Surface electromyography assessment of back muscle intrinsic properties

Journal of Electromyography and Kinesiology 13 (2003) 305–318 www.elsevier.com/locate/jelekin

Surface electromyography assessment of back muscle intrinsic properties Christian Larivie`re a,∗, A. Bertrand Arsenault b, Denis Gravel b, Denis Gagnon c, Patrick Loisel d a

d

Occupational Health and Safety Research Institute Robert-Sauve´, Montreal, Quebec, Canada b School of Rehabilitation, University of Montreal, Montreal, Quebec, Canada c Department of Kinanthropology, University of Sherbrooke, Sherbrooke, Quebec, Canada Department of Surgery, Faculty of Medicine, University of Sherbrooke, Sherbrooke, Quebec, Canada

Received 2 October 2002; received in revised form 27 November 2002; accepted 29 November 2002

Abstract The purpose of this study was to assess (1) the reliability and (2) the sensitivity to low back pain status and gender of different EMG indices developed for the assessment of back muscle weakness, muscle fiber composition and fatigability. Healthy subjects (men and women) and chronic low back pain patients (men only) performed, in a static dynamometer, maximal and submaximal static trunk extension tasks (short and long duration) to assess weakness, fiber composition and fatigue. Surface EMG signals were recorded from four (bilateral) pairs of back muscles and three pairs of abdominal muscles. To assess reliability of the different EMG parameters, 40 male volunteers (20 controls and 20 chronic low back pain patients) were assessed on three occasions. Reliable EMG indices were achieved for both healthy and chronic low back pain subjects when specific measurement strategies were applied. The EMG parameters used to quantify weakness and fiber composition were insensitive to low back status and gender. The EMG fatigue parameters did not detect differences between genders but unexpectedly, healthy men showed higher fatigability than back pain patients. This result was attributed to the smaller absolute load that was attributed to the patients, a load that was defined relative to their maximal strength, a problematic measure with this population. An attempt was made to predict maximal back strength from anthropometric measurements but this prediction was prone to errors. The main difficulties and some potential solutions related to the assessment of back muscle intrinsic properties were discussed.  2003 Elsevier Science Ltd. All rights reserved. Keywords: Low back pain; Back muscles; Fatigue; Recovery; Electromyography; Impairment evaluation; Reliability; Weakness; Dynamometry; Muscle composition

1. Introduction Chronic low back pain (CLBP) is associated with several anatomical or structural abnormalities such as atrophy of muscle mass or alteration of muscle fiber [43,57] characteristics and organizational abnormalities such as altered muscle coordination patterns [76,30], impaired proprioceptive abilities or slow psychomotor reaction time [15]. The focus of the present paper was placed on the evaluation of some intrinsic properties of back

Corresponding author. Tel.: +1-514-288-1551x217; fax: +1-514288-6097. E-mail address: [email protected] (C. Larivie`re). ∗

muscles, namely their fiber composition, strength and endurance. To fully understand these ‘muscle intrinsic properties’, the influence of some control mechanisms must be also considered. For example, muscle synergies and co-contraction as well as sharing of moment across muscles will be discussed in this paper. The assessment of control mechanisms of trunk muscles is certainly a field of research that needs developments to better understand the way the central nervous operates to compensate for lumbar instability [60]. The alterations in the structure of back muscles might lead to weakness and fatigability, two back muscle impairments recognized as a potential cause of the recurrent nature of LBP [43]. Muscle composition is usually assessed through biopsy techniques [43]. Back muscle

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strength is generally assessed under volitional effort using dynamometry, while endurance is quantified by measuring the maximum time that the subject can hold a given load. However, biopsy measurements are invasive and maximal performance measurements (strength, endurance) depend heavily on psychological [20,29] factors and the capacity of subjects to activate all motor units [1]. Previous findings suggest that it may also be possible to assess muscle weakness [10,55], muscle composition [24,35,77] and muscle fatigue [43] through the use of surface electromyography (EMG). The assessment of muscle intrinsic properties is possible only if the influence of control mechanisms is considered. This can be achieved with adequate standardization of the measurement protocol, but previous studies indicate that it is a difficult task. Some investigators show poor reliability of EMG spectral variables for testing the left–right difference between back muscles [62,69], or for estimating the rate of increase in back muscle fatigue through the slope decay of the median or mean frequency [54]. This might be explained by the variability in the electromyographic (EMG) signal caused by the variability of load-sharing between bilateral muscles [75]. This could be particularly true if variability of load sharing increases with muscle fatigue and back pain to minimize fatigue and pain symptoms. If this is correct, it would explain the poor reliability results reported by the only reliability study on EMG fatigue parameters involving CLBP subjects [54]. However, most studies assessing back muscle function have not controlled the coupled lumbar moments (lateral bending, axial rotation) during ‘purely sagittal’ extension efforts. This has often been identified as a possible cause of the alternating EMG activity or the uneven EMG activity between the left and right muscles of the erector spinae [3,44,64,72]. In an effort to further standardize previous measurement protocols, our group developed a triaxial static dynamometer to control coupled lumbar moments (lateral bending, axial rotation) during trunk extension tasks [38] and then reduce the variability in EMG variables by potentially decreasing the load-sharing between back muscles. The present paper summarizes results obtained using this measurement protocol. More specifically, the purpose of this study was to assess (1) the reliability and (2) the sensitivity to low back pain status and gender of different EMG indices developed for the assessment of back muscle weakness, muscle fiber composition and fatigability. Another objective was to discuss the main difficulties related to the use of these indices and to propose some potential solutions. 2. Methods This section describes the general methodology used in the different articles that were summarized in the

