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Adaptations during familiarization to resistive exercise
Journal of Electromyography and Kinesiology 17 (2007) 328–335 www.elsevier.com/locate/jelekin
Adaptations during familiarization to resistive exercise Kristina M. Calder, David A. Gabriel
*
Department of Physical Education and Kinesiology, Kinesiological Electromyographic Kinesiology Laboratory, Brock University, St. Catharines, Ont., Canada L2S 3A1 Received 18 August 2005; received in revised form 7 February 2006; accepted 25 February 2006
Abstract This study focused on adaptations during familiarization to resistive exercise. It was also determined if familiarization requires one or more sessions. Twenty-six sedentary, college-aged females were matched and randomly assigned to one of two groups. Measurements were obtained during the initial familiarization period (Group 1: 15 trials on 1 day, Group 2: 5 trials on each of three consecutive days), and during retention tests scheduled two weeks and 3 months after the first test session. Elbow flexion torque and surface electromyography (SEMG) of the biceps and triceps were monitored concurrently. There were no significant differences between groups for any of the criterion measures. There was a significant (p < 0.05) increase (12.4 Nm, or 38.8%) in maximal isometric elbow flexion torque. Biceps (agonist) root-mean-square (RMS) SEMG exhibited a significant (p < 0.05) increase of 95 lV (29%). Triceps (antagonist) RMS SEMG underwent alternating decreases then increases, and each change was significant (p < 0.05). The ratio of biceps to triceps RMS SEMG was used to assess cocontraction, and it followed the same pattern of change as triceps RMS SEMG. We concluded that both groups responded in the same way to testing, regardless of the pattern of the first 15 contractions. The increase in maximal isometric elbow flexion torque was due to neural drive to the bicep (agonist). There was a low level of triceps (antagonist) cocontraction to provide joint stability, and it was adjusted throughout the duration of testing. 2006 Elsevier Ltd. All rights reserved. Keywords: Biceps brachii; Elbow flexion; Electromyographic activity; Resistive exercise; Antagonist cocontraction
1. Introduction It is has been demonstrated that training-related changes in muscle strength are accompanied by a reduction in antagonist cocontraction (Carolan and Cafarelli, 1992). The explanation is quite reasonable: decreased antagonist cocontraction allows agonist muscle strength to manifest itself unimpeded by contraction of the opposing muscle group (Kamen, 1983). However, other studies on antagonist cocontraction following resistance training have reported mixed findings (Colson et al., 1999; Rutherford et al., 2001). Two studies have examined the effects of eccentric training on the shape of the elbow flexion torque–velocity curve while monitoring agonist–antagonist SEMG concurrently *
Corresponding author. Tel.: +1 905 688 5550x4362; fax: +1 905 688 8364. E-mail address: [email protected] (D.A. Gabriel). 1050-6411/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2006.02.006
(Colson et al., 1999; Rutherford et al., 2001). A significant increase in the overall magnitude and shape of the torque– velocity curve and in the amplitude of agonist SEMG activity at each velocity was observed by both research groups. The two studies also reported distinct, non-significant trends in antagonist cocontraction, but in opposite directions. Rutherford et al. (2001) observed a mean increase of 50% while Colson et al. (1999) reported an 11% decrease. Subject variability was implicated in both studies as the reason for non-significant findings. Participants in the study by Colson et al. (1999) had two ‘‘task familiarization’’ sessions within 1-week prior to beginning 7 weeks of resistive exercise. Rutherford et al. (2001) did not report that subjects had any familiarization period; participants did however practice eccentric contractions twice a week for 4 weeks. Thus, one possibility is that alterations in antagonist cocontraction occur during the task familiarization period prior to the initiation of
K.M. Calder, D.A. Gabriel / Journal of Electromyography and Kinesiology 17 (2007) 328–335
strength training studies. It has been theorized that antagonist cocontraction is a strategy that individuals employ when they are unfamiliar with the task requirements (Basmajian, 1977; Carolan and Cafarelli, 1992). The first several maximal effort contractions may be where the changes in cocontraction occur. While interpreting the results of training studies it is important to consider the time-course of measurement (or observation) and the familiarization or practice effects that may be intentionally or unintentionally embedded within the experimental protocol. This is particularly important due to the existing literature that has shown altered cocontraction due to skill acquisition (Englehorn, 1983; Gabriel and Boucher, 2000; Hobart et al., 1975; Moore and Marteniuk, 1986) and to maintain joint stiffness (Baratta et al., 1988; Solomonow et al., 1988). The purpose of this paper was, therefore, to study adaptations in cocontraction during familiarization to resistive exercise in novice participants. A secondary aim was to compare the effects multiple familiarization sessions to an equal number of practice trials within one session for stabilizing strength measures amidst what may be a rapid period of skill acquisition. The results of this study will clarify existing knowledge on rapid alterations in cocontraction during basic strength measurements and provide support for experimental design decisions that relate reliable measurement of muscular strength along a time scale that is appropriate for the given research questions. 2. Methods 2.1. Subjects Twenty-six females (aged 18–32) participated in this study. Females were selected because they have smaller error variances than males during isometric testing (Kroll, 1970). The participants fit the following criteria: they had a body mass index (BMI) of less than 30; they were righthand dominant; and they had not performed any upperlimb resistant training within the past year. The physical characteristics of the participants are presented in Table 1. An informed consent form was read and signed prior to participating in the study in accordance with Brock University’s human ethics board. A written questionnaire to monitor activity levels was administered prior to the first Table 1 Descriptive characteristics for the massed and distributed groups. Means and standard deviations for age, weight, height, forearm length and body mass index (BMI) Physical characteristics
Group 1 (M ± SD)
Group 2 (M ± SD)
Age (yr) Weight (kg) Height (cm) Forearm length (cm) BMI (kg m2) Strength (Nm) Sample size
24.08 ± 3.52 59.47 ± 6.69 164.65 ± 5.31 23 ± 1.2 21.88 ± 1.59 13.52 ± 3.7 13
23.15 ± 3.74 59.93 ± 8.01 165.76 ± 6.09 23 ± 1.5 21.87 ± 3.26 14.65 ± 3.8 13
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session, and again on the last re-test to ensure no new activities were initiated during the study. 2.2. Measurement schedule The focus of this paper was on the familiarization period, prior to initiating a strength training regimen. Testing consisted of only 15 contractions to minimize metabolic and hypertrophic adaptations (Phillips, 2000). Re-tests were scheduled 2 weeks and 3 months after first session. If physiological adaptations occurred, such as a change in muscle fiber cross-sectional area, it would be dissipated over the rest intervals (Mujika and Padilla, 2001), leaving behind the effects due to familiarization. Since another five contractions were performed at each re-test, the total number of contractions was 25. Isometric contractions were used because the moment arm, length-tension of the muscle, and electrode location with respect to the muscle fibers remain constant during isometric contractions. Moreover, the force-velocity effects are eliminated as well (Dowling, 1997). Because we are studying the first few contractions, subjects had to be matched and randomly assigned to their groups based on predicted elbow flexion strength. A regression equation that used weight and circumference of the upper arm was constructed based on data obtained from an earlier study (Gabriel et al., 2001a). Participants were ranked on predicted elbow flexion strength, and then assigned by matched pairs into one of two groups. Measurements were obtained during the initial familiarization period (Group 1: 15 trials on 1 day, Group 2: 5 trials on each of three consecutive days), and during retention tests scheduled 2 weeks and 3 months after the first test session (Fig. 1). 2.3. Recording torque and SEMG activity All subjects listened to a pre-recorded tape to standardize each test session so that the maximal isometric elbow flexion contractions were 5-s in length with a 3-min rest period. This tape instructed them when to flex at the elbow as hard and as fast as possible when they heard ‘‘flex’’ in the recorded ‘‘ready and flex’’ statement. After 5 s the tape stated, ‘‘rest’’, and the subject were told to relax until told to ‘‘flex’’ again. Participants were first seated in a testing chair (Fig. 2). Velcro straps were used to increase stability and minimize extraneous movements. The upper limb was supported at the back of the arm while shoulder and elbow were maintained at 90 of flexion in sagittal plane. The wrist was placed in a half-supinated position and secured within a cuff using Velcro straps, just below the styloid process. The wrist cuff unit was rigidly couple to the load cell (JR3 Inc., Woodland, CA) so the application of force was always perpendicular to the forearm. Prior to electrode placement, the skin was lightly abraded and cleaned with rubbing alcohol to reduce signal
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Fig. 1. Experimental set-up and subject test position.
