Length: »2500 Words
Reading Time: »8-10 Minutes
Target Audience: » Athletes,Coaches & Sports Scientists
Author: » David Nolan & Dr. Cody Haun
As practitioners, we should aspire to embody evidence-based practice in our coaching. Originating from the medical field, evidence-based practice can be envisioned as a three-legged stool, with each leg representing a pillar of the evidence-based model:
Best current research evidence
Personal experience & expertise
Athlete preferences & current situation
As coaches making informed training decisions, our job is to make sure we don’t fall off this stool during our practice. We can be quite stable balancing on two legs (two pillars), but when we try and simply rely on a single leg for stability, we stray too close to complete collapse at any moment.
When working at the elite level, it is almost impossible to be a purely evidence-led practitioner, simply because scientific literature on elite athletes specifically is sparse. High-quality applied research in elite cohorts is lacking, and therefore, we must often rely on our own expertise and the data we collect on our athletes rather than relying on the cushion of empirical support. Whether research evidence is lacking or plentiful to guide our training decisions, it is our moral duty as coaches to apply the scientific method to our approach. To better ensure effective methods, we must collect and analyze data from our athletes to allow us to draw objective conclusions about our methods and truly tailor training to individual athletes.
“The goal is to turn data into information, and information into insight”
- Carly Fiorina (8)
Training response phenotype can be considered as an individual athlete’s observable performance and physical characteristic changes in response to a specific dose of training. We contend that by harnessing the power of data collected from training and monitoring, we can manufacture useful information on individual athletes and gain critical insight into expected performance outcomes and risk of injury. This can ultimately allow us to make more informed coaching decisions. This process necessitates a continual loop of data collection, analysis, interpretation, and application. Some of us may be hesitant implementing a formal data collection system with our athletes, believing it makes the coaching process too “black and white” and undervalues the art of coaching. But as coaches we have always collected data, often unconsciously. Every time we observe an athlete in training, we are collecting numerous streams of data and then rely on subconscious analysis (e.g., “gut feeling”) and our own recall ability further down the road, which is an inherently flawed approach. For better results, we must strive to collect meaningful data in an efficient, systematic, valid, and reliable way that is easily accessible in order to facilitate objective analysis of our methods and well-informed decision making. If you are not yet convinced, we posit the question: what is the alternative to systematic data collection? Consistently relying on our own fallible brains alone to collect, store, and compute a plethora of data for multiple athletes on a daily basis?
When it comes to group responses, akin to what we observe in most training studies, we can, with relative consistency, predict what the average response will be to a specific training stimulus. We know what training stimulus generally leads to increased hypertrophy, maximal strength, power etc. Yet, what is often overlooked is the degree of heterogeneity of response in these studies. While on a group level, we may observe an average response, the magnitude of variance in individuals is often staggering.
At the elite level, whether working with a team or individual, we want to provide the optimal training stimulus to achieve our desired adaptation. Adaptive outcomes to training stimuli are not uniform across athletes and effective data collection allows us to gain valuable insight into their training response phenotype. It is critical for us to determine where on the spectrum of response our athletes reside.
If we conceptualise a training stimulus through an over-simplified, first principles approach we can view any training approach through the following components:
- Number of muscle contractions performed in a given time-frame
- The force requirements per muscle contraction
- The velocity of the muscle contraction
- The joints and ranges of motions involved in the movement(s)
- How often these contractions are performed in a given time-frame
These components correspond to the four main variables we manipulate as coaches:
Volume (e.g., total work completed)
Intensity (e.g., work per unit time)
Frequency (e.g., number of times work is completed in a given time-frame)
Exercise / movement selection (e.g., the kinematic nature of the work)
As coaches we evaluate available evidence, and manipulate the above variables in the way we believe will lead to the desired outcome. Data collection allows us to determine how effective our approach is in eliciting the desired adaptation, while also allowing us to identify if our athletes have an unexpected training response phenotype.
But to what degree does heterogeneity of response exist? Is the level of inter-individual difference in training response phenotype something we should actually be concerned about?
