Scholarly article on topic 'Biofeedback improves activities of the lower limb after stroke: a systematic review'

Biofeedback improves activities of the lower limb after stroke: a systematic review Academic research paper on "Medical engineering"

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Journal of Physiotherapy
OECD Field of science
Keywords
Stroke / Physical therapy techniques / Exercise therapy / Rehabilitation / Review systematic / Meta-analysis / Randomized controlled trials

Abstract of research paper on Medical engineering, author of scientific article — Rosalyn Stanton, Louise Ada, Catherine M. Dean, Elisabeth Preston

Question Is biofeedback during the practice of lower limb activities after stroke effective in improving performance of those activities, and are any benefits maintained after intervention ceases? Design Systematic review with meta-analysis of randomised trials. Participants People who have had a stroke. Intervention Biofeedback during practice of sitting, standing up, standing, or walking. Outcome measures Continuous measures of activity congruent with the activity trained. Results 22 trials met the inclusion criteria and 19 contained data suitable for analysis. Effect sizes were calculated as standardised mean differences because different outcome measures were used. Since inclusion of all trials produced substantial statistical heterogeneity, only trials with a PEDro score >4 (11 trials) were included in the final analysis (mean PEDro score 5.7). In the short-term, biofeedback improved lower limb activities compared with usual therapy/placebo (SMD=0.49, 95% CI 0.22 to 0.75). Lower limb activities were still improved compared with usual therapy/placebo 1 to 5 months after the cessation of intervention (SMD=0.41, 95% CI 0.06 to 0.75). Conclusion Augmenting feedback through the use of biofeedback is superior to usual therapy/placebo at improving lower limb activities in people following stroke. Furthermore, these benefits are largely maintained in the longer term. Given that many biofeedback machines are relatively inexpensive, biofeedback could be utilised more widely in clinical practice.

Academic research paper on topic "Biofeedback improves activities of the lower limb after stroke: a systematic review"

Stanton et al: Biofeedback in stroke

Biofeedback improves activities of the lower limb after stroke: a systematic review

Rosalyn Stanton, Louise Ada, Catherine M Dean and Elisabeth Preston

The University of Sydney, Australia

Question: Is biofeedback during the practice of lower limb activities after stroke effective in improving performance of those activities, and are any benefits maintained after intervention ceases? Design: Systematic review with meta-analysis of randomised trials. Participants: People who have had a stroke. Intervention: Biofeedback during practice of sitting, standing up, standing, or walking. Outcome measures: Continuous measures of activity congruent with the activity trained. Results: 22 trials met the inclusion criteria and 19 contained data suitable for analysis. Effect sizes were calculated as standardised mean differences because different outcome measures were used. Since inclusion of all trials produced substantial statistical heterogeneity, only trials with a PEDro score > 4 (11 trials) were included in the final analysis (mean PEDro score 5.7). In the short-term, biofeedback improved lower limb activities compared with usual therapy/placebo (SMD = 0.49, 95% CI 0.22 to 0.75). Lower limb activities were still improved compared with usual therapy/placebo 1 to 5 months after the cessation of intervention (SMD = 0.41, 95% CI 0.06 to 0.75). Conclusion: Augmenting feedback through the use of biofeedback is superior to usual therapy/placebo at improving lower limb activities in people following stroke. Furthermore, these benefits are largely maintained in the longer term. Given that many biofeedback machines are relatively inexpensive, biofeedback could be utilised more widely in clinical practice. [Stanton R, Ada L, Dean CM, Preston E (2011) Biofeedback improves activities of the lower limb after stroke: a systematic review. Journal of Physiotherapy 57: 145-155]

Key words: Stroke, Physical therapy techniques, Exercise therapy, Rehabilitation, Review systematic,

Meta-analysis, Randomized controlled trials

Introduction_

Provision of specific feedback is important for effective skill learning (Thorndike 1927, Trowbridge and Cason 1932). Following stroke, patients usually need to re-learn to perform motor activities. Learning requires practice, and feedback is important for practice to be effective (Annett and Kay 1957, Wallace and Hagler 1979). Although feedback is a common part of stroke rehabilitation, the most effective method of implementation of feedback in this population remains unknown (van Vliet and Wulf 2006). During rehabilitation, patients will receive intrinsic biological feedback via sensory systems, and therapists traditionally provide extrinsic (ie, augmented) feedback within their role as 'coach'. This extrinsic feedback will either take the form of knowledge of results (ie, information about the accuracy of the activity) or knowledge of performance (ie, information about the way in which the activity was carried out). Biofeedback (ie, feedback about physiological processes) can be delivered using technology to provide information about performance. Biofeedback may have advantages over therapist feedback in that it delivers continuous, accurate information in order to enhance performance (Salmoni et al 1984). However, since biofeedback delivers feedback concurrently rather than terminally, any enhanced performance may not be retained and motor learning may not occur (van Vliet and Wulf 2006). The question therefore arises as to whether biofeedback is superior to usual therapist feedback or intrinsic patient feedback in enhancing motor learning.

Biofeedback can be delivered through various senses, such as visual, auditory, and tactile systems, and can

provide information about the kinematics, kinetics, and/ or electromyography (EMG) of activities. Previous reviews examining the effect of biofeedback have tended to focus on one aspect and have therefore often failed to produce clear findings due to insufficient data to perform a meta-analysis (Langhorne et al 2009). For example, one review that examined biofeedback during one activity (walking), separated the interventions into biofeedback providing kinematic, temporospatial, or kinetic information, and was unable to conduct a meta-analysis (Tate and Milner 2010). Other reviews that examined only one type of biofeedback have found that EMG feedback does not improve outcome either at the impairment or activity level (Woodford and Price 2009) or that ground reaction force feedback does not improve balance or mobility (Barclay-Goddard et al 2009, van Peppen et al 2006).

This systematic review examines the effect of biofeedback more broadly in enhancing the training of motor skills after stroke. Unlike previous reviews, it includes clinical trials where any form of biofeedback was provided during the practice of the whole activity (rather than practice of part of the activity) and where outcomes were measured during the same activity. The focus is on activities involving the lower limb such as sitting, standing up, standing and walking, since independence in these activities has a significant influence on quality of life and ability to participate in activities of daily living. Although there has been one previous review of biofeedback for lower limb activities (Glanz et al 1995), only outcomes at the impairment level were measured.

