Effects of iVR Treadmill on Gait in PD

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.