Peak and average rectified EMG measures: Which method of data reduction should be used for assessing core training exercises?☆

Article Outline

• Abstract
• 1. Introduction
• 2. Methods
  • 2.1. Subjects
  • 2.2. Data collection
    • 2.2.1. Equipment set-up
    • 2.2.2. Experimental procedure
    • 2.2.3. Exercise details
      • 2.2.3.1. Normalisation exercises
      • 2.2.3.2. Core stability and strength exercises
  • 2.3. Data processing
  • 2.4. Statistical analysis
    • 2.4.1. Intra-subject variability derived from a single subject
    • 2.4.2. Intra-subject variability derived from multiple subjects
• 3. Results
  • 3.1. Variability of peak and ARV EMG during MVC exercises
  • 3.2. Variability of peak and ARV EMG during core exercises
  • 3.3. Interrelationship between ARV and Peak EMG variables during core exercises
• 4. Discussion
• 5. Conclusions
• References
• Biography
• Copyright

Abstract 

Core strengthening and stability exercises are fundamental for any conditioning training program. Although surface electromyography (sEMG) is used to quantify muscle activity there is a lack of research using this method to investigate the core musculature and core stability. Two types of data reduction are commonly used for sEMG; peak and average rectified EMG methods. Peak EMG has been infrequently reported in the literature with regard to the assessment of core training while even fewer studies have incorporated average rectified EMG data (ARV). The aim of the study was to establish the repeatability of peak and average rectified EMG data during core training exercises and their interrelationship. Ten male highly trained athletes (inter-subject repeatability group; age, 18±1.2years; height, 176.5±3.2cm; body mass, 71±4.5kg) and one female highly trained athlete (intra-subject repeatability group; age; 27years old; height; 180cm; weight; 53kg) performed five maximal voluntary isometric contractions (MVIC) and five core exercises, chosen to represent a range of movement and muscle recruitment patterns. Peak EMG and ARV EMG were calculated for eight core muscles (rectus abdominis, RA; external oblique, EO; internal oblique, IO; multifidis, MF; latissimus dorsi, LD; longissimus, LG; gluteus maximus, GM; rectus femoris, RF) using sEMG. Average coefficient of variation (CV%) for peak EMG across all the exercises and muscles was 45%. This is in comparison to 35% for the ARV method, which was found to be a significant difference (P<0.05), therefore implying that the ARV method is the more reliable measure for these types of exercise. Analysis of the inter-subject and intra-subject CV% values suggest that these exercises and muscles are sufficiently repeatable using sEMG. Five muscles were highly correlated (R>0.70; RA, EO, MF, GM, LG) between peak and ARV EMG suggesting, that for these core muscles, the two methods provide a similar evaluation of muscle activity. However, for other muscles (IO, RF, LD) the relationship was found to range from poor to moderate (R=0.10–0.70). The relationship between peak and ARV EMG was also affected by exercise type. Dynamic low and high-threshold exercises and asymmetrical low-threshold exercises had a moderate correlation between the variables (R=0.74–0.81), while the static exercise showed a poor correlation (R=0.46). It can be concluded that there are similarities between the two EMG variables, however due to the effect of type of exercise and muscle on the EMG data, both methods should be included in any future EMG study on the core musculature and core stability exercises.

Keywords: EMG, ARV EMG, MVIC, Core stability, Core strength

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1. Introduction 

The core refers to the musculature of the shoulder stabilisers, trunk and the upper leg muscles (Lehman, 2006, McGill, 2002, Elphinston, 2004, Santana, 2003). Almost all athletic movements involve the core and consequently, core exercises are incorporated into most sports training programs. Core training uses a combination of dynamic and static exercises, some of which are asymmetrical (e.g. birddog (McGill, 1999) resulting in alternating demands on the left and right side of the body) and some are performed on unstable surfaces and with a small base of support (Posner-Mayer, 1995, Cosio-Lima et al., 2003, Check, 1999). The resulting muscle activity occurs not only to move the limbs/objects into the desired position but also to maintain body posture (McGill, 1999). Regularly performing these types of exercises is believed to result in improvements to core stability and core strength due to improvements in proprioception, muscle recruitment and muscular/body control (Stanton et al., 2004, Trappe and Pearson, 1994, Hubley-Kozey and Vezina, 2002, Hasegawa, 2004, Hakkinen et al., 2001). This in turn may lead to an improvement in overall sporting performance by, for example, enhancing force transfer through the body (McGill, 1999). Surface electromyography (sEMG) has been used to quantify muscle activity during dynamic and static body movements during core musculature training (Axler and McGill, 1997, McGill, 2001). Such data if proven to be reliable would be vital to coaches/athletes and would enable them to identify exercises which can maximise training adaptations and hence improve core stability and/or core strength. Despite this, the biomechanics of core exercises are not fully understood (Akuthota and Nadler, 2004).