Biofeedback for stroke rehabilitation has been known about for decades (eg, since Basmajian et al 1975). However it

is not commonly used despite its relatively low cost. For biofeedback to be implemented widely into clinical practice, its effect as a form of augmented feedback to enhance motor skill learning needs to be determined. Therefore, the research questions for this systematic review were: In adults following stroke,

1. Is biofeedback during the practice of lower limb activities effective in improving those activities? and

2. Are any benefits maintained after intervention ceases?

In order to make recommendations based on the highest level of evidence, this review included only randomised or quasi-randomised trials with patients following stroke using biofeedback during whole task practice to improve activities of the lower limb.

Method_

Identification and selection of trials

Searches were conducted of MEDLINE (1950 to September 2010), CINAHL (1981 to September 2010), EMBASE (1980 to September 2010), PEDro (to September 2010), and the Cochrane Library (to September 2010) databases for relevant articles without language restrictions, using words related to stroke and randomised, quasi-randomised or controlled trials and words related to biofeedback (such as biofeedback, electromyography, joint position, and force) and lower limb activities (such as sitting, sit to stand, standing, and walking) (see Appendix 1 for full search strategy). Titles and abstracts (where available) were displayed and screened by one reviewer to identify relevant trials. Full paper copies of relevant trials were retrieved and their reference lists were screened. The methods of the retrieved papers were extracted and reviewed independently by two reviewers (RS and EP) using predetermined criteria (Box 1). Disagreement or ambiguities were resolved by consensus after discussion with a third reviewer (LA).

Box 1. Inclusion criteria

Design

• Randomised trial or quasi-randomised trial

Participants

• Adults

• Diagnosis of cerebrovascular stroke

• Any level of disability and any time after stroke

Intervention

• Experimental intervention includes biofeedback using any signal (EMG, force, position) via any sensory system (visual, auditory, tactile)

• Part of intervention must be biofeedback during practice of the whole activity

• Practice of whole activity must involve movement (such as reaching in sitting or weight shift in standing)

Outcome measures

• Measure/s of lower limb activity (sitting, standing up, standing or walking)

• Measure/s must be congruent with the activity trained

• Measure/s of activity must involve movement

Assessment of characteristics of trials

Quality: The quality of included trials was assessed by extracting PEDro scores from the Physiotherapy Evidence Database. Rating of trials on this database is carried out by two independent trained raters and disagreements are resolved by a third rater. Where a trial was not included on the database, it was assessed independently by two reviewers who had completed the PEDro Scale training tutorial on the Physiotherapy Evidence Database.

Participants: Trials involving adult participants of either gender, at any level of initial disability, at any time following stroke were included. Age, gender, and time since stroke were recorded to describe the trials.

Intervention: The experimental intervention could be of any type of biofeedback, ie, using any signal (position, force, EMG) via any sense (visual, auditory, tactile). At least some of the intervention had to involve practice of the whole activity and practice of the activity had to involve movement (such as reaching in sitting or weight shift in standing). The control intervention could be nothing, placebo, or usual therapy in any combination. Type of biofeedback, activity trained, and duration and frequency of the intervention were recorded to describe the trials.

Outcome measures: Measures of lower limb activity congruent with the activity in which biofeedback was applied were used in the analysis. Where multiple measures for one activity were reported, a measure was chosen that best reflected the aim of the biofeedback intervention (eg, step length). The measures used to record outcomes and timing of measurement were recorded to describe the trials.

Data analysis

Data were extracted from the included trials by one reviewer and cross-checked by a second reviewer. Information about the method (ie, design, participants, lower limb activity trained, intervention, measures) and data (ie, number of participants and mean (SD) of outcomes) were extracted. Authors were contacted where there was difficulty extracting and interpreting data from the paper.

Post-intervention scores were used to obtain the pooled estimate of the effect of intervention in the short term (after intervention) and in the longer term (some time after the cessation of intervention). Since different outcome measures were used, the effect size was reported as Cohen's standardised mean difference (95% CI). A fixed-effect model was used initially. In the case of significant statistical heterogeneity (I2 > 50%), a sensitivity analysis to confirm the source of the heterogeneity was carried out. The analyses were performed using the MIXa program (Bax et al 2006, Bax et al 2008). Possible sub-group analyses, such as by lower limb activity (eg, standing up compared with walking), by signal (eg, force compared with position), by sense (eg, auditory compared with visual feedback), were identified a priori.

Results_

Flow of trials through the review

The electronic search strategy identified 1431 trials (excluding duplicates). After screening titles and abstracts, 46 potentially relevant full papers were retrieved. An

Table 2. PEDro scores* for included trials (n = 22).

Trial Random allocation Concealed allocation Groups similar at baseline Participant blinding Therapist blinding Assessor blinding < 15% dropouts Intention-to-treat analysis Between-group difference reported Point estimate and variability reported Total (0 to 10)

Aruin et al (2003) Y N N N N N N N Y Y 3

Bradley et al (1998) Y N N N N Y Y N Y N 4

Chen et al (2002) Y N Y N N N N N Y Y 4

Cheng et al (2001) Y N Y N N N Y N Y Y 5

Cheng et al (2004) N N Y N N N Y N Y Y 4

Colborne et al (1993) Y N N N N N N N Y Y 3

Cozean et al (1998) Y N Y N N Y Y N Y Y 6

Engardt etal(1993; 1994a/b) Y N Y N N N Y N Y Y 5

Eser et al (2008) Y N Y N N Y N N Y Y 5

Geiger et al (2001) Y N Y N N N Y N Y Y 5

Gok et al (2008) Y Y N N N Y Y N Y Y 6

Grant et al (1997) Y N Y N N N Y N Y Y 5

Intiso et al (1994) Y N Y N N Y Y N Y Y 6

Jonsdottir et al (2010) Y N Y N N Y Y Y Y Y 7

Kerdoncuff et al (2004) Y N N N N N N N Y Y 3

Lin et al (1998) Y N Y N N N N N Y Y 4

Mandel et al (1990) Y N N N N Y N N Y N 3

Montoya et al (1994) Y N Y N N N N N Y Y 4

Morris et al (1992) Y Y Y N N Y Y N Y Y 7

Sackley & Lincoln (1997) Y N Y N N Y Y N Y Y 6

Schauer & Mauritz (2003) Y N Y N N N Y N N Y 4

Walker et al (2000) Y N Y N N N N N Y Y 4

'PEDro scores from website www.pedro.org.au

Titles and abstracts screened • From electronic databases (n = 1431)

