Personalized Gait Training With Feedback to Reduce Knee Pain From Osteoarthritis
Phase: NA
Conditions: Osteoarthritis
Sponsor: VA Office of Research and Development

 
 
 
 
Long-Term Effectiveness of Walking Training in Patients with Knee Osteoarthritis 
 
NCT02767570 
 
09/21/2021 
 

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1.1 Participants and enrollment 
Individuals with medial compartment knee osteoarthritis were recruited for this parallel, 
participant-blinded randomized controlled trial. All participants provided informed consent in 
accordance with Stanford University Institutional Review Board protocols. Participants were 
recruited via clinician referrals, approved searches of medical record databases, and 
advertisements in print media and online. Eligible individuals were adults (18+) who met the 
following criteria: 1) medial compartment knee osteoarthritis grade between one and three on the 
Kellgren-Lawrence (KL) scale as determined from radiographs by a radiologist with more than 
20 years of experience, 2) medial knee pain of three or greater on an 11-point numeric rating 
scale (NRS), 3) able to walk safely on a treadmill without an ambulatory aid for 25 minutes, and 
4) BMI less than 35 kg/m2. 
An a priori power analysis was conducted prior to the start of the study. For the primary 
hypothesis that there would be a greater reduction in pain for the altered compared to the 
consistent FPA group, an effect size of 0.57 was assumed (Brosseau et al., 2012; Shull, personal 
communication). To achieve 80% power with an alpha of 0.05, 39 participants per group were 
needed, so our recruitment goal was 40 subjects per group.  
 
Interested individuals were initially screened with a telephone or web-based 
questionnaire. Eligible individuals then underwent an in-person screen to confirm the presence, 
location, and severity of knee pain. Anterior-posterior weightbearing radiographs were obtained 
and given a KL grade. Participants who met the radiographic inclusion criteria were invited to 
the motion laboratory for a gait assessment (see Section 1.2). Those who were able to reduce 
their KAM peak by at least 5% with an FPA modification were randomized. Prior to study 
initiation, we generated a block randomization spreadsheet (block size=8, 1:1) and stored it on 

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the study coordinator’s computer. Once a participant met all inclusion criteria, their group 
allocation was revealed by unhiding a row in the randomization spreadsheet. Study staff were not 
blinded to group allocation during gait retraining sessions but were blinded for data processing 
and analysis. We enrolled 68 of the desired 80 participants, and the trial was concluded four 
months prior to the final subject’s final visit due to an institutional shutdown caused by the 
COVID-19 pandemic. 
1.2 Gait Analysis and Retraining 
 
Participants completed 13 visits to the motion laboratory for gait evaluation and 
retraining over the one-year study duration. During all visits, they walked on a force-
instrumented treadmill (Bertec Corporation, Columbus, OH, USA) in an 11-camera optical 
motion capture volume (Motion Analysis Corp., Santa Rosa, CA, USA). Forces and marker 
positions were collected at 2000Hz and 100Hz, respectively, and data were low-pass filtered at 
15 Hz (4th order, zero-lag Butterworth). MATLAB R2015b (MathWorks, Inc., Natick, MA, 
USA) was used to compute the KAM and FPA as well as to deliver real-time vibrotactile 
feedback through two C2 vibrotactile motors (Engineering Acoustics, Inc., Casselberry, FL, 
USA) affixed to the proximal tibia as described in Uhlrich et al. (Uhlrich et al., 2020, 2018).  
 
Prior to randomization, participants visited the motion laboratory two times to evaluate if 
an FPA modification could reduce their peak KAM. During the initial familiarization visit, we 
identified participants’ self-selected walking speed and they practiced walking with the 
following conditions until they felt comfortable: natural walking without feedback, 10° toe-in 
(relative to natural FPA) with feedback, and 10° toe-out with feedback. One week later, 
participants completed the FPA personalization (i.e., week 0) visit. After warming up on the 
treadmill for five minutes, participants completed a two-minute natural walking trial. They then 

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practiced walking with 5° and 10° toe-in and toe-out for at least one minute. Finally, the 
participants walked for two minutes at each of the four FPA modifications in a random order. 
Each participant’s larger KAM peak was identified from the natural walking trial, and the FPA 
modification that maximally reduced this peak was identified. For individuals whose early and 
late-stance KAM peaks were within 5% of one another, the FPA that maximally reduced one 
peak without increasing the other was selected. Only individuals who were able to reduce their 
KAM peak by at least 5% (Erhart-Hledik et al., 2012) were included and randomized as 
described in Section 1.1. 
 
