Materials and Methods
A comprehensive review of the science literature was conducted to assess the latest
approaches in evaluating skeletal muscle health. The National Institutes for Health
National Library of Medicine (PubMed.gov) and Google Scholar search engines were
utilized, specifically, to identify recent publications related to dynamic imaging
of skeletal muscle, peripheral neuromuscular assessments, muscle echogenicity, blood
flow restriction, biomarkers of skeletal muscle function, skeletal muscle quality,
body composition, and pain assessment.
Dynamic imaging
Muscle function and characteristics can be measured in several ways and many
laboratory- and hospital-based studies utilize EMG and imaging instrumentation,
such as magnetic resonance imaging. Financial and time constraints can limit
access and the ability to employ many of these techniques when clinicians and
practitioners are seeking a deeper understanding of muscle function. A viable
alternative to some of the more expensive and less accessible options is
ultrasound imaging ([Fig. 1]) [8]. Specifically for musculoskeletal ultrasound
imaging, there are established methods detailing how to measure muscle
thickness, cross-sectional area, echogenicity, etc. [9]. Most of these methods rely on the patient or participant to be in
a static, rested state, which controls the environment with hopes to avoid any
artifact or false representation of the muscle’s morphology. However, many of
the patient populations included in ultrasound imaging studies and clinical
scenarios are likely to experience some sort of injury and/or exhibit
symptomology that revolves around pain during movement.
Fig. 1 Dynamic imaging (B-mode) of lateral abdominal wall
musculature using an elastic belt to keep linear transducer fixed to the
abdomen throughout movement. Created with BioRender.com. [rerif]
Ultrasound imaging when used in a dynamic, innovative manner, capturing images
during contraction and even exercise, provides an understanding of muscle
function not just static characteristics [8].
When a patient or participant is experiencing pain and dysfunction during
movement, activities of daily living, or exercise, the consequences are well
documented, especially in those with chronic low back pain. Due to the known
contribution of abdominal, hip, and pelvic muscles to low back pain-related
dysfunction, ultrasound imaging is a useful tool to view the complex layering of
those muscles [8]. Part of the injury assessment
process could include viewing muscles when individuals move, and they experience
pain or avoid movement due to the fear of pain. Capturing muscle thickness,
cross-sectional area, and quality can be done reliably in many positions [9]
[10], commonly in
the lateral abdominal wall and posterolateral hip. The application of dynamic
ultrasound imaging has been described recently for sport-specific and body
part-focused rehabilitation exercises [11]
[12] . Visualizing the musculature of the lateral
abdominal wall is possible by fixing the ultrasound probe to the anterolateral
abdomen with a belt ensuring the probe stays in the same position even while the
individual moves. Documented methods describing this technique include capturing
images and videos while walking, balancing, squatting, planking, swinging a golf
club, etc. [11]
[12]
[13]
[14]. Populations included in these studies span from healthy,
asymptomatic individuals to those experiencing low back pain and chronic ankle
instability.
The utility of dynamic ultrasound imaging is shown by recent studies that have
reported muscle thickness changes from a static, rested position to an active,
contracted position. Activation ratios may also be calculated by dividing the
contracted thickness by the rested thickness and were first established in
hook-lying tabletop positions. Functional activation ratios divide thickness
during exercise (e. g., peak knee flexion during a single leg squat) by a
static, starting position (e. g., standing). A preferential activation ratio
[15] involves comparing the thickness of one
muscle to others within the same image. For example, when imaging the lateral
abdominal wall, the change in thickness of the transverse abdominis could be
divided by the change in thickness of the entire lateral abdominal wall ([Fig. 1]). This preferential ratio provides
insight into how much the transverse abdominis changes its thickness relative to
the other muscles during contraction. A greater preferential activation ratio
indicates that the transverse abdominis is the predominant muscle changing
thickness out of the entire lateral abdominal wall. Dynamic ultrasound imaging
also allows for an innovative, clinical approach to visual biofeedback. As the
patient visualizes their muscles during a prescribed exercise, activity of daily
living, or pain-provoking position, there is an opportunity to show the patient
how they can contract the muscle either at a different time or how to increase
thickness in general. Methods have been established for sport-specific
ultrasound biofeedback, specifically viewing the obliques during a golf swing
[12]. Brightness, B-mode, and Motion, M-mode
can be used for dynamic imaging and biofeedback. Based on the goal of dynamic
imaging, B-mode may apply when viewing a reference, static image or to view a
frame-by-frame breakdown of a muscle moving through a task. M-mode may be
appropriate when synchronizing dynamic ultrasound with other muscle measurement
tools capturing in the time domain, such as timing of activation with
electromyography [11].
