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

Abstract

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
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Fatigue monitoring in high performance sport: A survey of current trends.
J. Aust. Strength Cond. 20(1)12-23. 2012 © ASCA
Peer Review
FATIGUE MONITORING IN HIGH PERFORMANCE SPORT: A SURVEY OF
CURRENT TRENDS
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
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.
Keywords - training monitoring, neuromuscular fatigue, overtraining, overreaching.
INTRODUCTION
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
A

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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.
METHODS
Subjects
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.
Survey
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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coaches in the high performance sport arena about their current practices provided a further basis for the construction
of the questionnaire.
Procedures
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.
RESULTS
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
specificity.

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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-

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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
(restwise.com)(n=1). Two other respondents indicated they relied on asking the athlete how they felt, either at rest or
during high intensity training efforts.
DISCUSSION
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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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
trends.
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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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
employed.
PRACTICAL APPLICATIONS
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
TRAINING MONITORING IN HIGH PERFORMANCE SPORT
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
 Both
4. What level of sports performance are you involved in?
 Professional
 Elite/nonprofessional
 Semi-professional
 State level
 Other (please specify)
5. Which sport(s) do you work with on a daily/weekly basis?
 Australian Rules Football
 Basketball
 Boxing
 Cricket
 Cycling
 Football (Soccer)
 Hockey
 Martial Arts
 Netball
 Rugby League
 Rugby Union
 Rowing
 Swimming
 Tennis
 Track & Field

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 Volleyball
 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?
 Yes
 No
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
 1-2
 2-4
 4-6
 >6
11. How many hours per week do you (or your colleagues) spend ANALYSING training monitoring data?
 < 1
 1-2
 2-6
 6-10
 > 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?
 Remotely
 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
 2
 3
 4
 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?
 RESTQ
 DALDA
 TQR
 POMS
 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
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 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?
 No
 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.
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