Craig Pickering

Sports Science Monthly – July 2021

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Every month we take a deep dive into the latest research in sports science. In this month’s edition we look at the newest frontiers in sport science, the link between energy intake and adaptations, and much more.

As always, the full Sports Science Monthly 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 Monthly is all about, the April 2016 edition is available in its entirety for free.

This Month’s Topics

New frontiers in sports science

Quick Summary – Sports science is an ever-expanding topic, that is becoming of increased interest to “mainstream” science due to its link to improving human health and disease. This was recently illustrated by a special topic collection in the high-level science journal Nature, which looked at some of the more recent innovations in the area of sports science, such as heat adaptation, mitochondria, microbes, and data science.

Sports science, and its various related sub-disciplines, have, historically at least, been viewed as a little bit niche. This is especially true in the scientific community, which has a plethora of sub-disciplines, with the more basic sciences—biology, chemistry, and physics—often getting the most attention. However, earlier this year, Nature, arguably the leading journal within science (the only other journal with the ability to make such a claim is the appropriately named Science), ran an “outlook” series on sports science, and various innovations that are occurring within the field. As sports science often does not get such mainstream attention, let’s celebrate this by taking a closer look at some of the articles.

Beating the Heat

The ever-increasing globalization of sport, and especially athletics, means that major competitions are increasingly likely to be held in locations that are hot, humid, or both. When I competed at the 2007 World Championships, the trackside temperature during the 100m heats was 42 degrees Celsius. That is exceptionally hot; the track was too hot to touch, and the officials had to spread water on the start line before each race so the athletes could put their hands on the floor. I was lucky, however; the 100m is not a prolonged event, and so, after only a few minutes in the heat, I could head into an air conditioned room to recover. However, as I was warming up for the 100m heats, the men’s marathon was finishing. I remember that, due to the extreme heat, a lot of these athletes were in a very bad way; the British athletes had to come to the medical tent to get an ice massage and reduce their body temperature. I saw the race winner in anti-doping, and he was in such a bad way that he had to be carried to the toilet to deliver his sample by four anti-doping officials; I can imagine that it took him hours to have sufficient urine to produce a sample.

At the 2018 Commonwealth Games, which were held on the Gold Coast, Callum Hawkins was winning the men’s marathon, until, due to the 30 degree heat, he collapsed. If you watch this video of the incident, you can see just how much of a bad way he was in. Whilst Hawkins subsequently made a full recovery, other athletes have not been so lucky; it seems like, almost every year, there is a story of an athlete that unfortunately succumbs to heat exhaustion in some capacity, and this is true across many different sports. Sporting event organisers are becoming increasingly aware of the risks of heat exhaustion to their competitors; the out-of-stadia endurance events at the 2019 World Athletics Championships, for example, were held after midnight when the temperatures were coolest, and the marathon and race walk events at the Tokyo 2020+1 Olympics will be held in Sapporo, which is notably cooler than Tokyo.

For prolonged endurance events, “heat” is often defined as temperatures over 30 degrees Celsius. This is surprisingly low, which means that athletes need to take the risks seriously; fortunately, over the past decade or so, research into heat acclimation has become increasingly prevalent. As such, athletes can now prepare themselves to perform in the heat, typically by exposing themselves to heat in a controlled manner in the days, weeks, and months leading up to the target competition. An example session is exercising in a way to increase their core temperature to around 38.5 degrees (1 degree above normal), before aiming to maintain this core temperature for a further 30-60 minutes. This can be done by either continuing to exercise (active heat acclimation), or by sitting in a sauna or hot bath (passive heat acclimation). As the number of these sessions accumulates, athletes began to undergo a variety of physiological adaptations, such as an increased sweat rate, along with a lowering of heart rate and resting core temperature, and improved exercise tolerance. All of these are useful when exercising in the heat, but they’re also useful for improving performance in all environments, which is why athletes are now starting to utilise heat as a way of maximising training adaptations outside of its positive effects on performance in hot conditions. This is evidenced by the increased popularity of heat acclimation methods; prior to the 2015 World Athletics Championships, only 15% of athletes reported undergoing an acclimation period of between 5-20 days. For the 2019 World Athletics Championships, this number had jumped to just over 50%.