EMG data processing is complex and the muscle activity can be summarised using different output variables (De Luca, 1997). Two of the more common summary measures are peak EMG and Average Rectified Variable EMG (ARV EMG). The calculation of both variables involves normalising the EMG data which involves the subject performing a preliminary restrained exercise (i.e. isokinetic, isometric and isotonic exercises) that elicits an assumed maximal voluntary isometric contraction (MVIC) of a given muscle (Ekstorm et al., 2005). The peak EMG variable can then be expressed as a percentage of this MVIC (McGill, 1999, Axler and McGill, 1997, Vezina and Hubley-Kozey, 2000, Arokoski et al., 1999). The peak EMG variable gives a measure of the maximal activity of the given muscle during the exercise and has been used to quantify muscle activity during core exercises (Axler and McGill, 1997). In contrast, the ARV EMG is a measure of the area under the normalised EMG time-series curve divided by the time period (Hatton et al., 2008, Edwards et al., 2008, Merletti, 1999) (Fig. 1). This variable will include an indication of any submaximal activity which may occur during the stabilisation of the body (Comerford, 2007) particularly when performing the exercise on an unstable surface or with a small base of support as occurs during many routine core exercises. Previous research on the core muscle activations patterns (Hildenbrand and Noble, 2004, Warden et al., 1999) has found that by using different EMG data reduction procedures, differences in the subsequent level of muscular activity during core stability exercises are reported. For example, Hildenbrand and Noble (2004) used mean integrated EMG activity by calculating the area under the rectified EMG curve and dividing this by the elapsed time for 5 repetitions. Meanwhile, Warden et al. (1999) used peak EMG values for the same muscles during the same sit up techniques by identifying the greatest EMG value during the exercise repetitions. Subsequently the two studies reported differing levels of EMG activity for the same muscles and concluded that this could have been due to the different data reduction procedures. This highlights the potential importance of measuring more than one EMG processing method.

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• Fig. 1. 

  Diagram of the ARV EMG and Peak EMG processing method. Integral, repetition duration and peak values for the processed EMG were taken between the onset and offset points. Also shown is the method of establishing the onset and offset values for each repetition. (A) Baseline data to calculate onset threshold and (B) baseline data to calculate offset threshold.

In addition to functional relevance another consideration when choosing a summary measure for EMG is the variability of the data both within and between subjects (De Luca, 1997, De Luca, 1993, Burdon, 2006, Basmajian and De Luca, 1985). Factors such as cross talk (Farina et al., 2004, Winter et al., 1994) and the quasi-random nature of the EMG signal due to differing neural recruitment patterns makes the signal susceptible to large variations between measurements (De Luca, 1997). While it has been found that by following careful data collection procedures, reliable sEMG data can be obtained (Komi and Buskirk, 1970, Kadaba et al., 1985, Giroux and Lamontagne, 1990, Finni et al., 2007, Finucane et al., 1998, Golhofer et al., 1990, Goodwin et al., 1999), the variability in the measures can be high (10–30%) (Jackson et al., 2008). Furthermore, although no published data on the coefficients of variation (CV) for the core musculature exists, CV values of 30–50% from ultrasound studies have been reported (Mannion et al., 2008). It is therefore expected that variability is a likely problem for assessing core musculature which could obscure interpretation of differing demands and muscle roles during core exercises.

The aims of this study are twofold. The first aim of the study is to quantify the variability of peak and ARV EMG data during core training exercises and the second is to establish which method may be the more appropriate for the assessment of muscular activity during core stability and core strength exercises.

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2. Methods 

2.1. Subjects 

Eleven athletes (10 male, age, 18±1.02years; height, 177±1.5cm; body mass, 76±2.1kg; one female, age, 18±1.2years; height, 175.5±1.1cm; body mass, 71±1.8kg) volunteered to participate in the study. The 10 male subjects performed the protocol on a single day. The EMG electrodes were positioned on the muscle landmarks (see below) at the beginning of the day and remained in position until the end of the day’s data collection. A single subject (female, age; 27years old; height; 180cm; weight; 53kg) repeated the protocol on three separate days. In this case the electrode landmark was marked with a permanent marker to ensure the same placement on the following days. Experimental test protocols were approved by the Teesside University Ethical Committee. All subjects were highly trained and experienced in performing core stability and strength exercises thus minimising the potential for any learning effects. The subjects were in full health and did not report any feelings of pain when performing the tests.

2.2. Data collection 

2.2.1. Equipment set-up 

EMG signals were recorded from 8 right-sided core muscles (Table 1). The reference electrode was placed on the right iliac crest. Muscles were selected on the basis that they are important to core stability and core strength (Elphinston, 2004, Hubley-Kozey and Vezina, 2002, Hakkinen et al., 2001, McGill et al., 2003, McGill, 1991, Marshall and Murphy, 2003, Hubley-Kozey and Vezina, 2002, Hodges et al., 1999, Gardner-Morse et al., 1995, Faries and Greenwood, 2007). Each landmark was identified using anatomical landmarks by a qualified physiotherapist and shaved and cleaned using alcoholic wipes. All electrodes were securely taped to the skin and the data was subsequently high pass filtered at 20Hz to reduce any movement artefacts. EMG data was collected (sampling rate 1000Hz) using Delsys Wireless Myomonitor III device with surface electrodes (Delsys DE-2.3 Single Differential Surface Electrode; inter-electrode distance 1cm; bar type electrode, contact dimensions 10×1mm, 99.9% Ag; Gain 1000; Bandwidth 20–450Hz; common mode rejection ratio of −92dB, pre-amplifier gain 1000V/V ±1%, input impedance of >10¹⁵Ω//0.2pf) and saved using Delsys EMGWorks Acquisition software.

Table 1. Eight core muscles were analysed using sEMG; abbreviations and placements. Locations based on previous research.

Cram J. Introduction to surface electromyography. 2nd ed. Jones & Bartlett Publishers; 2008.2.2.2. Experimental procedure 

Due to the athletes being familiar with performing core exercises, the learning effects of performing these exercises are expected to be low given the highly trained nature of the sample. Any learning effect was further minimised by introducing the exercises one week prior to data collection. Subjects were provided with a written explanation of each exercise, shown a demonstration and practised each MVIC and core exercises at the required repetition rate. On the day of testing sEMG data was first recorded with the muscles fully relaxed (subject lay prone on the floor) to define the baseline for each muscle channel.