Fig. 2 Muscle belly displacement versus time the phases of
contraction elicited by TMG with key parameters comprise displacement
(Dm), contraction time (Tc), delay time (Td), contraction velocity (Vc)
(Vc=[90%Dm-10%Dm/Tc]), sustain time (Ts), and half-relaxation time (Tr).
(Top). An example of (TMG) placement for the lumbar erector spinae
(bottom).
Contractile properties
Tensiomyography (TMG) is a non-invasive and objective assessment tool used to
evaluate skeletal muscle contractile properties and peripheral neuromuscular
function. It provides valuable information about muscle contraction
characteristics, muscle fiber composition, and muscle fatigue. TMG has gained
popularity in sports science, rehabilitation, and research settings due to its
ability to provide real-time and reliable data on muscle function [16]
[17]
[18]. TMG involves the application of an external
electrical stimulus to the muscle belly, causing a muscle twitch response that
is measured through a displacement sensor tip ([Fig.
2]). It primarily assesses muscle contractile properties, including
muscle displacement, contraction time, and muscle relaxation time. These
parameters reflect muscle stiffness, contractile speed, and muscle fiber
recruitment patterns [19]
[20]. One of the key advantages of TMG is its
ability to provide objective and quantitative data. Traditional assessment
methods, such as manual muscle testing or electromyography, often rely on more
subjective, post-acquisition interpretation or qualitative measures. TMG, on the
other hand, offers standardized numerical values and objective measurements,
reducing the potential for human error and enhancing the reliability of the
assessment [20]
[21]. By tracking changes in muscle contractile properties, one can
evaluate the effectiveness of training programs and make adjustments
accordingly. TMG can also help identify muscle imbalances and guide targeted
interventions to restore balance and optimize performance.
TMG involves elicited, involuntary isometric contractions that are generated
using a single 1-ms wide biphasic wave. By utilizing proprietary computer
software, a twitch curve is generated based on data from the sensor, which
allows for the determination of six primary parameters. The y-axis of this curve
represents muscle displacement in millimeters, while the x-axis represents time
in milliseconds. The key TMG parameters comprise displacement (Dm), contraction
time (Tc), delay time (Td), contraction velocity (Vc) (Vc=[90%Dm–10%Dm/Tc]),
sustain time (Ts), and half-relaxation time (Tr). Displacement (Dm) pertains to
the highest radial displacement achieved by the muscle and is linked to muscle
stiffness. Contraction time (Tc) represents the duration between 10% and 90% of
Dm on the positive slope of the twitch curve. Delay time (Td) is a temporal
parameter that measures the duration from the initiation of the electrical
stimulus to when the muscle belly reaches 10% of Dm or peak displacement.
Half-relaxation time (Tr) refers to the time taken for the muscle displacement
to decrease from 90% of its maximum to 50% of Dm on the negative slope of the
curve. Sustain time (Ts) is defined as the time between 50% Dm on both the
negative and positive slopes of the curve. Contraction velocity (Vc) is a
calculated metric that aims to quantify the rate of muscular contraction. Since
Vc is a derived measure, authors have employed various methods to compute it.
The most commonly used calculation involves dividing the change in Dm between
10% and 90% by Tc. This approach enhances the usefulness of the Tc parameter and
provides a more reliable measure of contraction speed by mitigating the
influence of Dm. This is important as peak radial displacement values have been
shown to impact contraction time values due to the inherent shape of the twitch
curve [20]
[21].
TMG can also help identify specific muscle deficits or imbalances that may
contribute to functional limitations or recurring injuries. With this
information, tailored rehabilitation programs can be developed to address these
deficits and promote optimal recovery [18]
[21]. Moreover, TMG can be a valuable research tool
for investigating muscle adaptations to training, comparing different training
protocols, or studying the effects of injury or disease on muscle function.
Researchers can use TMG to examine changes in muscle contractile properties
following specific interventions or to explore how different training modalities
affect muscle performance.
While TMG has shown great promise as an assessment tool, it is important to note
its limitations. TMG primarily focuses on muscle contractile properties and does
not provide direct information about neural activation or muscle force
production. It is also important to consider that TMG measurements may be
influenced by factors such as skin impedance, adipose tissue thickness, and
anatomical variations. These factors need to be considered during data
interpretation to ensure accurate and meaningful results.
