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Is the Concept, Method, or Measurement to Blame for Testing Error? An Illustration Using the Force-Velocity-Power
Pierre Samozino1, Jean Romain Rivière1, Pedro Jimenez-Reyes2
1Univ Savoie Mont Blanc, Interuniversity Laboratory of Human Movement Sciences, EA 7424, Chambéry,France.
Abstract:
When poor reliability of "output" variables is reported, it can be difficult to discern whether blame lies with the measurement (ie, the inputs) or the overarching concept. This commentary addresses this issue, using the force-velocity-power (FvP) profile in jumping to illustrate the interplay between concept, method, and measurement reliability. While FvP testing has risen in popularity and accessibility, some studies have challenged the reliability and subsequent utility of the concept itself without clearly considering the potential for imprecise procedures to impact reliability measures. To this end, simulations based on virtual athletes confirmed that push-off distance and jump-height variability should be <4% to 5% to guarantee well-fitted force-velocity relationships and acceptable typical error (<10%) in FvP outputs, which was in line with previous experimental findings. Thus, while arguably acceptable in isolation, the 5% to 10% variability in push-off distance or jump height reported in the critiquing studies suggests that their methods were not reliable enough (lack of familiarization, inaccurate procedures, or submaximal efforts) to infer underpinning force-production capacities. Instead of challenging only the concept of FvP relationship testing, an alternative conclusion should have considered the context in which the results were observed: If procedures' and/or tasks' execution is too variable, FvP outputs will be unreliable. As for some other neuromuscular or physiological testing, the FvP relationship, which magnifies measurement errors, is unreliable when the input measurements or testing procedures are inaccurate independently from the method or concept used. Field "simple" methods require the same methodological rigor as "lab" methods to obtain reliable output data.
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