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The Training Adaptability Prediction Problem Explained: Part 1

Writer's picture: Jo ClubbJo Clubb

This post explores the Training Adaptation Predictability Problem (TAPP), highlighting the variability in training responses and its implications for designing and monitoring effective athlete training programmes.


The training process is a delicate balance between stress and recovery. Load monitoring systems are essential tools to capture, plan, and positively influence this process. Advances in tracking technology have made it easier than ever to objectively measure mechanical parameters of training. However, have we become overly reliant on these objective metrics of movement?


Training theory and periodisation aim to prescribe the optimal balance between training and recovery to elicit adaptation. Yet, a wealth of research demonstrates that athletes do not necessarily respond to a training stimulus in a predictable manner. Since mechanical training stressors are not the solitary drivers of performance and injury outcomes, the implications for the planning and monitoring of load on athletes must be considered.


In this post, we examine the evidence for variability in training responses, both between individuals and within the same individual over time. This phenomenon, which I’ve discussed with the brilliant John Kiely as the Training Adaptability Prediction Problem (TAPP), presents a significant challenge for sports science practitioners. Designing, monitoring, and adjusting training programmes requires acknowledging this unpredictability. Accordingly, we’ll explore how effective athlete monitoring programmes can be structured to help combat the TAPP.

 


Between-Athlete Differences: The Unpredictable Reality of the General Adaptation Syndrome


Conventionally, we prescribe training in mechanical terms. Training plans are designed and quantified via empirical description of volumes, intensities and frequencies. We most commonly report our athletes’ training experiences in terms of distances, accelerations, decelerations, and speeds.


Such prescription is rooted in Hans Selye’s General Adaptation Syndrome (GAS), which suggests that the body follow a predictable trajectory when responding to stress: alarm, resistance, and, in some cases, exhaustion. Periodisation is built upon this concept, in which we attempt to prescribe an optimal balance between training and recovery to elicit adaptation and supercompensation.


In reality, this trajectory is far from straightforward. Individuals respond differently to the same stimulus. In athletes, extensive research demonstrates they do not necessarily respond to training in a predictable manner.


To illustrate, large variation has been witnessed between individuals in health outcomes, including:


  • physical activity (Bouchard and Rankinen, 2001)

  • resistance training (Hubal et al, 2005)

  • endurance and sprint interval training (Bonafiglia et al., 2016)

  • high intensity interval training (Astorino et al, 2014)

  • ergogenic aids including caffeine ingestion (Astorino and Roberson, 2010) and vitamin D supplementation (Didriksen et al, 2013).

A figure from the Journal of Strength and Conditioning Research showing individual % change from baseline in two groups, PRE vs POST in white dots on the left and PRE vs PEAK in black dots on the right.
Comparison between individual changes in maximal power output analyzed with a PRE-POST vs. PRE-PEAK approach Morin et al., 2022

While much of this research is based on sedentary or recreationally-active individuals, variation is also evident in well-trained populations. For instance, Morin et al., (2020) observed inconsistent magnitude and timing of adaptations in competitive sprinters in response to a ten-week high-resistance sprint training block.


They concluded the same heavy resistance sprint training programme likely induced individually variable delayed adaptations for performance and mechanical variables. Their findings highlight the limitations of arbitrary, time-based assessments, advocating instead for more individualised interpretations of training adaptations.





Within-Athlete Differences: Psycho-emotional Factors Add Further Complexity


The individual response to a training stimulus may also differ within-individuals, often influenced by the psycho-emotional context surrounding the athlete. For example, injury risk is elevated during times of academic stress in student-athletes, particularly in those who play regularly (Mann et al., 2016). Interestingly, research in well-trained triathletes suggests psychological stress can have an even greater impact on signs and symptoms of injury and illness than physical training factors (Main et al., 2010). The intertwining of psychological and physical stressors further challenges the notion of a predictable, one-size-fits-all training response.


Yann Le Meur sports science infographic of stress, illness and injury in college football players, publiched by Bryan Mann and colleagues in the Journal of Strength and Conditoning Research. The graphic shows injury/illness odds are twice as hgh during weeks of high academic stress in college student athletes.

The impact of psychological stress on health, immunity, and injury outcomes in athletic populations is so great that physical and psychological stress have been described as ‘cumulative and synergistic’ (Clow and Hucklebridge, 2001). I’m sure many of us have observed negative effects on physical outcomes in our athletes (and maybe ourselves too) who are suffering with “real world” life stressors.


Consequently, the individualised response to any given exercise stimuli will vary extensively between individuals, and within the same individual in different contexts based on the complex integration of nature and nurture (Pickering and Kiely, 2018). Therefore, a holistic athlete monitoring system that incorporates both dose and response is essential to support each individual in their training process.


 

Tracking Individual Responses to Mechanical Stressors


Recognising the TAPP makes athlete monitoring an essential practice to illuminate how each individual is responding to the training plan. This requires looking beyond mechanical training load measures alone, to also capture an individual’s training response.


There are a multitude of objective assessments available to try to capture training response, including; biochemical, cardiovascular, and neuromuscular options. Of course, each approach has their own pros and cons, and it is up to practitioners to determine which response assessments will translate best to their own individual environment. In addition, the data architecture should be designed to allow objective response data stream to be viewed alongside each individual’s training load. This enables practitioners gauge how well an athlete is coping physically with their training programme.


Incorporating these measures into regular training routines – e.g., monitoring hamstring strength in the gym or heart rate during standardised warm-ups – offers ongoing insights without adding excessive testing burden. This approach, often referred to as “invisible monitoring”, is an appealing way to gain greater insight.


