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Fatigue Monitoring in High Performance Sport: A Survey of Current Trends


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BSTRACT Research has identified a plethora of physiological, biochemical, psychological and performance markers that help inform coaching staff about when an athlete is in a state of fatigue or recovery. However use of such markers in the regular high performance training environment remains undocumented. To establish current best practice methods for training monitoring, 100 participants involved in coaching or sport science support roles in a variety of high performance sports programs were invited to participate in an online survey. The response rate was 55% with results indicating 91% of respondents implemented some form of training monitoring system. A majority of respondents (70%) indicated there was an equal focus between load quantification and the monitoring of fatigue and recovery within their training monitoring system. Interestingly, 20% of participants indicated the focus was solely on load quantification, while 10% solely monitored the fatigue/recovery process. Respondents reported that the aims of their monitoring systems were to prevent overtraining (22%), reduce injuries (29%), monitor the effectiveness of training programs (27%), and ensure maintenance of performance throughout competitive periods (22%). A variety of methods were used to achieve this, based mainly on experiential evidence rather than replication of methods used in scientific publications. Of the methods identified for monitoring fatigue and recovery responses, self-report questionnaires (84%) and practical tests of maximal neuromuscular performance (61%) were the most commonly utilised.
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Journal of Australian Strength & Conditioning
March 2012 | Volume 20 | Issue 1
Fatigue monitoring in high performance sport: A survey of current trends.
J. Aust. Strength Cond. 20(1)12-23. 2012 © ASCA
Peer Review
Kristie-Lee Taylor1,2
Dale W. Chapman1,2, John B. Cronin3, Michael J. Newton2, Nicholas Gill3
1 Department of Physiology, Australian Institute of Sport, Belconnen, ACT, Australia
2 School of Exercise and Health Sciences, Edith Cowan University, Perth, Australia
3 Sport Performance Research Institute of New Zealand, AUT University, Auckland NZ
Research has identified a plethora of physiological, biochemical, psychological and performance markers that
help inform coaching staff about when an athlete is in a state of fatigue or recovery. However use of such
markers in the regular high performance training environment remains undocumented. To establish current best practice
methods for training monitoring, 100 participants involved in coaching or sport science support roles in a variety of high
performance sports programs were invited to participate in an online survey. The response rate was 55% with results
indicating 91% of respondents implemented some form of training monitoring system. A majority of respondents (70%)
indicated there was an equal focus between load quantification and the monitoring of fatigue and recovery within their
training monitoring system. Interestingly, 20% of participants indicated the focus was solely on load quantification, while
10% solely monitored the fatigue/recovery process. Respondents reported that the aims of their monitoring systems
were to prevent overtraining (22%), reduce injuries (29%), monitor the effectiveness of training programs (27%), and
ensure maintenance of performance throughout competitive periods (22%). A variety of methods were used to achieve
this, based mainly on experiential evidence rather than replication of methods used in scientific publications. Of the
methods identified for monitoring fatigue and recovery responses, self-report questionnaires (84%) and practical tests of
maximal neuromuscular performance (61%) were the most commonly utilised.
Keywords - training monitoring, neuromuscular fatigue, overtraining, overreaching.
Athlete fatigue is a difficult concept to define, making its measurement equally problematical (1, 18). Muscle
physiologists often describe fatigue simply as an acute exercise-induced decline in muscle force (17). Within applied
exercise science research, fatigue is most commonly referred to as a reduced capacity for maximal performance (31).
Given this characterisation, it would seem that the most relevant way to measure fatigue would be directly, via a
maximal test of performance in the athlete’s competitive event. There are of course a number of difficulties associated
with this approach. Most significantly, repeated maximal performance efforts are likely to contribute to a fatiguing
effect, which is impractical, especially during a competitive season. Additionally, accurately defining maximal
performance in a number of sporting pursuits, particularly team sports, is challenging if not impossible at this point in
time. As well, such a “blunt force” approach to monitoring performance does not indicate the underlying physiological
changes associated with performance fluctuations (4). Therefore, monitoring performance and functional capacity
during athletic training is generally reliant on indirect markers of maximal performance or relevant physiological and/or
psychological characteristics (25, 31, 43).
A multitude of such markers are available to assist in informing coaching staff when an athlete is in a state of fatigue
or recovery, and while the research in this area is plentiful, no single, reliable diagnostic marker has yet been identified
(4, 31). Also, while numerous markers of fatigue have been identified and studied in relation to the diagnosis of
overreaching and overtraining syndromes (see (23, 33, 51) for reviews), less work has been published using such
markers during regular training and competition in high performing athletes. Despite a lack of scientific confirmation in
the use of such markers for fatigue monitoring and predicting non-functional overreaching in athletes involved in
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March 2012 | Volume 20 | Issue 1
regular training and competition schedules, anecdotal evidence suggests that most coaches and support staff involved
in high performance sport programs have adopted monitoring systems that rely on a range of these markers to
provide insight into their athlete’s state of fatigue and readiness for training and/or competition.
As there is a paucity of information in the scientific literature on the current training monitoring methods being
employed in high performance sports programs, the purpose of the current research was to gather information on the
type of training monitoring systems that are considered current best practice. Specifically, information pertaining to the
purpose of the monitoring systems, data collection methods, and their perceived effectiveness were examined via an
online survey sent to a variety of coaching and support staff within the Australian and New Zealand high performance
sport sector.
This descriptive study utilised an online survey electronically mailed to 100 individuals identified via their employment
within high performance programs across a variety of sports. The survey response rate was 55%. The majority of
respondents who affirmed their use of training monitoring systems were employed as the head strength and
conditioning coach within their program (n=30), with other respondents identifying themselves as sports scientists
(n=12), high performance managers/sports science co-ordinators (n=9), head coach (n=3) or other (n=1). Of the 55
respondents, five indicated that they do not use any form of training monitoring and were thereafter excluded from the
analyses. The respondents all worked with elite/non-professional athletes or professional athletes across a variety of
sports (see Figure 1). Ethical approval was granted by the Institutional Human Research Ethics Committee.
Figure 1 -
Number of respondents representing various sports, with colours differentiating the level of
performance. This figure represents the 55 respondents, 53% of whom reported being involved
with multiple sports.
The survey (Appendix A) divided the topic of ‘training monitoring’ into two distinct areas; a) the quantification of
training load, and b) monitoring of the fatigue/recovery responses to training or competition loads. The results
presented herewith primarily relate to methods employed for monitoring athlete fatigue. Participants completed the
online survey in three parts; (A) demographic questions including whether or not a training monitoring system was
utilised, (B) items assessing the purpose and perceived value of the training monitoring system and how the data was
collected and analysed, and (C) details of which methods are used for quantifying training load and for monitoring
fatigue. Questions were based on methods identified within the scientific literature surrounding fatigue monitoring,
training load quantification and the modelling of fitness-fatigue responses. In addition personal communications with
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March 2012 | Volume 20 | Issue 1
coaches in the high performance sport arena about their current practices provided a further basis for the construction
of the questionnaire.
Subjects were contacted electronically whereby the purpose of the survey was explained and a link to the online
survey provided. They were informed that by completing and returning the survey that their consent to use the
information was assumed. Upon completion of the survey all respondents were asked to indicate their availability for
providing greater detail on selected responses if required by the principal researcher. Of the 50 respondents who
indicated the use of a training monitoring system, 39 indicated their willingness to participate in follow-up questioning.
Of these 39 participants, 28 were successfully reached via email correspondence with 17 responses received,
permitting a subset of responses to be collated. Follow up questions included details concerning; the protocols used
for performance testing, items included in custom designed self-report forms, the performance indicators used for
tracking performance changes in training/competition, reasons for the (non) use of hormonal profiling, and the
magnitude of change typically considered important for each of the parameters monitored.
Statistical Analysis
Frequency analysis for each question was conducted with results presented as absolute frequency counts or
percentages of those in agreement or disagreement. Only one question used a Likert scale, where respondents were
asked to rate the value of their training monitoring system to the overall performance of their athletes on a 5 point
scale (1=minimal value; 5=extremely valuable). In addition to a frequency analysis, the mean response ± standard
deviation is presented for this item.