As technology develops, the monitoring of athletes for heat-associated issues is becoming more realistic. Athletes can now swallow a tiny thermometer in a pill, which can monitor their core temperature. This sounds positive—during a race, medical staff can monitor the core temperatures of athletes—but it raises an interesting ethical dilemma; at what stage should medical staff intervene if their thermometer data is rapidly rising? As is often common in sports science, the more we know, the more questions we have.

Mitochondria and performance

Mitochondria are the “cells within our cells” that serve as the site of aerobic respiration; the way we produce energy during prolonged, sub-maximal efforts. We’ve probably all seen a diagram of a mitochondria; small, bean-like structures within a cell. However, recent research has begun to suggest this common diagram is misleading; the mitochondria actually form extensive networks within cells, and are all interconnected in a fluid manner. Instead of viewing them as a single, bean-like structure, we should perhaps view them as a mass of seaweed. These connections are fluid; the mitochondria also move around the cell, and are continually joining and separating with other mitochondria.

Given the importance of mitochondria in producing energy, and the importance of energy production in performance, for athletes in sports with an aerobic component, maximising mitochondrial adaptations to exercise is crucial. Recently, research has focused on how best to do this. One method is to deplete the athlete’s stores of muscle glycogen, and then undertake fasted training; a topic I have covered many times in these columns. The stress of low energy availability during exercise stimulates enhanced mitochondrial breakdown and regeneration, driving adaptations. Altitude exposure also increases mitochondrial density; one month increases the density by about 8%, according to some studies. Heat training, discussed above, also enhances mitochondrial adaptations to exercise. This is potentially due to the release of Heat Shock Proteins, some of which drive mitochondrial growth.

One issue in the field of understanding mitochondrial adaptations to training is being able to view the actual mitochondria themselves. Typically, this would be done via a muscle biopsy, where a piece of muscle tissue is cut away from the body for analysis. This is obviously highly invasive and, as a result, elite athletes typically don’t want to do it—an issue which hampers research. However, a new technique, called near-infrared spectroscopy, is proving very promising; here, researchers can measure mitochondrial function in the muscle via a scanner, removing the need for a biopsy. As this technique develops, it should lead to a vastly improved understanding of the mitochondria themselves, and how they support exercise adaptation—and which techniques are most effective in improving performance.

Microbes and performance

Interest in the human gut microbiome has exploded in the last decade or so, with a focus on areas such as health and disease. This link is potentially very interesting, particularly as alterations in the bacteria in the gut have been linked with health issues elsewhere in the body, such as the brain—suggesting that there is continuous communication between brain and gut, which can influence health. Interest in the microbiome within sports science has also began to accelerate—although to a lesser degree than in mainstream health and wellbeing—and is something I’ve written about for HMMR Media a couple of times before; for example, here. The microbiome has the potential to be hugely interesting, in part, because of how diverse it is, as well as how each individual has their own unique bacterial make-up.

We know of many factors that influence the gut microbiome, including current (and former) diet, age, stress, method of birth (there are different bacterial exposures from a vaginal and caesarean birth, for example), and previous use of medications such as antibiotics. Whilst the evidence of the influence of each of these factors is consistently growing, researchers are also beginning to understand the influence of exercise on the human microbiome. Typically, greater diversity of our gut bacteria is positive, and so researchers generally believe that anything that increases the microbiome diversity is a positive intervention. In the small number of studies that have explored the relationship between exercise and gut bacteria, higher fitness levels were correlated with increased bacterial diversity. The direction of this correlation is unclear; does being fitter increase your diversity, or does increased diversity increase your fitness? It’s also not clear whether this relationship is causal—i.e. changes in one variable cause a change in the other—which is why we need to remain cautious at this stage. One confounding variable is likely to be diet; fitter people potentially consume different foods to less fit people, which may be a driver of their differences in fitness.

