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Copyright © 2016 American College of Sports Medicine
Translating Fatigue to Human Performance
Roger M. Enoka1 and Jacques Duchateau2
1Department of Integrative Physiology, University of Colorado, Boulder, CO
2Laboratory of Applied Biology and Neurophysiology, ULB Neuroscience Institute,
Université Libre de Bruxelles (ULB), Bruxelles, Belgium
Accepted for Publication: 24 November 2015
Translating Fatigue to Human Performance
Roger M. Enoka1 and Jacques Duchateau2
1Department of Integrative Physiology, University of Colorado, Boulder, CO
2Laboratory of Applied Biology and Neurophysiology, ULB Neuroscience Institute, Université
Libre de Bruxelles (ULB), Bruxelles, Belgium
Correspondence: Roger M Enoka
Department of Integrative Physiology
University of Colorado
Boulder, CO 80309-0354
This work was partially supported by an award from National Institute of Child Health & Human
Development (R03HD079508) to RME. The authors declare no conflicts of interest. Understandably,
ACSM is unable to endorse either the data or its interpretation as presented in this article.
Medicine & Science in Sports & Exercise, Publish Ahead of Print
Copyright © 2016 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
Despite flourishing interest in the topic of fatigue—as indicated by the many presentations on
fatigue at the 2015 annual meeting of the American College of Sports Medicine—surprisingly
little is known about its impact on human performance. There are two main reasons for this
dilemma: (1) the inability of current terminology to accommodate the scope of the conditions
ascribed to fatigue, and (2) a paucity of validated experimental models. In contrast to current
practice, a case is made for a unified definition of fatigue to facilitate its management in health
and disease. Based on the classic two-domain concept of Mosso, fatigue is defined as a disabling
symptom in which physical and cognitive function is limited by interactions between
performance fatigability and perceived fatigability. As a symptom, fatigue can only be measured
by self-report, quantified as either a trait characteristic or a state variable. One consequence of
such a definition is that the word fatigue should not be preceded by an adjective (e.g., central,
mental, muscle, peripheral, and supraspinal) to suggest the locus of the changes responsible for
an observed level of fatigue. Rather, mechanistic studies should be performed with validated
experimental models to identify the changes responsible for the reported fatigue. As indicated
by three examples (walking endurance in old adults, time trials by endurance athletes, and
fatigue in persons with multiple sclerosis) discussed in the review, however, it has proven
challenging to develop valid experimental models of fatigue. The proposed framework provides
a foundation to address the many gaps in knowledge of how laboratory measures of fatigue and
fatigability impact real-world performance. Key words: performance fatigability, perceived
fatigability, endurance, critical power, multiple sclerosis
Copyright © 2016 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
Despite a substantial literature on the influence of fatigue on human performance, relatively
little progress has been made in translating this knowledge into practice. One of the key
impediments has been the scope of its usage, with fatigue denoting reductions in physical and
cognitive function that extend from an exercise-induced impairment of motor performance
through to the sensations of tiredness and weakness that accompany some clinical conditions.
As a consequence, knowledge on fatigue has become compartmentalized within such disciplines
as clinical medicine, ergonomics, physiology, and psychology. The resulting fragmentation of
work on fatigue has led to the absence of a consensus definition of fatigue, the emergence of
terminology that tends to exclude individuals not familiar with a particular discipline, and the
development of questionable experimental models.
Contemporary research on the physiology of fatigue, for example, is often based on the
Mosso dichotomy in which the phenomenon of force diminution is regarded as distinct from the
sensations that arise from prolonged muscular activity (19). Mechanistic studies on the
physiology of fatigue, therefore, have largely focused on identifying rate-limiting adjustments in
central and peripheral processes that limit human performance (7) (Figure 1). Despite the many
studies that have adopted the central-peripheral dichotomy, however, two key limitations with
this approach have precluded the development of a consensus understanding on what causes
The first limitation is the implicit assumption that the adjustments in neuromuscular activity
needed to counteract an exercise-induced decrease in force capacity and thereby sustain task
performance are independent from those involved in generating the accompanying sensations.
Although it is possible to isolate the decline in contractile function during a fatiguing contraction
by recording electrically evoked forces (6), adjustments in the activation signal discharged by
Copyright © 2016 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
motor neurons during a voluntary contraction begin before a detectable reduction in muscle force
and are attributable to changes occurring within the muscle (11,20). Moreover, sensory feedback
transmitted by group III/IV afferents can influence both the integration of synaptic input by
spinal motor neurons and contribute to perceptions of pain (42). Such observations indicate that
it is not possible to identify the etiology of fatigue by attempting to disentangle the decline in
muscle force from sensations about fatigue, especially during long-lasting contractions (68).