Included participants visited the motion laboratory for six once-per-week gait retraining 
visits (weeks 1-6). In both the consent form and during the first week of gait retraining, 
participants were told that everyone would receive biofeedback teaching them to walk 
consistently with a personalized target FPA that was selected from the week 0 visit. They were 
told that the only difference between groups was the target FPA. The target FPA for the altered 
FPA group was the FPA that maximally reduced their peak KAM from week 0, and the target for 
the consistent FPA group was the average natural FPA from week 0. During gait retraining 
visits, participants performed a two-minute pre-training evaluation trial without feedback, four 
blocks of biofeedback (12-24 minutes total) following a faded feedback scheme (Barrios et al., 
2010), and a one-minute post-training evaluation trial without feedback. Participants were 
instructed to practice their FPA outside the lab for at least 20 minutes daily and, once 
comfortable, to walk with this FPA at all times. Participants completed daily logs of walking 
time and the percentage of time walking with their target FPA. During each visit, study staff 
reviewed the log and encouraged participants to meet the walking goals.  

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Following training, participants visited the lab for five evaluation and refresher training 
visits at week 10, month 3, month 6, month 9, and year 1 after the week 0 visit. Evaluation and 
retraining trials were similar to the week 1-6 retraining visits, and participants received 18 
minutes of biofeedback.  
1.3 Self-reported and activity outcomes 
 
Pain, function, and activity outcomes were assessed for the week preceding the week 1, 
week 6, and year 1 visits. NRS pain was evaluated by study staff prior to the visit as the typical 
pain over the past week in the medial compartment. The WOMAC survey was completed on a 
computer. Participants carried an Omron HJ-323U pedometer (Omron Corporation, Kyoto, 
Japan) for one week and daily steps were averaged, excluding the first and last day of registered 
steps. Participants also estimated the percentage time walking at their target FPA (i.e., 
compliance) for the week preceding the year 1 visit. 
1.4 MRI 
Femoral cartilage microstructure was evaluated at week 0 and at year 1 using quantitative 
MRI (T1 and T2). All scans were performed at 3T (DISCOVERY MR750, General Electric 
Company, Boston, MA, USA) using a 16-channel phased-array flexible coil. T2 relaxation times 
were calculated using a 3D quantitative Double Echo in Steady State (qDESS) sequence 
(Chaudhari et al., 2018, 2017) (TR=24.96 ms, TE1/ TE2=7.54/42.38 ms, FA=30, 
FOV=160x160x120 mm3, voxel size=0.3125x0.3125x1.5 mm3, scan time=5 min 32 s). T1 
relaxation times were estimated using a magnetization-prepared pseudo-steady-state 3D Fast 
Spin Echo sequence (Spin Lock Frequency=500 Hz, TR/TE=1292/16 ms, flip angle=90, 
FOV=160x160x120 mm3, voxel size=0.5x0.625x3 mm3, Spin Lock Time = 1, 10, 30, 60 ms, 

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total scan time=5 min 12 s). T2 was calculated using signal models from the two DESS echoes 
(Sveinsson et al., 2017) and T1 values were calculated pixelwise using a mono-exponential 
decay model. All follow-up scans were non-rigidly registered to the qDESS baseline scan. 
Subsequently the medial and lateral weight-bearing portions of femoral cartilage were manually 
segmented and applied to all scans. The voxelwise difference between follow-up and baseline 
scans was calculated and mean difference T1 and T2 (T1 and T2) values were extracted. A 
denoising filter was applied to the difference maps to eliminate noise and spurious peaks, while 
preserving larger clusters of longitudinal changes. Image processing was performed using 
QMRITools (Froeling, 2019), and registrations were implemented using Elastix (Klein et al., 
2010). 
1.5 Statistical analysis 
All primary and secondary analyses were performed using intent-to-treat approaches. The 
percent of missing data ranged from 1-32% and the data were normally distributed. Missing data 
were imputed using the Markov chain Monte Carlo method (Schafer, 1997) for Continuous 
Variables because the pattern of missing data was arbitrary and all outcome variables were 
continuous.  
To test group differences in the primary outcomes of change in NRS medial pain and 
change in KAM from week 0 to year 1, linear regression models were used, adjusting for 
baseline scores. Linear regression models were also used to test group differences in the 
secondary outcomes of T1 and T2 in the medial and lateral compartments. The threshold for 
significance was set at alpha = 0.05. 
 
 

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