However, some of the early results are very promising. In one study, researchers collected stool samples from a group of people competing in the Boston Marathon, with collections taken at various time points before and after the race. The researchers found that, immediately after the marathon, there was a large spike in the proportion of a particular type of bacteria, Veillonella, with the spike only occurring in the marathon runners, and not the sedentary controls. Veillonella metabolizes lactate, something which is obviously produced at high levels during a marathon; by producing more of this bacteria in response to the marathon, the athlete’s bodies were better able to use the lactate produced as a fuel source. When the scientists then transplanted the Veillonella bacteria from the runners into mice, the mice could run for about 13% longer than mice who didn’t receive the bacterial transplant.

This leaves us in a place of obvious potential; supplementing with specific gut bacteria may enhance performance. As with any potential intervention, this opens the door to abuse—so called “poop doping”. The use of targeted bacteria for performance enhancement is not yet ready for prime time, but it’s a fascinating area to keep our eyes on over the coming years.

Using data science to predict injuries

Injuries are very costly in elite sport, both in terms of lost time and finances. If an athlete is injured, they either can’t perform, or can’t undertake the level of training required to be successful. As a result, injuries lead to underperformance, which costs money; for a professional team, this financial cost comes in terms of wages—paying a player who can’t take part—whilst in individual sports, the direct financial burden is borne by the athlete, who loses money by being unable to compete.

As a result, being able to reduce the incidence and severity of injuries is of high importance to all involved in elite sport. Often, we talk about injury prevention; in reality, it is highly unlikely that we can prevent all injuries (outside of just not doing sport). Instead, we want to just limit how often injuries happen, and how severe they are, to enable athletes to be able to perform at their best. One aspect of prevention (or risk reduction) is prediction; can we predict when an injury might occur, before it happens, and then stop it from actually occurring? Prediction is the holy grail in elite sport, but often the word is used to mean “associated with” or “linked to”—which are not the same as prediction.

One issue with prediction when it comes to injuries is the positive predictive value of whatever method we use; that is to say, if we predict an athlete will get injured, how often do they actually break down? A 100% positive predictive test would be one in which all the athletes predicted to get injured actually did, and none of the athletes who weren’t predicted to get injured actually get injured. In the real-world, what we see is that many injury “prediction” models are too sensitive, and not sufficiently specific; they identify a large number of athletes who are predicted to get injured, but only a small number actually do. This means that, when using these models, many athletes would undergo an injury prevention intervention (e.g. missing training) that was unnecessary, because they wouldn’t have gotten injured anyway.

When it comes to prediction, the best approach appears to be a multivariate one. There are so many aspects of athlete health, wellbeing and performance that can be quantified, that it is more likely that there is an issue with too much, as opposed to too little, data in the modern high performance sports setting. However, the utilization of this data is often poor; it is collected, but not much is done with it. By ensuring that all the required data can be stored and analyzed correctly, data scientists are able to build models to understand which data holds the greatest explanatory or predictive value, which, in turn, allows coaches and sports scientists to stop the collection of data which is not useful. The useful data can then be analyzed as part of a complex modeling process, hopefully enabling us to better understand who gets injured, and why.

However, as we get more data and ever-more complex models, we run into a different problem. Using data science techniques such as neural networks, it is possible to “train” a computer to spot patterns and make decisions. This can be useful, but it’s important to point out that both the computer, and the data scientist using the computer, don’t know why or how the model or algorithm came up with a given answer—just that it did. Under these circumstances, the algorithm is termed a black box; the challenge here is that understanding why a decision has been made is often important to many coaches and practitioners, especially if we want to make interventions based on the information provided.

The final section of this article highlights an important point; data should inform, not replace, subjective decision making on the part of athlete and/or coach. Whilst a player may have a high risk of injury, according to the given statistical model, the player/coach may decide that, given the importance of the match, it is worth playing. Keeping in mind that the information provided from data science should be used to assist humans make decisions is important; although humans are prone to bias in ways that machines aren’t, they’re also better able to understand the unique context of each individual decision—something that, in the case of sports injuries, may prove to be the missing link.

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