impedance. Skin impedance was maintained below 10 kX (Grass EZM Electrode Impedance Meter, Astro-Med Inc., Warwick, RI). The surface electromyographic (SEMG) signal was recorded from the biceps brachii using a bipolar surface electrode (DE-2.1, Delsys Inc., Boston, MA). The electrode was placed on the distal third of the biceps belly, away from the motor point towards the distal tendon. Another electrode was placed between the distal tendon and the top of the belly of the triceps brachii lateral head to monitor activity in the antagonist muscle. An adhesive ground electrode was also secured over the clavicle. To ensure that electrode placements remained consistent throughout the experiment, indelible ink was used to mark their locations. Participants were asked to maintain these markings themselves by using indelible ink over the fading
tracings, or by coming into the lab to have the lines retraced. Due to the long time period between testing sessions, digital images of the electrode markings were obtained following the first training session. A tape measure was placed on the subjects arm, starting at the tip of the third finger, and kept taut along the midline of the arm. The digital picture acted as an additional reference for electrode locations. The biceps brachii maximum M-wave was evoked during each session to assess the possibility of cross-talk in triceps antagonist coactivity. The M-wave was evoked with a cathode placed in the axillary fold over the musculocutaneous nerve. The cathode and anode electrodes were connected in a series with an isolation unit (Grass Telefactor SUI8, Astro-Med, Inc., West Warwick, RI) and a stimula-
Fig. 2. Measurement schedule.
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tor (Grass Telefactor S88, Astro-Med Inc., West Warwick, RI) that delivered a square-wave pulse, 1 ms in duration (Calder et al., 2005). The biceps brachii maximum M-wave was not detectable in the triceps SEMG recordings. The bipolar surface electrodes (DE-2.1 Single Differential, Delsys Inc.) pre-amplified the signal with a fixed gain of 10. The bioamplifiers (Bagnoli 4, Delsys Inc., Boston, MA) increased the signal further (100·) before it was band-passed filtered (20–450 Hz). All signals were digitized at 2048 Hz using a 16-bit A/D converter (NI PCI-6052E, National Instruments, Austin, TX) within a range of ±10 V. The force and SEMG signals were collected using a computer-based oscillograph and data acquisition system (DASYLab, DASYTEC National Instruments, Amherst, NH). The data were stored for further analysis on a Pentium III personal computer (Seanix Technology Inc., Blaine, WA). 2.4. Data reduction A 2-s window in the middle of the 5-s isometric contraction was used to calculate the root-mean-square (RMS) amplitude of surface electromyographic (SEMG) activity for the biceps and triceps brachii (Gabriel et al., 2001b). The ratio of biceps brachii (BB) to triceps brachii (TB) RMS SEMG was also calculated to provide a measure of cocontraction (Hebert et al., 1991). This was calculated by dividing triceps brachii RMS SEMG by biceps brachii RMS SEMG. Thus, a decrease in the ratio means a decrease in cocontraction and vice versa. The mean torque value of that window was also obtained (Fig. 3). Data reduction was accomplished using MATLAB (The Mathworks, Inc., Natick, MA). The criterion measures were the mean torque, RMS SEMG amplitude of the biceps
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(agonist activity) and RMS SEMG amplitude of the triceps (antagonist activity), and the ratio of BB-to-TB RMS SEMG amplitude. 2.5. Statistical analysis The data were organized into five blocks (Blocks) of five trials (Trials). For Group 1, the first three blocks of five trials were completed in one session. Group 2 performed the first three blocks of five trials across consecutive days. There was then a re-test scheduled 2 weeks after the last contraction of Block 3; this first re-test was Block 4. Another re-test was scheduled 3 months after Block 3. The last block of five trials was Block 5. A split-plot factorial analysis of variance (ANOVA) was used to test for significant differences. There was one between groups factor (distribution of the first 15 contractions) and two within groups factors, Blocks and Trials. Tukey’s honestly significant difference (HSD) test was used for post hoc testing of the means (Kirk, 1968). All statistical procedures were performed in SYSTAT (SPSS Inc., Chicago, IL). A significance level of p < 0.05 was adopted for this study. The intraclass correlation analysis of variance (ANOVA) model was used to determine the reliability of the criterion measures (Calder et al., 2005). This is a fully nested ANOVA model wherein trials were nested within blocks, which were also nested within subjects. Subjects were then classified as the main effect (between-subjects). The resulting mean squares (MS) were then used to construct the intraclass correlation coefficient (ICC) that is influenced by the true score variance, error variance due to blocks, and error variance due to trials. The reliability was estimated by:
Fig. 3. A representative trial from one subject to show the recorded force from the elbow flexors (top panel), surface electromyographic (SEMG) activity of the biceps (middle panel) and the triceps (bottom panel) brachii during a maximal isometric elbow flexion strength trial. The vertical line shows the 2-s data window, centered at the middle of the contraction.
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r2true
ICC ¼ r2e1
¼
r2true MS
þ
r2e
2 a0
r2e
þ a0
1
n0
Trials
MS
Blocks MS Trials n0 MS Subjects MS Blocks ¼ a0 n0
r2e2 ¼ r2true
In these equations, a 0 is number of blocks, n 0 is number of trials, r2e2 is error variance due to blocks, r2e1 is error variance due to trials, and r2true is the true score variance. The standard error of measurement (SEM) was used to assess the reliability of the criterion measures within subjects (Weir, 2005). The SEM was calculated as follows: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SEM ¼ SD 1 ICC; where the SD was the standard deviation of the scores was determined from the ANOVA. The SD of the scores was derived from the total sum of squares error (SStotal): rffiffiffiffiffiffiffiffiffiffiffiffi SStotal SD ¼ . N 1 3. Results The means and standard deviations for the physical characteristics of the subjects indicate that the two groups were matched well (Table 1). The split-plot factorial ANOVA yielded no significant differences between the two groups for any of the criterion measures. This was not due to a lack of power as the effect sizes ranged between 0.03 and 0.46. Further, there were no significant Group · Block interaction terms for any of the criterion measures, indicating that the two groups responded to testing in the same way. There were no significant main or higher order effects for Trials. The only significant main effect was for Blocks. The trial data were therefore collapsed within each block, and the block data were collapsed across groups for further analysis using a one-way repeated measure ANOVA (Table 2).
