Journal of Electromyography and Kinesiology
Volume 21, Issue 1 , Pages 49-58, February 2011

Biofeedback effectiveness to reduce upper limb muscle activity during computer work is muscle specific and time pressure dependent

  • Pernille Vedsted

      Affiliations

    • Alectia A/S, Sofiendalsvej 9, 9200 Ålborg, Denmark
    • ,
  • Karen Søgaard

      Affiliations

    • Institute of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
    • ,
  • Anne Katrine Blangsted

      Affiliations

    • Institute of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
    • ,
  • Pascal Madeleine

      Affiliations

    • Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Fredriks Bajersvej 7, DK-9220 Aalborg, Denmark
    • ,
  • Gisela Sjøgaard

      Affiliations

    • Institute of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
    • Corresponding Author InformationCorresponding author. Tel.: +45 6550 3456, mob.: +45 2498 4063; fax: +45 6550 3480.
    web address

Received 13 April 2010; received in revised form 14 June 2010; accepted 14 June 2010. published online 12 July 2010.

Article Outline

Abstract 

Continuous electromyographic (EMG) activity level is considered a risk factor in developing muscle disorders. EMG biofeedback is known to be useful in reducing EMG activity in working muscles during computer work. The purpose was to test the following hypotheses: (1) unilateral biofeedback from trapezius (TRA) can reduce bilateral TRA activity but not extensor digitorum communis (EDC) activity; (2) biofeedback from EDC can reduce activity in EDC but not in TRA; (3) biofeedback is more effective in no time constraint than in the time constraint working condition. Eleven healthy women performed computer work during two different working conditions (time constraint/no time constraint) while receiving biofeedback. Biofeedback was given from right TRA or EDC through two modes (visual/auditory) by the use of EMG or mechanomyography as biofeedback source. During control sessions (no biofeedback), EMG activity was (mean±SD): 2.4±1.1, 2.5±2.1, and 9.1±3.1%max-EMGrms for right and left TRA and EDC, respectively. During biofeedback from TRA, activity was reduced in right TRA (1.7±1.6%max-EMGrms) and left TRA (1.2±2.0%max-EMGrms) compared to control. During biofeedback from EDC, activity in EDC was reduced (8.3±3.3%max-EMGrms) compared with control. During time constraint, activity was reduced in right TRA (1.9±1.3%max-EMGrms), left TRA (1.5±1.5%max-EMGrms), and EDC (8.4±3.2%max-EMGrms), during biofeedback compared to control. Conclusion: biofeedback reduced muscle activity in TRA by ∼30–50% and in EDC by ∼10% when given from the homologous or bilateral muscle but not from the remote muscle, and was significant in the time constraint condition; while feedback source and presentation mode showed only minor differences in the effect on reducing homologous muscle activity. This implies that biofeedback should be given from the most affected muscle in the occupational setting for targeting relief and prevention of muscle pain most effectively.

Keywords: EMG biofeedback, Electromyography, Mechanomyography, M. trapezius, M. extensor digitorum communis

 

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1. Introduction 

Prevalence of musculoskeletal disorders is often reported in the upper extremities by subjects working with repetitive monotonous work (Kilbom, 1994, Ohlsson et al., 1995). Repetitive monotonous work has been observed when working with computer input devices, and related to disorders in the forearm and neck/shoulder region (Hägg, 2000, Jensen et al., 1998, Jensen, 2003). Continuous and low-force muscle activity is found to characterize computer mouse work and suggested to be accompanied by symptoms and development of work-related musculoskeletal disorders (WRMD) in the upper extremities (Sjøgaard et al., 2000). Elevated electromyography (EMG) activity level for prolonged periods of time is found as a predictor of muscular pain in the neck-shoulder region (Madeleine et al., 2003), indicating an increased muscle activity together with an inability to relax the muscle in the painful state. Insufficient relaxation of muscle has been considered as a possible underlying mechanism in developing WRMD (Veiersted et al., 1993).

EMG biofeedback can contribute to an awareness of a sustained muscle activation pattern in patients suffering from myalgia and WRMD. A reduction of unnecessary muscle activity for a given task and conceivably an interruption in the sustained muscle activity can be the result of biofeedback. EMG biofeedback has earlier been used to reduce EMG muscle activity in both m. trapezius and forearm muscles (Gerard et al., 2002, Hermens and Hutten, 2002, Palmerud et al., 1995, Poppen et al., 1988). Finger flexor EMG biofeedback and typing force feedback presented to touch typists, reduced the 90th percentile typing force, finger flexor, and extensor EMG by 10–20% (Gerard et al., 2002). EMG biofeedback from the upper trapezius resulted in a 7–12% reduction in the EMG amplitude in both right and left trapezius during standardised typing tasks in subjects with computer-work related myalgia (Hermens and Hutten, 2002). The involved muscles during computer work, which are evaluated in the present study, work differently regarding the contraction mode. The trapezius muscle (TRA) acts as a shoulder stabilizer and contracts primarily statically, whereas m. extensor digitorum communis (EDC) contracts repetitively and dynamically during e.g. mouse clicking and thus is a prime mover during computer work. Due to differences in the contraction mode, TRA and EDC can also respond distinctively in the muscle activation pattern when introduced to biofeedback.

A systematic literature search reviewed the effect of augmented feedback on motor function of the upper extremities (van Dijk et al., 2005). Different therapeutic interventions using feedback, such as EMG biofeedback, kinetic or kinematic feedback, showed no difference in effectiveness of improving motor function. However, a number of aspects have to be addressed when considering possible different outcomes of biofeedback effectiveness that will be summarized below. It is speculated that time pressure and the stress it may cause, which are common risk factors during computer work (Punnett and Bergqvist, 1999), impedes the effectiveness of the biofeedback. Therefore it is pertinent to test the working condition under which biofeedback is implemented together with a subjective evaluation of the usefulness of the feedback.

