BJSM The team paramedic or sports trainer, who was present at every practice session and soccer match of the team, was responsible for recording the soccer injuries in both study groups. Therefore, he/she used the Web-Based Injury System (BIS) developed by the Netherlands Organization for Applied Scientifi c Research (TNO).21 22 BIS uses the basic guidelines of the consensus statement on injury defi nitions and data collection procedures in soccer.23 The system captures epidemiological information on sports injuries (location, duration and type), aetiology (intrinsic and extrinsic risk factors), consequences of injuries (eg, work/school/ sports absenteeism) and the volume and type of medical treatment, using so-called injury and recovery forms. Outcomes Player characteristics recorded were age, height, weight, years of experience as a soccer player and soccer injuries sustained during the previous year (number and location). The primary outcome of the study was the injury incidence per 1000 h of soccer participation (I). This was calculated according to the formula I=(n/e)×1000, where n is the number of soccer injuries and e the total exposure time expressed as total hours of soccer participation. The Poisson model was used to obtain 95% confi dence intervals (CI). Exposure and all soccer injuries were recorded during the 2009–2010 competitive season, from the fi rst competition match until the last regular competition match of the season. Table 1 shows the used defi nitions. These are in accordance with the consensus statement by Fuller et al.23 In addition, team and player compliance was recorded by all coaches in the intervention group using the exposure form. Sample size Approximately 70% of all soccer players aged between 18 and 40 years (mainly men) get injured.24 sults reported by Junge et al17 and Heidt et al,13 Based on the rewe estimated that the The11 programme would result in a 25% reduction of soccer injuries in our study. With a power of 0.80 and α of 0.05, this meant that 90 players in each group had to take part in the study during an entire soccer season. Given an estimated infl ation factor for cluster randomisation effects of 1.8,18 and assuming a drop-out rate of 26%,17 the research staff aimed to include a minimum of 219 players in each group at the start of the season. Assuming 19 players per team, 12 teams were included in each group. Statistical methods The statistical procedures were performed with SPSS 17 (SPSS Inc., Chicago, Illinois, USA) and R (V2.13.2). Baseline characteristics, measured as continuous variables, were expressed as mean and standard deviation (SD). Ordinal or categorical variables such as injury history were expressed as percentages. The following outcome parameters were analysed: injury incidence, proportion of injured players and injury profi le. Because of their skewed distribution, exposure and absenteeism were presented as median and interquartile range (IQR). The categorical parameters representing injury profi le were expressed as percentages. Table 1 Used defi nitions in data collection23 Injury Any physical complaint sustained by a player that results from a soccer match or soccer practice session, irrespective of the need for medical attention or time loss from soccer activities Recurrent injury Match exposure Training exposure An injury of the same type and at the same site as a previous injury and which occurs after a player’s return to full participation from the index injury Play between teams from different clubs Team based and individual physical activities under the control or guidance of the team’s coaching or fi tness staff that are aimed at maintaining or improving players’ football skills or physical condition The outcome parameters of the intervention and control groups were compared using a univariate T-test and MannWhitney U-test for the continuous parameters, and χ2 analysis for categorical parameters. Signifi cant differences between the two study groups at baseline were included as covariates (ANCOVA) to test the intervention effect. To evaluate any effect of the programme during the season, survival curves (based on Cox regression) for both study groups were compared.25 Additionally, Cox regression for recurrent events was used to compare the two groups, enabling both fi rst time and recurrent injuries (adjusted for the time periods during each player had been on the team) to be used in the analysis. 26 were considered signifi cant. Two-tailed p values less than 0.05 Secondary outcomes were the absolute number of injuries, the proportion of injured players, as well as soccer injury characteristics (absenteeism, injury mechanism, recurrence, body part). Severity of injuries is reported as absenteeism in days.23 8 Sport & Geneeskunde | december 2012 | nummer 5 Pagina 7
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