2.2.3. Exercise details 

2.2.3.1. Normalisation exercises 

The choice of MVIC exercise does result in some variability between measurements (Ekstorm et al., 2005, Enoka and Fuglevand, 1993). However, previous research has established that the use of numerous static MVIC exercises does result in the least variability of data and that this method is suitable for the normalisation process of EMG data (Bolgla and Uhl, 2005, Yang and Winter, 1984). Previous studies (Ekstorm et al., 2005, Konrad et al., 2001) have recommended using more than one MVIC exercise to ensure a maximum activation for a muscle. Accordingly, five maximal voluntary isometric exercises (MVIC) were performed three times (with oneminute rest between each) for 10s to ensure a true MVIC from each muscle (details of each MVIC exercise can be found in Table 2). In order to minimise the effect of the muscle length–tension relationship on the resultant EMG output (Urquhart et al., 2005, Howard and Enoka, 1991) the MVIC exercises were performed in a similar body position to those of the core stability exercises (Table 3). For each subject, for the resisted exercises, the amount of weight needed to prevent any body angle movement occurring was established and then used during the MVIC exercises (this ranged from 20 to 35kg of free weights). Each MVIC exercise was performed three times for 10s with a twominutes rest period between each repetition. Subjects were given verbal encouragement during each MVC and core exercise to help ensure a maximum and consistent effort during the EMG data collection period.

Table 2. The MVIC exercises performed (based on Brandon (2006)).

Table 3. Description of core exercises (∗ based on Brandon (2006)).

2.2.3.2. Core stability and strength exercises 

Five core stability and core strength exercises were performed (Table 3). The exercises were selected based on previous research that highlights them as important in developing core stability and core strength (Axler and McGill, 1997, Akuthota and Nadler, 2004, Faries and Greenwood, 2007, Hodges, 1999, Jeffreys, 2002, Kibler et al., 2006, Liemohn et al., 2005, McGill, 1998). These included low-threshold (less demanding, posture related exercises which focus on muscle recruitment) and high-threshold exercises (greater stress on the core musculature thus promoting core strength development) (Comerford, 2007). Some of the exercises are classified twice. For example the sit-hold-twist with resistance exercise is classified as both a dynamic high-threshold exercise and an asymmetrical exercise.

The core exercises were performed continually for a minute and then repeated with oneminute rest between each set. The order of the exercises was performed in a crossover randomised design for each subject. The duration and number of repetitions over which these exercises were performed varied due to the demands of the exercises (Table 3) but these were subsequently time-normalised to muscle activity per second to enable direct comparisons between the exercises. Repetition rates were determined by a certified UK strength and conditioning coach and monitored during testing using a stopwatch. Subjects were instructed to perform controlled, smooth movements in order to minimise the variability of the EMG signal (Konrad et al., 2001).

2.3. Data processing 

Raw sEMG signals for both MVIC and the core exercises were bandpass filtered at 20–450Hz and analysed using Acknowledge software program (Biopac Systems Inc., Goleta, CA). A Root Mean Square (RMS) method with a moving average window of 50ms was adopted. To identify the start and end of the repetitions for the dynamic exercises (for the MVIC and static exercises, the middle 5s were used) onset and offset values were calculated using the equation below (Hatton et al., 2008, Edwards et al., 2008, Di Fabio, 1987, Hodges and Bui, 1996) (see Fig. 1). The onset of the repetitions was accepted when the muscle activity exceeded the mean resting value by more than three standard deviations for over 30ms and the cessation of the repetition established when the activity fell below the mean resting value by more than 3 standard deviations for over 30ms (Edwards et al., 2008).

Peak and ARV EMG values were obtained for both the MVIC (to enable normalisation of the EMG signals) and core exercises. Peak values were established by calculating the peak EMG activity during a 5s period for each of the three MVIC repetitions for each muscle. ARV EMG values were established by calculating the average muscle activity per second for each muscle during each MVIC exercise. These values were used to normalise the EMG data during the core exercises.

To establish peak and ARV EMG values during the core exercises, three repetitions of each exercise were analysed. The EMG data was normalised by expressing the peak EMG value as a percentage of the peak EMG value for a subject’s highest corresponding MVIC trial and for each muscle. The highest normalised EMG data value from the three repetitions was then used in all subsequent analysis as the peak EMG value. To calculate the ARV EMG, the sum of the EMG area under the curve was divided by the total number of data points between the onset and offset times, to give an ARV in volts for the repetition (Hatton et al., 2008, Edwards et al., 2008). This was normalised as a percentage of the maximum EMG activity during the MVIC exercises. An average (mean) value was obtained from three repetitions of each exercise for each muscle.

2.4. Statistical analysis 

2.4.1. Intra-subject variability derived from a single subject 

The within day coefficient of variation (CV) was established using the intra-subject data. CV measures were used as this calculation of reliability standardises the standard deviation (SD) to the mean and so removes the variability of the data due to the magnitude of the mean (Reed et al., 2002). As it is proposed that greater SDs will be seen when greater mean muscle activity during the core exercises are performed due to the differing techniques used and the subsequent greater demands placed on the core musculature resulting in the higher activation levels. The CV was established using the equation stated below for each day (day 1 sets 1–3, day 2 sets 4–7, day 3 sets 8–10). The greatest minimum to maximum CV difference occurring on any of these days was expressed as an indication of within day variation and the difference between these values, used as an indication of between day variation (Yang and Winter, 1983).