Innovation applications to determine blood flow restriction occlusion
pressures
Traditional exercise-based approaches are not well tolerated by several
clinical populations, an issue researchers/practitioners continually attempt
to circumvent [22]
[23]
[24] . An exciting strategy to
address this issue includes combining exercise with limb occlusion (blood
flow restriction [BFR]) due to its ability to induce similar or even
superior benefits compared to traditional non-occluded exercise [25]
[26]
[27]. During BFR exercise, low exercise loads
(e. g., lighter weights/resistance) are used while completing a standardized
5-min exercise scheme. Blood flow restriction exercise uses a small
inflatable cuff applied to the uppermost portion of a limb to restrict
venous blood from exiting the exercising limb, which facilitates robust
physiological responses that may underly the subsequent increases in muscle
strength, mass, and endurance (i. e., fatigue resistance) observed following
chronic BFR exercise. These adaptations have been demonstrated in
asymptomatic [22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31] and some symptomatic populations [32]
[33]
[34]. Importantly, BFR resistance exercise has also been shown to
be safe and to elicit positive effects in post-surgical [35]
[36] older
adults [32]
[37]
[38] and hospitalized patients
[39]. Specifically, the safety and
effectiveness of BFR exercise has been routinely demonstrated with the
implementation of the standard 75 repetition (1×30, 3×15) scheme [28]
[40].
Additionally, the exercise is performed at 30% of 1RM (i. e., a low
weight/resistance) and with the small, inflated cuff set to a pressure
corresponding to 40–80% of arterial occlusion pressure (i. e., approximately
40–80% of systolic blood pressure) [41]
[42].
Despite its high versatility and implementation in both research, clinical,
and athletic settings, the application of BFR is limited. Specifically, the
gold standard for the determination of total arterial occlusion pressure
(TAOP) and application of BFR requires the use of pulsed wave Doppler ([Fig. 3]). Typically, this necessitates
trained personnel to operate and interpret ultrasound-based readings (i. e.,
arterial and venous blood flow) with the concomitant modulation of pneumatic
pressures. Theoretically, minute variations in the determination of TAOP
changes the BFR pressure applied which has been demonstrated to alter the
physiological responses [41]
[43] and discomfort (e. g., higher pressures
result in greater discomfort). Therefore, it is imperative that TAOP is
determined accurately and precisely. As a result, the current applications
of BFR exercise are somewhat limited to research and clinical settings
whereby these procedures can be performed accurately and reliably, although
data are limited in this regard. For example, among a small sample of males
(n=13), pulsed wave Doppler ultrasound exhibited moderate/high reliability
(intraclass correlation coefficient [ICC]=0.796), while the coefficient of
variation (COV) was 5.6% (Bezerra et al., 2017). There were, however, no
relative measures of reliability reported (e. g., standard error of the
measurement [SEM] or minimal difference [MD]), which limits the application
of these findings [44].
Fig. 3 Illustrates the gold standard for the determination of
TAOP, which requires at least one technician. More recently,
innovative approaches have been developed that leverage proprietary
algorithms to determine TAOP and do not require a trained
technician(s). These devices can often be controlled wirelessly from
a mobile app and detached from the air source facilitating greater
utility in exercise and rehabilitation settings. Created with
BioRender.com. [rerif]
An exciting strategy to circumvent the determination of TAOP is the
algorithm-based determination of TAOP. Specifically, several commercially
available devices independently estimate TAOP using proprietary engineering
that likely leverages known variables (e. g., limb width, mean arterial
pressure) that affect TAOP. For example, in a small sample (n=10), one study
[45] examined test-retest reliability of
an algorithm-based determination of TAOP and reported high reliability
(ICC>0.953; COV=2.97%), but the validity of this device was not examined.
Algorithm-based devices such as the one previously examined and others may
exhibit greater utility than current research-based practices for the
determination of TAOP that requires ultrasound and trained personnel, but
their algorithms should be critically examined to prevent potential adverse
effects. Specifically, researchers [42]
[46]
[47] have
done substantial and meaningful work identifying predictors, equations,
and/or algorithms that can estimate TAOP, but these methods are not without
error. Thus, there is an inherent need to determine if commercially
available BFR devices implementing algorithms to determine TAOP are
clinically sound (i. e., reliability and validity). Furthermore, there is
insufficient data examining the reliability of TAOP using pulsed wave
Doppler (the criterion method). Regardless, commercially available devices
may provide viable alternatives to the criterion determination of TAOP
ultimately facilitating the larger implementation of BFR.