present paper [22,37,39–41]. Different sub-samples of subjects and tasks were analyzed in these studies. The reader is referred to the original articles for details concerning the inclusion–exclusion criteria and demographic characteristics of each sub-sample. The number of subjects and specific analyses corresponding to each of these studies were summarized in the results section. 2.1. Subjects and tasks Healthy subjects (men and women) and male patients with a chronic low back pain (CLBP) syndrome were recruited. The healthy females (n = 13; age: 26 ± 4 yr; height: 1.67 ± 0.05 m; mass: 61 ± 7 kg) were similar in age to the healthy males (n = 12; age: 27 ± 5 yr; height: 1.75 ± 0.05 m; mass: 74 ± 11 kg). Briefly, CLBP was defined as a daily or almost daily lumbar or lumbosacral pain with or without proximal radicular pain (limited distally to the knees) for at least three months. Exclusion criteria for the healthy subjects (men and women) were as follows: had back pain in the previous year or exceeding one week; lost a working day because of back pain; and consulted for a back problem. All subjects (healthy and CLBP) that went through a surgery of the pelvis or spinal column were excluded. At the arrival in the laboratory, questionnaires about perception of functional disability (Oswestry questionnaire [19]), pain intensity (10 cm visual analogue scale), physical activity level [2] were self-administered and anthropometric measures were collected. The effect of electromechanical delay was neglected in the computation of both NME EMG indices and MFT10–80. Each electrode site was marked and the thickness of the subcutaneous tissues was measured twice (left side electrode sites only) with a Harpenden skinfold caliper. The subjects performed, while standing in a dynamometer (Fig. 1), different static trunk extension efforts using the L5/S1 extension and axial rotation moments (to be minimized) as visual feedback. The minimization of lateral bending moments with the use of an extra visual feedback was not possible without interfering with the extension task (too much visual feedback at a time). The protocol followed the following sequence: two to four submaximal contractions to become familiar with the visual feedback, two maximal voluntary contractions (MVC), three 7 s ramps (0–100% MVC) and two static trunk extension tasks at 75% MVC, that correspond to a 30 s fatigue task and a 5 s contraction task to estimate the recovery. These two tasks were separated by a 60 s rest period. At the end of the measurement protocol, two MVC trials were performed to obtain the maximal EMG of abdominal muscles. A padded bar was fixed to the apparatus in front of the subject to stabilize the thorax when producing trunk flexion or rotation efforts. Three 4 s maximal efforts were produced consecutively in the same trial: (1) flexion, (2) rotation to the left and (3)

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Fig. 2. EMG recording and nomenclature used to identify the electrode sites. The active bipolar electrodes were positioned bilaterally on the multifidus at the L5 level (MU-L5-Left and MU-L5-Right), iliocostalis lumborum at L3 (IL-L3-L and IL-L3-R), longissimus at L1 (LO-L1-L and LO-L1-R) and over the belly of the longissimus at the T10 level (LO-T10-L and LO-T10-R) following the recommendations of Defoa et al. [14] with regard to muscle fiber direction. A reference silver-silver chloride electrode was positioned over the T8 spinous process. Fig. 1. Experimental setup. The dynamometer consists of a triaxial force platform (Advanced Mechanical Technology Incorporated, model MC6-6-1000) mounted on a steel frame that allows the stabilization of the feet, knees and pelvis (details in [38]). The subjects stood in the dynamometer with the trunk erect and the knees straight. Trunk extension was generated against a padded bar fixed on the surface of the force platform and adjusted at the T4 level.

2.2. Electromyography

peak L5/S1 extension moment was computed. For each ramp contraction, EMG RMS values of 250 ms windows at each 5% force level from 10 to 80% MVC were computed and spectral analysis (512 points, Hanning window processing, fast Fourier transform) was applied to extract the corresponding median frequency (MF) values. Finally, a series of 250 ms windows of EMG data, 75% overlapping, were taken from the data of the fatigue and recovery tests and spectral analysis was applied to each time-window to obtain the corresponding MF values. Different EMG indices were then calculated (list of abbreviations in Table 1) as described below.

The EMG signals from four pairs of back muscles (Fig. 2) and three pairs of abdominal muscles were collected (bandpass filter: 20–450 Hz; preamplification gain: 1000; sampling rate: 2048 Hz) with active surface electrodes (Delsys Inc., MA). After the skin was shaved and abraded with alcohol, the electrodes on the back muscles were positioned. Fig. 2 shows the selected muscles and the corresponding abbreviations. Electrodes on the abdominal muscles (rectus abdominis, external oblique and internal oblique) were positioned according to McGill [50].

2.3.1. Weakness EMG indices [37] Back muscle weakness was assessed using the neuromuscular efficiency (NME) concept which was defined as the slope of the relationship (quantified by linear regression) between the extension moment at L5/S1 (Y axis) and RMS (X axis) across the force levels (NME10– 80). A second NME parameter was computed from the MVC contraction as the ratio of the peak L5/S1 extension moment to its RMS value (NMEmvc). More efficient muscle contractions are characterised by steeper slope (NME10–80) and higher ratio (NMEmvc).

2.3. Data processing

2.3.2. Muscle composition EMG indices [37] Back muscle composition was assessed by three EMG parameters presumably sensitive to muscle fiber composition: (1) the slope (MFT10–80) and (2) the intercept

rotation to the right. At least two minutes of rest was given between efforts except between the 75% MVC fatigue and recovery tasks (60 s rest).

