Methodological variance associated with normalization of occupational upper trapezius EMG using sub-maximal reference contractions

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Journal of Electromyography and Kinesiology 19 (2009) 416–427 www.elsevier.com/locate/jelekin

Methodological variance associated with normalization of occupational upper trapezius EMG using sub-maximal reference contractions Jennie A. Jackson a,b,c,*, Svend Erik Mathiassen b, Patrick G. Dempsey c a

Department of Kinesiology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1 b Centre for Musculoskeletal Research, University of Ga¨vle, SE-80176 Ga¨vle, Sweden c Liberty Mutual Research Institute for Safety, Hopkinton, MA 01748, USA Received 15 March 2007; received in revised form 7 November 2007; accepted 7 November 2007

Abstract Objectives: To quantify the variance introduced to trapezius electromyography (EMG) through normalization by sub-maximal reference voluntary exertions (RVE), and to investigate the effect of increased normalization efforts as compared to other changes in data collection strategy on the precision of occupational EMG estimates. Methods: Women performed four RVE contractions followed by 30 min of light, cyclic assembly work on each of two days. Work cycle EMG was normalized to each of the RVE trials and seven exposure parameters calculated. The proportions of exposure variance attributable to subject, day within subject, and cycle and normalization trial within day were determined. Using this data, the effect on the precision of the exposure mean of altering the number of subjects, days, cycles and RVEs during data collection was simulated. Results: For all exposure parameters a unique component of variance due to normalization was present, yet small: less than 4.4% of the total variance. The resource allocation simulations indicated that marginal improvements in the precision of a group exposure mean would occur above three RVE repeats for EMG collected on one day, or beyond two RVEs for EMG collected on two or more days. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Exposure variability; Variance components; Assembly work; Trapezius EMG

1. Introduction In ergonomic applications raw EMG, in arbitrary electrical units, is customarily normalized. Normalization reduces signal variability between and within subjects due to physical characteristics irrelevant to the risk of developing work related musculoskeletal disorders (WMSDs), including thickness of tissue overlying the muscle of interest. Thus, normalization is intended to produce biomechanically meaningful values (Mirka, 1991), measured on

* Corresponding author. Address: Department of Kinesiology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1. E-mail address: [email protected] (J.A. Jackson).

1050-6411/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2007.11.004

a standardized scale, which allow comparisons between subjects, days and conditions (Mathiassen et al., 1995). While dynamic or multilevel normalization procedures may be attractive for some dynamic movements in some occupational settings (Mirka, 1991) the use of a single static reference posture has become standard in ergonomics studies, particularly those focusing on the upper trapezius muscle (for overview see Mathiassen et al., 1995, 2002). Both maximal voluntary exertions (MVE) and sub-maximal voluntary exertions, also called relative or reference voluntary exertions (RVE), are commonly employed. The RVE approach has, however, been proposed to offer physiologic and ethical advantages (Mathiassen et al., 1995). Under either approach, normalization can be performed using the value from a single or a set of reference contractions. Repeated RVEs and MVEs are associated with test–

J.A. Jackson et al. / Journal of Electromyography and Kinesiology 19 (2009) 416–427

retest variability; in both cases coefficients of variation have been reported in the range from 6% to 15% (Attebrant et al., 1995; Bao et al., 1995; Veiersted, 1991). This shows that normalization is itself a stochastic process which will introduce error to subsequent estimates of exposure parameters from normalized EMG (Mathiassen et al., 1995). Some authors have even suggested that the magnitude of variance introduced through normalization can, in certain cases, be larger than the variance eliminated (Yang and Winter, 1984). Access to specific variance components for all factors influencing an occupational exposure is paramount both for designing studies and for interpreting experimental results (Burdorf and Van Tongeren, 2003; Kromhout et al., 1993; Mathiassen et al., 2003; Rappaport, 1991). In addition, the variance components themselves may convey important ergonomic information and be useful in quantifying key elements of individual motor control strategy and/or the latitude afforded to individuals in performing a task by using different working techniques (Mathiassen et al., 2003; Mathiassen, 2006). In all of these applications, methodological variance is an issue. Few studies have, however, discussed the significance of methodological variance in design and interpretation of studies (Mathiassen et al., 2002; Mathiassen, 2006; Rappaport, 1991) and none have assessed, in quantitative terms, the unique component of variance due to normalization and its magnitude relative to other sources of exposure variance between and within subjects (Mathiassen et al., 2002, 2003).

417

information from the health questionnaire potential subjects were excluded if, in the last six months, they had experienced repeated feeling of numbness, tingling or pins and needles sensations in any of their hands, forearms, elbows, shoulders or neck or had a history of musculoskeletal disorders or rheumatoid conditions. A total of 23 women participated in the job training phase, however, only 15 were able to pass the timed proficiency test required to enter the experimental phase (see Section 2.3). The mean age of the 15 women completing the experimental phase of the study was 33.3 years (SD 9.1, range 18–45), mean height 1.66 m (SD 0.06, range 1.58–1.75), mean weight 69.9 kg (SD 16.3, range 45.4–113.4) and mean BMI 25.4 (SD 5.4, range 17.7–39.2). 2.2. Experimental task and pace

The primary goal of this study was to quantify the amount of variance introduced to EMG exposure parameters uniquely through the methodological process of normalizing raw trapezius EMG to sub-maximal RVE contractions, and to compare the magnitude of variance attributable to normalization to that attributable to subject, day within subject, and work cycle within day. The secondary goal of this study was to illustrate the effect of different resource allocation strategies during data collection and normalization on the precision of mean occupational exposures.