2.4.2. Intra-subject variability derived from multiple subjects 

The variability of the summary measures were calculated using the log-transformed CV (Bamman et al., 1997) for each type of core exercise to assess the variability of the scores (the normalised EMG values) as a percentage of their mean for each of the core muscles. This was then subjected to back-transformation of the RMS error (Hopkins, 2000) as stated below (Yang and Winter, 1983):

(where O=ERMS).

Two-way mixed consistency ICC values (using SPSS version 12.0) were computed on the sEMG data using peak and ARV values from the core exercises. ICC values were calculated using ICC (3,1) and the equation below (Shrout and Fleiss, 1979):

where BMS, between-subjects mean square; EMS, error mean square; k is the number of repetitions).

To establish the measurement error between the trials, consecutive pairs of trials were examined (trials 1 and 2 and trials 2 and 3). All three trials were then compared to establish total measurement error (CV). If this three trial CV value was below 26% this was reported, if the value was above 26%, the two trial CV value that showed the lowest variation was reported. This was adopted because, based on previous work on the arm (Ekstorm et al., 2005) and leg muscles (Bamman et al., 1997, Knutson et al., 1994), an acceptable limit of variation for sEMG to enable further data to be collected on any muscle or exercise would be a CV value of below 26% and an ICC value of >0.7. These limits were chosen to allow for the uncontrollable quasi-random nature of the EMG signal but removes EMG signals that show great variation within subjects due to for example, difficult electrode placement. These values that show a large variation between trials would make the identification of any significant changes in performance in subsequent analysis impossible.

The interrelationship between the peak and ARV EMG variables were analysed by calculating the standard deviations and r values (Pearsons correlation coefficient) (Atkinson and Nevill, 1998) for each muscle and exercise type.

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3. Results 

3.1. Variability of peak and ARV EMG during MVC exercises 

Between-trial and between-day variability during the MVC exercises derived from a single subject are shown in Table 4. CVs are shown for the muscles in the exercises that elicited a maximum in three or more of the data sets performed. Between-trials CVs ranged from 0% to 70% for peak muscle activity and from 2% to 61% for ARV muscle activity. Between-day CVs ranged from 6% to 57% for peak EMG muscle activity (excluding LG during the sit up [CV=93%]) and from 8% to 51% for ARV EMG (excluding LD during the sit up [CV=89%]). For both peak and ARV EMG, the lowest variability occurred for RF and MF muscles and the highest occurred for LD and LG muscles.

Table 4. Within-subject CV between day (mean) and within day variation (min and max range) derived from a single subjects (n=1) during the MVIC exercises. Min=minimum %CV within a day (3 trials), Max=maximum %CV within a day (3 trials).

Between-trials variability derived from multiple subjects are shown in Table 5. Peak EMG CV ranged from 7% to 27% while ARV EMG CV ranged from 8% to 33%.

Table 5. Within-subject CV (log-transformed) and 95% confidence levels (in brackets) derived from multiple subjects (n=10) during the MVIC exercises. Values are shown for muscles in exercises that elicited a maximum in more than 3 subjects.

aUsed only 2 of 3 trials (trials 1 and 2).

bUsed only trials 2 and 3 following pairwise correlation comparison tests.

3.2. Variability of peak and ARV EMG during core exercises 

Between-trials and between-day variability during the core exercises derived from a single subject are shown in Table 6. Between-trials CV ranged from 1% to 65% for peak EMG and from 0% to 56% for ARV EMG (excluding EO during the weighted squat (CV=88%). Between-day CV ranged from 7% to 66% for peak EMG (excluding RA during the weighted squat CV=77%) and from 7% to 54% for ARV EMG (excluding LG during the side bridge CV=61%). LG and EO muscles showed the largest variation between trials and between days for both peak and ARV EMG measures. The RF, GM and MF muscles were most repeatable muscle activity both between-days and between-trials.

Table 6. Within-subject CV between days (mean) and within day (min and max range) derived from a single subject (n=1) during the core stability and core strength exercises.

Min=minimum %CV within a day (3 trials), Max=maximum %CV within a day (3 trials).

Between-trials variability derived from multiple subjects are shown in Table 7. Peak EMG CV (Table 7) ranged from 5% to 28%, while ARV EMG CV% ranged from 2% to 28%.

Table 7. Within-subject CV (log-transformed) derived from multiple subjects (n=10) during the core stability and strength exercises (95% confidence levels shown in brackets).

aUsed only 2 of 3 trials (trials 1 and 2).

bUsed only trials 2 and 3 following pairwise correlation comparison tests.

Within subject ICC values during the core exercises are shown in Table 8. Values over 0.7 were deemed to be sufficiently reliable.

Table 8. Within-subject ICC during the core stability and core strength exercises (95% confidence limits are shown in brackets).

aUsed only 2 of 3 trials (trials 1 and 2).

bUsed only trials 2 and 3.

3.3. Interrelationship between ARV and Peak EMG variables during core exercises 

The average CV for peak EMG across all the exercises and muscles was 45% and 35% for peak EMG and ARV EMG, respectively, and was found to be a significant difference (P<0.05).