Potentially relevant papers retrieved for evaluation of full text

(n = 58)

• From electronic databases (n = 46)

• From reference lists (n = 12)

Papers included

in review

(n = 24, 22 trials)

Papers excluded after screening titles/abstracts • From electronic databases (n = 1385)

Papers excluded after

evaluation of full text

(n = 34)

• Research design not RCTorQCT (n = 11)

• Participants not hemiplegic stroke >18 (n = 0)

• Intervention not biofeedback (n = 1)

• Intervention not during activity (n = 22)

• Aim not to improve at activity level (n = 4)

• No appropriate measure of activity

(n = 7)

• Comparison with alternative intervention (n = 0)

• Same participants reported in other trial (n = 1)

Figure 1. Identification and selection of studies. Papers may have been excluded for failing to meet more than one inclusion criterion.

additional 12 potentially relevant trials were obtained following hand screening the reference lists of included trials and previous systematic reviews (1531 references screened). After being assessed against the inclusion criteria, 24 papers reporting 22 randomised trials were included in this review (Figure 1). Table 1 on the eAddenda provides a summary of the excluded papers.

Characteristics of included trials

The 22 trials involved 591 participants and investigated biofeedback as an intervention to improve activities of the lower limb following stroke. Activities trained included standing up (2 trials), standing (9 trials), and walking (11 trials). The quality of included trials is presented in Table 2 and a summary of the trials is presented in Table 3. Additional information was obtained from the authors for two trials (Jonsdottir et al 2010, Intiso et al 1994).

Quality: The median PEDro score of the included trials was 4.5, with a mean of 4.7 and a range of 3 to 7. Concealed allocation of randomisation occurred in 9% of trials, assessor blinding in 41%, intention-to-treat analysis in 9%, and less than 15% loss to follow-up in 59%. No trials blinded participants or therapists.

Participants: Across the trials, the mean age ranged from 55 to 71 years, and 59% of participants were male. The mean time after stroke ranged from less than 1 month to 4 years, with 71% of the trials carried out within 6 months after stroke.

Intervention: Experimental interventions included biofeedback of ground reaction force from a force platform via visual and/or auditory feedback (13 trials); muscle activity from EMG via visual and/or auditory feedback (5 trials); joint position from an electrogoniometer via visual and auditory feedback (3 trials); and limb position via auditory feedback (1 trial). Visual feedback was used in 10 trials; auditory in 6 trials; and a combination of both in 6 trials. The duration of intervention was from 2 to 8 weeks, with a frequency of between 1 and 5 days/week. Session times varied, ranging from 15 min to one hour. The experimental group received either biofeedback only (3 trials) or biofeedback plus usual therapy (19 trials). In the three trials where the experimental group received biofeedback only, the control intervention was nothing (1 trial) or usual therapy only (2 trials). In the 19 trials where the experimental group received biofeedback plus usual therapy, the control group received placebo plus usual therapy (2 trials), or usual therapy (17 trials).

Outcome measures: For standing up, weight distribution between the lower limbs was measured (2 trials). For standing, the measures used were directional control during reaching in standing (3 trials), Berg Balance Scale (3 trials), Rivermead Mobility Index (1 trial), gross function subscale of the Rivermead Motor Assessment (1 trial), and the balance component of the Fugl-Meyer-Lindmark (1 trial). For walking, all trials measured gait parameters such as step/stride length or width of base of support or speed (11 trials). Outcomes were measured after intervention (20 trials) and from 1 to 5 months after cessation of intervention (11 trials).

Effect of biofeedback

The short-term effect of biofeedback on activity limitations was examined by pooling data after intervention from 17 trials comprising 411 participants using a fixed-effect model. Biofeedback improved lower limb activities compared with usual therapy/placebo (SMD = 0.41, 95% CI 0.21 to 0.62) (see Figure 2 on the eAddenda for the detailed forest plot). There was, however, substantial statistical heterogeneity (I2 = 65%), indicating that the variation between the results of the trials is above that expected by chance. The results of a sensitivity analysis revealed that the heterogeneity was best explained by the quality of the trials. When low quality trials (ie, seven trials with PEDro score 3 and 4) were excluded from the analysis, the magnitude of the effect was similar (SMD = 0.49, 95% CI 0.22 to 0.75) but with less heterogeneity (I2 = 43%) (Figure 3, see Figure 4 on eAddenda for the detailed forest plot).

Table 3. Summary of included trials (n = 22).

Trial Design Participants LL activity Intervention Outcome measures during activity

Aruin et al (2003) RCT Bfbk+UT vs UT n=16 Age (yr) = 65 (SD 4) Gender = 11M,5F Time since stroke = < 1 mth Walking Exp = Step width from distance sensor via auditory fbk 70 min/day x 10 day Con = no Bfbk during walking practice 70 min/day x 10 day Both = usual therapy • Step width • Follow-up = 0,10day

Bradley et al (1998) RCT Bfbk+UT vs plac+UT n = 23 Age (yr) = 71 Gender = 12 M,11 F Time since stroke = 1 mth Walking Exp = LL mm activity from EMG via auditory + visual fbk 3/wk x6wk Con = placebo Bfbk during walking practice 3/wk x6wk Both = usual therapy • Speed, step length • Follow-up = 0,6 wk, 3 mth

Chen et al (2002) RCT Bfbk+UT vs UT n = 41 Age (yr) = 57 (SD 11) Gender = 13 M, 28 F Time since stroke = 3 mth Standing Exp = wt distr from force platform via visual fbk 20 min x 5/wkx2 wk Con = no Bfbk intervention Both = usual therapy • Smoothness of weight distribution • Follow-up = 0,6 mth

Cheng et al (2001) RCT Bfbk+UT vs UT n = 54 Age (yr) = 63 (SD 8) Gender = 33 M, 21 F Time since stroke = 3 mth Standing up Exp = wt distr from force platform via visual + auditory fbk 20 min x 5/wkx3 wk Con = no Bfbk intervention Both = usual therapy • % BW thru paretic lower limb • Follow-up = 0 wk, 6 mth