When asked to rate the value of their training monitoring system to the overall performance of their athletes, 38% rated it
extremely valuable, with a mean response of 3.9 ± 1.1. Respondents indicated that the most important purpose of their
training monitoring systems were injury prevention (29%), monitoring the effectiveness of a training program (27%),
maintaining performance (22%) and preventing overtraining (22%). The majority of respondents indicated that there was
an equal focus on load quantification and the monitoring of fatigue and recovery within the training monitoring system
(70%), while others indicated the focus was solely on load quantification (20%) or solely the monitoring of
fatigue/recovery (10%).
Most respondents spend between 0-4 hours per week collecting training monitoring data, while approximately 30%
require 4 hours or more per week to collect their data. Approximately 75% of respondents indicated that the analysis of
their data generally takes between 1-6 hours per week, while approximately 20% of respondents spent greater than 6
hours weekly on data analysis. Generally, results are fed-back to the athletes and/or other staff on the day of
assessment, with 50% of respondents requiring less than 1 hour and 42% getting results processed in less than one day.
Of the methods identified for monitoring fatigue responses to training and competition, self-report questionnaires were
most common (84%), with 11 respondents relying solely on self-reported measures in their monitoring systems. Fifty-five
per cent of respondents indicated that they collected self-report information on a daily basis (22% every session; 33%
once per day), while others used the forms multiple times per week (24%), weekly (18%), or monthly (2%) (Figure 2A).
The type of self-report forms most commonly used were custom designed forms (80%), with the Recovery-Stress
Questionnaire for Athletes (30) (13%), Profile of Mood States (37) (2%) and Daily Analysis of Life Demands (2%) in
minor use. Follow-up responses from 14 respondents who indicated the use of custom designed forms revealed their
forms typically included 4-12 items measured on Likert point scales typically ranging from either 1-5 or 1-10. Perceived
muscle soreness was most frequently signified as an important indicator of an athlete’s recovery state. Sleep duration
and quality, and perceptions of fatigue and wellness were also identified as highly important components of the custom
designed forms. When asked their reasons for not employing one of the self-report questionnaires frequently reported in
the scientific literature, a common theme in the responses was that they were too extensive, requiring too much time for
athletes to complete (influencing compliance and adherence) and for support staff to analyse, and that they lacked sport
Journal of Australian Strength & Conditioning
March 2012 | Volume 20 | Issue 1
Figure 2 -
Frequency of administration of (A) self-report questionnaires and (B) performance tests.
After the use of questionnaires for the monitoring of fatigue, 61% of respondents indicated the use of some form of
performance test within their monitoring system. Practical tests of performance included maximal jump and/or strength
assessments, overground sprints, submaximal cycling or running tests, and sports specific performance tests (Figure 2).
These tests were commonly implemented on a weekly or monthly basis (33% and 30%, respectively), although more
frequent testing was performed by 36% of respondents (Figure 1B). Within this category of performance tests, jump tests
were most popular, used by 54% of respondents. Follow up questioning revealed a variety of equipment used by
respondents in the assessment of jump performance, including linear position transducers, force plates, contact mats,
and vertical jumping apparatus (e.g. Vertec or Yardstick). Of the 11 follow-up respondents who reported using jump
assessments, all used a counter-movement jump (CMJ) for maximum height, with one respondent also using a broad
jump, and another using a concentric-only squat jump in addition to the CMJ. Six practitioners assessed CMJ
performance in an unloaded condition (hands on hips or holding a broomstick across the shoulders), and five assessed
loaded CMJ performance using a 20kg Olympic bar.
In the performance test category, the next most popular performance tests were sport specific test protocols (20%),
strength tests (16%), and submaximal running or cycling tests (14%), with a range of other tests identified that didn’t fit
into any of the above categories.
Figure 3 -
Frequency of use of performance tests by sport.
Other than self-report questionnaires and performance tests, tracking performance in sporting activity was another
popular method for monitoring fatigue and recovery, with 43% of respondents indicating this as a component of their
fatigue monitoring system. This method is most popular in Australian Rules Football (n=9), Football (Soccer) (n=4),
Rugby League (n=4), Rugby Union (n=3), Swimming (n=3) and Cycling, Rowing and Track and Field (n=2 each). Follow-
Journal of Australian Strength & Conditioning
March 2012 | Volume 20 | Issue 1
up responses were received from seven survey respondents. Those involved in field based sports (n=6) all indicated the
use of global positioning system (GPS) units to measure a large range of performance indicators from their athletes both
in training and competition. Most common were measures of work rate (e.g. metres covered per minute), time spent in
high intensity work ranges, and total distance, although numerous other variables were mentioned including the coaches
rating of performance, number of tackles performed and other game statistics. One respondent also indicated the use of
a measure of “body load”, based on data obtained from an accelerometer.
A variety of other forms of fatigue monitoring were suggested by survey respondents. Four participants indicated that
they use hormonal profiling as a component of their training monitoring system, and other respondents reported the use
of musculoskeletal screenings (n=1), resting heart rate (n=1), and a commercially available athlete monitoring system
( Two other respondents indicated they relied on asking the athlete how they felt, either at rest or
during high intensity training efforts.
The cumulative fatigue associated with successive overload training and/or frequent competition is an accepted part of
modern coaching practice. While anecdotal evidence suggests that a wide variety of methods for monitoring fatigue
are practiced in high performance sports programs, the details of what is considered best practice in these
environments is not yet detailed in the literature. The results from this survey describe this landscape, and present
evidence that a number of methods historically investigated in the scientific literature, such as resting heart rate
indices and biochemical monitoring, are not popularly employed at the coal face of high performance sport.
In the population surveyed a high usage of self-report questionnaires for monitoring fatigue was indicated across a
wide variety of sports and levels of performance. Support for such instruments and methods for monitoring fatigue
and/or overtraining is provided by a large body of scientific investigations showing mood disturbances coinciding with
increased training loads (7, 19, 22, 28, 29, 35, 39) and reduced performance (13, 42). It is likely that the popularity of
self-report questionnaires for monitoring fatigue in high performance athletic settings is largely due to the simplicity of
data collection and analysis which is then reflected in the regularity of the data collection, with 55% of respondents
collecting this information on a daily basis. A large percentage of those surveyed opted to rely on their own custom
designed self-report forms rather than those that have been used in scientific investigations. Further questioning
highlighted the need for self-report forms to be concise and targeted to the monitoring situation, which the established
versions reported in the literature are not. Accordingly respondents have designed their own forms, generally
consisting of 5-12 items using 1-5 or 1-10 point Likert scales, or by modifying existing questionnaires by placing
greater emphasis on ratings of muscle soreness, physical fatigue and general wellness. A dearth of experimental data
exists investigating the effectiveness of such self-designed forms for monitoring fatigue, with few published reports
available questioning the effectiveness of modified versions of existing questionnaires. Despite this lack of empirical
evidence validating the modified forms, follow up respondents indicated they were confident that their modified self-
report items provided them valid information, and that in their opinion scientific confirmation is unnecessary.
When asked what types of changes prompt the coaching or support staff to adjust an athlete’s training or competition
load based on their responses to the self-report questionnaires, a number of methods were identified. The majority of
respondents indicated a reliance on visually identifying trends in individual data (decline for successive days/sessions);
however another common method involved the use of individual “red flags” to identify meaningful changes in
responses. The determination of a “red flag” was often based on arbitrary cut-off values or thresholds considered
important by the coaching or support staff. One respondent provided a value for this arbitrary cut-off value (5% below
the mean value); with others only stating that a “significant” drop below the athletes mean score is flagged as
important. In relation to muscle soreness scores in particular, multiple respondents reported the use of the intra-
individual standard deviation (SD) values to highlight changes outside of the individual’s normal variation. Respondents
utilising this quantitative approach for identifying “red flags” typically used values of ±1 SD in relation to the mean,
although the magnitude of these values were not reported. To our knowledge such methods for identifying unusual
changes in regular performance due to fatigue are yet to be reported in the scientific literature.