Novel serological neoepitopes as biomarkers of skeletal muscle structure
and function
Biomarkers are defined as measurable indicators of biological processes or
responses to an exposure or intervention [48]
. They represent an indispensable tool in human biology that allows
researchers to map the complex physiological pathways that underpin healthy
and altered physiological function [49] . The
use of serological biomarkers has the benefit of being relatively
non-invasive and easy to perform [50]
[51], which has led to the widespread
development of assays for various biological markers. In this regard,
neoepitopes have emerged as a class of serological peptide biomarkers with
diagnostic and prognostic potential that may also have utility as minimally
invasive indicators of health status, physiological response, and/or disease
susceptibility ([Fig. 4]).
Fig. 4 Detection of serological peptide fragments. 1.
Proteolytic cleavage of collagen protein 2. Peptide fragments enter
blood stream, 3. Blood sample obtained, 4. Antibodies raised against
neoepitope markers, 5. Quantification vis assay e. g., flow
cytometry. Created with BioRender.com. [rerif]
In skeletal muscle, neoepitopes may be exposed following post-translational
modifications (PTMs) to specific muscle proteins [52]. Proteolytic cleavage is a particularly interesting PTM as it
creates peptide fragments and the potential for novel neoepitopes on the
carboxy- or aminoterminal ends of cleaved peptides [53]
[54]. Being smaller than their
intact parent proteins, peptide fragments can enter the circulation more
readily [55] where antibodies can be raised
against specific neoepitopes exposed on these fragments. Since serological
neoepitope biomarkers consist of a unique combination of parent proteins and
PTMs [56], they are thought to be reflective
of tissue specific physiological or pathological remodeling processes rather
than overall muscle size or quality and may therefore serve as ideal
biomarkers for early the detection of various myopathies [57]
[58] . It is
also thought that serological neoepitope biomarkers have the potential to
provide insight into net changes in protein metabolism, which is currently
limited to operationally complex and invasive stable isotope techniques
[58].
Much of the research examining neoepitopes in skeletal muscle has focused on
temporal changes to extracellular matrix (ECM) collagens following various
interventions. Nedergaard et al. (2013) examined changes in collagen type VI
fragment degraded by matrix metalloproteinases 2 and 9 (C6M) and type VI
collagen N-terminal globular domain epitope (IC6) among young and old men at
baseline after 2 weeks of unilateral immobilization and 4 weeks of
remobilization with thrice weekly resistance training, respectively [59]. They found significant correlations
between IC6 and muscle mass at baseline, and between C6M and the change in
muscle mass from 2 weeks to 4 weeks of remobilization in young but not old
men [59]. The same group also reported
significant associations between lean body mass, IC6, collagen type III
synthesis (Pro-C3) and the IC6/C6M ratio among matched controls in the 25B
cohort of the Danish Head and Neck Cancer Group (DAHANCA) trial [60]. In another study, Sun et al. (2015)
examined the temporal profile of neoepitope peptides Pro-C3, C-terminus
α3(VI) chain (Pro-C6) and C6M following 8 weeks immobilization and
remobilization. They reported significant associations between Pro-C3, C6M
and lean body mass at baseline, a significant upregulation of both Pro-C3
and Pro-C6 following immobilization and remobilization indicative of muscle
remodeling, and an inverse relationship between Pro-C6 and changes in muscle
mass [61]. Consistent with this, work by
Nielsen et al. (2013) also indicates that higher levels of Pro-C3 predict
greater muscle mass in healthy individuals [62]. More recently, Reule et al., 2016 found that the ratio of
type II collagen collagenase cleavage neoepitope (C2C) to C propeptide of
type II procollagen (CP2) was responsive to leucine-rich amino acid
supplementation administered in conjunction with 12-weeks of combined
aerobic strength and balance training [63].
They report a significantly greater decrease in the acute phase (0–3 hours)
C2C/CP2 post-training response to a downhill walking stress test when
compared to the placebo group, indicating a lower disturbance in joint
homeostasis that coincided with a significant attenuation of acute phase
quadricep MVC strength loss [63]. Several
other serological biomarkers, including C-terminal agrin fragment (CAF) and
matrix metalloproteinase-2 degraded titin fragment (titin-MMP2), also appear
to be strong discriminators of normal versus aberrant skeletal muscle
outcomes including muscle wasting/atrophy and protein turnover [61]
[64].
