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Issue 6 June Volume 35 Issue 5 May Volume 35 Issue 4 April Volume 35 Issue 3 March Volume 35 Issue 2 February Volume 35 Issue 1 January Volume 34 Issue 12 December Volume 34 Issue 11 November Volume 34 Issue 10 October Volume 34 Issue 9 September Volume 34 Issue 8 August Volume 34 Issue 7 July Volume 34 Issue 6 June Volume 34 Issue 5 May Volume 34 Issue 4 April Volume 34 Issue 3 March Volume 34 Issue 2 February Volume 34 Issue 1 January Volume 33 Issue 12 December Volume 33 Issue 11 November Further, there are no guarantees that monitoring training load will result in successful performances, therefore the resources required may not be provided.
In addition, a clear rationale identifying why the monitoring is occurring, what will be monitored, how often monitoring will occur, and how the data are interpreted and presented back to the coaching staff is required. Finally, the ability and opportunity to implement change and provide feedback is critical to a successful monitoring system, and, if this does not occur, many attempts at monitoring are not sustainable [ 1 ]. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available to athletes, coaches, and scientists.
However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker of fatigue described in the literature. Given the definition described in Sect. However, there are numerous difficulties regarding maximal testing in athletes. Maximal tests may add to existing fatigue in an athlete, which may be problematic around competition phases [ 4 ]. A taper may also be required to determine true performance capabilities, which is often impractical. When fatigued, athletes may also lack motivation to produce a maximal effort that is not for competitive purposes.
For many sports, particularly team sports, it is extremely difficult to replicate or even define maximal performance [ 5 ]. When monitoring training load, the load units can be thought of as either external or internal. Traditionally, external load has been the foundation of most monitoring systems. External load is defined as the work completed by the athlete, measured independently of his or her internal characteristics [ 6 ].
An example of external load in road cycling would be the mean power output sustained for a given duration of time i. While external load is important in understanding work completed and capabilities and capacities of the athlete, the internal load, or the relative physiological and psychological stress imposed is also critical in determining the training load and subsequent adaptation.
Indeed, it may be the relationship between external and internal loads that may aid in revealing fatigue.
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For example, using the cycling external load mentioned above, the power output may be maintained for the same duration; however, depending on the fatigue state of the athlete, this may be achieved with a high or low heart rate or a high or low perception of effort. It is this uncoupling or divergence of external and internal loads that may aid in differentiating between a fresh and a fatigued athlete [ 1 ]. To gain an understanding of external training load, a number of technologies are available to athletes and coaches.
Training and competition can be recorded and data can be analyzed to provide information on a number of parameters, including average power, normalized power, speed, and accelerations. The reliability of GPS for monitoring movement is influenced by factors such as sample rate, velocity, and duration and type of task [ 8 ].
From the available literature, it appears that the higher the velocity of movement, the lower the GPS reliability [ 8 ]. Further, the reliability is also reduced when assessing tasks that require a change of direction and GPS does not quantify the load of jumping, kicking the ball, and tackling actions [ 8 ].
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Typically, when using TMA for monitoring, arbitrary speed thresholds are set [ 9 ]. These categories may include walking, jogging, running, striding, sprinting, etc. It is becoming increasingly popular to associate TMA data with arbitrary and individualized speed thresholds.
Lovell and Abt [ 9 ] compared TMA data from video analysis as arbitrary units with units expressed as individual speed thresholds from pre-determined maximal treadmill running speeds. While this approach may be time-consuming, recent data suggest that individualized speed thresholds may provide practically significant information regarding training loads [ 9 ]. These assessments have become popular due to the simplicity of administration and the minimal amount of additional fatigue induced [ 10 ].
Common variables from jump test measurements include mean power, peak velocity, peak force, jump height, flight time, contact time, and rate of force development [ 5 , 10 ]. Equipment requirements for jump testing may include contact mats, portable or non-portable force platforms, and rotary encoders.