Elbow flexion torque increased 12.4 Nm (38.8%) from Blocks 1 to 5 (p < 0.05). Post hoc testing revealed that each block was significantly different from the other, except Blocks 2 and 3. The ICC for elbow flexion torque was 0.88. Bicep brachii (agonist) RMS SEMG followed the same pattern of change as elbow flexion torque. There was a significant increase (p < 0.05) across the five blocks, totaling 95 lV (29%). Post hoc analysis further showed that Blocks 2 and 3 were the only ones not significantly different from one another. The ICC for biceps brachii (agonist) RMS SEMG was 0.97. There was an alternating decrease then increase in triceps brachii (antagonist) RMS SEMG from the first to last block, with each block significantly (p < 0.05) different from the other. Triceps brachii (antagonist) RMS SEMG had an ICC of 0.72. The ratio of BB-toTB RMS SEMG followed the same significant (p < 0.05) changes as observed for triceps brachii (antagonist) RMS SEMG. 4. Discussion This study explored adaptations that occur during the task familiarization period. We also addressed the question of whether the familiarization period can be completed in a single session, or if multiple sessions are necessary. The strongest interpretations are those based on torque. The two groups responded to testing in the same way, indicating that familiarization can be completed in a singlesession. However, both groups also exhibited further increases in maximal strength upon re-test, 2-weeks and 3 months after the first test session. Biceps RMS SEMG increased monotonically across the five blocks. Triceps RMS SEMG alternated decreases with increases across the five blocks. The same was true for the ratio of BB-toTB RMS SEMG. 4.1. Reliability of the criterion measures The reliability of the criterion measures was good. When subjects are able to reproduce their own score, the scores are tightly grouped around the subject’s own mean. In this
Table 2 The means (M), standard deviations (SD), standard error of measurement (SEM), percent sources of variance for the intraclass correlation coefficients (ICCs) for elbow flexion torque, biceps brachii (BB) and triceps brachii (TB) root-mean-square (RMS) surface electromyographic (SEMG) amplitude, and the ratio of BB-to-TB RMS SEMG activity (ratio) Torque (Nm) (M ± SD) 32.0 ± 8.0 33.7 ± 8.3 34.1 ± 8.6 37.7 ± 8.6 44.4 ± 7.3 SEM r2Trials r2Blocks r2true ICC
1.9 N m 8% 36.1% 55.9% 0.88
BB SEMG (lV) (M ± SD)
TB SEMG (lV) (M ± SD)
Ratio (M ± SD)
324 ± 177 350 ± 203 362 ± 175 389 ± 222 419 ± 213
67 ± 51 58 ± 44 74 ± 52 33 ± 44 99 ± 44
0.213 ± 0.165 0.191 ± 0.155 0.200 ± 0.126 0.083 ± 0.093 0.266 ± 0.136
8 lV 8.4% 12.3% 79.3% 0.97
5 lV 5% 62% 33% 0.72
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way, the scores of one subject are very different from the scores of another, and the between subjects means squares error is high. This is also reflected as a high true score variance. Measures that are highly reliable have a true score variance ðr2true Þ that accounts for the greatest percentage of the total variance (Weir, 2005). The variance between subjects was 79.3% of the total variance in biceps (agonist) RMS SEMG scores and the ICC was 0.97. For elbow flexion torque, participants were consistent at reproducing their own scores but those scores increased across blocks. The scores for the later blocks for the weaker subjects were therefore similar to the scores for the stronger subjects for the earlier blocks. Thus, the differences between subjects became more obscured. The ICC for elbow flexion torque was reduced to 0.88. This is considered very good because the variance between subjects still occupied the greatest percentage (55.9%) of the total variance. Another factor affecting reliability is the range of scores. If the range is limited, it is difficult to differentiate between subjects, even if they are very consistent at reproducing their own scores. A limited range therefore artificially lowers the ICC. The range for triceps (antagonist) RMS SEMG scores was 237 lV. This is 5-times lower than the range of scores (1216 lV) for biceps (agonist) RMS SEMG. Thus, the ICC (0.72) for triceps (antagonist) RMS SEMG was artificially low due to a limited range of scores. This measure can still be considered to have good reliability, and the small SEM (5 lV, or 7%) reinforces this view. 4.2. Elbow flexion torque Participants continued to increase maximal isometric elbow flexion strength across the five blocks of testing, even over the two rest intervals. If there was a physiological adaptation, such as a change in muscle fibre cross-sectional area, detraining would have occurred during the rest intervals (Mujika and Padilla, 2001). There are several studies that report a similar phenomenon for maximal isometric contractions of the wrist flexors (Kroll, 1963), knee extensors (Hood and Forward, 1965; Knight and Kamen, 2001; Schenck and Forward, 1965; Warshal, 1979), plantar flexors (Kamen, 1983), elbow extensors (Gabriel et al., 2001b), and several intrinsic finger muscles (Kamen et al., 1995; Patten et al., 2001). It appears to be a robust observation: only a few contractions were required to significantly improve the strength of a muscle through familiarization. Familiarization can be linked with the cognitive phase of motor learning wherein the participant is primarily concerned with understanding the demands of the task (Fitts and Posner, 1967). Strength gains that occur during the early phase of resistance training have been attributed to ‘‘neural factors’’ as measured by SEMG activity (Moritani and de Vries, 1979). These neural factors as described in Sections 4.3 and 4.4 may also reflect task learning.
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This next question is: how much familiarization is required for a study that utilizes dynamic contractions, under more complicated motion patterns? The simplicity of maximal isometric elbow flexion explains why 15 contractions performed within a single session was equivalent to 15 contractions completed over 3 consecutive days. A meta-analysis by Donovan and Radosevich (1999) indicated that more difficult tasks require practice to be spaced across several sessions. When the task is highly complex, the practice spacing allows the learner to process the cognitive aspects of the skill, and allows for mental practice (Lee and Genovese, 1988). 4.3. Biceps SEMG activity To provide insight into the mechanisms for changes in strength, we evaluated the SEMG activity of the biceps and triceps brachii. The amplitude of SEMG activity is used in physiological studies to monitor neural drive to the muscle (Moritani and de Vries, 1979). The current work showed that the biceps brachii (agonist) RMS SEMG increased 95 lV (29%) across blocks. Moritani and deVries (Lee and Genovese, 1988) reported a 223 lV increase in the muscle activation of the biceps. However, their subjects followed an 8-week training program for the elbow flexors wherein they trained three times per week. An increase in SEMG amplitude is indicative of either increased recruitment of additional motor units or increased firing rates; it is not possible to differentiate between the two potential mechanisms (Farina et al., 2004). During the familiarization period, it is possible that participants learned how to activate the muscles. However, research on the expression of muscular strength using the twitch interpolation (TI) technique demonstrated that participants can achieve activation levels (AL) close to 100%. DeSerres and Enoka (1998) report an AL of 97.8% for their younger subjects while Yue et al. (2000) observed an AL of 98.5%. Allen and colleagues (Allen et al., 1998) used the TI technique to show that, when the biceps brachii is maximally activated, force measured at the load cell can be increased by recruitment of the brachioradialis and shoulder flexion. The shoulder was placed in 90 of flexion for testing to minimize its contribution. Therefore, in the present data, there was a distinct possibility that familiarization involved greater recruitment of the brachioradialis, but it does not explain the rise in biceps brachii (agonist) RMS SEMG. 4.4. Triceps SEMG activity One of the ideas motivating this paper was that trainingrelated decreases in antagonist cocontraction allows the agonist muscle to contract unimpeded by contraction of the opposing muscle group. The result would be an increase in joint torque. In the present study, we showed that increases in maximal elbow flexion torque paralleled increases in biceps brachii (agonist) RMS SEMG, while