A simulated industrial assembly task combining gross motor movements and fine motor skills was performed in half hour bouts interspaced with 15 min of rest (Fig. 1). To complete the assembly subjects removed a base plate from the in-box, placed it in a wooden jig, drove screws into four holes in the plate using a counter-balanced pneumatic drill (nine holes were present and subjects were required to select the four correct locations), turned over the assembly, added four spacers (1 per screw), placed on a top plate, fastened the top plate in place using one wing nut per screw and placed the completed product in the ‘out-box’. Subjects worked seated at a station adjusted individually to facilitate an elbow flexion angle of 90° when seated with feet positioned flat on the floor and with upper arms relaxed and hanging vertically beside the trunk with hands resting atop the workstation. Materials were provided in bins and boxes affixed to the top of the workstation. Data from six work bouts and three pacing conditions was collected on two days: data from one pacing condition collected during one work bout on each of the two days will be discussed in this paper. Using the Maynard Operation Sequence Technique (MOSTÒ) (Zandin, 1990) the assembly task pre-determined cycle time (PT) was calculated. To simulate industrial work time pressures akin to those experienced by workers paid on piecework, an average performance time standard of 110 PT (110% as fast as the operational pre-determined standard time) was selected. For the condition considered in the present study, subjects worked under simulated assembly line conditions with new pieces becoming available every 51 s (corresponding to 110 PT pace). During each 30 min work bout 36 assemblies (cycles) were completed; 15 min of rest followed each work bout.

2. Methods

2.3. Training and experimental protocols

Data were collected as part of a larger study that investigated cycle-to-cycle consistency among subjects performing simulated industrial assembly and disassembly tasks. This study received ethical approval from the Internal Review Board at the Liberty Mutual Research Institute for Safety.

Three days of paid training were provided with a work pace proficiency test at 120 PT given on the final day. Proficiency at 120 PT was required for one pacing condition explored in the larger study and also secured proficiency at 110 PT. Subjects were required to pass the 10 min work pace test in order to advance to the experimental phase of the study. Subject visits were interspaced by a minimum of one day of rest during both training and experimental phases of the study. Each experimental day began with a 5 min ‘warm up’ while subjects re-oriented themselves to the task by completing six assemblies. Subjects were set-up with EMG and performed rest and RVE trials prior to the onset of the work bouts.

1.1. Study objectives

2.1. Subjects Healthy, right-handed women, ages 18–45, were recruited from the community. All recruits completed a health questionnaire and signed an informed consent after receiving an explanation of the study task and testing standards. Using reported

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in-box

drill

screws spacers

top plates reflective marker

wing-nuts

TrCr EMG TrCa EMG

out-box

Fig. 1. Experimental set-up: workstation layout, EMG recording sites and reflective marker placement.

2.4. Experimental measures

2.5. Data selection and processing

EMG was collected from two sites on the right trapezius muscle: Trapezius–Cranial (TrCr), electrode centre 2 cm lateral to the midpoint of the line between C7 and the acromion process and, Trapezius–Caudal (TrCa), electrode centre located at the midpoint of the vertical line joining the TrCr landmark and the superior border of the scapula – see Fig. 1. At each recording site the skin was shaved then cleaned with alcohol prior to applying a disposable two snap Ag–AgCl electrode, 2 cm inter-electrode distance, (Noraxon Dual Electrode – Arizona, USA) aligned along the length of the muscle fibers. A single snap ground electrode (Noraxon Single Electrode – Arizona, USA) was positioned atop a prominent thoracic vertebra (usually T1). A rest file and four RVE trials were collected at the beginning of each experimental day. The rest file was collected while the subject sat at the workstation relaxed, leaning back into the chair, looking straight ahead and with arms resting, palm down, on her lap. Normalization trials were collected while the subject sat upright, looking straight ahead at a marker positioned at eye level, arms abducted 90° in the frontal plane with elbows fully extended, wrists straight and palms down (Mathiassen et al., 1995). RVE trials were 15 s in duration interspaced by 30 s of rest: meticulous attention was paid to the standardization of normalization postures. EMG signals were pre-amplified (gain 500) at a distance of 6.5 cm from the recording site and telemetrically transmitted to the central receiver (Noraxon TeleMyo 2400R, Noraxon, USA). Signals were sampled at 1000 Hz, band-pass filtered (Butterworth 10–500 Hz), A/D converted using a 12 bit National Instruments A/D card and continuously monitored in real time while recorded using EvaRT software (Motion Analysis, California, USA). Synchronized analog data was collected from limit switches triggered when a plate was removed from the in-box to start the assembly (on) and when the completed product was placed in the out-box (off). Although not discussed here, subjects were also fitted with motion analysis reflective markers, EMG was collected from the forearm and assembly work bouts were interspaced with disassembly work bouts of equal duration.

Post data collection, signals were Butterworth filtered (30 Hz high pass filter), offset corrected (removal of electrical noise bias) and RMS converted (moving window, 100 ms). The observed RMS signal was corrected for noise using resting EMG values, according to the principle: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RMSrest adjust ¼ RMS2unadjust  RMS2rest Next, data files were chopped to individual cycles using the analog spikes generated by the limit switches. Individual cycles were removed from the data set if they contained any of the following:  erroneous or missing ‘on’ or ‘off’ signal(s);  screw jam resulting in an incomplete assembly;  obvious non-biological noise on an EMG channel. All accepted cycles were then normalized to each of the four RVE trials (RVE 1–4) and for each cycle the following exposure parameters were calculated: amplitude probability distribution function (APDF) values at the 1st, 10th, 50th, 90th and 99th percentiles (Jonsson, 1982); time spent at ‘rest’, defined as amplitude less than 6.7% RVE; and time at ‘large’ levels of activation, defined as amplitude larger than 100% RVE. The criterion for ‘rest’ and ‘large’ levels of activation were selected based on their correspondence to approximately 1% and 15% MVE, ˚ kesson et al., 1997; Bao et al., 1995). In summary, respectively (A the normalized data set comprised seven exposure parameters, each with 4, 320 values (less any deleted cycles): 15 subjects  2 days  36 cycles  4 normalization trials. The same five APDF percentiles were also calculated for all cycles using the non-normalized EMG data. 2.6. Variance component analyses Pooled data sets were formed and the total variability of each data set was partitioned to the appropriate variance components using the following nested, random effects model with crossed sub-factors (Searle et al., 2006), or a modified version of the model as detailed in the text below:

