Effects of Real-time Feedback During Decline Walking on Kinematic and Kinetic Gait Parameters (Up & Down)
Phase: NA
Conditions: Healthy
Sponsor: Klaus Widhalm

 
 
 
 
 
 
 
 
 
 
 
Effects of real-time feedback during decline 
walking on kinematic and kinetic gait 
parameters (Up & Down): study protocol 
 
 
 
Date: 17.12.2020 
Version: 2.0 
Contact: Klaus Widhalm (klaus.widhalm@fh-campuswien.ac.at) 
 
 

2 of 14 
 
 
 
Abbreviations 
cm 
 
 
 
centimeter 
dFLA  
 
 
dynamic functional leg alignment 
fKROM  
 
 
frontal knee range of motion 
GRAIL  
 
 
Gait Real-time Analysis Laboratory 
ICC 
 
 
 
Intraclass Correlation Coefficient 
kg 
 
 
 
kilogram 
LW 
 
 
 
level walking 
m 
 
 
 
meter 
OA 
 
 
 
osteoarthritis 
vGRF  
 
 
vertical ground reaction force 
 
 
 

3 of 14 
Synopsis 
Title of the project 
Up & Down - Effects of real-time feedback during decline walking on kinematic and kinetic 
gait parameters 
Name and address of the sponsor 
FH Campus Wien – University of Applied Sciences 
Department of Health Sciences 
Favoritenstrasse 226 
1100 Vienna 
AUSTRIA 
 
Authors of the research protocol 
Klaus Widhalm, Sebastian Durstberger, Peter Putz 
Scientific project staff 
Klaus Widhalm, Principal Investigator 
Sebastian Durstberger, Researcher Physiotherapy 
Peter Putz, Statistical Analysis 
 
Short summary 
Control of the dynamic functional leg alignment (dFLA) and biomechanical load are 
important joint related aspects regarding the development of osteoarthritis (OA). Research 
on level walking with feedback on load related parameters provided innovative treatment 
possibilities. Concerning walking on sloped surfaces, fundamental biomechanical 
knowledge exists. However, deeper insights into the control of the dFLA during decline 
walking, 
and 
the 
usefulness 
of 
real-time 
feedback 
are 
missing. 
This study is set up as cross-sectional observation of gait under four conditions, which 
follows a randomized sequence in order to avoid carry over effects. Thirty (30) participants 
aged between 18 and 35 years will be included. They will complete a three-dimensional 
gait analysis on a 5-m ramp with 10° inclination. Afterwards they will be observed under 
four different conditions a) self-paced walking b) self-paced walking with internal focus of 

4 of 14 
attention, c) self-paced walking with real-time feedback, and d) condition c speed-matched 
walking, on a 10° declined split belt treadmill. The primary outcome parameter will be the 
frontal knee range of motion (fKROM). Secondary outcomes include the ground reaction 
force loading rate, spatial-temporal parameters, sagittal frontal and transversal 
kinematics, and kinetics for the lower extremities. 
The findings should improve the understanding of effects of real-time feedback on the 
control of the dFLA and lower limb loading. Results will be published in a peer-review 
journal. 
 
 
 

5 of 14 
Rationale and background information 
The prevalence of symptomatic osteoarthritis (OA) was estimated to be about 3-9% in 
European adults aged over 19 and increases towards to 9-15% in those over 60 years1. 
Biomechanical loading was identified as one of highly relevant parameters concerning the 
development of knee OA and the management of conservative OA therapy2,3. In this 
context, kinematic and kinetic parameters have been studied thoroughly during level 
walking. Previous studies reported on altered gait parameters in incline and increasing 
inclination walking4,5.  However, declined walking is, due to higher vertical ground reaction 
forces (vGRF), even more challenging for the musculoskeletal system6-8 and the control of 
the dynamic functional leg alignment (dFLA). Especially knee and hip joint compression 
forces increase with the grade of inclination9. The medio-lateral load distribution and 
loading velocity generally is associated with knee valgus / varus thrust10, which was 
reported for level walking (LW) up to 6° in healthy persons11,12. Beside one x-ray based 
study data for declined walking is missing10. As loading and neuromuscular control have 
been shown to be important in the prevention and treatment of knee OA13,14, feedback is 
an effective tool supporting patients15,16. Especially real-time feedback targeting at the 
sensorimotor control of the dFLA during walking has not been investigated yet.  
Assessment of uphill or downhill locomotion has mainly been studied using instrumented 
ramps6,17. Only few studies have been done on instrumented treadmills with 
incliniation5,18,19. But in those the kinetic measurement technology is not sufficient 
comparable. Furthermore, knowledge on the agreement of ramp and treadmill derived 
kinetic and energy data is missing. 
To our knowledge, the influence of motor control interventions as internal focus of attention 
and real-time feedback on prior mentioned biomechanical factors in declined walking have 
not been studied before. 
Study goals and objectives 
This cross-sectional observation of decline walking under four conditions, aims at 
enhancing the understanding of the effects of real-time feedback on lower limb gait 
kinematics and kinetics. Furthermore, the agreement of kinematic and kinetic parameters 
of 10° decline and incline walking between ramp and treadmill will be assessed. 
The primary research question is, whether real-time feedback alters the frontal knee range 
of motion when compared to self-paced walking on the one hand, and matched speed 
walking on the other hand. The secondary aspect focuses on the effect of lower limb loading 
in decline walking. Thirdly, parameters of interest will be compared between 
declined/inclined ramp and declined/inclined treadmill walking. Consequently, underlying 
hypotheses were phrased as follows:  
 

