Accepted Manuscript Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toeout Gait in Individuals with Knee Osteoarthritis Michael A. Hunt , PhD Judit Takacs , MSc Katie Hart , MPT Erika Massong , MPT Keri Fuchko , MPT Jennifer Biegler , MPT PII:
S0003-9993(14)00411-0
DOI:
10.1016/j.apmr.2014.05.016
Reference:
YAPMR 55855
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 2 April 2014 Revised Date:
9 May 2014
Accepted Date: 19 May 2014
Please cite this article as: Hunt MA, Takacs J, Hart K, Massong E, Fuchko K, Biegler J, Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toe-out Gait in Individuals with Knee Osteoarthritis, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2014), doi: 10.1016/ j.apmr.2014.05.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Running Head: Visual feedback comparison for gait modification
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Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toe-out Gait in Individuals with Knee Osteoarthritis
Department of Physical Therapy, University of British Columbia, Vancouver, BC.
Address Correspondence to:
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Dr. Michael A. Hunt Department of Physical Therapy University of British Columbia 212-2177 Wesbrook Mall Vancouver, BC Canada V6T 1Z3 Email: [email protected]
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Michael A. Hunt PhD1 Judit Takacs MSc1 Katie Hart MPT1 Erika Massong MPT1 Keri Fuchko MPT1 Jennifer Biegler MPT1
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Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toe-out Gait in Individuals with Knee Osteoarthritis
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ABSTRACT
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Objective: To compare performance error and perceived difficulty during toe-out gait
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modification in people with knee osteoarthritis (OA) across three different types of visual
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feedback: mirror, raw video, and real-time biofeedback of toe-out angle.
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Design: Repeated-measures, within-subject trial
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Setting: University motion analysis laboratory
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Participants: Twenty individuals with knee OA (11 female; age = 65.4 +/- 9.8 years)
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participated in this study. Seven participants had mild knee OA, nine had moderate knee OA, and
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four had severe knee OA.
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Interventions: Participants were trained to walk on a treadmill while matching a target
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indicating a ten degree increase in stance phase toe-out compared to toe-out angle measured
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during self-selected walking. The target was provided visually via the three types of feedback
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listed above and were presented in a random order.
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Main Outcome Measures: Kinematic data were collected and used to calculate the difference
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between the target angle and actual performed angle for each condition (toe-out performance
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error). Difficulty was assessed using a numerical rating scale (0-10) provided verbally by
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participants.
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Results: Toe-out performance error was significantly less when using the real-time biofeedback
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compared to the other two methods (p = 0.025; mean difference vs. mirror = 2.05°; mean
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difference vs. raw video = 1.51°). Perceived difficulty was not statistically different between the
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groups (p = 0.51).
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Conclusions: Though statistically significant, the 2 degree differences in toe-out gait
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performance error may not necessitate the large economic and personnel costs of real-time
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biofeedback as a means to modify movement in clinical or research settings.
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Key words: feedback, gait modification, knee osteoarthritis, rehabilitation
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List of Abbreviations:
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OA
osteoarthritis
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KAM
knee adduction moment
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KL
Kellgren and Lawrence
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Hz
hertz
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ANOVA
analysis of variance
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CI
confidence interval
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INTRODUCTION
70 Movement modification is an integral component of rehabilitation for a number of
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musculoskeletal pathologies, either after the initial injury or following surgery. In general, the
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goal of movement modification is to optimize the biomechanics of functional movement patterns
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in order to improve function and/or reduce pain in light of a given musculoskeletal impairment.
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Using a variety of methods of feedback of performance, patients are trained to modify one or
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more movement parameters to achieve this goal, with the chosen parameters tailored to the
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pathology. In the knee osteoarthritis (OA) research literature, there have been a number of
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studies published recently that have utilized laboratory-based motion analysis equipment to
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provide real-time biofeedback of gait characteristics intended to reduce knee joint loading during
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walking.
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Excessive and unbalanced knee joint loads are a well-accepted risk factor for the development
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and progression of knee OA.1 The magnitude of the external knee adduction moment (KAM) has
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been correlated with medial tibiofemoral joint load during walking,2, 3 with established
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relationships with many clinically-relevant outcomes including disease progression.4
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Accordingly, many intervention studies aiming to reduce knee joint loading in this patient
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population have included the KAM as a primary outcome measure, including gait modification.
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Given the difficulty in calculating and displaying KAM magnitudes in real-time, most knee OA
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gait modification studies have targeted factors shown in cross-sectional studies to influence the
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KAM such as walking speed,5 knee adduction angle,6 lateral trunk lean angle,7 and toe-out angle
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(foot progression angle).8
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92 Increasing the toe-out angle, in particular, represents a potentially attractive treatment option
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given that increased toe-out angles during gait have been linked with a reduced risk of
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radiographic knee OA progression. Specifically, Chang et al showed that each five degree
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increase in self-selected toe-out angle at baseline was associated with a 40% reduction in the risk
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of OA progression over an 18 month period,9 suggesting that a conscious increase in toe-out
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angle during gait may be an effective strategy to reduce the risk of knee OA progression.
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Through lateralization of the centre of pressure and reducing the lever arm of the ground reaction
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force as it passes by the knee,10 an increase in toe-out angle has been shown to significantly
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reduce the late stance peak KAM during walking in adults with11, 12 and without13 knee OA. Toe-
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out gait has been shown to increase the early stance peak KAM during walking,12 yet an
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increased early stance peak KAM has been shown in people with mild knee OA compared to
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controls,14 and has also been linked to the progression of knee OA.4 However, the fact that most
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people with knee OA naturally adopt a toe-out gait,15 combined with the reported reductions in
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risk of OA progression described above,9 make toe-out gait modification a potentially attractive
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option for conservative treatment of the disease.
