Avoiding systematic errors in isometric squat-related studies without pre-familiarization by using sufficient numbers of trials

  • Pekünlü E
  • Özsu I
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Abstract

There is no scientific evidence in the literature indicating that maximal isometric strength measures can be assessed within 3 trials. We questioned whether the results of isometric squat-related studies in which maximal isometric squat strength (MISS) testing was performed using limited numbers of trials without pre-familiarization might have included systematic errors, especially those resulting from acute learning effects. Forty resistance-trained male participants performed 8 isometric squat trials without pre-familiarization. The highest measures in the first 'n' trials (3 ≤ n ≤ 8) of these 8 squats were regarded as MISS obtained using 6 different MISS test methods featuring different numbers of trials (The Best of n Trials Method [BnT]). When B3T and B8T were paired with other methods, high reliability was found between the paired methods in terms of intraclass correlation coefficients (0.93-0.98) and coefficients of variation (3.4-7.0%). The Wilcoxon's signed rank test indicated that MISS obtained using B3T and B8T were lower (p < 0.001) and higher (p < 0.001), respectively, than those obtained using other methods. The Bland- Altman method revealed a lack of agreement between any of the paired methods. Simulation studies illustrated that increasing the number of trials to 9-10 using a relatively large sample size (i.e., ≥ 24) could be an effective means of obtaining the actual MISS values of the participants. The common use of a limited number of trials in MISS tests without pre-familiarization appears to have no solid scientific base. Our findings suggest that the number of trials should be increased in commonly used MISS tests to avoid learning effect-related systematic errors ABSTRACT FROM AUTHOR

Author-supplied keywords

  • isometric testing standards
  • learning effect
  • maximal isometric strength
  • number of trials
  • testing study assumptions

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Authors

  • Ekim Pekünlü

  • Ilbilge Özsu

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