The second limitation is that most of the physiological processes involved in performing a
voluntary action, extending from the generation of the motor command down to the cross bridge
cycle, can be challenged under appropriate experimental conditions and thereby contribute to the
development of fatigue. For example, the decline in maximal voluntary contraction (MVC)
force—a classic index of muscle fatigue—that develops during sustained low-intensity activity is
largely attributable to a reduction in the activation signal generated by the nervous system,
whereas the reduction in MVC force after high-intensity activity is more likely due to a decline
in contractile function (68,72). Indeed, much of the literature on the physiology of fatigue is
focused on exploiting a range of experimental protocols to quantify the capabilities of the
neuromuscular system in various contexts (18,22,34,46,59). This work has clearly demonstrated
that the rate-limiting adjustments during fatiguing contractions vary across conditions, which has
become known as the task dependency of fatigue (19). A critical question that emerges from this
work, however, is which of these impairments limit real-world performance?
The purpose of this brief review is to present a framework that can be used to evaluate the
functional significance of fatigue-related adjustments. The framework comprises a taxonomy
that can encompass the scope of the conditions ascribed to fatigue and a general approach to
determine the influence of fatigue on human performance. To achieve this goal, we propose that
fatigue be conceptualized as a disabling symptom in which physical and cognitive function is
limited by interactions between performance fatigability and perceived fatigability.
The original scheme proposed by Mosso remains relevant (Figure 1), but needs to be
broadened to accommodate the contemporary scope of conditions ascribed to fatigue during
human performance. As suggested in the taxonomy proposed by Kluger et al. (37), the concept
of fatigue should acknowledge its two attributes: (1) performance fatigability – the decline in an
objective measure of performance over a discrete period of time; and (2) perceived fatigability –
changes in the sensations that regulate the integrity of the performer (Figure 2). Fatigue is
defined in terms of fatigability to normalize the level of fatigue reported by an individual relative
to the demands of the task that produces it. Individuals who are less fatigable, for example, are
able to meet a greater demand to reach the same level of fatigue. When a person reports the level
of fatigue during ongoing activity, therefore, the value at a specific time point will depend on the
rates of change in its two attributes. As indicated in Figure 2, the attribute of performance
fatigability depends on the contractile capabilities of the involved muscles and the capacity of the
nervous system to provide an adequate activation signal derived from descending commands and
afferent feedback for the prescribed task. In contrast, the attribute of perceived fatigability is
derived from the initial value and the rate of change in sensations that regulate the integrity of
the performer based on the maintenance of homeostasis and the psychological state of the
In contrast to performance fatigability, perceived fatigability can be assessed when a person
is either at rest (21,23) or performing physical activity (57,62). Elevated perceived fatigability at
rest is attributable to deviation of one or more of the modulating factors (e.g., core temperature,
hydration, motivation, and pain) from normal baseline values. Conversely, perceived fatigability
during onging activity is derived from rates of change in the modulating factors and are used to
regulate the pace of the performance and thereby control the development of fatigue. Although
the proposed scheme (Figure 2) suggests that the influence of psychological factors on fatigue is
mediated via perceived fatigability, it remains to be determined whether or not psychological
factors can have a direct effect on fatigue without involving perceived fatigability.
Most of the factors listed in Figure 2 are the familiar candidates that have been demonstrated
to represent rate-limiting adjustments under selected conditions (17,24,38,50,52,58,68) (Figure
1). One of the unique features of the proposed scheme, however, is the explicit inclusion of
psychological factors as significant contributors to the construct of fatigue. Although a potential
role for psychological factors in the development of fatigue is occasionally acknowledged in
studies on healthy individuals (43), they assume a more prominent role in the fatigue reported by
various clinical cohorts. For example, the level of fatigue (Fatigue Severity Scale) reported by
individuals with either multiple sclerosis or Parkinson’s disease is significantly correlated with
the level of depression (37) and represents a deviation in one of the modulating factors (mood)
from normal baseline values. Similarly, lesions in the ascending reticular activating system
suggest that reductions in arousal may mediate some aspects of fatigue in individuals with post-
polio syndrome (37). Even in healthy individuals, however, psychological factors, such as
performance feedback, can influence the average power produced by trained cyclists when
performing a time trial as quickly as possible, which presumably coincides with maximal ratings
of perceived exertion at the end of the task (66).
Although the taxonomy illustrated in Figure 2 lists many of the factors that can influence
each attribute of fatigue (performance fatigability and perceived fatigability), the scheme
acknowledges that most voluntary actions performed by humans involve significant interactions
between the two domains. For example, several of the modulating factors that contribute to
perceived fatigability, including the levels of blood glucose (49), core temperature (50), arousal
(36), and mood (64), can all modulate the capacity of the individual to generate the required
amount of voluntary activation, which is a factor that influences performance fatigability.
Similarly, afferent feedback generated during high-intensity exercise can influence the
adjustments required to maintain homeostasis and thereby contribute to perceived fatigability
(33,59). The key feature of this scheme is that the level of fatigue experienced by an individual
emerges from the many adjustments that occur in the modulating factors within and between
each fatigability attribute. With this construct, fatigue is defined as a disabling symptom in
which physical and cognitive function is limited by interactions between performance fatigability
and perceived fatigability. As indicated in Figure 2, the level of fatigue experienced by an
individual can be modulated by challenges to homeostasis, disturbances in the psychological
state, reductions in contractile function, and limitations in the capacity to provide an adequate
activation signal to the involved muscles.