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While these studies assessing toe-out modification, and other recent studies that have examined
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gait modification as a means to reduce the KAM,16-19 have shown promising results, the training
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methods used limit the feasibility in the clinical setting. Specifically, these studies have utilized
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expensive motion capture systems or tactile feedback devices that are available in very few
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physical therapy clinics. Further, to generate useable gait data from these devices requires
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extensive computer programming and calculation skills that few physical therapists possess. As a
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result, though these studies have been useful in determining the biomechanical efficacy of
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specific gait modifications such as toe-out, the ability to deliver these specific treatments
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clinically remains unclear. A comparison of real-time biofeedback with clinically-available
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methods of feedback of toe-out gait performance would provide information on what benefits
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this resource-intensive method may have when modifying gait parameters. However, we are
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unaware of any studies currently published in the literature that have compared different methods
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of visual feedback when training toe-out gait modification, including toe-out gait. As a result, the
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purpose of this study was to compare performance error using different methods of visual
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feedback of performance when training toe-out gait modification. A secondary purpose was to
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assess the perceived difficulty and preference of each type of feedback by the end-users. It was
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hypothesized that use of the real-time biofeedback would result in less performance error and
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would be preferred by people with knee OA.
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METHODS
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Participants
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Participants with knee pain were recruited for this study using three separate strategies: 1) from
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the university and surrounding community using advertisements, 2) from a laboratory database
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of previous study participants, and 3) from a longitudinal study assessing changes in clinical and
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biomechanical outcomes following toe-out gait modification (data for the current study were
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collected prior to the intervention). The presence of medial compartment osteoarthritis was
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confirmed using the Kellgren and Lawrence (KL) classification scale (KL grade 2 or higher)20
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from standing, semi-flexed posteroanterior knee radiographs. Individuals were excluded if they
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had a history of lower limb joint replacement, had knee surgery or injections in the past six
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months, had rheumatoid arthritis, had self-reported osteoarthritis in other lower limb joints, or
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were unable to walk on a treadmill unaided for 15 minutes. This research was approved by the
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university’s clinical research ethics board and all participants gave informed consent prior to
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testing. Based on pilot work, with a pre-determined estimated effect size of 0.6 between
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conditions, an alpha of 0.05, and statistical power of 0.80, 20 participants were required for this
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study.
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145 Procedure
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All participants attended one testing session. Twenty-two passive reflective markers were placed
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on each participant according to a modified Helen Hayes marker set21 and kinematic data were
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collected using ten high-speed motion capture camerasa sampling at 120 Hz. An assessment of
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normal gait biomechanics was conducted as participants walked over ground and shod at a self-
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selected speed. A total of five trials were conducted, and analysis was limited to the limb with
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knee OA in the cases of unilateral involvement, or the most symptomatic limb in the cases of
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bilateral knee OA. Kinematic data were immediately processed using commercially-available
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softwarea to determine each individual’s self-selected toe-out angle during walking – defined as
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the mean angle of a line connecting the heel (calcaneus) and toe (head of 2nd metatarsal) markers
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with respect to the forward progression of the body – during foot-flat. Foot-flat was determined
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as the period between cessation of vertical displacement of the toe marker and initiation of
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vertical movement of the heel marker.
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Gait Modification Training and Data Collection
162 Participants were trained to increase their toe-out angle by ten degrees using three randomly-
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presented methods that provided feedback of toe-out gait performance: 1) a mirror, 2) raw video,
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and 3) real-time biofeedback of toe-out angle. Ten degrees was chosen as the target angle as it
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represents an amount of change that would produce relevant reductions in the risk of OA
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progression9, and larger changes (> 10 degrees) in toe-out angle during gait have been shown to
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be difficult to achieve in people with knee OA12. Toe-out gait modification was performed on a
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treadmill at a speed equal to that exhibited during the self-selected, over ground walking
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assessment for each participant. Prior to each feedback method, participants stood on a protractor
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device (Figure 1) and were instructed to increase their toe-out angle to the target angle (ten
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degree increase compared to self-selected gait). This was performed to calibrate the feedback
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target with the anatomical orientation of the lower limb and to obtain a comparator measure of
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toe-out angle using the motion analysis system. Briefly, while standing on the protractor device,
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a green line depicting this target was placed on the mirror overlaying the reflection of the foot as
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viewed by the participant (Figure 2a), on the video screen overlaying the raw video image of the
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foot (Figure 2b), or on the video screen overlaying the calculated, real-time toe out angle (Figure
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2c). Real-time toe-out angle was calculated using commercially-available softwarea and
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streamed directly on-screen. Once the green target line was in place, ten seconds of kinematic
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data were collected and the calculated mean toe-out angle during this standing posture was used
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as a comparator against the toe-out angle exhibited during treadmill walking trials.
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Participants were permitted to practice the toe-out gait on the treadmill for approximately 2-3
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minutes for each feedback method and rest was provided between methods. When they indicated
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to the assessor that they were comfortable with the feedback, and that they were confident that
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they were performing the gait modification consistently, data collection commenced and 15
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seconds of kinematic data were obtained. Participants reported their difficulty in performing the
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gait modification immediately after each feedback method using an 11-point numerical rating
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scale (terminal descriptors of 0 = “no difficulty” and 10 = “unable to perform). Upon completion
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of all three training methods, the participants were asked to rank the feedback methods in terms
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of preference.
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Data Processing and Statistical Analysis
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Toe-out angle was calculated for each feedback method as the mean value during foot-flat across
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the first ten consecutive full gait cycles. Toe-out performance error was then calculated as the
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difference between the mean value measured during walking and the mean value obtained whilst
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standing on the protractor device prior to each feedback method: negative values represented as
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undershooting the target angle, while positive values represented overshooting. Two types of
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statistical comparisons were made using repeated measures analysis of variance (ANOVA).
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First, toe-out performance error and perceived difficulty were compared among the three
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feedback methods, regardless of order of presentation. Second, given the potential for a learning
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effect, toe-out performance error was compared among the first, second, and third training
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methods, regardless of the actual feedback type. In instances of a significant ANOVA result,
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Tukey’s honest significant difference (HSD) tests were used to determine the nature of between-
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condition differences. Preference of feedback method was examined using a chi-square test. All
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statistical analyses were conducted using the Statistical Package for the Social
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Sciences (SPSS; ver. 20)b, and a statistical significance level with a two-sided test of p = 0.05
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was used for all comparisons.
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RESULTS
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Twenty individuals with knee OA (11 female; age = 65.4 +/- 9.8 years; body mass index = 29.8
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+/- 6.4 kg/m2; duration of symptoms = 87.6 +/- 76.3 months) participated in this study. Seven
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participants had mild knee OA (KL grade 2), nine had moderate knee OA (KL grade 3), and four
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had severe knee OA (KL grade 4).
