Advances in Skeletal Muscle Function · PDF file2 Advances in Skeletal Muscle Function...

Volume 1 | Issue 2 | June 2017

Advances in Skeletal Muscle Function Assessment

ISSN 2536-1392 (Online)

Official Journal of the International Society of Tensiomyography

ADVANCES IN SKELETAL MUSCLE FUNCTION ASSESSMENTVOLUME 1, ISSUE 2

EDITORIAL

Swarup Mukherjee

CONTRACTILE RATE OF MUSCLE DISPLACEMENT ESTIMATED FROM THE SLOPE OF THE DISPLACEMENT-TIME CURVE USING TENSIOMYOGRAPHY

Article type: Original researchHannah V. Wilson, Mark I. Johnson, Peter Francis

ASSESSMENT OF SKELETAL MUSCLE ENDURANCE USING TWITCH ELECTRICAL STIMULATION AND ACCELEROMETER-BASED MECHANOMYOGRAPHY

Article type: Original researchKevin K. McCully, Thomas B. Willingham

THE USE OF TENSIOMYOGRAPHY TO EVALUATE NEUROMUSCULAR PROFILE AND LATERAL SYMMETRY IN COMPETITIVE FEMALE SURFERS

Article type: Original researchHelen J. Gravestock, Matthew J. Barlow

INFLUENCE OF JOINT ANGLE AND BICEPS BRACHII ISOMETRIC CONTRACTION INTENSITY ON ELECTROMYOGRAPHIC AND MECHANOMYOGRAPHIC RESPONSES

Article type: Original researchSwapan Mookerjee, Matthew J. McMahon, Sam Meske

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Advances in Skeletal Muscle Function Assessment, Volume 1, Issue 22

EDITORIAL

‘Two down and a lot to go’. The second issue wraps-up the first volume of the journal. It has been a tremen-

dously rewarding year for the ASMFA editorial team and we are only getting better with time. New plans and

partnerships are being worked out for an aggressive growth of the journal and to provide the authors with

greater scholarly incentives to publish in the journal.

The second issue further builds-up on the first with high-quality articles on muscle function assessment from

both young and accomplished researchers. The teams from Centre for Pain Research and Musculoskeletal

Health Research Group, both from the Leeds Beckett University UK provide evidence on the most valid es-

timate of measuring the greatest muscle displacement using tensiomyography. The second article by the

researchers from the Department of Kinesiology, University of Georgia USA evaluate the reproducibility and

validity of twitch electrical stimulation and accelerometer-based mechanomyography for the assessment of

skeletal muscle endurance. The third article from researchers in Leeds Beckett University UK report the con-

tractile properties and muscle stiffness to determine the lateral muscle function symmetry using tensiomyo-

graphy in competitive female surfers. The fourth article by the researchers from the Department of Exercise

Science, Bloomsburg University of Pennsylvania USA report the influence of joint angle and biceps brachii iso-

metric contraction intensity on electromyographic and mechanomyographic using linear slope coefficients.

Hope you enjoy the issue and find it purposeful in your work.

Dr Swarup Mukherjee, MD, PhD

Editor-in-Chief


ABSTRACT

Tensiomyography (TMG) can estimate the intrinsic contractile potential of a muscle using data between 10 and 90% of the displacement-time curve. However, it is yet to be determined whether this data represents the greatest rate of displacement i.e. the most valid estimate of the maximal shortening velocity of a muscle. The aim of this secondary analysis of data gathered from 10 participants who had maximal displacement (Dm) of the rectus femoris assessed using TMG, was to compare the rate of displacement using data from 0 – 100% of Dm; 10 – 90% of Dm and the most linear phase of the displacement-time curve. One-way analysis of variance (ANOVA) indicated that rate of displacement increased as data bands narrowed towards the most linear phase of the displacement-time curve (P<0.001). Rate of displacement explained the greatest proportion of variance in total Tc when estimated from the linear phase (R2=0.601; P=0.008). Rate of displacement estimated from data points between 10 – 90% of Dm had a strong association with rate of displacement estimated from the linear phase (r=0.996; P<0.001). The most valid estimate of maximal rate of displacement comes from the linear phase of the displacement-time curve.

KEY WORDS: Muscle function; Contractile rate of force development; Tensiomyography

CONTRACTILE RATE OF MUSCLE DISPLACEMENT ESTIMATED FROM THE SLOPE OF THE DISPLACEMENT-TIME CURVE USING TENSIOMYOGRAPHY

* Corresponding author at:Hannah V. Wilson, School of Clinical and Applied Sciences, Leeds Beckett University, City Campus, Leeds LS1 3HE, United Kingdom, Telephone: +44 113 2063375, Fax: 0113 2063314., E-mail: [email protected]

Hannah V. Wilson, MSc 1, 2*, Mark I. Johnson, PhD 1, Peter Francis, PhD 2

1. Centre for Pain Research, School of Clinical and Applied Sciences, Leeds Beckett University2. Musculoskeletal Health Research Group, School of Clinical and Applied Sciences, Leeds Beckett University


INTRODUCTION

Tensiomyography (TMG) is a non-invasive method for meas-uring skeletal muscle contractile properties [1]. A pulsed electrical current is delivered via two electrodes applied to the surface of the skin to evoke a phasic contraction of un-derlying skeletal muscle (Figure 1). The muscle contraction displaces a probe positioned perpendicular to the skin to record muscle displacement, from which maximum displace-ment (Dm) has been described as a surrogate measure of contractile force [2]. A characteristic waveform is produced when displacement is plotted against time from which con-traction time (Tc) can be determined (Figure 2). Contractile rate of force development (RFD) is defined as the slope of the force-time curve and is a representative measure of the rate of force generated by the neuromuscular system [3]. Typi-cally, RFD is obtained from a voluntary isometric contraction or via an involuntary twitch contraction and is normally used to provide information about the intrinsic contractile proper-ties of a muscle which cannot be inferred from maximal force or Dm [4, 5]. RFD is thought to be an important determinant of the maximum force and velocity that can be exerted by an individual during functional tasks such as sprint running or recovering from a postural perturbation to avoid a fall. These contractions require 50 – 200 ms which is consider-ably shorter than the time to reach maximum force (~300ms).

on the assumption that this will encapsulate the linear phase of the contraction, and will exclude the initiation of contrac-tion and the plateau when reaching maximum contraction. A strong association between Tc and the percentage of type 1 muscle fibres (r=0.930), measured using histochemical tech-niques, has been demonstrated [10]. However, it is yet to be determined if the data between 10 – 90% represents the greatest rate of displacement i.e. the most valid estimate of maximal shortening velocity during muscle contraction. Fur-thermore, the effect of widening the data range sampled (0 – 100%) or narrowing the data ranged sampled (the linear phase of contraction) on the rate of displacement has not been investigated. Therefore, the aim of this technical report is to describe a comparison of three estimates of the rate of displacement using data from 0 – 100% of Dm, 10 – 90% of Dm, and the linear phase of the displacement-time curve. The association between the three estimates of rate of displace-ment, Dm and total Tc (0 – 100% Dm) was also determined. We hypothesised that Dm would explain a greater propor-tion of the variance in rate of displacement when sampled from wider time points e.g. 0 – 100%, due to both measures being dependent on factors influencing maximal strength. Conversely, we hypothesised that total Tc would explain a greater proportion of the variance in rate of displacement when sampled from narrower time points i.e. the linear phase of the displacement-time curve.

MATERIALS AND METHODS

This technical report describes a secondary analysis of data that was collected as part of a study that evaluated the ef-fect of kinesiology taping and a no tape control on Dm and Tc. The study used a within-subject repeated measures de-sign, with each participant undertaking one 60 minute ex-periment during which Dm and Tc data were collected be-fore, during and after the application of either kinesiology tape or no kinesiology tape (control, Figure 3). Study partici-pants were a convenience sample of 62 healthy (mean + SD age 23.55 ± 5.60 years; height 169.68 ± 17.03 cm; body mass 69.16 ±13.40 kg) staff and students from Leeds Beck-ett University, recruited via word-of-mouth and posters displayed within the University. Exclusion criteria included: ≤18 years; pregnant; taking medication; those wearing an implantable medical device (i.e. pacemaker); those who do not consider themselves as healthy; have major long-term illness; have lower limb and/or lower back injury; experi-ence disturbances to skin sensation (i.e. numbness, sensi-tivity or tingling) or have a dermatological condition(s) (i.e. dermatitis, eczema, bacterial/ fungal infection or allergy to adhesive plasters). Participants were asked to refrain from participating in vigorous activity 72 hours prior to the labo-ratory visit and to refrain from consuming stimulants (i.e. caffeinated products) or exercising within 12 hours of the laboratory testing session. Participants provided written informed consent and all procedures were performed in accordance with the most recent version of the Declaration of Helsinki. The study was approved by the Research Ethics Committee of Leeds Beckett University.Data used in the secondary analysis described in this technical report was extracted from 10 of the 62 partici-

The TMG system software uses an algorithm to calculate Tc using predetermined data points (10% and 90% of Dm) extracted from the rising waveform [6-9]. The rationale for using 10% and 90% as cut off points is apparently based

Figure 1. TMG set up.

Figure 2. TMG waveform characteristics for contraction time (Tc) and maximum displacement (Dm).


pants (mean + SD, age 22.40 ± 4.53 years; height 173.02 ± 9.51 cm; body mass 70.65 ±13.69 kg). Data was se-lected by allocating each participant a code, noting it on a piece of paper, and selecting 10 of these from a bag containing all 62 participants. If the TMG wave met the criteria for acceptance (see below) it was entered into the secondary analysis. If not, then the data was ‘reject-ed’ and another piece of paper was selected from those remaining in the bag. A total of two data points were re-jected before a sample of 10 was achieved.

