Rating of Fatigue Scale
Rating of fatigue is one of the newest developments in fatigue monitoring, and it has so far been shown to have good face validity and high-levels of convergent validity . This scale is designed to provide a holistic measure of how fatigued an individual feels.
This is done through the use of an 11-point Likert scale with diagrammatic selections. This two-part system seems to make the rating easier for participants and, therefore, provides a more accurate way of determining perceived fatigue levels. Not only does the Rating of Fatigue scale appear to have a high-correlation with physiological markers, but it also appears to be capable of differentiating between perceived exertion during recovery and during exercise . These findings suggest that the Rating of Fatigue scale may be a superior way of monitoring fatigue in comparison to many traditional methods.
Countermovement jump (CMJ) testing is a popular method for monitoring fatigue due it is simplicity, and because it takes little time to measure . CMJ tests can be used to measure power, velocity, and/or jump displacement , and has been shown to be sensitive to match-induced fatigue [17, 19-22]. Furthermore, in a meta-analysis of 151 research articles, it was reported that the average CMJ height is more sensitive to neuromuscular fatigue than the highest CMJ height .
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Heart Rate Variability
The interest in heart rate variability (HRV) appears to be growing year-on-year as a tool for measuring the body’s reaction to training and its associated levels of fatigue . HRV is simply a measure of the differences in time from heart beat to heart beat. As HRV reflects the autonomic nervous system function, and thus stress, it is frequently used in the athletic world to identify periods of optimal training, monitor recovery status, and to flag any potential overtraining .
One of the main drawbacks of using HRV is that it is somewhat difficult and time-consuming to measure. HRV requires an athlete to be at complete rest and as relaxed as possible for the most accurate measure . This could be time-consuming, as getting an athlete to relax can potentially take several minutes or more. Other variables, such as body positioning (e.g. sitting vs. standing), or the time of day, can also play a role in the tests accuracy . Due to the high number of variables involved in this measurement, it is suggested that HRV should not be used as a sole measure of fatigue and readiness, but rather in conjunction with other testing protocols .
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While historically not the most popular method used, testing hormonal levels in saliva has been shown to provide an accurate measure of fatigue . Testing has shown that levels of hormones such as cortisol and testosterone taken from saliva samples change during and after physical activity, and that an individual’s biochemical response can be determined by looking at these changes .
For example, throughout a rugby match, levels of cortisol have been shown to increase to around 2.5 times the pre-match levels (i.e. baseline), and return back to baseline within 4-hours . On the other hand, testosterone levels appear to decrease. During the 5-day recovery period, cortisol levels appear to be lower, and testosterone levels higher, in comparison to baseline testing, which can result in a high testosterone-cortisol ratio . The high testosterone-cortisol ratio observed after competition, and during recovery, is most likely required to restore the break-down of homeostasis induced by the rugby match .
By looking at the testosterone-cortisol ratio in comparison to their baseline scores, the practitioner can determine how an athlete is coping with training or competition, as well as how they are recovering. The advantage of testing markers found in saliva is that they can be very accurate . However, performing these tests requires expensive equipment and resources, therefore limiting their practicality.
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In summary, no matter what test is chosen, it is important to have solid baseline measures. It should also be understood that comparing test findings with a baseline measure from different phases of the year than you are currently in (e.g. competitive season, pre-season, post-season), will not allow for meaningful comparison. For example, if an athlete is in the middle of a competitive season, the baseline measure should be from the beginning of that competitive season. This way the baseline data is generated while the athlete is in a physical and mental state most similar to that in which they will be training and performing in moving forward.