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A significant difference was found when comparing toe-out performance error among the three
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feedback methods, regardless of order of presentation (F2,38 = 4.07, p = 0.025) (Table 1). Post-
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hoc analysis indicated that the toe-out performance error was significantly less when using the
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real-time biofeedback (3.81 +/- 1.75°) compared to both the mirror (5.86 +/- 3.47°; mean
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difference (95% CI) compared to real-time biofeedback = -2.05 (-3.13, -0.97)°; p < 0.001) and
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raw video (5.32 +/- 2.66°; mean difference (95% CI) compared to real-time biofeedback = -1.51
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(-2.94, -0.09)°; p = 0.04). Toe-out performance error was not significantly different between the
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mirror and raw video methods (mean difference (95% CI) = -0.54 (-2.57, 1.49)°; p = 0.59).
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Though toe-out performance error was largest during the first training condition regardless of
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type of feedback (mean = 6.19 +/- 3.20°), it was not significantly greater than the second (mean
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= 4.38 +/- 2.85°; p-value compared to first condition = 0.06) or third (mean = 4.67 +/- 2.17°; p-
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value compared to first condition = 0.08) condition (F2,38 = 3.27, p = 0.06). Based on the
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randomization, the first training condition involved seven participants with video, eight
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participants with mirror, and five participants with real-time biofeedback.
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Though participants reported less difficulty when performing the toe-out gait modification using
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the real-time biofeedback, differences were not statistically significant among the three feedback
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methods (F2,38 = 0.69, p = 0.51) (Table 1). Finally, ten participants preferred the real-time
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biofeedback the most – compared to five each for the mirror and video methods. Differences in
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participant preference were not statistically significant among the three methods (χ2 = 4.80, p =
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0.31) (Table 2).
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DISCUSSION
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This study presents data comparing performance error during toe-out gait modification training
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using three different visual feedback methods in people with knee OA. Results indicate that use
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of a laboratory-grade motion analysis system that can collect, analyze, and display toe-out angle
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data in real-time produced the least amount of toe-out performance error compared to raw video
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or a standard wall mirror. However, though there were trends towards more people preferring the
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real-time biofeedback and reporting less difficulty with it compared to the other two types of
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feedback, neither of these comparisons reached statistical significance. Given the two degree
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performance differences observed in this study, conclusions regarding the use of each type of
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feedback in the clinical or research setting must be made by taking into account the significant
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cost and resource differences between the three feedback methods, the movement in question,
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the required accuracy, as well as the availability of required equipment and skilled personnel.
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is sparse. Indeed, only two studies have been published that compare different feedback
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modalities during gait modification relevant to this patient population – both of which used
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young, healthy individuals and both of which used the KAM directly rather than a kinematic
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modifier of the KAM. Wheeler et al compared changes in KAM magnitude during treadmill
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walking in young healthy individuals using direct feedback of KAM values either displayed
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visually or provided via tactile feedback.17 They found nearly identical reductions in KAM
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between the two methods. Though participants rated the “awkwardness” of each method
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similarly, trial duration was much longer with the tactile feedback, which may suggest a longer
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learning effect for that method. Dowling et al also examined the use of tactile feedback
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pertaining to the location of foot pressure to reduce the KAM in young, healthy individuals and
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compared its effectiveness with verbal feedback.22 Participants were required to minimally load
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the lateral side of the foot during walking, which was achieved either through tester verbal
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feedback or via tactile feedback on their shoe which provided information regarding lateral foot
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pressure magnitude. Though both conditions produced significant KAM reductions compared to
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a no feedback control, no differences in KAM reduction were observed between the two
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methods. Other than differences in sample demographics and feedback methods (and required
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bodily senses such as vision, touch, or hearing), another difference between these studies and the
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current study was that they permitted participants to choose any combination of suggested gait
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modifications to achieve the KAM reductions. Thus, the effects of different feedback methods on
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a single gait modification could not be ascertained. However, despite the methodological
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differences among these studies, these findings suggest minimal differences based on feedback
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training method in the achievement of gait modification to directly or indirectly reduce KAM
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magnitudes.
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In the current study, participants had positive comments regarding the real-time biofeedback
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training method. They particularly commented on the ease in matching the vertical line
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pertaining to the toe-out angle with the vertical target line on-screen. In contrast, they reported
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difficulty matching the image of their foot with video feedback with the target line, likely as the
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images provided were in the frontal plane while the toe-out modification occurred in the
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transverse plane. Finally, difficulty tracking the movement of the foot in mirror was reported due
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to the fact that the image provided was a mirror image opposite to the actual movement. Indeed,
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the performance errors in excess of five degrees on a task requiring a ten degree change likely
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reflect these perceived issues and the fact that this was the first time performing toe-out gait
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modification for these individuals. Other methods of training toe-out gait have been used in the
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past with many studies simply using target lines on the floor.11, 13, 23 Though this has the
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advantage of matching the target line with the movement in the transverse plane, it does require
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the patient to look down during walking which results in a more unnatural gait pattern and may
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produce safety issues if used in an uncontrolled environment. Consistent with current delivery of
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gait modification in the clinical setting, this approach may be more beneficial in the initial
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learning of the task, with the more functionally-appropriate methods used during the refinement
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of the new motor program. Indeed, the treadmill walking used in the current study was
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conducive to delivery of the visual feedback in a controlled environment, but other methods of
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re-training gait over ground should be examined. Further research is needed to identify the
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optimal progression of learning this new gait pattern.
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associated with laboratory-based motion analysis. Dynamic accuracy of current optical motion
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analysis systems has been reported to be on the magnitude of approximately 1 degree,24 which is
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less than the toe-out error exhibited in all three conditions in this study. However, when
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comparing the toe-out errors calculated in this study, the “true” error when using the real-time
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biofeedback could have ranged from 2.81° to 4.81° (i.e. calculated error of 3.81 degrees +/- 1
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degree of measurement error), while the error when using the mirror could have ranged from
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4.86° to 6.86°. Further, higher errors would be expected when taking into consideration other
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factors such as skin movement artifact. Therefore, though the differences observed in the present
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study achieved statistical significance, whether these differences represented actual differences
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after accounting for measurement error is less clear.