 

Subjective Insights are Critical


Subjective wellness markers, like how athletes feel, often provide superior responsiveness to training stress compared to objective measures (Saw et al., 2015; Thorpe et al., 2016). While jump testing, muscle strength assessments, or biochemical markers can each provide insight into how an athlete is responding, perhaps the best approach is to simply ask how they’re feeling!


Of course, questionnaires are not without issue. We’re all familiar with debates regarding honesty, compliance, and buy-in. I do argue however, that at least some of this responsibility rests with practitioners and their education and communication to athletes and key stakeholders about the process.


I feel many practitioners have given up on subjective questionnaires, especially in light of abundant objective technologies available to them. But considering the TAPP, overlooking subjective questionnaires is missing a critical opportunity to understand how each individual is responding to their training programme. Montull and colleagues (2022) argue that subjective monitoring outperforms objective monitoring due, in part, to the “outstanding potential of the human neurobiological system to dynamically, and rapidly, integrate massive amounts of personal and environmental information”. Recognising the TAPP, subjective wellness questionnaires, when implemented effectively, provide a critical opportunity to capture how each individual is responding to their programme.


Similarly, the Rating of Perceived Effort (RPE), despite perhaps declining popularity, is an individual’s appraisal of training intensity. It aligns with cognitive appraisal theory (Lazarus & Folkman, 1984), which posits that the stress response depends on the individual’s interpretation of the stressor and their coping resources.


As a practitioner, my perspective on subjective measures evolved significantly thanks to collaborations with psychology colleagues like Drs Katy Tran-Turner and Desaree Festa in Buffalo, as well as reading works such as John Kiely’s essay on stress. This integration of psychology and physiology is key to addressing the TAPP.



Integrating Multiple Measures for Further Insight


No single metric can comprehensively represent training load or response. Effective monitoring systems should incorporate multivariate approaches. Research, such as Weaving et al. (2017), has repeatably demonstrated that training load is complex and cannot be represented by one number. As mentioned earlier, this reiterates the importance of data infrastructure to enable a dynamic and flexible approach to data analysis and visualisation of various training load and response measures.


Combining internal and external load measures can give us a holistic view of an athlete's training dose. Most simply, ratios between internal and external load have been used as a representation of work efficiency, such as the Training Efficiency Index (Delaney et al., 2018), although they should be interpreted cautiously due to oversimplification associated with ratios.


Given the TAPP, it is likely that relationships between external and internal load are more complex and individualised. As such, advanced data analytics (e.g., machine learning) may provide greater insight, as demonstrated by Lacome and colleagues (2018) and our more recent project led by Mauro Mandorino (2024). I explore combining internal and external load further in the video below taken.



 

Managing the TAPP Beyond the Monitoring System


The Training Adaptation Predictability Problem highlights the challenge of reliably predicting how individuals will response to planned training stimuli. This unpredictability underscores the need for practitioners to design processes that effectively manage athlete training programmes despite this limitation.


While this discussion has focused on training load and response measurements as tools for managing the TAPP, it’s essential to remember that training stress is not purely physical. As John Kiely emphasises in his essay on stress, an individual’s emotional and cognitive backdrop significantly influences their response to training. To manage the TAPP effectively, we must also foster positive cognitive and emotional environments for athletes. This essential discussion will be the focus of Part 2.

 


With thanks to John Kiely, whose thought-provoking discussions and comments shaped this philosophy.



FAQs on the Training Adaptation Predictability Problem


What is the Training Adaptation Predictability Problem (TAPP)?

The TAPP refers to the challenge of reliably predicting how individuals will respond to planned training stimuli. Variability exists both between athletes and within the same athlete over time due to a combination of physical, psychological, and environmental factors.


Why is individual variability in training responses significant for sports science practitioners?

Athletes do not always respond predictably to training plans. This variability impacts how practitioners design, monitor, and adjust training programmes to optimise performance and reduce injury risk. Recognising this variability is essential to providing individualised and effective training strategies.


What are some key examples of variability in training responses?

Studies have demonstrated variability in responses to various training modalities, including:

  • Resistance training (Hubal et al., 2005)

  • Endurance and sprint interval training (Bonafiglia et al., 2016)

  • Ergogenic aids like caffeine and vitamin D (Astorino & Roberson, 2010; Didriksen et al., 2013)

These differences exist even in well-trained populations, highlighting the limitations of one-size-fits-all training approaches.


How do psycho-emotional factors influence training responses?

Psychological stress, such as academic or life stress, can significantly impact injury risk, illness susceptibility, and recovery. Research shows that psychological and physical stressors are cumulative and synergistic, necessitating a holistic approach to athlete monitoring and management.


What role does athlete monitoring play in managing the TAPP?

Athlete monitoring systems help identify how individuals are responding to training programmes. These systems should combine both training load and response metrics, incorporating objective and subjective measures to provide a more comprehensive view of the athlete's status.


Are subjective measures like wellness questionnaires and RPE still relevant?

Yes, subjective measures are critical for understanding individual responses. Research suggests that wellness questionnaires often outperform objective metrics in capturing how athletes are coping with training stress. These tools align with the cognitive appraisal theory, which underscores the importance of individual perception in stress responses.


How can data infrastructure enhance athlete monitoring systems?

A robust data infrastructure enables dynamic and flexible analysis of training load and response measures. This is particularly important for combining internal and external load data, as well as response data streams, and utilising advanced analytics like machine learning to better understand individualised patterns.

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