Mental demands have been associated with increased muscle activity (Jensen et al., 1998). Psychological stress and cognitive demands can elicit non-postural muscle activity (Lundberg et al., 1994), which can contribute to an over-activity of single MU. During standardised finger movements and mouse clicking, motor unit (MU) activity in EDC was shown in spite of no physical requirement (Søgaard et al., 2001). MU activity was detected both before and after ended double click and even during contra lateral finger movement. This suggests a continuous activation of MU throughout computer work. Increased muscle activity above resting level in the non-keying contra lateral trapezius has been demonstrated during keying tasks (Blangsted et al., 2004). Therefore, there is a need for means to reduce the muscle activity in both the ipsi and contra lateral trapezius muscle during computer mouse work.

Various biofeedback sources, such as force, goniometry, EMG, and mechanomyography (MMG) have been used in earlier studies (Madeleine et al., 2002b, Madeleine et al., 2006, Tax et al., 1990). MMG has been used to study the response of muscle activity (Blangsted et al., 2005a, Madeleine et al., 2002a, Søgaard et al., 2003) and acute muscle pain (Madeleine and Arendt-Nielsen, 2005). The MMG, measured as the oscillation on the surface of the muscle, is suggested to reflect the ‘mechanical counterpart’ to the motor unit electrical activity as measured by EMG (Barry et al., 1985). Changes due to fatigue in motor-unit recruitment, firing pattern, and synchronisation are suggested to be revealed in the MMG (Dalton and Stokes, 1993, Orizio et al., 2003) and therefore believed to be complementary to the surface EMG (Madeleine et al., 2002b). MMG has shown to be more prone than EMG to detect fatigue due to low-level contractions (Blangsted et al., 2005b, Søgaard et al., 2003) and to be more useful as a biofeedback source than EMG in TRA during computer work (Madeleine et al., 2006). However, it is still unknown if such biofeedback conditions can be generalized to other muscle regions, i.e. the forearm.

Biofeedback can be presented in various modes, i.e. auditory (Gerard et al., 2002), visually (Palmerud et al., 1995), and vibratory signal (de Korte et al., 2008, Vollenbroek-Hutten et al., 2006). However, previous studies have not demonstrated one mode being more effective than the other with regard to reducing muscle activity. Muscle recruitment patterns or neural commands can depend upon biofeedback mode. Differences in co-activation relationships were observed for position control versus force control tasks (Buchanan and Lloyd, 1995) perhaps due to differences in the sensory organs and their neural afferent pathways. Thus, humans react faster on e.g. a sound than a visual source, since fewer synapses exist when motor control takes place as a consequence of an auditory vs. visual input despite the speed of light being higher than speed of sound. Therefore, auditory biofeedback can have a better effect in terms of reducing muscle activity as well as being less disturbing regarding the work task than visual feedback.

In summary, there is a need for further optimization of the biofeedback tool, therefore, the overall purpose of the present study was to test biofeedback from upper limb muscles as a means to reduce their activity during standardised computer work; and to reveal if biofeedback effectiveness depends on computer task working condition manipulated by time pressure. More specifically, the aim was to test the following hypotheses: (1) Unilateral biofeedback from upper m. trapezius (TRA) can reduce bilateral TRA activity but not activity in more remote muscle, e.g. m. extensor digitorum communis (EDC); (2) biofeedback from EDC can reduce activity in EDC but not in TRA; and (3) biofeedback is more effective in the no time constraint than in the time constraint working condition. Additionally, the study was designed to reveal whether visual vs. auditory biofeedback or biofeedback from electrical vs. mechanical muscle activity was most efficient.

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2. Methods 

2.1. Subjects 

Eleven female subjects with no history of neuromuscular disorders participated in the study – thus representing the working population most vulnerable to attract musculoskeletal disorders. All subjects were right-handed and experienced in the use of a computer mouse. The mean±SD age, body mass, and height of the women were 40.4±9.3years, 66.3±11.8kg, and 167.9±6.9cm, respectively. Informed consents were obtained from all participants and the study was approved by the local ethical committee (No. 19990062) and conducted in conformity with the Declaration of Helsinki. The experimental setup in the present study is from the same experimental series as reported by (Madeleine et al., 2006).

2.2. Experimental procedure 

The subjects were seated at a standardised and adjustable computer work station in an upright position with flexed elbows and pronated forearms resting horizontally in the sagittal plane. Elbows and forearms were supported throughout the experiment. The table and chair were adjusted to accommodate each subject and ensure the most comfortable position. The subjects performed standardised computer work for 3min during two different working conditions with time constraint and no time constraint while receiving biofeedback. The biofeedback was given from two different muscles (right TRA or right EDC), which primarily are affected by WRMD, through two different modes (visual or auditory) by the use of EMG as the biofeedback source. The design of the present study was a full 23 factors design. Additionally, six biofeedback sessions were performed with MMG as biofeedback source from TRA with feedback mode audio and visual as well as from EDC with only biofeedback mode audio, by which each subject in total completed 14 different biofeedback sessions in a randomised order. Additionally, prior to, halfway through, and after the biofeedback sessions, control sessions with time constraint (1 time)/no time constraint (2 times) were performed in randomized order. During the control sessions, the tasks were identical to the work sessions but no biofeedback was given.