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Study Limitations
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The primary methodological limitation of the present study was the lack of a comparator
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condition; thus, though the participants were able to utilize the biofeedback to successfully
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modify gait, it was unclear what additive effect, if any, the methods of visual feedback had on
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motor learning. A “no feedback” or verbal feedback control condition was not used as the aim
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was to modify gait performance by a specific quantifiable amount. Clearly, instructing the
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participants to “toe-out more” would have produced more toe-out during gait, but without any
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method to standardize the amount, a comparison with the methods used in this study would not
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have been appropriate. When prescribing movement modification clinically, therapists often
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provide qualitative instructions without emphasizing a specific amount of change; this is in
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contrast to the present study where a specific 10 degree change in toe-out gait was desired.
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Further, a ten degree change may not be achievable for all individuals (especially those who
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already exhibit large toe-out angles during walking) and thus the reliability and appropriateness
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of gait modification may be influenced by the type of modification and the individual patient in
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question. Another limitation was the lack of longitudinal data examining the amount of retention
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of the new gait pattern using the different feedback methods. Instead, the objective of the present
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study was to examine any initial differences between the visual feedback methods. As stated
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above, large percentage errors (58.6% in the case of mirror training) would likely be reduced
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with further training. Future research is required to determine what types of feedback best
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correlate with changes in adherence, performance, and ultimately clinical outcomes with gait
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modification. Unfortunately, that was beyond the scope of the present study.
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CONCLUSION
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This study found a small but statistically significant difference in performance of toe-out gait in
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people with knee OA based on the method of feedback provided. Performance error was best
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when using real-time biofeedback of performance compared to raw video or a standard wall
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mirror. However, the two degree differences between the methods may suggest that the current
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practice of utilizing a mirror or video camera are acceptable methods for the purpose of
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movement modification for a variety of orthopaedic injuries in the clinical setting. Future
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research is required to compare feedback methods for different movement modifications and in
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different patient groups, comparing visual feedback methods with other types of feedback such
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as verbal, haptic, or auditory. More importantly, longitudinal research comparing different types
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of feedback of performance is needed to compare motor learning differences and retention
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outside the clinical setting as well as examine changes in clinical outcomes such as symptoms.
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This information is needed to ensure optimal rehabilitation outcomes.
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Lynn S, Kajaks T, Costigan P. The effect of internal and external foot rotation on the adduction moment and lateral-medial shear force at the knee during gait. J Sci Med Sport 2008;11:444-51.
14.
Mills K, Hunt MA, Ferber R. Biomechanical deviations during level walking associated with knee osteoarthritis: A systematic review and meta-analysis. Arthritis Care Res 2013;65:1643-65.
15.
Hunt MA, Birmingham TB, Bryant D, Jones IC, Giffin JR, Jenkyn TR et al. Lateral trunk lean explains variation in dynamic knee joint load in patients with medial compartment knee osteoarthritis. Osteoarthritis Cartilage 2008;16:591-9.
16.
Hunt MA, Simic M, Hinman RS, Bennell KL, Wrigley TV. Feasibility of a gait retraining strategy for reducing knee joint loading: Increased trunk lean guided by real-time biofeedback. J Biomech 2011;44:943-7.
17.
Wheeler JW, Shull PB, Besier TF. Real-time knee adduction moment feedback for gait retraining through visual and tactile displays. J Biomech Eng 2011;133:041007.
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Barrios J, Crossley K, Davis I. Gait retraining to reduce the knee adduction moment through real-time visual feedback of dynamic knee alignment. J Biomech 2010;43:220813.
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Shull PB, Shultz R, Silder A, Dragoo JL, Besier TF, Cutkosky MR et al. Toe-in gait reduces the first peak knee adduction moment in patients with medial compartment knee osteoarthritis. J Biomech 2013;46:122-8.
20.
Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis 1957;16:494-502.
21.
Kadaba MP, Ramakrishnan HK, Wootten ME, Gainey J, Gorton G, Cochran GV. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. J Orthop Res 1989;7:849-60.
22.
Dowling AV, FIsher DS, Andriacchi TP. Gait modification via verbal instruction and an active feedback system to reduce peak knee adduction moment. J Biomech Eng 2010;132:071007.
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van den Noort JC, Schaffers I, Snijders J, Harlaar J. The effectiveness of voluntary modifications of gait pattern to reduce the knee adduction moment. Human Movement Science 2013;32:412-24.
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Hassan EA, Jenkyn TR, Dunning CE. Direct comparison of kinematic data collected using an electromagnetic tracking system versus a digital optical system. J Biomech 2007;40:930-5.
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List of Suppliers:
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b IBM, 1 New Orchard Road. Armonk, NY 10504-1722
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c Sony Canada, 115 Gordon Baker Road. Toronto, ON M2H 3R6
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a Motion Analysis Corporation, 3617 Westwind Blvd. Santa Rosa, CA 95403
Figure Headings:
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Figure 1. Protractor device that participants stood on to guide placement of the green tape target line for each biofeedback training method, and to compare target toe-out data with actual toe-out angle exhibited during treadmill walking trials.
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Figure 2a. Mirror training method. Participants were instructed to match the reflection of their study foot with the angulation of the tape target placed on the mirror positioned 3.0 metres directly in front of the participant.
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Figure 2b. Video training method. A video camerac was placed directly in front of the participant and raw video was streamed live to the video screen. Participants were instructed to match the video projection of their study foot with the angulation of the tape target placed on the screen positioned 3.2 metres directly in front of the participant.
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Figure 2c. Real-time biofeedback training method. Toe-out data were streamed live to the video screen (thin black line). Participants were instructed to match the stance portion of the black line – denoting foot-flat – with the vertical tape target placed on the screen positioned 3.2 metres directly in front of the participant.