According to the proposed definition, fatigue is a single entity that does not need to be
modified by an accompanying adjective, such as central fatigue, mental fatigue, muscle fatigue,
peripheral fatigue, physical fatigue, and supraspinal fatigue. Although the descriptors are often
intended to imply the likely locus of the adjustments in the modulating factors that limit
performance, the distinctions are too vague to be meaningful. Moreover, the literature on fatigue
would be more coherent if the practice was to state the primary outcome variable and describe
how it was influenced by the protocol, without suggesting that the study examined a particular
type of fatigue.
Studies on performance fatigability, for example, should focus on the key outcome variable,
which could include the duration that a task can be sustained (endurance time or time to task
failure), the time it takes to complete a prescribed task (walking endurance, time trial), or the rate
of change in such variables as MVC force, power production, the amplitude of evoked responses,
reaction time, rating of perceived exertion, heart rate, mean arterial pressure, and core
temperature. Critically, however, the outcome variable must be an ecologically valid measure of
human performance, such as those specified in the NIH Toolbox, indices of athletic prowess, and
standardized clinical assessments. Such an approach would facilitate the development of
guidelines on how to manage fatigue in health and disease (3,37,77). An analogous construct has
been proposed to monitor the fatigue experienced by athletes across a training cycle (26).
HUMAN PERFORMANCE AND FATIGUE
We now consider three examples of what we know about how fatigue influences human
performance. Similar approaches have been used by other groups (24,38,63). In each of our
examples, a performance criterion is identified and then what is known about the fatigue-related
adjustments that limit this performance is discussed. In addition to providing a framework that
can be extended to other activities, the examples included in the current review identify specific
gaps in knowledge that need to be addressed in future work.
Walking Endurance of Healthy Adults
The National Institutes of Health (NIH) developed a multidimensional set of measures,
known as the Toolbox, to provide brief yet comprehensive tests of neurological health and
function across the lifespan. The NIH Toolbox has four domains: cognition, emotion, motor, and
sensation (55). Motor function—the ability to perform physical tasks—is quantified with tests of
endurance, locomotion, manual dexterity, non-vestibular balance, and strength. Endurance,
which the Toolbox assesses as the distance that can be walked in 2 min, characterizes the global
and integrated responses of the pulmonary, cardiovascular, and musculoskeletal systems. The
distance that can be walked in 2 min varies across the lifespan (Figure 3) and walking endurance
is reduced in old adults (65-102 yrs) who self-report elevated levels of fatigue (61,70).
Moreover, time to complete another test of walking endurance (400 m) is a significant predictor
of mortality in old adults (71).
Given that walking endurance provides a clinically relevant measure of human performance,
Justice et al. (30) compared the associations between walking endurance and measures of
neuromuscular function in young (22 ± 4 yrs) and old (75 ± 6 yrs) adults. Of particular interest
was the strength of the association between walking endurance (500-m walk) and a laboratory
test of leg-muscle fatigability. Due to the significant loss of function in the dorsiflexor muscles
of old adults (4,54,67), the test of fatigability involved supporting a submaximal mass (20% of
the one-repetition maximum) with the dorsiflexor muscles for as long as possible while seated
with the foot in a neutral position. The two groups (young and old) experienced a similar decline
in maximal voluntary contraction (MVC) torque at task failure (~23%), but endurance time for
this particular fatiguing contraction was longer for the young adults (15.5 ± 0.9 min) than the old
adults (8.9 ± 0.6 min) even though the young adults were stronger.
In a subsequent analysis of the data reported by Justice et al. (30), the outcome measures
were entered into a stepwise, multiple-regression model to predict the variance in 500-m walk
time for each group of participants. Separate models emerged for young and old adults, with
more of the variance explained for young adults (R2 = 0.42) than for old adults (R2 = 0.31;
Figure 4). The three explanatory variables in the model for old adults also emerged as predictors
in the model for the young adults: MVC force for the knee flexors, 1-RM load for the
dorsiflexors, and force steadiness when supporting a 20% 1-RM load with the dorsiflexors. The
model for young adults included an additional two explanatory variables: endurance time for the
measure of leg-muscle fatigability and dorsiflexor MVC force. These findings indicate that
dorsiflexor fatigability during a sustained, submaximal contraction was moderately related to
walking endurance for young adults, but not for old adults. Additional studies are needed to
determine if more of the variance in walking endurance can be explained by other tests of
performance fatigability, such as actions that engage multiple joints and those involving the
stretch-shortening cycle (39).
In contrast to the absence of an association between walking endurance and the test of
performance fatigability used by Justice et al. (30), another measure of fatigue has been found to
be associated with walking endurance in old adults. In a sample of 1,155 adults (65-102 yrs),
Vestergaard et al. (70) examined the association between fatigue and physical function by
categorizing participants into those who self-reported elevated levels of fatigue and those who
did not. Fatigue was assessed with two questions from the Center for Epidemiologic Studies-
Depression Scale based on experience in the past week: (1) I feel that everything I did was an
effort; (2) I could not get going. Possible answers were: (a) rarely (<1 day); (b) some of the time
(1-2 days); (c) occasionally (3-4 days); or (d) all of the time (5-7 days). Those individuals who
reported ≥3 days for either question were classified as fatigued, the prevalence of which was
greater for women (29.1%) than for men (15.3%).