Assessment of Muscle Contractile Properties

Participants were positioned quietly resting supine on a plinth to ensure recording took place under static and relaxed muscular conditions (Figure 1). A triangular pad was placed under the dominant knee to maintain the knee joint at an angle of 120˚ (180˚ corresponded to knee full extension). A TMG-S1 stimulator (EMF-Furlan and Co. d.o.o., Ljubljana, Slovenia) was used to deliver a single monophasic 1-ms electrical pulse via two square (5 x 5 cm, Med-Fit) self-adhesive electrodes placed on the skin overlying the rectus femoris. A sensor probe (GK40, Panoptik d.o.o., Ljubljana, Slovenia), positioned perpendicular to the muscle belly recorded muscle dis-placement using a sampling rate of 1 kHz. Probe loca-tion was determined using anatomical landmarks of the greater trochanter and lateral condyle. A horizontal line was marked across the thigh using a dermatological pen, at the midpoint between the two landmarks. Rectus femoris borders were identified manually by the tester via resisting an isometric knee extensor contraction. The sensor was positioned at the midpoint between the bor-ders, along the marked horizontal line, representing the midpoint of the greater trochanter and lateral femoral condyle. The electrodes were positioned 2.5 cm distal and proximal to the probe position, along the vertical axis, creating a 5 cm inter electrode distance.

Criteria for acceptance of a displacement-time curve

Baseline, pre-intervention displacement-time curves were used for this secondary analysis. The criteria for exclu-sion of a displacement-time curve were a) an incomplete wave-form or b) a double peak of which the the second is higher. Examples of accepted and excluded waveforms are shown in Figure 4.

Secondary Analysis

The following data were extracted for each displacement-time curve:

• The slope of the displacement-time curve between 0 – 100%.

• The slope of the displacement-time curve between 10% and 90%

• The slope of the displacement-time curve between the lower and upper bounds of the most linear contractile phase.The process of identifying the most linear phase of the line through visual inspection, involved plotting 0-100% of Dm graphically (Figure 5). The linear phase was identified using the line function on Microsoft Excel. Subsequently, a straight line was drawn and manipulated such that the straight line overlay the maximum number of data values along the con-tractile wave. Contractile rate of displacement was extracted from the slope of the line equation, where ‘m’ represents the rate of development (mm/ms) in the line equation; y = mx + c. A Shapiro-Wilk test was conducted to assess whether vari-ables were normally distributed. Mean and standard devia-tion (SD) values are reported.

Figure 4. Examples of included (accepted) and excluded (disqualified) displacement-time curves. A: Accepted TMG contraction wave; B: Disqualified TMG contraction wave due to the second peak of the displacement-time curve exceeding the first peak; C: Disqualified TMG contraction wave due to the lack of the full wave development; D: Disqualified TMG contraction wave due to the second peak of the displacement-time curve (Dm, mm) exceeding the first peak as well as the lack of the full wave development.

Figure 3. Time-line of experimental procedures used in the study from which data was collected for this secondary analysis.

Figure 5. Process for determining the slope of the displacement-time curve between the lower and upper bounds of the most linear contractile phase. Raw data was extracted and plotted for the upward phase of the maximal contraction wave between 0% and 100% Dm using the X Y scatter plot command in Microsoft Excel. Visual inspection was used to identify the greatest linear phase of the wave and data extracted between the lower and upper boundaries (solid lines). This data was plotted on a separate X-Y scatter and a trend line added and line equation calculated.


Statistical Analysis

Differences between methods were assessed using a one-way analysis of variance. Linear regression was used to determine the proportion of variance in rate of displacement explained by Dm and total Tc. Statistical significance was defined as P < 0.05. Sta-tistical analysis was conducted using IBM SPSS statistical package

Participant code

Dm (mm)

Tc (ms)

Dm/Tc (mm/ms)

Data Range: 0 – 100%.

Rate of displacement development

(mm/ms)

Data Range: 10 – 90%.

Rate of displacement development

(mm/ms)

Data Range: Linear phase, lower bound

to upper bound. Rate of displacement

development (mm/ms)

2 7.81 79 0.10 0.13 0.18 0.19

4 10.65 55 0.19 0.23 0.41 0.46

5 6.93 57 0.12 0.14 0.24 0.28

16 4.02 71 0.06 0.08 0.17 0.21

27 5.34 62 0.09 0.19 0.38 0.43

34 10.58 65 0.16 0.21 0.33 0.38

38 11.35 73 0.16 0.10 0.15 0.18

44 7.31 59 0.12 0.15 0.21 0.28

49 10.96 53 0.21 0.24 0.43 0.48

50 10.67 64 0.17 0.21 0.34 0.38

Table 1. Maximal displacement (Dm), total contraction time (Tc) and rate of Dm calculated from three separate time points (n=10).

for Windows, Version 23.0 (IBM Corp. Released 2015. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp.).

RESULTS

Descriptive statistics are displayed in Table 1.

Rate of displacement was associated with maximal Dm when estimated between 0 – 100% of contraction (Table 2). Rate of displacement increased as the data sampled ap-proached the linear phase of contraction (P <0.001) and is illustrated by box plots in Figure 6. Rate of displacement explained a greater proportion of the variance in total Tc as

Mean ± SD (mm/ms) Dm Tc Rate of discplacement

Linear Phase

r2 (p-value) r2 (p-value) r2 (p-value)

Rate of displacement (0 – 100% Dm) 0.14 ± 0.05 0.851 (<0.001)* 0.285 (0.112) 0.349 (0.072)

Rate of displacement (10 – 90% Dm) 0.28 ± 0.11 0.128 (0.310) 0.525 (0.018) 0.985 (<0.001)

Rate of displacement (Linear Phase) 0.33 ± 0.12 0.108 (0.353) 0.601 (0.008)

Table 2. The association between rate of displacement at different time points, displacement, contraction time and the linear phase of the displacement-time curve. Abbreviations: maximal displacement (Dm), total contraction time (Tc).

the data sampled became closer to the linear phase (Table 2, Figure 7, 8). Rate of displacement estimated from the linear phase was not associated with rate of displacement estimated between 0 – 100% but had a strong association with rate of displacement estimated between 10 – 90% of contraction (Table 2).


DISCUSSION

This secondary analysis estimated rate of displacement us-ing data from 1) the onset of contraction to Dm (0-100%) 2) between 10 and 90% of Dm and 3) from the linear phase of the displacement-time curve. The findings support our hy-pothesis that the rate of displacement was greatest when estimated from the linear phase rather than the 10 – 90% of contraction parameter suggested by TMG manufactur-ers to calculate Tc. Rate of displacement was only associ-ated with Dm when estimated between 0 – 100% of the displacement-time curve which suggests that data within 10 – 90% of contraction is not dependent on physiology associated with Dm. Rate of displacement estimated from the linear phase had the strongest association with total Tc

although this association was similar when estimated be-tween 10 – 90%. Furthermore, while rate of displacement between 0 – 100% was not associated with rate of displace-ment estimated from the linear phase, rate of displacement estimated between 10 – 90% was strongly associated with that of the linear phase.

Our results indicate that investigators who seek to quantify the greatest rate of displacement should use the most lin-ear phase of the displacement-time curve. Depending on the physiological component of muscle contraction under investigation this is a potentially important finding. Rate of displacement is greatest and demonstrates a stronger asso-ciation with total Tc when estimated from the linear phase of contraction compared to that estimated between 10 and 90% of contraction. However, the correlation between meth-ods is excellent (r=0.993, p<0.001) and therefore either may be used to estimate rate of displacement. There are parallels in our estimation of rate of displacement and that of rate of force development (RFD) estimated from an isometric con-traction. RFD becomes increasingly dependent on factors associated with maximal strength as the time from the on-set (wider sampling frame) of contraction increases. In fact, maximal strength can explain 80% of the variance in volun-tary RFD when estimated from later phases (150 – 250 ms) of the contraction (Andersen and Aagard, 2006). Dm in the present report could explain 85% of the variance in rate of displacement when estimated between 0 – 100% but could not explain any of the variance when estimated at intervals between 10 – 90% of the displacement-time curve. Our in-voluntary contractions required <80 ms and the early phase of contractions (<40ms) are highly dependent on the rate of cross-bridge cycling which is highly dependent on the myosin heavy chain composition of a muscle. This may help to ex-plain the lack of association between Dm and rate of displace-ment at time points less than 90% of the displacement-time curve. Furthermore, rate of displacement between 0 – 100% although strongly associated with Dm was not associated with total Tc, or rate of displacement at any point between 10 – 90% of the displacement-time curve. These findings com-bined may suggest that physiological factors associated with Dm (i.e. force) and rate of displacement estimated from the entire contraction may be different from those responsible for total Tc and rate of Dm estimated at different time points between 10 – 90% of contraction.

In conclusion, this secondary analysis provides preliminary evidence that the most valid estimate of rate of displacement comes from the linear phase of the displacement-time curve. Nevertheless, our analysis raises more questions than it an-swers such as:a) What are the alterations that occur in the relationships described above when a greater sample size is used?b) What is the reliability of estimate for rate of displacement at varying time points along the displacement-time curve?c) What is the rate of displacement at specified time points from the onset of contraction e.g. 0 – 10%; 0 – 20% etc.?d) What is the variance in the number of data points used to obtain the linear phase?e) What is the variance around the upper (90%) and lower (10%) boundaries used to estimate Tc?We hope that this technical report catalyses further research in this field.

Figure 8. The association between rate of displacement (0-100%) and Dm.

Figure 6. The increase in rate of displacement approaching the linear phase of the displacement-time curve (Linear P = Linear Phase).

Figure 7. The association between the rates of displacement from the linear phase against total Tc.