Cheng et al (2004) Q-RCT Bfbk+UT vs UT n = 52 Age (yr) = 61 (SD 17) Gender = 32 M, 20 F Time since stroke = 3 mth Standing Exp = wt distr from force platform via visual fbk 20 min x 5/wkx3wk Con = no Bfbk intervention Both = usual therapy • Smoothness of weight distribution • Follow-up = 0,3 wk, 6 mth

Colborne et al (1993) CT-RCT Bfbk vs UT n = 8 Age (yr) unknown Gender unknown Time since stroke = 17 mth Walking Exp = Ankle joint angle from elgon via visual + auditory fbk 30 min x 2/wkx 4 wk Con = usual therapy 30 min x 2/wkx 4 wk • Speed, step length • Follow-up 0, 5 wk

Cozean et al (1988) RCT Bfbk+UT vs UT n=18 Age (yr) = 55 Gender = 10M,6F Time since stroke = unknown Walking Exp = Ankle muscle activity from EMG via visual + auditory fbk 30 min x 3/wkx 6 wk Con = placebo Bfbk during walking practice 30 min x 3/wk x6wk Both = usual therapy • Stride length and cycle time • Follow-up = 0,6wk

Engardt et al (1993, 1994a, 1994b) RCT Bfbk+UT vs UT n = 40 Age (yr) = 65 (SD 8) Gender = 25 M, 15 F Time since stroke = 1 mth Standing up Exp = wt distr from force platform via auditory fbk 45 min x 5/wkx 6 wk Con = no Bfbk during standing up practice 45 min x 5/wkx 6 wk Both = usual therapy • % BW thru paretic lower limb • Follow-up = 0,6wk

Design Participants

LL activity Intervention

Outcome measures during activity

Eser et al RCT n = 41 Standing Exp = wt distr from force platform via visual fbk • Rivermead Mobility Index

(2008) Bfbk+UT Age (yr) = 61 (SD 12) 15 min x 5/wkx3 wk • Follow-up = 0,4wk

vs UT Gender = 25 M, 16 F Con = no Bfbk intervention

Time since stroke = 6 mth Both = usual therapy

Geiger et al RCT n =13 Standing Exp = wt distr from force platform via visual fbk • Berg Balance Scale

(2001) Bfbk+UT Age (yr) = 60 (SD 16) 15 min x 2-3/wkx 6 wk • Follow-up 0, 4 wk

vs UT Gender = 9M,4F Con = no Bfbk during standing practice

15 min x 2-3/wkx 6 wk

Time since stroke = 4 mth

Both = usual therapy

Gok et al RCT n = 30 Standing Exp = Wt distr from unstable platform via visual fbk • Smoothness of weight

(2008) Bfbk+UT Age (yr) = 57 (SD 8) 20 min x 5/wkx 4wk distribution

vs UT Gender = 17 M,13 F Con = no Bfbk intervention • Follow-up 0, 4 wk

Time since stroke = 18 mth Both = usual therapy

Grant et al RCT n=16 Standing Exp = Wt distr from force platform via visual fbk • Berg Balance Scale

(1997) Bfbk+UT Age (yr) = 65 (SD 3) 30 min x 5/wk (inpt) and 2/wk (outpt) x8wk • Follow-up = 0, 8, 12 wk

vs UT Gender = 10M,6F Con = no Bfbk during standing practice

Time since stroke = 1 mth 30 min x 5/wk (inpt) and 2/wk (outpt) x8wk

Both = usual therapy

Intiso et al RCT n=16 Walking Exp = Ankle muscle activity from EMG via auditory fbk • Speed, step length

(1994) Bfbk+UT Age (yr) = 57 (SD15) 30 sessions/2 mth • Follow-up = 0,2 mth

vs UT Gender = 9 M,7F Con = no Bfbk during walking practice

30 sessions/2 mth

Time since stroke = 10 mth

Both = usual therapy

Jonsdottir RCT n = 20 Walking Exp = Ankle muscle activity from EMG via auditory fbk • Speed, stride length

et al Bfbk Age (yr) = 62(SD11) 45 min x 3/wkx7wk • Follow-up = 0, 7, 13 wk

(2010) vs UT Gender = unknown Con = usual therapy

Time since stroke = 4yr 45 min x 3/wk x 7wk

Kerdoncuff RCT n = 25 Standing Exp = Wt distr from force platform via visual fbk • Fugl-Meyer-Lindmark Scale

et al Bfbk+UT Age (yr) = 60 (SD 14) 15-20 min x 5/wk x3wk (balance component)

(2004) vs UT Gender = 15 M, 10 F Con = no Bfbk during standing practice • Follow-up = 0,3wk

15-20 min x 5/wk x3wk

Time since stroke = 1 mth

Both = usual therapy

Lin & Chung RCT n = 20 Walking Exp = Wt distr from force platform via visual fbk • Speed, step length, cadence,

(1998) Bfbk+UT Age (yr) = 57 40 min x 3/wk x4wk step width

vs UT Gender = unknown Con = no Bfbk intervention • Follow-up = 0,4wk

Time since stroke = >6 mth Both = usual therapy

Mandel et al RCT n = 37 Walking Exp = Ankle muscle activity from EMG via auditory + visual fbk • Speed

(1990) Bfbk Age (yr) = 57 (SD13) 24 sessions/12 wk • Follow-up = 0, 12 wk, 3 mth

vs Gender = 25 M, 12 F Con = no intervention

nothing Time since stroke = >6 mth

Design Participants

LL activity Intervention

Outcome measures during activity

Montoya et al (1994)

RCT Bfbk+UT vs UT

Age (yr) = 62

Gender =8M,6F

Time since stroke = <6 mth

Walking

Exp = Step length from moving platform via auditory + visual fbk 45 min x 2/wkx 4 wk

Con = no Bfbk during walking practice 45 min x 2/wkx 4 wk

Both = usual therapy_

Step length Follow-up = 0,4wk

Morris et al Q-RCT n = 26 (1992) Bfbk+UT Age(yr) = 64(SD11)

vs UT Gender=12M,14F

Time since stroke = 2 mth

Walking

Exp = Knee angle from elgon via auditory fbk 30 min x 5/wk x4wk

Con = no Bfbk during walking practice 30 min x 5/wkx 4 wk

Both = usual therapy_

Follow-up = 0,4,8 wk

Sackley & Lincoln

(1997)