In general, the health behaviors and prevalence of clinical conditions was relatively similar
for the two groups of participants for both sexes. The exceptions were that men who were
classified as being fatigued, but not women, were more sedentary, had more comorbid
conditions, reported a greater prevalence of coronary heart disease and chronic
bronchitis/emphysema, and had greater levels of C-reactive protein in blood samples.
Nonetheless, the fatigued participants of both sexes had weaker handgrip strength, greater
activity limitations (Instrumented Activities of Daily Living), and worse walking endurance. For
example, average speed during the 400-m walk was less for the fatigued groups (men: 1.29 and
1.22 m/s; women: 1.14 and 1.05 m/s) and the percentage of those individuals who could not
complete the 400 m walk was greater for the fatigued groups (men: 5.9 and 21.0%; women: 6.9
and 14.8%). These findings indicate that an elevated trait level of fatigue is associated with
reduced walking endurance (a measure of performance fatigability) in old adults.
Time Trials by Endurance Athletes
The peak power that a muscle can produce during sustained activity declines as the duration
of the task increases. The relation between power production and task duration can be
characterized with a hyperbolic function, with the asymptote corresponding to critical power and
the curvature denoting the finite amount of work that can be performed above critical power at
each level of power production (24,28). Critical power is located at the boundary between heavy
and severe exercise, and has been characterized as the greatest exercise intensity at which the
physiological adjustments can accommodate the challenge to intramuscular homeostasis.
Exercise can be sustained for durations up to a maximum of ~30 min when performed at
intensities above critical power; that is, in the severe domain.
An individual’s critical power, which provides a measure of performance fatigability, can be
estimated with a single, 3-min test on a cycle ergometer in a laboratory (69). The test requires
participants to cycle at a maximal cadence with the ergometer resistance set midway between the
power outputs that correspond to the gas exchange threshold and maximal oxygen consumption
as determined during a ramp protocol. The participant is required to achieve maximal power
production quickly and to maintain as high a cadence as possible until told to stop. Critical
power is quantified as the average power produced during the last 30 s of the test and the
curvature of the hyperbolic function is estimated from the power-time integral above critical
The validity of the method as a measure of human performance was examined by comparing
the ergometer-based estimate of critical power with the time it took 10 competitive cyclists to
complete a 16.1 km time trial (8). Such time trials are performed at an intensity similar to
critical power. The time trial was completed in 27.1 ± 1.2 min (mean ± SD) and critical power
was estimated as 309 ± 34 W (4.20 ± 0.41 W/kg). Time trial performance was significantly
correlated with critical power (r2 = –0.69, P < 0.01), indicating that faster times were associated
with greater values for critical power. A similar correlation was found between time trial
performance and total work (power-time integral) performed during the 3 min test. These
findings indicate that the critical power protocol represents a valid model for mechanistic studies
of performance fatigability during time trials, at least in highly trained individuals.
As one example of such a mechanistic study, Klass et al. (36) compared the influence of
three drug conditions on the performance of 10 cyclists and triathletes on a time trial. The drug
conditions, one of which was administered in each experimental session, comprised a placebo or
a reuptake inhibitor for either noradrenaline (reboxetine) or dopamine (methylphenidate). The
reuptake inhibitors result in each neurotransmitter lingering longer among synaptic contacts and
thereby augmenting the actions it elicits. On each occasion, the participants performed a time
trial in which they were required to complete a fixed amount of work (the equivalent of 30 min at
75% of maximal power production) on a cycle ergometer as quickly as possibly. The rating of
perceived exertion (6-20 scale) was reported during the test. Before and after each time trial,
contractions were evoked with transcranial magnetic stimulation and nerve-muscle stimulation to
estimate the level of voluntary activation.
The average power produced during the time trial was depressed when noradrenaline
reuptake was inhibited relative to the other two conditions (Figure 5). As a consequence, the
time to complete the simulated time trial was similar for the placebo (30.8 ± 2.1 min) and the
inhibition of dopamine reuptake (29.7 ± 1.4 min) conditions, but significantly longer for the
noradrenaline condition (33.7 ± 3.6 min). Consistent with these results, the level of voluntary
activation, as assessed with transcranial magnetic stimulation, did not differ from baseline values
(~97%) to those measured at 10 min after the time trial for either the placebo (95 ± 1.5%) or
dopamine (95 ± 1.4%) groups, but was depressed for the noradrenaline group (89 ± 3.0%). In
contrast, the rating of perceived exertion at the end of the time trial did not differ for the three
groups (18.0 ± 1.4, 18.1 ± 1.1, and 18.7 ± 1.1, respectively). These results suggest that the
average power produced during each time trial was regulated by adjustments that influence the
rating of perceived exertion and that these adjustments were compromised when noradrenaline
reuptake was inhibited relative to the other two conditions.