Rate

of D

ispl

acem

ent 0

- 10

0% (m

m/m

s)


REFERENCES

1. Valenčič V, Knez N. Measuring of skeletal muscles’ dynamic properties. Artif Organs 1997; 21(3): 240-242.2. Šimunic B, Degens H, Rittweger J, Narici M, Mekjavic IB, Pišot R. Noninvasive estimation of myosin heavy chain composition in human skeletal muscle. Med Sci Sports Exerc 2011; 43 (9): 1619-1625.3. Peñailillo L, Blazevich A, Numazawa H, Nosaka K. Rate of force development as a measure of muscle damage. Scand J Med Sci Sports 2015; 25(3): 417-427.4. Hunter AM, Galloway SD, Smith IJ, Tallent J, Ditroilo M, Fairweather MM, et al. Assessment of eccentric exercise-induced muscle damage of the elbow flexors by tensiomyography. J Electromyogr Kinesiol 2012; 22 (3): 334-341. doi: 10.1016/j.jelekin.2012.01.009.5. Andersen LL, Aagaard P. Influence of maximal muscle strength and intrinsic muscle contractile properties on contractile rate of force development. Eur J Appl Physiol 2006; 96 (1): 46-52.6. Dahmane R, Djordjevič S, Šimunič B, Valenčič V. Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. J Biomech 2005; 38 (12): 2451-2459.7. Ditroilo M, Hunter AM, Haslam S, De Vito G. The effectiveness of two novel techniques in establishing the mechanical and contractile responses of biceps femoris. Physiol Meas 2011; 32 (8): 1315-1326. doi: 10.1088/0967-3334/32/8/020.8. Šimunič B, Križaj D, Narici M, Pišot R. Twitch parameters in transversal and longitudinal biceps brachii response. Ann Kin 2010; 1: 61-80.9. Tous-Fajardo J, Moras G, Rodríguez-Jiménez S, Usach R, Doutres DM, Maffiuletti NA. Inter-rater reliability of muscle contractile property measurements using non- invasive tensiomyography. J Electromyogr Kinesiol 2010; 20 (4): 761-766. doi: 10.1016/j.jelekin.2010.02.008.10. Dahmane R, Valenčič V, Knez N, Eržen I. Evaluation of the ability to make non-invasive estimation of muscle contractile properties on the basis of the muscle belly response. Med Biol Eng Comput 2001; 39 (1): 51-55.11. Shaban J, Wilson HV, Caseley A, Johnson MI, Francis P. The effect of kinesiology taping on rectus femoris muscle belly displacement and contraction time. Sports Therapy Organisation Conference May 2016.


ABSTRACT

Previous studies have used twitch electrical stimulation and accelerometer-based mech-anomyography (aMMG) to evaluate muscle function in clinical populations. However, the reproducibility and validity of the methodology has not been defined. This study evaluat-ed the reproducibility and validity of twitch electrical stimulation and aMMG as an assess-ment of muscle endurance. Participants were healthy males and females 21.8±1.9 years of age. Muscle twitch acceleration was measured using an accelerometer placed over the surface of the muscle. The relationship between acceleration and torque was measured during twitch stimulation of the vastus lateralis muscle. Muscle endurance of the fore-arm and gastrocnemius was measured during 9 minutes of twitch electrical stimulation, in three stages (3min/stage) of increasing frequency (2Hz, 4Hz, and 6Hz). An Endurance Index (EI) was calculated as the percent of acceleration at the end of each stimulation stage rela-tive to the peak acceleration. Oxygen saturation was measured using near-infrared spec-troscopy. Results showed that acceleration correlated with torque during twitch electrical stimulation of the vastus lateralis (mean R2= 0.96±0.04; p<0.05). Measures of forearm EI reproducibility were CV= 2.5-7.4%. EI was significantly higher (12.1%) in the gastrocne-mius compared to the forearm (p<0.01). Muscle oxygen saturation was not reduced dur-ing stimulation of the forearm (72.6±9.8% at 2Hz, 73.2±11.6% at 4Hz, and 71.0±12.5% at 6Hz) compared to resting baseline (74.3±15.1%) (p>0.1). This study found that EI is a reproducible measure of muscle endurance that is not influenced by declines in oxygen saturation.

KEY WORDS: Fatigue; Fatigability; Near-infrared spectroscopy; Muscle contraction

ASSESSMENT OF SKELETAL MUSCLE ENDURANCE USING TWITCH ELECTRICAL STIMULATION AND ACCELEROMETER-BASED MECHANOMYOGRAPHY

* Corresponding author at:Kevin K. McCully, E-mail:[email protected], Telephone: (706) 542-1129, Address: 330 River Road Athens, GA 30602

Kevin K. McCully, Ph.D. 1,*, Thomas B. Willingham, Ph.D.1

1 Department of Kinesiology, University of Georgia, Athens, GA, USA


A recent study has used aMMG to measure the accelera-tion generated by muscle movement during electrically stimulated muscle twitches in persons with neurological disease [25]. However, the reproducibility and validity of the methodology were not defined. The purpose of the pre-sent study was to evaluate the reproducibility and validity of measures of muscle endurance using twitch electrical stimulation and aMMG. We performed four different ex-periments to: 1) compare twitch acceleration to force meas-urements, 2) determine the reproducibility of the muscle endurance test at different levels of stimulation current, 3) compare the endurance index of the forearm and calf mus-cles, and 4) test whether oxygen saturation falls during the endurance index test. We hypothesized that aMMG meas-ures of endurance will be reproducible, not influenced by changes in the level of electrical stimulation current, that the forearm muscles will be more fatigable than the calf, and that twitch electrical stimulation would not lower mus-cle oxygen levels sufficiently to suggest muscle endurance was limited by oxygen delivery.

METHODS

Experimental Protocols

Four different experiments were performed. Experiments included two upper extremity tests (forearm muscle endur-ance test with reproducibility and forearm oxygen satura-tion test) and two lower extremity tests (vastus lateralis acceleration versus torque test and calf muscle endurance test). All experiments were performed on separate days by the same tester.

Participants

Twenty young, healthy participants (11 female, 9 male) 21.8±1.9 years of age were tested. Ten participants (6 female, 4 male) participated in the upper extremity tests and ten participants (5 female, 5 male) participated in the lower extremity tests. The study was conducted with the approval of the Institutional Review Board at the University of Georgia (Athens, GA), and all participants gave written, informed consent before testing.

Forearm Muscle Endurance Test

Muscle endurance was measured in the forearm muscles (flexor carpi ulnaris, palmaris longus, and flexor carpi ra-dialis). The forearm muscle endurance test was performed with the participants in the supine position, the upper ex-tremity in the supinated position, and the limb secured with padded Velcro straps at the wrist. Muscle endurance was assessed by measuring acceleration during a series of electrically stimulated muscle twitches at three frequen-cies (Fig. 1a). The muscle endurance test included a total of 9 minutes of twitch electrical stimulation: 3 minutes of stimulation at frequencies of 2Hz, 4Hz, and 6Hz. Each frequency bout was separated by ~3 seconds of no stimu-lation which provided a distinguishing marker in the data between each frequency.Reproducibility of the forearm muscle endurance test was assessed by performing a second test on a separate day at

INTRODUCTION

Muscle dysfunction in persons with neuromuscular and cardiovascular diseases can contribute to impairments in physical function [1-3]. An important characteristic of skel-etal muscle function is the ability to perform repeated con-tractions while maintaining function (either force or move-ment). The inability to sustain muscle contractions has been termed fatigue or fatigability [4,5] and is often quantified as a decline in force or power output during continuous or re-petitive muscle contractions [6]. Alternatively, the ability to sustain repeated muscle contractions has been described as muscle endurance [7]. While a number of different ex-perimental protocols have been used to evaluate muscle fatigue or endurance, these protocols are not always practi-cal or applicable to clinical populations [8-11]. In vivo measures of muscle function have historically used either maximal voluntary activation or high frequency elec-trical stimulation to induce muscle contractions [3, 9]. How-ever, some clinical populations may not be able to volun-tarily activate their muscles, and the use of high frequency stimulation to produce tetanic, high force contractions may have adverse effects [3,12]. High force contractions have higher risk of orthopedic injury [13], greater chance of ex-ercise-induced muscle damage [14], blood flow occlusion [15], and lower participant tolerability (electrical stimula-tion at high frequencies/current levels) [16]. Low frequency electrical stimulation produces low force muscle twitches which may be more easily tolerated and can decrease the potential for musculoskeletal injury in clinical populations compared to high force, tetanic contractions [14]. Muscle twitches also have higher rates of cross bridge turnover compared to isometric contractions which may increase en-ergy demand while decreasing force production and lower-ing risk. Furthermore, the maintenance of relative muscle oxygen saturation levels during muscle twitches also has the advantage of allowing measures of muscle endurance under conditions that are not influenced by contraction in-duced ischemia [17].Previous studies assessing muscle endurance have also re-lied on measures of force production and focused on a few representative muscle groups, such as the vastus lateralis and biceps brachii [12,18], in part due to the ease of use of isokinetic ergometers. Focusing on large, isolated muscles may be a limitation of previous studies as clinical expres-sion of disease/injury is nonuniform and muscles are dif-ferentially affected by pathology. Alternatively, measuring changes in muscle movement using accelerometers allows the application of fatigue/endurance tests to muscle groups where force measurements may be difficult [19]. Acceler-ometer-based mechanomyography (aMMG) measures the acceleration generated from changes in muscle architec-ture that occur during contraction and does not require the use of isokinetic ergometers [20,21]. Previous studies have shown that aMMG signals correlate with measures of force and electromyography during muscle contractions, and measures of acceleration from aMMG have been used to assess muscle endurance during fatiguing contractions [18, 22-24]. To summarize, testing muscle endurance in clinical populations may require the use of surface neuromuscular stimulation, low force contractions, and the measurement of muscle movement rather than force.


a different level of electrical stimulation current. The initial electrical stimulation current (high current) was identified as the highest current the participant reported as tolerable for the 9 minute testing period (30-40mA). The second current intensity (lower current) used was 10mA less than the established high current intensity for each participant (20-30mA). It was necessary that all high and low currents generated a palpable contraction. Because there were no significant mean differences in the endurance index of the forearm between high and low current conditions, the re-sults of these tests were also used to calculate indices of reproducibility.

Calf Muscle Endurance Test

Muscle endurance was measured in the calf muscle (gas-trocnemius). For the calf muscle endurance test, partici-pants were supine with the calf suspended and the limb secured with padded Velcro straps at the knee and ankle. The stimulation protocol was the same 9 minute protocol (2, 4, and 6 Hz) used for the forearm muscles.

Comparison of Acceleration and Torque

Comparisons of acceleration and torque measurements were made during electrical stimulation of the vastus lat-eralis muscle. Knee extension torque was measured using Biodex System 4 Pro dynamometer (NY, USA). Participants were seated in an upright position with the right knee at 70 degrees of knee flexion. The participant was provided a lap safety belt to limit movement. Torque was measured at an acquisition frequency of 100 Hz. The accelerometer was placed on the vastus lateralis muscle and aMMG meas-urements were recorded simultaneously during the torque measurements (Figure 2a). Twitch torque and acceleration were measured at six levels of increasing current (50, 55, 60, 65, 70, and 75 mA).