Fatigue was also commonly assessed by respondents via tests of functional performance, with maximal jump
assessments most popular within this category. Vertical jumping in particular has been touted as a convenient model
to study neuromuscular function and has been used in a multitude of studies investigating the time course of recovery
from fatiguing training or competition (3, 8, 11, 15, 21, 27, 32, 36, 41, 44, 47, 53). The utility of vertical jumps as a
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March 2012 | Volume 20 | Issue 1
practical measure of neuromuscular fatigue is reflected by the adoption of such testing procedures in the high
performance sporting environment. However, a wide variety of protocols and equipment are available for measuring a
range of outcome variables associated with vertical jumping performance, and little consensus exists as to the optimal
methods or variables of interest for accurately measuring the state of fatigue or recovery in individual athletes. Vertical
jump performance during periods of heavy loading has been monitored using vane jump and reach apparatus (9, 14,
15, 20, 38), contact or switch mats (24, 53), and force platforms (11, 32, 36, 40, 44). Within the population surveyed
respondents also indicated the use of the above equipment; with the most popular being linear position transducers, or
force plates in combination with linear position transducers. The use of force plates in combination with linear position
transducers is not a regularly reported method for monitoring changes in performance due to fatigue in overreaching or
overtraining studies, but is used widely for the assessment of vertical jump performance in numerous other settings
and interventions (e.g. (12, 16, 45, 46)). Cormack et al., (10) monitored changes in vertical jump performance
performed on a force plate following an Australian Rules Football match and reported that only six of the 18 force-time
variables analysed during single and 5-repetition jumps had declined substantially following the match. In particular
there was a lack of sensitivity of jump height to fatigue which supported the earlier work of Coutts and colleagues (15).
Of further interest was that the pattern of response in these parameters varied greatly during the recovery period (from
24h to 120h post match) (10). This research highlights the considerable differences in changes in vertical jump
performance based on the performance variable of interest. The responses to further questions regarding jump
assessment protocols indicated that jump height remained popular among the variables being assessed in fatigue
monitoring systems, however numerous other kinetic and kinematic variables, such as peak and mean velocity, peak
and mean power, and peak force were also monitored. Many of the respondents indicated that they were still unsure of
which parameter(s) are most useful, and thus continued to monitor numerous variables in the hope of gaining a better
understanding of how they changed in relation to each other, as well as attempting to establish their relationship with
changes in performance. Similar to the self-report questionnaires, the magnitude of change in these variables
considered important was often based on visual analysis of trends or arbitrary threshold values (±5-10%), with two
respondents indicating the use of individual SD values (±1 SD) to identify changes outside of normal intra-individual
Longer-term negative adaptions to training stress often involve changes in the autonomic nervous system which may
be reflected in concomitant alterations in resting heart rate (HR), heart rate variability (HRV) measures and heart rate
responses to maximal or submaximal exercise (2, 5). Results from the current survey indicated that heart rate
monitoring during submaximal tests are popular, while resting heart rate indices, including heart rate variability, are
less commonly monitored. Follow-up questioning regarding custom designed self-report forms did however reveal that
resting heart rate was commonly included as an item on these self-report forms, suggesting that its popularity may not
have been truly represented in responses during the initial survey. The continued use of HR and HRV measures is in
contrast to reported opinion in that although there are significant modifications after short-term fatigue (in resting heart
rate and HRV), long-term fatigue (HR during a submaximal workloads) or both (maximal HR), the moderate amplitude
of those alterations limits their clinical usefulness since the expected differences fall within the day-to-day variability of
those measures (6).
It is interesting that although a large number of scientific investigations have explored the effectiveness of
biochemical monitoring for assessing fatigue and/or adaptive states (for extensive reviews see (49, 50, 52)), only four
survey participants indicated that this is a component of their training monitoring system. Follow-up questioning
suggested that the limited popularity is likely due to the large time, cost and expertise required for the analysis, as well
as perceived difficulties in linking changes in biochemical parameters to performance outcomes. In addition, time of
day, diet, and presence of injury influence biochemical concentrations, requiring well standardised sampling conditions
which are often difficult to realise in the training environment (48, 49). There also exists considerable variation within
and between individuals, influencing the reliability of measures and the availability of reference values indicating a
“normal” exercise tolerance (49). These methodological issues, along with the inconvenience of collecting samples
make this method difficult to implement on a regular basis, which is supported by the findings of the current study.
For all types of assessment, where decisions about an athlete’s state of fatigue or recovery are made on the basis of
changes in an outcome variable that isn’t the performance itself, there is a need to identify a threshold at which
negative changes in performance are considered large enough to be meaningful. Commonly this threshold value
referred to as the smallest worthwhile change (SWC) in performance. These SWC values for each test parameter
change from population to population. However, the reporting of these values in the literature by the people
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March 2012 | Volume 20 | Issue 1
implementing such tests is not widespread. If this reporting practice can be encouraged it will add greatly to the
knowledge base and assist in gaining an understanding of what changes are practically important based on the type of
sporting performance involved. It is also important that these values fall outside of the typical error of the assessed
variable in order for changes to be confidently interpreted (26). Currently data on the relationship between SWC and
typical error has been presented for vertical jumps on a force platform (10) and heart rate values during submaximal
running tests (34). To our knowledge few data exist describing the practically important changes associated with item
analyses on self-report questionnaires, limiting the ability to make decisions using critical thresholds based on changes
in these parameters. Instead coaches and practitioners rely on these self-report questionnaires as a tool to highlight
possible problems in an athlete’s fatigue or recovery state, with only a few employing statistical methods to quantify
what they consider practically important changes within an individual. To date, changes in these values have only
anecdotally been linked with reductions in performance.
Based on the current findings that significant time investment is allocated to training monitoring and that the
respondents place a high value on their systems for ensuring maximal performance of their athletes, it seems that
more research in this popular area will assist in enhancing current best practice. While there appears to be plentiful
research focused on the development of training monitoring systems and their validation in high performance sports
environments, the current results suggest that the protocols adopted by coaches and support staff at the coal face of
elite sport do not entirely reflect the most current evidence available in the scientific literature. A more focused
research approach on the development and validation of methods for monitoring fatigue and recovery via practical
tests of maximal neuromuscular performance is warranted, given the wide variety of methods and protocols currently
It is critical for coaches of high performing athletes to have a training plan, yet it is also highly important to be able to
adjust the plan based on how the athlete is adapting or coping with the imposed training and competition demands. To
do this effectively the coach requires information based on each individual athlete’s recovery abilities in response to
various training stressors. In high performance sporting environments, self-report questionnaires identifying perceived
changes in muscle soreness, feelings of fatigue and wellness, sleep quality and quantity and a variety of other
psychosocial factors are relied upon for “flagging” athletes in a state of fatigue. Results from the survey indicate that
custom-designed forms are preferred to those existing in the scientific literature because of the time required for
completion. This concern is understandable given the time pressures in high performance environments, however
shortened versions of the REST-Q are available. Use of a shortened REST-Q would provide a more scientifically valid
method for collecting such information and provide support staff with a more reliable cross-reference to broader
exercise applications.
Vertical jump tests are also frequently used to assess neuromuscular function, using a variety of equipment and
assessment protocols. While limited data are available, unpublished observations from our research group suggests
that unloaded jumps are more useful for monitoring fatigue than loaded variations. Similarly we have observed that
eccentric displacement in a CMJ is most sensitive to fatigue induced by periods of high loading. Mean power, peak
velocity and peak force are also useful variables to monitor. Within the population surveyed CMJs are most popularly
employed, however there may also be value in monitoring a variety of different types of jumps (e.g. static-,
countermovement- and drop- jumps), since experimental evidence suggests differential responses depending on the
fatiguing stimulus.
While only a few practitioners reported using physiological parameters measured during submaximal exercise tasks to
monitor training responses, feedback from these respondents along with recent research suggests that such tasks
may provide a useful monitoring tool. In contrast, limited evidence exists supporting the use resting heart rate indices
for these purposes due to large day-to-day variability.
Biochemical monitoring is not a popular form of athlete monitoring in the population surveyed, mostly due to the high
costs associated as well as the extended time required to process results. There is however plentiful research
supporting its use in monitoring athletes susceptible to non-functional overreaching or overtraining, and therefore may
be useful in circumstances where the practical limitations can be worked around.
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Lastly, when deciding on any assessment method, careful consideration should be given to the magnitude of change
considered important for each of the measurement variables. Respondents indicated arbitrary thresholds of 5-10% or
± 1SD, but the consequence of changes beyond these thresholds is unknown. The reporting of typical variation in
these values during normal training and periods of high stress may assist practitioners in determining the most
appropriate monitoring protocols and threshold levels. With this concept at the forefront of decision making, the
authors believe that practitioners seeking to effectively monitor the fatigue state of their athletes should at least be
using a shortened version of the REST-Q while monitoring changes in eccentric displacement, mean power, peak
velocity or peak force in unloaded CMJs. Each of these variables should be consistently monitored during a period of
low intensity training determine an individual’s normal variation so as to effectively determine “red flag” thresholds.