The association between physiological adjustments and ―volitional fatigue‖—when ratings of
perceived exertion were presumably maximal—during high-intensity exercise was demonstrated
in another study when McKenna et al. (44) compared the influence of an antioxidant compound
on cycling performance. The antioxidant administered during the study was N-acetylcysteine,
which can influence the activity of Na+,K+ pumps in muscle and prolong the duration that high-
intensity exercise can be sustained. In a cross-over design in which the influence of the drug was
compared with saline, McKenna et al. (44) examined the performance and adjustments exhibited
by eight endurance-trained athletes when they cycled on an ergometer for 45 min at 70% of peak
oxygen consumption and then as long as possible at 92% of maximal oxygen consumption. The
influence of the antioxidant compound was characterized by measuring maximal Na+,K+ pump
activity in biopsy samples from vastus lateralis and the associated changes in plasma [K+].
Infusion of the antioxidant had a significant influence on maximal Na+,K+ pump activity,
plasma [K+], and endurance time relative to the saline condition. The duration that the cyclists
could sustain the cycling task was prolonged by 24 ± 8% for the antioxidant condition and this
improvement in performance was accompanied by lesser changes in both maximal Na+,K+ pump
activity and plasma [K+] when normalized to the amount of work that was performed. The
attenuation of the decline in maximal Na+,K+ pump activity during the antioxidant condition
suggests that the accumulation of reactive oxygen species contributes significantly to the
inactivation of Na+,K+ pumps during high-intensity exercise. Because the task was performed
for as long as possible for the two conditions, the results suggest the contraction-associated
modulation of Na+,K+ pump activity can influence the neural pathways involved in generating
the maximal level of tolerable exercise (76).
Taken together, these findings indicate that the physiological adjustments exhibited by
endurance athletes during high-intensity exercise are strongly associated with ratings of
perceived exertion. Key gaps in knowledge for this level of human performance include
information about the relative significance of the different adjustments that contribute to the
generation of perceived fatigability (1) and the extent to which tolerance of these perceptions can
be improved with exercise interventions (29).
Fatigue in Multiple Sclerosis
Multiple sclerosis (MS) is an inflammatory disease that progressively impairs the ability of
neurons to generate and conduct action potentials. Although the course of the disease varies
among individuals, it invariably involves the development of difficulties with walking and
elevated levels of fatigue (10,40,47). One large survey found that 74% of respondents (n =
9,077) reported severe fatigue, and worsening fatigue was associated with the development of
limitations in walking (25).
Several studies have examined the association between the fatigue reported by individuals
with MS and performance fatigability during standardized laboratory tests. In one such study,
Steens et al. (64) compared the characteristics of 20 persons who had one form of the disease
(relapsing-remitting MS) with a control group of age- and sex-matched individuals. Fatigue was
assessed with the Fatigue Severity Scale (FSS), which is a validated and reliable instrument that
quantifies the impact of fatigue on activities of daily living in individuals with MS (41). The
FSS is a unidimensional scale that largely focuses on the physical aspects of fatigue.
Performance fatigability was quantified as the decline in force during a sustained, isometric
MVC with a hand muscle (first dorsal interosseus). This muscle was chosen because it is less
likely than limb muscles to be impacted by the deconditioning associated with the relatively
sedentary lifestyle of individuals with MS. In addition, mood was evaluated using the depression
subscore of the Hospital Anxiety and Depression Scale questionnaire.
Participants in the MS group exhibited significantly greater levels of fatigue (FSS: 5.3 ± 0.9)
and depression (4.8 ± 3.3) than those in the control group (2.9 ± 0.6 and 0.9 ± 1.0, respectively).
There was a moderate association between FSS score and depression for the MS group (r2 =
0.36), but not for the control group. MVC force for the right hand muscle was not significantly
different between groups, but when standardized as a Z score the MVC force was less for the MS
group relative to the control group (–0.7 ± 1.0). Normalized MVC force (Z score) was
significantly associated with the level of voluntary activation assessed during the MVCs for the
MS group (r2 = 0.33), but not for the control group. The decline in force during the fatiguing
contraction (sustained MVC for 124 s) was similar for the MS (61 ± 17%) and control (63 ±
12%) groups, and was significantly associated with MVC force (partial r2 = 0.15).
The assocation between FSS score and the measure of performance fatigability (decline in
MVC force) was examined with a multiple-regression analysis. A significant amout of the
variance in the FSS score for the participants in the MS group (R2 = 0.45), but not the control
group, was explained with a model that included the measure of performance fatigability (partial
r2 = 0.37) and normalized MVC score (partial r2 = 0.35). The model indicates that the
individuals with MS who had greater FSS scores exhibited a greater amount of performance
fatigability than would be expected on the basis of the normalized MVC score. However, more
of the variance in FSS scores for the MS group was explained by including the depression score
in the model (R2 = 0.77), which may have arisen from some of the FSS items concerning social
aspects of fatigue.