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TABLE 1. Mean (sd) toe-out error and perceived difficulty for each of the three feedback methods. Negative values represent undershooting the target angle. Perceived difficulty was measured using an 11-point numerical rating scale (0 = “no difficulty”, 10 = “unable to perform”). P-value corresponds to the main effect of toe-out or perceived difficulty. Video Mirror Real-time p-value biofeedback Toe-out error (º) -5.32 (2.66) -5.86 (3.47) -3.81 (1.75) *† 0.03 Perceived difficulty (0-10) 5.4 (3.0) 5.1 (2.8) 4.7 (2.6) 0.51
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TABLE 2. Frequency distribution (%) of feedback training method preference rankings. Participants were asked to rank their preferences immediately after completing training using all three methods. First Second Third Video (n) 5 (25) 8 (40) 7 (35) Mirror (n) 5 (25) 6 (30) 9 (45) Real-time biofeedback (n) 10 (50) 6 (30) 4 (20)
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S0003-9993(14)00411-0
DOI:
10.1016/j.apmr.2014.05.016
Reference:
YAPMR 55855
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 2 April 2014 Revised Date:
9 May 2014
Accepted Date: 19 May 2014
Please cite this article as: Hunt MA, Takacs J, Hart K, Massong E, Fuchko K, Biegler J, Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toe-out Gait in Individuals with Knee Osteoarthritis, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2014), doi: 10.1016/ j.apmr.2014.05.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Running Head: Visual feedback comparison for gait modification
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Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toe-out Gait in Individuals with Knee Osteoarthritis
Department of Physical Therapy, University of British Columbia, Vancouver, BC.
Address Correspondence to:
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Dr. Michael A. Hunt Department of Physical Therapy University of British Columbia 212-2177 Wesbrook Mall Vancouver, BC Canada V6T 1Z3 Email: [email protected]
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Michael A. Hunt PhD1 Judit Takacs MSc1 Katie Hart MPT1 Erika Massong MPT1 Keri Fuchko MPT1 Jennifer Biegler MPT1
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Comparison of Mirror, Raw Video, and Real-time Visual Biofeedback for Training Toe-out Gait in Individuals with Knee Osteoarthritis
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ABSTRACT
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Objective: To compare performance error and perceived difficulty during toe-out gait
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modification in people with knee osteoarthritis (OA) across three different types of visual
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feedback: mirror, raw video, and real-time biofeedback of toe-out angle.
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Design: Repeated-measures, within-subject trial
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Setting: University motion analysis laboratory
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Participants: Twenty individuals with knee OA (11 female; age = 65.4 +/- 9.8 years)
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participated in this study. Seven participants had mild knee OA, nine had moderate knee OA, and
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four had severe knee OA.
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Interventions: Participants were trained to walk on a treadmill while matching a target
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indicating a ten degree increase in stance phase toe-out compared to toe-out angle measured
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during self-selected walking. The target was provided visually via the three types of feedback
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listed above and were presented in a random order.
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Main Outcome Measures: Kinematic data were collected and used to calculate the difference
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between the target angle and actual performed angle for each condition (toe-out performance
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error). Difficulty was assessed using a numerical rating scale (0-10) provided verbally by
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participants.
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Results: Toe-out performance error was significantly less when using the real-time biofeedback
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compared to the other two methods (p = 0.025; mean difference vs. mirror = 2.05°; mean
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difference vs. raw video = 1.51°). Perceived difficulty was not statistically different between the
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groups (p = 0.51).
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Conclusions: Though statistically significant, the 2 degree differences in toe-out gait
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performance error may not necessitate the large economic and personnel costs of real-time
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biofeedback as a means to modify movement in clinical or research settings.
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Key words: feedback, gait modification, knee osteoarthritis, rehabilitation
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List of Abbreviations:
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OA
osteoarthritis
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KAM
knee adduction moment
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KL
Kellgren and Lawrence
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Hz
hertz
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ANOVA
analysis of variance
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CI
confidence interval
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INTRODUCTION
70 Movement modification is an integral component of rehabilitation for a number of
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musculoskeletal pathologies, either after the initial injury or following surgery. In general, the
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goal of movement modification is to optimize the biomechanics of functional movement patterns
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in order to improve function and/or reduce pain in light of a given musculoskeletal impairment.
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Using a variety of methods of feedback of performance, patients are trained to modify one or
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more movement parameters to achieve this goal, with the chosen parameters tailored to the
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pathology. In the knee osteoarthritis (OA) research literature, there have been a number of
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studies published recently that have utilized laboratory-based motion analysis equipment to
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provide real-time biofeedback of gait characteristics intended to reduce knee joint loading during
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walking.
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Excessive and unbalanced knee joint loads are a well-accepted risk factor for the development
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and progression of knee OA.1 The magnitude of the external knee adduction moment (KAM) has
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been correlated with medial tibiofemoral joint load during walking,2, 3 with established
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relationships with many clinically-relevant outcomes including disease progression.4
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Accordingly, many intervention studies aiming to reduce knee joint loading in this patient
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population have included the KAM as a primary outcome measure, including gait modification.
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Given the difficulty in calculating and displaying KAM magnitudes in real-time, most knee OA
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gait modification studies have targeted factors shown in cross-sectional studies to influence the
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KAM such as walking speed,5 knee adduction angle,6 lateral trunk lean angle,7 and toe-out angle
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(foot progression angle).8
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92 Increasing the toe-out angle, in particular, represents a potentially attractive treatment option
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given that increased toe-out angles during gait have been linked with a reduced risk of
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radiographic knee OA progression. Specifically, Chang et al showed that each five degree
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increase in self-selected toe-out angle at baseline was associated with a 40% reduction in the risk
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of OA progression over an 18 month period,9 suggesting that a conscious increase in toe-out
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angle during gait may be an effective strategy to reduce the risk of knee OA progression.
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Through lateralization of the centre of pressure and reducing the lever arm of the ground reaction
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force as it passes by the knee,10 an increase in toe-out angle has been shown to significantly
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reduce the late stance peak KAM during walking in adults with11, 12 and without13 knee OA. Toe-
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out gait has been shown to increase the early stance peak KAM during walking,12 yet an
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increased early stance peak KAM has been shown in people with mild knee OA compared to
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controls,14 and has also been linked to the progression of knee OA.4 However, the fact that most
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people with knee OA naturally adopt a toe-out gait,15 combined with the reported reductions in
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risk of OA progression described above,9 make toe-out gait modification a potentially attractive
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option for conservative treatment of the disease.