APPENDIX A Copy Of Survey
PART A Demographics
1. What is your position?
Head Coach
Head Strength & Conditioning Coach/Trainer
High Performance Manager or Sport Science Coordinator
Sport Scientist
Other (please specify)
2. Does your employment require you to work with:
one team/squad only (single sport)
more than one team/squad (multiple sports)
3. Do you mostly work with team sport or individual athletes?
Team sports
Individual events
4. What level of sports performance are you involved in?
State level
Other (please specify)
5. Which sport(s) do you work with on a daily/weekly basis?
Australian Rules Football
Football (Soccer)
Martial Arts
Rugby League
Rugby Union
Track & Field
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March 2012 | Volume 20 | Issue 1
Other (please specify)
6. What is the age range of the athletes you work with?
<15 years
15-20 years
21-25 years
>25 years
7. Do you have a training monitoring system in place to quantify training load and/or monitor fatigue?
PART B - Monitoring Practices
8. What is the main purpose of your training monitoring system? (one response only)
Reduce injuries
Maintain performance
Prevent overtraining
Monitor the effectiveness of a training program
9. What is the main focus of the system?
Load quantification
Monitoring fatigue/recovery
Equal focus on load quantification and fatigue monitoring
10. How many hours per week do you (or your colleagues) spend COLLECTING training monitoring data?
< 1
11. How many hours per week do you (or your colleagues) spend ANALYSING training monitoring data?
< 1
> 10
12. How is your data collected?
Using specialist software
Via a custom web interface
Entered directly into excel
Pen and paper
Other (please specify)
13. After collecting data, how long does it take to get feedback to the athletes and/or other staff?
Less than 1 hour
Less than 1 day
1-2 days
1 week
More than 1 week
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14. Do you monitor your athletes remotely or do you have daily face-to-face contact with them?
Face-to-face (daily)
Face-to-face (weekly)
Other (please specify)
15. Rate the value of your monitoring system to the overall performance of your athletes
1 = Minimal value
5 = Extremely valuable
PART C Methods
16. Which of the following do you use to quantify training load?
Sessional RPE
External workload calculation (e.g. distance, time, kg lifted)
Heart rate trimps
GPS data
Sport specific workload measurement device (e.g. SRM)
None of the above
Other (please specify)
17. Which of the following do you use to monitor athlete fatigue/recovery?
Self-report questionnaires
Performance tests (e.g. jumps, 20m sprint etc)
Hormonal profiling
Tracking performance in their sporting activity
Other (please specify)
18. If applicable, how frequently do you use each of the following to monitor athlete fatigue/recovery?
19. If you use self-report questionnaires, which do you currently use?
Custom designed forms
Other (please specify)
20. If applicable, which type of performance test(s) do you use to monitor fatigue/recovery?
Submaximal running/cycling test
Jump tests
Strength tests
Overground sprint tests
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March 2012 | Volume 20 | Issue 1
Sport specific test protocol
None of the above
Other (please specify)
21. When an athlete is identified as being fatigued, how do you modify their training?
Prescribe fewer training sessions
Modify the length/intensity of prescribed sessions
Make recommendations to the head coach that training load be reduced
Prescribe extra recovery sessions
Other (please specify)
22. Do you modify training based only on individual fatigue responses or do you also look at the team/squad trends?
Only individual trends
A mixture of team/squad and individual trends
Team/squad trends only
Not applicable, I only monitor individual athletes
Other (please specify)
PART D - Thank you and Follow-up
Thank you once again for taking the time to answer the above questions.
Would you agree to be contacted for a follow-up telephone call regarding your responses?
Yes via telephone
Yes via email
If you answered yes, please supply your name, organisation and the most convenient contact telephone number or
email address below.
1. Abiss, C. & Laursen, P. Is part of the mystery surrounding fatigue
complicated by context? Journal of Science and Medicine in
Sport. 10: 277-279. 2007.
2. Achten, J. & Jeukendrup, A. Heart rate monitoring. Applications and
Limitations. Sports Medicine. 33: 517-538. 2003.
3. Andersson, H., Raastad, T., Nilsson, J., Paulsen, G., Garthe, I., &
Kadi, F. Neuromuscular fatigue and recovery in elite female soccer:
effects of active recovery. Medicine and Science in Sports and
Exercise. 40: 372-380. 2008.
4. Bishop, P., Jones, E., & Woods, K. Recovery from training: a brief
review. Journal of Strength and Conditioning Research. 22:
1015-1024. 2008.
5. Borresen, J. & Lambert, M. Autonomic control of heart rate during
and after exercise. Sports Medicine. 38: 633-646. 2008.
6. Bosquet, L., Merkari, S., Arvisais, D., & Aubert, A. Is heart rate
monitoring a convenient tool to monitor over-reaching? A
systematic review of the literature. British Journal of Sports
Medicine. 42: 709-714. 2008.
7. Bresciani, G., Cuevas, M., Molinero, O., Almar, M., Suay, F.,
Salvador, A., De Paz, J., Marquez, S., & Gonzalez-Gallego, J.
Signs of overload after an intensified training. International
Journal of Sports Medicine. 32: 338-343. 2011.
8. Byrne, C. & Eston, R. The effect of exercise-induced muscle
damage on isometric and dynamic knee extensor strength and
vertical jump performance. Journal of Sports Sciences. 20: 417-
425. 2002.
9. Callister, R., Callister, R., Fleck, S., & Dudley, G. Physiological and
performance responses to overtraining in elite judo athletes.
Medicine and Science in Sports and Exercise. 22: 816-824.
10. Cormack, S., Newton, R., & Mcguigan, M. Neuromuscular and
endocrine responses of elite players to an Australian Rules Football
match. International Journal of Sports Physiology and
Performance. 3: 359-374. 2008.
11. Cormack, S., Newton, R., Mcguigan, M., & Cormie, P.
Neuromuscular and endocrine responses of elite players during an
Australian Rules Football season. International Journal of Sports
Physiology and Performance. 3: 439-453. 2008.
12. Cormie, P., Mccaulley, G., Triplett, T., & Mcbride, J. Optimal
Loading for maximal power output during lower-body resistance
exercises. Medicine and Science in Sports and Exercise. 39:
340-349. 2007.
13. Coutts, A. & Reaburn, P. Monitoring changes in rugby league
players' perceived stress and recovery during intensified training.
Perceptual and Motor Skills. 106: 904-916. 2008.
14. Coutts, A., Reaburn, P., Piva, T., & Murphy, A. Changes in selected
biochemical, muscular strength, power, and endurance measures
during deliberate overreaching and tapering in rugby league
players. International Journal of Sports Medicine. 28: 116-124.
15. Coutts, A., Reaburn, P., Piva, T., & Rowsell, G. Monitoring
overreaching in rugby league players. European Journal of
Applied Physiology. 99: 313-324. 2007.
16. Dugan, E., Doyle, T., Humphries, B., Hasson, C., & Newton, R.
Determining the optimal load for jump squats: a review of methods
and calculations. Journal of Strength and Conditioning
Research. 18: 668-674. 2004.
17. Edwards, R. Human muscle function and fatigue, in Human muscle
fatigue: Physiological mechanisms, R. Porter and Whelen, J., eds.
London. Pitman Medical, 1981. pp. 1-18.
Journal of Australian Strength & Conditioning
March 2012 | Volume 20 | Issue 1
18. Enoka, R. & Duchateau, J. Muscle function: what, why and how it
influences muscle function. Journal of Physiology (Lond). 586:
11-23. 2008.
19. Faude, O., Meyer, T., Urhausen, A., & Kindermann, W. Recovery
training in cyclists: ergometric, hormonal and psychometric findings.
Scandinavian Journal of Medicine and Science in Sports. 19:
433-441. 2009.
20. Fry, A., Kraemer, W., Lynch, J., Triplett, T., & Perry Koziris, L. Does
short-term near-maximal intensity machine resistance training
induce overtraining? Journal of Strength and Conditioning
Research. 8: 188-191. 1994.