The large dynamic range and complexity of both the proteome and various PTMs
[65] represents a significant challenge
for the identification of new muscle-specific neoepitope biomarkers and
targeting reagents. Nevertheless, while this area of research is currently
in its infancy, the limited current literature indicates that serological
neoepitope biomarkers of cleaved circulating peptide fragments are promising
candidates for assessing skeletal muscle structure and function.
Skeletal muscle quality
Studies have increasingly shown that there is a disassociation between skeletal
muscle strength and mass [66]
[67]. For example, the National Institutes on
Aging’s longitudinal Health, Aging, and Body Composition study showed that
adults≥70 years of age lost ~3×more muscle strength than mass on an annual basis
[66]. Even more compelling, older adults that
gained muscle mass still lost muscle strength [68]. In addition, immobilization and bed rest studies have shown that
muscle strength and mass show divergent timelines, with strength rapidly
diminishing before detectable declines in muscle mass [69]
[70]
[71]. These concepts have given rise to the concept of muscle quality
and its methods of assessment [72].
The measurement of echo intensity has emerged as a potential tool for studying
skeletal muscle quality and estimating intramuscular fat content, providing
insights that may be unique from measures of muscle size [73]. Echo intensity is a quantitative measure of
brightness in ultrasound images, reflecting the echogenicity of tissues ([Fig. 5]). In skeletal muscle, echo intensity is
influenced by physiological factors such as muscle fiber arrangement [74], connective tissue content [75], and the presence of intramuscular fat [76]. Higher echo intensity values are associated
with muscle pathologies like atrophy, fibrosis, and fatty infiltration [77], while lower values are observed in young,
healthy muscles. MRI studies suggest that echo intensity’s ability to estimate
intramuscular fat content appears promising [77]
[78]. Echo intensity is associated
with several functional outcomes [79]
[80] and it appears to be sensitive in detecting
differences between age groups [81]. Additional
research is needed to understand why large differences in echo intensity are
often seen when comparing groups with distinct characteristics like age or
training status, whereas smaller changes are detected in response to exercise or
rehabilitation interventions [82]. One potential
explanation is that intrinsic physiological factors like muscle fiber type
distribution, connective tissue content, and intramuscular fat infiltration
change slowly over time. Group differences may represent the cumulative result
of prolonged exposure to factors like aging. In contrast, short-term
interventions elicit more modest echo intensity changes, as muscle structural
characteristics do not radically transform within days or weeks. Longer training
studies are needed to determine if more marked echo intensity changes can be
induced over time with sustained exposure to stimuli like exercise. It is also
important to recognize that echo intensity is affected by methodological
factors, such as probe tilt [83] and participant
positioning [84]. Recent evidence suggests that
researchers new to echo intensity analyses provide reliable measurements [85] and small adjustments in image depth to
accommodate muscles of different sizes are acceptable [86].
Fig. 5 Example B-mode ultrasound images and echo intensity (EI)
analyses of the vastus lateralis for an older (top) and younger (bottom)
male. Note the vastly different pixel distributions for the two images.
These images are a fairly accurate depiction of published findings, as
many studies have reported higher echo intensity among older adults.
Most published echo intensity studies have relied on ImageJ software (National
Institutes of Health, Bethesda, MA, USA), in which investigators manually
analyze a muscle’s region of interest. While these approaches are well
established, they can be time-consuming and subjective, making it difficult to
conduct large-scale analyses quickly. However, emerging technologies are likely
to streamline the measurement process, reduce subjectivity, and enhance the
accuracy of echo intensity analysis. Automated or semi-automated region of
interest selection algorithms have recently been introduced to target specific
muscle regions [87]
[88]. In the future, computerized analysis of ultrasound images will
enable the precise quantification of echo intensity values, ensuring consistent
and reproducible evaluations. In addition, small probes and wireless technology
that integrate with laptops, tablets, and smartphones will likely make the
analysis of echo intensity much more accessible and rapid.
Overall, the measurement of echo intensity has emerged as an innovative approach
for studying skeletal muscle quality and estimating intramuscular fat content.
