Download Suppl_3

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.