Bfbk+UT

plac+UT

n = 26

Age (yr) = 66(SD11) Gender = 20 M,6F Time since stroke = 5 mth

Standing

Exp = Wt distr from force platform via visual fbk 20 min x 3/wkx 4 wk

Con = placebo Bfbk during standing practice 20 min x 3/wkx 4 wk

Both = usual therapy_

Rivermead Motor Assessment (gross function subscale)

Follow-up 0, 4, 12 wk

Schauer & Mauritz

(2003)

RCT n = 23

Bfbk+UT Age (yr) = 60 (SD 12)

vs UT Gender = unknown

Time since stroke = 1.5 mth

Walking

Exp = Position of heel strike via auditory fbk 20 min x 5/wkx3 wk

Con = no Bfbk during walking practice 20 min x 5/wkx3 wk

Both = usual therapy_

Speed, stride length Follow-up = 0,3wk

Walker et al RCT n = 32

(2000) Bfbk+UT Age(yr) = 64(SD14)

vs UT Gender = 20 M, 12 F

Time since stroke = 1 mth

Standing

Exp = Wt distr from force platform via visual fbk 30 min x 5/wk until D/C

Con = no Bfbk during standing practice 30 min x 5/wk until D/C

Both = usual therapy_

Berg Balance Scale Follow-up = 0, D/C (5-8 wk), 10 wk

RCT = randomised clinical trial, Q-RCT = quasi-randomised clinical group, BW = bodyweight, LL = lower limb, fbk = feedback, wt distr =

trial, CT = cross-over trial, Bfbk = biofeedback, UT = usual therapy, M/F = Male/Female, weight distribution, inpt = inpatient, outpt = outpatient, EMG = electromyography, elgon :

Exp = experimental group, Con = control = electrogoniometer

Cozean I-

Engardt h

Eser I-

Geiger I--

Grant i--

Intiso |-

Jonsdottir

Morris I— Sackley & Lincoln

■ -1

i-1-1--1-1-1

-3-2-10 1 2 3

Figure 3. SMD (95% CI) of effect of biofeedback on lower limb activities after intervention by pooling data from 10 high quality trials (n = 241).

The long-term effect of biofeedback on activity limitations was examined by pooling data after the cessation of intervention from 5 high quality trials comprising 138 participants using a fixed-effect model. Biofeedback improved activity compared with usual therapy/placebo (SMD = 0.41, 95% CI 0.06 to 0.75, I2 = 42%) (Figure 5, see Figure 6 on the eAddenda for the detailed forest plot).

Cheng 2001 Grant Jonsdottir Morris Sackley & Lincoln

—I— -1

Figure 5. SMD (95% CI) of effect of biofeedback on lower limb activities 1-5 months after the cessation of intervention by pooling data from 5 high quality trials (n = 138).

Subgroup analysis by activity found that the short-term effect of biofeedback on standing up could only be examined in one high quality trial comprising 40 participants. Biofeedback tended to increase standing up compared with usual therapy (SMD = 0.54, 95% CI -0.09 to 1.17). The short-term effect of biofeedback on standing could be examined by pooling data after intervention from five high quality trials comprising 125 participants, using a fixed-effect model. Biofeedback increased standing compared with usual therapy/placebo (SMD = 0.42, 95% CI 0.05 to 0.78, I2 = 69%, see Figure 7 on the eAddenda for the detailed forest plot) and the magnitude of the effect was the same using a random-effects model (SMD = 0.42, 95% CI -0.08

to 0.93). The short-term effect of biofeedback on walking could be examined by pooling data after intervention from four high quality trials comprising 76 participants, using a fixed-effect model. Biofeedback increased walking compared with usual therapy (SMD = 0.57, 95% CI 0.10 to 1.03, I2 = 0%, see Figure 8 on the eAddenda for the detailed forest plot).

Discussion_

This systematic review provides evidence that biofeedback has a moderate effect (Cohen 1988) in improving activities of the lower limb such as standing up, standing, and walking in the short term compared with usual therapy/placebo. Furthermore, the benefits are still present in the longer term although slightly diminished. This suggests that learning has taken place in addition to short-term improvements in performance. Biofeedback delivers feedback that is continuous, objective and concurrent with the activity, ie, knowledge of performance. In healthy populations, evidence suggests that concurrent feedback is beneficial to performance, but detrimental to learning (van Vliet and Wulf 2006). However, this review provides evidence that after stroke the provision of concurrent biofeedback during the practice of activities resulted in learning because lower limb activities were permanently improved.

The mean PEDro score of 4.7 for the 22 trials included in this review represents only moderate quality. However, in order to decrease the substantial amount of statistical heterogeneity, only higher quality trials (PEDro score > 4) were included in the final meta-analyses. This resulted in the 11 trials contributing to the findings having a mean PEDro score of 5.7, adding to the credibility of the conclusions. There was some clinical heterogeneity in these trials. Participant characteristics of age and gender were similar, and the time since stroke was generally subacute (70%), with three trials of participants whose time post stroke was chronic (10 mth, 18 mth, 4 yr). There was a range of duration of intervention (3 to 8 weeks), however the majority of trials examined interventions of 4 to 6 weeks in duration. Taken together, this suggests that the findings are credible and can be generalised cautiously.

Our subgroup analysis of lower limb activities suggests that biofeedback may be slightly more effective at improving walking (SMD 0.57) than standing (SMD 0.42). However, another explanation may be that the tools used to measure outcome were usually more congruent with the activity practised in trials of walking (eg, outcome of biofeedback of step length during walking practice measured as step length during walking) than in trials of standing (eg, outcome of biofeedback of weight distribution during standing practice measured with the Berg Balance Scale). In terms of walking, our result is similar to Tate and Milner (2010) who reported a moderate-to-large effect of all types of biofeedback on walking (7 trials, no meta-analysis). In contrast, Woodford and Price (2009) reported no effect of biofeedback on walking speed (SMD 0.13, 95% CI -0.55 to 0.80, 3 trials) and Langhorne et al (2009) reported being unable to draw conclusions. However, this may have been because these systematic reviews performed meta-analyses only on trials that measured exactly the same aspect of walking, eg, speed or step length, and this usually resulted in small numbers of trials available for analysis. In terms of standing, our finding is in contrast to Barclay-Goddard

Stanton et al: Biofeedback in stroke

et al (2009) and van Peppen et al (2006) who both reported no effect of biofeedback (force information via visual feedback) on standing, with Berg Balance Scale effects of MD -2, 95% CI -6 to 2 (2 trials) and SMD -0.20, 95% CI -0.79 to 0.39 (2 trials).