2.3. Standardised computer work 

The standardised computer work was performed by duplicating various graphs that were shown in the upper right corner of the computer screen (pixel resolution: 0.3mm, screen resolution: 1024×768pixels) (Birch et al., 2000). The graphs were completed by clicking with the mouse cursor in a specific order on six circular targets (target point: 10mm) displayed on the main part of the computer screen. A straight line was automatically drawn between two consecutively activated targets. A new graph came up when the former graph was completed. Number of completed graphs and mouse clicks were registered. When working with time constraint, the task was to complete each graph within 10s, whereas during no time constraint there was no time limit. During time constraint, the subjects were occasionally unable to complete all graphs within the designated 10 s resulting in a larger number of graphs being initiated than completed, and only the latter value was assessed. The subjects were instructed to complete as many graphs as possible within the 3min during both working conditions.

2.4. Maximal voluntary contractions and reference contractions 

To measure maximal EMG muscle activity for TRA and EDC, subjects seated on an experimental chair of adjustable height performed isometric maximal voluntary contractions (MVC) as bilateral shoulder elevation and wrist extension, respectively, prior to the computer sessions. Subjects were at all times verbally encouraged during the maximal exertion as well as provided with force feedback. The procedure was repeated three times with in-between breaks of 90s. If the highest force was measured in the last MVC contraction, an additional contraction was performed but the maximum number of MVC contractions was limited to five. MVC measured for bilateral shoulder elevation were performed with braces placed above both acromions with the arms hanging passively alongside the body. MVC measured for wrist extension was performed in a seated position with the elbow flexed 90° and the forearm supported in a pronated horizontal position in the sagittal plane. The force transducers (Alpha Beam 250N, BLH Electronics, USA) was placed on both shoulders touching the acromion when the subjects were sitting in the relaxed position with their arms hanging alongside the body. The highest measured EMGrms value during the MVCs was taken as the max-EMGrms of that muscle.

2.5. Electromyography and mechanomyography 

EMG was recorded by bipolar surface electrodes (oval shape: 24mm long, 16mm wide, size of the recording area: circular with a 5mm diameter) Ag–AgCl electrodes, Neuroline 72001-K, Medicotest A/S, Ølstykke, Denmark). For the upper right TRA, the electrodes were placed on each side of the MMG accelerometer (30mm apart), which was placed approximately 20mm laterally from the mid distance between cervical vertebra C7 and acromion (Jensen et al., 1993). For the upper left TRA, the electrodes were placed parallel and identical to the placement of the electrodes on the right TRA. For EDC, the electrodes were placed on each side of the MMG accelerometer (30mm apart), which was placed one third of the distance between the lateral epicondyle and the radial styliod process (Delagi et al., 1981). Before mounting the electrodes, the skin was shaved, rubbed, and cleaned with ethanol. The impedance was checked to be below 12kOhm. The EMG signal was amplified 25 times, band-pass filtered in the frequency range 5–400Hz, and sampled with a sampling frequency of 1kHz. The signals were visually checked for noise and were then off-line digitally band-pass filtered (4th-order Butterworth filter) at 5–400Hz. The EDC EMG may be affected by cross talk from nearby forearm muscles, therefore, special care was taken during electrode positioning. Further, the cross talk was demonstrated to be minimal by having the subject perform a few practical tests while recording the EMG (Finsen et al., 2005).

MMG from the right TRA and EDC used as a biofeedback source was recorded by a piezoelectric accelerometer (Bang & Olufsen Technology, Struer, Denmark). The technical specifications of the accelerometer were as follows: diameter: 17.6mm, weight: 2.9g, sensitivity: 30pC/ms−2, linear transmission within a frequency range from 0.1 to 800Hz. The accelerometer was attached to the skin with double sided adhesive tape. The transducer was coupled to a charge amplifier (Brüel & Kjær Nexus, Nærum, Denmark) operating with a selected bandwidth of 1–100Hz and sampled at 1kHz. The root mean square (rms) values of the EMG (EMGrms) were computed over 1s non-overlapping epoch and averaged over the 3min recordings. The resting EMG signal was recorded during 20s “instructed rest”.

All EMGrms obtained during the sessions were normalized to the maximal EMGrms measured during the MVC (%max-EMGrms). The maximal EMGrms and rms value of the MMG (MMGrms) during MVC were calculated over a 1s period with a moving window of 100ms duration. Analogue signals were digitised synchronously via a National Instrument® (Austin, Texas, USA) 12-bit PCI-MIO16-E4 A/D board. Analysis was performed by MatLab, Version 6.5.1, Release 13 (© 1994–2005 Mathworks Inc., MA, USA).

2.6. Biofeedback 

The biofeedback program was developed as a graphical user friendly interface developed in LabVIEW™ (National Instruments®, Austin, Texas, USA) where the epoch length, threshold value, and number of epochs in test were set manually (Madeleine et al., 2006). The rms values of the high pass filtered (2nd-order Butterworth filter, Fc=5Hz) collected biofeedback source signal (either EMG or MMG) were computed over non-overlapping 1s epochs corresponding to the set epoch length (Madeleine et al., 2006). The number of epochs was set to three. The biofeedback program generated a squared trigger signal coded on two bits and sampled synchronously with the EMG and MMG analogue signals at 1kHz. An rms value of the last epoch above the threshold but less than three consecutive values above the threshold led to a 1V trigger signal. An rms value of the last epoch above the threshold led to a 3V trigger signal resulting in biofeedback given to the subject. Thus, if three consecutive 1s epoch’s rms values (corresponding to a time period of 3 epochs=3s) were above the threshold value biofeedback was generated. The threshold level of muscle activity used during computer work was determined individually during several trials (Table 1). The threshold was defined as the value where the subject was able to reduce the level of muscle activity in the given feedback muscle sufficiently to change the biofeedback status. Biofeedback was presented either visually (change colour bar on screen from red to green) or auditory (turn off generated tone (560Hz sinusoid)). The defined threshold value was normalized to the maximal EMGrms or MMGrms measured during the MVC (%max-EMGrms or %max-MMGrms, respectively). The duration of given biofeedback above threshold, i.e. amount of time where three consecutive rms values were above the threshold value, was computed as percentage of the 3min recordings.

