Background Longitudinal injury surveillance may provide valuable information regarding relationships between initial and any subsequent injuries. The Subsequent Injury Categorisation (SIC) (Finch & Cook, 2013) has been proposed as a method for examining these relationships.
Objective To identify priority injury problems and potential risk factors for injury and subsequent injury in international cricket.
Design Prospective injury surveillance using consensus methods and SIC.
Setting International cricket.
Participants All players from one national team squad during over 2 years (2011–2013).
Risk factor assessment The contrasting; injury types, rates and severity, according to body area, position and activity (training or match) at time of injury, and SIC.
Main outcome measurement Training and match injury incidence (per 100 days), prevalence (% of players unavailable) and average days-lost per injury were calculated for each variable along with the percentage of injuries within each SIC. Further analysis of the SIC is in process.
Results 225 injuries were sustained (73 time-loss and 152 non time-loss), with an overall incidence of 10/100 days (7/100 days matches, 3/100 days training), prevalence of 13% and average of 32 days-lost per injury. Ankle, knee and hand regions had the highest incidence, but lumbar and abdomen regions had the greatest days-lost per injury. Fast bowlers had the highest incidence (6/100 days) and prevalence (8%). Wicket-keepers had the greatest days-lost per injury (68 days). Thirty-five percent of the 219 subsequent injuries sustained were coded as being related to a previous injury. Twelve percent of all subsequent injuries were a recurrence.
Conclusions International cricketers have a high rate of non time-loss injury, particularly to the ankle, knee and shoulder whereas thigh strains and lower back injuries result in the greatest time-loss. Injury prevention strategies should target these problems, particularly in fast bowlers. Analysis of the SIC may provide further insight into injury risk predicators.
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