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While these studies assessing toe-out modification, and other recent studies that have examined
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gait modification as a means to reduce the KAM,16-19 have shown promising results, the training
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methods used limit the feasibility in the clinical setting. Specifically, these studies have utilized
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expensive motion capture systems or tactile feedback devices that are available in very few
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physical therapy clinics. Further, to generate useable gait data from these devices requires
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extensive computer programming and calculation skills that few physical therapists possess. As a
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result, though these studies have been useful in determining the biomechanical efficacy of
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specific gait modifications such as toe-out, the ability to deliver these specific treatments
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clinically remains unclear. A comparison of real-time biofeedback with clinically-available
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methods of feedback of toe-out gait performance would provide information on what benefits
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this resource-intensive method may have when modifying gait parameters. However, we are
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unaware of any studies currently published in the literature that have compared different methods
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of visual feedback when training toe-out gait modification, including toe-out gait. As a result, the
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purpose of this study was to compare performance error using different methods of visual
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feedback of performance when training toe-out gait modification. A secondary purpose was to
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assess the perceived difficulty and preference of each type of feedback by the end-users. It was
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hypothesized that use of the real-time biofeedback would result in less performance error and
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would be preferred by people with knee OA.
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METHODS
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Participants
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Participants with knee pain were recruited for this study using three separate strategies: 1) from
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the university and surrounding community using advertisements, 2) from a laboratory database
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of previous study participants, and 3) from a longitudinal study assessing changes in clinical and
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biomechanical outcomes following toe-out gait modification (data for the current study were
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collected prior to the intervention). The presence of medial compartment osteoarthritis was
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confirmed using the Kellgren and Lawrence (KL) classification scale (KL grade 2 or higher)20
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from standing, semi-flexed posteroanterior knee radiographs. Individuals were excluded if they
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had a history of lower limb joint replacement, had knee surgery or injections in the past six
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months, had rheumatoid arthritis, had self-reported osteoarthritis in other lower limb joints, or
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were unable to walk on a treadmill unaided for 15 minutes. This research was approved by the
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university’s clinical research ethics board and all participants gave informed consent prior to
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testing. Based on pilot work, with a pre-determined estimated effect size of 0.6 between
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conditions, an alpha of 0.05, and statistical power of 0.80, 20 participants were required for this
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study.
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145 Procedure
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All participants attended one testing session. Twenty-two passive reflective markers were placed
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on each participant according to a modified Helen Hayes marker set21 and kinematic data were
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collected using ten high-speed motion capture camerasa sampling at 120 Hz. An assessment of
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normal gait biomechanics was conducted as participants walked over ground and shod at a self-
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selected speed. A total of five trials were conducted, and analysis was limited to the limb with
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knee OA in the cases of unilateral involvement, or the most symptomatic limb in the cases of
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bilateral knee OA. Kinematic data were immediately processed using commercially-available
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softwarea to determine each individual’s self-selected toe-out angle during walking – defined as
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the mean angle of a line connecting the heel (calcaneus) and toe (head of 2nd metatarsal) markers
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with respect to the forward progression of the body – during foot-flat. Foot-flat was determined
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as the period between cessation of vertical displacement of the toe marker and initiation of
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vertical movement of the heel marker.
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Gait Modification Training and Data Collection
162 Participants were trained to increase their toe-out angle by ten degrees using three randomly-
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presented methods that provided feedback of toe-out gait performance: 1) a mirror, 2) raw video,
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and 3) real-time biofeedback of toe-out angle. Ten degrees was chosen as the target angle as it
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represents an amount of change that would produce relevant reductions in the risk of OA
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progression9, and larger changes (> 10 degrees) in toe-out angle during gait have been shown to
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be difficult to achieve in people with knee OA12. Toe-out gait modification was performed on a
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treadmill at a speed equal to that exhibited during the self-selected, over ground walking
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assessment for each participant. Prior to each feedback method, participants stood on a protractor
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device (Figure 1) and were instructed to increase their toe-out angle to the target angle (ten
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degree increase compared to self-selected gait). This was performed to calibrate the feedback
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target with the anatomical orientation of the lower limb and to obtain a comparator measure of
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toe-out angle using the motion analysis system. Briefly, while standing on the protractor device,
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a green line depicting this target was placed on the mirror overlaying the reflection of the foot as
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viewed by the participant (Figure 2a), on the video screen overlaying the raw video image of the
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foot (Figure 2b), or on the video screen overlaying the calculated, real-time toe out angle (Figure
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2c). Real-time toe-out angle was calculated using commercially-available softwarea and
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streamed directly on-screen. Once the green target line was in place, ten seconds of kinematic
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data were collected and the calculated mean toe-out angle during this standing posture was used
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as a comparator against the toe-out angle exhibited during treadmill walking trials.
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Participants were permitted to practice the toe-out gait on the treadmill for approximately 2-3
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minutes for each feedback method and rest was provided between methods. When they indicated
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to the assessor that they were comfortable with the feedback, and that they were confident that
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they were performing the gait modification consistently, data collection commenced and 15
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seconds of kinematic data were obtained. Participants reported their difficulty in performing the
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gait modification immediately after each feedback method using an 11-point numerical rating
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scale (terminal descriptors of 0 = “no difficulty” and 10 = “unable to perform). Upon completion
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of all three training methods, the participants were asked to rank the feedback methods in terms
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of preference.
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Data Processing and Statistical Analysis
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Toe-out angle was calculated for each feedback method as the mean value during foot-flat across
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the first ten consecutive full gait cycles. Toe-out performance error was then calculated as the
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difference between the mean value measured during walking and the mean value obtained whilst
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standing on the protractor device prior to each feedback method: negative values represented as
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undershooting the target angle, while positive values represented overshooting. Two types of
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statistical comparisons were made using repeated measures analysis of variance (ANOVA).
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First, toe-out performance error and perceived difficulty were compared among the three
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feedback methods, regardless of order of presentation. Second, given the potential for a learning
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effect, toe-out performance error was compared among the first, second, and third training
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methods, regardless of the actual feedback type. In instances of a significant ANOVA result,
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Tukey’s honest significant difference (HSD) tests were used to determine the nature of between-
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condition differences. Preference of feedback method was examined using a chi-square test. All
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statistical analyses were conducted using the Statistical Package for the Social
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Sciences (SPSS; ver. 20)b, and a statistical significance level with a two-sided test of p = 0.05
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was used for all comparisons.