J.A. Jackson et al. / Journal of Electromyography and Kinesiology 19 (2009) 416–427

Esdcr ¼ l þ as þ bsd þ csdc þ nsdr þ esdcr

ð1Þ

where Esdcr is the experimental value of the exposure parameter obtained for subject, s, on day, d, for cycle, c and RVE trial, r; l is the grand mean across all s, d, c and r; as is the random effect of subject on the value of the exposure parameter for s = 1, 2, . . . , ns; bsd is the random effect of day within subject for d = 1, 2, . . . , nd; csdc is the random effect of cycle within day and subject for c = 1, 2, . . . , nc; nsdr is the random effect of normalization trial repeat within day and subject for r = 1, 2, . . . , nr; and esdcr is the residual error term which includes the interaction between cycle and normalization trial. All effects, as, bsd, csdc, nsdr and esdcr, are assumed to be independently and identically distributed, have zero covariance between any pair of values and to have a mean of zero: as  i:i:d:ð0; r2a Þ; nsdr  i:i:d:ð0; r2n Þ;

bsd  i:i:d:ð0; r2b Þ;

csdc  i:i:d:ð0; r2c Þ;

419

2.6.2. Non-normalized EMG data The total variance of exposure parameters calculated from non-normalized data was partitioned to individual components according to the model: Esdc ¼ l þ as þ bsd þ esdc

ð4Þ

where esdc is the residual error term which contains the variance due to cycle within day and subject. 2.6.3. Normalized EMG data The total variance of exposure parameters calculated from normalized data was partitioned to individual components according to the overall nested, random effects model with crossed sub-factors given in (1). 2.7. Stability of variance component estimates

and esdcr  i:i:d:ð0; r2e Þ

where r2a ; r2b ; r2c and r2n are the true variance components for subject, day, cycle and normalization trial, respectively. For all models, estimates of the variance components for subject, day, cycle and normalization trial, s2s ; s2d ; s2c and s2r , respectively, were calculated using type III ANOVA algorithms in SPSS 10.0 (Illinois, USA) with a custom design identifying the appropriate nested and crossed effects. ANOVA algorithms were selected given their good performance when the ratio of variance components between subjects to within subjects is larger than 1 (which was shown true for the majority of exposure parameters), and when the data set is ‘‘reasonably well balanced” (Swallow and Monahan, 1984) (true of the present data set). A further basis for selecting ANOVA algorithms over, for instance, Restricted Maximum Likelihood (REML) algorithms, is their robust nature to non-normally distributed data (Kromhout et al., 1993; Rappaport, 1991) as resulted from the current study. All negative estimates of variance (1% of the calculated values in the present data set) were replaced with zero values as has commonly occurred in the literature (Mathiassen et al., 2003; Searle et al., 2006). To permit comparison between exposure parameters, coefficients of variation (CVs) were calculated for all variance component estimates using the generic formula: qffiffiffiffi CV h ¼ s2h  m1  100% ð2Þ where m is the grand mean of the pooled data set and s2h is the estimated variance component of interest. 2.6.1. Repeatability of reference contractions To determine the within day and between days test–retest repeatability of the RVE trails a variance components analysis was run on the pooled data set containing RVE trial data from all subjects and days according to the model: Esdr ¼ l þ as þ bsd þ esdr

ð3Þ

where esdr is the residual error term which contains the variance due to repeated normalization test contractions within day. To facilitate comparison with previously reported values of repeatability for RVE test contractions, specific coefficients of variation were calculated for the within day (CVWD.RVE) and between days (CVBD.RVE) variance, entering components s2r and s2d as estimated from model (3) in the generic CV formula (Eq. (2)).

To investigate the stability of the variance component estimates derived for normalized EMG data (see Section 2.6.3), a jack-knife procedure was used (Efron and Tibshirani, 1993). One at a time, a single subject’s data was removed from the pooled data set; this created 15 secondary data sets of size (ns  1) * 2 days * 36 cycles * 4 normalization trials. Variance components were estimated for each of the secondary data sets according to model (1). To further investigate the magnitude and type of variability exhibited by individual subjects, variance components within each subject were determined. The magnitude of variance accounted for by day, cycle and normalization was calculated from the data set containing all cycles, days and RVE trials for that subject using the nested model with crossed sub-factors: Edcr ¼ l þ bd þ cdc þ ndr þ edcr

ð5Þ

where edcr is the residual error term which includes the interaction between cycle and normalization trial. 2.8. Precision analysis The effect of different resource allocation strategies on the precision of the group mean (mg) was estimated by mathematical simulation using the following model (Mathiassen et al., 2003; Searle et al., 2006): s2mg ¼ ½s2s þ ðs2d þ ðs2c =nc Þ þ ðs2r =nr ÞÞ=nd =ns

ð6Þ

where s2mg is the variance of the group mean. In this model, a value of ns = 1 reflects the case when a single subject is considered as a representation of the entire population. Allocation effects on the precision of the mean exposure of an individual (mi) were also examined using the equation: s2mi ¼ ½s2d þ ðs2c =nc Þ þ ðs2r =nr Þ=nd

ð7Þ

where s2mi is the variance of that specific individual’s mean exposure. This equation is employed when the single subject in question is the only one of interest and the generalization of findings to a larger population is irrelevant; for example, in the surveillance of person-specific ergonomic interventions. In both models, iterative values for the number of subjects, days, cycles and normalization trial repeats (ns, nd, nc and nr) were entered together with the experimentally determined variance components ðs2s ; s2d ; s2c ; s2r , cf. Section 2.6.3).

