Whilst athletes and coaches have long focused on the physical aspects of performance enhancement, such as training program design and exercise selection, it is only relatively recently that we have started to pay attention to how stress and sleep might also influence both the magnitude of adaptations seen following a training program, and competition performance. Based on this recent research, we have an increased understanding of the need to account for psychological stress, including, in the case of younger athletes, academic work load, when developing optimal training programs.
Stress and training
As an example of this recent research, a paper published in the Journal of Strength and Conditioning Research in 2008 explored the impact of stressful life events on improvements in strength seen following 12 weeks of resistance training. Here, the authors had 135 university students undertake weight training sessions lasting 90 minutes twice per week for the duration of the 12-week training program. Prior to starting the training program, the subjects also filled out two questionnaires aiming to measure stressful life events and perceived social support.
Using this information, the study authors were able to stratify the subjects into a “low stress” and “high stress” group, and compare for differences in training response between the groups. Whilst, overall, the subjects in this study improved their bench press 1-rep max by 13% and their back squat 1-rep max by 25%, there were differences between the groups in how much they improved. The subjects in the low stress group typically saw greater improvements in strength than those in the high group, along with greater improvements in muscle size in both the arms and thighs. Based on these results, the authors stated that it appears that high life stress has the potential to reduce the adaptations seen from training, potentially partly due to increases in cortisol concentrations.
Stress and health
A more recent paper from the same journal explored the interaction between stress, illness, and injury. In this case, author Bryan Mann and his team recruited 101 athletes from a college football team across a season, categorizing different periods as high physical stress, high academic stress, and low academic stress. The athletes were twice as likely to suffer an injury during periods of high academic stress compared to periods of low academic stress, again demonstrating the role life stress—either real or perceived—can have on performance-related outcomes.
These findings are not outliers. A 2012 paper from Frontiers in Physiology reported that the lower the levels of self-reported stress, and the greater the perception of an individual’s ability to deal with psychological stress, the more likely they were to demonstrate greater improvements in aerobic fitness following a training program. Personality factors, such as increased trait anxiety and susceptibility to stress, have been associated with increased injury rates in soccer players. Negative life events not only reduce the speed of recovery from a training session, but also negatively impact running economy, making athletes less efficient and requiring them to use more energy to carry out the same amount of work. An increased exposure to, and lack of ability to cope with, psychological stress is also associated with the development of overtraining syndrome. Finally, increased levels of stress are associated with a blunted affective (i.e. mood) response to exercise, such that those individuals with higher stress levels are less likely to enjoy exercise, and are also less motivated to undertake it—two aspects which could harm performance in athletes.
All of this tells us that understanding the stress levels of our athletes is an important aspect of monitoring, and can hugely influence training program design. This has perhaps best been explored by my academic supervisor John Kiely in his HMMR Classroom Video Lesson, as well as his recent paper on periodization.
Stress and sleep
An obvious question is – if you know your athletes are in a period of time where they are increasingly susceptible to stress, what can they do about it? There is a well-established link between stress and sleep, with poor sleep increasing levels of stress, and increased stress reducing sleep duration and quality. As such, enhancing sleep quality is a potentially important method of managing stress in athletes, especially when we consider that research tends to suggest that athletes don’t get sufficient sleep anyway, and are prone to sleep disturbances around important competitions.
Recently, I was fortunate enough to be able to collaborate with the sports science team from the youth academy of AFC Bournemouth in the Premier League. We analyzed wellness and sleep related data they had across a season—the results were recently published as a paper in Sports Performance and Science Reports. To summarize, they collected data on 42 players (average age 16.5 years) from their academy in the form of a five-question sleep and wellness questionnaire. The questions revolved around the duration of sleep the previous evening (rounded to the nearest half-hour), along with quality of sleep, self-rated feelings of muscle soreness, fatigue, and stress (all scored out of 7).
To analyze the data, I pulled out two main blocks to compare: the pre-season training period (lasting from July 3rd to August 11th), and the late-season competition period (March 5th to May 7th). The late season period is stressful from a psychological perspective; the league season is drawing to an end, with cup finals coming up and the battle for final placings, and contracts are due for renewal, with many players likely to be released. Conversely, the pre-season period is likely to be a period of low psychological, but high physiological, stress. We wanted to see if any of the wellness data collected changed between these two time periods.
Of the 1709 full data points, defined as answers to all 5 questions on a given day, 908 occurred during the pre-season, and 801 during the late season. Overall, we found a small increase in self-reported stress in the late- compared to pre-season period (Cohen’s d = 0.23). This increased feeling of stress was accompanied with what was termed “trivial” (an effect size of between 0 and 0.2) increase in self-reported sleep duration, and a decrease in quality of sleep. This suggests that the increased feelings of sleep are potentially occurring despite the fact that the players are sleeping for longer, perhaps because their sleep quality is worse. Importantly, there was no real change in the self-reported feelings of fatigue or muscle soreness, suggesting that the lack of sleep quality or increase in sleep duration was not due to increases in physical stress. As a result, because interventions aimed at improving sleep quality are low cost and potentially high impact, the academy sports science staff decided this was an area they wanted to look into more closely in future, in the hope that it would enable their players to potentially better manage their stress as the season progresses.
Low cost, high impact
Of course, it’s important to recognize some limitations associated with this study. It’s entirely possible that the players were mistaken as to how stressed they were, or how deep their sleep was. After all, these were self-reported measures, which are subject to bias (although they have been demonstrated to be effective). Additionally, because we didn’t measure it, it’s not clear what effect this increase in self-reported stress had on performance or injury rates. Those limitations aside, I’d suggest that, given the well-established wide benefits of enhancing sleep, and the well-replicated negative effects of stress on performance, all athletes and coaches look at their sleep practices to ensure they’re getting the most out of their training. This isn’t groundbreaking, but it represents a quick win, so it’s worth exploring.
A side note on statistics:
Ok, time for a brief aside on some statistics. The journal this was published in has a preference for the use of the magnitude-based inference (MBI) method of statistical analysis. What this means is that they don’t want to see just p-values reported. Personally, I think this is a good thing—I’ve previously written about some issues with p-values here and here—and, more importantly, a p-value doesn’t actually tell you much about the size of the effect. The MBI method does, as well as giving you some insight into the likelihood of how “true” this effect is—that is, is it a real improvement, or potentially false? This method has recently been criticized, primarily because it potentially inflates the rate of false positives, and there is quite an intense debate currently unfolding around its use. That said, I like the method, and I think that, when sample size is low, and no real harm can occur through a false positive, such as in sports training, it can be a worthwhile method to make better informed decisions.