In a subsequent study with similar groups of participants, Steens et al. (65) compared the
adjustments in the activation signal generated by the nervous system during a fatiguing
contraction with the hand muscle. The variance in performance fatigability (decline in MVC
force) was explained by different outcome measures for the two groups of participants. Much of
the variance for the decline in force exhibited by the control group was explained (R2 = 0.49) by
the initial MVC force (strength) of the hand muscle. As reported in some other studies on the
performance fatigability of healthy individuals (27), the strongest individuals were the most
fatigable. Although the participants in the MS group experienced a similar reduction in force as
the control group, the primary explanatory variable (R2 = 0.51) was the decline in voluntary
activation as assessed by nerve-muscle stimulation (–20.1 ± 20.6%). The level of voluntary
activation at the start of the fatiguing contraction did not differ between the two groups (~90%)
and declined only slightly by the end of the fatiguing contraction for the control group (–7.8 ±
11.8%). The regression model for the MS group indicated that those individuals with greater
FSS scores experienced larger declines in voluntary activation.
To assess the cortical adjustments during the fatiguing contraction, Steens et al. (65)
compared the changes in blood oxygen level-dependent (BOLD) contrast for the two groups of
participants. As observed in other studies on healthy adults (53), the control group demonstrated
a widespread increase in cortical activity during the fatiguing contraction despite a small
decrease in voluntary activation and the progressive decline in maximal force. One potential
explanation for the increase in cortical activity during fatiguing contractions is that it
compensates for the reduction in excitation provided to spinal motor neurons (35,45).
Nonetheless, the increase in cortical activity is not sufficient to overcome the adjustments
responsible for the decrease in force during strong contractions. Consistent with this
interpretation, participants in the MS group did not increase cortical activity and this was
accompanied by a larger decline in voluntary activation. The similar decline in MVC force for
the two groups, therefore, must be explained by other adjustments for the MS group. Indeed, the
peak force elicited by a pair of stimuli—a measure of force capacity and the efficacy of
excitation-contraction coupling—declined during the fatiguing contraction and was negatively
related to the reduction in voluntary activation for the MS group (R2 = 0.25). Thus, greater
decreases in voluntary activation were associated with lesser declines in the evoked twitch forces
Taken together, these findings indicate that the trait level of fatigue in persons with MS is
associated with a measure of performance fatigability, the strength of the muscles involved in the
fatiguing contraction, and the capacity to sustain a high level of voluntary activation during the
fatiguing contraction. The caveat, however, is that these studies were performed on a hand
muscle and future studies need to examine similar associations in limb muscles. In addition,
little is known about the influence of fatigue and performance fatigability on mobility, such as
walking endurance, in individuals with MS.
TRANSLATING FATIGUE TO PERFORMANCE
By combining the proposed taxonomy (Figure 2) with the information presented in the
preceding examples, we can complete the framework on how to translate knowledge on fatigue
into practice. The foundation of this approach is the definition of fatigue as a symptom, which
means it can only be measured by self-report. Classic measures of fatigue, such as the time to
complete a prescribed task, the reduction in MVC force, and the decline in power production, are
indices of performance fatigability, but do not provide a measure of the intensity of the
symptom. Rather, the assessment of fatigue requires the individual to interpret relevant
physiological and psychological factors by providing responses to standardized questions
Trait and State Properties
As with many symptoms, fatigue can be assessed either as a trait characteristic or as a state
variable. The trait level of fatigue represents the average amount of fatigue experienced during
the preceding several days, whereas state measures reflect the rate of change in key adjustments
during a fatiguing task. In the example of walking endurance in healthy adults, Vestergaard et
al. (70) used a standardized instrument (Center for Epidemiologic Studies-Depression Scale) to
determine the level of fatigue experienced by old adults during the preceding week. Such a
measure indicates the trait level of fatigue, which Vestergaard et al. (70) used to assign the study
participants to either a control group or a fatigue group. The relevance of the approach was
underscored by finding significant differences in health status and performance criteria between
the two groups of participants. Similarly, there are several instruments (e.g., Fatigue Severity
Scale, Mobility-Tiredness Scale, Modified Fatigue Impact Scale, Multidimensional Fatigue
Inventory, Recovery-Stress Questionnaire, Situational Fatigue Scale) that rely on self-reported
responses about the preceding several days or weeks to quantify the trait level of fatigue
experienced by individuals with various illnesses (3,5,37,47,64) and in athletes during a training
The measure of fatigue at a specific instant in time indicates the state level of fatigue. This
can be accomplished by asking the performer to answer one or more questions about the level of
fatigue ―right now‖. One instrument often used to assess the state level of fatigue is the visual
analog scale, which can be anchored by ―Fatigue is absent‖ on the left and ―Most fatigue ever‖
on the right (12). Another option is to use the fatigue scale from the Profile of Mood States
questionnaire, which has been demonstrated to provide a reliable and valid measure of the state
level of fatigue across a wide range of cohorts (51). Participants are asked to provide a response
on a 5-point scale (0 = not at all, 1 = a little, 2 = moderately, 3 = quite a bit, 4 = extremely) to
five terms: Worn out, Fatigued, Exhausted, Sluggish, and Weary. The combined score indicates
the state level of fatigue and can be compared at selected time points during a protocol. For
example, Dishman et al. (14) used the Profile of Mood States fatigue scale to compare the
influence of cycling exercise on the state level of fatigue in individuals who reported an elevated
trait level of fatigue. Participants were assigned to either a 6-wk intervention (low- or moderate-
intensity cycling) or a control group. The state level of fatigue was compared before and after
cycling exercise at three time points during the 6-wk protocol. Participants in the low-intensity
group, but not the other two groups (moderate-intensity exercise or control), exhibited less of an
increase in the state level of fatigue after an exercise bout at weeks 3 and 6 of the study. These
findings were interpreted to indicate that low-intensity exercise can be used to manage clinical
symptoms and mood disorders, such as fatigue.