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3. Results

100 90

Variance components calculated for the pooled data set containing all EMG reference contractions were 6297.9, 5771.4 and 266.4 mV2, respectively, for variance attributable to subject, day and trial (grand mean = 231.5 mV). The coefficient of variation for the normalization trial variance component ðs2r Þ was 0.07 within day (CVWD.RVE) and 0.33 between days (CVBD.RVE).

Proportion of Variance (%)

3.1. Repeatability of reference contractions

80 70

s2e

60

s2r

50

s2c s2d

40

s2s

30 20 10

3.2. Exposure variability

0

Mean exposure values of normalized EMG from the superior Trapezius–Cranial (TrCr) site were slightly higher than those at the inferior site, Trapezius–Caudal (TrCa) but were not significantly different (paired t-test: criteria of p > 0.05). Both sites showed the same rank of factors from most to least variable and the proportions of total variance attributable to subject, day, cycle and normalization were quite similar between sites. Only data from the superior site, TrCr, will be presented below since this site has been more frequently studied and reported in the literature (Table 1). For all exposure parameters a unique component of variance due to normalization trial was present, was the smallest factor and contributed 0.5–4.4% of the total variability; variance attributable to subject was the largest component across all APDF parameters (Fig. 2). In the case where a specific individual is considered without any generalization to larger populations, the relative proportion of variance attributable to normalization will increase dramatically since between subjects variance is no longer an issue. For example, for the parameter APDF50 the average proportion of variance attributable

APDF 1

APDF 10

APDF 50

APDF 90

APDF 99

Exposure Parameter

%time < %time > 6.7%RVE 100%RVE

Fig. 2. Variance components for subject ðs2s Þ, day ðs2d Þ, cycle ðs2c Þ, normalization trial ðs2r Þ and residual error ðs2e Þ from all seven exposure parameters expressed as a proportion of the total variance.

to normalization was 24.8% for a specific individual while it was only 3.1% under the group approach (Fig. 2). Variance components calculated for the pooled data set containing non-normalized APDF 50 data (grand mean 229.7 mV) totalled 10,316.5 mV2 with 72%, 25% and 3% of this total attributed to the factors subject, day and residual error (predominantly due to cycle), respectively (cf. model (4)). Compared to the variance components calculated for normalized APDF 50 data (Table 1, column 4), the variance components for the non-normalized data had considerably larger absolute magnitudes, with the exception of s2c (cycle-to-cycle variability). Not surprisingly, the non-normalized data set attributed proportionally less variance to subject than did the normalized data set (72% compared to 85%), but showed an increase in variance attributed to day within subject (25% compared to 10%). Since the statistical models used to calculate variance

Table 1 Variance components from superior site of the trapezius muscle, TrCr APDF 1

APDF 10

APDF 50

APDF 90

APDF 99

<6.7% RVE

>100% RVE

s2s

194.0 (141.4–215.3)

868.5 (602.51–951.7)

5876.1 (1570.6–6385.8)

22,959.8 (3781.8–24,915.4)

62,275.9 (8717.0–67,521.6)

0.02 (0.00–0.09)

629.0 (513.8–705.6)

s2d

125.1 (49.5–135.2)

199.9 (151.5–216.0)

724.9 (258.9–784.8)

2132.5 (544.6–2304.6)

4422.8 (959.6–4780.2)

2.4 (0.1–2.6)

157.6 (125.0– 170.4)

s2c

60.3 (43.6–64.7)

62.8 (46.3–67.2)

98.5 (59.7– 104.2)

315.4 (144.0–335.1)

1,549.3 (818.144–1651.0)

0.6 (0.2–0.6)

30.1 (26.4–32.0)

s2r

15.8 (4.1–17.0)

52.0 (22.1–55.9)

215.7 (127.4–231.8)

676.9 (327.6–727.5)

1771.6 (818.4–1903.8)

0.01 (0.0–0.01)

18.5 (16.4–19.9)

s2e

0.4 (0.2–0.5)

0.4 (0.3–0.5)

0.8 (0.4–0.9)

3.2 (1.1–3.4)

14.7 (6.2–15.8)

0.01 (0.0–0.01)

0.4 (0.3–0.4)

Total s2

395.7

1183.6

6916.0

26,087.8

70,034.2

3.0

835.8

Grand mean

25.9 (22.1–27.2)

56.9 (49.6–60.1)

117.9 (98.5–122.6)

204.6 (164.2–211.9)

338.9 (267.2–350.9)

0.6 (0.3–0.8)

48.4 (45.2–51.0)

Values in parentheses indicate ranges obtained using the jack-knife procedure. Grand mean expressed in % RVE for APDF percentiles and in % time for remaining exposure parameters; variances in corresponding units squared.

J.A. Jackson et al. / Journal of Electromyography and Kinesiology 19 (2009) 416–427

components (i.e. models (1) and (4)) did not contain the same set of factors (s2r not present in raw EMG analyses), expressing lower variance components as a proportion of s2s may be a more judicial comparison. Using that approach, the ratio of s2d =s2s was 35% for non-normalized data and 12% for normalized data; as expected, when no normalization efforts were made a larger proportion of the total variance was attributable to day. Given the large differences in exposure parameter mean values, CV values for all variance components are presented in Table 2 to facilitate comparison; the CV for normalization trial repeat (CVBR) was similar across all APDF parameters and ranged from 0.12 to 0.15.

