Statistics from Altmetric.com
When dealing with small cohorts, as is typical in elite sport, the well-known four-step ‘sequence of prevention’ described by van Mechelen et al 1 (figure 1) potentially represents a vicious circle: When introducing a prevention measure, an otherwise reasonable call for targeting specific subgroups (ie, relevant groups of athletes, injury locations and specific injury causes) may undermine study power, breaking down an already-small baseline cohort into undersized pieces. Consequently, statistical testing becomes impossible.
To illustrate the problem we (1) discuss a recently implemented preventive measure in alpine ski racing as an example, (2) highlight the influence of sample size and effect size on study power and the possibility for statistical hypothesis testing and (3) provide a solution to increase study power for comparable injury prevention initiatives in elite sports.
General effects but underpowered in subgroups
In elite alpine ski racing, we recently tested potential preventive measures (eg, ski equipment changes) that target a specific body part (eg, knee/ACL injuries),2 their specific mechanisms (eg, aggressive ski–snow interaction driven by the skiing equipment)3 4 and specific disciplines (eg, different ski alterations in downhill, super-G and giant slalom).5 Based on this research process,6 the International Ski Federation (FIS) introduced new equipment rules for the 2012–2013 season.
The effect of these changes was assessed by repeating step 1 …
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