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Why Sports and Health Technology have become a Burden not a Boost


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I often get asked about the current state of the “industry”, whether that be sports medicine and strength & conditioning. Specifically I get asked about the advancements, but my response has been surprising to others. Our ability to identify injuries and reduce their occurrence is worse than 10 years ago. Why? Because we have more access to information and the stakes are higher for job security, causing us to rely on too much data.

So how much is too much data? And what is this nefarious term “data” to which we refer? Well, we will define “data” broadly for our context as either variables or tests. And you generally want no more than five pieces. That means five variables total, which can come from multiple sources or just one, ideally adding up to five.

An article out of Mayo Clinic and LaTrobe University outlines the key reasons behind such a recommendation. The authors determined that an R squared of 0.2 is our designated threshold level for clinically meaningful correlation. R-squared (R2) represents the variance for a dependent variable that’s explained by an independent variable. So an R2 of 0.2 indicates 20% of the variation can be explained by the model, or a maximum of 5 variables.

The authors went on to highlight the 5 areas to provide such insights:


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Anthropometrics: size/proportion of the body (i.e. a deeper notch of the femur reduces ACL risk).

Strength: the ability to produce force. 

Biomechanics: how structures work together to produce movement, basically the sequencing of movement. 

Proprioception: sense of body position in space, best measures here usually involve balance. 

Psychological: often ignored, but scientifically exists as a kinesiophobia which has validated screens like this.

A common litmus test we use with colleagues internally & externally is what you focus on for injury rehabilitation or prevention. If the answer is prolonged and/or overtly academic in nature (i.e. “it depends”), then your big 5 have not been solidified yet. 

Rather than a deeper dive into more tests/variables, focus on educating the individual to also allow more frequent data collection. After all, our life is a journey and other than anthropometrics, none of these factors are concretely set, unless you quit already.

Reference 

Systematic Selection of Key Logistic Regression Variables for Risk Prediction Analyses: A Five-Factor Maximum Model