Electrical Stimulation

All muscle contractions were induced by applying twitch (pulse duration=200μs) electrical stimulation (Theratouch 4.7, Rich-Mar, USA). Electrodes (5.08cm x 10.16cm) were placed 2 cm proximal and 2 cm distal of the accelerometer over the muscle of interest.

Accelerometer-based Mechanomyography

The acceleration generated from surface oscillations dur-ing muscle contraction was measured using a triaxial, wire-less accelerometer (WAX9, Axivity, UK). The accelerometer was secured to surface of the skin at the belly of the mus-cle using double sided adhesive tape. The orientation of the accelerometer was the same for all tests. Data was col-lected from the accelerometer at an acquisition frequency of 100Hz via a Bluetooth communication port on a laptop computer.

Oxygen Saturation during Twitch Stimulation Test

Muscle oxygen saturation was measured in the forearm during the same electrical stimulation protocol used for the muscle endurance test. A physiological calibration

was performed immediately following the final bout of electrical stimulation using an arterial occlusion [26]. Continuous-wave near-infrared spectroscopy (CW-NIRS) (Oxymon Mk III, Artinis Medical Systems) was used to quantify changes in muscle oxygen saturation. The CW-NIRS probed was secured to surface of the skin at the belly of the muscle using double sided adhesive tape in same location used to measure aMMG. Adipose Tis-sue Thickness (ATT) was measured at the site of CW-NIRS probe placement using ultrasound (LOGIQ, GE Health-Care). The ATT was used to determine the appropriate distance between the CW-NIRS receivers and transmitter so that oxygen was measured at the depth of the skeletal muscle. CW-NIRS measurements were recorded at an ac-quisition frequency of 10Hz and visualized in real time. The physiological range of optical density (OD) units was used to calibrate HbO2 CW-NIRS signals as previously de-scribed [26]. Briefly, a blood pressure cuff (Hokanson, 20c, Bellevue, Washington) was rapidly inflated (Hokanson, E20, Bellevue) proximal to the elbow joint to achieve a complete vascular occlusion after 30 seconds of electri-cal stimulation to increase metabolic rate. The vascular occlusion was maintained until the minimal optical densi-ty (OD) units value (0% oxygen saturation) was identified by a plateau in the HbO2 signal (~5min) [26]. The cuff was then released and peak hyperemia was measured (100% oxygen saturation).

Data Analysis

Acceleration was measured throughout the muscle en-durance test (Figure 1a). Muscle twitch acceleration was quantified as the resultant acceleration (Ar) from all three axes .Raw acceleration data was analyzed using custom written routines in MATLAB R2014b (MathWorks Inc., USA). Peak to peak (p-p) analysis was employed to determine the magnitude of acceleration measured during each contrac-tion. To eliminate the influence of extraneous vibrations and other sources of noise, peak to peak signal magnitude was calculated from the initial acceleration waveform at the time of each twitch (Figure 1b).

Figure 1. a) Example of acceleration data from twitch stimulation of the forearm at 2 Hz, 4 Hz, and 6 Hz. Each stimulation frequency lasted 3 minutes. b) Example of peak and end twitch acceleration of the forearm. Stimulation frequency was 6 Hz. The peak magnitude of acceleration (p-p) for each twitch was used to determine the strength of the twitch contraction.


Acceleration data from the muscle endurance test was ana-lyzed for measures of peak acceleration (Ap), End Accelera-tion (Ae), and Endurance Index (EI) (Fig. 1b). Ap was calcu-lated as the maximum peak to peak value obtained prior to the referenced measurement, Ae was calculated as the average peak to peak value of the last three twitches at the end of a 3 minute stage of stimulation frequency, and EI was defined at the percent of acceleration observed at the end of a stimulation relative to the peak value: EI = (Ae/Ap) ×100. Therefore, higher EI values indicate high muscle endurance, or less fatigability. Statistical analysis was performed using IBM SPSS Statistics 22 (IBM®, Armonk, New York). One-way repeated measures ANOVA was used to identify changes in EI values during muscle endurance test. Post-Hoc Bonferroni corrections for multiple comparisons were used to identify differenc-es in peak accelerations and EI values at each frequency of stimulation. Two-way repeated measures ANOVA was used to compare measures of EIs between low and high currents and between muscle groups and identify interac-tions. Data reported at means (±standard deviation) unless otherwise specified. Significance was accepted at p < 0.05 for all comparisons.

RESULTS

Comparison of Acceleration and Torque

An example of simultaneously collected acceleration and torque in the vastus lateralis muscle is shown in Figure 2a. Acceleration strongly correlated with torque measure-ments from knee extension (mean R2=0.96±0.04) during twitch stimulation at increasing current levels (Figure 2b).

tion between current and frequency on measures of EI, and EI was not different at any frequency between the low and high current levels (p>0.6) (Figure 3b). The coefficients of variation (CV) determined from the low and high current level experiments were CV= 2.49 ± 3.67% for the 2Hz stage, CV= 7.36 ± 8.11%; for the 4Hz stage, and CV= 4.30 ± 3.09% for the 6Hz stage. Because there was little decline in acceleration observed during the 2Hz and 4Hz stages of contractions, EI values were similar between individuals at these first two stages which resulted in weak interclass correlation coefficients (ICC): 2 Hz ICC=0.11, 4 Hz ICC=0.36; both p>0.05. The ICC for 6 Hz was significant: ICC=0.98, p<0.01.

Figure 2.a) Example of simultaneously collected acceleration and toque data from twitch stimulation of the vastus lateralis at 6 Hz. b) An individual example of the correlation of acceleration and torque produced at 6 levels of increasing stimulation current at the vastus lateralis (50, 55, 60, 65, 70, 75mA).

Reproducibility of the Endurance Tests at Different Current Levels

Lower stimulation current levels resulted in lower accelera-tion values in the forearm muscle for all three stimulation frequencies (Figure 3a). There was no significant interac-

Figure 3.a) Peak to peak (p-p) acceleration values from twitch electrical stimulation of the forearm during 2Hz, 4Hz, and 6Hz of electrical stimulation at high and low current. *indicates significantly different than low current (p<0.05). b) Endurance Index (EI) of the forearm during 2 Hz, 4 Hz, and 6 Hz electrical stimulation. No significant differences were found in measures of EI between high and low current. *indicates significantly different than EI at 2 Hz stimulation (p<0.05). # indicates significantly different than EI at 4Hz stimulation (p<0.05).

EI of the Calf versus the forearm

The calf muscles showed very little evidence of fatigue with the stimulation frequencies used, with mean EI values above 95% for all three frequencies (Figure 4). There was a significant interaction of muscle group and frequency on measures of EI, and EI values from the calf were significant-ly higher compared to the forearm (p < 0.01).

Figure 4. Endurance Index (EI) of the forearm and calf during 2Hz, 4Hz, and 6Hz electrical stimulation at high stimulation current. † indicates significantly different than EI at calf (p<0.05).


Oxygen Saturation during EI tests in the forearm

Muscle oxygen saturation in the forearm muscles was 74.3±15.1% at baseline and did not decreased during the three stages of electrical stimulation (Figure 5). Adipose tis-sue thickness was 0.52±0.16 cm, and did not correlate with oxygen saturation values during the EI tests.

DISCUSSION

The present study systematically evaluated the use of low frequency twitch electrical stimulation and aMMG as an as-sessment of muscle endurance. In agreement with previ-ous studies, we found that twitch acceleration correlated with torque and may provide a reliable measure of muscle contractile strength [18,22-24]. Our measures of EI in the forearm of healthy control subjects were also similar to those previously reported [25]. The reproducibility of EI asmeasured by CV was less than 10% for all stages of stimu-lation, which is comparable to other methods of measur-ing muscle endurance and fatigability [27-29]. We found that measures of muscle endurance were not influenced by changes in muscle oxygen saturation or level of stimu-lation current. Furthermore, EI was greater in the calf com-pared to forearm, suggesting that this methodology can be used to evaluate differences in muscle function between muscles or in the presence of pathology. Previous studies have used EI as measured by aMMG and twitch electrical stimulation to noninvasively evaluate muscle function in population with neuromuscular disease [25]. The use of low frequency twitch stimulation produces lower force contractions which may be better tolerated and more inclusive to clinical populations compared to high frequency tetanic contractions [14,16,25]. One limi-tation to measuring muscle endurance in clinical popula-tions is that factors such as decreases in bone mass, in-creases in pain sensitivity, and autonomic dysreflexia can increase the risk associated with the use of high intensity muscle contractions [30-32]. Furthermore, high frequency electrical stimulation may cause muscle damage in some

clinical populations and influence measures of muscle en-durance [14]. In addition, aMMG signals generated during tetanic contractions require complex processing and can be difficult to interpret as changes in force development [33]. Measures of EI derived from twitch acceleration may provide a more direct measure of muscle contractility and simplified analysis process.We found that EI was not influenced by intensity of stimu-lation current. This is important because supramaximal stimulation currents are not easily obtained with surface muscle electrical stimulation in people with intact sensory nerves. Because supramaximal muscle activation can be poorly tolerated, the use of submaximal current levels may be necessary in future studies measuring EI in humans, and establishing the stability of EI measurements at varying levels of current is important to the clinical applicability of the method [16]. Previous studies using acceleration from aMMG to evaluate endurance did not evaluate the impact of the intensity of electrical stimulation current [18,34,35]. Studies using T2 MRI imaging have shown that increasing the intensity of neuromuscular electrical stimulation cur-rent results in increased cross sectional area of muscle activation and increased toque production [36]. MRI stud-ies suggest that increasing current activates muscle with a uniform motor unit recruitment pattern in spatial proximity to the electrodes, which is different from the recruitment pattern by motor unit size seen with voluntary muscle acti-vation [37]. The finding that the EI did not change with cur-rent level suggests the muscle fiber characteristics of the additional muscle activated with the higher current were not different from the muscle activated by the lower cur-rent levels. This finding is consistent with previous studies that have shown increasing stimulation intensity did not alter measurements of mitochondrial capacity [38].We found that oxygen saturation as measured by NIRS did not decrease during the progressing twitch protocol em-ployed in the present study. Oxygen saturation levels rep-resent the balance between oxygen utilization and oxygen delivery. Previous studies evaluating muscle endurance have used high force, tetanic contractions which have been shown to reduce blood flow and oxygen delivery and may influence measures of muscle endurance [15,39]. In our study, the maintenance of oxygen saturation suggests that the muscle endurance measurements were not influenced by inadequate oxygen delivery. We chose to present our results as a muscle endurance in-dex. Muscle endurance was quantified as the preservation of muscle contractile movement during repeated twitch stimulations, which is the inverse of measuring fatigue as the loss of movement. Muscle endurance has been previ-ously reported as a measure of muscle fatigability [7,40]. The advantage of reporting muscle endurance is that the preservation of muscle contractile strength should be di-rectly rather than inversely related to muscle function and health. Previous studies have chosen to evaluate the loss of voluntary muscle contractile strength as task failure which may be more appropriate when studying voluntary exercise [41,42]. In our study, the use of electrical stimula-tion eliminates the influence of central factors, and the use of stimulation frequencies below those seen in voluntary contractions reduces the likelihood that failure of neuro-muscular transmission could play a role in the results [43].