It is possible that some of the positive effect of biofeedback could be explained by the amount of practice carried out by the experimental group compared with the control group. When analysing only those trials where the control group practised the same activity for the same amount of time as the experimental group, with the only difference being the substitution of biofeedback for therapist feedback in the experimental group, the effect of biofeedback was still clinically and statistically significant (SMD 0.51, 95% CI 0.20 to 0.83, I2 = 47%, fixed-effect model of 8 trials, see Figure 9 on eAddenda for detailed forest plot) and of a similar magnitude to the original analysis (SMD 0.49, 95% CI 0.22 to 0.75). This suggests that improvement in lower limb activities is due to the type of feedback (ie, biofeedback compared with therapist feedback during usual therapy) rather than the amount of practice. Why might biofeedback be more effective than therapist feedback? An observational study of therapist-patient interactions during therapy found that the content of feedback was motivational rather than informative, with specific feedback rarely given (Talvitie 2000). As early as 1932, Trowbridge and Casen demonstrated that the content of feedback is important, with feedback containing specific information regarding ways to improve future practice, enhancing learning more than motivational feedback. By its very nature, biofeedback provides specific information that can be used to adapt the next attempt at the task.

This review has some potential limitations. Several of these limitations may have led to an overestimate of the effect of biofeedback. First, there was a lack of blinding of participants and therapists since this is not always possible in trials of biofeedback. Second, even after including only high quality trials in the meta-analysis, the results are potentially affected by small trial bias, with an average number of 27 participants per trial (range 13-54 participants). Third, when multiple measures were reported, the measure used in the meta-analyses was the measure most congruent with the aim of the intervention, which may have introduced selection bias. On the other hand, the inclusion of trials that compared biofeedback only with usual therapy only does not distinguish the effect of biofeedback precisely, making the result from this systematic review a more conservative estimate of the effect. However, given that only one trial with this design was included in the meta-analysis, it is unlikely to have had a large impact. Additionally, as is usual with trials of complex interventions, the outcome measures were not the same. This meant that we had to calculate a standardised mean difference from the meta-analysis, which is less clinically useful than a mean difference. Finally, only half of the trials measured the outcomes some time after the cessation of intervention. There is a need for a large high quality trial with adequate power and follow-up to investigate the effect of biofeedback in this population.

In conclusion, this systematic review provides evidence that augmenting feedback through the use of biofeedback is superior to usual therapy/placebo at improving lower limb activities in people after stroke. Importantly, it appears superior to therapist feedback. Furthermore, these

benefits are largely maintained in the longer term. Given that many biofeedback machines are relatively inexpensive, biofeedback could be utilised more widely in clinical practice. ■

Footnote: aMIX-Meta-Analysis Made Easy Version 1.61.

eAddenda: Table 1, Figures 2, 4, 6, 7, 8, and 9 available at jop.physiotherapy.asn.au

Acknowledgements: The authors gratefully acknowledge Tien-Hsin Chang, Oktay Irmak, Helen Preston, J Rebecca Winbom, and Nikki Yang for assistance with translation. We would also like to thank Domenico Intiso and Johanna Jondottir for providing us with additional information and data.

Correspondence: Assoc Prof Louise Ada, Discipline of Physiotherapy, Faculty of Health Sciences, The University of Sydney, Australia. louise.ada@sydney.edu.au

References

Annett J, Kay H (1957) Knowledge of results and skilled performance. Occupational Psychology 31: 69-79.

Aruin AS, Hanke TA, Sharma A (2003) Base of support feedback in gait rehabilitation. International Journal of Rehabilitation Research 26: 309-312.

Barclay-Goddard R, Stevenson T, Poluha W, Moffatt MEK, Taback SP (2009) Force platform feedback for standing balance training after stroke (Cochrane Review). Cochrane Database of Systematic Reviews Issue 1.

Basmajian JV, Kukulka CG, Narayan MG, Takebe K (1975) Biofeedback treatment of foot-drop after stroke compared with standard rehabilitation technique: effects on voluntary control and strength. Archives of Physical Medicine & Rehabilitation 56: 231-236.

Bax L, Yu LM, Ikeda N, Tsuruta H, Moons KG (2006) Development and validation of MIX: comprehensive free software for meta-analysis of causal research data. BMC Medical Research Methodology 6: 50.

Bax L, Yu LM, Ikeda N, Tsuruta H, Moons KGM (2008) MIX: comprehensive free software for meta-analysis of causal research data. Version 1.7. http://mix-for-meta-analysis.info

Bi HY, Zhang H, Zheng P-Y (2007) Effect of EMG biofeedback treatment of stroke patients (Chinese). Medical Journal of Quli (Quli Yixue Zazhi) 22: 522-523.

Binder S, Moll CB, Wolf SL (1981) Evaluation of electromyographic biofeedback as an adjunct to therapeutic exercise in treating the lower extremities of hemiplegic patients. Physical Therapy 61: 886-893.

Boonsinsukh R, Panichareon L, Phansuwan-Pujito P (2009) Light touch cue through a cane improves pelvic stability during walking in stroke. Archives of Physical Medicine & Rehabilitation 90: 919-926.

Bourbonnais D, Bilodeau S, Lepage Y, Beaudoin N, Gravel D, Forget R (2002) Effect of force-feedback treatments in patients with chronic motor deficits after a stroke. American Journal of Physical Medicine & Rehabilitation 81: 890-897.

Bradley L, Hart BB, Mandana S, Flowers K, Riches M, Sanderson P (1998) Electromyographic biofeedback for gait training after stroke. Clinical Rehabilitation 12: 11-22.

Burnside IG, Tobias HS, Bursill D (1982) Electromyographic feedback in the remobilization of stroke patients: a controlled trial. Archives of Physical Medicine & Rehabilitation 63: 217222.

Ceceli E, Dursun E, Cacki A (1996) Comparison of joint-position biofeedback and conventional therapy methods in genu recurvatum after stroke: 6 months' follow-up. European Journal of Physical Medicine and Rehabilitation 6: 141-144.