Across the exercises the ARV EMG standard deviations ranged from 3 to 67.9, while an absolute standard deviation range of 3.4–54.7 was observed for peak EMG (Table 9). When all the muscles were averaged, the standard deviation for the static exercise (side bridge) was 15.2 for ARV EMG and 15 for peak EMG compared with the high intensity exercises (overhead squat and medicine ball sit twist) of 19.5 and 17 (ARV EMG and peak EMG, respectively), which suggests that static exercises are less variable, although these differences were found to just miss the required significance level (P=0.05). The dynamic, asymmetrical, high-threshold exercises resulted in the highest standard deviation values (medicine ball sit twist; ARV EMG 19 v peak EMG 17.8, respectively, Table 9), while the asymmetrical, low-threshold exercises (birddog and bent leg curl-up; ARV EMG 17.4, peak EMG 15.8) resulted in the lowest standard deviation values which implies that dynamic, high-threshold exercises are more variable than low-threshold exercises. However these differences were again found to just miss the required significance level between the two EMG methods (low-threshold: P=0.51; high-threshold P=0.58). Some exercises reported a high SD despite showing an acceptable level of within subject variability. This implies that there is greater between subject variability than within subject variability.

Table 9. Interrelationship between peak EMG and ARV EMG as %MVIC for the eight muscles for each core stability and core strength exercise. Also shown are the SD values (in brackets) and the Pearson’s correlations coefficients between Peak and ARV EMG measures.

A poor correlation was observed between peak and ARV EMG for the static and dynamic high-threshold exercises (r<0.56), while the other exercises reported strong correlations between the EMG methods (r>0.70) (Table 9). This suggests that peak EMG and ARV EMG values are related during certain types of core stability training exercises but not others. Peak EMG and ARV EMG values were observed to be highly correlated for RA (0.96), GM (0.77), LG (0.97), MF (0.75) and EO (0.93) (Table 9). However a poor correlation was found between the measures for the other muscles (IO=0.10; LD=0.46; RF=0.68).

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4. Discussion 

A functionally relevant and reliable measure is important to sports practitioners in order to understand the demands placed on the core musculature during exercises. It has been recognised that ARV and peak EMG activity are important parameters in the assessment of EMG data (De Luca, 1997, Hatton et al., 2008, Edwards et al., 2008, Burdon, 2006). This is the first study to compare these measures during core exercises.

Three studies have evaluated the repeatability of sEMG data collection on the core musculature when performing core stability and core strength exercises (Edwards et al., 2008, Liemohn et al., 2005, Knutson et al., 1994). Behm (Knutson et al., 1994) found ICCs for the isometric side bridge support exercise of 0.96 and 0.98 for the dynamic birddog exercise, which can be classed as excellent. Liemohn et al. (2005) observed ICCs for the front support bridge exercise of 0.90 and values ranging from 0.71 to 0.95 for other low-threshold core stability exercises. Similarly, Edwards et al. (2008) observed a high repeatability (ICC>0.9) for the vastus medialis and vastus lateralis muscles during a sit-to-stand movement. The current study has reported similar ICCs values for some muscles during similar low-threshold core stability exercises (for example, LD during the side bridge and bent leg curl-up exercises and the IO during the birddog exercise; ICC>0.7). However, some muscles analysed during the core stability exercises did result in lower ICC values than those previously reported (ICC<0.7). This maybe due to the more complex exercise movements being performed and the greater number of core muscles being analysed, with not all of these muscles being continually involved in all the exercises which would result in a greater variability in the data. Despite this, many of the exercises and muscles did result in acceptable levels of ICC and CV. Generally, the ICC and CV% values in this study indicate that for the muscles analysed the MVIC and core exercises performed were sufficiently repeatable to acceptable levels that have previously been reported to be acceptable in EMG studies (Atkinson and Nevill, 1998, Pfeiffer et al., 2006).

In general our data show that level of the variability was influenced by the exercise being undertaken. It was found that low-threshold exercises were more repeatable exercises than high-threshold exercises. This interpretation is supported by previous studies that have found for example, that sitting tasks are more variable than prone tasks (Jackson et al., 2008), cycling tasks are more variable than climbing stairs (Pfeiffer et al., 2006) and studies that have observed high CV average values of over 80% during highly dynamic taekwondo kicks (Aggeloussis et al., 2007). Overall the ARV EMG variable was significantly (P<0.05) less variable (average CV 35%) when compared with peak EMG muscle activity (average CV 45%), therefore ARV EMG is the least variable measure of muscle activity produced during core training exercises. The more variable peak EMG values could be due to inconsistencies in balance correction muscle activity which would result in unpredictable, short but large bursts of muscular activity to bring the centre of mass back into a balanced position (for example, during the medicine ball sit twist exercise where there is an unstable base of support along with a highly dynamic movement) (McGill, 1999). In some subjects this occurs and reached peak values whereas in others it did not, subsequently increasing the variation in muscle activity seen between subjects.

As there is a dearth of previous research using the ARV EMG method during core exercises it is not possible to directly compare the ARV EMG muscle activity during core exercises with previous work. Data for peak EMG activity of selected core muscles is available but differences in the experimental parameters of different studies can affect the data recorded; e.g. bandwidth selection, electrode placement, repetition rate and the use of different MVC exercises (Merletti, 1999). However, despite the possible differences between our study and other studies, there are similarities in the peak EMG values. For example, during the side bridge exercise in the current study, the RA muscle elicited a muscular contraction of 43% of the MVIC compared to 48% (McGill, 2002) and 50% (Axler and McGill, 1997) stated in previous studies. The side bridge also resulted in similar activation levels (47% and 47%) to previous research (40% and 58%) (Behm et al., 2002) for the LG muscle and the MF muscle, respectively, during the birddog exercise. Therefore there is evidence that the relative magnitudes reported in the current study are similar to previous studies.

