Craig Pickering

Sports Science Quarterly – Q1 2025

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Every quarter we take a deep dive into the latest research in sports science. In this edition we look at the latest research on wellness questionnaires, flexible periodization models, supplements, and much more.

As always, the full Sports Science Quarterly is available exclusively to HMMR Plus Members. You can browse the past topics on our archive page. The first topic below is free to everyone, but sign up now to read about all the latest research. To get an idea of what Sports Science Quarterly is all about, the April 2016 edition is available in its entirety for free.

This Quarter’s Topics

Just how useful are wellness scores?

Quick Summary – Wellness scores are widely used in sport; this study suggests that they are not great at predicting performance within training sessions.

In the last decade, the use of athlete monitoring tools has exploded, with athletes, coaches, and sports teams utilising a variety of methods in an attempt to understand an athlete’s fatigue and readiness to train, and potentially predict injury and performance. A commonly utilised monitoring technique is that of wellness monitoring; here, measures typically utilise questionnaires and similar tools, with the goal being to develop an holistic understanding of an athlete’s training response. Common wellness monitoring tools, that you might have heard of, include the Profile of Mood States (POMS) and the Recovery-Stress Questionnaire for Athletes (REST-Q).

In the last 5 years or so, research has begun to explore just how useful these tools are. Different studies report varying results; some research supports a significant dose-response relationship between measures of wellness and external load—for example, the more load an athlete experiences, the lower their wellness. Other studies report no relationship, or at least no dose response relationship, which makes predictions hard. Other research suggests that the effects of fatigue on wellness scores are not reproducible—i.e., the same amount of fatigue, at different times, leads to different wellness scores. Interestingly, when team sport players win a game, and/or play at home, their wellness scores are better. This latter point isn’t all that unexpected—wellness scores are typically subjective in nature, and our subjectivity is therefore affected by other, external factors. However, if we’re a coach or performance support team member, we want to ensure that, if we’re collecting data, it’s useful. The rise of advanced analytical techniques, such as machine learning, have helped in this area – we can now analyse large swathes of data in order to better understand what is signal and what is noise.

That is where this study comes in; here, the researchers aimed to examine the predictive capability of wellness questionnaires on changes in internal and external load. Players from three different sports—international cricket, professional and semi-professional rugby league, and soccer—had provided their data across multiple seasons (2013 – 2018). The wellness measures were collected through a commercially available program (Catapult), via an 8- or 5-item wellness questionnaire (cricket utilised the 5-item questionnaire). Training load was quantified through a variety of measures, including session rating of perceived exertion (sRPE) and GPS measures (specifically total distance and metres covered per minute). In total, 14,109 data points were analysed.

Overall, the results demonstrated that there was a very limited predictive capacity of wellness scores in relation to both internal (sRPE) and external (total distance and metres per minute) training measures. This finding held up across different analysis techniques, and across all sports. For a given individual, there was minimal change in wellness score on a day-to-day basis, which suggests that either a) the wellness questionnaires aren’t sensitive enough to pick up on any changes in wellness, or b) the athletes completing wellness scores aren’t putting much thought into them when they fill them out. This last point also relates to survey fatigue – the more surveys an athlete has to fill out over time, the less time, effort, and thought they tend to put into them. This is especially true when the athlete doesn’t believe that coaches or performance support staff will either view the results, or make any actionable changes based off the results themselves.

What does all this mean? Well, it’s a clear reminder that we should continually audit the data we collect to ensure that it is useful. Collecting wellness data just for the sake of it leads to the survey fatigue problems seen in the study, which limits the effectiveness of collecting the data—and potentially other data too. Alongside this, regular audits would highlight that there is minimal day-to-day change in wellness scores, meaning that any true change in wellness is not being effectively captured by the currently utilised methods. Remember – just because you can collect data on something, doesn’t always mean you should!

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