Physiological vs subjective measuring to asses overtraining.

Overtraining (OT) is an ever-present and problematic topic in the realm of sporting performance. The persistent increases in volume/load or intensity are often necessary drivers to improve athletic performance, however, the maladaptation to the stimulus provided through inadequate recovery can, in turn, lead to OT (Halson, 2014). Issues with OT occur to the detriment of performance by restricting an athlete’s level to recover and therefore put in optimal performances. Thus, the management of fatigue and workload needs to be carefully monitored to enable an athlete to push high-level performances and results consistently.

OT syndrome can be considered to have a pathology of performance decrement (> 2 months) alongside possibly more severe symptomatology and maladapted physiology (psychologic, neurologic, endocrinologic, immunologic systems), and an additional stressor not explained by other diseases (Meeusen et al., 2006). OT syndrome is however, often confused and intertwined with more applicable methodologies of training, notably with overreaching phases. That of functional overreaching where there may be a temporary decline in performance allowing an supercompensation phase to occur, resulting in an overall improvement in performance in the longer term (Kreher & Schwartz, 2012).

A consistent problematic theme within assessing OT is the quantification of what can be considered OT, and the premise of being able to minimise the risks of OT via quantifiable measures. In terms of physiological measurement, there is the increase of Creatine Phosphokinase (CPK) and Lactate Dehydrogenase (LDH) that occur after bouts of intense exercise, and although this doesn’t signify an overtraining effect per se, excessive levels of CPK found in the blood can indicate that damage has occurred at a cellular level, and although a possible short term marker in the presence of intense exercise, in the longer term this may also be a marker that OT may be imminent (Callegari et al., 2017; Gleeson, 2002). However, more work is required to quantify these measures for chronic OT as these markers are generally looked at for acute responses to training. Further physiological tests used is the blood urea nitrogen test, whereby the concentration of nitrogenous waste (e.g. urea, 3-methyl histidine and uric acid) in blood plasma may be another method to quantify the possible onset of OT, due to presence of the aforementioned waste products, providing a measure of muscle protein breakdown from the catabolic nature of OT (Gleeson, 2002). Unfortunately, just as the CPK levels test, this measure can be skewed in the short term from repeated bouts of high-intensity exercise, and Urea levels can also be elevated significantly in the presence of dietary protein too. Therefore, although a possible good measurement tool it probably has too many mitigating factors to be considered for use on its own.

The physical biomarkers previously mentioned are good possibilities concerning quantifying OT, however, each biomarker has a large degree of inherent variability from subject to subject. Therefore, a trusted baseline value for each biomarker and athlete should be acquired before being exposed to over-reaching or overtraining, to ensure the outcomes have value and meaning. These possible measures are also relatively invasive and require the taking of blood samples for analysis, which would have to be planned for and causes a barrier to obtain this data, besides those who have regular access to the facilities required.

More accessible measures that can be acquired over long periods, include the introduction of the use of heart rate variability (HRV). HRV has become a popular modality to measure, as it is now more accessible to athletes that have a budget or those that do not have regular access to expensive kit or labs. This is due to its addition as a metric on several smartwatches and other wearable devices. HRV is the fluctuation of the length of intervals between heartbeats (Malik et al., 1996). As stated on a company’s website that market the use of HRV, “HRV has been shown in numerous studies to positively correlate with athletic performance and training adaptation, to negatively correlate with the risk of overtraining” (Capodilupo, 2016). The paradigm through which HRV may offer a useful measure of OT is due to HRV being a respondent factor to numerous environmental and physiological provocations. Higher levels of HRV are an associative factor with improved regulation and homeostasis of the nervous system functions, consequently the ability to cope with stressors placed upon it, physical exertion and training being one such stressor. Conversely, a low level of HRV manifests in a heartbeat of repetitive regularity and its association with lower levels of recovery and readiness, due to the reduced ability for the body to cope with stressors, like extra training load, placed upon it. The upshot of such a methodology is the non-invasive nature and ability for constant monitoring, alongside its accessible nature of the technology required and software, like that featured on Apple watches and Garmin wearables, alongside Whoop whose whole product is based on HRV. This is a technology that can be utilised to monitor the athletes training and the possibility of overtraining. A study did find relationships with HRV and OT through the use of Electrocardiographic (ECG) data collected at 500 Hz, and converting it from analogue to digital, however, one of the issues within this study was the inability to repeat it due to equipment requirements (Mourot et al., 2004). Since this study, there have been advancements in wearable technology that has developed an algorithm to measure HRV and stress, supported by the discovery that the prescription of moderate and high-intensity sessions according to HRV is a more optimal approach of programming training sessions, compared to an individually predefined arrangement for 3000m runners (Vesterinen et al., 2013). Thus, leading to HRV’s possible usefulness in predicting OT and the management of fatigue to avoid OT.

Whilst the physiological measures carry the weight of appearing to be hard and fast determinants of whether an athlete is in a state of OT or not, the more subjective measures have been noted as not proving any less reliable (Saw et al., 2016). The issue of collecting enough relevant and current information about levels of performance and fatigue is key to ascertaining whether an athlete is converging upon overtraining. The use of more subjective measures and assessments allows for the regular collection and storage of such data, although its applicability is reliant on the athlete’s ability to answer the questions in a meaningful and honest fashion. Figure 1. (McLean et al., 2010) offers a practical and rudimentary version of this very practice. This readiness questionnaire has been used in professional settings with great effect, as the first port of call when ascertaining players readiness to play and train. Subsequently highlighting if any changes to the schedule are required to prevent OT.

Figure 1. Readiness and wellness questionnaire (McLean et al., 2010, p. 370)

Subjective measures have repeatedly been demonstratred as reputable at identifying impaired recovery and overall well-being, stimulated by the change in chronic training load (Dodson, 2007; Filaire et al., 2001; Saw et al., 2016). During long term monitoring, this can aid the early identification of factors leading towards OT, as the development is often more gradual than acute and, in these circumstances, can be trickier to determine or spot due to its gradual nature. One of the issues with the subjective measure is that, much like the physical biomarkers, there often needs to be a decently sized collection of data to be able to determine the trends and patterns before OT can begin to be identified, thusly hampering the newer athlete in the system without a back catalogue of data. Furthermore, the subjective nature means there is no set biological baseline for monitoring, allowing for greater individuality, but also greater room for subjective interpretation of the data. It has been observed that a questionnaire-based system for changes in fatigue and readiness to train after 3 day cycling, correctly predicted 78% of the subjects for functional overreaching or acute fatigue (ten Haaf et al., 2016).

In conclusion, the ability of invasive physiological testing appears to be no better at the prediction of overtraining in well-trained subjects than that of a simple questionnaire. This idea falls in-line with other works in this area (Hooper & Mackinnon, 1995; Kreher, 2016; Saw et al., 2016; ten Haaf et al., 2016) whereby the recommendation is for low-cost and easily accessed questionnaires, due to their repeatability and relative ease of collection. This offers an easy solution when monitored and measured alongside other physical markers, namely the non-invasive and relatively low-cost use of HRV now available.

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References

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