Figure 5. Oxygen Saturation in the forearm during as measured by continuous wave near infrared spectroscopy (CW-NIRS) during 2Hz, 4Hz, and 6Hz electrical stimulation. No significance differences between oxygen saturation during stimulation and baseline were found (p>0.05). Values are means and SD.


A potential limitation of the current study was that a physiological mechanism responsible for the reduction in muscle contractility was not identified. A number of physi-ological mechanisms have been implicated in the decline in force production during muscle fatigue and have been reviewed elsewhere [44]. However, previous studies using muscle endurance or fatigue tests have not established physiological mechanisms associated with their tests [11, 18,21]. In our study, the higher EI values found for the leg muscles compared to the forearm muscles are consistent with higher reported values for mitochondrial capacity in the leg versus forearm muscles [45,46]. Our study per-formed four experiments to characterize the endurance index using electrical twitch stimulation and aMMG. While we consider our results to be promising, additional studies, including studies form other laboratories, will be need to evaluate the usefulness of this test and the application to various clinical populations.

CONCLUSION

Developing inclusive methods of quantifying muscle en-durance is critical to evaluating disease progression and the efficacy of rehabilitation interventions in clinical popu-lations. The present study demonstrated the viability of EI as an assessment of muscle endurance that does not re-quire voluntary muscle activation, the use of high force te-tanic contractions, or the use of ergometers. The endurance index was independent of stimulation current, with CV less than 10%, and higher in muscles that are expected to have higher mitochondrial contents. Our results suggest that EI measures have the potential to reproducibly evaluate mus-cle endurance in various muscles and clinical populations that may not be measureable by existing methodologies.

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ABSTRACT

The aim of this study was to determine the contractile properties and muscle stiffness to assess lateral symmetry in competitive female surfers. Fifteen competitive female surfers volunteered to participate in the study. Tensiomyography was used to derive maximum muscle belly displacement, and time delay duration of the Biceps Brachiis, Biceps Femoris, Deltoid, Gastrocnemius lateral head, Rectus Femoris, Tibialis Anterior, Triceps Brachii and Vastus Medialis. No significant differences between right and left limbs at in any of the tested muscles were observed (p > 0.05). Competitive female surfers showed that upper body muscles had the ability to generate force rapidly dur-ing contractions, while the lower body muscles generated force at a slower rate. Surf specific training seems to have had an influence on the contractile properties, and stiff-ness of these muscles. The neuromuscular profile provided here provides further nor-mative data to this unique population.

KEY WORDS: Surfing; TMG;Muscle stiffness; Muscle balance;Fibre type

THE USE OF TENSIOMYOGRAPHY TO EVALUATE NEUROMUSCULAR PROFILE AND LATERAL SYMMETRY IN COMPETITIVE FEMALE SURFERS

* Corresponding author at:Leeds Beckett University, email: [email protected], Telephone: 0113 812 3250, Postal address: Carnegie Hall G05, Headingley Campus, Leeds, LS6 3QQ, UK.

Helen J. Gravestock 1,*, Matthew J. Barlow 2

1 Leeds Beckett University2 Carnegie School of Sport, Leeds Beckett University


INTRODUCTION

Surfing is a physically demanding sport that is intermittent in nature and its growing popularity has led to increased scien-tific interest in the sport. Surfing was recently recommended for inclusion in the Tokyo 2020 Olympics and as such, devel-oping a corpus of knowledge relating to performance of the sport is important to assist athletes, coaches and sports sci-entists in preparation for this event. Previous studies have investigated the physiological demands of surfing [1,2,3,4] reporting that surfers spend 3.8 - 8.1 % of the total time surf-ing with paddling contributing 35 – 54 %, waiting 28 – 42 % and miscellaneous activities such as “duck diving”, wading, and “wipe-outs” contributing 2.5 – 5 %. Of these activities, only the surfing element is judged in order to generate scores, but paddling can represent the most demanding element, and is critical to ensure the surfer is in the correct position to capitalise on high scoring wave opportunities when they arise. Typically during these activities, average heart rate has been shown to be between 64-80 % of laboratory tested maximum heart rate [1,5]. Furthermore, in response to the demands of the sport, surfers have been identified as having good aerobic fitness with an average VO2peak of ~46.83 ml•kg-1•min-1 with power at VO2peak, anaerobic power and the power at lactate threshold being related to ranking in surfing [1,6]. Anthropo-metrically surfers are shorter than other age-matched athletes [7] and it is suggested that muscularity is positively associated with ability and competitive ranking in both male and female surfers [2,8].

Despite the popularity of the sport, apparently there is very little data about the neuromuscular profile in female surf-ers. While some data does exist regarding joint stiffness, leg power and proprioception [9], the neuromuscular profiles of individual muscles have yet to be quantified, especially to assess lateral symmetry. Tensiomyography is a non-invasive [10] evaluative method for ascertaining the neuromuscular profile of a muscle, through detecting the muscular reaction to electrical stimulation [11]. As such, TMG is an ideal method for competition-day assessment as no physical effort is re-quired of the individual being evaluated [10]. TMG measures the radial displacement, and time response of the stimulated muscle during the evoked isometric contraction to evaluate stiffness, contractile properties of the muscle [10]. Previous research has indicated suitable validity and reliability of the manufacturer-informed protocol for using TMG [12-14]. The aim of this descriptive study was to investigate the appli-cability of Tensiomyography assessment in elite female surf-ers to derive normative neuromuscular data for this athletic population, specifically to investigate measures of muscle stiffness and contractile properties between right and left limbs to assess lateral symmetry.

METHODS

Participants

Fifteen competitive female surfers (age, 23.8 ± 4.4 years; height, 165.5 ± 5.5 cm; mass, 63.4 ± 5.6 kg) volunteered to participate in the present study. The surfers were recruited at the English National Surfing Championship held at Wa-

tergate Bay, Cornwall, England in May 2015. These surfers were likely to train rigorously and also compete regularly in high level surfing competitions. This study was approved by the Local Research Ethics Committee within the school of Sport, Carnegie Faculty, Leeds Beckett University and was consistent with the requirements for human experi-mentation in accordance with the Declaration of Helsinki. Informed written consent was obtained when participants were fully informed of the associated risks and benefits of the study prior to participation.

Procedures

All measures were performed at the contest venue and in-cluded stature measured to the nearest 0.5 cm (Seca 225, Birmingham UK), and body mass which was measured to the nearest 0.01 kg using a digital scale (SECA 770, Birmingham UK). A Tensiomyography device (TMG measurement system, TMG-BMC Ltd., Ljubljana, Slovenia) was used to evaluate neuromuscular profiles. All TMG assessments were made by the same scientist researcher who was experienced in taking these measurements. Each muscle was tested once on both the right, and left limbs at the following sites; Biceps Brachii (BB), Biceps Femoris (BF), Deltoid (DT), Gastrocnemius lateral head (GL), Rectus Femoris (RF), Tibialis Anterior (TA), Triceps Brachii (TB) and Vastus Medialis (VM). Quadriceps muscle sites were assessed with the participant positioned supine, and the knee flexed at 120°, and hamstring sites were as-sessed lying in prone position with the knee in full exten-sion. Upper body sites were taken with the participant in an upright seated position.

A displacement transducer sensor (TMG, Panoptik, Ljubljana, Slovenia) was positioned perpendicular to the muscle axis [15], at the maximal bulge of the muscle belly for each par-ticipant individually to account for anatomical differences [16]. In line with previous research [16], self-adhesive elec-trodes (Med Fit, SA 10, Taiwan) were placed approximately 5 cm away from the sensor with the positive electrode po-sitioned superior to the negative electrode. To initiate the isometric contraction, a single twitch electrical stimulus with 30 mA starting amplitude lasting 1 ms was delivered to the muscle site using an electrostimulator (TMG-BMC, Ljubljana, Slovenia). Pulses increased by 10 mA until the maximal dis-placement of the muscle belly was obtained [15]. A 10 sec-ond rest period was ensured between stimulations to avoid fatigue [12] or post activation potentiation effects [17]. The stimulation resulting in the greatest radial displacement of the muscle was considered for analysis.

The muscle contraction parameters obtained included maximum radial muscle belly displacement (Dm; measured in mm), and time delay (Td; measured in ms), for the tested muscle to reach 10 % of the maximum Dm. The Dm repre-sents the stiffness of the muscle [18], where high values represent low muscle tone, tendon stiffness or fatigue, and low Dm values represent high muscle tone and increased stiffness of the tendon [16]. Td represents muscle fibre type that is dominant in the assessed muscle [18]. Here, high values are indicative of type I muscle fibres and a low value represents a majority type II fibres [16].


Statistical analysis

Data are presented as mean and standard deviation. Nor-mality was tested through Kolmogorov-Smirnov test. To compare lateral symmetry (right vs left limb) a dependent sample t-test was computed, unless data were non-para-metric (Td VM, Td TA, and Td TB), a Wilcoxon signed-rank test was used. Significance level was set at p < 0.05. All data were analysed using the Statistical Package for the Social Sciences for windows (version 22.0, SPSS, Chicargo, USA).

