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Supplement 3 – Summary of the included results indicating the relationship between injuries and/or illnesses and success or failure performance outcomes Study (year) Study design Number of participants, sport(s), level of competition, age (mean ± SD), team seasons (where appropriate) Definition of an success and/or failure Definition of injury and/or illness Statistical method Key findings Athletics Raysmith and Drew (2016) Prospective cohort 33 track and field athletes across 5 consecutive international seasons, age not reported, 76 athlete seasons Success: achieving their key performance goal during the season e.g. personal best time or finishing position at a major event Sports incapacity: unable to participate in training for greater than 24 hours due to an injury or illness. These were converted to “modified training weeks” RR and attributable risks; Mixed model logistic regression Success: Athletes who achieved >80% of training weeks were seven times more likely to reach their goal (RR 7.16, 95%CI 1.84-27.89). Remaining injury/illness free significantly increased the chance of success (RR 2.26, 95%CI 1.33-3.83). Sustaining ≤2 injuries or illnesses in the season increased chance of success three-fold (RR 3.13, 95%CI 1.43-6.84). Failure: Athletes who achieved <80% of training weeks were twice as likely to fail (RR 1.97, 95%CI 1.44-2.71). Sustaining >2 injuries or illnesses in the season increased chance of failure two-fold (RR 1.79, 95%CI 1.24-2.57). Remaining injury/illness free halved the chance of failure (RR 0.47, 95%CI 0.220.99). For every week modified there was 26% reduction in the odds of reaching their goal (OR 0.74, 95%CI 0.58-0.94, p=0.01). Basketball Podlog et al (2015) Retrospective cohort 30 professional basketball teams over period of 30 years, age not reported, 685 team seasons In-season games won Sports incapacity: unable to participate in a match due to an injury or illness. No distinction was possible between the two time-loss events (injury and illness). Linear mixed models with random effects for team Observed modest inverse correlation between number of missed-games due to injury/illness and percentage of games won (r= -0.29, p<0.0001). Football Arnason et al (2004) Prospective cohort 17 elite and first division Icelandic football clubs, 301 athletes, mean age 24, range 16-38, 17 team seasons Final league standing Sports incapacity: unable to participate in a match or training session Linear regression with final league standing as the outcome and total days injured as the independent variable Observed trend towards lower final standing position by club and total days injured (B 13.2±7.3. p=0.092). Bengsston et al (2013) Retrospective analysis of prospective cohort 26 professional clubs across ten countries over nine seasons, unknown number of athletes, unknown age, 234 team seasonsc Competition result win/loss/draw Sports incapacity: unable to participate in a match or training session General Estimating Equations (GEE) with logit link fitted to matchlevel data All time-loss injuries: Increases odds of draw (OR 1.39, 95%CI 1.15-1.69, p=0.01) or loss (OR 1.66, 95%CI 1.38-1.98, p<0.001) were observed if ≥2 injuries occurred. No observed relationship if only one injury recorded in the game. Time-loss injuries with >1 week absence: Observed increased odds of loss (OR 1.28, 95%CI 1.11-1.48, p=0.001) if one injury occurred. No relationship with a draw if only one injury occurred. Observed increase in odds of both draw (OR 2.14, 95%CI 1.60-2.88, p<0.001) and loss (OR 1.98, 95%CI 1.41-2.80, p<0.001) if ≥2 injuries occurred. Carling et al (2015) Case study 1 professional club across five consecutive seasons, approx. 140 athletes, unknown age, 5 team seasons Championship winning season Sports incapacity: unable to participate in a match or training session Descriptive statistics, one-way MANOVA for injury-related variables Squad utilisation: In the championship winning season the club utilised the lowest number of players (84.0% versus 84.689.3%). In the winning season, 10 players participated in >75% of the total minutes compared with 6, 6, 5 and 4 in the other seasons achieving this threshold. Injury-related variables: Lower incidence in the winning season compared with one (p<0.001) but not all other seasons (p>0.05), average working days missed due to injury (p<0.001) and percentage of squad unavailable due to injury (p<0.01). Dauty and Collon (2011) Case study Hagglund et al (2013) Prospective cohort 1 professional club across 15 consecutive seasons, approx. 173 athletes, age not reported, 15 team seasons 24 professional teams across nine countries over 11 seasons, number and age of athletes not reported, 155 team seasons Final league standing Sports incapacity: unable to participate in a match or training session Pearson product moment correlation coefficient No correlation observed between injury incidence and final league standing. No correlation observed between injury and final league standing when stratified for severity. UEFA Season Club Coefficient (UEFA SCC), final league ranking and points per league matchb Sports incapacity: unable to participate in a match or training session GEE to fit a linear regression on team-level data; Adjusted for change of head coach Adjusted analyses results: Significant relationship between injury burden (β 0.01, 95%CI -0.017 to -0.002, p=0.01), match availability (β -0.09, 95%CI -0.01 to -0.16, p=0.03) and final league ranking. No relationship between injury incidence and these variables. Significant relationship between injury burden (β 0.002, 95%CI -0.003 to -0.001, p<0.001), match availability (β 0.02, 95%CI 0.009 to 0.028, p<0.001), incidence (β -0.02, 95%CI 0.046 to 0.002, p=0.035) and points per league match. Significant relationship between injury burden (β - 0.021, 95%CI -0.042 to -0.001, p=0.043), match availability (β -0.205, 95%CI -0.042 to -0.001, p<0.043) and points per league match but not incidence. Eirale et al (2012) Prospective cohort 