For the best MVC contraction, the EMG root mean square (RMS) value of a 250 ms window centered at the

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Table 1 List of abbreviations (by category) used to identify the EMG indices and recording sites (muscles) EMG index

Description (units)

Weakness EMG indices (neuromuscular efficiency) Gradient (slope) of the linear relationship between the extension moment at L5/S1 (Y axis) and RMS (X axis) across NME10-80 the force levels (ramp contractions) Ratio of the peak L5/S1 extension moment to its RMS value (MVC contraction) NMEmvc Muscle composition EMG indices Initial median frequency of the EMG power spectrum (intercept of time-series) at the start of the fatigue test (Hz) IMF75 Intercept of the linear relationship between the MF (Y axis) and the extension moment at L5/S1 (X axis) across the IMFT10-80 force levels (ramp contractions) Gradient (slope) of the linear relationship between the MF (Y axis) and the extension moment at L5/S1 (X axis) across MFT10-80 the force levels (ramp contractions) Fatigue and recovery EMG indices IMF Initial median frequency of the EMG power spectrum (intercept of time-series) at the start of the fatigue test (Hz) Initial median frequency of the EMG power spectrum (intercept of time-series) at the start of the recovery test (Hz) IMFrec IRMS Initial EMG root mean square (intercept of time-series) at the start of the fatigue test (µV) MFslp Gradient (slope of time-series) of the Median Frequency of the EMG power spectrum during the fatigue test (Hz/s) REC Recovery of IMF (absolute value in Hz) %REC Relative recovery of IMF (%) RMSslp Gradient (slope of time-series) of the EMG root mean square during the fatigue test (µV/s) EMG recording sites (muscles) IL-L3-L & R Iliocostalis lumborum at the L3 level, left & right sides LO-L1-L & R Longissimus at the L1 level, left & right sides LO-T10-L & R Longissimus at the T10 level, left & right sides MU-L5-L & R Multifidus at the L5 level, left & right sides

(IMFT10–80) of the relationship between the MF (Y axis) and the extension moment at L5/S1 (X axis) across the force levels (ramp contractions) as quantified by linear regression, and (3) the intercept (IMF75) of the linear regression applied to the time-series of MF (fatigue contraction). Analyses of the power in different frequency bands (20–60, 60–120, 120–180, 180–240, 240– 300 Hz) of the power spectrum were also examined between healthy and CLBP male subjects. 2.3.3. Fatigue and recovery EMG indices [40] Linear regression was applied to the time-series of RMS and MF to get their initial values or intercept (IRMS, IMF) and to estimate their rate of change (slope of their respective linear regression) corresponding to the fatigue increase (RMSslp, MFslp). The lowest MFslp and the highest RMSslp among all muscles, corresponding to the most fatigable muscle, were also retained as EMG fatigue indices. The same procedure (spectral analysis, linear regression) was applied on the MF estimates of the 75% MVC recovery test to compute the corresponding IMF (IMFrec). Two recovery EMG indices (%REC and REC) were computed as illustrated in Fig. 3. All the EMG indices described above were computed for each muscle (n = 8), all muscle pairs (mean of left and right homologous muscles), and for the back muscles as a whole (mean of all eight muscles). The effect of electromechanical delay was neglected in the computation of both NME EMG indices and MFT10–80. Each EMG parameters computed from the ramp contrac-

tions (NME10–80; MFT10–80; IMFT10–80) were averaged across bilateral muscle pairs and the three ramps. 2.4. Reliability assessment Forty (20 healthy and 20 CLBP male subjects) were assessed on three sessions at least two days apart within a two-week period. The generalizability theory [67] was used as a framework to estimate the reliability of EMG indices and to estimate the reliability for various measurement designs. Reliability was assessed for all EMG parameters, each group (healthy and CLBP male subjects) and each muscle or muscle pair by the intraclass correlation coefficient (ICC) and standard error of measurement (SEM) expressed as a percentage of the grand mean (across days) [37,40,41]. 2.5. Estimation of recovery time required after the fatigue test to perform a second fatigue trial It was predicted from the reliability assessment of EMG fatigue indices that the averaging of measures across two fatigue tests performed within the same session would increase the reliability by about 13% [40]. However, enough rest between fatigue tests is required to allow a complete recovery. Thus, a study was conducted to evaluate if rest intervals of 10 or 15 min allow the back muscles to recover completely, from an EMG point of view, after performing a fatiguing contraction [41]. Twelve healthy males performed the fatigue trials

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Fig. 3. Illustration of the computation of fatigue and recovery EMG indices based on median frequency (MF) estimates. The upper plots show the L5/S1 extension moment during the 30 s fatigue (upper-left) and 5 s recovery (upper-right) contractions separated by a 60 s rest period. The lower plots show the corresponding MF estimates for a given back muscle. The fatigue index is represented by the slope of the MF-time linear relationship (lower-left plot). Two recovery indices were computed (lower-right plot): (1) the percent recovery of the IMFr (%REC), where IMFf and IMFr represents the intercept of the linear regression equation computed from the fatigue and recovery MF time-series and FMFf the final MF of the fatigue contraction MF time-series, and (2) the difference between IMFr and FMFf (REC).