10000

day

cycle

100

150

421 normalization

2

variance (%RVE )

1000

100

10

1

0.1 0

3.3. Stability of variance components

50

200

250

300

350

mean APDF 50 (%RVE) day

10000

cycle

normalization

1000

variance (%time2)

The stability of the pooled variance component estimates according to the jack-knife procedure is given in Table 1. The proportion of total variance attributable to normalization was consistent for all exposure parameters, with a maximum uncertainty range of 5.2% (APDF 99). While the absolute variance component estimates showed a substantial dispersion (as did the grand mean), their relative proportions proved quite certain for all mid-range APDF parameters. As an example, the proportions of variance for APDF 50 ranged from 78% to 86% (subject), 10% to 12% (day), 1% to 3% (cycle) and 2% to 6% (normalization). The parameter ‘time at rest’ was the least stable with proportions of variance attributable to subject ranging from 0% to 22% and to day ranging from 25% to 82%, however, the proportion of variance attributable to normalization proved stable, ranging from 0.1% to 3.4%. The stability of the pooled estimates of within subject variance was further investigated by graphing variance components calculated at the level of the individual subject with respect to the subject’s mean exposure (Fig. 3a and b). Subjects differed substantially on the magnitudes of s2d , s2c and s2r for all parameters. Mean exposure values were reasonably consistent among most subjects, however, some outliers are evident. The rank of variance component magnitudes was not consistent across subjects; for example, most subjects showed more variance between days than between cycles or normalization trials ðs2d > s2c ; s2r Þ, a few demonstrated the most variance between cycles ðs2c > s2r ; s2d Þ and one subject showed the highest amount of variance between normalization trials ðs2r > s2d ; s2c Þ. For a particular subject the rank of variance component magni-

100

10

1

0.1 0

20

40

60

80

100

mean time > 100 %RVE (% cycle time) Fig. 3. Individual variance components (n = 15) for day ðs2d Þ, cycle ðs2c Þ and normalization ðs2r Þ plotted relative to the individual’s mean exposure value for parameters (a) APDF 50 and (b) percent time spent above 100% RVE. Each column of data represents one subject as indicated by the dashed lines.

tudes was typically constant across all exposure parameters. Mid- and upper-range APDF parameters (i.e. APDF 50, 90 and 99; Fig. 3a) showed a clear trend of increased variance with increased mean value (R2 > 0.82) for s2r and s2c and, to a lesser degree, for s2d (R2 = 0.63–0.79). In contrast, end range exposure parameters (APDF 1, percent time <6.7% RVE, and percent time >100% RVE; Fig. 3b) showed either a weak or no relationship between the magnitudes of mean and variance.

Table 2 Coefficients of variation between subjects (CVBS), days (CVBD), cycles (CVBC) and normalization trials (CVBR) derived from variance components in Table 1 using Eq. (2)

CVBS CVBD CVBC CVBR

APDF 1

APDF 10

APDF 50

APDF 90

APDF 99

<6.7% RVE

>100% RVE

0.54 0.43 0.30 0.15

0.52 0.25 0.14 0.13

0.65 0.23 0.08 0.12

0.74 0.23 0.09 0.13

0.74 0.20 0.12 0.12

0.26 2.80 1.36 0.21

0.52 0.26 0.11 0.09

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3.4. Precision estimates

1 RVE 24

2 RVEs 4 RVEs

CV of mean (%)

23

22

21 1

10

number of cycles

1 RVE 28

2 RVEs 4 RVEs

27

CV of mean (%)

The structure of the precision analysis models plainly shows that increasing the number of repeats of a higher level hierarchical factor (for example s2s in Eq. (6) or s2d in Eq. (7)) will have a larger impact on the precision of the mean than a corresponding increase in the number of repeats of a lower level factor (for example s2c or s2r ). To illustrate numerically, s2mg values from the simulations run for 1 normalization trial repeat, 1 cycle and 1 day changed from 6915.2 to 691.5 when the number of subjects in the group was increased from 1 to 10. If instead the number of subjects remained 1 but the number of days for that subject was increased to 10, the variance on the mean decreased only to 6826.6. In contrast, crossed sub-factors, such as s2c and s2r , do not have a strict hierarchy and their effect on the precision of the mean is directly related to their relative magnitudes. In Eqs. (6) and (7), if s2c > s2r then the precision of the mean will be more effectively improved by increasing the number of cycles, nc, than by increasing the number of normalization trials, nr; the opposite will be true if s2r > s2c . Mid-range parameters, APDF 50, 90 and 99, all demonstrated larger variance components for normalization ðs2r Þ than for cycle ðs2c Þ. For these parameters, the increase in the precision of the mean was greater when the number of normalization trials was increased from 1 to 2 than when the number of cycles was increased from 1 to 10 under the group approach for ten subjects and one day (Fig. 4a). Further, this figure shows that subsequent increases in the number of normalization trials will lead to smaller gains in the precision of the mean; with 10 cycles, increasing the number of RVE repeats from 1 to 2 decreased the CV from 22.2% to 22.0%, while a further increase from 2 to 4 RVEs led to a CV of 21.9%. Increasing the number of cycles or normalization trials resulted in larger changes to the precision of the mean for the individual approach (Fig. 4b) than for the group approach: increasing the number of RVE repeats from 1 to 2 decreased the CV from 26.2% to 24.6% and a further increase from 2 to 4 RVEs led to a CV of 23.8%. The fact that increased normalization efforts have a greater impact in the individual case is also evident when comparing the two estimation models (Eqs. (6) and (7)). All non-mid-range parameters had larger values of s2c than s2r and therefore were more sensitive to increased numbers of cycles than normalization trials. Iterations using the precision algorithms were conducted to determine the number of RVE repeats required such that additional RVE repeats would result in a less than 1% gain in precision of the mean. Simulations showed that for a group approach with EMG collected on one day, three RVE repeats correspond to this criterion, irrespective of the number of subjects or cycles; for a model in which EMG was collected on two or more days, the criterion was achieved with only two RVE