Table 1. Range of the biofeedback threshold level of muscle activity used during computer work determined individually based on several trials.
Biofeedback muscleRange of threshold level
EMGEDC3.5–14.5%max-EMGrms
TRA0.5–4.3%max-EMGrms
MMGEDC5.5–30%max-MMGrms
TRA1.5–30%max-MMGrms

n=11. EMG, electromyography; MMG, mechanomyography; EDC, right m. extensor digitorum communis; TRA, right m. trapezius; %max-EMGrms, maximal EMGrms obtained during MVC; %max-MMGrms, maximal MMGrms obtained during MVC.

2.7. Subjective evaluation of usefulness 

The subjective evaluation of the usefulness of the feedback was also rated on a verbal 4-point scale with respect to the question “was the biofeedback useful?” by either answering I fully disagree (1), I partly disagree (2), I partly agree (3), or I fully agree (4) after ended work session. Usefulness was determined by the subject evaluating if they were able to respond to the biofeedback by attempting to reduce the muscle activity.

2.8. Statistics 

The statistical analyses were performed by SPSS 12.0 (©SPSS Inc. 1989–2003, Chicago, USA). Values are presented as means±SD. The statistical significance level was set to 0.05, and tests of one-sided hypotheses were deemed significant if a two-sided P-value was less than 0.1 Due to biofeedback being hypothesized to have a reducing effect on EMG muscle activity, P-values below 0.1 were reported significant. Acceptance of increased risk of type 1 error or a false positive result is legitimatized as biofeedback offered to workers – if being effective – is regarded as beneficial. To test the isolated effect of the biofeedback factors and working conditions on muscle activity compared to control sessions, where no biofeedback was given, all biofeedback sessions were pooled for each response muscle. Averaged values within the two biofeedback muscles (TRA vs. EDC) and working conditions (time constraint/no time constraint) were compared to averaged values over the three control sessions. Control sessions vs. biofeedback sessions were then evaluated by a two-way analysis of variance (ANOVA) with subjects as random factor and biofeedback muscles (TRA vs. EDC) and working conditions (time constrtaint/no time constraint) as the fixed factor. If main effect of given factor on muscle activity was shown, a two-sided Dunnett t-test was run as a post hoc test. Muscle activity in both TRA was analysed by Friedman repeated measures ANOVA on Ranks due to non-normality distribution. If main effect of given factor on muscle activity was shown, a Tukey test was run as a post hoc test.

To test differences between biofeedback conditions five-way ANOVA, with subjects as random factor and biofeedback muscles (TRA vs. EDC), biofeedback modes (visual vs. auditory), biofeedback sources (EMG vs. MMG), and working conditions (time constraint/no time constraint) as fixed factors, was applied to detect possible main effect of factors on muscle activity in right and left TRA and right EDC and to detect 2-ways interactions between factors. Four-way ANOVA were performed if significant interactions were seen between the fixed factors.

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3. Results 

3.1. Control sessions 

No significant differences were shown between the three control sessions regarding time constraint vs. no time constraint in muscle activity for either right or left TRA, or EDC (Table 2). The control sessions were therefore pooled and the means±SD were 2.4±1.1, 2.5±2.1, and 9.1±3.1%max-EMGrms for the right and left TRA and the EDC, respectively.

Table 2. The muscle activity (%max-EMGrms) from right and left TRA and right EDC during the three control sessions with time constraint/no time constraint.
Working conditionRight EDCRight TRALeft TRA
TC8.5±3.21.9±1.02.3±2.6
No TC9.5±3.12.5±1.32.0±1.4
No TC10.1±3.13.3±2.33.6±3.8

Values are mean±SD, n=11. TC, time constraint; No TC, no time constraint; EDC, right m. extensor digitorum communis; TRA, right or left m. trapezius;

3.2. Effect of biofeedback muscles 

Muscle activity in right TRA was reduced by ∼30% when feedback was given from right TRA compared to control (1.7±1.2 vs. 2.4±1.1%max-EMGrms, P=0.07) (Fig. 1, (a). In left TRA, muscle activity was significantly reduced by ∼50% when feedback was given from right TRA compared to control (1.2±1.2 vs. 2.5±2.1%max-EMGrms, P=0.05) (Fig. 1, (b). Muscle activity in the EDC was also significantly reduced by ∼10% when feedback was given from EDC compared to control (8.3±3.4 vs. 9.1±3.1%max-EMGrms, P=0.003) (Fig. 1, (c). However, no significant effect was seen of feedback from TRA on muscle activity in EDC; nor of feedback from EDC on muscle activity of either right or left TRA (Fig. 1).

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  • Fig. 1. 

    The muscle activity (%max-EMGrms) from (a) right TRA, (b) left TRA, and (c) right EDC during biofeedback from right TRA or EDC or during control sessions with no biofeedback (CON). EDC, right m. extensor digitorum communis; TRA, right m. trapezius; EMG, electromyography; MMG, mechanomyography; %max-EMGrms, maximal EMGrms obtained during MVC. Values are mean±SD, n=11. ∗Indicate significant difference. Note: the y axis does not origin at 0 in c).