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RESULTS
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Twenty individuals with knee OA (11 female; age = 65.4 +/- 9.8 years; body mass index = 29.8
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+/- 6.4 kg/m2; duration of symptoms = 87.6 +/- 76.3 months) participated in this study. Seven
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participants had mild knee OA (KL grade 2), nine had moderate knee OA (KL grade 3), and four
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had severe knee OA (KL grade 4).
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A significant difference was found when comparing toe-out performance error among the three
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feedback methods, regardless of order of presentation (F2,38 = 4.07, p = 0.025) (Table 1). Post-
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hoc analysis indicated that the toe-out performance error was significantly less when using the
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real-time biofeedback (3.81 +/- 1.75°) compared to both the mirror (5.86 +/- 3.47°; mean
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difference (95% CI) compared to real-time biofeedback = -2.05 (-3.13, -0.97)°; p < 0.001) and
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raw video (5.32 +/- 2.66°; mean difference (95% CI) compared to real-time biofeedback = -1.51
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(-2.94, -0.09)°; p = 0.04). Toe-out performance error was not significantly different between the
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mirror and raw video methods (mean difference (95% CI) = -0.54 (-2.57, 1.49)°; p = 0.59).
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Though toe-out performance error was largest during the first training condition regardless of
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type of feedback (mean = 6.19 +/- 3.20°), it was not significantly greater than the second (mean
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= 4.38 +/- 2.85°; p-value compared to first condition = 0.06) or third (mean = 4.67 +/- 2.17°; p-
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value compared to first condition = 0.08) condition (F2,38 = 3.27, p = 0.06). Based on the
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randomization, the first training condition involved seven participants with video, eight
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participants with mirror, and five participants with real-time biofeedback.
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Though participants reported less difficulty when performing the toe-out gait modification using
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the real-time biofeedback, differences were not statistically significant among the three feedback
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methods (F2,38 = 0.69, p = 0.51) (Table 1). Finally, ten participants preferred the real-time
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biofeedback the most – compared to five each for the mirror and video methods. Differences in
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participant preference were not statistically significant among the three methods (χ2 = 4.80, p =
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0.31) (Table 2).
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DISCUSSION
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This study presents data comparing performance error during toe-out gait modification training
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using three different visual feedback methods in people with knee OA. Results indicate that use
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of a laboratory-grade motion analysis system that can collect, analyze, and display toe-out angle
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data in real-time produced the least amount of toe-out performance error compared to raw video
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or a standard wall mirror. However, though there were trends towards more people preferring the
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real-time biofeedback and reporting less difficulty with it compared to the other two types of
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feedback, neither of these comparisons reached statistical significance. Given the two degree
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performance differences observed in this study, conclusions regarding the use of each type of
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feedback in the clinical or research setting must be made by taking into account the significant
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cost and resource differences between the three feedback methods, the movement in question,
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the required accuracy, as well as the availability of required equipment and skilled personnel.
253 Data comparing types of feedback of performance during gait modification relevant to knee OA
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is sparse. Indeed, only two studies have been published that compare different feedback
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modalities during gait modification relevant to this patient population – both of which used
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young, healthy individuals and both of which used the KAM directly rather than a kinematic
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modifier of the KAM. Wheeler et al compared changes in KAM magnitude during treadmill
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walking in young healthy individuals using direct feedback of KAM values either displayed
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visually or provided via tactile feedback.17 They found nearly identical reductions in KAM
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between the two methods. Though participants rated the “awkwardness” of each method
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similarly, trial duration was much longer with the tactile feedback, which may suggest a longer
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learning effect for that method. Dowling et al also examined the use of tactile feedback
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pertaining to the location of foot pressure to reduce the KAM in young, healthy individuals and
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compared its effectiveness with verbal feedback.22 Participants were required to minimally load
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the lateral side of the foot during walking, which was achieved either through tester verbal
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feedback or via tactile feedback on their shoe which provided information regarding lateral foot
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pressure magnitude. Though both conditions produced significant KAM reductions compared to
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a no feedback control, no differences in KAM reduction were observed between the two
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methods. Other than differences in sample demographics and feedback methods (and required
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bodily senses such as vision, touch, or hearing), another difference between these studies and the
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current study was that they permitted participants to choose any combination of suggested gait
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modifications to achieve the KAM reductions. Thus, the effects of different feedback methods on
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a single gait modification could not be ascertained. However, despite the methodological
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differences among these studies, these findings suggest minimal differences based on feedback
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training method in the achievement of gait modification to directly or indirectly reduce KAM
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magnitudes.
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In the current study, participants had positive comments regarding the real-time biofeedback
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training method. They particularly commented on the ease in matching the vertical line
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pertaining to the toe-out angle with the vertical target line on-screen. In contrast, they reported
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difficulty matching the image of their foot with video feedback with the target line, likely as the
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images provided were in the frontal plane while the toe-out modification occurred in the
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transverse plane. Finally, difficulty tracking the movement of the foot in mirror was reported due
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to the fact that the image provided was a mirror image opposite to the actual movement. Indeed,
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the performance errors in excess of five degrees on a task requiring a ten degree change likely
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reflect these perceived issues and the fact that this was the first time performing toe-out gait
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modification for these individuals. Other methods of training toe-out gait have been used in the
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past with many studies simply using target lines on the floor.11, 13, 23 Though this has the
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advantage of matching the target line with the movement in the transverse plane, it does require
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the patient to look down during walking which results in a more unnatural gait pattern and may
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produce safety issues if used in an uncontrolled environment. Consistent with current delivery of
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gait modification in the clinical setting, this approach may be more beneficial in the initial
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learning of the task, with the more functionally-appropriate methods used during the refinement
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of the new motor program. Indeed, the treadmill walking used in the current study was
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conducive to delivery of the visual feedback in a controlled environment, but other methods of
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re-training gait over ground should be examined. Further research is needed to identify the
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optimal progression of learning this new gait pattern.
