi.org/10.1016/j.ptsp.2014.05.003

of

heraMaffulli, 2009; Rebella, Edwards, Greene, Husen, & Brousseau,2008). In the present pilot study, our preliminary results showedthat athletes who presented a pre-participation health problem inthe month preceding the championship were about six times more

Table 4Athletes, exposure, injury and illness in different discipline categories.

Sprints

PopulationRegistered athletes (n) 151Competing athletes (excluding DNS) (n) 137Athlete participations (n) 221InjuriesNumber of injuries (n) 16Training (n) 1Competition (n) 15

Injuries per 1000 registered athletes 106.0Injuries per 1000 competing athletes (excluding DNS) 116.8Injuries per 1000 athlete participations 72.4Injuries in competition per 1000 competing athletes (excluding DNS) 109.5Time-loss injuries0 days (n) 121e7 days (n) 28e28 days (n) 2>4 weeks (n) 0

Time-loss injuries per 1000 registered athletes 26.5Time-loss injuries per 1000 competing athletes (excluding DNS) 29.2Time-loss injuries per 1000 athlete participations 18.1IllnessesNumber of illnesses (n) 8Illnesses per 1000 registered athletes 53.0Illnesses per 1000 competing athletes (excluding DNS) 58.4Illnesses per 1000 athlete participations 36.2Number of time-loss illnesses (n) 0

a Since some athletes competed in more than one discipline, this is not the sumregistered.

P. Edouard et al. / Physical Tlikely to suffer a newhealth problem during the championship thanthose who did not. The training load before the championships alsoseems to be a risk factor. Therefore more attention should befocused on athletes who have endured heavy training loads andsuffered previous health problems before championships. Themoderate performance with regard to injury specicity may belargely explained by the fact that most of injuries were caused byextrinsic factors (track conditions) which might not appear to beassociated with pre-participation health status. However, the re-sults clearly indicate that more research into pre-participationhealth screening before Athletics championships is warranted.Since this pilot study aimed to evaluate the feasibility of using aPHQ to identify risk factors discussion of these results focussedrather on the conceptual aspects than the clinical results alone.

4.2. I&I incidence and characteristics during the European AthleticsIndoor Championship

The major strength of this I&I surveillance study was that 92% ofathletes were covered by national medical teams who returned thereport forms with 100% response rate. This suggests a higherquality of data than during previous Athletics indoor champion-ships (Edouard, Depiesse, Hertert, et al., 2013; Feddermann et al.,2014), and supports the interest of the presentation of these data.However, although there were no statistically signicant differ-ences, the number of injuries and illnesses reported for athleteswith medical teams and those without indicated a possible differ-ence; this issue should be highlighted in further studies.

The recorded incidence of injuries and time-loss injuries inGoteborg 2013 was higher than during the previous Athletics In-door Championships (Edouard, Depiesse, Hertert, et al., 2013;

Please cite this article in press as: Edouard, P., et al., Extending in-comperisk factor screening: A pilot study, Physical Therapy in Sport (2014), httpFeddermann et al., 2014), suggesting that a low proportion of ath-letes covered by medical teams and low response rates by medicalteams has resulted in an underestimation of the risk of injuryduring indoor championships.

Hurdles Middle andlong distances

Jumps Throws Combinedevents

Total

58 135 183 43 30 577a

52 128 176 40 30 563100 206 242 56 175 1000

6 15 8 1 14 600 1 0 0 0 26 14 8 1 14 58

103.4 111.1 43.7 23.3 466.7 104.0115.4 117.2 45.5 25.0 466.7 106.660.0 72.8 33.1 17.9 80.0 60.0

115.4 109.4 45.5 25.0 466.7 103.0

3 10 5 0 6 363 1 2 1 4 130 2 1 0 3 80 2 0 0 1 3

51.7 37.0 16.4 23.3 266.7 41.657.7 39.1 17.0 25.0 266.7 42.630.0 24.3 12.4 17.9 45.7 24.0

2 10 7 1 1 2934.5 74.1 38.3 23.3 33.3 50.338.5 78.1 39.8 25.0 33.3 51.420.0 48.5 28.9 17.9 5.7 29.01 5 1 0 0 7

registered athletes in each different event group but the total number of athletes

py in Sport xxx (2014) 1e9 7Diagnoses of injuries were in agreement with previous studies(mostly hamstring strains and ankle sprains) (Alonso et al., 2009,2010, 2012; Edouard, Depiesse, Branco, et al., 2013), but therewere also many knee and lower leg skin lesions. Lower limb skinlesions could be explained by the higher number of contact injuries(contact with other athletes, immobile ormoving objects, and falls).The cause behind the injuries observed differed from those notedduring previous Athletics championships: the majority werecaused by eld play (or track) conditions. Small track size, trackinclination, or race tactics could be an explanation, suggesting aninteresting direction for the prevention of indoor Athletics injuries.In line with previous studies we also found overuse injuriesaccounted for one-third of all injuries (Alonso et al., 2009, 2010,2012; Edouard, Depiesse, Hertert, et al., 2013), and others sports(Clarsen et al., 2013; Engebretsen et al., 2013). Better understandingof overuse injuries is needed but possible prevention measurescould be: early treatment of acute injuries, eliminating periods ofovertraining and improving preventative strengthening and re-covery programs (Alonso et al., 2012; Edouard, Depiesse, Branco,et al., 2013; Jacobsson et al., 2013).