21. Girard, O., Lattier, G., Micaleff, J.-P., & Millet, G. Changes in
exercise characteristics, maximal voluntary contraction, and
explosive strength during prolonged tennis playing. British Journal
of Sports Medicine. 40: 521-526. 2006.
22. Halson, S., Bridge, M., Meeusen, R., Busschaert, B., Gleeson, M.,
Jones, D., & Jeukendrup, A. Time course of performance changes
and fatigue markers during intensified training in trained cyclists.
Journal of Applied Physiology. 93: 947-956. 2002.
23. Halson, S. & Jeukendrup, A. Does overtraining exist? An analysis
of overreaching and overtraining research. Sports Medicine. 34:
967-981. 2004.
24. 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: 3-8. 2009.
25. Hoffman, J. & Kaminsky, M. Use of performance testing for
monitoring overtraining in elite youth basketball players. Strength
and Conditioning Journal. 22: 54-62. 2000.
26. Hopkins, W., Marshall, S., Batterham, A., & Hanin, J. Progressive
statistics for studies in sports medicine and exercise science.
Medicine and Science in Sports and Exercise. 41: 3-12. 2009.
27. Hortobagyi, T., Lambert, N., & Kroll, W. Voluntary and reflex
responses to fatigue with stretch-shortening exercise. Canadian
Journal of Sport Science. 16: 142-150. 1991.
28. Jürimäe, J., Mäestu, J., Purge, P., & Jürimäe, T. Changes in stress
and recovery after heavy training in rowers. Journal of Science
and Medicine in Sport. 7: 335-339. 2004.
29. Kellman, M. & Gunther, K. Changes in stress and recovery in elite
rowers during preparation for the Olympic games. Medicine and
Science in Sports and Exercise. 32: 676-683. 2000.
30. Kellman, M. & Kallus, W. Recovery-Stress Questionnaire for
Athletes. Champaign, IL: Human Kinetics, 2001.
31. Knicker, A., Renshaw, I., Oldham, A., & Cairns, S. Interactive
processes link the multiple symptoms of fatigue in sport
competition. Sports Medicine. 41: 307-328. 2011.
32. Kraemer, W., Fry, A., Rubin, M., Triplett-Mcbride, T., Gordon, S.,
Koziris, L., Lynch, J., Volek, J., Meuffels, D., Newton, R., & Fleck,
S. Physiological and performance responses to tournament
wrestling. Medicine and Science in Sports and Exercise. 33:
1367-1378. 2001.
33. Kuipers, H. & Keizer, H. Overtraining in elite athletes. Review and
directions for the future. Sports Medicine. 6: 79-92. 1988.
34. Lamberts, R. & Lambert, M. Day-to-day variation in heart rate at
different levels of submaximal exertion: implications for monitoring
training. Journal of Strength and Conditioning Research. 23:
1005-1010. 2009.
35. Lieberman, H., Kellogg, M., & Bathalon, G. Female marine recruit
training: mood, body composition, and biochemical changes.
Medicine and Science in Sports and Exercise. 40: S671-S676.
36. Mclean, B., Coutts, A., Kelly, V., Mcguigan, M., & Cormack, S.
Neuromuscular, endocrine, and perceptual fatigue responses
during different length between-match microcycles in professional
rugby league players. International Journal of Sports Physiology
and Performance. 5: 367-383. 2010.
37. Mcnair, D., Lorr, M., & Dopplemann, L. Profile of mood states
manual. San Diego: Educational and Industrial Testing Service,
38. Montgomery, P., Pyne, D., Cox, A., Hopkins, W., Minahan, C., &
Hunt, P. Muscle damage, inflammation, and recovery interventions
during a 3-day basketball tournament. European Journal of Sport
Science. 8: 241-250. 2008.
39. Morgan, W., Brown, D., Raglin, J., O'connor, P., & Ellickson, K.
Psychological monitoring of overtraining and staleness. British
Journal of Sports Medicine. 21: 107-114. 1987.
40. Nindl, B., Leone, C., Tharion, W., Johnson, R., Castellani, J.,
Patton, J., & Montain, S. Physical performance responses to 72 h of
military operational stress. Medicine and Science in Sports and
Exercise. 34: 1814-1822. 2002.
41. Nosaka, K., Abbiss, C., Watson, G., Wall, B., Suzuki, K., & Laursen,
P. Recovery following an Ironman triathlon: A case study.
European Journal of Sport Science. 10: 169-165. 2010.
42. Raglin, J., Koceja, D., Stager, J., & Harms, C. Mood,
neuromuscular function, and performance during training in female
swimmers. Medicine and Science in Sports and Exercise. 28:
372-377. 1996.
43. Robson-Ansley, P., Gleeson, M., & Ansley, L. Fatigue management
in the preparation of Olympic athletes. Journal of Sports
Sciences. 27: 1409-1420. 2009.
44. Ronglan, L., Raastad, T., & Borgeson, A. Neuromuscular fatigue
and recovery in elite female handball players. Scandinavian
Journal of Medicine and Science in Sports. 16: 267-273. 2006.
45. Sheppard, J., Cormack, S., Taylor, K., Mcguigan, M., & Newton, R.
Assessing the force-velocity characteristics of the leg extensors in
well-trained athletes: The incremental load power profile. Journal
of Strength and Conditioning Research. 22: 1320-1326. 2008.
46. Sheppard, J., Hobson, S., Barker, M., Taylor, K., Chapman, D.,
Mcguigan, M., & Newton, R. The effect of training with accentuated
eccentric load counter-movement jumps on strength and power
characteristics of high-performance volleyball players.
International Journal of Sports Science and Coaching. 3: 355-
363. 2008.
47. Thorlund, J., Michalsik, L., Madsen, K., & Aagaard, P. Acute
fatigue-induced cahnges in muscle mechanical properties and
neuromuscular activity in elite handball players following a handball
match. Scandinavian Journal of Medicine and Science in
Sports. 18: 462-472. 2008.
48. Thuma, J., Gilders, R., Verdun, M., & Loucks, A. Circadian rhythm
of cortisol confounds cortisol responses to exercise: implications for
future research. Journal of Applied Physiology. 78: 1657-1664.
49. Urhausen, A., Gabriel, H., & Kindermann, W. Blood hormones as
markers of training stress and overtraining. Sports Medicine. 20:
251-276. 1995.
50. Urhausen, A. & Kindermann, W. Biochemical monitoring of training.
Clinical Journal of Sport Medicine. 2: 52-61. 1992.
51. Urhausen, A. & Kindermann, W. Diagnosis of overtraining. What
tools do we have? Sports Medicine. 32: 95-102. 2002.
52. Viru, A. & Viru, M. Biochemical Monitoring of Sport Training:
Human Kinetics Publishers, 2001.
53. Welsh, T., Alemany, J., Montain, S., Frykman, P., Tuckow, A.,
Young, A., & Nindl, B. Effects of intensified military field training in
jumping performance. International Journal of Sports Medicine.
29: 45-52. 2008.
... Athlete monitoring systems (AMS) typically capture information relating to an athlete's response to training and training load. A survey of practitioners in elite Australasian sport found that 91% utilised an athlete monitoring system, (Taylor et al., 2012) with injury prevention, performance and training programme optimisation amongst the most important stated purposes of an AMS (Halson, 2014;McGuigan et al., 2021). AMS can play an important role in elite sport, as lost training days through illness or injury are a significant issue. ...
... The use and implementation of AMS have therefore received significant research attention to mitigate the risks of athlete maladaptation (Saw et al., 2017). However, the current crosssport trends in monitoring, recording and analysing elite athletic training and performance either go unreported or, have primarily focussed on Australasian or invasion sports, (McGuigan et al., 2021;Taylor et al., 2012) with limited insights available for a more diverse range of sports, or sports in the United Kingdom (UK). Without a clear understanding of what current monitoring practices across sports in the UK are, it is challenging to provide evidence-based guidance for AMS in elite sport. ...
... Athlete monitoring systems have been reported to include a variety of performance, laboratory or field tests and athlete self-report measures (ASRM) specific to the sport (McGuigan et al., 2021;Taylor et al., 2012). Of 55 respondents to one survey, (Taylor et al., 2012) customised ASRM were employed by 84% and collected daily by 55% of practitioners working in elite sport. ...