The asymmetrical low-threshold exercise (Birddog) had the strongest relationship between the ARV EMG and peak EMG variables (r=0.81) due to the low variation in the CV values for the ARV EMG and peak EMG variables. In comparison, the static exercise (side bridge) resulted in the weakest relationship between the ARV EMG and peak EMG (r=0.46) (although these differences were found to lie just outside the level of significance, P<0.05). This is despite this exercise reporting the lowest SD between subjects for the muscles. This difference maybe due to the nature of the exercise and the two EMG data reduction methods, with one measuring activity over time and the other peak activity. The side bridge exercise recruits many core muscles throughout the duration of the exercise (high ARV EMG activity) but not to a great level (low peak EMG activity) which would result in the poor correlation between these two EMG data reduction methods. This suggests that for core exercises (although not resulting in a significant difference here) the intensity of the exercise performed may influence the resultant muscular activity and subsequently how this muscular activity should be reported when using EMG measurements.

It was found that peak and ARV EMG are related (i.e. r>0.70) for five of the eight muscles and three of the five types of exercises (r>0.70). Peak and ARV EMG measures also identified the same exercise that elicited the highest muscle activity for four of the eight muscles (e.g. EO – Medicine ball sit twist, LG, Overhead squat). This suggests that peak and ARV EMG are in some way related, however, this relationship is affected by the type of exercise performed and the muscle being analysed. Differences were observed between peak and ARV EMG for overall resultant muscle activity during exercises. For example, a peak EMG value of 73% MVIC was observed for the GM muscle during the birddog exercise, while ARV EMG resulted in 56% MVIC of maximal activation. In contrast, during the static exercise, ARV EMG measured a higher level of activation for the GM compared to the peak EMG value (47% vs. 33% MVIC) (Table 9). The results suggest that due to the different demands on the body (Brandon, 2006), high-threshold activities generally result in greater peak EMG activity (a result of the larger forces and faster movements that the muscles have to overcome), while the lower threshold activities result in a greater ARV EMG activity (due to the postural corrections and longer muscle activations to maintain stability during slower movements). Therefore by reporting the average rectified EMG value, that has largely been overlooked in past studies, alongside the peak EMG value, a greater appreciation can be gained about the type and extent of muscle activity produced during well-used core stability training exercises.

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5. Conclusions 

The ARV EMG variable is significantly less variable when measuring the muscle activity of the core musculature compared to the peak EMG variable. Peak and ARV EMG values are correlated for some but not all core exercises. It is suggested that ARV EMG data should be recorded alongside the peak EMG measure when assessing core exercises.