6 of 14 
Primary null hypothesis: Means of the outcome “frontal knee range of motion” are equal 
across the four conditions a) self-paced walking, b) self-paced walking with internal focus 
of attention, c) self-paced walking with real-time feedback, and d) condition c speed-
matched walking. Predefined contrasts hypothesize that: 
1) The outcome of self-paced walking with internal focus of attention and self-paced 
walking with real-time feedback is equal to speed matched walking (bc=d) 
2) The outcome of self-paced walking with internal focus of attention and self-paced 
walking with real-time feedback is equal to self-paced walking (bc=a) 
3) The outcome of self-paced walking with real-time feedback is equal to self-paced 
walking with internal focus of attention (c=b) 
 
Primary alternative hypothesis: Means of the outcome “frontal knee range of motion” are 
NOT equal across the four conditions a) self-paced walking, b) self-paced walking with 
internal focus of attention, c) self-paced walking with real-time feedback, and d) condition 
c speed-matched walking. Two-sided testing will be applied, as knowledge from existing 
literature is only moderate.  Predefined contrasts hypothesize that: 
1) The outcome of self-paced walking with internal focus of attention and self-paced 
walking with real-time feedback is NOT equal to speed matched walking (bc≠d) 
2) The outcome of self-paced walking with internal focus of attention and self-paced 
walking with real-time feedback is NOT equal to self-paced walking (bc≠a) 
3) The outcome of self-paced walking with real-time feedback is NOT equal to self-
paced walking with internal focus of attention (c<b) 
 
Secondary null hypothesis: Means of the outcome “ground reaction force loading rate” are 
equal across the four conditions a) self-paced walking, b) self-paced walking with internal 
focus of attention, c) self-paced walking with real-time feedback, and d) condition c speed-
matched walking. Predefined contrasts are for this outcome concordant with those of the 
primary hypothesis. 
Secondary alternative hypothesis: Means of the outcome “ground reaction force loading 
rate” are NOT equal across the four conditions a) self-paced walking, b) self-paced walking 
with internal focus of attention, c) self-paced walking with real-time feedback, and d) 
condition c speed-matched walking. Two-sided testing will be applied, as knowledge from 
existing literature is only moderate. Predefined contrasts are for this outcome concordant 
with those of the primary hypothesis. 
 
Tertiary null hypothesis:  

7 of 14 
1) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between decline ramp walking and speed-
matched decline treadmill walking is lower than 0.75 (ICC). 
2) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between decline ramp walking and self-paced 
decline treadmill walking is lower than 0.75 (ICC). 
3) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between incline ramp walking and speed-
matched incline treadmill walking is lower than 0.75 (ICC). 
4) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between incline ramp walking and self-paced 
incline treadmill walking is lower than 0.75 (ICC). 
Tertiary alternative hypothesis: 
1) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between decline ramp walking and speed-
matched decline treadmill walking is at least 0.75 (ICC). 
2) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between decline ramp walking and self-paced 
decline treadmill walking is at least 0.75 (ICC). 
3) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between incline ramp walking and speed-
matched incline treadmill walking is at least 0.75 (ICC). 
4) The agreement of continuous kinematic and kinetic outcomes of the lower limb 
(see list of outcome parameters) between incline ramp walking and self-paced 
incline treadmill walking is lower at least 0.75 (ICC). 
 