RESULTS

Descriptive statistics for maximum radial displacement (Dm) and time delay duration (Td) from TMG assessments are shown in Table 1. No statistically significant differences (p < 0.05) were found between right and left limbs for ei-ther Dm or Td in each of the muscle assessed.

Muscle TMG Parameter Right Limb Left Limb Right limb vs. left limb(p-value)

BB Dm (mm) 13.86 ± 3.68 13.00 ± 4.29 0.35

Tc (ms) 28.12 ± 4.22 28.57 ± 5.00 0.14

BF Dm (mm) 4.79 ± 2.35 4.80 ± 2.24 0.12

Tc (ms) 29.46 ± 4.93 27.74 ± 5.01 0.11

DT Dm (mm) 3.18 ± 1.67 3.70 ± 2.85 0.09

Tc (ms) 18.23 ± 1.54 18.78 ± 2.31 0.43

GL Dm (mm) 5.63 ± 2.46 7.79 ± 11.64 0.68

Tc (ms) 25.67 ± 2.92 29.71 ± 13.22 0.32

RF Dm (mm) 5.66 ± 2.55 9.92 ± 12.25 0.29

Tc (ms) 25.58 ±1.98 24.67 ± 2.02 0.83

TA Dm (mm) 1.62 ± 0.65 4.09 ± 9.03 0.88

Tc (ms) 25.87 ± 8.88 30.61 ± 29.41 0.69

TB Dm (mm) 7.57 ± 4.27 10.33 ± 11.94 0.94

Tc (ms) 22.62 ± 4.16 22.57 ± 3.25 0.82

VM Dm (mm) 6.20 ± 2.26 5.15 ± 1.87 0.17

Tc (ms) 23.94 ± 2.23 24.93 ± 3.26 0.51

DISCUSSION

Measured responses of electrically stimulated muscles were collated from competitive female surfers. The results showed no significant (p < 0.05) difference in neuromus-cular profile at any tested muscle site between right and left limbs of competitive female surfers for either Dm or Td variables measured using tensiomyography. This sug-gested that the surfers were well-trained and that both

Table 1: TMG parameters for the Biceps Brachii (BB), Biceps Femoris (BF), Deltoid (DT), Gastrocnemius lateral head (GL), Rectus Femoris (RF), Tibialis Anterior (TA), Triceps Brachii (TB) and Vastus Medialis (VM) of both the right and left limbs.

the right and left limb musculature respond similarly to training [15].

The Dm parameter is associated with muscle stiffness, in addition to the adaptations of cross sectional area of the muscle [16] and can also be influenced by tendon stiffness [19], where amplitude of Dm response is inversely related to muscle belly stiffness. Despite the limited research on TMG in females, two studies have analysed the neuromus-cular profile of the major muscles used in other sport mo-dalities. Similar to our results, a study on female kayakers also did not find any significant differences between the left and right side in Dm for deltoid, trapezius or latissimus dorsi [16]. Likewise, from the data presented by Rodriguez Ruiz et al. [18], no substantial differences were found in Dm between left and right sides for vastus medialis, rectus femoris, vastus lateralis and bicep femoris in beach volley-ball players. By comparison, the Dm results of the present

study show that rectus femoris was lower compared to beach volleyball players. Thus competitive female surfers have stiffer rectus femoris muscles compared to female beach volley ball players (all other muscle sites were with-in 1.5 mm difference). This could be due to the different physiological profiles of these sports that produce changes in the muscle which affect the neuromuscular profile [20]. In surfing, control and dynamic balance during wave rid-ing is paramount, as such lower body strength and power


exercises have been recommended for female surfers [21]. It could be that during a surf the rectus femoris contributes to such balance, and is therefore more toned than beach volleyball athletes that typically perform greater explosive movements, such as vertical jumps [18]. Indeed, in wind-sailing, the rectus femoris has been shown to be active to stabilise the leg during standing [22]. However, as surf judging criteria favours performances of radical, yet con-trolled manoeuvres with the greatest speed, explosive leg power would appear to be pivotal for success in the sport [7]. In previous surfing literature, vertical leg muscle stiff-ness has been assessed during hopping in a range of surf-ing abilities [9]. Here, higher levels of surfing experience was associated with lower leg stiffness in female surfers. Although these results differed compared to the present study, it is difficult to directly compare the stiffness of a specific muscle, measured here, to the stiffness of the leg as a whole. The Td variable indicates the fibre type distribution within the assessed muscle [23]. Again similar to the Dm variables, no significant differences between right and left limbs were identified. In comparison to the female kayakers, Td of the deltoid was very similar [16]. This may indicate similar fibre type composition at the deltoid between the two sports. When surfing, a deficit in shoulder joint strength may im-pair sprint paddling and ability to catch waves [21]. Like-wise, the role of shoulder joint musculature is important in kayaking, particularly during the recovery phase of a stroke [24]. Where muscle samples have been taken previously, the deltoid was reported to have a higher percentage of type I fibres than type II fibres [25]. Based on the 30 ms threshold implied by Rey et al. [20], the female surfers in the present study, and female kayakers from previous research would be interpreted as having majority type II fibres at the deltoid (18.23 and 17 ms respectively). This is notable considering females typically demonstrate slower firing rates compared to males [26]. However, it could be the case that adaptation has occurred in the muscle fibre profile of the deltoid due to specific training, competition loads [27] and that a high proportion of this time is spent paddling [3]. In contrast to the deltoid, displaying the shortest Td duration, the biceps femoris displayed the longest. Explosive bursts in upper body activity are prevalent in surfing when paddling out to catch a wave. This sport specific demand may produce faster muscles in the upper extremities, in comparison to the more fatigue resistant muscles of the lower body.

In the present study, contralateral limb was selected as the independent variable in assessing lateral symmetry, based on its use elsewhere in the literature [16,18]. It could be that a comparison between dominant and non-dominant limbs may be more appropriate due to reported increases in mus-cle strength on the dominant side [28]. However, where this approach was taken elsewhere, no statistically significant differences were reported, and so it could be that such a comparison is not justified [29]. Future work may consider averaging both right and left sides together as no significant differences were found here or in previous similar research [16,18], or between dominant and non-dominant limbs else-where [15]. This study supports the method pooling bilat-eral TMG data, a technique that has been used previously

[30]. Lateral symmetry may therefore be more valuable for assessing lateral differences due to injury on an individual basis, opposed to analysing group mean differences.

CONCLUSION

In conclusion, tensiomyography is a useful and non-invasive method for assessment of the neuromuscular characteristics in elite female surfers. Our results provide normative neuro-muscular values for this unique population. This study has established lateral neuromuscular symmetry for a range of muscle sites, and contributes to the literature on the neu-romuscular profile of competitive surfers. Surf coaches and female athletes should consider neuromuscular responses to electrical stimulation to monitor adaptations to training and recovery in preparation for competition. Acknowledgements

The authors would like to acknowledge the participants for their time.

CONFLICT OF INTEREST

All authors have declared there is not any potential conflict of interests concerning this article.

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ABSTRACT

Purpose: This study was designed to: a) examine the influence of elbow joint angle and contraction intensity on electromyographic (EMG) and mechanomyographic (MMG) re-sponses using linear slope coefficients and, b) further describe these relationships utiliz-ing polynomial regression. Methods: 14 male subjects (mean ± SD, age 22.1 ± 2.3 years) performed maximal voluntary isometric contractions (MVC) at elbow flexion angles of 60°, 90°, and 120°. Subjects then performed 35 second contractions at two MVC levels (50%, 75%) for each joint angle. EMG and MMG were recorded simultaneously from the biceps brachii. The center 30 second segment of the signal was utilized to determine the root-mean-square (RMS). Results: No significant effect of elbow joint angle was found for the EMG (p = 0.52) and MMG (p = 0.12) slope coefficient analysis, as well as contrac-tion intensity (EMG: p = 0.61; MMG: p = 0.50). Composite polynomial regression revealed that the MMG-Time relationships were best fit with linear models at 120° (50% MVC: p = 0.025; 75% MVC: p = 0.019), while non-linear relationships best described the 60° (50% MVC, 75% MVC: p < 0.001) and 90° (50% MVC, 75% MVC: p < 0.001) joint angles. Conclusions: Results indicate that motor control strategies are not significantly different between elbow joint angles when utilizing linear regression models. However, polyno-mial regression revealed elbow joint angle specific MMG-Time relationships. Non-linear, MMG-Time relationships are influenced by elbow joint angle during short-term, sustained isometric contractions.

KEY WORDS: Muscle strength; Torque; Regression analysis

INFLUENCE OF JOINT ANGLE AND BICEPS BRACHII ISOMETRIC CONTRACTION INTENSITY ON ELECTROMYOGRAPHIC AND MECHANOMYOGRAPHIC RESPONSES

* Corresponding author at:Swapan Mookerjee, Ph.D., Bloomsburg University, Department of Exercise Science, 400 East 2nd St., Bloomsburg, PA, 17815Tel: (570) 389-4743, Fax: (570) 389-5047, E-mail: [email protected]

Swapan Mookerjee 1,*, Matthew J. McMahon 1, Sam Meske 1

1 Department of Exercise Science, Bloomsburg University of Pennsylvania, Bloomsburg, PA

mailto:[email protected]


INTRODUCTION

During isometric contractions, the joint angle or muscle length, as well as the contraction intensity influences mo-tor control strategies and muscle fatigue. Several investi-gations have demonstrated this based on force data, and information gathered from electromyographic (EMG) and mechanomyographic (MMG) signals [1-4]. Studies utilizing short duration (≤ 6 sec) contractions have shown greater torque, EMG, and MMG values at shorter muscle lengths [1,3,4]. Joint angle has also been shown to influence the EMG and MMG versus torque relationship [2]. Lastly, sus-tained contractions at longer lengths have produced great-er EMG and MMG amplitude slope coefficients [5]. The isometric strength curve for elbow flexion follows an ascending - descending pattern with maximum torque generation occurring anywhere within 80° - 100° [6-9]. Fur-thermore, EMG and MMG responses are known to be influ-enced by force of the contracting muscle [10, 11]. Previous investigators have studied EMG and MMG signal responses by manipulating elbow joint angle and intensity concur-rently [12,13]. However, apparently no comparisons of these signals were made between the joint angles [12,13]. Sustained exhaustive isometric contractions have been defined by either: a) a percentage decline in force/torque; or b) deviation from a standardized joint angle [12-18]. Testing models using pre-defined exhaustive criteria do not necessarily reflect muscle responses during many oc-cupational tasks or activities of daily living. To our knowl-edge, only two studies have examined the EMG and MMG responses using shorter duration (< 60 seconds), sustained isometric muscle actions. A non-linear increase in EMG-Time and MMG-Time relationships was reported for 30 second and 60 second duration tasks [5,19]. Several work tasks involving material handling, carrying, and packing require sustained elbow flexor activity for shorter durations (e.g. 30 seconds). Limited published in-formation is available with regard to the effect of elbow joint angle and contraction intensity on EMG and MMG re-sponses of the biceps brachii. Previous investigations have included these as independent variables during fatiguing isometric contractions [12,13]. However, these authors did not conduct any further analyses of relational differences between joint and intensity combinations. Therefore, the purpose of this study was twofold: a) identify the potential influence of elbow joint angle and contraction intensity on the EMG-Time and MMG-Time relationships utilizing linear slope coefficients and, b) further describe the nature of these relationships using polynomial regression models.