Athlete monitoring systems (AMS) aid performance optimisation and support illness/injury prevention. Nonetheless, limited information exists on how AMS are employed across elite sports in the United Kingdom. This study explored how athlete monitoring (AM) data, in particular athlete self-report measures, were collected, analysed and disseminated within elite sports. Thirty elite sports practitioners representing 599 athletes responded to a survey on their AM methodologies. The majority, 83%, (n = 25) utilised an AMS, and a further 84% (n = 21) stated the collection of their AMS data was underpinned by a scientific rationale. Athlete self-report measures (ASRM) were the most commonly employed tool, with muscle soreness, sleep and energy levels amongst the most frequently collected measures. The ubiquitous use of custom single-item ASRM resulted in considerable variability in the questionnaires employed, thus potentially impacting questionnaire validity. Feedback processes were largely felt to be ineffective, with 44% (n = 11) respondents indicating that athletes did not receive sufficient feedback. Some respondents indicated that AMS data was never discussed with athletes and/or coaches. Overall, significant disparities exist in the use of athlete monitoring systems between research and elite sports practice, and the athlete, coach and practitioner experience of monitoring risks being poor if these disparities are not addressed.
... Risk of injury is often closely associated with lower levels of endurance, fitness and strength, and good structural, biomechanical and mental adaptation to specific tasks and training environment [4,5]. However, determining a unified definition of fatigue is not a straightforward process [6][7][8][9][10][11][12]. ...
... Typically, these questionnaires gather information on parameters such as muscle soreness, tiredness, sleep health and current stress levels [170]. Self-report questionnaires are widely employed to assess fatigue in sport, of which there is a plethora [12,[171][172][173]. This is predominantly thought to be due to their low-cost, relative ease of implementation and flexibility, enabling them to be administered across a wide range of individual and team sport settings [174]. ...
... Nonetheless, recent research has demonstrated that even those questionnaires explicitly developed for athletic populations have only adequate measurement properties, with weak content validity and reliability a particular concern [180,181]. Interestingly, there is widespread evidence that sporting organisations have increasingly chosen to employ their own customised self-report questionnaires over the questionnaires published in the literature [12,182,183]. This divergence between research and practice likely stems from the perception that the existing questionnaires lack sports specificity and are too extensive in nature [12]. ...
Fatigue is a phenomenon associated with decreases in both physical and cognitive performances and increases in injury occurrence. Competitive athletes are required to complete demanding training programmes with high workloads to elicit the physiological and musculoskeletal adaptations plus skill acquisition necessary for performance. High workloads, especially sudden rapid increases in training loads, are associated with the occurrence of fatigue. At present, there is limited evidence elucidating the underlying mechanisms associating the fatigue generated by higher workloads and with an increase in injury risk. The multidimensional nature and manifestation of fatigue have led to differing definitions and dichotomies of the term. Consequently, a plethora of physiological, biochemical, psychological and performance markers have been proposed to measure fatigue and recovery. Those include self-reported scales, countermovement jump performance, heart rate variability, and saliva and serum biomarker analyses. The purpose of this review is to provide an overview of the fatigue and recovery plus methods of assessments.
... Therefore, measuring these subjective markers is necessary to better understand NMF and recovery [27]. A recent survey on the use of fatigue-monitoring tools on high performance athletes in team-sport settings describes a high acceptance of the self-report questionnaires in various disciplines and competition levels to assess overall well-being [28]. The validated self-report forms are cus- The psychobiological state by prolonged periods of demanding cognitive activity (or mental fatigue) affects the individual perception of fatigue [26]. ...
... Therefore, measuring these subjective markers is necessary to better understand NMF and recovery [27]. A recent survey on the use of fatigue-monitoring tools on high performance athletes in team-sport settings describes a high acceptance of the self-report questionnaires in various disciplines and competition levels to assess overall well-being [28]. The validated self-report forms are custom-designed forms, such as the Profile of Mood States Questionnaire (POMS), or the Recovery-Stress Questionnaire for Athletes (REST-Q), which are among the most widely used, and which may assist staff in monitoring the complex psychophysiological stress associated with high degrees of fatigue and poor recoveries [27]. ...
... The benefit of vertical jumps as a practical measure of NMF is indicated by the high degree of the adoption of these testing procedures in high-performance sports settings. These tests have been used in many studies to investigate the recovery times from demanding practices or competitions [28]. Vertical jump tests are practical, well accepted by elite players, and are valid and reliable, which makes them potentially valuable for detecting and quantifying fatigue in in-field conditions [46]. ...
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Neuromuscular fatigue is defined as a reduction induced by exercise in the maximal voluntary force that a muscle or group of muscles can generate. An accumulation of work or an incomplete force restoration can significantly influence the neuromuscular performance in both the short and long terms. Thus, fatigue management is essential for controlling the training adaptations of athletes and reducing their susceptibility to injury and illness. The main individualized monitoring tools used to describe fatigue are questionnaires and subjective assessments of fatigue, biochemical markers, sprint tests, and vertical jump tests. Among the subjective measures, the rating of the perceived exertion has been widely used because of its simplicity and high validity. In terms of the objective measures, one of the more frequently employed tools by practitioners to assess neuromuscular fatigue is the countermovement jump. Because of its high validity and reliability, it is accepted as the reference standard test in sports, in general, and particularly in team sports. Our review aims to clarify how all these indicators, as well as several devices, can help coaches in different sports contexts to monitor neuromuscular fatigue, and how these procedures should be used to obtain data that can be used to make decisions in complex environments.
... A major challenge to understanding the consequences of concurrent training in soccer is selecting appropriate assessment and monitoring tools to capture the complex underpinning to fatigue, which is likely multi-factorial. Practitioners and researchers have used several tools that include internal, external, and athlete self-reported measures, which might be important factors to make evidence-based decisions on appropriate training (Taylor et al., 2012;Buchheit et al., 2013a;Buchheit et al., 2013b;Akenhead and Nassis, 2016;Thorpe et al., 2016;Carling et al., 2018;Fitzpatrick et al., 2019). The interaction of numerous training load metrics, physiological responses, physical function measures, and performance indices has previously been investigated to identify measures and interactions of performance, fatigue and recovery (Wallace and Baumeister, 2002;Coutts et al., 2007;Elloumi et al., 2012;Mooney et al., 2013;Brink et al., 2018;Evans et al., 2022). ...
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Aim: To profile the etiology and recovery time-course of neuromuscular function in response to a mixed-content, standard training week in professional academy soccer players. We concurrently examined physical performance, cognitive function, and perceptual measures of mood and wellness states to identify a range of simple tests applied practitioners could use in the field as surrogate measures of neuromuscular function. Methods: Sixteen professional academy soccer players completed a range of neuromuscular, physical, perceptual, mood, and cognitive function tests at baseline and after a strenuous training day (pitch and gym), with retest at 24, 48, and 72 h, and further pitch and gym sessions after 48 h post-baseline. Maximal voluntary contraction force (MVC) and twitch responses to electrical stimulation (femoral nerve) during isometric knee-extensor contractions and at rest were measured to assess central nervous system (voluntary activation, VA) and muscle contractile (potentiated twitch force, Q tw,pot ) function. Results: Strenuous training elicited decrements in MVC force post-session (−11%, p = 0.001) that remained unresolved at 72 h (−6%, p = 0.03). Voluntary activation (motor nerve stimulation) was reduced immediately post-training only (−4%, p = 0.03). No change in muscle contractile function (Q tw,pot ) was observed post-training, though was reduced at 24 h (−13%, p = 0.01), and had not fully recovered 72 h after (−9%, p = 0.03). Perceptions of wellness were impaired post-training, and recovered by 24 h (sleepiness, energy) and 48 h (fatigue, muscle soreness, readiness to train). Countermovement jump performance declined at 24 h, while RSI (Reactive Strength Index) decrements persisted at 48 h. No changes were evident in adductor squeeze, mood, or cognitive function. Conclusion: Elite youth soccer training elicits substantial decrements in neuromuscular function, which are still present 72 h post-strenuous exercise. Though central processes contribute to post-exercise neuromuscular alterations, the magnitude and prolonged presence of impairments in contractile function indicates it is the restitution of muscular function (peripheral mechanisms) that explains recovery from strenuous training in academy soccer players.