Chen IC, Cheng PT, Chen CL, Chen SC, Chung CY, Yeh TH (2002) Effects of balance training on hemiplegic stroke patients. Chang Gung Medical Journal 25: 583-590.

Cheng PT, Wu SH, Liaw MY, Wong AM, Tang FT (2001) Symmetrical body-weight distribution training in stroke patients and its effect on fall prevention. Archives of Physical Medicine & Rehabilitation 82: 1650-1654.

Cheng PT, Wang C-M, Chung C-Y, Chen C-L (2004) Effects of visual feedback rhythmic weight-shift training on hemiplegic stroke patients. Clinical Rehabilitation 18: 747-753.

Cho S-H, Shin H-K, Kwon Y-H, Lee MY, Lee Y-H, Lee C-H, et al (2007) Cortical activation changes induced by visual biofeedback tracking training in chronic stroke patients. Neurorehabilitation 22: 77-84.

Cohen J (1988) Statistical Power Analysis for the Behavioural Sciences (2nd ed.). New York: Academic Press.

Colborne GR, Olney SJ, Griffin MP (1993) Feedback of ankle joint angle and soleus electromyography in the rehabilitation of hemiplegic gait. Archives of Physical Medicine and Rehabilitation 74: 1100-1106.

Cozean CD, Pease WS, Hubbell SL (1988) Biofeedback and functional electric stimulation in stroke rehabilitation. Archives of Physical Medicine & Rehabilitation 69: 401-405.

Dobkin BH, Plummer-D'amato P, Elashoff R, Lee J (2010) International randomized clinical trial, stroke inpatient rehabilitation with reinforcement of walking speed (SIRROWS), improves outcomes. Neurorehabilitation and Neural Repair 24: 235-242.

Dursun E, Hamamci N, Donmez S, Tuzunalp O, Cakci A (1996) Angular biofeedback device for sitting balance of stroke patients. Stroke 27: 1354-1357.

Engardt M, Ribbe T, Olsson E (1993) Vertical ground reaction force feedback to enhance stroke patients' symmetrical body-weight distribution while rising/sitting down. Scandinavian Journal of Rehabilitation Medicine 25: 41-48.

Engardt M (1994a) Long-term effects of auditory feedback training on relearned symmetrical body weight distribution in stroke patients. A follow-up study. Scandinavian Journal of Rehabilitation Medicine 26: 65-69.

Engardt M (1994b) Rising and sitting down in stroke patients. Auditory feedback and dynamic strength training to enhance symmetrical body weight distribution. Scandinavian Journal of Rehabilitation Medicine 31: 1-57.

Eser F, Yavuzer G, Karakus D, Karaoglan B (2008) The effect of balance training on motor recovery and ambulation after stroke: a randomized controlled trial. European Journal of Physical & Rehabilitation Medicine 44: 19-25.

Gao L, Tong F, Li J-P (2009) Effect of EMG biofeedback for improving lower extremity function in hemiplegic patients (Chinese). Chinese Journal of Neuroimmunology and neurology (Zhongguo Shenjing Mianyi he Shenjingbing Xue Zazhi) 16: 209-210.

Geiger RA, Allen JB, O'Keefe J, Hicks RR (2001) Balance and mobility following stroke: effects of physical therapy interventions with and without biofeedback/forceplate training. Physical Therapy 81: 995-1005.

Glanz M, Klawansky S, Stason W, Berkey C, Shah N, Phan H, et al (1995) Biofeedback therapy in poststroke rehabilitation: a meta-analysis of the randomized controlled trials. Archives of Physical Medicine & Rehabilitation 76: 508-515.

Gok H, Geler-Kulcu D, Alptekin N, Dincer G (2008) Efficacy of treatment with a kinaesthetic ability training device on balance and mobility after stroke: A randomized controlled study. Clinical Rehabilitation 22: 922-930.

Grant T, Brouwer BJ, Culham EG, Vandervoort A (1997) Balance retraining following acute stroke: a comparison of two methods. Canadian Journal of Rehabilitation 11: 69-73.

Heller F, Beuret-Blanquart F, Weber J (2005) [Postural biofeedback and locomotion reeducation in stroke patients] (French). Annales de Readaptation et de Medecine Physique 48: 187-195.

Hurd WW, Pegram V, Nepomuceno C (1980) Comparison of actual and simulated EMG biofeedback in the treatment of hemiplegic patients. American Journal of Physical Medicine 59: 73-82.

Intiso D, Santilli V, Grasso MG, Rossi R, Caruso I (1994) Rehabilitation of walking with electromyographic biofeedback in foot-drop after stroke. Stroke 25: 1189-1192.

John J (1986) Failure of electrical myofeedback to augment the effects of physiotherapy in stroke. International Journal of Rehabilitation Research 9: 35-45.

Jonsdottir J, Cattaneo D, Recalcati M, Regola A, Rabuffetti M, Ferrarin M, et al (2010) A task-oriented biofeedback to improve gait in individuals with chronic stroke: motor learning approach. Neurorehabilitation & Neural Repair 24: 478-485.

Kaur B, Manikandan N (2009) Effect of auditory feedback on lower limb weight bearing symmetry and gait parameters in patients with hemiparesis. Indian Journal of Physiotherapy & Occupational Therapy 3: 1-5.

Kerdoncuff V, Durufle A, Petrilli S, Nicolas B, Robineau S, Lassalle A, et al (2004) [Interest of visual biofeedback training in rehabilitation of balance after stroke] (French). Annales de Readaptation et de Medecine Physique 47: 169-176.

Krebs HI, Hogan N, Volpe BT, Aisen ML, Edelstein L, Diels C (1999) Overview of clinical trials with MIT-MANUS: a robot-aided neuro-rehabilitation facility. Technology & Health Care 7: 419-423.

Langhorne P, Coupar F, Pollock A (2009) Motor recovery after stroke: a systematic review. Lancet Neurology 8: 741-754.

Lee MY, Wong MK, Tang FT (1996) Clinical evaluation of a new biofeedback standing balance training device. Journal of Medical Engineering and Technology 20: 60-66.

Lin JJ, Chung KC (1998) [Evaluate a biofeedback training on the dynamic and static balance for preambulation in hemiplegic patients] (Chinese). Chinese Journal of Medical and Biological Engineering 18: 59-65.