Alternatively, the state level of fatigue can be assessed with an instrument that is developed
for a specific cohort. For example, Barry and colleagues devised a Fatigue and Energy Scale to
measure the state level of fatigue after performing a bout of physical activity in individuals with
chronic fatigue syndrome (32). One of the characteristics of the syndrome is the prolonged
exacerbation of symptoms after performing physical activity. The approach involved convening
focus groups comprising afflicted individuals to inform the development of a self-report
instrument, examining the psychometric properties of the instrument in two case-control
challenge studies, and assessing the ecological validity of the instrument. The resulting
instrument (Fatigue and Energy Scale) comprised two domains—physical and mental fatigue—
that each included three items. As intended, the Scale was able to capture the state level of
fatigue reported by individuals with chronic fatigue syndrome after performing a bout of
physical activity. With the increasing prevalence of fatigue being included in the diagnosis of
various clinical conditions, there is an urgent need for similar instruments that can enable
clinicians to quantify condition-specific levels of fatigue.
However, a word of caution is necessary about the use of self-report measures to assess the
state level of fatigue in some cohorts, such as athletes and special operations forces personnel.
Due to the competitive nature of these individuals, they are more likely than not to understate
responses to questions about fatigue (1). Nonetheless, some assessment of fatigue is essential for
these individuals to optimize the training load and lessen the likelihood of injuries and
To determine the influence of fatigue on human performance, the proposed approach
involves three levels of analysis: (1) select a criterion measure of human performance that is
modulated by fatigue; (2) identify a laboratory test that is a strong predictor of performance on
the criterion measure; and (3) conduct mechanistic studies to determine the relative significance
of the adjustments in the modulating factors (Figure 2) that limit performance on the laboratory
test. Of the three examples discussed previously (walking endurance of healthy adults, time
trials by endurance athletes, and fatigue in multiple sclerosis), most progress has been made on
determining the influence of fatigue on the performance of endurance athletes during a time trial.
The time it takes a trained cyclist to complete a time trial is reasonably correlated with the
absolute critical power for the individual (8,9), which depends on the capacity of intramuscular
adjustments to accommodate the challenge to homeostasis during high-intensity exercise
(24,28,69). Given a specific critical power capability, however, performance during an
individual time trial may be limited by factors that contribute to perceived fatigability, such as
those that establish the performer’s psychological state. Power production can be modulated, for
example, by interventions that manipulate homeostasis and thereby alter ratings of perceived
exertion (36,44). Future studies are needed to determine the relative significance of the various
adjustments that influence ratings of perceived exertion.
In contrast, much less is known about how fatigue influences walking endurance of healthy
adults and the fatigue reported by individuals with multiple sclerosis. Walking endurance, which
is a strong predictor of health status (61,62,70), is reduced in old adults who exhibit an elevated
trait level of fatigue, but there is not yet a validated test of fatigability that predicts performance
on tests of walking endurance. Without an appropriate laboratory measure, it is not possible to
identify the adaptations responsible for declines in walking endurance with advancing age and
thereby develop appropriate countermeasures.
Although more is known about the influence of fatigue—as quantified with validated
questionnaries—on motor function in persons with multiple sclerosis (13,48,74) than is known
about walking endurance, significant gaps in knowledge remain. The elevated trait level of
fatigue reported by individuals with relapsing-remitting MS is strongly associated with the
strength of a hand muscle, the performance fatigability of the same hand muscle during a
sustained maximal contraction, and perceived fatigability as indicated by a depression score
(64,74). Despite comparable measures of performance fatigability (decline in MVC force), the
explanatory variables were related to muscle strength for the control group and voluntary
activation for the MS group (65). Additional studies are needed with this clinical cohort to
examine such issues as the adjustments that limit voluntary activation during fatiguing
contractions, the adjustments that establish performance fatigability on a laboratory test and how
these relate to the trait level of fatigue, and the interaction between fatigue and limitations in
Some insight on these issues can be gleaned from randomized controlled trials of strength
training performed by individuals with relapsing-remitting MS. Consistent with the finding that
some of the variance in the trait level of fatigue (Fatigue Severity Scale) can be explained by the
strength of a hand muscle (64), a 10-wk strength-training program that increased the strength and
endurance of leg muscles also reduced the trait level of fatigue (Modified Fatigue Impact Scale)
but did not improve walking endurance (2-min walk) (15). However, the decrease in fatigue was
significantly correlated (r = 0.40) with a test of leg-muscle endurance, but not with the gains in
muscle strength. Similar strength gains were achieved in another 12-wk intervention and these
were accompanied by correlated (r = 0.30) reductions between the trait level of fatigue (Fatigue
Severity Scale) and depression (Major Depression Inventory), but there was no association
between the improvement in fatigue and increases in either muscle strength (knee extensors) or
functional capacity (cumulative score based on chair-rise test, ascending-stairs test, 10-m walk
test, and the 6-min walk test) (13). These findings suggest that some of the fatigue reported by
individuals with relapsing-remitting MS can be explained by depression and the reduced capacity
to sustain high levels of voluntary activation, and that the functional consequences are likely
attributable to an inability to compensate for the disease-related decline in muscle activation.