Illness incidences per 1000 athlete days during Goteborg 2013was higher than during previous outdoor championships (12.6 vs.4e7.6) (Alonso et al., 2010, 2012; Edouard, Depiesse, Branco, et al.,2013). The difference in climatic conditions, the season (winter)and air cleaning systems might explain these differences (Edouard,Depiesse, Hertert, et al., 2013), but also the fact that all the athletesat this championship were staying at the same hotel. In agreementwith previous published recommendations, illness preventionmeasures should pay attention to temperature and climatic con-dition changes, air cleaning systems, and implement the recom-mendations for infectious disease prevention (Alonso et al., 2012;

tition Athletics injury and illness surveillance with pre-participation://dx.doi.org/10.1016/j.ptsp.2014.05.003

heraEdouard, Depiesse, Branco, et al., 2013; Engebretsen et al., 2010;Hanstad, Ronsen, Andersen, Steffen, & Engebretsen, 2011; Tillett& Loosemore, 2009; ).

4.3. Strengths and limitations

The rst major strength of this study was the high quality of I&Isurveillance. However, the sample size of athletes (n 577) couldbe considered as too small to draw conclusions on I&I incidencesand characteristics during indoor championships. The second ma-jor strength is that it is the rst study, to our knowledge, that hasaimed to calculate/determine in-championship I&I risk factors us-ing pre-participation characteristics of the athletes. Although thisstudy should be considered as a pilot study and the results shouldbe interpreted with caution given the small number of study par-ticipants, it provides valuable insights into how the informationfrom the PHQ can be used effectively in future studies and how tomaximise athlete participation.

The study's limitations include the retrospective design of thePHQ which could induce a recall bias, although this should havebeen minimal as we asked subjects only about the 4 weeksimmediately before the competition. There may have been a pop-ulation bias for the risk factor analyses since there was a slightlyhigher illness and health problem rate in the sub-cohort of athletes,although the 74 athletes was representative of all 577 athletesregarding age, gender, countries and disciplines. It is also uncertainwhether the athletes who reported a health problem were fullyrecovered at the time of the championships. So there could havebeen a bias, reporting the same health problem in the PHQ andduring the championships. The metrological characteristics of thePHQ should be analysed/determined in further study. These pre-liminary results regarding risk factors and the predictive perfor-mance of the decision algorithms need to be conrmed in futurestudies, where the nal decision algorithms can be prospectivelyevaluated using new populations of athletes.

5. Conclusions

The athletes' self-reported PHQs allowed the development of I&Irisk assessment algorithms for pre-participation screening of in-dividuals at risk. Injuries suffered at indoor championship can to alarge extent be attributed to extrinsic factors, such as small tracksize, track inclination, and race tactics. This study highlights inter-esting areas for future research and the importance of routinelycollecting I&I data at championships.

Conict of interestNone declared.

Ethical approvalThe Saint-Etienne University-Hospital Ethical Committee

approved this study; Institutional Review Board Information:IORG0004981.

FundingNone declared.

Acknowledgements

The authors greatly appreciate and would like to thank themedical staff of the EAA Indoor Championships (Dr Peter Bergh(SWE)) for their cooperation and the team physicians who vol-unteered their time to collect the data for this project: Dr UlrichLanz (AUT), Dr Claire Sneyers (BEL), Dr Dzmitry Savenia (BLR), Dr

P. Edouard et al. / Physical T8Angel Lozanov (BUL), Mario Dusak (CRO), Dr Jiri Neumann (CZE), Dr

Please cite this article in press as: Edouard, P., et al., Extending in-comperisk factor screening: A pilot study, Physical Therapy in Sport (2014), httpJosena Espejo (ESP), Dr Rauno Anders Elokiuru (EST), Dr IlkkaTulikoura (FIN), Dr Jean-Michel Serra (FRA), Dr Robin Chakraverty(GBR), Dr Steffen Brand (GER), Theodoros Vlachopoulos (GRE), IgazBalint (HUN), Emma Gallivan (IRL), Dr Guisseppe Fischetto (ITA),Mareks Osovskis (LAT), Dr Daluis Barkauskas (LTU), Joost Vollaard(NED), Peter Eemers (NED), Klas Eliasson (NOR), Michal Roba-kowski (POL), Dr Isabel Crespo (POR), Dr Vasile Osanu (ROU), JustinRadutu (ROU), Dr Vasily Avramenko (RUS), Nasif Khalid (SLO),Tobias Powalla (SUI), Dr Jenny Lingman-Framme (SWE), Omer FatihAkca (TUR), Dr Natalia Boiarska (UKR). The authors would also liketo thank Vincent Pichot (Laboratory EA4607 SNA-EPIS, JeanMonnetUniversity of Saint-Etienne, PRES Lyon, Saint-Etienne, France) andPaul Vercherin (Department of medical and health sciences, Uni-versity Hospital of Saint-Etienne, Saint-Etienne, France) for theirinvaluable help and contribution for the statistical analyses.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ptsp.2014.05.003.

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P. Edouard et al. / Physical Therapy in Sport xxx (2014) 1e9 9Please cite this article in press as: Edouard, P., et al., Extending in-comperisk factor screening: A pilot study, Physical Therapy in Sport (2014), httptition Athletics injury and illness surveillance with pre-participation://dx.doi.org/10.1016/j.ptsp.2014.05.003

Extending in-competition Athletics injury and illness surveillance with pre-participation risk factor screening: A pilot study1 Introduction2 Methods2.1 Study design and data collection procedures2.2 Definitions2.3 Development of the pre-participation health questionnaire2.4 Confidentiality and ethical approval2.5 Data analysis

3 Results3.1 Pre-participation risk factors study3.2 Injury and illness surveillance study

4 Discussion4.1 Pre-participation risk factors study4.2 I&I incidence and characteristics during the European Athletics Indoor Championship4.3 Strengths and limitations

5 ConclusionsConflict of interestEthical approvalFundingAcknowledgementsAppendix A Supplementary dataReferences