... Given the combined stress of hypoxia and potential increases in TLs, individual monitoring is especially important during altitude training camps where large inter-individual differences in responses to hypoxia may manifest (Chapman, 2013). While multiple indices for monitoring TLs, training responses and acclimatization have been suggested (Saw et al., 2016;Sperlich et al., 2016;Mujika et al., 2019), the invasive and/or time-consuming nature of measuring blood markers and implementing multi-item questionnaires, for example, makes their daily use with a large group of elite athletes impractical and unpopular in applied settings (Taylor et al., 2012;McGuigan et al., 2021). As such, there is a need for exploring simple tools and measures that are readily available to athletes and their coaches (Saw et al., 2018). ...
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Objective: To monitor the daily variations and time course of changes in selected variables during a 17–21-day altitude training camp at 1,800 m in a group of elite cross-country skiers (9 women, 12 men) and biathletes (7 women, 4 men). Methods: Among other variables, resting peripheral oxygen saturation (SpO2rest), resting heart rate (HRrest) and urine specific gravity (USG) were monitored daily at altitude, while illness symptoms were monitored weekly. Before and after the camp, body composition (i.e., lean and fat mass) and body mass were assessed in all athletes, while roller-skiing speed at a blood lactate concentration of 4 mmol·L−1 (Speed@4mmol) was assessed in the biathletes only. Results: Neither SpO2rest, HRrest nor USG changed systematically during the camp (p > 0.05), although some daily time points differed from day one for the latter two variables (p < 0.05). In addition, body composition and body mass were unchanged from before to after the camp (p > 0.05). Eleven out of 15 illness episodes were reported within 4 days of the outbound or homebound flight. The five biathletes who remained free of illness increased their Speed@4mmol by ~ 4% from before to after the camp (p = 0.031). Conclusions: The present results show that measures typically recommended to monitor acclimatization and responses to altitude in athletes (e.g., SpO2rest and HRrest) did not change systematically over time. Further research is needed to explore the utility of these and other measures in elite endurance athletes at altitudes typical of competition environments
... Vertical jump tasks are frequently used by strength and conditioning professionals to monitor an athlete's ability to rapidly generate force, adaptions to training interventions, or the fatigue accumulated in response to both competition and training (18,21,30). Traditionally, these measurements have been performed in laboratory or institutional settings using force plates (24), which enable the direct measurement of the force applied during the jump. ...
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Force plates are commonly used when assessing vertical jumping performance but are not always affordable or practical tools for all testing situations. Twenty-four participants volunteered to take part in a study investigating the agreement between bilateral force plates and a new commercially available contact mat that records jump height, flight-time (FT), and FT of individual limbs during both countermovement (CMJ) and squat (SJ) jumps. Each participant performed six jumps of each type while standing on a contact mat placed upon a pair of in-ground force plates. When compared to the force plate via ordinary least products regression, the contact mat agreed with force plate CMJ and SJ jump height, individual limb FT during CMJs, and left-leg FT during SJs. The bilateral contact mat provided valid assessment of individual limb FT during CMJs, but not SJs. Practitioners can therefore use a bilateral contact mat interchangeably with bilateral force plates to measure SJ and CMJ performance.
... The predominant approach undertaken by professional sporting organizations in monitoring athletes is the implementation of selfreport measures such as questionnaires and diaries which are relatively simple and form an inexpensive approach to monitoring athlete responses (14). Supporting literature for the implementation of self-report measures advocates the use of questionnaires with evidence of good validity and reliability (29). In reality, however, the more common approach is fort high-performance staff to develop their own short-from with athlete selfreport measures in an attempt to improve compliance and increase relevance (25). ...
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Objective: This study explored psychophysiological stress in professional Australian Rules football athletes across the course of one competitive season. Methods: A sample of eight players listed with one professional Australian football club participated in this study. Each week during the competitive season (22 weeks), players self-reported their general fatigue and sleep using a paper-based scale, as well as providing a salivary cortisol measure. Testing occurred 48-hours after competition. Participants’ weekly performance rating scores based on a points system metric of players’ data obtained during competitive matches were also recorded by the club each week. Results: A significant inverse relationship was observed between cortisol and performance ratings, sleep and fatigue, and sleep and performance ratings. There was a significant predictive relationship observed, with cortisol levels and performance rankings (R2 = .35, F (6,74) = 7.06, p<.001). There was no significant relationship between performance and fatigue or performance and sleep. Conclusions: This study shows a significant relationship between performance outcomes and psychophysiological stress in professional Australian football players. Professional clubs should look towards objective assessment protocols to measure athlete psychological stress to enhance current practice of self-report stress measures.
... Additionally, the relevance of augmented jumping ability to soccer performance is theoretical and a causal link cannot be made based on current evidence, however, jumping ability is a commonly used performance test to monitor fatigue/readiness. 28 A larger testing battery including multiple performance measures including skill-specific components would strengthen the evidence reported here. ...
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The purpose of this study was to investigate the time-course of decrements in physical performance following a pre-match warm-up in soccer players. Knowledge of this information could be used to inform re-warm-ups and pre-pitch entry practices of soccer substitutes. Data were collected over five sessions with 12 male youth professionals (15-17 yrs). Across the five sessions each player performed countermovement jumps (CMJ) and drop jumps (30 and 40 cm), pre-warm-up, immediately post-warm-up, and following 10-, 20-, 30-, 40-, and 50-minutes of inactivity. Physical performance was assessed by jump height and calculation of reactive strength index (RSI). Hierarchical generalized linear models (HGLMs) were fitted within a Bayesian framework to identify plausible time to achieve 10 to 50% decrements of the initial pre to post warm-up improvement. Mean improvements of 5.4 cm (95%CrI: 4.8 to 6.0), 0.24 ms-1 (95%CrI: 0.19 to 0.29), and 0.32 ms-1 (0.27 to 0.36) were obtained for the CMJ, and RSI measured from the 30 and 45 cm box, respectively. Decrements for all assessments were non-linear with the steepest rates of decline measured in the initial periods following warm-up. High probabilities were calculated (p ≥ 0.979) that up to 50% of the initial warm-up improvement for the CMJ would be lost between 20 and 30 minutes. The results of this study provide a guide for future research and practitioners managing the pre-pitch entry of soccer substitutes. It is suggested that practitioners consider and assess the effectiveness of exposing players to a re-warm-up between 20- and 30-minutes prior to pitch entry to maintain performance capabilities.
... This method with the incorporation of a stretching-shortening movement, like Countermovement Jump (CMJ) can provide very deep information about fatigue 18 as good relationships between sprint ability and CMJ capacity has been veri ed 18 . Thereby, scienti c evidence have found CMJ performance to be an objective marker of fatigue and supercompensation 19,20 , as neuromuscular fatigue have been associated with a decrease in the average CMJ height 9 , making this method also very usual to asses neuromuscular fatigue 19,21 . However, when using RSA to assess neuromuscular fatigue, the number of sprints induces great variability between athletes 22 , and when using CMJ it is important to consider that the same fatiguing stimuli can elicit different effects between individuals in the results 23 . ...
Football is a very demanding sport where many injuries take place because of fatigue. Thermography can be used to detect fatigue and prevent its consequences, through thermal asymmetries in the bilateral body areas, but its adequacy for elite footballers has not been widely studied. Therefore, the objective of the present investigation was to determine the suitability of thermography to detect fatigue in male football players. Twenty elite male football players were gathered into pair of subgroups (low [<0,2 ºC] vs high thermal asymmetry [≥0,2 ºC]) based on a thermography session of the lower limbs (anterior and posterior part). Afterwards, players performed a vertical jump test (CMJ) before and after a Repeated Sprint Test (RSA) (6x30 m/20"). No significant differences (p ≤0,05) were found in any of the RSA test variables between the low and high thermal asymmetry groups in thighs and calves, while the low thermal asymmetry hamstring group reported lower %Diff and higher %Best in some sprints. As for vertical jump, the low thermal asymmetry hamstring group reported significantly higher values in the CMJ Post-RSA. In accordance with these results, thermography seems to be an useful tool to detect fatigue in elite footballers, as hamstrings temperature asymmetries over 0,2 ºC seem to be an indicative of it.