10 professional firstdivision clubs in Qatar, unknown athlete numbers, age not reported, 10 team seasons Final league position, number of games won, number of goals scored, goal difference and total points Sports incapacity: timeloss in daysa Injury incidence in the club Spearman’s correlation coefficient calculated for both injury incidence rate and injury severity against measures of team success Strong relationship observed between clubs which had lower injury incidence and higher league position (r=0.93, p<0.01), more games won (r=0.88, p<0.01), more goals scored (r=0.89, p<0.01), greater goal difference (r=0.82, p<0.01) and total points in the season (r=0.93, p<0.01). No association between total days lost due to injury and the above success outcome variables. Taekwondo Feehan et al (1995) Prospective cohort 48 national level taekwondo (TKD) athletes, age 22.2 ± 5.7), 48 athlete competitions (single tournament) Win-loss record in the official first round only Kazemi (2012) Retrospective case series 45 international level Taekwondo athletes over a 10 year period, age 24.6 (± 5.6) years, 75 athlete competitions Medals won during approved World Taekwondo Federation Championships Australian Football Verrall et al (2006) Case series One professional club across 2 seasons, 20 hamstring cases, age not reported, 2 season years Coach rating of performance using 10point scale Sports incapacity or clinical assessment: unable to participate in normal training for at least one session or required at least one visit to a health professional for treatment Three definitions utilised: a circumstance forcing the Taekwondo athlete to leave the competition (sports incapacity); a circumstance for which the referee or athlete had to cease competition; a circumstance for which the athlete requested medical attention (clinical assessment) Fisher’s exact test No association between fight outcome and a history of TKD or non-TKD injuries in the previous 12 months or current injury at time of competition. Logistic regression with GEE to account for correlations among intraathlete data Competitors were 88% less likely to medal for every injury sustained in competition (OR 0.12, 95%CI 0.02-0.90, p=0.04). Winners trended towards having sustained greater pre-competition injuries (not statistically significant) (OR 1.30, 95%CI 0.87-1.95). Hamstring injury (clinical assessment) Friedman’s test with pairwise comparisons of significant variables (Wilcoxon Signed-Rank test) Significantly reduced performance occurred in first two games after return to sported compared to the entire season (p<0.001) and two games prior to the injury (p<0.001). Rugby League Gabbett (2004) Case study 32 semi-professional rugby league players, age not reported, 1 team season Success: win/loss as determined by final points differential Sports incapacity and/or medical attention and/or athlete self-report Pearson product moment correlation coefficient No statistically significant relationships. Lower injury rates (incidence) tended to be associated with more points in attack (r=-0.45), fewer point conceded (r=0.38), greater points differential (r=0.48) and greater metres gained (r=-0.24) Sports incapacity: unable to participate in a match or training session Incidence rate ratios using Poisson regression Win-loss record: Performance outcomes: metres gained, points scored, points conceded, final points differential, completion rate of attacking sets of tackles Ice Hockey Emery et al (2001) Retrospective analysis of two cohort studies 277 Pee-wee and Bantom ice-hockey teams, 4099 athletes, age not reported, 277 team seasons Success: Outcome of each game, measured as win, lose or draw Teams with a >50% win record had 25% lower incidence of any injuries (95%CI 0.60-0.93, p<0.05) and 36% lower incidence of injuries with >7 days of time loss (95% 0.46-0.91, p<0.05). Total team game penalty minutes No relationship was observed with concussion or the number of penalty minutes. Rugby Union Williams et al (2015) Prospective cohort 15 professional teams across 7 consecutive seasons, 1462 athletes, age not reported, approx. 105 team seasons Main success outcome: Premiership league points tally and Eurorugby Club Ranking (ECR). Secondary success outcomes: Final league ranking, points differential and tries scored a Sports incapacity: unable to participate in a match or training session for greater than 24 hours Linear mixed models for within-team and between-team effects; Pearson product moment correlation coefficient A highly negative association for injury burden and injury days per team-match in both within team and between team success as measured by the ECR and premiership points tally (70-100% likelihood). An unclear relationship of injury days per team-match on the between-teams model when assessed for the ECR. Clear negative associations between injury burden (r= -0.56), injury days per team-match (r=-0.31) and league points tally. A clear negative association between injury burden (r=-0.50) and ECR. A possibly trivial negative association was observed with injury days per team-match (r=-0.28) and ECR was observed. unclear whether data refers to training and competition or competition alone. bUEFA SCC represents a team’s international performance in the European cups. The coefficient is based on the results of teams competing in the UCL and EL tournaments. Teams are awarded points based on stage achieved in the tournaments, and the result in group stage matches. UEFA SCC is determined by the sum of all points won in the current season, plus 20% of the national association coefficient over the same period (the association coefficient takes into account the results of all teams from each association). Final league ranking and points per league match (‘total league points/league matches played’) were used to represent a team’s domestic league performance; c estimate team seasons (clubs x seasons). RR, risk ratio; OR, odd ratio; CI, confidence interval; MANOVA, multiple analysis of variance; GEE, general estimating equations.