Methods/Design 
Trial design 
The study is set up as cross-sectional observation of gait under four conditions, which 
follows a randomized sequence in order to avoid carry over effects. Chronologically, the 
assessments will start with the tertiary observation, investigating agreements between 
declined/inclined ramp and treadmill walking. Thereafter, three out of four conditions (self-
paced walking, self-paced walking with internal focus of attention, and self-paced walking 
with real-time feedback) are tested under block randomization of their six possible 
permutations (ABC, BCA, CAB, CBA, ACB, BAC). With three conditions (n=3), this 
represents the smallest number of permutations, equaling a Williams design, where each 
condition precedes each other condition equally often and each treatment occurs equally 

8 of 14 
frequently at each position20. The fourth corresponding condition (speed-matched 
walking), can technically not take place before the corresponding real-time feedback 
condition, and is therefore exempted from the sequence randomization and always follows 
after self-paced walking with real-time feedback. Numbers of participants assessed for 
eligibility, allocated to assessments, and analyzed will be recorded by a modified CONSORT 
flow chart21, without consideration of losses to follow-up, due the cross-sectional study 
design. Figure 1 illustrates a flowchart of the trial design. 
Figure: Flow diagram of the progress through the phases of the trial21  
 
Sample size estimation and study population 
As knowledge and studies concerning the effect of real-time feedback during decline 
walking is missing, sample size calculation based on proven effects is not applicable. 
Therefore sample size was arbitrary estimated with 25 based on the number of participants 
included in topic-specific studies, ranging from 14 to 4015,22-24. Taking into account a drop 
out of 20% we will recruit 30 participants. 
Assessed for eligibility (n= 30) 
Excluded (n=  ) 
   Not meeting inclusion criteria (n= ) 
   Declined to participate (n= ) 
   Other reasons (n= ) 
Allocated to assessment (n= ) using a 
randomized crossover design all test 
subjects 
 
 self-paced decline treadmill walking (n= ) 
 self-paced decline treadmill walking with 
internal focus of attention (n= ) 
 self-paced decline treadmill walking with 
real-time feedback +  speed-matched 
decline treadmill walking (n= ) 
Allocation 
Analysis 
Randomized (n= ) 
Enrollment 
Allocated to assessment (n= ) of ramp walking and speed-matched 
declined treadmill walking 
 Complete allocated assessment (n=  ) 
 Did not complete allocated assessment (give reasons) (n=  ) 
Analysed 
 Excluded from analysis (give Reason) (n= ) 

9 of 14 
Those healthy participants will be included, according to the following inclusion criteria: 1) 
age from 18-35 years, 2) body mass index from 18.5 to 29.99 kg/m², 3) no chronical joint 
diseases and/or OA surgery, and 4) no neuro-motor diseases. Participants will be excluded 
from the analysis in case of 1) non-physiological and non-symmetrical gait patterns, and 
2) severe outliers (more than two standard deviations). 
 
Recruitment, informed consent and randomization 
Students and staff of the FH Campus Wien – University of Applied Sciences will be 
addressed with the study information via in-house corridor monitors and an email 
newsletter.  If interested, they may contact the primary investigator. A research team 
member will check eligibility and provide study details. An assessment date will be agreed 
in case of willingness and eligibility of the potential participant. Written informed consent 
will be obtained from the participants prior to the assessments. The participants will 
randomly be assigned to one of the three sequences, by means of a prepared sequence 
list as derived from a random sequence generator online tool. Due to the nature of the 
studied conditions, it will neither be possible to conceal the allocation, nor to blind 
participants or researchers. A participant will pass through all assessments within one day. 
Shopping vouchers of 30 € will be offered as incentive for participation. 
 
Safety considerations and participant insurance 
No adverse event are to be expect. The participants will be instructed by the principal 
investigator on treadmill walking and the safety system. The safety system will consist of 
a safety harness that will be attached to a suspension system, two handrails, light gates 
at the front and back of the belt and an emergency button at the operating desk. The 
participants will have five minutes of familiarization with the treadmill operated with 
matched walking speed from the level walking trials. All participants will be insured by a 
clinical trial insurance. 
 