Heterogeneity of response to training interventions
While some of the intricacies and nuances of the muscle hypertrophy response to training require further teasing out in the research, as practitioners we have a reasonable understanding of the main pillars required in a training program to elicit muscle hypertrophy. Based on our current understanding of the evidence it would be wise to structure a hypertrophy-specific program adhering to the core principles of:
Progressive increases in training volume throughout the mesocycle (7,11,13)
Ensure an adequate degree of muscular tension through the termination of sets in reasonably close proximity to muscular failure (9,14)
Using a per muscle-group training frequency that allows for the most effective distribution of sufficient training volume across the microcycle, while accounting for personal preference(12)
If these tenets of training are adhered to, we can be reasonably confident that the average response would be increased muscle hypertrophy. Yet, the magnitude and direction of response can vary greatly between individuals.
A 2016 study by Ahtiainen et al.(1) pooled the data of ten separate, but similar, resistance training interventions collected over fifteen years in untrained individuals (n=287). These lengthy training interventions (20-24 weeks) consisted of whole-body resistance training with progressive increases in both training volume and intensity over the course of the intervention. As we might expect, the intervention resulted in an average increase in muscle size of 4.8% (not surprising in a group of untrained individuals engaging in resistance training for the first time). Yet, when we examine the individual responses we see a different picture. Across the nearly 300 participants, the hypertrophic response varied from -11% to +30% (fig.1). Almost 30% of participants were categorised as “low responders”, showing little to no increases in muscle size after ~6 months of progressive resistance training, with a considerable number displaying decreases in muscle size (some by more than a tenth of their baseline size).
Given the large range in age (22 to 78 years) and ratio of males (n=183) to females (n=104) the authors posited that age and sex may have been explanatory factors in the heterogeneity of response observed, but further analysis of the data failed to show this relationship.
Figure 1. Variance in muscle hypertrophy response to resistance training (1)
Similar variances have been observed in other cohorts(4,10) with one study reporting a hypertrophy response variance of -2 to 59%(15). While the reason for such a diverse response to resistance training is not yet precisely known, it has been suggested that a combination of factors such as genetics(2,9) and molecular responses(16) play a significant role in the observed heterogeneity of hypertrophic response to resistance training. Research on hypertrophy response heterogeneity in trained individuals is currently lacking.
What may present even more of a challenge for coaches from a performance standpoint is that similar variability of response exists in maximal strength.
Ahtiainen et al.,(1) also measured strength outcomes in its cohort of 287 participants. The average increase in 1RM leg press strength was 21% but the variability in response was even greater than observed in the hypertrophy outcomes, with 1RM responses ranging from -8% to +60% (fig.2).
Figure 2. Variance in muscular strength response to resistance training (1)
9 weeks of leg extension only training (3.wk-1) in untrained individuals resulted in an average maximal force increase of 22%, yet some trainees actually got weaker after this intervention with individual responses ranging from -1% to +44%(5). 12 weeks of elbow flexor training in 335 untrained men and women resulted in increases of strength ranging from 0% - 250%, with the ACTN3 genotype associated with increased strength response in the female cohort only(3). Similar variability in strength measures have been observed in other large cohorts consisting of both men and women representing diverse age profiles(1,10). Akin to hypertrophic responses, research examining variability in strength outcomes responses in trained individuals is lacking.
The phenomenon of training response phenotype is not just isolated to muscle strength and hypertrophy but has been extensively observed in response to aerobic training also. In a homogenous cohort, a standardised moderate intensity aerobic training program resulted in Vo2max responses ranging from 0% - +40%(2). Training with high anaerobic demands, such as sprint interval training also results in varied response when assessed by V̇O2peak (6).
Heterogeneity of response to sprint interval training
Figure 3. Variance in V̇O2peak response to sprint interval training(6)
Exactly why we do not observe uniform responses to standardised training protocols in homogenous cohorts is still a burning question in the research, and poses a very real challenge for us as coaches in the real world.
The term “non-responder” to training is arguably inaccurate. There is always a response to a training stimulus, it just may not be the one we expect or hope for, or the magnitude of change may not be detectable. Often we see those categorised as “non-responders” adapting positively to an increase in training volume(6). It is not that these individuals don’t respond to training at all, but rather may require an increased or altered training stimulus than the average individual. Conversely a “high-responder” may require less training to achieve the desired adaptation. Similarly, individual responses to certain intensity ranges vary greatly. We see some athletes increase maximal strength at the greatest rates when the majority of their resistance training is done at high loads, while others excel with moderate loads. The same is observed anecdotally with exercise selection. As coaches our aim is to understand what combination of volume, intensity, frequency, and exercise selection results in the desired adaptation for our athletes’ training response phenotype.