Importantly, these measurements can be done with ultrasound devices that are
portable and less expensive than MRI, and minimal training is required. These
measurements are particularly useful when complementing measures of muscle size
(e. g., cross-sectional area or volume) and physical function. For more detailed
information, the reader is encouraged to review two recent echo intensity
reviews by Stock and Thompson [73] and Wong et
al. [82].
Practical body composition & novel use of bioelectrical impedance
analysis (BIA)
Laboratory-based methods of body composition estimation potentially offer an
error reduction of approximately 50% in body fat estimation compared to
field-based methods. However, these improvements are partially attributable
to the ability to control numerous physiological assumptions [89]. The inherent error of body composition
measurement is further complicated using either population-specific or
generalized prediction equations that likely compound the error in some
individuals or groups. For athletes, the need for dietary restriction,
adequate hydration, and standardized physical activity further exacerbates
the issue.
Whole body estimation of body fat percentage is limited to its greatest
utility in large population/epidemiological evaluations or public health
settings. Site- or region-specific values to evaluate tissue distribution
may be more useful for practitioners and athletes. Dual-energy X-ray
absorptiometry enables potential evaluation of total body and site-specific
fat mass, lean soft tissue mass, and bone mineral density [89] . However, these devices are expensive,
vary across manufacturers, and require low-dose radiation exposure, all of
which may be prohibitive in many practical settings.
A viable alternative is the direct evaluation of skinfold thickness either at
a specific site or as a sum of several sites throughout the body, instead of
using prediction equations to determine body density. While standardized
protocols and trained evaluators are required, this method seems least
affected by the inherent variation in the typical restrictions recommended
for body composition assessments [90].
Although less commonly reported in the literature, recent attempts have been
made to provide normative data for the summation of skinfold measurements
[90]
[91].
As skinfold thickness does not directly assess skeletal muscle, it is
recommended to evaluate this information alongside anthropometric data such
as regional circumferences (limb, hip/waist, etc.) and/or body mass [92]
[93]. [Fig. 6] illustrates how changes in skinfold
thickness can be translated to body fat, while circumferences or body mass
can serve as a proxy for muscle mass and the interaction between these
values.
Fig. 6 Changes in site-specific skinfold thickness and
circumference (or body mass) as potential proxies of body fat and
muscle mass, respectively. The examples provided indicate trends
toward and away from a) muscle growth/hypertrophy, b)
adiposity, c) muscle growth/hypertrophy with leanness, and
d) muscle growth/hypertrophy with adiposity. Created with
BioRender.com. [rerif]
This combined information can also be used to calculate corrected girth
values (which account for an adjustment of skinfold thickness to determine
musculoskeletal cross-sectional area) or lean mass index (a log-based
adjustment for body mass and summed skinfolds) [94] . Notably, DeFreitas et al. [95] demonstrated that, despite underestimating muscle
cross-sectional area compared to peripheral quantitative computed tomography
(pQCT), both a corrected girth equation [96]
and a regression equation [97] adequately
tracked changes in this value during an eight-week resistance training
program. A recent review by Duarte et al. [98] discusses numerous validated anthropometric equations for
limb-specific muscle mass estimation.
BIA offers a unique approach for body composition assessment by estimating
the fat-free body, generally assumed to be ~73% hydrated, rather than fat
mass [89]. However, the basic assumptions of
body shape (i. e., the segments of the body are perfect cylinders), as well
as the use of prediction equations (either through published work or
developed by the device manufacturer), introduce similar problems to other
measurement methods. Therefore, it is becoming increasingly common to
directly evaluate the raw bioelectric parameters [resistance (R), reactance
(Xc), and phase angle (PhA)] recorded by these devices (typically at
50 kHz).
From a practical perspective, R may represent cellular hydration, Xc may
represent cell membrane integrity, and PhA is calculated as the arctangent
ratio of Xc to R. The latter variable may be considered representative of
intra- and extra-cellular fluid (or ICW/ECW ratio) and/or cell body mass,
which has been suggested as an indicator of cellular health. These values
can be considered separately or plotted together through bioelectrical
impedance vector analysis (BIVA). The resultant vectors have been shown to
differentiate between competitive levels and types of athletes [99]. Interestingly, Kim et al. [100] demonstrated the discriminative potential
of BIVA by distinguishing between female fashion models, dancers, and
gymnasts in a manner similar to somatotyping with increasing mesomorphy
(i. e., muscularity) and decreasing ectomorphy (i. e., linearity) across
these groups. Furthermore, a recent systematic review reported that PhA
increases with age and is higher in athletes than controls as well as in
males than females [101].