Examination Procedures 
At first, height will be measured with a stadiometer to the nearest 0.5 cm (SECA 213), and 
weight with a medical scale (Marsden M-420). Furthermore, joint range of motion and 
functional hamstring flexibility will be assessed. Thereafter, all participants will be equipped 
with 43 retroreflective markers on bony landmarks and 16 markers on four rigid clusters 
on thighs and shanks. Five level walking trials will captured to evaluate if gait pattern is 
physiological or not. Participants will have to walk on a five-meter ramp with 10° inclination 
which is equipped with 24 infrared cameras (Vicon, Oxford, United Kingdom) and two force 
plates (Kistler, Winterthur, Switzerland). After seven left and seven right foot strikes on 

10 of 14 
the force plates for up and down walking each, the participants will be guided to the Gait 
Real-time Analysis Interactive Lab (GRAIL, Motekforce Link, Amsterdam, Netherlands), 
which is located next door. The GRAIL is a dual-belt treadmill equipped with two force 
plates, a 180° virtual reality screen and a motion capturing system with ten infrared 
cameras. After the familiarization phase on the treadmill with an inclination of 10° and with 
matched speed of incline walking, three times a period of 20 seconds will be recorded 
within 3 minutes. The investigator will explain the self-paced mode of the treadmill to the 
participant afterwards. The self-paced mode will adapt the belt speed automatically based 
on the position of the participant on the treadmill. Following a familiarization phase, data 
will be recorded as before. 
This procedure will be repeated on the treadmill with a declination of 10°. Thereafter, the 
three following tasks will be performed in the order as specified in the sequence list. 
Self-paced decline walking  
The participants will be instructed to walk on the declined treadmill with self-selected 
speed. They will complete a five-minute familiarization period and a three-minute 
measurement period. Within the measurement phases, three times a period of 20 seconds 
of self-paced walking will be recorded.  
Self-paced decline walking with internal focus of attention  
The participants will be instructed to walk on the declined treadmill with self-selected 
speed. During walking, they will have to concentrate on an internal focus of attention, as 
keeping their spine as long towards the ceiling as possible. The participants will complete 
a five-minute familiarization period and a three-minute measurement period. In the 
measurement phase, three times a period of 20 seconds of self-paced walking  
Self-paced decline walking with real-time feedback, followed by speed-matched 
decline walking 
The participants will be instructed to walk on the declined treadmill with self-selected 
speed. During walking, they will have to concentrate on specific real-time feedback 
presented on the projection screen. This feedback visualization will represent the simplified 
frontal knee alignment movement using the participants knee position in relation to the 
ankle. The participants will complete a five-minute familiarization period and a three-
minute measurement period. In the measurement phases, three times a period of 20 
seconds of self-paced walking will be recorded. After this task, a control trial will take place 
with matched speed and without any feedback instruction. 
 
Ethical and data protection aspects 
This study will be submitted to the ethics committee of the Medical University of Vienna. 
With respect to participating students, there is a teacher-student relationship between 

11 of 14 
researcher and participants. Besides general ethical principles, i.e. voluntary participation, 
data protection and confidentiality, provision of contact details of the research team, it will 
be emphasized in the course of obtaining informed consent, that refusing or withdrawing 
consent will not have any consequences on the remaining academic education. Participants 
will be assigned a continuous study code (UD01 – UD30) to protect identifiable data. The 
assignment key will be locked in the study manager office, separate from identifiable study 
data. 
 
Outcome parameters 
Height, bodyweight and inter-anterior superior iliac spine distance will be measured at the 
beginning of all sessions. Furthermore, flexibility of hamstrings and triceps surae will be 
assessed clinically. The primary outcome of this study is the frontal knee range of motion 
during the stance phase. Stance phase will be defined between heel-strike and toe-off of 
the observed leg. A second focus is on the ground reaction force loading ratio. Thirdly, the 
kinematics for the sagittal, frontal and transversal plane for hip, knee and ankle and the 
external moments for the sagittal and frontal plane for hip, knee and ankle will be assessed 
as well as step time, step length, step width, cadence and walking speed (table 1).  
Area 
Outcome Measure 
Kinematic 
3 planes of hip angle range of motion (°) 
Minimum sagittal hip angle (°)  
Maximum sagittal hip angle (°) 
3 planes of knee angle range of motion (°) 
Minimum sagittal knee angle (°)  
Maximum sagittal knee angle (°) 
Sagittal ankle angle range of motion(°) 
Minimum sagittal ankle angle(°)  
Maximum sagittal ankle angle(°) 
3 planes of pelvis angle range of motion (°) 
3 planes of trunk angle range of motion (°) 
Minimum sagittal trunk angle (°) 
Minimum sagittal trunk angle (°) 
Kinetics 
First peak load (N) 
Moment of first peak load (%) 
Average loading rate (N s-1) 
Maximum loading rate (N sec-1) 
Knee joint forces (N/kg) 
3 planes of minimum of hip external moments (Nm/kg) 
3 planes of maximum of hip external moments (Nm/kg) 
3 planes of minimum of knee external moments (Nm/kg) 
3 planes of maximum of knee external moments (Nm/kg) 
Minimum of sagittal ankle external moment (Nm/kg) 
Maximum of sagittal ankle external moment (Nm/kg) 