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Completion of an Ironman triathlon results in muscle damage, indicated by reductions in muscle function and muscle soreness. However, the time course of recovery from this damage has received little attention. The purpose of this case study was to examine the time course of changes in blood markers of muscle damage and inflammation, muscle function, muscle soreness, and economy of motion following an Ironman event. An experienced well-trained male triathlete aged 35 years completed the Western Australian Ironman triathlon in 11 h 38 min 41 s (winner's time: 8 h 3 min 56 s). Before and on several occasions in the 15 days after the event, the participant performed an incremental cycling test to exhaustion, running economy test at 12 km · h (2% incline), maximal isometric knee flexion and extension at 90° knee flexion, and maximal squat and countermovement jumps. Venous blood samples and muscle soreness were also assessed. Maximal oxygen consumption, efficiency of motion, maximal muscle strength, and jump performance were all markedly reduced (4.5–54%) following the event, but returned to baseline within 15, 8, 2, and 8 days following the event, respectively. Muscle soreness and blood markers peaked 2–24 h after the race but returned to baseline within 8 days. In conclusion, although the Ironman triathlon induces marked muscle damage, a trained triathlete recovered almost completely within approximately one week, without the use of any therapeutic interventions after the event.
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To examine variations in neuromuscular and hormonal status and their relationship to performance throughout a season of elite Australian Rules Football (ARF). Fifteen elite ARF players performed a single jump (CMJ1) and 5 repeated countermovement jumps (CMJ5), and provided saliva samples for the analysis of cortisol (C) and testosterone (T) before the season commenced (Pre) and during the 22-match season. Magnitudes of effects were reported with the effect size (ES) statistic. Correlations were performed to analyze relationships between assessment variables and match time, training load, and performance. CMJ1Flight time:Contraction time was substantially reduced on 60% of measurement occasions. Magnitudes of change compared with Pre ranged from 1.0+/-7.4% (ES 0.04+/-0.29) to -17.1+/-21.8% (ES -0.77+/-0.81). Cortisol was substantially lower (up to -40+/-14.1%, ES of -2.17+/-0.56) than Pre in all but one comparison. Testosterone response was varied, whereas T:C increased substantially on 70% of occasions, with increases to 92.7+/-27.8% (ES 2.03+/-0.76). CMJ1Flight time:Contraction time (r=.24+/-0.13) and C displayed (r=-0.16+/-0.1) small correlations with performance. The response of CMJ1Flight time:Contraction time suggests periods of neuromuscular fatigue. Change in T:C indicates subjects were unlikely to have been in a catabolic state during the season. Increase in C compared with Pre had a small negative correlation with performance. Both CMJ1Flight time:Contraction time and C may be useful variables for monitoring responses to training and competition in elite ARF athletes.
Over the last 20 years, heart rate monitors (HRMs) have become a widely used training aid for a variety of sports. The development of new HRMs has also evolved rapidly during the last two decades. In addition to heart rate (HR) responses to exercise, research has recently focused more on heart rate variability (HRV). Increased HRV has been associated with lower mortality rate and is affected by both age and sex. During graded exercise, the majority of studies show that HRV decreases progressively up to moderate intensities, after which it stabilises. There is abundant evidence from cross-sectional studies that trained individuals have higher HRV than untrained individuals. The results from longitudinal studies are equivocal, with some showing increased HRV after training but an equal number of studies showing no differences. The duration of the training programmes might be one of the factors responsible for the versatility of the results. HRMs are mainly used to determine the exercise intensity of a training session or race. Compared with other indications of exercise intensity, HR is easy to monitor, is relatively cheap and can be used in most situations. In addition, HR and HRV could potentially play a role in the prevention and detection of overtraining. The effects of overreaching on submaximal HR are controversial, with some studies showing decreased rates and others no difference. Maximal HR appears to be decreased in almost all ‘overreaching’ studies. So far, only few studies have investigated HRV changes after a period of intensified training and no firm conclusions can be drawn from these results. The relationship between HR and oxygen uptake (V̇O2) has been used to predict maximal oxygen uptake (V̇O2max). This method relies upon several assumptions and it has been shown that the results can deviate up to 20% from the true value. The HR-V̇O2 relationship is also used to estimate energy expenditure during field conditions. There appears to be general consensus that this method provides a satisfactory estimate of energy expenditure on a group level, but is not very accurate for individual estimations. The relationship between HR and other parameters used to predict and monitor an individual’s training status can be influenced by numerous factors. There appears to be a small day-to-day variability in HR and a steady increase during exercise has been observed in most studies. Furthermore, factors such as dehydration and ambient temperature can have a profound effect on the HR-V̇O2 relationship.
Endurance training decreases resting and submaximal heart rate, while maximum heart rate may decrease slightly or remain unchanged after training. The effect of endurance training on various indices of heart rate variability remains inconclusive. This may be due to the use of inconsistent analysis methodologies and different training programmes that make it difficult to compare the results of various studies and thus reach a consensus on the specific training effects on heart rate variability. Heart rate recovery after exercise involves a coordinated interaction of parasympathetic re-activation and sympathetic withdrawal. It has been shown that a delayed heart rate recovery is a strong predictor of mortality. Conversely, endurance-trained athletes have an accelerated heart rate recovery after exercise. Since the autonomic nervous system is interlinked with many other physiological systems, the responsiveness of the autonomic nervous system in maintaining homeostasis may provide useful information about the functional adaptations of the body. This review investigates the potential of using heart rate recovery as a measure of training-induced disturbances in autonomic control, which may provide useful information for training prescription.
Jay R. Hoffman, PhD, CSCS, *D, FACSMDepartment of Health and Physical EducationThe College of New JerseyMeir KaminskyIsrael Basketball OrganizationTel Aviv, IsraelKeywords: fitness; basketball; overtraining; elite athletes; conditioning.THE OVERTRAINING SYNDROMEmay be considered a continuum ofnegative adaptations to training.Symptoms of overtraining appearwhen the training stimulus hasreached the point where either orboth training intensity and trainingvolume has become too excessiveand is coupled with inadequate restand recovery. Initial stages of over-training are generally accompaniedby subjective feelings of fatigue andstaleness but may or may not beaccompanied by decrements inperformance. As this continuumproceeds, these subjective feelingsof fatigue and staleness become as-sociated with decreases in perfor-mance. When the training stimulusis excessive and recovery and adap-tation do not occur within an antic-ipated time, the athlete is consid-ered to be overreaching. With adecrease in the training stimulusand adequate rest, complete recov-ery will usually occur within a weekor two (14). This recovery may alsocoincide with an overcompensationand improved performance. Often,overreaching is a planned phase ofmany training programs. When the imbalance betweentraining and recovery continues foran indefinite period of time, theathlete will progress from a stage ofoverreaching and fatigue to themore serious problem of overtrain-ing. Overtraining may be the cul-mination of repeated warningsthat went unacknowledged or un-noticed by the athlete or coach.Many times, a plateau or a decre-ment in performance will be metwith frustration on the part of theathlete and/or coach. This may bethe initial symptom of what wasearlier referred to as overreaching.However, the athlete or coach mayignore these signs, and instead ofreducing the training stimulus andresting, they increase the trainingstimulus, thinking that the athleteneeds to train harder to get pastthis plateau. This will result in adownward spiral of events that cul-minates in chronic fatigue and insignificant performance decre-ments that are associated with theovertraining syndrome. Recoveryfrom overtraining may be quitelong (exceeding 6 months; 14).
To examine the efficacy of a 3-week, high-intensity, resistance exercise protocol for inducing overtraining, 9 subjects trained their lower body on a squat-simulating resistance exercise machine. Five subjects performed a training (Trn) protocol 5 days a week to elicit an overtraining response. Four subjects performed a control (Con) protocol 2 days a week. Test batteries of sprints, jumps, and strength tests were performed four times during the study at l-week intervals (Tl, T2, T3, T4). One-RM performances increased for the Trn group by T2 and remained augmented through T4. Overtraining did not occur, but other performances were attenuated for the Trn group. Increased sprint times for 9.1 m and 36.6 m were evident by T2 for the Trn group and remained slower through T4. Leg extension torque decreased for the Trn group by T4. Future attempts to induce intensity-dependent overtraining for study should use greater training intensities or different training modalities and should monitor physiological factors that may contribute to this phenomenon. (C) 1994 National Strength and Conditioning Association
A training stimulus can be effective only if intensity and duration of the training workload correspond to the present individual workload capacity. On this narrow strait between training below an effective threshold on the one hand and overtraining on the other, sports medicine has different blood parameters at its disposal, which we discuss in this paper. (C) Lippincott-Raven Publishers.