3.3. Effect of working conditions 

A comparison between the effect of overall biofeedback sessions during time constraint and no time constraint working conditions and the control sessions was performed. A significant reduction in right and left TRA muscle activity was observed during computer tasks with time constraint performed with feedback compared to control (right: 1.9±1.3 vs. 2.4±1.1%max-EMGrms, P=0.08, and left: 1.5±1.5 vs. 2.5±2.1%max-EMGrms, P=0.09) (Fig 2). No such significant reduction was observed in neither right nor left TRA when working with no time constraint. Only EDC muscle activity was significantly reduced during computer tasks performed with biofeedback compared with control both during time constraint (8.4±3.2 vs. 9.1±3.1%max-EMGrms, P=0.002) and during no time constraint (8.7±3.2 vs. 9.1±3.1%max-EMGrms, P=0.08).

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  • Fig. 2. 

    The muscle activity (%max-EMGrms) from (a) right TRA and (b) left TRA during overall biofeedback when working with time constraint, no time constraint, or during control sessions with no biofeedback (CON). TRA, right m. trapezius; TC, time constraint; No TC, no time constraint; %max-EMGrms, maximal EMGrms obtained during MVC. Values are mean±SD, n=11. ∗Indicate significant difference.

3.4. Interactions of biofeedback muscle, mode, and source 

When comparing the effect of biofeedback sessions with control sessions, the reduction observed in muscle activity depended on the biofeedback muscle and the working condition. Even though biofeedback tested overall had no effect on TRA during no time constraint, a more detailed analysis comparing the various biofeedback situations showed several significant findings.

In left TRA (Table 3), a significantly lower muscle activity (P=0.07) was observed only when working with no time constraint and receiving biofeedback from right TRA compared to EDC. In EDC, a significantly lower muscle activity was observed (Table 3) (P=0.04) only when working with no time constraint and receiving feedback from EDC compared to TRA.

Table 3. The muscle activity (%max-EMGrms) from right and left TRA and right EDC during the control sessions and the 14 biofeedback sessions.
Response muscleBiofeedback modeBiofeedback muscleWorking conditionsControl sessions
Time constraintNo time constraint
Biofeedback sources
EMGMMGEMGMMG
Right TRAAudioEDC2.3±1.92.3±2.62.2±1.52.7±2.62.4±1.1
TRA1.7±1.91.4±1.21.8±1.31.4±1.2
VisualEDC2.2±2.2 2.5±3.2
TRA1.8±2.11.4±1.12.3±2.01.6±1.7
Left TRAAudioEDC2.2±3.41.7±2.11.2±1.23.0±4.92.5±2.1
TRA1.3±1.41.1±0.90.8±0.90.8±0.8
VisualEDC2.3±4.2 2.9±5.4
TRA1.3±1.60.6±0.51.1±1.12.5±4.9
Right EDCAudioEDC8.1±3.38.2±3.68.3±3.48.6±3.49.1±3.1
TRA8.6±2.98.5±2.98.6±2.99.2±3.6
VisualEDC8.3±3.4 8.2±3.6
TRA8.8±3.38.6±3.48.8±3.59.4±3.3

Values are mean±SD, n=11. Response muscle. muscle from which EMG muscle activity is recorded; Audio, auditory biofeedback; Visual, visual biofeedback; EDC, right m. extensor digitorum communis; TRA, right m. trapezius; EMG, electromyography; MMG, mechanomyography.

A significantly lower muscle activity (P=0.09) was observed in the left TRA during feedback through MMG compared to EMG when working with no time constraint. Moreover, a significantly lower muscle activity (P=0.03) was observed in EDC, when receiving feedback through EMG compared to MMG during no time constraint. However, neither auditory nor visual biofeedback had an effect on muscle activity in any response muscle. The biofeedback sessions during time constraint showed no effect of any biofeedback factor.

3.5. Subjective evaluation of usefulness, and productivity 

Independent of feedback variable, 84% of the subjects reported the feedback to be useful (Fig. 3). Receiving feedback during no time constraint was reported to be useful by a larger number of subjects compared to the usefulness of feedback during time constraint. Further, a larger number of subjects agreed with the feedback being useful when feedback was given from EDC compared to feedback from TRA. Independent of feedback variable, the duration of biofeedback above the threshold level, i.e. amount of time where three consecutive RMS values were above the threshold value, was approximately 30% of the sessions working time (Table 4).

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  • Fig. 3. 

    The subjective evaluation of the usefulness of the biofeedback by answering the question “Was the biofeedback useful?” by either answering I fully disagree (1), I partly disagree (2), I partly agree (3), or I fully agree (4) after ended work session. Answers within the two biofeedback muscles, biofeedback modes, and working conditions were pooled. The box-and-whisker plot summarizes the Δ: difference in usefulness of biofeedback and work type factors (left y axis) and the usefulness of all biofeedback sessions (right y axis). For Δ usefulness, the subtraction order is indicated on the categorical axis. Bold line indicates the median (50th centile), error bars indicate the 95% central range (2.5 and 97.5centiles), the maximal and minimal box values indicate the quartiles (25 and 75centiles), and open circles indicate values outside the 95% central range. EDC, right m. extensor digitorum communis; TRA, right m. trapezius; TC, time constraint; No TC, no time constraint; EMG, electromyography; MMG, mechanomyography.

Table 4. The duration of the muscle activity above the threshold level relative to the 3min recordings (%). Values within the two biofeedback muscles, biofeedback modes, biofeedback sources, and working conditions were pooled.
Duration of feedback variable above threshold (%)
Biofeedback muscleEDC27.1±18.5
TRA32.4±25.7
Working conditionsTime constraint30.2±24.1
No time constraint30.0±22.0
Biofeedback modeAudio29.5±20.9
Visual30.8±25.6
Biofeedback sourceEMG33.5±25.0⁎
MMG25.6±19.3

Mean values±SD. EDC, right m. extensor digitorum communis; TRA, right m. trapezius; Audio, auditory biofeedback; Visual, visual biofeedback; EMG, electromyography; MMG, mechanomyography.