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cocontraction was constantly adjusted throughout the duration of the study. The reliability and SEM for triceps brachii (antagonist) RMS SEMG indicate that these results were are not due to measurement error. The M-wave data also confirmed that cross-talk from the biceps did not contaminate the triceps SEMG. The findings therefore put into question the proposed relationship between antagonist cocontraction and the expression of training-related changes in maximal isometric joint torque. There are different computational approaches to quantifying the degree of antagonist contraction, each with its own advantages and disadvantages (Damiano et al., 2000; Kellis, 1998). However, the main limitation of the computational approach is that the results are highly dependent upon the method used (Damiano et al., 2000; Kellis, 1998). For this reason, we used both triceps (antagonist) RMS SEMG values and a derived measure. The derived measure was the ratio of BB-to-TB RMS SEMG activity to assess antagonist cocontraction. It provided no new information above that for triceps (antagonist) RMS SEMG. In fact, it distorted the percent magnitude of change across blocks and interpretations based on these values would be misleading. Kellis (1998) advocates a consideration of the moment arms (MA) and physiological cross-sectional areas (PCSA) of the muscles involved when interpreting the functional significance of antagonist SEMG. Cadaveric work has determined that the combined PCSA of the extensors is 14.9 cm2 while for the flexors it is 13.2 cm2 (Murray et al., 2000). However, at 90 of elbow flexion, the MA of the biceps brachii is near its maximum of 4.7 cm while that of triceps brachii is near its minimum of 1.9 cm (Murray et al., 1995). The vast difference in moment potential (MA · PCSA) between the flexors and extensors at 90 of elbow flexion was evident in this study, because an increase in the ratio of BB-to-TB RMS SEMG activity to 0.266 failed to reduce maximal isometric elbow flexion moment. The triceps brachii would therefore have to generate a great deal more activity than 99 ± 44 lV to reduce maximal isometric elbow flexion torque (Kaufman et al., 1991). The mean values of 33–99 lV for triceps (antagonist) RMS SEMG were similar to those obtained in an earlier study by Gabriel and Kroll (1991) with a similar task and female participants. It is reasonable to suggest that this low-level of cocontraction served only to stabilize the joint, and not modulate agonist force output. However, antagonistic cocontraction is also necessary to evenly distribute pressure across the articulating surfaces, and avoid a focal stress point that would lead to wear, a potential factor in degenerative joint disease (Baratta et al., 1988; Lindscheid, 1982; Solomonow et al., 1988). On a scale bounded by the range of triceps brachii (antagonist) RMS SEMG scores, it would appear that there were large increases and decreases in the level of cocontraction, especially when expressed as a percent change. The statistically significant alterations were not functional and may be attributed to good reliability and
a small SEM. If the changes were practically significant, we would expect that maximal isometric elbow flexion torque would covary with levels of triceps brachii (antagonist) RMS EMG, but it did not. When considered on a much larger scale of the biceps, analogous to plotting both the biceps and triceps means same graph, the triceps provided a ‘‘relatively’’ constant level of stabilization. 5. Conclusion The two groups responded to testing in the same way, indicating that familiarization can be completed in a single-session. There was a progressive increase in maximal isometric elbow flexion strength across all five blocks of testing. Thus, the increase continued despite rest intervals during which detraining should have occurred. The increase in maximal isometric elbow flexion torque was due an increase in SEMG activity of the flexors (agonist), which followed a similar pattern of change. There was a low-level of triceps (antagonist) cocontraction that served to stabilize the joint, but was not directly involved in the expression of strength. These conclusions are valid only for isometric contractions. There are additional neural and biomechanical factors related to concentric or eccentric joint actions wherein antagonist muscle function may play a greater role. Acknowledgement This study was supported by the Natural Sciences and Engineering Research Council of Canada. References Allen GM, McKenzie DK, Gandevia SC. Twitch interpolation of the elbow flexor muscle at high forces. Muscle Nerve 1998;21:318–28. Baratta R, Solomonow M, Zhou BH, Letson D, Chuinard R, D’Ambrosia R. The role of antagonist musculature in maintaining knee stability. Am J Sports Med 1988;16:113–22. Basmajian JV. Motor learning and control: a working hypothesis. Arch Phys Med Rehabil 1977;58:38–41. Calder K, Hall LA, Lester SM, Inglis GI, Gabriel DA. Reliability of the biceps brachii m-wave. J Neuro Eng Rehabil 2005;2:33. Carolan B, Cafarelli E. Adaptations after isometric resistance training. J Appl Physiol 1992;73:911–7. Colson S, Pousson M, Martin A, Van Hoecke J. Isokinetic elbow flexion and coactivation following eccentric training. J Electromyogr Kinesiol 1999;9:13–20. Damiano DL, Martellotta TL, Sullivan DJ, Granata KP, Abel MF. Muscle force production and functional performance in spastic cerebral palsy: relationship of cocontraction. Arch Phys Med Rehabil 2000;81:895–900. DeSerres SJ, Enoka RM. Older adults can maximally activate the biceps brachii by voluntary command. J Appl Physiol 1998;84:284–91. Donovan JJ, Radosevich DJ. A meta-analytic review of the distribution of practice effect: now you see it, now you do not. J Appl Psychol 1999;84:795–805. Dowling JJ. Use of electromyography for the non-invasive prediction of muscle forces. Sports Med 1997;24:82–96. Englehorn RA. Agonist and antagonist muscle EMG activity changes with skill acquisition. Res Quart Exerc Sport 1983;54:315–23.
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Mujika I, Padilla S. Muscular characteristics of detraining in humans. Med Sci Sports Exerc 2001;33:1297–303. Murray WM, Delp SL, Buchanan TS. Variation of muscle moment arms with elbow and forearm position. J Biomech 1995;28:513–25. Murray WM, Buchanan TS, Delp SL. The isometric functional capacity of muscles that cross the elbow. J Biomech 2000;33:943–52. Patten C, Kamen G, Rowland DM. Adaptations in maximal motor unit discharge rate to strength training in young and older adults. Muscle Nerve 2001;24:542–50. Phillips SM. Short-term training: when do repeated bouts of exercise become training?. Can J Appl Physiol 2000;25:185–93. Rutherford OM, Purcell C, Newham DJ. The human force–velocity relationship: activity in the knee flexor and extensor muscles before and after eccentric practice. Eur J Appl Physiol 2001;84:133–40. Schenck JM, Forward EM. Quantitative strength changes with test repetitions. Phys Ther 1965;45:562–9. Solomonow M, Baratta R, Zhou BH, D’Ambrosia R. Electromyogram coactivation patterns of the elbow antagonist muscles during slow isokinetic movement. Exp Neurol 1988;100:470–7. Warshal D. The reliability of isometric strength gain in therapeutic assessment. Am Corr Ther J 1979;33:188–91. Weir JP. Quantifying test–retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 2005;19:231–40. Yue GH, Ranganathan VK, Siemionow V. Evidence of inability to fully activate human limb muscle. Muscle Nerve 2000;23:376–84. Kristina M. Calder received a B.Phed. degree in physical education and kinesiology and M.Sc. degree in biomechanics from Brock University in 2002 and 2004, respectively. She is currently a doctoral student in the School of Rehabilitation Therapy at Queen’s University. Her research interests include biological signal processing, electrophysiology, fatigue, and repetitive strain injuries.
David A. Gabriel received a Ph.D. from McGill University in 1995 and completed a post-doctoral fellowship at the Mayo Clinic in 1997. He is currently an associate professor in the Department of Physical Education and Kinesiology at Brock University, where he teaches biomechanics and directs the Electromyographic Kinesiology Laboratory. He also holds cross-appointments in the Departments of Biology and Neuroscience. His research interests involve mathematical modeling of the SEMG signal, non-invasive methods for detecting motor unit firing patterns, and training-related changed in the SEMG signal and their functional significance.
