This report is based on an exploratory post hoc analysis of
data from the intervention group in a randomised controlled
trial.33 All statistical analyses were conducted using SPSS
for Windows version 15.0 and STATA version 10.0. We used
a Poisson regression model based on generalised estimating
equations taking cluster effects into account as a per protocol
analysis to compare the rate ratios (RR) of the risk of injury
between teams as well as players (independent of club) stratifi
ed into tertiles of compliance according to the number of
prevention sessions completed: low, intermediate and high.
We used χ2 tests to compare categorical variables between
these subgroups and one-way analysis of variance to compare
continuous variables. To investigate the relation between the
coaches’ attitudes and compliance with the warm-up programme,
logistic regression analyses were used with compliance
as the dependent variable. Attitudes among coaches
who represented teams with high compliance were compared
with attitudes among coaches from low-compliance teams.
The teams who completed both the intervention study and
the study of attitudes were included in this analysis. To investigate
the relation between the coaches’ attitudes and their
teams’ injury risk, logistic regression analyses were used
with injury risk as the dependent variable. The results are
presented as OR with 95% CI and p values. The summary
measure of injury incidence (i) was calculated according to
the formula i=n/e, where n is the number of injuries during
the study period and e the sum of exposure time expressed in
player hours of match, training or in total. Descriptive data for
exposure, compliance with the warm-up programme, injury
incidences and attitudes towards injury prevention training
are presented as means with standard errors or 95% CI. RR
are presented with 95% CI. Two tailed p values of 0.05 or less
were regarded as significant.