METHODS

Subjects

Fourteen healthy male subjects (mean ± SD, age 22.1 ± 2.3 years, stature 180.1 ± 5.8 cm, mass 90.2 ± 10.3 kg, body fat 14.8 ± 4.3 %) possessing 7.2 (± 3.4) years of strength train-ing experience volunteered to participate in this study. Subjects completed a written informed consent document, a health history questionnaire, and received explanation of

the possible risks associated with testing. All procedures were previously approved by the Institutional Review Board for Human Subjects.

Orientation Session

The importance of an orientation session has been dem-onstrated in previous test-retest studies [20]. Therefore, all subjects were previously familiarized with the testing pro-cedures. An isometric elbow flexion movement of the right arm was performed on a Biodex System 3 isokinetic dy-namometer (Biodex Medical System, Inc., Shirley, New York, USA). This practice session consisted of one maximal and one submaximal contraction at elbow joint angles of 60°, 90°, and 120°. Direct visual feedback was provided via ref-erence lines on the display monitor of the testing system.

Testing Protocol

Pre-TestAll testing was conducted a minimum of 48 hours post-orientation. Prior to testing, subjects performed a five-minute warm-up on a rowing machine (Concept II Indoor Rower, Morrisville, VT, USA) at a load of 40 watts and a rate of 30 strokes∙min-1. A three-exercise, upper body static stretching routine followed the warm-up. All testing was performed in accordance to manufacturer’s recommended guidelines (Biodex Medical System, Inc., Shirley, New York, USA). Subjects were seated on the dynamometer chair with 90° hip flexion and secured with pelvic and shoulder restraints, to minimize extraneous body movements. Ad-justments of seat height and dynamometer location were made to ensure proper individual alignment. The axis of rotation of the dynamometer was aligned to each subject’s lateral epicondyle of the right humerus.

Maximal TestingThe testing protocol began with a single, six-second MVC at joint angles of 60°, 90°, and 120°. The selection of these joint angles was based on the known ascending – descend-ing nature of elbow flexor strength [6]. The contraction order was randomized and a two-minute rest interval was provided between joint angles. Verbal encouragement was given to subjects in an attempt to elicit a maximum effort. Peak torque was denoted as the highest value achieved at each joint angle. These values were subsequently used to calculate the specific intensity level for all submaximal tri-als.

Submaximal TestingFollowing maximal testing, each subject performed a sin-gle submaximal contraction at intensity levels of 50% and 75% MVC for each of the three selected joint angles. Each contraction trial was sustained for 35 seconds. Five min-utes of rest was permitted between joint angle trials, and 10 minutes between intensities (50%, 75% MVC). Testing trials (both intensities and joint angles) were randomized to avoid an order affect. Subjects received direct visual feedback via a 41-cm computer monitor set at a viewing distance of 1-m. A reference line was set at the exact cal-culated intensity level with a lower limit line set at 10% below that value.


Electromyographic Procedures

Bipolar (2.54 × 2.54 cm center-to-center) silver/silver chloride surface electrodes (EL504, BIOPAC Systems, Inc., Goleta, CA, USA) were placed over the belly of the biceps brachii mus-cle using an elbow flexion angle of 90°. Electrode placement was one-third the distance between the acromion process and the fossa cubit [21]. The inter-electrode distance was 25 mm to allow for placement of the MMG sensor. The reference electrode was placed over the head of the ulna [21]. Elec-trode sites were prepared by carefully shaving, abrading, and scrubbing the skin surface with isopropyl alcohol to ensure an inter-electrode impedance of < 5,000 Ω. The EMG signal was pre-amplified (gain: x 1,000) using an electromyogram amplifier (EMG 100C, BIOPAC Systems, Inc., Goleta, CA, USA, bandwidth 1.0-5,000 Hz, CMRR: 110 dB).

Mechanomyographic Procedures

A capacitive accelerometer (Kistler K-Beam 8305B10, Am-herst, NY, USA, bandwidth 0.0 – 180 Hz, dimensions 2.2 cm × 2.2 cm × 0.9 cm, mass 6.5 g, sensitivity 100 mV/g) was placed between the proximal and distal EMG electrodes on the biceps brachii to record the MMG signal. Double-sided adhesive tape (3M, St. Paul, MN, USA) and a Nylatex wrap (Chattanooga, Wixson, TN, USA) was used to attach, stabi-lize and ensure proper contact pressure of the sensor. The MMG signal was preamplified (gain: x 2) using a signal con-ditioning unit (K-Beam 5210, Amherst, NY, USA) then passed through a universal interface module (UIM100C, BIOPAC Systems, Inc., Goleta, CA, USA) for recording and analysis.

Data Analysis

The raw EMG and MMG signals were sampled at 1,000 Hz us-ing a multi-channel data acquisition unit (MP150, BIOPAC Sys-tems, Inc., Goleta, CA, USA) and stored in a personal computer (ThinkPad R61; Lenovo, China) for analysis. All signal process-ing was performed off-line using AcqKnowledge software (version 4.1, BIOPAC Systems, Inc., Goleta, CA, USA). Isometric torque was adjusted for gravity and low-pass filtered with a 20 Hz cutoff. The EMG signal was bandpass filtered at 10 Hz – 500 Hz to center the waveform on the zero volt baseline and remove high frequency noise. A 2 – 100 Hz bandpass filter was applied to zero-center the MMG signal. Setting the upper limit at 100 Hz did not result in the omission of useful por-tions of the signal, since no significant frequency components have been reported beyond this level [22]. All subsequent analyses were performed on the filtered signals. The amplitude of the EMG and MMG signal was quantified using the root-mean-square (RMS). The initial and final 2.5 seconds for the submaximal trials and the initial and final 1second time intervals for the MVC data were discarded from the analysis. These time intervals were selected to avoid the

gross lateral movement of active muscle fibers at initiation of contraction [23,24]. For both submaximal intensities the RMS was calculated over 30 consecutive, non-overlapping, one-second epoch windows. For MVC data, the highest EMG and MMG RMS value which occurred during the middle four sec-onds (of the six second muscle action) was used to represent the peak value.

Statistical Analysis

Three separate, one-way analyses of variance (ANOVA) were used to determine differences between elbow joint angle MVC values for peak torque, EMG amplitude, and MMG am-plitude. The effect of elbow joint angle (60°, 90°, 120°) and contraction intensity (50%, 75% MVC) on the relationship between biceps brachii EMG and MMG amplitude versus contraction time was examined using linear slope coeffi-cients. Two separate, two-way repeated measures ANOVA (intensities × joint angles) were used to test for the effect of joint angle and intensity on the EMG and MMG amplitude linear slope values. All ANOVA tests were performed using Sigma Plot v. 12.0 (San Jose, CA, USA).Polynomial regression analyses (linear, quadratic, cubic) were utilized to further examine the relationships between EMG and MMG amplitude versus contraction time, on an individual and composite (mean) basis. Determination of statistical significance was done hierarchically, beginning with a linear fit and followed by successively higher-degree polynomials. At each step, the proportion of variance of the dependent variable incremented by a higher-degree poly-nomial (e.g., r2 change in SPSS) was tested for significance by using the F-test described by Pedhazur [25]:

where N = number of subjects; K1= degrees of freedom for the larger R2; K2= degrees of freedom for the smaller R2. SPSS software v. 19.0 (Chicago, IL, USA) was used for all poly-nomial regression analyses. An alpha level of P ≤ 0.05 was considered significant for all statistical comparisons.

RESULTS

Maximal Voluntary Contraction

Mean (± SD) 100% MVC values for peak torque, EMG am-plitude, and MMG amplitude are reported in Table 1. One-way ANOVA indicated no significant effect of elbow joint angle for torque (p = 0.27), EMG (p = 0.63), and MMG (p = 0.81) values. Although non-significant, torque was great-est at an elbow joint angle of 90°, followed by 60° and 120° (Table 1).

60° 90° 120°

Torque (N∙m) 92.71 ± 20.52 101.57 ± 21.08 89.93 ± 16.32

EMG (mV) 1.30 ± 0.62 1.14 ± 0.50 1.31 ± 0.44

MMG (m∙s-2) 0.07 ± 0.05 0.06 ± 0.02 0.07 ± 0.04

Table 1. Mean (± SD) values for peak torque, EMG amplitude, and MMG amplitude during 100% MVC elbow flexion at joint angles of 60°, 90°, and 120°


EMG Amplitude

Table 2 presents the mean (± SD) EMG linear slope coef-ficients for elbow joint angle and intensity. Two-way re-peated measures ANOVA revealed no significant effect of elbow joint angle (p = 0.52) or intensity (p = 0.61) when examining slope coefficients between angles and across the intensities, respectively.

MMG Amplitude

Table 4 presents the mean (± SD) MMG linear slope coef-ficients for elbow joint angle and percentage of MVC. Two-way repeated measures ANOVA revealed no significant ef-fect of elbow joint angle (p = 0.12) or intensity (p = 0.50) when examining between angles and across the intensi-ties, respectively.