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References 

1. Aggeloussis N, et al. Repeatability of electromyographic waveforms during the naeryo Chagi in taekwondo. J Sports Sci Med. 2007;6:6–9
   • View In Article
2. Akuthota V, Nadler SF. Core strengthening. Arch Phys Med Rehabil. 2004;85(3 Suppl. 1):S86–S92
   • View In Article
   • MEDLINE
3. Arokoski JP, et al. Back and hip extensor muscle function during therapeutic exercises. Arch Phys Med Rehabil. 1999;80(7):842–850
   • View In Article
   • Abstract
   • Full-Text PDF (4895 KB)
   • CrossRef
4. Atkinson G, Nevill A. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med. 1998;26(4):217–238
   • View In Article
   • MEDLINE
   • CrossRef
5. Axler CT, McGill SM. Low back loads over a variety of abdominal exercises: searching for the safest abdominal challenge. Med Sci Sports Exerc. 1997;29(6):804–811
   • View In Article
   • MEDLINE
   • CrossRef
6. Bamman M, et al. Evaluation of surface electromyography during maximal voluntary contraction. J Strength Cond Res. 1997;11(2):68–72
   • View In Article
   • CrossRef
7. Basmajian J, De Luca C. Muscles alive: their functions revealed by electromyography. 5th ed.. Baltimore: Williams and Williams; 1985;
   • View In Article
8. Behm DG, Anderson K, Curnew RS. Muscle force and activation under stable and unstable conditions. J Strength Cond Res. 2002;16(3):416–422
   • View In Article
   • MEDLINE
   • CrossRef
9. Bolgla L, Uhl T. Reliability of electromyographic normalisation methods for evaluating the hip musculature. J Electromyogr Kinesiol. 2005;17(1):102–111
   • View In Article
   • Abstract
   • Full Text
   • Full-Text PDF (321 KB)
   • CrossRef
10. Brandon R. Core training menus. In:  Taylor J editors. Core stability: injury free performance. London: Pye, J.A.; 2006;p. 57–90
    • View In Article
11. Burdon A. Surface electromyography. In: BASES biomechanics testing guidelines; 2006.
    • View In Article
12. Check P. Swissball exercises for swimming, soccer and basketball. Sports Coach. 1999;21:12–13
    • View In Article
13. Comerford MJ. Performance stability: module 1 stability for performance: course 1: core stability concepts. The ‘Performance Matrix’. UK: Comerford & Performance Stability; 2007.
    • View In Article
14. Cosio-Lima LM, Reynolds KL, Winter C, Paolone V. Effects of physioball and conventional floor exercises on early phase adaptions in back and abdominal core stability and balance in women. J Strength Cond Res. 2003;17:721–725
    • View In Article
    • MEDLINE
    • CrossRef
15. De Luca C. Use of the surface EMG signal for performance evaluation of back muscles. Muscle Nerve. 1993;16:210–216
    • View In Article
    • CrossRef
16. De Luca C. The use of surface electromyography in biomechanics. J Appl Biomech. 1997;13:135–163
    • View In Article
17. Di Fabio RP. Reliability of computerized surface electromyography for determining the onset of muscle activity. Phys Ther. 1987;67:43–48
    • View In Article
    • MEDLINE
18. Edwards L, et al. Effect of shoe heel light on vastus medialis and vastus lateralis electromyographic activity during sit to stand. J Orthop Surg Res. 2008;3(2):1749–1799
    • View In Article
19. Ekstorm RA, Soderberg GL, Donatelli RA. Normlisation procedures using maximum voluntary isometric contractions for the serratus anterior and trapezius muscles during surface EMG analysis. J Electromyogr Kinesiol. 2005;15:415–428
    • View In Article
20. Elphinston J. Getting to the bottom of things. Sportex Dyn. 2004;2:12–16
    • View In Article
21. Enoka R, Fuglevand A. Neuromuscular basis of the maximum voluntary force capacity of muscle. In:  Grabiner M editors. Current issues in biomechanics. Champaign: Human Kinetics; 1993;p. 215–236
    • View In Article
22. Faries MD, Greenwood M. Core training: stabilising the confusion. Strength Cond J. 2007;29(2):10–25
    • View In Article
23. Farina D, Merletti R, Enoka R. The extraction of neural strategies from the surface EMG. J Appl Physiol. 2004;96:1486–1495
    • View In Article
24. Finni T, et al. Measurement of EMG activity with textile electrodes embedded into clothing. Physiol Meas. 2007;28(11):1405–1419
    • View In Article
    • CrossRef
25. Finucane S, et al. Reproducibility of electromyographic recordings of submaximal concentric and eccentric muscle contractions in humans. Electromyogr Clin Neurophysiol. 1998;109:290–296
    • View In Article
26. Gardner-Morse M, Stokes IA, Laible JP. Role of muscles in lumbar spine stability in maximum extension efforts. J Orthop Res. 1995;13(5):802–808
    • View In Article
    • MEDLINE
    • CrossRef
27. Giroux B, Lamontagne M. Comparison between surface electrodes and intramuscular wire electrodes in isometric and dynamic conditions. Electromyogr Clin Neurophysiol. 1990;30:397–405
    • View In Article
28. Golhofer A, et al. Reproducibility of electromyographic patterns in stretch-shortening type contractions. Eur J Appl Physiol. 1990;60:7–14
    • View In Article
29. Goodwin P, et al. Reliability of leg muscle electromyography in vertical jumping. Eur J Appl Physiol. 1999;79:374–378
    • View In Article
    • CrossRef
30. Hakkinen K, Kraemer WJ, Newton RU, Alen M. Changes in electromyographic activity, muscle fibre and force production characteristics during heavy resistance/power strength training in middle-aged and older men and women. Acta Physiol Scand. 2001;171(1):51–62
    • View In Article
    • MEDLINE
    • CrossRef
31. Hasegawa I. Using the overhead squat for core development. NSCA’s Perform Training J. 2004;3(6):19–21
    • View In Article
32. Hatton A, et al. The effect of textured surfaces on postural stability and lower limb muscle activity. J Electromyogr Kinesiol. 2008;19(5):957–964
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (389 KB)
    • CrossRef
33. Hildenbrand K, Noble L. Abdominal muscle activity while performing trunk-flexion exercises using the ab roller, AB slide, fitball and conventionally performed trunk curls. J Athletic Training. 2004;39(1):37–43
    • View In Article
34. Hodges PW. Is there a role for transversus abdominis in lumbo-pelvic stability?. Man Ther. 1999;4(2):74–86
    • View In Article
    • CrossRef
35. Hodges P, Bui B. A comparison of computer based methods for the determination of onset of muscle contraction using electromyography. Electroencephalogr Clin Neurophysiol. 1996;101:511–519
    • View In Article
    • MEDLINE
    • CrossRef
36. Hodges P, Cresswell A, Thorstensson A. Preparatory trunk motion accompanies rapid upper limb movement. Exp Brain Res. 1999;124(1):69–79
    • View In Article
    • MEDLINE
    • CrossRef