(30 s contractions at 75% MVC) three times separated successively by a 15 min (between trial 1 and 2) and a 10 min (between trial 2 and 3) rest period. 2.6. Assessment of abdominal coactivity during the fatigue test The flexion moment produced by the antagonist muscles (abdominals) during the extension fatigue test was estimated for 22 healthy males [22]. The maximal EMG of each back and abdominal muscle was computed across the corresponding MVC contractions to normalize EMG signals. A detailed lumbar spine model [8,51] was implemented to partition the L5/S1 joint moment between 54 muscle fascicles and estimate joint forces. An EMG-assisted by optimization (EMGAO) approach [7] was used to adjust EMG predicted muscle forces to balance the joint moment. For all back muscles, the average EMG signals recorded during the first 5 s of the 30 s fatigue test were used as constant values to compensate for fatigue progression. To estimate the degree of trunk muscle coactivity produced by the abdominals, all the individual muscle moments were summed signwise. Coactivity was resolved axis by axis at each instant of the fatigue cycle by comparing the net L5/S1 moment about a given axis to the signed sum of the individual muscle moments about the same axis. The sum of all the individual muscle moments opposed to the net joint moment about a given axis represented coactivity at this

instant of the task. This was performed for the first and last 5 s of the fatigue test to quantify the effect of fatigue on coactivity.

2.7. Prediction of back strength using anthropometric data

The assessment of back muscle relative endurance (% maximal strength) requires the measurement of maximal back strength which remains problematic with CLBP patients. A study was conducted to evaluate if a multiple regression equation using anthropometric measurements could predict the back strength of healthy men subjects with accuracy [39]. Briefly, several anthropometric measures were collected on 42 healthy men. The possible anthropometric correlates to back strength were identified based on previous literature and included global indices of body size, specific measures of the trunk segment and limbs, and derived variables related to body composition and muscularity. Stepwise multiple linear regression analysis was performed to predict back strength with age and 26 anthropometric variables as independent variables.

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3. Results 3.1. Assessment of weakness 3.1.1. Reliability The NME parameters (NME10–80 and NMEmvc) averaged across bilateral muscles showed good to excellent reliability results for both healthy and CLBP groups (ICCs from 0.65 to 0.96; SEMs from 12 to 25%). 3.1.2. Sensitivity The healthy subjects showed higher (t-test, P = 0.004) peak extension moment at L5/S1 (n = 20; 252 Nm, SD: 40) than CLBP subjects (n = 20; 202 Nm, SD: 62) and men generated a significantly (P = 0.000) higher extension moment (n = 12; 274 Nm, SD: 38) than women (n = 13; 191 Nm, SD: 26). However, only the NMEmvc index showed significant differences (P ⱕ 0.05) between these groups. These differences occurred for the MU-L5 (healthy vs CLBP subjects) and for the LO-L1 (men vs women) muscles. However, higher values (more efficient muscle contractions) were obtained for the CLBP and female groups, which was in opposition to our expectations. 3.2. Assessment of muscle composition 3.2.1. Reliability The IMFT10–80 and IMF75 parameters showed excellent reliability for both groups (ICCs from 0.68 to 0.91; SEMs ⱕ10%) while modest results were achieved for MFT10–0 (ICCs from 0.36 to 0.71; SEMs from 189 to 598%). 3.2.2. Sensitivity The MF based parameters (MFT10–80, IMFT10–80, IMF75) were not sensitive to the differences in muscle composition expected between groups. Between group differences were observed in the MF data across the force levels (especially between healthy and CLBP men for the MU-L5 muscles, Fig. 4) but large inter-individual variability was present. Consequently, the difference was not significant. 3.2.3. Frequency banding analysis The power contained in the different frequency bands of the EMG power spectrum was equivalent for the same extension moment at L5/S1. A typical example is depicted in Fig. 5 for the MU-L5 muscle group. 3.3. Assessment of fatigability 3.3.1. Reliability The level of reliability obtained in the present study for individual muscles was, at best, acceptable. However, it was demonstrated that measurement strategies

Fig. 4. Median frequency (MF in Hz) as a function of the L5/S1 extension moment (Nm) during a 0–100% ramp contraction of the multifidus muscles (MU-L5). Each curve represents the mean of all healthy males (n = 20) and all CLBP (n = 20) subjects. The non-linear appearance of both curves and their apparent difference (not statistically significant) were observed only for this muscle group.

involving the averaging of multiple measurements sites can, at times, increase the reliability to an acceptable level. Only the EMG indices based on MF estimates (MFslp) showed acceptable reliability. Reliable EMG indices were achieved for both healthy and CLBP subjects in particular conditions, namely when (1) electrodes are positioned on medial back muscles (MU-L5 level and LO-L1) instead of more laterally positioned back muscles such as IL-L3 and LO-T10 and (2) measures were averaged across bilateral muscles. The most reliable EMG indices were the bilateral average of medial back muscles (ICC range: 0.68–0.91; SEM range: 5–35%) and the average of all back muscles (ICC range: 0.77–0.91; SEM range: 5–30%). With regards to EMG indices of fatigue, the identification of the most fatigable muscle also lead to satisfactory results (ICC range: 0.74– 0.79; SEM range: 21–26%). However, among the EMG recovery indices, only REC was reliable for medial muscles (MU-L5 and LO-L1) and when bilateral averaging was performed. According to the results of the Dstudy (generalizability theory), it was predicted that the averaging of measures across two fatigue tests performed within the same session would increase the reliability by about 13%. 3.3.2. Sensitivity The EMG fatigue indices (RMSslp, MFslp) showed no difference between men and women (Table 2). Even accounting for skinfold thickness in an analysis of covariance was unsuccessful because this covariate remained statistically unsignificant. Significant differences were observed between healthy and CLBP men for all but one comparison (MFslp, LO-T10) (Table 2). Unexpectedly, the corresponding results suggest that the back muscles of healthy men fatigued more rapidly than

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Fig. 5. L5/S1 extension moment (Nm) as a function of power in the different frequency bands (µV2×Hz) during a 0–100% ramp contraction for the multifidus muscles (MU-L5). From top to bottom, the plots corresponding to the frequency band 1 (20–60 Hz) to 5 (240–300 Hz). Each curve represents the mean of all healthy (n = 20) and all CLBP (n = 20) subjects. The curve corresponding to the CLBP men follow exactly the course of the curve corresponding to healthy men, and this at each frequency band.

