Current Issues in Sports Science and Strength and Conditioning Research

Sports science and strength and conditioning research is a relatively young field, leading to unresolved issues compared to older fields like physics, chemistry, mathematics, and biology.
One issue is the reliance on poorly validated surrogate outcomes to inform decisions about performance.
Example: Using EMG to justify exercise selection based on greater muscle activation.
- EMG only measures electrical activity, not force, force production location, or force production sequence.
- While research (e.g., Brokantreras's PhD work) shows the glute thrust induces greater muscle activation than exercises like the front squat, it's questionable whether this leads to greater performance gains (sprinting speed, jumping height, change of direction ability) over time.
Another Example: Using methods of measuring cross sectional area when you are looking at hypertrophy development.
- Gold standard measures are DEXA or ultrasound.
- Measurements such as limb circumference or muscle thickness are surrogate outcomes. These can be impacted by hydration status, nutritional intake, and other factors.
- Therefore, these surrogate outcomes may not be a valid and strong support for decision-making.

Many studies comparing different periodization or programming models have limited durations.
Example: A 2002 study by Ria et al. compared linear periodization and daily undulating periodization on upper and lower body strength over twelve weeks.
- While the study concluded that daily undulating programming might be better for improvements in lower and upper body strength due to a statistically significant difference between groups from weeks 0 to 12, most of the change occurred in the first six weeks.
- The daily undulating group experienced a more novel stimulus, leading to greater initial gains, while the linear periodization group had less adaptation due to habitual exposure.
When evaluating intervention studies, consider whether the observed effects are due to a novel training stimulus or a true difference between programming models after the novelty period has subsided.

Historically, sports science and strength and conditioning research has over-relied on p-values to determine the importance and clinical meaning of results.
Statistical significance only indicates whether a result is more extreme than expected from the population.
It's crucial to consider the clinical or practical meaningfulness of results alongside statistical significance.
Focusing solely on statistical significance leads to positive result bias, where only statistically significant findings are deemed important.
Non-significant results may remain unpublished, leading to wasted resources as researchers unknowingly repeat previously disproven hypotheses.
Researchers may also selectively emphasize outcomes that align with predetermined expectations rather than examining all results.
When evaluating research, consider non-significant findings and assess the replicability of positive outcomes based on sample size and p-value magnitude (lower p-values, such as less than 0.01, are more replicable).

When conducting research, focus on specific dependent variables of interest rather than running multiple analyses and selectively reporting statistically significant ones.
P-hacking involves running multiple analyses to find statistical significance and obtain positive outcomes, increasing the likelihood of publication.
P-hacking increases the risk of false positives due to the increased family-wise error rate, which should be corrected using Bonferroni or Holm-Bonferroni approaches.

A significant issue in sports science and strength and conditioning literature is the non-reporting of effect sizes alongside statistical significance.
Non-reporting of effect sizes reduces the ability to:
- Quantify the magnitude of an effect to determine its meaningfulness.
- Compare the magnitude of effects across different studies.
Effect sizes can be standardized (e.g., Cohen's D, Hedges' G) or unstandardized (raw absolute values).
The absence of effect sizes prevents the synthesis of evidence to support decision-making processes.
Without knowing the magnitude of an effect, it's difficult to determine whether a statistically significant result is practically meaningful and warrants changes in training strategies.

When using research literature to support decisions as strength and conditioning coaches, critically evaluate the results relative to the aims and methods.
Ensure the results align with the aims and methods, dependent variables are well-defined, hypotheses are clear, and the statistical approach is appropriate.
Be aware of the limitations when using evidence to support coaching decisions.
Consider the implications of using poor surrogate measures of performance (e.g., muscle activation data) to inform exercise choices within a training program.