Although there is widespread acceptance of the notion that fatigue can limit human
performance, there are considerable gaps in knowledge on the underlying mechanisms and how
these can be managed. This dilemma is largely attributable to the inability of current
terminology to accommodate the scope of the conditions ascribed to fatigue and a paucity of
valid experimental models. In an attempt to provide a more unifying rubric, it is proposed that
fatigue be defined as a symptom in which physical and cognitive function is limited by
interactions between performance fatigability and perceived fatigability. As a symptom, fatigue
can only be measured by self-report, quantified as either a trait characteristic or a state variable.
The trait level of fatigue represents the average amount of fatigue experienced during the
preceding several days and depends on the absolute values of the modulating factors that
contribute to perceived fatigability. In contrast, the state level of fatigue reflects the rate of
change in the modulating factors that contribute to both performance and perceived fatigability
during ongoing activity.
The two measures of fatigability (performance and perceived) normalize the observed fatigue
to the demands associated with specific tasks. Less fatigable individuals, for example, are able
to tolerate a greater demand to reach a given level of fatigue. Performance fatigability depends
on the contractile capabilities of the involved muscles and the capacity of the nervous system to
provide an adequate activation signal for the prescribed task. Perceived fatigability is derived
from the sensations that regulate the integrity of the performer based on the maintenance of
homeostasis and the psychological state of the individual. Perceived fatigability can be
measured at rest or during physical activity, whereas performance fatigability is quantified as the
rate of change in a criterion outcome due the adjustments made during a fatiguing task.
To determine how fatigue influences human performance, it is necessary to identify those
modulating factors most responsible for establishing the levels of fatigability that contribute to
the observed trait and state levels of fatigue. This can only be accomplished, however, after the
development of a reliable laboratory test that is strongly associated with a criterion measure of
human performance. Once an ecologically valid laboratory test has been determined,
mechanistic studies can probe the critical adjustments and thereby suggest countermeasures to
moderate the influence of the two attributes of fatigue on human performance. As indicated by
the three examples discussed in this paper, much remains to be learned about how knowledge on
fatigue can be translated into practice. The proposed framework provides a foundation to
address these gaps in knowledge.
This work was partially supported by an award from National Institute of Child Health & Human
Development (R03HD079508) to RME. The authors thank Andrew Jones, Benjamin Barry, Inge
Zijdewind, Katrina Maluf, and Robyn Capobianco for comments on a draft of the manuscript.
The authors declare no conflicts of interest. Understandably, ACSM is unable to endorse either
the data or its interpretation as presented in this article.
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1. The physiological processes that can contribute to fatigue are classically categorized into two
domains, those that establish the level of muscle activation (central) and those that influence
contractile function (peripheral). Reprinted from Enoka (16).
2. The proposed taxonomy suggests that fatigue be defined as a self-reported disabling symptom
derived from two interdependent attributes: perceived fatigability and performance
fatigability. Each of the attributes has two domains that themselves depend on the status of
4-7 modulating factors. The list of modulating factors is not intended to be all-inclusive, but
rather it provides an initial set that can be expanded as new experimental findings emerge.
By defining fatigue in terms of fatigability, the level of fatigue reported by an individual
depends on the rates of change in the two attributes and thereby normalizes it to the demands
of the task being performed. Adapted from Kluger et al. (37).
3. Normative data (mean ± SE) for the distance walked by individuals ranging in age from 3 yrs
to 85 yrs. There were approximately 200 participants in each age group: 3, 4, 5,…16, 17, 18-
29, 40-49, 50-59, 60, 69, 70-85 yrs. Note that the distance walked by the 5-yr group was
similar to that for the oldest group (70-85 yrs). Data from Kallen et al. (31).
4. Associations between observed and predicted 500-m walk times for old (A) and young (B)
adults. The relations were derived from a multiple-regression analysis of the data reported
by Justice et al. (30).
5. Average power (% initial) produced by 8 trained cyclists during a ~30-min time trial
performed on three occasions. On each occasion, the participant was tested after oral
ingestion of either placebo or one of two reuptake inhibitors (dopamine or noradrenaline).
The goal on each occasion was to perform the prescribed amount of work as quickly as
possible. *P < 0.05 relative to the initial level of power production. Data from Klass et al.