Table 2 Effect of gender (healthy men vs healthy women) and low back status (healthy and CLBP men) on EMG fatigue indicesa Healthy men (n=12)

Healthy women (n=13)

MFslp: slope of the MF to time relationship (Hz/s) Multifidus ⫺1.50 (0.91) ⫺1.27 (0.54) Ilioc. lumb. ⫺0.60 (0.42) ⫺0.60 (0.34) Longissimus-L1 ⫺0.99 (0.58) ⫺0.75 (0.29) Longissimus-T10 ⫺0.49 (0.39) ⫺0.58 (0.29) Mean all muscles ⫺0.89 (0.54) ⫺0.80 (0.32) Most fatigable ⫺1.64 (0.93) ⫺1.39 (0.54) RMSslp: slope of the RMS to time relationship (µV/s) Multifidus 1.50 (1.47) 0.87 (0.52) Ilioc. lumb. 2.59 (3.38) 1.24 (0.60) Longissimus-L1 1.34 (1.08) 1.24 (0.83) Longissimus-T10 1.44 (1.43) 1.52 (1.03) Mean all muscles 1.72 (1.80) 1.22 (0.55) Most fatigable 3.01 (3.54) 2.42 (1.45) a

P (t-test)

Healthy men (n=20)

0.441 0.977 0.203 0.496 0.594 0.400

⫺1.34 ⫺0.60 ⫺0.79 ⫺0.40 ⫺0.78 ⫺1.46

0.159 0.168 0.798 0.880 0.346 0.583

0.93 1.84 1.27 1.51 1.39 2.66

(0.52) (0.33) (0.45) (0.27) (0.35) (0.55)

(0.70) (1.91) (0.83) (0.99) (0.97) (2.09)

CLBP Men (n=20)

P (t-test)

⫺0.82 (0.49) ⫺0.35 (0.23) ⫺0.46 (0.30) ⫺0.26 (0.22) ⫺0.47 (0.28) ⫺0.97 (0.52)

0.003 0.008 0.010 0.088 0.004 0.006

0.29 (0.32) 0.52 (0.58) 0.41 (0.44) 0.61 (0.54) 0.45 (0.42) 1.02 (0.85)

0.000 0.005 0.000 0.001 0.000 0.002

Significant differences (P ⱕ 0.05) are identified by bold characters

those of CLBP men and both fatigue indices demonstrates consistent results in this respect.

3.4. Estimation of recovery time required after the fatigue test to perform a second fatigue trial

3.3.3. Assessment of the cocontraction of abdominals During the fatigue test, coactivity increased for most subjects (18 out of 22) while the L5/S1 moments (extension, lateral bending, axial rotation) remained stable. The model predicted an average coactivity increase from 21 Nm (SD: 15) to 33 Nm (SD: 21).

No significant differences (one-way ANOVAs between the three trials, α = 0.05) were obtained for the different EMG indices (RMSslp, MFslp, IRMS, IMF) computed across the three fatigue trials. This suggested that complete muscle recovery was allowed with either a 10 or 15 min rest period. The corresponding reliability results showed that the averaging of two measures lead

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to an ICC increase ranging from 0.02 to 0.10 and a SEM decrease ranging from 2 to 10% depending on the EMG index of fatigue.

of MF estimates to muscle force or by a curvilinear relationship (bell shaped), depending on the back muscle investigated [36].

3.5. Prediction of back strength using anthropometric data

4.2. Back muscle weakness

The peak L5/S1 extension moment identified as the back strength was used as the dependent variable. The Pearson correlation coefficients revealed poor to valuable relationship (range: 0.01–0.60) between independent variables and back strength. The final prediction regression model included two variables (FFM: fat free mass and THORD: thoracic depth) that accounted for 39% of the variance and the corresponding equation was: Back strength ⫽ 14.740 ⫹ 5.814 ⫻ FFM ⫺ 4.959 ⫻ THORD (adjusted R 2 = 0.391, standard error of the estimate = 42.5 Nm)

4. Discussion 4.1. Reliability In general, similar reliability was obtained for the control subjects and CLBP patients for all EMG indices. These results were not expected because the only reliability study available for CLBP patients [54] demonstrates moderate ICCs of 0.47 and 0.39 for the left and right longissimus at L3, respectively, for the MFslp index. We attribute our satisfactory results to the stable clinical status of the CLBP patients (Oswestry score, VAS pain rating) during the reliability assessment and to the minimization of axial rotation moments using the visual feedback from the dynamometer [40]. In most cases (especially for MFslp), averaging measures across bilateral muscles or across all electrode sites increased the reliability of EMG indices of back muscles. The averaging across bilateral muscles ignores the difference that may exist between bilateral muscles when, for example, unilateral pain occurs. However, even if these bilateral differences exist, it appears that they cannot be detected reliably as shown in our reliability study [40] and in other studies [62,69]. We conclude from our reliability studies that the assessment of back muscle impairments through EMG analysis necessitates the use of multiple electrodes to achieve reliable results. The reliability of the MFT10–80 slope parameter computed during ramp contractions was the only EMG index showing poor reliability. This was attributed to the flatness of the MF to moment relationship (slopes close to zero), which was explained by either a lack of sensitivity