12 of 14 
Area 
Outcome Measure 
Frontal knee abduction moment impulse (Nm/kg s) 
Frontal knee adduction moment impulse (Nm/kg s) 
Spatio-temporal 
parameters  
Walking velocity (m sec-1) 
Cadence (steps min-1) 
Step length (m) 
Step width (m) 
Stance phase duration (% gait cycle) 
Swing phase duration (% gait cycle) 
Table 1: List of tertiary outcome parameters 
Kinematic data will be time normalized from heel strike to heel strike, kinetic data from 
heel strike to toe-off. Individual participant data will expressed as average per condition. 
Time normalization, data processing and parameter calculations will be done with self-
developed scripts in MATLAB 2018a (The Mathworks, Natrick, MA, USA). 
 
Statistical analysis 
All metric parameters will be analyzed with SPSS 26 (IBM Corporation, Armonk, NY, USA). 
Data will be tested for normality using the Shapiro-Wilk Test, and graphical inspections of 
Q-Q-plots. Differences between ramp-walking and matched speed / self-paced walking on 
the inclined / declined treadmill will be analyzed using paired two-tailed t-test and tested 
for agreement using ICC [3.1, absolute agreement, single measures] with SEM of the ICC. 
To evaluate differences between the four conditions (decline walking, internal focus of 
attention, real-time feedback, and matched speed walking), a one-factorial analysis of 
variance with repeated measures will be used. Contrasts will be employed to test 
aforementioned pre-specified hypotheses. In view of the legitimation given with the 
hypothesis, two-sided testing will be applied. One-dimensional statistical parameter 
mapping (1dSPM)25 will be used to examine the steady parameters of the gait analysis. All 
other statistical tests than those related to the primary and secondary hypotheses are 
considered exploratory and statistical testing will be limited to tests as pre-specified by the 
hypotheses. Consequently. Bonferroni-Holm method will be used in terms of correcting for 
multiple testing. Alpha will be set to 0.05; exact p-values will be reported though. 
 
Dissemination of results and publication policy 
A manuscript will be submitted to a journal with peer review process (e.g. Gait & Posture) 
aiming for an open access publication. Coded raw data will be made available upon request 
and for non-commercial purposes after publication of the results. 
 
Timeline of the project 

13 of 14 
Nov 2020 
Submission to the local ethical committee (Medical University 
of Vienna) 
Dec 2020 
Ethical approval obtained 
Jan 2021 
Recruitment 
Jan – Apr 2021 
Field work 
Mar – May 2021 
Data analysis 
May – June 2021 
Finalization of manuscripts 
 
 
Limitations and problems anticipated 
The condition speed-matched walking cannot be included into the randomization 
procedure. Due to the nature of the studied conditions, it will neither be possible to conceal 
the allocation, nor to blind participants or researchers. Another limitation is that different 
force plates are used for ramp-walking and treadmill walking, as well as the continuous 
quality of declined treadmill force plate data cannot be guaranteed. Thereby a small 
systematic error can maybe affect the kinetic outcome. Findings of this study will apply 
only to healthy young adults. 
 
Conflict of interest and funding information  
The study team has no financial or academic conflict of interest to declare. No external 
funding will be requested. 

14 of 14 
References 
 
 
 