The evaluation of raw bioelectrical data can also be applied regionally, a
process known as electrical impedance myography or localized BIA. Initially
developed to examine diseased tissue and subsequently sarcopenic
individuals, its application in athletic populations has been established,
focusing on adaptations to exercise and return-to-play situations following
injury [102].
In adopting an “innovation through simplification” stance towards body
composition assessment, this section of the paper underscores the importance
of understanding and contextually applying the available methodologies. For
a comprehensive overview of available methods and a practical
decision-making tree, readers are encouraged to consult Kasper et al. [90]. Further, a detailed discussion on related
topics is provided by Lukaski & Raymond-Pope [99].
Skeletal muscle pain
Pain is a sensory and emotional experience impacted by the interaction of
biological and psychosocial factors [103]. One
biologic factor that impacts the perception of pain is skeletal muscle health.
In a state of inflammation or musculoskeletal pathology, myofascial trigger
points may develop within the tissue that contribute to a myofascial pain
syndrome [104]. Myofascial pain is prevalent with
30% of individuals seeking care for pain at a primary care office meeting the
criteria for myofascial pain. While the definition of a trigger point can vary,
there is general agreement among clinicians and scientists that myofascial pain
is a separate diagnosis from fibromyalgia [105]
and that trigger points contribute to myofascial pain syndrome [106].
Myofascial trigger points are localized, taut bands of skeletal muscle tissue
([Fig. 7]). A recent Delphi survey
established that two of the following three criteria should be present to
diagnose a myofascial trigger point: taut band, hypersensitive spot, and
referred pain [107]. During direct compression, a
“jump response” may be elicited with or without referred pain [106]. Myofascial trigger points are often both
painful to palpation and can be a generator of pain. While the mechanisms
underlying trigger points are multifactorial, repetitive eccentric contractions
or overuse may result in an abnormal increase in acetylcholine at the
neuromuscular junction of the muscle. Abnormal acetylcholine release may
generate a sustained muscle contraction, causing localized ischemia and a
palpable taut band [108]. Although myofascial
pain was traditionally thought to involve only peripheral changes, muscle
sensitization is also impacted by central nervous system sensitization [109].
Fig. 7 Overview of methods to identify a myofascial trigger point.
Myofascial trigger points may be identified with palpation or novel
imaging techniques. Although not comprehensive, novel imaging techniques
may include ultrasound or magnetic resonance imaging (MRI). Created with
BioRender.com. [rerif]
A diagnosis of myofascial pain syndrome relies on the palpation of myofascial
trigger points. However, a limitation in the clinical assessment is varied
inter-rater reliability in myofascial trigger point identification [110]. New approaches have been proposed to improve
the identification of trigger points, including pressure algometry and imaging.
Pressure pain threshold (PPT) is a cost-effective and clinically feasible
technique that may be employed to assess trigger points. During PPT, a device
with a small rubber tip (algometer) is applied over the muscle with an ascending
intensity until the individual reports that the sensation changed from
“comfortable pressure to slightly unpleasant pain” (pain threshold). The benefit
of this assessment is that the stimulus is quantifiable and, therefore, the
threshold for pain perception in response to pressure is measured. PPT is
significantly lower over trigger points and increases in areas without trigger
points [111]. Excellent intra and inter-rater
reliability is demonstrated for PPT application over trigger points [112].
Recent advances have also allowed for imaging of trigger points [113]. Imaging methods, including ultrasound,
magnetic resonance imaging, and infrared thermography have been developed as
objective measures to potentially address the limitations with reliability.
Imaging allows for objective characterization of the tissue consistent with the
definition of a trigger point. B-mode ultrasound imaging indicates trigger
points present as spherical, hypoechoic regions [56]. Ultrasound elastography indicates decreased vibration amplitudes
within the region, indicative of localized stiffness of the muscle [114] at the site of the trigger point. Blood flow
to myofascial trigger points is also distinct from healthy tissue [114]. Magnetic resonance imaging has been used to
examine trigger points; however, the evidence remains unclear on the benefit of
this imaging modality [115] for this purpose.
Trigger points are important contributors to myofascial pain and relevant to
clinical treatment of patients with myofascial pain. Innovations in
standardizing the definition of trigger points, along with advances in the
imaging of muscle, may help to make this phenomenon more objective.