Even though CLBP male and healthy female subjects showed lower back strength than healthy male subjects according to the dynamometry results, the NME EMG parameter showed no between group difference with the exception of the counter-intuitive results (CLBP men and healthy women with higher NME than healthy men) obtained for 2 of the 16 comparisons. Pain and fear of injury may have confounded the strength measures for CLBP subjects. Hence, the use of dynamometry to report back muscle weakness might at least partly explain the lower strength results observed for CLBP subjects relative to healthy individuals in some studies. Studies that report no differences between CLBP and healthy subjects support this assertion [31,70]. Nevertheless, it appears reasonable to hypothesize that back muscle weakness occurs for some CLBP patients given that the muscle structure (decrease of muscle mass, alteration of muscle fiber characteristics) is modified [28,43] and that these modifications are time dependent relative to pain symptoms [46]. The pain or fear of injury bias was unlikely between healthy males and females, which confirmed that the NME parameter was not a sensitive indicator of back strength. The NME concept, which might be a useful tool to assess weakness at simple joints, does not seem applicable to the spine where multiple muscles span several joints. The use of the net moments to compute the NME parameter might represent a significant problem when load sharing among back synergic muscles and coactivation of abdominal antagonist muscles occurs. 4.3. Back muscle composition None of the EMG spectral parameters were sensitive to low back status or gender. However, it is possible that the CLBP subjects involved in the present study were not impaired enough to show detectable changes in muscle fiber size and/or proportion. In the present study, the CLBP subjects were at work at the time of testing and showed minimal disability according to the gradation proposed by Fairbank et al. [19]. However, females have smaller muscle fiber than in men irrespective of fiber types or back muscles [44,71]. Then, the absence of difference between men and women suggested that these spectral parameters were not sensitive to muscle fiber size. Given the discrepancy between the MF curves of healthy and CLBP (Fig. 4), MU-L5 muscle), it was hypothesized that between group differences could be comprised in specific frequency bands of the power

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spectrum. Unfortunately, this was not the case. However, time-frequency analyses such as wavelet transforms might give different results, given their expected ability to analyze the EMG signal with specific time resolutions. Wavelet analysis decrease the variability of spectral estimates [34]. This might be especially useful for ramp contractions where the force level is changing through the contraction, also implying a possible change in muscle length. However, the relationship between muscle fiber composition (proportions, areas) and spectral parameters is confusing [4,25,26,35]. Our results suggested that the role of many confounding factors must be clarified before EMG can provide a non invasive alternative to muscle biopsy.

4.4. Back muscle fatigability

4.4.1. Sensitivity to gender The effect of gender on back muscle fatigability is not trivial. Muscle physiology studies generally demonstrate a higher muscular endurance in women [11,21]. This could be explained by the greater capacity of women to use oxidative phosphorylation to produce ATPs [21], given that muscle composition in terms of muscle fiber proportion type (type I vs type II) is the same in both genders for back muscles [44,71] and that the absolute load produced is apparently unrelated to gender differences [21]. Many studies comparing men to women through the Sorensen test report lower fatigue in women [13,33,45,59,73]. However, the relative weight of the trunk is generally lower for women resulting in a longer holding time in this test [32,33,59] but this hypothesis has yet to be tested adequately. Few studies contrasting men and women involved a fatigue test with the subject standing erect (trunk vertical) to eliminate the possible bias of relative trunk weight [16,32]. They showed conflicting results with Jorgensen & Nicolaisen [32], who demonstrate lower fatigue in women using a mechanical muscle fatigue criterion, and Elfving et al. [16] and the present study who show no difference using an EMG muscle fatigue criterion. It is possible that EMG is not sensitive enough to detect differences in muscle fatigue. However, these three studies also use a high intensity relative load (Elfving et al. [16]: 80% MVC; Jorgensen & Nicolaisen [32]: 60% MVC; the present study: 75% MVC) that would halt the circulation of blood flow within the back muscles [5]. The use of a fatigue test allowing partial or intermittent blood circulation might magnify the gender effect if women use more the oxidative phosphorylation pathway to produce ATPs. This is supported by previous studies performed on other muscle groups [18,49].

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4.4.2. Sensitivity to low back status Unexpectedly, healthy males showed more back muscle fatigue than CLBP males. This concurred with other findings [6] and strongly suggested that our CLBP did not produce a ‘true’ MVC at the beginning of the assessment so that the ‘true’ relative load (function of the MVC) during the fatigue test was smaller (in absolute and relative terms) for them. It becomes evident that the main drawback in the measurement of relative endurance is the need to get a valid estimate the MVC level. Thus, an attempt was made to predict back strength with the use of anthropometric measurements [39]. However, as demonstrated previously [48,53] and further substantiated in the present paper, this prediction was prone to errors. Thus, other measurement protocols must be developed to overcome this situation. The task generally used for EMG fatigue assessments does not correspond to a work task, and hence the muscle fatigue mechanisms involved are different, a condition which makes difficult the inference to muscle endurance in relation to work. The use of a fixed load to induce muscle fatigue would eliminate the need for measurement of back strength. At first sight, this measurement approach may appear inappropriate for the evaluation of endurance because absolute endurance partly depends on subject strength [63]. Practically, it is however important to test this form of endurance considering that a same force level is required for all individuals to be able to perform many working tasks. Another common characteristic of most EMG fatigue assessments is the use of a sustained static effort at a high level of back strength [44,65]. Although this allows the phenomenon of muscle fatigue to be quickly revealed through EMG, muscle endurance is not evaluated in conditions that correspond to tasks performed in the workplace (intermittent contractions at a low to moderate level of strength). The endurance of muscle fibers at high levels of strength (60–80% of the MVC) would be evaluated with complete occlusion of intramuscular blood flow instead of conditions with low to moderate levels of strength (25–60% of the MVC) creating partial intermittent occlusion of blood flow, and completely different fatigue mechanisms [17]. There is a need to apply EMG measurements to quantify muscle fatigue in more complex tasks performed intermittently at low to moderate intensity to better mimic occupational tasks [58]. Given the larger proportion of type II fibers in CLBP subjects [47], this form of muscle fatigue assessment should be efficient to demonstrate their reduced capacity to use the aerobic energy pathways (oxidative phosphorylation) which is generally more developed in type I fibers. Furthermore, given that changes in muscle capillarity generally accompany the oxidative capacity of a muscle [27], allowing partial blood flow could possibly highlight the impairment in muscle capillarity that would be expected in CLBP subjects (more type II fibers = decrease of oxi-