60° 90° 120°

50% MVC 0.003 ± 0.004 0.002 ± 0.007 0.005 ± 0.008

75% MVC 0.005 ± 0.011 0.002 ± 0.010 0.003 ± 0.011

Table 2. Mean (± SD) EMG amplitude linear slope coefficients during 50% and 75% MVC at elbow joint angles of 60°, 90°, and 120°

50% MVC 75% MVC

60° 90° 120° 60° 90° 120°

Linear 4 3 4 4 5 3

Quadratic 4 6 5 3 7 5

Cubic 4 4 5 6 1 4

Non-significant 2 1 0 1 1 2

Table 3. Frequency tallies of EMG amplitude versus time relationship regression models for individual subjects (n = 14)

60° 90° 120°

50% MVC 0.030 ± 0.019 0.027 ± 0.025 0.017 ± 0.022

75% MVC 0.025 ± 0.030 0.016 ± 0.019 0.017 ± 0.029

Table 4. Mean (± SD) MMG amplitude linear slope coefficients during 50% and 75% MVC at elbow joint angles of 60°, 90°, and 120°

Figure 1 depicts the results of the polynomial regression anal-yses for electromyographic amplitude versus time relation-ships. The composite relationships for 60° were best fit with a quadratic model for 50% MVC (r2 = 0.72, p < 0.001) and a cubic model for 75% MVC (r2 = 0.92, p = 0.002). At 90° the compos-ite relationship was best fit with a cubic model for 50% MVC (r2 = 0.81, p = 0.070) and 75% MVC (r2 = 0.74, p = 0.014). The best fit for the 120° joint angle was a quadratic model for 50% MVC (r2 = 0.94, p < 0.001) and 75% MVC (r2 = 0.79, p < 0.001). Individual subject counts from the EMG amplitude versus time polynomial regression analysis are presented in Table 3.

Figure 1. EMG-Time regression analysis - At 50% MVC, curves were best fit with a quadratic model at 60° and 120°, and a cubic model at 90°. At 75% MVC, the 120° joint angle revealed a quadratic fit, whereas the 60° and 90° joint angles indicated a cubic fit

Figure 2 depicts the results of the polynomial regression anal-yses for mechanomyographic amplitude versus time relation-ships. The composite relationship at 60° was best fit with a quadratic model for 50% MVC (r2 = 0.92, p = 0.025) and 75% MVC (r2 = 0.88, p = 0.019). At 90° the composite relationship was best fit with a quadratic model for 50% MVC (r2 = 0.94, p < 0.001) and 75% MVC (r2 = 0.86, p < 0.001). The 120° joint angle was best fit with a linear model for 50% MVC (r2 = 0.92, p < 0.001) and 75% MVC (r2 = 0.60, p < 0.001). Individual subject results from the MMG amplitude versus time polyno-mial regression analysis are presented in Table 5.

Figure 2. EMG-Time regression analysis - At 50% MVC, curves were best fit with a quadratic model at 60° and 120°, and a cubic model at 90°. At 75% MVC, the 120° joint angle revealed a quadratic fit, whereas the 60° and 90° joint angles indicated a cubic fit


DISCUSSION

This study explored the influence of elbow joint angle and contraction intensity on EMG and MMG signal character-istics during shorter-duration, sustained isometric con-tractions. Simple linear regression analyses enabled the comparison between joint angles and the two intensities, in an attempt to gain insight into the underlying motor con-trol strategies [5]. The EMG-Time and MMG-Time relation-ships have been previously demonstrated (Itoh et al. 2004; Kimura et al. 2004; Mamaghani et al. 2001, 2002; Orizio et al. 1989, 1992) [12-15,17,18]. However, in these previ-ous investigations, large variations in contraction time du-ration may have influenced motor unit activation patterns thereby affecting the EMG and MMG signals. Therefore, this investigation extended previous work by manipulating the joint angle and contraction intensity, while standardizing the contraction time (30 seconds). Elbow flexor strength has received considerable attention for decades; however, there is still disagreement about the point at which maximum isometric torque occurs. De-spite the statistically non-significant peak torque findings in our study, the highest torque values occurred at 90°. Willams and Stutzmann (1959) also found that maximum elbow flexor isometric strength occurred at 90°, whereas Doss and Karpovich reported maximum isometric values at elbow angles of 105-110° [9,26]. Other investigators have stated peak values at 120° or to range from 100-140° [27,28]. These differences have been noted to be due to differences in test protocols, equipment used, inter-sub-ject variability, forearm positioning, and altered muscle moments.Maximum isometric torque generation at the elbow joint has been reported to occur between 80-100° [6]. There-fore, in this study the settings included a selection within this range (90°), as well as above (120°) and below (60°) these joint angles. However, the manipulation of elbow joint angle did not significantly affect the linear relation-ship between the EMG or MMG amplitude and time. These findings are different from previous work which found the rate of EMG and MMG change to be influenced by the joint angle [5]. Signal characteristics are reported to be closely related to the tension or force of the contracting muscle [10,11]. Maximum torques in this study did not differ sig-nificantly across joint angles, although the trends were indeed reflective of the ascending-descending curve seen during elbow flexion. Furthermore, there was considerable inter-subject variability in torque outputs during these tests. These factors may have influenced the EMG-Time and MMG-Time relationships resulting in non-significant

differences in linear slope coefficients. It appears that in this type of exercise protocol, motor unit control strategies (recruitment and/or firing rate) of the biceps brachii are similar across the elbow joint angles.

EMG Polynomial Analyses

Previous investigations using longer duration (i.e., several minutes) protocols have reported increases in EMG am-plitude throughout the tests [12-15,17,18]. The pattern of relationship evident in this study may be an outcome of the second-by-second analysis of data from a standardized 30-second time window. Whereas, previous studies used longer time averaging across varying isometric contraction time durations [12-15]. The composite EMG-Time relation-ships exhibited a rising trend from the onset of contrac-tion to the midpoint (15 seconds) in all the test conditions. The latter portion of these relationships (15-30 seconds) showed either a plateauing or a declining trend. The ini-tial increase in the trend lines is reflective of greater motor unit recruitment (i.e., Type II), as well as firing rates [29,30]. Subsequent declines or plateauing can be attributed to de-recruitment of Type II motor units and attenuation in firing rate [29,30]. Individual tallies showed that the majority of test subjects exhibited either a quadratic or cubic model (Table 3) for the EMG-Time relationship. However, joint angle manipulation did not reveal any discernible patterns in these relation-ships across subjects. Individual and composite data ap-pear to indicate that motor unit control strategies of the biceps brachii are similar across the selected elbow joint angles. Further, the non-significant patterns between el-bow joint angles may be attributed to the considerable inter-subject variability in the EMG signal [31].

MMG Polynomial Analyses

Within the current study, the MMG-Time relationships re-vealed an influence of elbow joint angle, independent of intensity (% MVC). At 60° and 90° the data showed an initial increase until 15 seconds of work, which was followed by a plateauing or declining trend. In contrast, the MMG-Time re-lationship exhibited a linear increase at 120° of elbow flex-ion at both intensity levels (50%, 75% MVC). These findings are similar to previous work which found joint angle to af-fect the relationship between the MMG signal and contrac-tion time [5]. Furthermore, the non-linear MMG amplitude increase at 60° and 90° is similar to a previous study which also utilized a 30-second data analysis time-frame [19]. These polynomial relationships are reflective of fiber type recruitment patterns which influence the summation of lat-

50% MVC 75% MVC

60° 90° 120° 60° 90° 120°

Linear 7 3 2 6 4 4

Quadratic 5 4 6 3 5 4

Cubic 2 6 6 4 4 2

Non-significant 0 1 0 1 1 4

Table 5. Frequency tallies of MMG amplitude versus time relationship regression models for individual subjects (n = 14)


eral pressure waves represented by the MMG. The increasing MMG-Time relationship at 120° can be attributed to continu-ous recruitment of Type II muscle fibers, which are reported to produce greater pressure waves [18]. The synchronization of motor unit twitches has also been suggested to contribute to the increase in MMG amplitude [17]. The different trend at 60° and 90° (increase and plateau) may be explained by the initial increased recruitment of motor units (i.e., Type I and Type II) followed by de-recruitment of Type II [18,19]. The de-recruitment of these fast-twitch fibers could result in the attenuation of the MMG signal. Therefore, it appears that vary-ing elbow joint angles influenced myofibrillar lateral oscilla-tions, thereby affecting the MMG amplitude.The MMG-Time relationship has been previously demonstrat-ed to be dependent on the contraction intensity. During mod-erate level isometric exercise (i.e., 40-60% MVC) the MMG amplitude increased initially but plateaued during the latter half of the contraction [12,13,15,17,18]. At higher intensities (i.e., > 70% MVC) the MMG signal increased initially followed by a progressive decline [14,16-18]. As indicated earlier, these studies used longer contraction time durations. Interestingly, the MMG-Time relationship within the current investigation was not influenced by the two intensity levels (50%, 75% MVC). At 60° and 90°, the two intensities were best fit with a quadratic model, while the 120° joint angle exhibited a linear trend in both conditions. This may have been partly due to the shorter contraction duration used in this study.Individual MMG-Time regression models (Table 5) also re-vealed an influence of elbow joint angle. At 60° the majority of subjects exhibited a linear trend in the MMG versus time relationships. In contrast, as the joint angle increased, these relationships were best fit with quadratic and cubic models (Table 5). Although such trends were evident, there was con-siderable inter-subject variability in the MMG-Time relation-ship. Further, hierarchal regression indicated non-significant MMG-Time relationships in six subjects (Table 5). Previous studies have also reported a high degree of inter-individual variability which was attributed to differences in subcutane-ous tissue layers, motor control strategies, and muscle fiber type composition [31,32].

CONCLUSION

Elbow joint angle and contraction intensity did not signifi-cantly influence EMG and MMG linear slope coefficients. Secondly, polynomial regression analysis revealed linear models were best fit at a lower joint angle (at 60°), whereas increases in joint angle and intensity resulted in signifi-cant quadratic and cubic relationships. This study presents insight into MMG responses during standardized (i.e., 30 second) shorter duration contractions across varied joint angles, which have hitherto not been reported upon. Fu-ture investigations should explore MMG responses during standardized, longer duration (i.e., > 30 seconds), contrac-tions in additional muscle groups at the elbow joint.

CONFLICT OF INTEREST

The authors verify that there is no conflict of interest in this work.

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