As isokinetic and isoinertial dynamometry requires specialized and often expensive equipment and does not replicate sport-specific movements, they are often not utilized in applied settings for strictly monitoring purposes [ 10 ].
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The rating of perceived exertion RPE is one of the most common means of assessing internal load. The use of RPE is based on the notion that an athlete can monitor their physiological stress during exercise as well as retrospectively provide information regarding their perceived effort post training or competition. Evidence suggests that RPE correlates well with heart rate during steady-state exercise and high-intensity interval cycling training, but not as well during short-duration high-intensity soccer drills [ 11 ].
Further, a meta-analysis of the literature reported that while RPE is a valid means of assessing exercise intensity, the validity may not be as high as previously thought [ 12 ]. For example, weighted mean validity coefficients for heart rate HR , blood lactate, and percent of maximal oxygen uptake V O 2max were 0.
RPE is also often combined with other variables such as session duration, HR, and blood lactate to provide additional insights into the internal load experienced by the athlete. The session RPE method was developed to eliminate the need to utilize HR monitors or other methods of assessing exercise intensity. While the session RPE method may be simple, valid, and reliable, the addition of HR monitoring may aid in understanding some of the variance that it does not explain.
Monitoring HR is one of the most common means of assessing internal load in athletes. The use of HR monitoring during exercise is based on the linear relationship between HR and the rate of oxygen consumption during steady-state exercise [ 15 ]; however, percentage of maximum HR is often used to both prescribe and monitor intensity [ 14 ]. Due to the daily variation in HR, which may be up to 6. Examination of physiological and perceptual indicators of load at a fixed submaximal intensity can provide information on the state of fatigue of the athlete.
A TRIMP is a unit of physical effort that is calculated using training duration and maximal, resting, and average HR during the exercise session [ 18 ]. However, the authors recognize the technical and scientific expertise and resources required for this type of individualized internal load monitoring. Blood lactate concentration is sensitive to changes in exercise intensity and duration [ 24 ]; however, there are a number of potential limitations to the use of regular monitoring of lactate concentrations during training and competition.
These include inter- and intra-individual differences in lactate accumulation depending on ambient temperature, hydration status, diet, glycogen content, previous exercise, and amount of muscle mass utilized, as well as sampling procedures time and site [ 14 ].
Again, changes in these parameters at a fixed submaximal workload may be useful to identify physiological and perceptual changes in internal load. HR recovery HRR is the rate at which HR declines at the cessation of exercise and has been suggested to be a marker of autonomic function and training status in athletes [ 26 ]. The autonomic nervous system consists of the sympathetic and parasympathetic systems, with the rise in HR during exercise being the result of increased sympathetic activity in combination with a reduction in parasympathetic activity.
HRR is characterized by opposing autonomic nervous system activity, with an increase in parasympathetic activity and withdrawal of sympathetic nervous activity [ 27 ]. In a recent review on HRR and monitoring changes in training status [ 26 ], it is suggested that HRR improves with increased training status, remains unchanged when there is no change in training status, and decreases when training status is reduced.
It was then concluded that, with the exception of overreaching where research is conflicting , HRR could be used to monitor the accumulation of fatigue in athletes [ 26 ]. However, the considerations mentioned in Sect.
The measurement of resting or post-exercise HR variability HRV has been suggested to indicate both positive and negative adaptations to training [ 28 ]. However, the varying methodological approaches employed, as well as high day-to-day variability in environmental and homeostatic factors, have resulted in inconsistent findings in the scientific literature [ 28 ]. As such, HRV has been shown to increase without a change in fitness V O 2max [ 29 ] as well as decrease alongside increases in fitness [ 30 ].
Increases, decreases, and no change in HRV have also been reported in the over-training literature [ 31 ]. To overcome some of the inconsistencies in findings, it has been suggested that both weekly and 7-day rolling averages have higher validity than single-day measurements [ 32 ]. This is due to the lower co-efficient of variation compared with other indices, a lack of influence of breathing frequency, and that data can be collected over a short period of time and easily calculated.