⁎Significantly different from MMG as biofeedback source.

During the control sessions with no biofeedback, less completed graphs were monitored during time constraint compared with the productivity during no time constraint (20.4±1.9 vs. 22.8±3.2 graphs (P=0.06)) together with less mouse clicks (133.7±14.1 vs. 149.1±22.5 clicks, P=0.04) (Fig. 4). The same trend was evident in the biofeedback session, when comparing time constraint with no time constraint (P<0.0001) (Fig. 4). Introduction of biofeedback during no time constraint resulted in ∼5% less completed graphs (21.7±2.7 vs. 22.8±3.2 graphs (P=0.03)) together with ∼7% less mouse clicks (139.6±18.8 vs. 149.1±22.5 clicks, P=0.01) compared with no biofeedback during the control sessions (Fig. 4). Biofeedback during time constraint had no effect on productivity compared to that of the control sessions.

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  • Fig. 4. 

    The production of (a) graphs and (b) mouse clicks during standardized computer work in biofeedback sessions (BIO) and control session (CON) with time constraint (TC) or no time constraint (No TC). Values are mean±SD, n=11. ∗Indicate significant difference. Note: the y axis does not origin at 0.

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4. Discussion 

The main finding of present study was that biofeedback is a potential tool to reduce the activity in the muscle from which biofeedback is given as well as its contra lateral. No differences were found between auditory and visual feedback but the biofeedback tool was most effective during the time constraint working condition. The physiological response of TRA and EDC during biofeedback is in concert with the subjective evaluation of the biofeedback being a useful tool to reduce muscle activity.

4.1. Biofeedback criteria based on physiological mechanisms for WRMD 

The biofeedback method of this study had a time wise perspective eliciting biofeedback if the muscle activity was above the threshold level for longer than a preset time period. Thereby, biofeedback was not only based on the amplitude level exceeding a given individual threshold level. However, the range of threshold levels in this study can still have allowed for recruitment of the low-threshold MU and thereby not unloading them fully. A reduction in muscle activity as little as 1–2% of maximal EMG muscle activity, as seen in this study, may seem insignificant but physiologically it can have a favourable effect. The Henneman’s size principle comprises the suggestion of a fixed motor-unit recruitment and de-recruitment pattern order during repeated muscle activation pattern (Henneman, 1957). The low-threshold motor units are always recruited first and remain active until total relaxation of the muscle occurs; this can give an explanation for the link between insufficient muscle relaxation and muscle damage of the continuously activated motor units. This hypothesis is also known as the Cinderella hypothesis (Hägg, 1991), which provides a feasible explanation for the development of WRMD regarding the adverse muscle activation pattern when working with prolonged and low-force loads as in computer work. Accordingly, muscle relaxation is implied in the prevention of WRMD of the low-threshold MU. The firing rate of low-threshold motor units in trapezius has shown to increase with increasing contraction amplitude in the range of 1–10% of the EMG at maximal voluntary contraction (Westad et al., 2004). Thus, a substantial reduction in muscle activity may also correlate with a reduction in the firing rate of the activated MU. However, due to the range of the threshold levels in this study and thereby precluding a possible MU unloading, a stereotyped recruitment pattern of the low-threshold MU could consequently have resulted in an overload of the MU both mechanically and metabolically. Accumulation of metabolites, electrolyte disturbances, and altered Ca2+ homeostasis play a significant role in morphological changes and muscle damage (Berchtold et al., 2000, Jackson et al., 1984) and may extend to possible micro-damage of ligaments and tendons, triggering of inflammation/cytokines (King et al., 2009), which also can be the scenario in the surroundings of the low-threshold MU (Rosendal et al., 2005). The intracellular free Ca2+ concentration is proposed to be an essential regulator of many cell processes including oxidative status and metabolism and is the primary activator of many enzymes that are important for maintaining the structural integrity of the cells (Berchtold et al., 2000). Affecting recruitment and firing rate of low-threshold MU is likely to be the result of the present biofeedback method, and has thereby the potential to prevent development of WMSD. We recently demonstrated that subjects are able to accommodate feedback from individual muscle compartments and respond adequately by lowering requested muscle activity (Holtermann et al., 2009).

4.2. Effect of biofeedback muscle 

The results of the present study with regard to increasing the subject’s awareness of unnecessary muscle activity levels through biofeedback are consistent with earlier studies as the subjects reduced their muscle activity (Gerard et al., 2002, Hermens and Hutten, 2002, van Dijk and Hermens, 2006). Auditory biofeedback presented to subjects performing a gross-motor task, showed an increase in the relative rest time and a decrease in muscle activity (Voerman et al., 2004). Vollenbroek-Hutten et al. (2006) showed significant changes in muscle activation pattern after biofeedback training (Voerman et al., 2007, Vollenbroek-Hutten et al., 2006). Biofeedback in the aforementioned studies was given similar to that in the present study, i.e. when there was less than a preset relative time of rest in the given muscle (Hermens and Hutten, 2002), by which the subjects became aware of insufficient muscle rest.

Biofeedback may not only bring out beneficial outcomes, since a reduction in muscle activity in TRA with feedback from TRA has been shown to cause an increase in muscle activity in other shoulder muscles, which can result in just moving the WRMD to the synergists (Palmerud et al., 1995). However, the reduction observed in the contra lateral TRA muscle activity in the present study suggests that biofeedback can reduce motor activity in other muscles than the muscle from which biofeedback is given. Conversely, it was not possible to reduce the EMG activity level in TRA when feedback was given from the remote EDC and vice versa, suggesting a limitation in the ability to transfer biofeedback from one muscle region to an effective change in motor control in another region.

