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Training should be planned in a progressive manner in order to optimise potential adaptations. The fitness-fatigue paradigm (Plisk & Stone, 2003) illustrates that there are two after-effects of training, fitness and fatigue. In order to optimise an athlete’s preparedness for performance and training adaptations the effects of fatigue over time should be minimised. Excessive and poorly planned training loads can ultimately lead to non-functional overreaching and the overtraining syndrome. Monitoring an athlete prior to a training session to assess how ready they are for the session is often done in the applied setting to assess their levels of fatigue and potential to optimise the adaptations that can be realised from the planned session. This is known as the measurement of an athlete’s ‘readiness to train’.
Numerous variables and tests are used to assess an athlete’s ‘readiness to train’. Turner et al. (2016) noted that these include the following:
Research has been undertaken to investigate the role and benefit of assessing such ‘readiness to train’ metrics.
Psychometric measures, for example, which involve an athlete rating subjectively their perception of their response to training (Saw et al., 2015), e.g. energy levels, mood, stress levels, have been reported to be sensitive to changes in training load (Thorpe et al., 2015). Thorpe et al. (2015), for example, noted that in elite soccer players perceived ratings of fatigue were sensitive to daily fluctuations in total high-intensity running distance which provides some support for the use of subjective psychometric measures as ‘readiness-to-train’ markers.
The countermovement jump (CMJ) is also a common test often used to monitor an athlete’s power levels and ‘readiness to train’ profile. It is a simple, objective, time-efficient and non-invasive test that can be conducted via the use of force platforms, mobile apps, electronic switch contact mats and inertial measurement units (IMUs). Cormack et al. (2008) noted that the ratio of CMJ flight time to contraction time may be a useful metric to monitor the response to elite Australian Rugby Football competition and training. Similarly, the reactive strength index (RSI, ratio of height jumped to contact time) which is derived from a drop jump test, has been reported to be a test with the capacity to detect neuromuscular fatigue in elite youth soccer players (Hamilton, 2009).
‘Readiness to train’ markers are taken before an athlete begins their training sessions. Often these measures are taken in the morning after an athlete has woken up. In order to ensure that the reliability of the variables used is intact it is important that the same procedures and protocols are employed when measuring and recording the marker. Turner et al. (2016) provide a checklist that should be followed to ensure that any data collected is accurate. This includes:
In addition, it is vital that the tester follows the same protocol each time a test is conducted, such as a drop jump RSI test, and uses the same equipment, such as an IMU device. Only when these guidelines are followed can some reassurance be given that the data is accurate and reliable and thus useful for comparison purposes and the identification of an athlete’s ‘readiness to train’.
Essentially with either subjective or objective ‘readiness to train’ markers you are assessing the athlete’s response to the training that they have exposed to and their levels of fatigue. There is a multi-factorial aspect to fatigue and therefore it is recommended that multiple measures of an athlete’s ‘readiness to train’ are assessed (Turner et al., 2016). This may include, for example, the use of the DJ RSI and psychometric measures daily with an athlete as a method of pre-training monitoring.
If data is collected then it is important that the coach interprets the data and responses appropriately. Often with ‘readiness to train’ markers the appropriate intervention is a reduction in training volume and/ or intensity with the ultimate goal of enhancing the associated adaptation and avoiding the negative effects of non-functional overreaching and overtraining. No one ‘readiness to train’ marker should be looked at in isolation. Indeed Turner et al. (2016) noted that an athlete should score below normal on two ‘readiness to train’ tests before the coach would intervene and adjust the training intensity.
This raises the question of what constitutes a ‘below normal’ score on ‘readiness to train’ tests such as the drop jump RSI test. If the athlete, for example, has a 5% drop in RSI is this an issue or could this drop be related to error in the testing protocol or biological variability? Researchers have suggested that the coefficient of variation (CV) can be used to assess if a change in performance is a meaningful and true change (Beattie and Flanagan, 2015; Comyns et al. 2019; Turner et al., 2015). To assess CV have your athletes perform a number of trials of the test (e.g. 3 trials) and then determine the CV for each individual athlete by dividing the standard deviation by the mean and multiplying by 100. So if the athlete has an individual CV of 6% for the drop jump RSI and they report a 5% drop on a certain training day the drop would not be consider a ‘below normal’ decrease in performance. In this case no intervention is needed and only when the reported drop is greater than the individual CV should the coach intervene and adjust the volume and/ or intensity of the planned training.
Coefficient of variance (CV) = 100 X (Standard deviation / Mean)
Finally when considering if the change in the ‘readiness to train’ marker is of concern the coach should consider what phase of training the athlete is in. For example, during a period of planned functional overreaching, such as a training camp, the increased training load is likely to manifest itself as fatigue (Turner et al., 2016). This in turn will be reflected in the ‘readiness to train’ markers. In this scenario if no improvement in the markers is seen in the subsequent taper or unload period then the coach should intervene and adjust the training accordingly.
Beattie K. and Flanagan E.P. (2015). Establishing the reliability and meaningful change of the drop-jump reactive strength index. Journal of Australian Strength and Conditioning, 23, 12-18.
Comyns T.M., Flanagan, E., Fleming, S., Fitzgerald, E., and Harper, D. (2019). Inter-day reliability and usefulness of reactive strength index derived from two maximal rebound jump tests. International Journal of Sports Physiology and Performance, 14 (9), 1200-1204.
Cormack, S., Newton, R., McGuigan, M. and Cormie, P. (2008). Neuromuscular and endocrine responses of elite players during an AustralianRrules Football season. International Journal of Sports Physiology and Performance, 3(4), 439-53.
Hamilton, D. Drop jumps as an indicator of neuromuscular fatigue and recovery in elite youth Soccer athletes following tournament match play. Journal of Australian Strength and Conditioning, 17(4): 3-8. 2009.
Pedley, J., Lloyd, R., Read, P., Moore, I.S., and Oliver, J.L. (2017). Drop jump: A technical model for scientific application. Strength and Conditioning Journal, 39(5), 36-44.
Plisk, S.S. and M.H. Stone, 2003. Periodization strategies. Strength and Conditioning Journal, 25(6), 19-37.
Saw, A., Main, L. and Gastin, P. (2015). Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. British Journal of Sports Medicine, 0, 0-13.
Thorpe, R.T., Strudwick, A.J., Buchheit, M., Atkinson, G., Drust, B. and Gregson, W. (2015). Monitoring fatigue during the in-season competitive phase in elite soccer players. International Journal of Sports Physiology and Performance, 10(8), 958-64.
Turner, A.N., Bishop, C., Springham, N., and Stewart, P. (2016). Identifying readiness to train: when to push and when to pull. Professional Strength and Conditioning, 42, 9-14.