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dative capacity = decrease of capillarity) relative to healthy individuals. This should affect the EMG signal content, the MF being sensitive to the accumulation of metabolic byproducts that is amplified by muscle ischemia [52] or in other words, by an impairment in muscle blood flow that would go along with a low capillarity density. Unfortunately, this latter hypothesis is not supported in the literature because back muscle biopsy studies contrasting healthy and CLBP subjects do not include data on capillarity. 4.4.3. Assessment of the cocontraction of abdominals It was assumed that abdominal muscles would not fatigue during sustained trunk extension so that timerelated changes in the EMG of these muscles would reflect force modulations after EMGAO adjustment. This assumption appears reasonable because the relative level activation of the abdominal muscle remained below 20% of the maximal voluntary EMG on average (rectus abdominis (left and right): 6 ± 4% and 7 ± 6%; external oblique: 19 ± 13% and 17 ± 16%; internal oblique: 15 ± 10% and 18 ± 15%) at the beginning of the fatigue test (first 5 s) and the contraction was relatively short (30 s). The increase in trunk muscle coactivity might indicate a protective strategy to stabilize the lumbar spine but at the expense of additional shear and compression forces on lumbar joints. Furthermore, the back muscles must counteract this additional net flexion moment produced by the abdominals, a situation that should precipitate muscle fatigue. We can hypothesize that CLBP subjects employ more abdominal coactivity to stabilize their lumbar spine [9,60,61], so they would be more prone to back muscle fatigue. However, the verification of these hypotheses requires the modification of present EMG driven models to process adequately CLBP subjects data who cannot produce the required MVC to normalize EMG. 4.4.4. Load sharing between back muscles A phenomenon that also deserves attention in the assessment of back muscle fatigue is the variability of load sharing between the different components of the erector spinae. Van Diee¨ n et al. [75] have shown the presence of this phenomenon in back muscles, but others also suggest that it occurs in some circumstances. A drop in the activation of the lumbar muscles is reported for some subjects at the end of a fatigue test while the lumbar net moment is kept constant [12,59]. We inspected the results of our sample of 20 healthy subjects and observed this phenomenon in four of them (typical case in Fig. 6). Interestingly, the lowest MFslp among the eight electrode sites corresponding to the most fatigable muscle were all smaller than ⫺1.49 (MFslp range at all electrode sites: ⫺0.67 to ⫺3.00 in our 20 healthy subjects) for all of them meaning that these subjects were getting more fatigued than the others. This concurred

with Cooper and Stokes [12] who observed variable load sharing only with the more strenuous tasks involving higher levels of fatigue. Moreover, Oddsson et al. [59] observed this phenomenon only for their male sample (n = 10) because seven of them reached exhaustion before the end of their 60 s Sorensen test. None of the women (n = 10) reached exhaustion and no drop in EMG activation was apparent. The drop in the EMG activation of lumbar muscles might be linked to specific muscles (multifidus for instance which are generally fatigued earlier than the other components of the erector spinae (see MFslp— Table 2 and [64,68]). These muscles would be progressively switched off by the central nervous system when they reach exhaustion. Thus, the load must be taken by other erector spinae components to keep the net moment constant. Scheerlink-Bunkens and Jorgensen [66] observed a shift of the load from the lumbar to the thoracic erector spinae. However, the additional load put on these muscles cannot be sustained for a long time and the subject rapidly reach exhaustion. An additional increase of EMG of the LO-T10 level was observable in only two of our four cases (as in Fig. 6) but it is possible that the load was sometimes shifted to muscles not recorded with our EMG setup. Variable load sharing between synergic muscles is hypothesized as a muscle strategy to delay muscle fatigue [75]. This phenomenon might partly explain the weak reliability of RMSslp relative to MFslp EMG parameters because EMG RMS amplitude is more sensitive than MF to the load [56]. The standardization of the task and measurement protocol cannot exclude the influence of the central nervous system on the muscle recruitment patterns. Thus, appropriate quantification of load sharing would be required to act as a complementary EMG parameter to explain muscle fatigue. However, to achieve this, the changes in amplitude and spectral EMG parameters due to fatigue and variations in muscle load must be differentiated. A method has been proposed to achieve this goal [42] but it is based on the premise that MF increase monotonously with muscle force. Unfortunately, we observed that the MF remains stable or even decreases across the force levels [36] so that this method would be useless when applied to back muscles. Other methods must be developed and tested to resolve this problem. In summary, the assessment of back muscle fatigue to infer back muscle capacity at work is a complex task that should be addressed in different ways. Van Dieen et al. [75] identified three factors that explain muscular endurance: (1) anatomical factors (muscle composition, capillarization), (2) physiological factors (hormones, enzymes, energy stores), and (3) functional factors (motor control). Anatomical and physiological factors can be accommodated by fatigue tests involving the recruitment of the motor units commonly used in daily