4.3. Effect of working condition 

Overall biofeedback during the time constraint working condition resulted in significantly lower muscle activity in both TRA and the EDC, which opposes our hypothesis that feedback is more effective in the no time constraint than in the time constraint working condition. Few studies have tested the effect of biofeedback during stressful vs. less stressful working condition. The results of the present study are in contrast with an earlier study investigating the effect of biofeedback on muscle activation patterns during a stress task and a typing task (Vollenbroek-Hutten et al., 2006). In that study muscle relaxation measured as relative rest time was shortest during the stress task compared to the non stressful typing task. However, the same effect of the biofeedback intervention was found in both tasks. Likewise, biofeedback stimuli was introduced to subjects performing a gross-motor task with either 5-, 10-, or 20-s intervals, resulting in the 10-s interval being most effective in reducing trapezius muscle activity (Voerman et al., 2004). The 5-s interval caused the highest level of trapezius activation perhaps due to a mental stress evoked by such relatively high number of feedback stimuli provided to the subject. Mental demands have shown to increase TRA muscle activity (Laursen et al., 2002). This suggests that stressful working conditions induced muscle activity that is unnecessary for performing the task from a biomechanical point of view underlining the potential of using biofeedback in such condition.

During time constraint, less graphs and clicks were produced during both control and biofeedback sessions compared with no time constraint, which can be attributed to the design of the working condition during time constraint. However, it suggests that muscle activity can be lowered by consequently reducing the amount of work performed during computer work, as biofeedback – in this study – resulted in a reduction of the muscle activity during time constraint. Within the no time constraint working condition, biofeedback resulted in smaller productivity compared to control, which supports the lower muscle activity observed during some biofeedback situations and the suggestion of lower work amount being a requisite if lower muscular load is the intention. These results also bring support to the notion that there is little unnecessary EMG muscle activity that can be eliminated – especially in EDC. A recent study, showed precision demand and mental pressure to have an effect on productivity in terms of a decreased time per mouse click (Visser et al., 2004), indicating more clicks, i.e. higher productivity, for a given work period with increased precision demand and mental pressure. However, high time pressure resulted in lowered productivity during high precision and mental demands during standardized computer work (Birch et al., 2000) highlighting a trade-off between precision and speed of movement.

Exposure to psychosocial stress factors, i.e. time constraint, low decision latitude and social support; have been identified as a major risk factor for developing WRMD during computer work in terms of elevated muscle activity and raised secretion of cortisol and catecholamine (Lundberg et al., 1994, Melin and Lundberg, 1997). A hyperventilation theory of job stress attempted to explain a variety of biologically plausible mechanisms by which stress factors can increase the risk of WRMD (Schleifer et al., 2002). Hyperventilation may arise because of time constraint and induce respiratory alkalosis that can trigger a number of physiological responses that have deteriorating impact on muscle performance. Peripheral vasoconstriction can reduce blood flow to upper extremities and cause a decrease in muscle tissue oxygenation due to sustained, repetitive work. Recently, decreased local oxygen saturation was demonstrated in the forearm during more vs. less stressful computer mouse work (Heiden et al., 2005). The results of this study allow, however, no assumptions to be made whether such physiological responses occur during biofeedback in a time constraint working condition.

4.4. Effect of biofeedback mode and source 

The type of biofeedback mode during different muscle contractions can be of significance to elicit a beneficial change in muscle activity. Differences in recruitment levels and firing frequency behaviour have been reported to be task dependent (Madeleine et al., 2002b, Tax et al., 1990). It has been acknowledged that slower sensory integration exists to evoke muscle activity for visual input than for proprioceptive input due to a longer control loop from eyes to α motor neurones compared with the monosynaptic stretch reflex (Madeleine et al., 2002b). However, the present study showed no differences in the effectiveness of visual or auditory biofeedback. As the computer task performed in this study is a visual-based task, an additional visual input could be easier to respond to than an auditory input. An extra input source can be perceived as an extra load and thus disregarded. However, the subjective evaluation of the usefulness of the biofeedback showed the auditory feedback to be most likely preferable.

During the no time constraint working condition, MMG was more effective than EMG as biofeedback source in TRA while EMG was more effective than MMG in EDC, suggesting EMG as the most efficient biofeedback source during dynamic contractions. MMG has been hypothesized to relate to the dimensional changes of the fibres during lengthening and shortening (Oster and Jaffe, 1980) and observed to be larger during dynamic than static contractions (Vedsted et al., 2006) due to movement artefact contamination. This can also be a feasible explanation for the MMG being a more effective biofeedback source in TRA, as TRA primarily is a shoulder stabilizer and should contract statically and not dynamically during computer work. However, as EDC primarily contracts dynamically during computer work, it is speculated that a limitation in the ability to change the MU activation strategy exist during dynamic contractions or a reduction in the number of cross bridge cycling per MU discharge may be complex during a given dynamic contraction.

4.5. Subjective vs. objective evaluations 

The subjective evaluation demonstrated a larger number of subjects to agree on the usefulness of biofeedback when given from EDC than TRA, which may be explained by the EDC being the muscle with the highest workload, i.e. around 9%max-EMGrms, while the activity in TRA was less than 3%max-EMGrms. The subjects had not to respond on where they felt the largest reduction in muscle activity but this was measured to decrease around 1%max-EMGrms in both muscles when feedback was given from the homologous muscle; and since the workload was three times higher in EDC than TRA the relative reduction in terms of objective measure was largest in the TRA (30% decrease) and only 10% decrease in the EDC. Since subjective and objective measures give complementary information a combination of these measures are recommended. Most importantly, a recent randomized controlled study using biofeedback training demonstrated a significant reduction in muscle activity as well as significant increased frequency of short and long gaps, and relative rest time of the trapezius during computer work (Holtermann et al., 2008).

