Number of steps needed to investigate running biomechanics

NewsBot · Feb 12, 2021

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Implications of sample size
and acquired number of steps
to investigate running
biomechanics
Anderson Souza Oliveira & Cristina Ioana Pirscoveanu
Scientifc Reports | (2021) 11:3083

Low reproducibility and non-optimal sample sizes are current concerns in scientifc research, especially
within human movement studies. Therefore, this study aimed to examine the implications of diferent
sample sizes and number of steps on data variability and statistical outcomes from kinematic and
kinetics running biomechanical variables. Forty-four participants ran overground using their preferred
technique (normal) and minimizing the contact sound volume (silent). Running speed, peak vertical,
braking forces, and vertical average loading rate were extracted from> 40 steps/runner. Data stability
was computed using a sequential estimation technique. Statistical outcomes (p values and efect
sizes) from the comparison normal vs silent running were extracted from 100,000 random samples,
using various combinations of sample size (from 10 to 40 runners) and number of steps (from 5 to 40
steps). The results showed that only 35% of the study sample could reach average stability using up
to 10 steps across all biomechanical variables. The loading rate was consistently signifcantly lower
during silent running compared to normal running, with large efect sizes across all combinations.
However, variables presenting small or medium efect sizes (running speed and peak braking force),
required> 20 runners to reach signifcant diferences. Therefore, varying sample sizes and number of
steps are shown to infuence the normal vs silent running statistical outcomes in a variable-dependent
manner. Based on our results, we recommend that studies involving analysis of traditional running
biomechanical variables use a minimum of 25 participants and 25 steps from each participant to
provide appropriate data stability and statistical power.
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NewsBot · Apr 13, 2022

Implications of sample size
and acquired number of steps
to investigate running
biomechanics
Anderson Souza Oliveira & Cristina Ioana Pirscoveanu
Scientifc Reports | (2021) 11:3083

Low reproducibility and non-optimal sample sizes are current concerns in scientifc research, especially
within human movement studies. Therefore, this study aimed to examine the implications of diferent
sample sizes and number of steps on data variability and statistical outcomes from kinematic and
kinetics running biomechanical variables. Forty-four participants ran overground using their preferred
technique (normal) and minimizing the contact sound volume (silent). Running speed, peak vertical,
braking forces, and vertical average loading rate were extracted from> 40 steps/runner. Data stability
was computed using a sequential estimation technique. Statistical outcomes (p values and efect
sizes) from the comparison normal vs silent running were extracted from 100,000 random samples,
using various combinations of sample size (from 10 to 40 runners) and number of steps (from 5 to 40
steps). The results showed that only 35% of the study sample could reach average stability using up
to 10 steps across all biomechanical variables. The loading rate was consistently signifcantly lower
during silent running compared to normal running, with large efect sizes across all combinations.
However, variables presenting small or medium efect sizes (running speed and peak braking force),
required> 20 runners to reach signifcant diferences. Therefore, varying sample sizes and number of
steps are shown to infuence the normal vs silent running statistical outcomes in a variable-dependent
manner. Based on our results, we recommend that studies involving analysis of traditional running
biomechanical variables use a minimum of 25 participants and 25 steps from each participant to
provide appropriate data stability and statistical power.
Click to expand...

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