Adaptations during familiarization to resistive exercise Kristina M. Calder, David A. Gabriel
*
Department of Physical Education and Kinesiology, Kinesiological Electromyographic Kinesiology Laboratory, Brock University, St. Catharines, Ont., Canada L2S 3A1 Received 18 August 2005; received in revised form 7 February 2006; accepted 25 February 2006
Abstract This study focused on adaptations during familiarization to resistive exercise. It was also determined if familiarization requires one or more sessions. Twenty-six sedentary, college-aged females were matched and randomly assigned to one of two groups. Measurements were obtained during the initial familiarization period (Group 1: 15 trials on 1 day, Group 2: 5 trials on each of three consecutive days), and during retention tests scheduled two weeks and 3 months after the first test session. Elbow flexion torque and surface electromyography (SEMG) of the biceps and triceps were monitored concurrently. There were no significant differences between groups for any of the criterion measures. There was a significant (p < 0.05) increase (12.4 Nm, or 38.8%) in maximal isometric elbow flexion torque. Biceps (agonist) root-mean-square (RMS) SEMG exhibited a significant (p < 0.05) increase of 95 lV (29%). Triceps (antagonist) RMS SEMG underwent alternating decreases then increases, and each change was significant (p < 0.05). The ratio of biceps to triceps RMS SEMG was used to assess cocontraction, and it followed the same pattern of change as triceps RMS SEMG. We concluded that both groups responded in the same way to testing, regardless of the pattern of the first 15 contractions. The increase in maximal isometric elbow flexion torque was due to neural drive to the bicep (agonist). There was a low level of triceps (antagonist) cocontraction to provide joint stability, and it was adjusted throughout the duration of testing. 2006 Elsevier Ltd. All rights reserved. Keywords: Biceps brachii; Elbow flexion; Electromyographic activity; Resistive exercise; Antagonist cocontraction
1. Introduction It is has been demonstrated that training-related changes in muscle strength are accompanied by a reduction in antagonist cocontraction (Carolan and Cafarelli, 1992). The explanation is quite reasonable: decreased antagonist cocontraction allows agonist muscle strength to manifest itself unimpeded by contraction of the opposing muscle group (Kamen, 1983). However, other studies on antagonist cocontraction following resistance training have reported mixed findings (Colson et al., 1999; Rutherford et al., 2001). Two studies have examined the effects of eccentric training on the shape of the elbow flexion torque–velocity curve while monitoring agonist–antagonist SEMG concurrently *
Corresponding author. Tel.: +1 905 688 5550x4362; fax: +1 905 688 8364. E-mail address: [email protected] (D.A. Gabriel). 1050-6411/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2006.02.006
(Colson et al., 1999; Rutherford et al., 2001). A significant increase in the overall magnitude and shape of the torque– velocity curve and in the amplitude of agonist SEMG activity at each velocity was observed by both research groups. The two studies also reported distinct, non-significant trends in antagonist cocontraction, but in opposite directions. Rutherford et al. (2001) observed a mean increase of 50% while Colson et al. (1999) reported an 11% decrease. Subject variability was implicated in both studies as the reason for non-significant findings. Participants in the study by Colson et al. (1999) had two ‘‘task familiarization’’ sessions within 1-week prior to beginning 7 weeks of resistive exercise. Rutherford et al. (2001) did not report that subjects had any familiarization period; participants did however practice eccentric contractions twice a week for 4 weeks. Thus, one possibility is that alterations in antagonist cocontraction occur during the task familiarization period prior to the initiation of
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strength training studies. It has been theorized that antagonist cocontraction is a strategy that individuals employ when they are unfamiliar with the task requirements (Basmajian, 1977; Carolan and Cafarelli, 1992). The first several maximal effort contractions may be where the changes in cocontraction occur. While interpreting the results of training studies it is important to consider the time-course of measurement (or observation) and the familiarization or practice effects that may be intentionally or unintentionally embedded within the experimental protocol. This is particularly important due to the existing literature that has shown altered cocontraction due to skill acquisition (Englehorn, 1983; Gabriel and Boucher, 2000; Hobart et al., 1975; Moore and Marteniuk, 1986) and to maintain joint stiffness (Baratta et al., 1988; Solomonow et al., 1988). The purpose of this paper was, therefore, to study adaptations in cocontraction during familiarization to resistive exercise in novice participants. A secondary aim was to compare the effects multiple familiarization sessions to an equal number of practice trials within one session for stabilizing strength measures amidst what may be a rapid period of skill acquisition. The results of this study will clarify existing knowledge on rapid alterations in cocontraction during basic strength measurements and provide support for experimental design decisions that relate reliable measurement of muscular strength along a time scale that is appropriate for the given research questions. 2. Methods 2.1. Subjects Twenty-six females (aged 18–32) participated in this study. Females were selected because they have smaller error variances than males during isometric testing (Kroll, 1970). The participants fit the following criteria: they had a body mass index (BMI) of less than 30; they were righthand dominant; and they had not performed any upperlimb resistant training within the past year. The physical characteristics of the participants are presented in Table 1. An informed consent form was read and signed prior to participating in the study in accordance with Brock University’s human ethics board. A written questionnaire to monitor activity levels was administered prior to the first Table 1 Descriptive characteristics for the massed and distributed groups. Means and standard deviations for age, weight, height, forearm length and body mass index (BMI) Physical characteristics
Group 1 (M ± SD)
Group 2 (M ± SD)
Age (yr) Weight (kg) Height (cm) Forearm length (cm) BMI (kg m2) Strength (Nm) Sample size
24.08 ± 3.52 59.47 ± 6.69 164.65 ± 5.31 23 ± 1.2 21.88 ± 1.59 13.52 ± 3.7 13
23.15 ± 3.74 59.93 ± 8.01 165.76 ± 6.09 23 ± 1.5 21.87 ± 3.26 14.65 ± 3.8 13
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session, and again on the last re-test to ensure no new activities were initiated during the study. 2.2. Measurement schedule The focus of this paper was on the familiarization period, prior to initiating a strength training regimen. Testing consisted of only 15 contractions to minimize metabolic and hypertrophic adaptations (Phillips, 2000). Re-tests were scheduled 2 weeks and 3 months after first session. If physiological adaptations occurred, such as a change in muscle fiber cross-sectional area, it would be dissipated over the rest intervals (Mujika and Padilla, 2001), leaving behind the effects due to familiarization. Since another five contractions were performed at each re-test, the total number of contractions was 25. Isometric contractions were used because the moment arm, length-tension of the muscle, and electrode location with respect to the muscle fibers remain constant during isometric contractions. Moreover, the force-velocity effects are eliminated as well (Dowling, 1997). Because we are studying the first few contractions, subjects had to be matched and randomly assigned to their groups based on predicted elbow flexion strength. A regression equation that used weight and circumference of the upper arm was constructed based on data obtained from an earlier study (Gabriel et al., 2001a). Participants were ranked on predicted elbow flexion strength, and then assigned by matched pairs into one of two groups. Measurements were obtained during the initial familiarization period (Group 1: 15 trials on 1 day, Group 2: 5 trials on each of three consecutive days), and during retention tests scheduled 2 weeks and 3 months after the first test session (Fig. 1). 2.3. Recording torque and SEMG activity All subjects listened to a pre-recorded tape to standardize each test session so that the maximal isometric elbow flexion contractions were 5-s in length with a 3-min rest period. This tape instructed them when to flex at the elbow as hard and as fast as possible when they heard ‘‘flex’’ in the recorded ‘‘ready and flex’’ statement. After 5 s the tape stated, ‘‘rest’’, and the subject were told to relax until told to ‘‘flex’’ again. Participants were first seated in a testing chair (Fig. 2). Velcro straps were used to increase stability and minimize extraneous movements. The upper limb was supported at the back of the arm while shoulder and elbow were maintained at 90 of flexion in sagittal plane. The wrist was placed in a half-supinated position and secured within a cuff using Velcro straps, just below the styloid process. The wrist cuff unit was rigidly couple to the load cell (JR3 Inc., Woodland, CA) so the application of force was always perpendicular to the forearm. Prior to electrode placement, the skin was lightly abraded and cleaned with rubbing alcohol to reduce signal
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Fig. 1. Experimental set-up and subject test position.