So how do we best navigate this field of uncertainty when making training decisions?
As already stated, it is difficult to be “evidence-led” when working at the elite level, as empirical evidence is lacking. We have to lean on the other two pillars of the evidence-based model in this instance; our experience and the data we collect on the athletes themselves. In an ideal world any new athlete we work with would have a detailed log of their training history from which we could draw conclusions and make informed decisions. If this training history exists, it would be prudent to extrapolate that athlete’s training response phenotype based on how they have responded to certain intensities, volumes, frequencies and exercises in the past. However, this is often impossible since detailed training logs are often lacking. As a starting point, simply asking your athlete what they feel they respond best to can provide valuable insight, especially as we learn more about the role of psychological belief and athlete “buy-in” in physical programming.
In the absence of an athlete’s training history or input from the athlete themselves, it is best to rely on your own experiences in similar situations, coupled with your understanding of training theory and guidelines based on the best available empirical evidence, along with the induction of an athlete monitoring system we’ve alluded to above.
Whatever method you use to establish your initial training approach, it is important to note that this is simply your starting point. We can never accurately predict exactly how any given individual will respond to a specific training stimulus. We simply make our best estimation of what will result in the desired outcome and design a training protocol to accomplish this. From there we encourage practitioners to systematically collect and interpret on-going training performance and monitoring data to establish how our athlete is responding to the given approach. Over time we accumulate sufficient amounts of data to closer establish our athletes training response phenotype and “fine-tune” our programming. This method requires experimentation and a keen attention to detail.
Taking the time to collect and effectively analyse your athletes’ training and performance data and establishing their training response phenotype is a worthwhile endeavour allowing you to establish what volumes, intensities, frequencies and exercises they respond best to.
Conclusion:
Humans are complex biological systems, and with our current knowledge it is impossible to precisely predict how an individual will respond to a specific training stimulus. Large heterogeneity in training response has been demonstrated in the literature for a wide range of physical traits. As coaches the goal is to determine our athletes’ training response phenotype, which is done through a combination of calculated experimentation, effective data collection, and insightful analysis. Modern technology coupled with practical wisdom provides a mechanism to help accomplish these desires in a more efficient manner which, hopefully, will allow coaches to dedicate more time to coaching and less time to arduous tasks related to data engineering and management. While this is partly possible through the creative and coordinated use of multiple softwares as we have done in the past, Aplyft is a relatively new software we’ve been working on that aims to consolidate these pursuits into a single software, for coaches, and mobile application, for athletes, to facilitate an efficient workflow for coaches to program training, analyze data, and communicate with athletes.
Aplyft information:
Aplyft intends to provide the leading data-driven online coaching and training-related data management software, providing both coaches and clients with the tools required for effective online personal coaching and in-person athlete data management. Used by top organisations including the International Powerlifting Federation, The United States Basketball Academy, and Rugby Academy Ireland, Aplyft provides a centralised platform for coaching requirements and automatically generates valuable training data analytics allowing coaches to make more informed, evidence-based decisions for athletes and clients. Aplyft’s marketplace feature also allows expert coaches to offer their services to prospective clients from all over the globe.
Aplyft is currently offering a free two-week trial to test out the software and consider it for use in your own practice. You can easily sign-up at this link: https://portal.aplyft.com/sign-up
References:
1. Ahtiainen, J., Walker, S., Peltonen, H., Holviala, J., Sillanpää, E., & Karavirta, L. et al. (2016). Heterogeneity in resistance training-induced muscle strength and mass responses in men and women of different ages. AGE, 38(1). doi: 10.1007/s11357-015-9870-1
2. Bouchard, C., & Rankinen, T. (2001). Individual differences in response to regular physical activity. Medicine And Science In Sports And Exercise, 33(Supplement), S446-S451. doi: 10.1097/00005768-200106001-00013
3. Clarkson, P., Devaney, J., Gordish-Dressman, H., Thompson, P., Hubal, M., & Urso, M. et al. (2005). ACTN3 genotype is associated with increases in muscle strength in response to resistance training in women. Journal Of Applied Physiology, 99(1), 154-163. doi: 10.1152/japplphysiol.01139.2004
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