37. Hopkins WG. Measures of reliability in sports medicine and science. Sports Med. 2000;30(1):1–15
    • View In Article
    • MEDLINE
    • CrossRef
38. Howard J, Enoka R. Maximum bilateral contractions are modified by neurally mediated interlimb effects. J Appl Physiol. 1991;70:306–316
    • View In Article
39. Hubley-Kozey CL, Vezina MJ. Muscle activation during exercises to improve trunk stability in men with low back pain. Arch Phys Med Rehabil. 2002;83(8):1100–1108
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (131 KB)
    • CrossRef
40. Hubley-Kozey CL, Vezina MJ. Differentiating temporal electromyographic waveforms between those with chronic low back pain and healthy controls. Clin Biomech (Bristol, Avon). 2002;17(9–10):621–629
    • View In Article
41. Jackson JA et al. Comparison of the repeatability of submaximal and maximal methods commonly employed for normalisation of the erector spinae muscles in the thoracic and lumbar region. In: North American Congress on Biomechanics. Michigan; 2008.
    • View In Article
42. Jeffreys I. Developing a progressive core stability program. Strength Cond J. 2002;24(5):65–66
    • View In Article
43. Kadaba M, Wooten M, Gainey J. Repeatability of phasic muscular activity: performance of surface and intramuscular wire electrodes in gait analysis. J Orthop Res. 1985;3:350–359
    • View In Article
    • MEDLINE
    • CrossRef
44. Kibler WB, Press J, Sciascia A. The role of core stability in athletic function. Sports Med. 2006;36(3):189–198
    • View In Article
    • MEDLINE
    • CrossRef
45. Knutson L, et al. A study of various normalisation procedures for within day electromyographic data. J Electromyogr Kinesiol. 1994;4(1):47–59
    • View In Article
    • CrossRef
46. Komi P, Buskirk E. Reproducibility of electromyographic measurements with inserted wire electrodes and surface electrodes. Electromyography. 1970;4:357–367
    • View In Article
47. Konrad P, Schmitz K, Denner A. Neuromuscular evaluation of trunk-training exercises. J Athletic Training. 2001;36(2):109–118
    • View In Article
48. Lehman GJ. Resistance training for performance and injury prevention in golf. J Can Chiropr Assoc. 2006;50(1):27–42
    • View In Article
49. Liemohn WP, Baumgartner TA, Gagnon LH. Measuring core stability. J Strength Cond Res. 2005;19(3):583–586
    • View In Article
    • MEDLINE
    • CrossRef
50. Mannion A, et al. Muscle thickness changes during abdominal hollowing: an assessment of between-day measurement error in controls and patients with chronic low back pain. Eur Spine J. 2008;17:494–501
    • View In Article
    • CrossRef
51. Marshall P, Murphy B. The validity and reliability of surface EMG to assess the neuromuscular response of the abdominal muscles to rapid limb movement. J Electromyogr Kinesiol. 2003;13(5):477–489
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (333 KB)
    • CrossRef
52. McGill SM. Electromyographic activity of the abdominal and low back musculature during the generation of isometric and dynamic axial trunk torque: implications for lumbar mechanics. J Orthop Res. 1991;9(1):91–103
    • View In Article
    • MEDLINE
    • CrossRef
53. McGill SM. Low back exercises: evidence for improving exercise regimens. Phys Ther. 1998;78(7):754–765
    • View In Article
    • MEDLINE
54. McGill S. Stability: from biomechanical concept to chiropractice practice. J Can Chiropr Assoc. 1999;43:75–88
    • View In Article
55. McGill SM. Low back stability: from formal description to issues for performance and rehabilitation. Exerc Sport Sci Rev. 2001;29(1):26–31
    • View In Article
    • MEDLINE
    • CrossRef
56. McGill S. Low back disorders: evidence-based prevention and rehabilitation. Champaign, ILL: Human Kinetics; 2002;
    • View In Article
57. McGill SM, et al. Coordination of muscle activity to assure stability of the lumbar spine. J Electromyogr Kinesiol. 2003;13(4):353–359
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (255 KB)
    • CrossRef
58. Merletti R. Standards for reporting EMG data. J Electromyogr Kinesiol. 1999;9:3–4
    • View In Article
59. Merletti R. Standards for reporting EMG data. J Electromyogr Kinesiol. 1999;9(1):
    • View In Article
60. Pfeiffer KA, et al. Physical activates in adolescent giels variability in energy expenditure. Am J Prev Med. 2006;31(4):328–331
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (61 KB)
    • CrossRef
61. Posner-Mayer J. Swiss ball applications for orthopaedic and sports medicine. Longmont, CO: Ball Dynamics International Inc.; 1995;
    • View In Article
62. Reed G, Lynn F, Meade B. Use of coefficient of variation in assessing variability of quantitative assays. Clin Diagn Lab Immunol. 2002;9(6):1235–1239
    • View In Article
63. Santana J. Sport-specific conditioning: the serape effect – a kinesiological model for core training. Strength Cond J. 2003;25(2):73–74
    • View In Article
64. Shrout P, Fleiss J. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420–428
    • View In Article
    • CrossRef
65. Stanton R, Reaburn PR, Humphries B. The effect of short-term Swiss ball training on core stability and running economy. J Strength Cond Res. 2004;18(3):522–528
    • View In Article
    • MEDLINE
    • CrossRef
66. Trappe S, Pearson D. Effects of weight assisted dry-land strength training on swimming performance. J Strength Cond Res. 1994;8(4):209–213
    • View In Article
    • CrossRef
67. Urquhart DM, Hodges PW, Story IH. Postural activity of the abdominal muscles varies between regions of these muscles and between body positions. Gait Posture. 2005;22(4):295–301[Epub 2005 Jan 19]
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (211 KB)
    • CrossRef
68. Vezina MJ, Hubley-Kozey CL. Muscle activation in therapeutic exercises to improve trunk stability. Arch Phys Med Rehabil. 2000;81(10):1370–1379
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (153 KB)
    • CrossRef
69. Warden S, Wajswelner H, Bennell K. Comparison of AB shaper and conventionally performed abdominal exercises using surface electromyography. Med Sci Sports Exerc. 1999;31:1656–1664
    • View In Article
    • MEDLINE
    • CrossRef
70. Winter D, Fuglevand A, Archer S. Cross-talk in surface electromyography: theoretical and practical estimates. J Electromyogr Kinesiol. 1994;4:15–26
    • View In Article
    • CrossRef
71. Yang J, Winter D. Electromyography reliability in maximal and submaximal isometric contractions. Arch Phys Med Rehabil. 1983;64:417–420
    • View In Article
    • MEDLINE
72. Yang J, Winter D. Electromyographic amplitude normalisation methods: improving their sensitivity as diagnostic tools in gait analysis. Arch Phys Med Rehabil. 1984;65:517–521
    • View In Article
    • MEDLINE