4.6. Conclusion 

This study confirmed in line with previous findings that biofeedback can reduce muscle activation of the trapezius muscle by ∼30–50% and of the extensor digitorum communis muscle by ∼10% when working with standardised computer tasks. Importantly, the feedback source as well as presentation mode showed only minor differences in the effect on the reduction of homologous muscle activity, and we conclude these differences not to be of physiological significance. This implies that variable techniques can be used as promising tools in the applied work site conditions. There was some task dependency in the effectiveness of feedback that calls for a request to validate the effect magnitude of biofeedback in the specific working conditions. The effect range of biofeedback from a single muscle extends from effect on the homologous muscle to also include the bilateral muscle, but does not have any effect on a more remote muscle. This implies that biofeedback should be given from the most affected muscle in the occupational setting for targeting relief and prevention of muscle pain most effectively. Finally, biofeedback may be considered as effective means when formulating strategic guidelines for preventing musculoskeletal disorders and optimizing muscle activity during working conditions.

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Conflict of Interest 

All authors declare no conflict of interest.

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Acknowledgement 

The present study was supported by grants from The Danish Research Foundation, The medical Research Council (grant no. 9700565), and The European Community shared-cost RTD actions (QRLT 2000 00139) “Neuromuscular Assessment in Elderly Workers” (NEW).

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biography

Pernille Vedsted, was born in Aalborg, Denmark in 1974. She received the M.Sc. in Physical Education and Health from the Institute of Sports Science and Clinical Biomechanics, Faculty of Health Sciences, University of Southern Denmark in 2001. From the same institute she pursued a Ph.D in Exercise Physiology and Biomechanics in 2006 in cooperation with National Research Centre for the Working Environment, Copenhagen. She has been employed as research assistant and researcher at Institute of Sports Science and Clinical Biomechanics and National Research Centre for the Working Environment, Copenhagen. Currently she holds a position as Head of Department at Alectia A/S. The department consultants advise within working environment/occupational health towards all market segments. Pernille Vedsteds main areas of interest and advising are prevention of work-related musculoskeletal disorders, health promotion, and strategic health management.

biography

Karen Søgaard, received the M.Sc. in Physical Education from the August Krogh Institute, University of Copenhagen and at the same institution she pursued a Ph.D in human physiology in 1994. She spent 8months as a research fellow at the Department of Kinesiology at Simon Fraser University, Vancouver, Canada in 1995 and in 2001 5months at Prince of Wales Medical Research Institute, Sydney , Australia. Currently, she holds a professorship in Sports and Health Sciences at University of Southern Denmark, Odense, Denmark.

Her main field of competence is human exercise physiology with focus on muscle mechanics, metabolism and fatigue. She has more than 80 original papers in international peer reviewed scientific journals. She is involved in experiments focused on kinetics, biofeedback, motor coordination and muscle fatigue in humans and the relation to musculoskeletal disorders and rehabilitation. Recently, she has mainly been involved in large randomized controlled trial interventions focused on physical activity as prevention and rehabilitation for musculoskeletal disorders.

biography

Anne Katrine Blangsted received the M.Sc. degree in Physical Education/Human Physiology from the August Krogh Institute, University of Copenhagen in 1998. In 2005 she pursued a Ph.D. in Human Physiology at the Faculty of Health Sciences, University of Copenhagen in cooperation with the National Research Centre for the Working Environment, Denmark. From 1998–2008 she was employed as research assistant and researcher at the National Research Centre for the Working Environment, Denmark. Currently, she works in the field of clinical studies and patient registries.

biography

Pascal Madeleine was born in Toulouse, France, in 1969. He received the M.Sc. degree in biomedical engineering in 1991 from Paul Sabatier University, Toulouse, France and the Ph.D. degree in 1998 from Aalborg University, Denmark. In 2010, he received his Dr. Scient. degree from the Faculties of Engineering, Science and Medicine, Aalborg University, Denmark. He is currently employed as a Professor at the Center for Sensory–Motor Interaction (SMI), Department of Health Science and Technology at Aalborg University, Denmark. He is head of the research interest group within Physical Activity and Human Performance and director of the laboratory for Ergonomics and Work-related Disorders. He has published more 65 peer reviewed scientific journal publications and book chapters. His main area of research interests are the development and application of novel methods and technologies in Ergonomics and Sports.

biography

Gisela Sjøgaard completed M.S. degrees in mathematics and physical education and earned in 1979 her Ph.D. in muscle physiology at the faculty of natural science and her Dr. Med. Sci. in 1990 at the faculty of medicine at the University of Copenhagen. She was professor and head of the department of physiology at the National Institute of Occupational Health in Denmark, visiting professor at the University of Guelph, Canada and at the University of Michigan, USA, and holds presently a professorship in Sports and Health Sciences at University of Southern Denmark. She has published more than 120 original papers in international peer reviewed scientific journals as well as numerous educational publications. She has participated actively with presentations at more than 200 conferences including more that 60 invited lectures. Her main field of competence is human exercise physiology with focus on muscle mechanics, metabolism and fatigue. Special area of interest is neuromuscular control and muscle biochemistry, as well as their applications to work related musculoskeletal disorders.

PII: S1050-6411(10)00091-X

doi:10.1016/j.jelekin.2010.06.002

Journal of Electromyography and Kinesiology
Volume 21, Issue 1 , Pages 49-58, February 2011

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