impedance. Skin impedance was maintained below 10 kX (Grass EZM Electrode Impedance Meter, Astro-Med Inc., Warwick, RI). The surface electromyographic (SEMG) signal was recorded from the biceps brachii using a bipolar surface electrode (DE-2.1, Delsys Inc., Boston, MA). The electrode was placed on the distal third of the biceps belly, away from the motor point towards the distal tendon. Another electrode was placed between the distal tendon and the top of the belly of the triceps brachii lateral head to monitor activity in the antagonist muscle. An adhesive ground electrode was also secured over the clavicle. To ensure that electrode placements remained consistent throughout the experiment, indelible ink was used to mark their locations. Participants were asked to maintain these markings themselves by using indelible ink over the fading
tracings, or by coming into the lab to have the lines retraced. Due to the long time period between testing sessions, digital images of the electrode markings were obtained following the first training session. A tape measure was placed on the subjects arm, starting at the tip of the third finger, and kept taut along the midline of the arm. The digital picture acted as an additional reference for electrode locations. The biceps brachii maximum M-wave was evoked during each session to assess the possibility of cross-talk in triceps antagonist coactivity. The M-wave was evoked with a cathode placed in the axillary fold over the musculocutaneous nerve. The cathode and anode electrodes were connected in a series with an isolation unit (Grass Telefactor SUI8, Astro-Med, Inc., West Warwick, RI) and a stimula-
Fig. 2. Measurement schedule.
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tor (Grass Telefactor S88, Astro-Med Inc., West Warwick, RI) that delivered a square-wave pulse, 1 ms in duration (Calder et al., 2005). The biceps brachii maximum M-wave was not detectable in the triceps SEMG recordings. The bipolar surface electrodes (DE-2.1 Single Differential, Delsys Inc.) pre-amplified the signal with a fixed gain of 10. The bioamplifiers (Bagnoli 4, Delsys Inc., Boston, MA) increased the signal further (100·) before it was band-passed filtered (20–450 Hz). All signals were digitized at 2048 Hz using a 16-bit A/D converter (NI PCI-6052E, National Instruments, Austin, TX) within a range of ±10 V. The force and SEMG signals were collected using a computer-based oscillograph and data acquisition system (DASYLab, DASYTEC National Instruments, Amherst, NH). The data were stored for further analysis on a Pentium III personal computer (Seanix Technology Inc., Blaine, WA). 2.4. Data reduction A 2-s window in the middle of the 5-s isometric contraction was used to calculate the root-mean-square (RMS) amplitude of surface electromyographic (SEMG) activity for the biceps and triceps brachii (Gabriel et al., 2001b). The ratio of biceps brachii (BB) to triceps brachii (TB) RMS SEMG was also calculated to provide a measure of cocontraction (Hebert et al., 1991). This was calculated by dividing triceps brachii RMS SEMG by biceps brachii RMS SEMG. Thus, a decrease in the ratio means a decrease in cocontraction and vice versa. The mean torque value of that window was also obtained (Fig. 3). Data reduction was accomplished using MATLAB (The Mathworks, Inc., Natick, MA). The criterion measures were the mean torque, RMS SEMG amplitude of the biceps
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(agonist activity) and RMS SEMG amplitude of the triceps (antagonist activity), and the ratio of BB-to-TB RMS SEMG amplitude. 2.5. Statistical analysis The data were organized into five blocks (Blocks) of five trials (Trials). For Group 1, the first three blocks of five trials were completed in one session. Group 2 performed the first three blocks of five trials across consecutive days. There was then a re-test scheduled 2 weeks after the last contraction of Block 3; this first re-test was Block 4. Another re-test was scheduled 3 months after Block 3. The last block of five trials was Block 5. A split-plot factorial analysis of variance (ANOVA) was used to test for significant differences. There was one between groups factor (distribution of the first 15 contractions) and two within groups factors, Blocks and Trials. Tukey’s honestly significant difference (HSD) test was used for post hoc testing of the means (Kirk, 1968). All statistical procedures were performed in SYSTAT (SPSS Inc., Chicago, IL). A significance level of p < 0.05 was adopted for this study. The intraclass correlation analysis of variance (ANOVA) model was used to determine the reliability of the criterion measures (Calder et al., 2005). This is a fully nested ANOVA model wherein trials were nested within blocks, which were also nested within subjects. Subjects were then classified as the main effect (between-subjects). The resulting mean squares (MS) were then used to construct the intraclass correlation coefficient (ICC) that is influenced by the true score variance, error variance due to blocks, and error variance due to trials. The reliability was estimated by:
Fig. 3. A representative trial from one subject to show the recorded force from the elbow flexors (top panel), surface electromyographic (SEMG) activity of the biceps (middle panel) and the triceps (bottom panel) brachii during a maximal isometric elbow flexion strength trial. The vertical line shows the 2-s data window, centered at the middle of the contraction.
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r2true
ICC ¼ r2e1
¼
r2true MS
þ
r2e
2 a0
r2e
þ a0
1
n0
Trials
MS
Blocks MS Trials n0 MS Subjects MS Blocks ¼ a0 n0
r2e2 ¼ r2true
In these equations, a 0 is number of blocks, n 0 is number of trials, r2e2 is error variance due to blocks, r2e1 is error variance due to trials, and r2true is the true score variance. The standard error of measurement (SEM) was used to assess the reliability of the criterion measures within subjects (Weir, 2005). The SEM was calculated as follows: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SEM ¼ SD 1 ICC; where the SD was the standard deviation of the scores was determined from the ANOVA. The SD of the scores was derived from the total sum of squares error (SStotal): rffiffiffiffiffiffiffiffiffiffiffiffi SStotal SD ¼ . N 1 3. Results The means and standard deviations for the physical characteristics of the subjects indicate that the two groups were matched well (Table 1). The split-plot factorial ANOVA yielded no significant differences between the two groups for any of the criterion measures. This was not due to a lack of power as the effect sizes ranged between 0.03 and 0.46. Further, there were no significant Group · Block interaction terms for any of the criterion measures, indicating that the two groups responded to testing in the same way. There were no significant main or higher order effects for Trials. The only significant main effect was for Blocks. The trial data were therefore collapsed within each block, and the block data were collapsed across groups for further analysis using a one-way repeated measure ANOVA (Table 2).