Liu YH, Zhang SZ, Ge RL (2005) [Clinical observations in patients with EMG biofeedback treatment of hemiplegia ankle dorsiflexion disorder] (Chinese). Chinese Journal of Rehabilitation Medicine (Zhonghua Kangfu Yixue Zazhi) 20: 379-380.

Lu XS, He J (2002) [Clinical observations of EMG biofeedback on cerebral hemiplegia rehabilitation] (Chinese). Zhongguo Xinli Weisheng Zazhi 16: 398-399.

Lu XS, Gu X, Jiang YJ, He J (2003) [EMG biofeedback treatment clinical study of patients with stroke] (Chinese) Chinese Journal of Physical Medicine and Rehabilitation (Zhonghua Wuli Yixue yu Kangfu Zazhi) 25: 438-439.

Mandel AR, Nymark JR, Balmer SJ, Grinnell DM, O'Riain MD (1990) Electromyographic versus rhythmic positional biofeedback in computerized gait retraining with stroke patients. Archives of Physical Medicine and Rehabilitation 71: 649-654.

Mirelman A, Patritti BL, Bonato P, Deutsch JE (2010) Effects of virtual reality training on gait biomechanics of individuals post-stroke. Gait & Posture 31: 433-437.

Montoya R, Dupui P, Pages B, Bessou P (1994) Step length biofeedback device for walk rehabilitation. Medical and Biological Engineering and Computing 32: 416-420.

Morris ME, Matyas TA, Bach TM, Goldie PA (1992) Electrogoniometric feedback: its effect on genu recurvatum

Stanton et al: Biofeedback in stroke

in stroke. Archives of Physical Medicine and Rehabilitation 73: 1147-1154.

Mroczek N, Halpern D, McHugh R (1978) Electromyographic feedback and physical therapy for neuromuscular retraining in hemiplegia. Archives of Physical Medicine & Rehabilitation 59: 258-267.

Mudie MH, Winzeler-Mercay U, Radwan S, Lee L (2002) Training symmetry of weight distribution after stroke: a randomized controlled pilot study comparing task-related reach, Bobath and feedback training approaches. Clinical Rehabilitation 16: 582-592.

Mulder T, Hulstijn W, van der Meer J (1986) EMG feedback and the restoration of motor control. A controlled group study of 12 hemiparetic patients. American Journal of Physical Medicine 65: 173-188.

Olney SJ, Nymark JR, Zee B-Y (1993) Computer-assisted feedback and gait re-education in stroke patients: a two-centre randomized control trial. Canadian Journal of Rehabilitation 7: 31-33.

Petersen H, Magnusson M, Johansson R, Fransson P (1996) Auditory feedback regulation of perturbed stance in stroke patients. Scandinavian Journal of Rehabilitation Medicine 28: 217-223.

Sackley CM, Lincoln NB (1997) Single blind randomized controlled trial of visual feedback after stroke: effects on stance symmetry and function. Disability and Rehabilitation 19: 536-546.

Salmoni AW, Schmidt RA, Walter CB (1984) Knowledge of results and motor learning: A review and critical appraisal. Psychological Bulletin 95: 355-386.

Santilli V, Intiso D, Formisano R, Menghini G, Cugini E, Carlino N, et al (1993) [Functional recovery with EMG-BFB in post-stroke patients] (Italian). Rivista di Neurobiologia 39: 471476.

Schauer M, Mauritz K-H (2003) Musical motor feedback (MMF) in walking hemiparetic stroke patients: randomized trials of gait improvement. Clinical Rehabilitation 17: 713-722.

Shen XY, Cheng AL, Ma YH (2002) EMG biofeedback in the cerebral vascular disease early rehabilitation of lower limb function after the application (Chinese). Shanghai Medical Journal (Shanghai Yixue) 25: 108-109.

Talvitie U (2000) Socio-affective characteristics and properties of extrinsic feedback in physiotherapy. Physiotherapy Research International 5: 173-189.

Taskiran FH (1993) [The effect of EMG biofeedback for the stabilization of the ankle in hemiplegic patients] (Turkish). Istanbul Tip Fakultesi Mecmuaya 56: 49-52.

Tate JJ, Milner CE (2010) Real-time kinematic, temporospatial,

and kinetic biofeedback during gait retraining in patients: a systematic review. Physical Therapy 90: 1123-1134.

Thorndike EL (1927) The law of effect. American Journal of Psychology 39: 212-222.

Trowbridge EL, Casen H (1932) An experimental study of Thorndike's theory of learning. Journal of General Psychology 7: 245-258.

van Peppen RPS, Kortsmit M, Lindeman E, Kwakkel G (2006) Effects of visual feedback therapy on postural control in bilateral standing after stroke: A systematic review. Journal of Rehabilitation Medicine 38: 3-9.

van Vliet PM, Wulf G (2006) Extrinsic feedback for motor learning after stroke: What is the evidence? Disability and Rehabilitation 28: 831-840.

Walker C, Brouwer BJ, Culham EG (2000) Use of visual feedback in retraining balance following acute stroke. Physical Therapy 80: 886-889.

Wallace SA, Hagler RW (1979) Knowledge of performance and the learning of a closed motor skill. Research Quarterly 50: 265-271.

Wolf SL, Binder-Macleod SA (1983) Electromyographic biofeedback applications to the hemiplegic patient: changes in lower extremity neuromuscular and functional status. Physical Therapy 63: 1404-1413.

Wong AM, Lee MY, Kuo JK, Tang FT (1997) The development and clinical evaluation of a standing biofeedback trainer. Journal of Rehabilitation Research and Development 34: 322-327.

Woodford H, Price C (2009) EMG biofeedback for the recovery of motor function after stroke. Cochrane Database of Systematic Reviews CD004585.

Woolley-Hart A, Musa I, Rodger E (1977) The use of electromyographic biofeedback in the retraining of gait following stroke. Chest, Heart & Stroke Journal 2: 3-9.

Yavuzer G, Eser F, Karakus D, Karaoglan B, Stam HJ (2006) The effects of balance training on gait late after stroke: a randomized controlled trial. Clinical Rehabilitation 20: 960969.

Zheng H, Sun BM, Lu YH, Yu A, Shan L, Wei Z (2006) [Clinical rehabilitation of electromyographic biofeedback in acute hemiplegia] (Chinese). Chinese Journal of Physical Medicine and Rehabilitation (Zhonghua Wuli Yixue yu Kangfu Zazhi) 28: 620-621.

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