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Fig. 6. L5/S1 moment components (Nm) and electromyographic signals at eight electrode sites in function of time (s). The L5/S1 extension moment (upper left plot) was kept constant throughout the fatigue test, while the L5/S1 moments coupled to the extension moment (upper right plot: Mlat = lateral bending and Mrot = axial rotation) stayed at negligible values. See the decrease of MU-L5 activation (lower plots) and the corresponding increase of LO-T10 activation at the end of the fatigue test (these events are identified with arrows). The decrease of activation was also observed at IL-L3 and LO-L1 electrode sites but never at LO-T10 in other subjects.

activities and to some extent, allowing aerobic energy pathways to come into play (e.g. intermittent contractions). However, even with the level of task standardization of our measurement protocol, control mechanisms were not adequately controlled to isolate intrinsic muscle properties. Thus, these control mechanisms must be quantified and accounted for in future studies. The functional factors, which are defined by the muscle recruitment strategies (load sharing between synergetic muscles and coactivity of antagonistic muscles) have never been considered before but could help to explain why muscle fatigue occur more or less rapidly for some subjects. EMG driven models partitioning the lumbar net moments into individual muscle forces must be developed to account for the effect of muscle fatigue on the EMG signal, to estimate muscle forces from EMG without requiring the production of MVC contractions (problematic for CLBP subjects), and finally to built new biomechanical variables capturing the essential features of load sharing and cocontraction. Efforts have been initiated in this respect [23,48,74] and should be further encouraged.

Acknowledgements We acknowledge the financial support from the Re´ seau de recherche en adaptation re´ adaptation (REPAR/FRSQ) and by the Occupational Health and

Safety Research Institute Robert-Sauve´ (IRSST: Institut de recherche Robert-Sauve´ en sante´ et en se´ curite´ du travail). Christian Larivie`re was supported by a postdoctoral fellowship from the IRSST.

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Christian Larivie`re received a B.Sc. in physical education, a M.Sc. in kinanthropology and a Ph.D. in Clinical sciences from the University of Sherbrooke, Sherbrooke, Canada, in 1992, 1994 and 1999, respectively. From 1999 to 2000, he was a post-doctoral fellow in biomedical sciences at the University of Montreal, Quebec, Canada. He is currently researcher at the Occupational Health and Safety Research Institute Robert-Sauve´ , Montreal, Quebec, Canada. Since 1991, his research has focused on the development of linked models applied to lifting tasks and on the quantification of lumbar impairments through kinematic, kinetic and electromyographic measures.

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A. Bertrand Arsenault received a B.Sc. (Physical Therapy), M.Sc. (Kinesiology) and Ph.D. (Kinesiology) from University of Montreal, Simon Fraser University and University of Waterloo respectively. He practiced physical therapy for several years before joining, in 1980, the School of Rehabilitation of the University of Montreal and the Research Center of the Montreal Rehabilitation Institute. Since 1980, he has acted as a professor, director of graduate studies and director of the physical therapy program at the University of Montreal and as a researcher and director of this research center. He is now director of the School of Rehabilitation, Faculty of Medicine, University of Montreal. He has been involved in research activities focussing on the evaluation of the musculoskeletal system of stroke patients as well as of subjects suffering from back and neck pain. Denis Gravel is Professor at the School of Rehabilitation of the University of Montreal and researcher at the Research Center of the Montreal Rehabilitation Institute. After he received his B. Sc. in Physical Therapy in 1970, he practiced physical therapy for two years. Then, he completed his M.Sc. degree at the department of anatomy of the University of Montreal. From 1976 to 1983, he acted as a Professor at the School of Rehabilitation of the University of Montreal. From 1984 to 1991, he completed his Ph.D. degree in neurobiology at Laval University. From 1992 to 1996, he was granted from the FRSQ (Fonds de Recherche en Sante´ du Que´ bec) as a clinical researcher. His research interest focus on the evaluation of normal and pathologic motor function using electromyography, biomechanics and dynamometry techniques.

Denis Gagnon received a B.Sc. in physical education (1980) and M.Sc. in kinanthropology (1985) from University of Sherbrooke, and a Ph.D. in biomechanics in 1990 from University of Montreal. He is a Professor at the Department of Kinanthropology of the University of Sherbrooke and Director of the Occupational Biomechanics Laboratory. His research interests focus on the study of trunk muscle coactivity strategies during dynamic lifting and on the investigation of back muscle fatigue during static effort in healthy and low back pain individuals.

Patrick Loisel received his MD degree in 1971 from the Faculty of Medicine of Paris VI (France). He is a Fellow of the Royal College of Physicians and Surgeons of Canada (orthopaedic surgery) since 1986. He is full professor at the Faculty of Medicine of the Universite´ de Sherbrooke and presently involved in research, practice and teaching in work rehabilitation at Charles LeMoyne teaching Hospital (South Shore Montreal). His research work is in the development and validation of programs and tools for the prevention of work disability for musculoskeletal disorders. He is the head of the PREVICAP multidisciplinary clinical research team in work disability prevention. He is the director of the Quebec network for work rehabilitation (RRTQ) and the head of the work disability prevention CIHR strategic training program. He holds the research chair Bombardier/Pratt & Whitney in work rehabilitation at the Universite´ de Sherbrooke.