Parkinson Disease Clinical Trial
Official title:
Immersive Virtual Reality on Treadmill to Ecologically Assess Parkinson's Disease Patients' Gait
Parkinson's Disease (PD) patients suffer from gait impairments responsible for falls and bad quality of life: reduced speed and stride length, randomness in stride duration variability (reduced Long-Range Autocorrelations (LRA)). This pilot study will compare the spatio-temporal walking parameters and LRA of PD patients tested under three conditions: walking over ground, walking on a treadmill and walking on a treadmill in virtual reality. The aim is to determine the effect on gait of an optical flow recreated in virtual reality, by means of a virtual reality headset, on a treadmill.
1. BACKGROUND Parkinson's disease (PD) is the second most common degenerative neurological
disease. PD induces gait disorders that lead to increased risk of falls. These falls
seriously affect patients' quality of life and generate significant health care costs.
Unfortunately, gait disorders do not respond well to drug treatments and their
management is mainly based on rehabilitation treatment. The rehabilitation approach
comprises two steps: a functional assessment of locomotor capacities followed by
completion of a therapeutic physical exercise program.
Like heart rate, stride duration varies in the short and long term according to a
complex dynamic of temporal variations. These variations present long-range
autocorrelations (LRA): the stride duration does not vary randomly but in a structured
way. The study of LRA is based on complex mathematical methods requiring recording of
512 consecutive gait cycles. LRA are altered in PD patients whose gait rhythm is
excessively random. Alteration of LRA is correlated with neurological impairments (Hoehn
& Yahr scale and UPDRS) and patients' locomotor stability (ABC scale & BESTest).
Measurement of LRA would be the first available objective and quantitative biomarker of
stability and risk of falling in patients with PD.
Guidelines concerning rehabilitation programs for PD patients are based on education
(prevention of falls and inactivity,...), physical exercises, functional training
(double task, complex tasks,...), learning, adaptation strategies (cueing) and action
observation. The combination between immersive virtual reality (iVR), using an iVR
headset, and treadmill walking will be developped.
Treadmill walking has shown long-term effectiveness on PD patients' gait and quality of
life. A study carried out recently has shown that a single treadmill session reduces the
stride duration variability during the intervention. Although the treadmill seems to
improve patients' gait, it lacks an essential ecological component that allows humans to
stabilize gait: an optical flow, an environment that scrolls during walking. iVR allows
to give patients a visual flow when walking on treadmill as if they were walking
overground and patients could benefit from it. The purpose of this pilot study is to
compare the spatio-temporal gait parameters and gait variability parameters obtained
during three Walking conditions: over ground walking, treadmill walking, treadmill
walking with a VR headset.
2. METHODS
2.1 Participants : 10 patients suffering from idiopathic Parkinson's Disease will be
recruited from the local community and from the Neurology and the Physical and Rehabilitation
Medicine outpatient clinics of the Cliniques universitaires Saint-Luc (Woluwe-Saint-Lambert,
Belgium).
2.2 Functional assessment: Before the expermientations starts, all participants will undergo
a non harmful assessment including clinical tests and questionnaires
PD patients: Age, height, weight, sex, most affected side, Movement Disorder Society-Unified
Parkinson Disease Rating Scale (MDS-UPDRS), Mini Balance Evaluation Systems Test
(Mini-BESTest), Simplified version of the Activities-specific Balance Confidence Scale
(ABC-Scale), modified Hoehn & Yahr scale, Mini Mental State Examination (MMSE).
2.3 Procedure : Every participants will walk in three conditions in a randomized order. Each
condition lasted ±15 minutes in order to get 512 gait cycles mandatory to assess the presence
of LRA.
The first condition consists in Over ground Walking (OW). Participants will be asked to walk
on an rectangular track with rounded corner of 63.2 meters in CUSL at their comfortable
walking speed.
The second condition is Treadmill Walking (TW). Patients will walk on the treadmill at their
comfortable walking speed assessed before the condition with a 10 Meter Walk Test. During
this condition, patients will wear a non weight-bearing safety harness (Petzl, Volt, France).
The last condition consists in walking on a treadmill, at the same speed as during TW,
patients still wearing the non weight-bearing harness, while wearing a VR headset (VRTW). The
VR headset (HTC, Vive, Taïwan) consists of a kind of mask that the person comes to put on his
face. Two Fresnel lenses are in front of the person's eyes and the person looks at a display
that allows stereoscopic 3D through these lenses. The patient is then immersed in an
immersive virtual environment, cut off from the outside world. For this experiment, a VR
environment has been created by means of Unity software and using C# code. While walking on
the treadmill, the participants will see a quiet hallway surrounding them. The participants
will perceive an optic flow while walking on the treadmill. The speed of the optic flow
perceived inside the headset by the patient will be matched with the speed of the treadmill
to create the illusion of walking on an actual hallway. A cybersickness questionnaire
(Simulator Sickness Questionnaire, SSQ) will be completed before and just after VRTW. Indeed,
the use of a VR headset can cause dizziness, nausea, headaches and other symptoms in some
patients. We want to evaluate whether or not the use of an optical flow displayed in VR on a
treadmill leads to cybersickness.
2.4 Data acquisition: Two Inertial Measurement Units (IMU) (IMeasureU Research, VICON, USA)
will be taped on patients' both lateral malleoli. IMUs will be taped on the leg on the side
most affected by the disease. This system allowed to record ankle accelerations at 500 Hz.
The data will then be put on a computer and each peak of acceleration, corresponding to each
heel strike, will be detected by software internally developed to determine all stride
durations.
2.5 Gait assessment: Data will be extracted from 512 consecutive gait cycles which is
required to measure gait variability.
2.5.1 Spatiotemporal gait variables:
Mean gait speed, gait cadence and stride length will be measured as follow:
Mean gait speed (m.s-1) = Total walking distance (m)/ Acquisition duration (s) Gait cadence
(#steps.min-1) = Total number of steps (#)/Acquisition duration (min) Step length (m) = Gait
speed (m/s)*60/Gait cadence (steps/min)
2.5.2 Stride duration variability : Stride duration variability can be assessed 2 ways: in
terms of magnitude or in terms of organization (how stride duration evolves across
consecutive gait cycles).
2.5.2.1 Magnitude of the stride duration variability : To determine the effect of the RAS on
the magnitude of the stride duration variability during 512 gait cycles, the mean, the
standard deviation (SD) and the coefficient of variation (CV = [SD/mean] * 100) will be
assessed.
2.5.2.2 Temporal organization of the stride duration variability (LRA) : Temporal
organization of stride duration variability will be assessed by LRA computation using the
evenly spaced averaged version of the Detrended Fluctuation Analysis (DFA) to obtain α
exponent. The presence of LRA can be shown with α exponent values between 0.5 and 1.
Data will be treated by the mean of CVI Labwindows (C++).
2.6 Statistical analyses : Statistical analyses will be conducted using Sigmaplot 13. If the
normality test is passed, a one-way repeated measures ANOVA will be applied to determine the
effect of the various walking condition on spatiotemporal gait parameters (gait speed, gait
cadence, stride length) and on linear and nonlinear measures of stride duration variability
(CV, SD, H and α exponents). When a significant difference between groups is detected with
the ANOVA, a post hoc test will be performed to compare each mean with the other means to
isolate the groups from each other.
A paired t-test will also be conducted to determine a possible change in score on the SSQ
questionnaire after TW and after VRTW.
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