Elbow flexion torque increased 12.4 Nm (38.8%) from Blocks 1 to 5 (p < 0.05). Post hoc testing revealed that each block was significantly different from the other, except Blocks 2 and 3. The ICC for elbow flexion torque was 0.88. Bicep brachii (agonist) RMS SEMG followed the same pattern of change as elbow flexion torque. There was a significant increase (p < 0.05) across the five blocks, totaling 95 lV (29%). Post hoc analysis further showed that Blocks 2 and 3 were the only ones not significantly different from one another. The ICC for biceps brachii (agonist) RMS SEMG was 0.97. There was an alternating decrease then increase in triceps brachii (antagonist) RMS SEMG from the first to last block, with each block significantly (p < 0.05) different from the other. Triceps brachii (antagonist) RMS SEMG had an ICC of 0.72. The ratio of BB-toTB RMS SEMG followed the same significant (p < 0.05) changes as observed for triceps brachii (antagonist) RMS SEMG. 4. Discussion This study explored adaptations that occur during the task familiarization period. We also addressed the question of whether the familiarization period can be completed in a single session, or if multiple sessions are necessary. The strongest interpretations are those based on torque. The two groups responded to testing in the same way, indicating that familiarization can be completed in a singlesession. However, both groups also exhibited further increases in maximal strength upon re-test, 2-weeks and 3 months after the first test session. Biceps RMS SEMG increased monotonically across the five blocks. Triceps RMS SEMG alternated decreases with increases across the five blocks. The same was true for the ratio of BB-toTB RMS SEMG. 4.1. Reliability of the criterion measures The reliability of the criterion measures was good. When subjects are able to reproduce their own score, the scores are tightly grouped around the subject’s own mean. In this
Table 2 The means (M), standard deviations (SD), standard error of measurement (SEM), percent sources of variance for the intraclass correlation coefficients (ICCs) for elbow flexion torque, biceps brachii (BB) and triceps brachii (TB) root-mean-square (RMS) surface electromyographic (SEMG) amplitude, and the ratio of BB-to-TB RMS SEMG activity (ratio) Torque (Nm) (M ± SD) 32.0 ± 8.0 33.7 ± 8.3 34.1 ± 8.6 37.7 ± 8.6 44.4 ± 7.3 SEM r2Trials r2Blocks r2true ICC
1.9 N m 8% 36.1% 55.9% 0.88
BB SEMG (lV) (M ± SD)
TB SEMG (lV) (M ± SD)
Ratio (M ± SD)
324 ± 177 350 ± 203 362 ± 175 389 ± 222 419 ± 213
67 ± 51 58 ± 44 74 ± 52 33 ± 44 99 ± 44
0.213 ± 0.165 0.191 ± 0.155 0.200 ± 0.126 0.083 ± 0.093 0.266 ± 0.136
8 lV 8.4% 12.3% 79.3% 0.97
5 lV 5% 62% 33% 0.72
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way, the scores of one subject are very different from the scores of another, and the between subjects means squares error is high. This is also reflected as a high true score variance. Measures that are highly reliable have a true score variance ðr2true Þ that accounts for the greatest percentage of the total variance (Weir, 2005). The variance between subjects was 79.3% of the total variance in biceps (agonist) RMS SEMG scores and the ICC was 0.97. For elbow flexion torque, participants were consistent at reproducing their own scores but those scores increased across blocks. The scores for the later blocks for the weaker subjects were therefore similar to the scores for the stronger subjects for the earlier blocks. Thus, the differences between subjects became more obscured. The ICC for elbow flexion torque was reduced to 0.88. This is considered very good because the variance between subjects still occupied the greatest percentage (55.9%) of the total variance. Another factor affecting reliability is the range of scores. If the range is limited, it is difficult to differentiate between subjects, even if they are very consistent at reproducing their own scores. A limited range therefore artificially lowers the ICC. The range for triceps (antagonist) RMS SEMG scores was 237 lV. This is 5-times lower than the range of scores (1216 lV) for biceps (agonist) RMS SEMG. Thus, the ICC (0.72) for triceps (antagonist) RMS SEMG was artificially low due to a limited range of scores. This measure can still be considered to have good reliability, and the small SEM (5 lV, or 7%) reinforces this view. 4.2. Elbow flexion torque Participants continued to increase maximal isometric elbow flexion strength across the five blocks of testing, even over the two rest intervals. If there was a physiological adaptation, such as a change in muscle fibre cross-sectional area, detraining would have occurred during the rest intervals (Mujika and Padilla, 2001). There are several studies that report a similar phenomenon for maximal isometric contractions of the wrist flexors (Kroll, 1963), knee extensors (Hood and Forward, 1965; Knight and Kamen, 2001; Schenck and Forward, 1965; Warshal, 1979), plantar flexors (Kamen, 1983), elbow extensors (Gabriel et al., 2001b), and several intrinsic finger muscles (Kamen et al., 1995; Patten et al., 2001). It appears to be a robust observation: only a few contractions were required to significantly improve the strength of a muscle through familiarization. Familiarization can be linked with the cognitive phase of motor learning wherein the participant is primarily concerned with understanding the demands of the task (Fitts and Posner, 1967). Strength gains that occur during the early phase of resistance training have been attributed to ‘‘neural factors’’ as measured by SEMG activity (Moritani and de Vries, 1979). These neural factors as described in Sections 4.3 and 4.4 may also reflect task learning.
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This next question is: how much familiarization is required for a study that utilizes dynamic contractions, under more complicated motion patterns? The simplicity of maximal isometric elbow flexion explains why 15 contractions performed within a single session was equivalent to 15 contractions completed over 3 consecutive days. A meta-analysis by Donovan and Radosevich (1999) indicated that more difficult tasks require practice to be spaced across several sessions. When the task is highly complex, the practice spacing allows the learner to process the cognitive aspects of the skill, and allows for mental practice (Lee and Genovese, 1988). 4.3. Biceps SEMG activity To provide insight into the mechanisms for changes in strength, we evaluated the SEMG activity of the biceps and triceps brachii. The amplitude of SEMG activity is used in physiological studies to monitor neural drive to the muscle (Moritani and de Vries, 1979). The current work showed that the biceps brachii (agonist) RMS SEMG increased 95 lV (29%) across blocks. Moritani and deVries (Lee and Genovese, 1988) reported a 223 lV increase in the muscle activation of the biceps. However, their subjects followed an 8-week training program for the elbow flexors wherein they trained three times per week. An increase in SEMG amplitude is indicative of either increased recruitment of additional motor units or increased firing rates; it is not possible to differentiate between the two potential mechanisms (Farina et al., 2004). During the familiarization period, it is possible that participants learned how to activate the muscles. However, research on the expression of muscular strength using the twitch interpolation (TI) technique demonstrated that participants can achieve activation levels (AL) close to 100%. DeSerres and Enoka (1998) report an AL of 97.8% for their younger subjects while Yue et al. (2000) observed an AL of 98.5%. Allen and colleagues (Allen et al., 1998) used the TI technique to show that, when the biceps brachii is maximally activated, force measured at the load cell can be increased by recruitment of the brachioradialis and shoulder flexion. The shoulder was placed in 90 of flexion for testing to minimize its contribution. Therefore, in the present data, there was a distinct possibility that familiarization involved greater recruitment of the brachioradialis, but it does not explain the rise in biceps brachii (agonist) RMS SEMG. 4.4. Triceps SEMG activity One of the ideas motivating this paper was that trainingrelated decreases in antagonist cocontraction allows the agonist muscle to contract unimpeded by contraction of the opposing muscle group. The result would be an increase in joint torque. In the present study, we showed that increases in maximal elbow flexion torque paralleled increases in biceps brachii (agonist) RMS SEMG, while