26

25

24

23 1

10

number of cycles Fig. 4. Simulated effects of increasing the number of RVE trial repeats compared to increasing the number of cycle repeats on the precision of the estimated mean of an APDF 50 exposure parameter. Data collection during only one experimental day (nd = 1) was assumed for both approaches. (a) Group approach with n = 10 subjects and (b) individual approach. Trade-offs are illustrated by the thin black lines.

repeats. Using the individual approach, six RVE repeats were required before the defined plateau in precision occurred, irrespective of the number of days used in the model.

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4. Discussion 4.1. Repeatability of RVE contractions Variation in upper trapezius EMG between repeated reference contractions within a day may result from actual deviations in posture during RVE repeats and/or a change in involvement of the monitored portion of the upper trapezius while maintaining the reference posture. In the present study meticulous monitoring of the subject’s reference posture throughout subsequent RVE trials was achieved through strict definition of reference posture and consistency in the data collection team monitoring the postures; subjects were given precise instructions for how to sit, where to look and the angle at which they were to hold their arms, and feedback was provided throughout the trials based on visual inspection. A fluctuating involvement of (parts of) the upper trapezius is therefore the more likely explanation for differences between subsequent RVE trials. The experimentally determined CV for within day test–retest repeatability of RVE trials (CVWD.RVE) of 7% was similar to that previously published by Veiersted (6–7%) (Veiersted, 1991) in a study of 10 trapezius RVE repeats and was lower than CVWD.RVE values published for trapezius RVEs by Bao et al. (1995) and Attebrant et al. (1995) (11–13%) or by Yang and Winter (1983) for the triceps (10.3%). The similarity of current RVE repeatability values to previously published data indicate that our findings on the magnitude of variance contributed uniquely through normalization are likely applicable to previous studies using normalized trapezius EMG for assessing manual handling tasks (Mathiassen et al., 2002). Fewer studies have reported the test–retest values of RVE contractions between days (CVBD.RVE). Yang and Winter (1983) collected five triceps RVE trials on each of three days and reported the CVBD.RVE for RVE contractions at 30% and 50% of the maximum voluntary force to be 12–15%. Veiersted (1991) examined the variation attributable to changes in electrode placement by measuring three trapezius RVE trials on each of four days at the midpoint between the acromion and C7 and at 3 mm increments from that point moving longitudinally ±12 mm along the muscle fibers. In the same study he examined the variance due to changes in posture during repeated RVE trials, recording ten RVE repeats on one day at postures between 70° and 90° abduction. A CV value of 23% was reported for trapezius EMG repeatability by summing variance estimates obtained for these two independent sources of variation. This sum could be considered akin to a gross CVBD.RVE since it includes signal variability associated with both electrode replacement and postural variation. The design of the current study permits the determination of the ‘‘true” CVBD.RVE, i.e. a CVBD rid of contributions from within day sources of variability (cf. model 3). Despite this adjustment, the current study showed a larger amount of variability between experimental days (CVBD.RVE = 33%) than either of the aforemen-

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tioned papers. It is conceivable that the ±3 mm range of electrode repositioning achieved by Veiersted was more exact than in the present study, considering that we employed neither skin markings nor transparencies. 4.2. Contribution of normalization to overall exposure variability As expected, normalizing the EMG data decreased the absolute magnitude of variance attributed to both subject ðs2s Þ and day within subject ðs2d Þ compared to the non-normalized data. A decrease also occurred in the ratio s2d =s2s , indicating that the effect of normalization on between days variability was more pronounced than that on between subject variability. Despite meticulous monitoring of RVE postures, normalization per se introduced a unique component of variance and accounted for a maximum of 4.4% of the total variance of normalized EMG exposure parameters (Fig. 2). The estimated magnitude of variance due to normalization proved quite stable, as shown by the jack-knife confidence estimates for s2r (Table 1): over the 15 jack-knife simulations for each of the 7 exposure parameters the proportion of variance attributable to normalization was less than 5% for 101 of the 105 simulations, and never exceeded 7.2%. Considering the strict attention paid to normalization posture replication over subsequent trials, the s2r obtained may represent a ‘best-case scenario’ of the amount of variance that can be introduced through normalization, particularly when compared to what could be achieved in the field (Attebrant et al., 1995). When more than one sub-maximal reference trial is performed the corresponding RVE values can be averaged and subsequent occupational EMG data normalized to that value (method A–N), or, occupational EMG data can be normalized to each of the RVE values and then averaged (method N–A). An extensive review of trapezius normalization techniques recommended the A–N method (Mathiassen et al., 1995) and published data have predominantly followed this suggestion. It is, however, simpler from a mathematics perspective to estimate the precision effects of multiple RVEs using the N–A procedure, since this results in a data structure that can be expressed by model (1), or modifications there of, which method A–N does not. Accordingly, all variance component analyses presented in this paper assume the N–A order of operations during normalization. A small, systematic difference exists between normalized EMG amplitudes resulting from the two methods with method N–A resulting in slightly higher values (unless all RVEs all equal in magnitude, in which case the results are the same). However, in an analysis of data from this study, differences in the resulting variance components were minimal and not consistently higher for either method. For all exposure parameters and variance components the proportions of variance differed between the two processing methods by less than 0.4% with two RVE trials, and by less than 3% with four RVE trials. Further, the proportion of variance