1. 
Hunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet. 2019;393(10182):1745-1759. 
2. 
Fransen M, McConnell S. Exercise for osteoarthritis of the knee. Cochrane Database Syst 
Rev. 2008(4):CD004376. 
3. 
Vina ER, Kwoh CK. Epidemiology of osteoarthritis: literature update. Curr Opin Rheumatol. 
2018;30(2):160-167. 
4. 
Silder A, Besier T, Delp SL. Predicting the metabolic cost of incline walking from muscle 
activity and walking mechanics. J Biomech. 2012;45(10):1842-1849. 
5. 
Pacifico D, Visscher R, List R, Item-Glatthorn JF, Casartelli NC, Maffiuletti NA. Discriminant 
validity and reproducibility of spatiotemporal and kinetic parameters during treadmill 
walking in patients with knee osteoarthritis. Gait Posture. 2020;80:77-79. 
6. 
Lay AN, Hass CJ, Gregor RJ. The effects of sloped surfaces on locomotion: a kinematic and 
kinetic analysis. J Biomech. 2006;39(9):1621-1628. 
7. 
Schwameder H, Lindenhofer E, Muller E. Effect of walking speed on lower extremity joint 
loading in graded ramp walking. Sports Biomech. 2005;4(2):227-243. 
8. 
Kuster M, Sakurai S, Wood GA. Kinematic and kinetic comparison of downhill and level 
walking. Clin Biomech (Bristol, Avon). 1995;10(2):79-84. 
9. 
Alexander N, Schwameder H. Lower limb joint forces during walking on the level and slopes 
at different inclinations. Gait Posture. 2016;45:137-142. 
10. 
Farrokhi S, Voycheck CA, Gustafson JA, Fitzgerald GK, Tashman S. Knee joint contact 
mechanics during downhill gait and its relationship with varus/valgus motion and muscle 
strength in patients with knee osteoarthritis. Knee. 2016;23(1):49-56. 
11. 
Paterson KL, Sosdian L, Hinman RS, et al. The influence of sex and obesity on gait 
biomechanics in people with severe knee osteoarthritis scheduled for arthroplasty. Clin 
Biomech (Bristol, Avon). 2017;49:72-77. 
12. 
Sosdian L, Hinman RS, Wrigley TV, et al. Quantifying varus and valgus thrust in individuals 
with severe knee osteoarthritis. Clin Biomech (Bristol, Avon). 2016;39:44-51. 
13. 
Ageberg E, Roos EM. Neuromuscular Exercise as Treatment of Degenerative Knee Disease. 
Exercise and Sport Sciences Reviews. 2015;43(1). 
14. 
Richards R, van der Esch M, van den Noort JC, Harlaar J. The learning process of gait 
retraining using real-time feedback in patients with medial knee osteoarthritis. Gait 
Posture. 2018;62:1-6. 
15. 
Luc-Harkey BA, Franz JR, Blackburn JT, Padua DA, Hackney AC, Pietrosimone B. Real-time 
biofeedback can increase and decrease vertical ground reaction force, knee flexion 
excursion, and knee extension moment during walking in individuals with anterior cruciate 
ligament reconstruction. J Biomech. 2018;76:94-102. 
16. 
Tate JJ, Milner CE. Real-time kinematic, temporospatial, and kinetic biofeedback during gait 
retraining in patients: a systematic review. Phys Ther. 2010;90(8):1123-1134. 
17. 
Strutzenberger G, Alexander N, Bamboschek D, Claas E, Langhof H, Schwameder H. Uphill 
walking: Biomechanical demand on the lower extremities of obese adolescents. Gait 
Posture. 2017;54:20-26. 
18. 
Telhan G, Franz JR, Dicharry J, Wilder RP, Riley PO, Kerrigan DC. Lower limb joint kinetics 
during moderately sloped running. J Athl Train. 2010;45(1):16-21. 
19. 
Dewolf AH, Ivanenko YP, Lacquaniti F, Willems PA. Pendular energy transduction within the 
step during human walking on slopes at different speeds. PLoS One. 
2017;12(10):e0186963. 
20. 
Williams E. Experimental Designs Balanced for the Estimation of Residual Effects of 
Treatments. Australian Journal of Chemistry. 1949;2(2):149-168. 
21. 
Schulz KF, Altman DG, Moher D, Group C. CONSORT 2010 statement: updated guidelines 
for reporting parallel group randomised trials. BMJ (Clinical research ed). 2010;340:c332-
c332. 
22. 
Richards RE, van den Noort JC, van der Esch M, Booij MJ, Harlaar J. Effect of real-time 
biofeedback on peak knee adduction moment in patients with medial knee osteoarthritis: Is 
direct feedback effective? Clinical Biomechanics. 2018. 

15 of 14 
23. 
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. Arch Phys Med Rehabil. 2014;95(10):1912-1917. 
24. 
Agopyan H, Griffet J, Poirier T, Bredin J. Modification of knee flexion during walking with 
use of a real-time personalized avatar. Heliyon. 2019;5(11):e02797. 
25. 
Pataky TC, Robinson MA, Vanrenterghem J. Region-of-interest analyses of one-dimensional 
biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power. 
PeerJ. 2016;4:e2652.