299 One important consideration when interpreting the results of this study is the measurement error
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associated with laboratory-based motion analysis. Dynamic accuracy of current optical motion
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analysis systems has been reported to be on the magnitude of approximately 1 degree,24 which is
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less than the toe-out error exhibited in all three conditions in this study. However, when
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comparing the toe-out errors calculated in this study, the “true” error when using the real-time
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biofeedback could have ranged from 2.81° to 4.81° (i.e. calculated error of 3.81 degrees +/- 1
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degree of measurement error), while the error when using the mirror could have ranged from
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4.86° to 6.86°. Further, higher errors would be expected when taking into consideration other
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factors such as skin movement artifact. Therefore, though the differences observed in the present
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study achieved statistical significance, whether these differences represented actual differences
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after accounting for measurement error is less clear.
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Study Limitations
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The primary methodological limitation of the present study was the lack of a comparator
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condition; thus, though the participants were able to utilize the biofeedback to successfully
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modify gait, it was unclear what additive effect, if any, the methods of visual feedback had on
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motor learning. A “no feedback” or verbal feedback control condition was not used as the aim
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was to modify gait performance by a specific quantifiable amount. Clearly, instructing the
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participants to “toe-out more” would have produced more toe-out during gait, but without any
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method to standardize the amount, a comparison with the methods used in this study would not
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have been appropriate. When prescribing movement modification clinically, therapists often
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provide qualitative instructions without emphasizing a specific amount of change; this is in
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contrast to the present study where a specific 10 degree change in toe-out gait was desired.
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Further, a ten degree change may not be achievable for all individuals (especially those who
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already exhibit large toe-out angles during walking) and thus the reliability and appropriateness
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of gait modification may be influenced by the type of modification and the individual patient in
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question. Another limitation was the lack of longitudinal data examining the amount of retention
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of the new gait pattern using the different feedback methods. Instead, the objective of the present
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study was to examine any initial differences between the visual feedback methods. As stated
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above, large percentage errors (58.6% in the case of mirror training) would likely be reduced
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with further training. Future research is required to determine what types of feedback best
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correlate with changes in adherence, performance, and ultimately clinical outcomes with gait
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modification. Unfortunately, that was beyond the scope of the present study.
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CONCLUSION
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This study found a small but statistically significant difference in performance of toe-out gait in
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people with knee OA based on the method of feedback provided. Performance error was best
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when using real-time biofeedback of performance compared to raw video or a standard wall
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mirror. However, the two degree differences between the methods may suggest that the current
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practice of utilizing a mirror or video camera are acceptable methods for the purpose of
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movement modification for a variety of orthopaedic injuries in the clinical setting. Future
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research is required to compare feedback methods for different movement modifications and in
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different patient groups, comparing visual feedback methods with other types of feedback such
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as verbal, haptic, or auditory. More importantly, longitudinal research comparing different types
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of feedback of performance is needed to compare motor learning differences and retention
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outside the clinical setting as well as examine changes in clinical outcomes such as symptoms.
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This information is needed to ensure optimal rehabilitation outcomes.
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Lynn S, Kajaks T, Costigan P. The effect of internal and external foot rotation on the adduction moment and lateral-medial shear force at the knee during gait. J Sci Med Sport 2008;11:444-51.
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Mills K, Hunt MA, Ferber R. Biomechanical deviations during level walking associated with knee osteoarthritis: A systematic review and meta-analysis. Arthritis Care Res 2013;65:1643-65.
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Hunt MA, Birmingham TB, Bryant D, Jones IC, Giffin JR, Jenkyn TR et al. Lateral trunk lean explains variation in dynamic knee joint load in patients with medial compartment knee osteoarthritis. Osteoarthritis Cartilage 2008;16:591-9.
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Hunt MA, Simic M, Hinman RS, Bennell KL, Wrigley TV. Feasibility of a gait retraining strategy for reducing knee joint loading: Increased trunk lean guided by real-time biofeedback. J Biomech 2011;44:943-7.
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Shull PB, Shultz R, Silder A, Dragoo JL, Besier TF, Cutkosky MR et al. Toe-in gait reduces the first peak knee adduction moment in patients with medial compartment knee osteoarthritis. J Biomech 2013;46:122-8.
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Dowling AV, FIsher DS, Andriacchi TP. Gait modification via verbal instruction and an active feedback system to reduce peak knee adduction moment. J Biomech Eng 2010;132:071007.
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List of Suppliers:
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b IBM, 1 New Orchard Road. Armonk, NY 10504-1722
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c Sony Canada, 115 Gordon Baker Road. Toronto, ON M2H 3R6
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a Motion Analysis Corporation, 3617 Westwind Blvd. Santa Rosa, CA 95403
Figure Headings:
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Figure 1. Protractor device that participants stood on to guide placement of the green tape target line for each biofeedback training method, and to compare target toe-out data with actual toe-out angle exhibited during treadmill walking trials.
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Figure 2a. Mirror training method. Participants were instructed to match the reflection of their study foot with the angulation of the tape target placed on the mirror positioned 3.0 metres directly in front of the participant.
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Figure 2b. Video training method. A video camerac was placed directly in front of the participant and raw video was streamed live to the video screen. Participants were instructed to match the video projection of their study foot with the angulation of the tape target placed on the screen positioned 3.2 metres directly in front of the participant.
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Figure 2c. Real-time biofeedback training method. Toe-out data were streamed live to the video screen (thin black line). Participants were instructed to match the stance portion of the black line – denoting foot-flat – with the vertical tape target placed on the screen positioned 3.2 metres directly in front of the participant.
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TABLE 1. Mean (sd) toe-out error and perceived difficulty for each of the three feedback methods. Negative values represent undershooting the target angle. Perceived difficulty was measured using an 11-point numerical rating scale (0 = “no difficulty”, 10 = “unable to perform”). P-value corresponds to the main effect of toe-out or perceived difficulty. Video Mirror Real-time p-value biofeedback Toe-out error (º) -5.32 (2.66) -5.86 (3.47) -3.81 (1.75) *† 0.03 Perceived difficulty (0-10) 5.4 (3.0) 5.1 (2.8) 4.7 (2.6) 0.51
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TABLE 2. Frequency distribution (%) of feedback training method preference rankings. Participants were asked to rank their preferences immediately after completing training using all three methods. First Second Third Video (n) 5 (25) 8 (40) 7 (35) Mirror (n) 5 (25) 6 (30) 9 (45) Real-time biofeedback (n) 10 (50) 6 (30) 4 (20)
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