As is the case with the majority of tools to monitor elite athletes, longitudinal monitoring and an understanding of individual responses in HRV to training, taper and competition is critical. A relatively large amount of research has been conducted examining a range of biochemical, hormonal and immunological responses to exercise, primarily in a bid to monitor fatigue and minimize excessive fatigue and illness.
It is beyond the scope of this article to review the literature in this area; however, in short, no definitive marker has yet been identified. Serum creatine kinase activity is often a popular measure due to the simplicity of sample collection and analysis; however, variability of this measure is very high, and a poor temporal relationship with muscle recovery exists [ 10 ].
Salivary cortisol and testosterone measures have been shown to have some relationship to performance in the overreached athlete; however, the usefulness of these measures to quantify internal load on a regular basis has not been examined [ 33 ]. Other hormonal measures and suggested markers of immune function, such as salivary immunoglobulin A, natural killer cell activity, and neutrophil phagocytic activity have also not been examined on a routine basis, potentially due to both the expense and the time required for analysis [ 34 ].
In addition, these measures can be costly, time consuming and impractical in an applied environment [ 10 ]. Questionnaires and diaries can be a relatively simple and inexpensive means of determining the training load and subsequent responses to that training. However, both questionnaires and diaries rely on subjective information, which may need to be corroborated with physiological data [ 11 ]. A number of questionnaires are identified in the literature as well as being utilized by high-performance sport programs [ 5 ].
While questionnaires can provide simple and often useful subjective information, factors such as frequency of administration, time taken to complete the questions, sensitivity of questionnaire, type of response required written answers or circling responses , time of day of completion and the amount of time required for appropriate feedback should all be considered. Fatigued athletes often report impaired concentration and cognitive complaints [ 39 ]; therefore, investigation into psychomotor speed might provide insight into the cognitive load induced by exercise.
Psychomotor speed is most often assessed using computer-based reaction time and rapid visual information processing tasks and therefore can be affordable. While this measure may be applicable for examining overreached athletes, it is yet to receive research attention in the area of determining cognitive load as an indicator of internal load.
Sleep loss or deprivation can have significant effects on performance, motivation, perception of effort and cognition as well as numerous other biological functions [ 42 ]. Monitoring sleep quality and quantity can be useful for early detection and intervention before significant performance and health decrements are observed.
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The use of simple diaries indicating hours of sleep and perceived sleep quality can be useful. Actigraphy can provide data on bedtime, wake time, sleep-onset latency time taken to fall asleep , wake during sleep, and sleep efficiency estimate of sleep quality , as well as provide information on sleep routines.
Due to the increasing knowledge regarding the importance of sleep, sleep monitoring and assessment is becoming popular with elite athletes, coaches, and support staff.
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Current best practice methods for monitoring fatigue in high-performance sport were recently examined by Taylor [ 5 ]. From this assessment of monitoring, it appears that monitoring is incorporated by many staff in high-performance programs and that self-report measures are most commonly used, followed by practical sport-specific performance assessments.
Support staff and coaches are incorporating these techniques regularly, with the goal of minimizing fatigue and injury as well as examining the effectiveness of the training program. The nature of load monitoring required or indeed possible may vary greatly between team sport and individual sport athletes.
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Monitoring in team sports is often perceived to be more challenging due to the diverse range of training activities e. When monitoring team sport athletes, some of the most useful measures involve physiological changes, assessment of movement patterns and indicators of skills [ 1 ], with these measures being as sport-specific as possible. Movement patterns can be assessed by time-motion analysis or GPS tracking [ 1 ]. Other difficulties when assessing team sport competition performance include the influence of team tactics including those of the opposing team , environmental conditions, team cohesion, home or away competition and travel.
In individual sports such as cycling, swimming and triathlon, the fatigue is often the result of high training loads; the management of these loads through monitoring can be particularly important [ 1 ]. Load monitoring is often based on training volume, duration and intensity alongside indicators of perceptual fatigue such as RPE. Taylor, A. Doust, J.