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attributable uniquely to normalization differed by less than 1% between the N–A and A–N methods for all exposure parameters irrespective of the number of RVE repeats. We therefore believe that findings on normalization effects and resource allocation calculated in this paper are applicable to trapezius EMG data normalized using either method. 4.3. Exposure and variance component magnitudes 4.3.1. Mean exposure magnitudes Mean exposure levels for APDF 10, 50 and 90 exposure parameters reported in the current study (57%, 118% and 205% RVE, respectively) are higher than many of the previously reported values from studies investigating trapezius EMG during light upper extremity work: APDF 10 ranging from 5.7% to 21.6%, APDF 50 ranging from 28% to 73% and APDF 90 ranging from 78% to 95% RVE (Balogh et al., 1999; Hansson et al., 2000; Mathiassen et al., 2002; Mathiassen et al., 2003; Nordander et al., 2004). However, values from a study of surface EMG collected from female cashiers by Risse´n et al. (2002) were in line with the values reported in the present study: APDF 1, 50 and 90 values for right trapezius were 40%, 92% and 175% RVE, respectively, and for the left trapezius, 56%, 118% and 192% RVE, respectively (RVE posture-90° forward arm flexion). Differences in RVE postures employed in the above studies complicate comparison across studies, and with the present study. Although current results are at the higher end of the previously reported range of mean exposure values they are still within reasonable agreement. Thus, we believe that our results, including the variance components, can be of general interest and application to assessments of light occupational tasks involving the upper extremities. 4.3.2. Exposure variability between subjects Variability of exposure measures in previously published studies has been predominantly reported using gross overall measures, such as, the observed standard deviation (SD) of mean exposures among subjects or the corresponding coefficient of variation (Mathiassen et al., 2003; Nordander et al., 2004). Only a small number of studies have published variance component estimates for upper trapezius EMG and direct comparisons between studies are often hampered by insufficient provision of data to calculate the specific variance for each of the between and within subject factors (Mathiassen et al., 2002; Nordander et al., 2004). The study by Mathiassen et al. (2002) provided sufficient data for proportional comparison and also examined light assembly work in a laboratory setting; variance due to subject was reported to account for 57–62% of the total variance for exposure parameters APDF 10, 50 and 90 (Mathiassen et al., 2003). These values are clearly lower than the values reported in the present paper (73–88%). When comparing variance components between studies it is paramount to consider the high dependency of within day variance on the duration of the measurement unit: the

longer the duration of the individual measurement quanta, the lower the expected estimate of variance within day (Mathiassen et al., 2003). The measurement unit employed in the above referred study by Mathiassen et al. (2002) included data from two cycles for a total unit time of 136 s while the present study employed a 51 s measurement unit. Accordingly, it was expected that the 2002 study would have a lower proportion of variance within day and, subsequently, a larger proportion of variance attributed to subject than seen in the present study. The impression of a large between subject variability has been corroborated by several previously published studies presenting gross overall measures of exposure variability (gross CVBS) for the APDF 50 (or mean amplitude) exposure parameter during light manual handling tasks conducted in the laboratory: 0.63 (components picking) (Bao et al., 1995), 0.5 (assembly work) (Nakata et al., 1993), 0.5 (letter sorting) (Jørgensen et al., 1989), 0.4 (bank teller work) (Takala, 1991), 0.31 (light cyclic assembly) (Mathiassen et al., 2002), 0.28 (loosening of screws) (Feng et al., 1997), 0.15 (nut running) (Mathiassen et al., 2003). These gross CVBS values are not corrected for the inflating effect of lower level within subject sources of variance, and, while reported values are quite varied, they are still smaller than the ‘true’ CVBS of 0.65 reported here. Some of the divergence may be explained by the fact that variance components are estimated from a limited data sample and are therefore themselves subject to variability. It is also likely that differences between subjects vary by study as a result of the degree to which a subject can alter the way in which the study task is performed (Mathiassen et al., 2003) and of study populations being more or less homogeneous. If present estimates of between subject variance are unusually high, then estimates for the proportion of variance attributable to the process of normalization may be slightly lower than what one could expect with a more typical group of subjects. For example, consider parameters APDF 50 and 90 with presently reported CVBS values of 0.65 and 0.74, respectively. With a between subjects variance corresponding to a CVBS of only 0.5, which is a typical value in the literature, the proportion of total variance attributable to subject would have been 77% (compared to the reported values of 85% and 88%, respectively) and the proportion of variance attributable to normalization would increase from 3% to 5%. 4.3.3. Exposure variability between days and cycles within subject Trapezius EMG studies with repeated measures both within and between days have been scarce (Mathiassen et al., 2002; Trask et al., 2006) and therefore estimates of ‘true’ variance components for day and cycle have rarely been calculated. A few papers have reported data from multi-day trapezius EMG studies, but do not explicitly report the variance components (Fjellman-Wiklund et al., 2004; Nordander et al., 2004; Veiersted, 1991; Veiersted, 1996). Data published in the Mathiassen et al. study

