# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 2 Materials and Methods
### Statistical Analysis  
The selection of statistical methods was guided by the nature and variability of the data extracted from the included studies. Given that the studies reported outcomes using different scales and measurement units (e.g., EMG %MVC, stride length in meters, ground contact time in milliseconds), standardized mean differences (SMDs) were used to facilitate direct comparisons.  
A random-effects model was employed to account for expected heterogeneity across studies in terms of participant characteristics (e.g., track sprinters vs. team sport athletes), measurement techniques, and sprinting protocols. This model assumes that the true effects vary between studies, making it more appropriate for this context than a fixed-effects model.  
To assess heterogeneity, we used the I\({}^{2}\) statistic, which quantifies the percentage of total variation across studies that is due to heterogeneity rather than chance. Values above 50% were interpreted as moderate to high heterogeneity. Additionally, [PERSON]'s Q test was used to assess the statistical significance of observed heterogeneity.  
For evaluating the dose-response relationship between sprint velocity and muscle activation, we conducted a meta-regression analysis using ordinary least squares. This method is commonly used in meta-analyses to assess the effect of continuous moderators on outcomes. Our approach aligns with prior studies employing EMG-based regression modeling in sports and rehabilitation contexts.  
Standardized mean differences (SMDs) were calculated for each study to account for differences in measurement scales and to facilitate comparison across studies. Effect sizes were interpreted according to [PERSON]'s criteria, where 0.2 represents a small effect, 0.5 a moderate effect, and 0.8 a large effect.  
A random-effects model was employed due to the expected variability among studies in terms of participant populations, measurement techniques, and sprint conditions. The DerSimonian and Laird method was used to estimate between-study variance.  
Heterogeneity was assessed using the I\({}^{2}\) statistic, which quantifies the proportion of total variability due to heterogeneity rather than sampling error. Thresholds for interpretation were as follows: I\({}^{2}<25\%\) (low heterogeneity), 25-50% (moderate heterogeneity), and \(>50\%\) (high heterogeneity). Additionally, [PERSON]'s Q test was performed to assess statistical significance of heterogeneity. This approach aligns with current guidelines for evaluating consistency across studies in meta-analytic research [21; 22].  
Figure 1: The final PRISMA flowchart.  
Publication bias was evaluated using funnel plots and [PERSON]'s regression test, with statistical significance (\(p<0.05\)) indicating potential bias. In case of asymmetry in funnel plots, [PERSON]'s trim-and-fill method was applied to adjust for missing studies and provide a corrected estimate of the effect size. These methods have been widely recommended for assessing and correcting publication bias in systematic reviews [23, 24].  
To further explore the relationship between sprint speed and muscle activation, a meta-regression model was conducted. The regression aimed to quantify the dose-response effect of sprint velocity (m/s) on muscle activation (%MVC). The procedure for meta-regression followed best practices in the field, including appropriate handling of residual diagnostics and continuous moderators [25].