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cocontraction was constantly adjusted throughout the duration of the study. The reliability and SEM for triceps brachii (antagonist) RMS SEMG indicate that these results were are not due to measurement error. The M-wave data also confirmed that cross-talk from the biceps did not contaminate the triceps SEMG. The findings therefore put into question the proposed relationship between antagonist cocontraction and the expression of training-related changes in maximal isometric joint torque. There are different computational approaches to quantifying the degree of antagonist contraction, each with its own advantages and disadvantages (Damiano et al., 2000; Kellis, 1998). However, the main limitation of the computational approach is that the results are highly dependent upon the method used (Damiano et al., 2000; Kellis, 1998). For this reason, we used both triceps (antagonist) RMS SEMG values and a derived measure. The derived measure was the ratio of BB-to-TB RMS SEMG activity to assess antagonist cocontraction. It provided no new information above that for triceps (antagonist) RMS SEMG. In fact, it distorted the percent magnitude of change across blocks and interpretations based on these values would be misleading. Kellis (1998) advocates a consideration of the moment arms (MA) and physiological cross-sectional areas (PCSA) of the muscles involved when interpreting the functional significance of antagonist SEMG. Cadaveric work has determined that the combined PCSA of the extensors is 14.9 cm2 while for the flexors it is 13.2 cm2 (Murray et al., 2000). However, at 90 of elbow flexion, the MA of the biceps brachii is near its maximum of 4.7 cm while that of triceps brachii is near its minimum of 1.9 cm (Murray et al., 1995). The vast difference in moment potential (MA · PCSA) between the flexors and extensors at 90 of elbow flexion was evident in this study, because an increase in the ratio of BB-to-TB RMS SEMG activity to 0.266 failed to reduce maximal isometric elbow flexion moment. The triceps brachii would therefore have to generate a great deal more activity than 99 ± 44 lV to reduce maximal isometric elbow flexion torque (Kaufman et al., 1991). The mean values of 33–99 lV for triceps (antagonist) RMS SEMG were similar to those obtained in an earlier study by Gabriel and Kroll (1991) with a similar task and female participants. It is reasonable to suggest that this low-level of cocontraction served only to stabilize the joint, and not modulate agonist force output. However, antagonistic cocontraction is also necessary to evenly distribute pressure across the articulating surfaces, and avoid a focal stress point that would lead to wear, a potential factor in degenerative joint disease (Baratta et al., 1988; Lindscheid, 1982; Solomonow et al., 1988). On a scale bounded by the range of triceps brachii (antagonist) RMS SEMG scores, it would appear that there were large increases and decreases in the level of cocontraction, especially when expressed as a percent change. The statistically significant alterations were not functional and may be attributed to good reliability and
a small SEM. If the changes were practically significant, we would expect that maximal isometric elbow flexion torque would covary with levels of triceps brachii (antagonist) RMS EMG, but it did not. When considered on a much larger scale of the biceps, analogous to plotting both the biceps and triceps means same graph, the triceps provided a ‘‘relatively’’ constant level of stabilization. 5. Conclusion The two groups responded to testing in the same way, indicating that familiarization can be completed in a single-session. There was a progressive increase in maximal isometric elbow flexion strength across all five blocks of testing. Thus, the increase continued despite rest intervals during which detraining should have occurred. The increase in maximal isometric elbow flexion torque was due an increase in SEMG activity of the flexors (agonist), which followed a similar pattern of change. There was a low-level of triceps (antagonist) cocontraction that served to stabilize the joint, but was not directly involved in the expression of strength. These conclusions are valid only for isometric contractions. There are additional neural and biomechanical factors related to concentric or eccentric joint actions wherein antagonist muscle function may play a greater role. Acknowledgement This study was supported by the Natural Sciences and Engineering Research Council of Canada. References Allen GM, McKenzie DK, Gandevia SC. Twitch interpolation of the elbow flexor muscle at high forces. Muscle Nerve 1998;21:318–28. Baratta R, Solomonow M, Zhou BH, Letson D, Chuinard R, D’Ambrosia R. The role of antagonist musculature in maintaining knee stability. Am J Sports Med 1988;16:113–22. Basmajian JV. Motor learning and control: a working hypothesis. Arch Phys Med Rehabil 1977;58:38–41. Calder K, Hall LA, Lester SM, Inglis GI, Gabriel DA. Reliability of the biceps brachii m-wave. J Neuro Eng Rehabil 2005;2:33. Carolan B, Cafarelli E. Adaptations after isometric resistance training. J Appl Physiol 1992;73:911–7. Colson S, Pousson M, Martin A, Van Hoecke J. Isokinetic elbow flexion and coactivation following eccentric training. J Electromyogr Kinesiol 1999;9:13–20. Damiano DL, Martellotta TL, Sullivan DJ, Granata KP, Abel MF. Muscle force production and functional performance in spastic cerebral palsy: relationship of cocontraction. Arch Phys Med Rehabil 2000;81:895–900. DeSerres SJ, Enoka RM. Older adults can maximally activate the biceps brachii by voluntary command. J Appl Physiol 1998;84:284–91. Donovan JJ, Radosevich DJ. A meta-analytic review of the distribution of practice effect: now you see it, now you do not. J Appl Psychol 1999;84:795–805. Dowling JJ. Use of electromyography for the non-invasive prediction of muscle forces. Sports Med 1997;24:82–96. Englehorn RA. Agonist and antagonist muscle EMG activity changes with skill acquisition. Res Quart Exerc Sport 1983;54:315–23.
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Mujika I, Padilla S. Muscular characteristics of detraining in humans. Med Sci Sports Exerc 2001;33:1297–303. Murray WM, Delp SL, Buchanan TS. Variation of muscle moment arms with elbow and forearm position. J Biomech 1995;28:513–25. Murray WM, Buchanan TS, Delp SL. The isometric functional capacity of muscles that cross the elbow. J Biomech 2000;33:943–52. Patten C, Kamen G, Rowland DM. Adaptations in maximal motor unit discharge rate to strength training in young and older adults. Muscle Nerve 2001;24:542–50. Phillips SM. Short-term training: when do repeated bouts of exercise become training?. Can J Appl Physiol 2000;25:185–93. Rutherford OM, Purcell C, Newham DJ. The human force–velocity relationship: activity in the knee flexor and extensor muscles before and after eccentric practice. Eur J Appl Physiol 2001;84:133–40. Schenck JM, Forward EM. Quantitative strength changes with test repetitions. Phys Ther 1965;45:562–9. Solomonow M, Baratta R, Zhou BH, D’Ambrosia R. Electromyogram coactivation patterns of the elbow antagonist muscles during slow isokinetic movement. Exp Neurol 1988;100:470–7. Warshal D. The reliability of isometric strength gain in therapeutic assessment. Am Corr Ther J 1979;33:188–91. Weir JP. Quantifying test–retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 2005;19:231–40. Yue GH, Ranganathan VK, Siemionow V. Evidence of inability to fully activate human limb muscle. Muscle Nerve 2000;23:376–84. Kristina M. Calder received a B.Phed. degree in physical education and kinesiology and M.Sc. degree in biomechanics from Brock University in 2002 and 2004, respectively. She is currently a doctoral student in the School of Rehabilitation Therapy at Queen’s University. Her research interests include biological signal processing, electrophysiology, fatigue, and repetitive strain injuries.
David A. Gabriel received a Ph.D. from McGill University in 1995 and completed a post-doctoral fellowship at the Mayo Clinic in 1997. He is currently an associate professor in the Department of Physical Education and Kinesiology at Brock University, where he teaches biomechanics and directs the Electromyographic Kinesiology Laboratory. He also holds cross-appointments in the Departments of Biology and Neuroscience. His research interests involve mathematical modeling of the SEMG signal, non-invasive methods for detecting motor unit firing patterns, and training-related changed in the SEMG signal and their functional significance.