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discussed above (Mathiassen et al., 2002) permitted calculation of the relative proportion of variance due to day ðs2d Þ for parameters APDF 10, 50 and 90 (33.5%, 34.0% and 31.0%, respectively); these values were much larger than proportions obtained in the present study (17.0%, 10.4% and 8.1%, respectively). As previously noted, it is difficult to compare proportions of variance between studies given the dependence of s2c on the measurement unit of time. The absolute magnitudes of the variance components s2s and s2d are not, however, affected by cycle time and so the ratio of s2d =s2s can be compared across studies. For the APDF 10, 50 and 90 parameters reported by Mathiassen et al. (2002), the ratio of s2d =s2s ranged from 0.5 to 0.6 while the current study shows much lower ratios of 0.1–0.2 for these parameters. This difference may be a result of the relatively large variance between subjects found in the current study, as previously discussed. Prior studies have proposed a considerable part of between day variance to be due to variance induced through normalization rather than actual differences in how the worker completed the task on the different days (Mathiassen et al., 2003; Nordander et al., 2004).The present study was able to parse out and quantify a unique effect for both day, s2d , and cycle, s2c , independent of the effect due to normalization, s2r . For all exposure parameters the magnitude of the variance component for day was larger than that for both cycle and normalization and thus we could reject the hypothesis that gross variation between days is predominantly due to normalization effects. The finding of a unique component for variance between cycles is consistent with several previous studies (Hammarskjo¨ld et al., 1990; Mathiassen et al., 2002, 2003; Mo¨ller et al., 2004). As with variability in repeated RVEs, this variance could result from a change in movement patterns during assembly and/or a change in the involvement of the monitored parts of the trapezius muscle. By extension, it is also reasonable to expect that motor patterns would differ between days due, for instance, to fluctuations in strategy, motivation, proficiency or fatigue.

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affect on lowering the variance of group exposure measure means, while increasing the number of days is most effective in the individual approach. Simpler and less costly solutions, such as increasing the number of data cycles or the number of normalization trials collected, will also increase the precision of the mean, albeit to a lesser extent. Fig. 4 illustrates that the trade-off between these two alternatives can be worth-while considering on the basis of variance components from the literature or pilot data for the study. 4.5. Conclusions In the present EMG study of trapezius activity during cyclic assembly work, variance attributable to the process of normalization accounted for no more than 4.4% of the total variance within and between subjects. The magnitude of the variance due to normalization was considerably less than the unique component of between day variance and thus we could reject previous hypotheses that gross between day variability in EMG is predominantly due to normalization effects. Mid-range exposure parameters, APDF 10, 50, 90 and 99, were found to have similar relative sizes of variance components and showed a higher consistency in individual mean exposure values and values of variance components within subject than did low level exposure parameters, such as percent time below 6.7% RVE (corresponding to approximately 1% MVE). Increased numbers of RVE trials will attenuate imprecision introduced through normalization, however, increasing the number of RVE repeats beyond three resulted in improvements of less than 1% to the precision of a group mean for data collected on one day only. Two repeats were equally effective for data collected on more than one day. For mean exposures of individual subjects, up to six repeats were still considered effective in improving precision. The reported variability attributable uniquely to sub-maximal normalization is likely a best-case scenario for values that would be encountered in laboratory settings or in the field. With this in mind, we suggest our procedures and results can be used as a benchmark for studies in similar occupational settings.

4.4. Precision and resource allocation Acknowledgements The use of EMG requires substantial resources in terms of time, equipment, cost and competence and therefore experiments examining trapezius EMG commonly employ fewer than 15 subjects; in many cases this can lead to insufficient statistical power (Mathiassen et al., 2002). It is therefore important to carefully consider whether the size of a planned study is sufficient, and what values are optimal for the allocation of samples of subjects, days and trials within day: precision analyses permit such explorations into different resource allocations. Precision algorithms (Eqs. (6) and (7)) show that increasing the number of subjects will have the greatest

This study was conducted at the Liberty Mutual Research Institute for Safety in Hopkinton, Massachusetts where the first author was then employed, and the second author was a Visiting Scholar. Continuation of the work occurred at the Centre for Musculoskeletal Research, University of Ga¨vle, Sweden and was made possible by support from the University of Ga¨vle, the National Science and Engineering Research Council of Canada and the University of Waterloo. The authors would like to thank N.V. O’Brien for his technical expertise and work in data collection.

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Jennie Jackson received her MSc in Human Kinetics from the University of Guelph (Guelph, Ontario, Canada) in 2002. She worked as a research associate at the Liberty Mutual Research Institute for Safety in Hopkinton, Massachusetts for two years. She is currently a PhD candidate in Spinal Biomechanics at the University of Waterloo (Waterloo, Ontario, Canada). Her research interests focus on quantifying physical workload in the upper extremity and spine during occupational and athletic tasks and investigating variability during task performance.

Svend Erik Mathiassen is Professor and Research Director at the Centre for Musculoskeletal Research, CBF, at the University of Ga¨vle, Sweden, and adjunct professor at the Curtin University of Technology in Perth, Australia. He worked at the Swedish National Institute for Working Life between 1987 and 1999, and earned his PhD in work physiology in 1993 at the Karolinska Institute in Stockholm, Sweden. He has authored more than 50 papers in international journals. His main research interest is in collection, analysis and interpretation of data on biomechanical exposures in working life, with a particular emphasis on issues related to diversity and variation in exposure. How can variation be measured with a high cost-efficiency? Which kinds and sizes of variation have positive physiological effects? How can sufficient variation be achieved in a production system?

J.A. Jackson et al. / Journal of Electromyography and Kinesiology 19 (2009) 416–427 Patrick G. Dempsey received his BS degree from the University of Buffalo (Buffalo, New York) and MS and PhD degrees from Texas Tech University (Lubbock, Texas). He is a Principal Research Scientist at the Liberty Mutual Research Institute for Safety in Hopkinton, Massachusetts. His primary research interests are in the area of musculoskeletal disorder prevention, and in particular workplace assessment of exposures that pose risk to the low back and upper extremities.

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