Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05790759 |
| Other study ID # |
NMSK - Lheureux 03 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2020 |
| Est. completion date |
March 15, 2020 |
Study information
| Verified date |
March 2023 |
| Source |
Cliniques universitaires Saint-Luc- Université Catholique de Louvain |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
Parkinson's Disease (PD) patients suffer from gait impairments responsible for falls and bad
quality of life: reduced speed and stride length, randomness in the temporal organization of
stride duration variability (reduced Long-Range Autocorrelations (LRA)). For years, auditory
cueing has been used to modulate PD gait and its effect on LRA is known. Less is known
regarding the effects of haptic cueing on PD gait and especially on LRA. This pilot study
will compare the spatio-temporal gait parameters and LRA of PD patients tested under three
conditions: walking without cueing, walking with auditory cueing and walking with haptic
cueing by means of rhythmic vibrations on the patients' wrists.
Description:
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 at
least 256 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, and adaptation strategies such as the use of
rhythmic sensory cueing. Auditory Cueing (AC) has been used for years for clinical and
research purposes and its effects on spatio-temporal gait parameters and LRA are known.
Less is know regarding Haptic/Somatosensory Cueing (HC). A few research were conducted
to study the influence of HC on PD spatio-temporal gait parameters but to the best of
our knowledge, none has yet addressed its effects on LRA. The aim of this present study
was to compare PD saptio-temporal gait parameters and LRA under three conditions :
walking without cueing, walking with AC and walking with HC.
2. METHODS 2.1 Participants : 10 patients suffering from idiopathic Parkinson's Disease
were 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 underwent 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 walked in three conditions in a randomized order. Each
condition lasted ±10 minutes in order to get 256 gait cycles mandatory to assess the presence
of LRA using the evenly spaced averaged version of the Detrended Fluctuations Analysis (DFA).
The first condition was the control condition (CC), patients walking without any cueing on a
rectangular track with rounded corner of 63.2 meters in CUSL at their comfortable walking
speed.
The second condition was the Auditory Cueing Condition (ACC) and consisted in walking on the
same rectangular track using auditory cueing by the mean of a smartphone app called
Soundbrenner. This app allowed to precisely deliver rhythmic auditory stimulations through
earphones paced 10% faster than each patient's preferred step frequence assessed before the
experiment.
The last condition was the Haptic Cueing Condition (ACC) and consisted in walking on the same
rectangular track using haptic cueing by the mean of a vibratory device called Soundbrenner
Pulse and attached to each patient's wrist located on their most affected side. The
Soundbrenner app on the smartphone was connected to the Soundbrenner Pulse by Bluetooth to
deliver rhythmic vibratory stimulations also paced 10% faster than each patient's preferred
step frequence, the same frequence as during ACC.
2.4 Data acquisition: Two Inertial Measurement Units (IMU) (IMeasureU Research, VICON, USA)
were taped on patients' both lateral malleoli. This system allowed to record ankle
accelerations at 500 Hz. The data were then put on a computer and each peak of acceleration,
corresponding to each heel strike, was detected by software internally developed to determine
all stride durations.
2.5 Gait assessment: Data were extracted from 256 consecutive gait cycles which is required
for LRA computation.
2.5.1 Spatiotemporal gait variables:
Mean gait speed, gait cadence and stride length were 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 256 gait cycles, the mean, the
standard deviation (SD) and the coefficient of variation (CV = [SD/mean] * 100) were
assessed.
2.5.2.2 Temporal organization of the stride duration variability : Temporal organization of
stride duration variability were 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 were treated by the mean of CVI Labwindows (C++).
2.6 Statistical analyses : Statistical analyses were conducted using Sigmaplot 13. If the
normality test passed, a one-way repeated measures ANOVA was 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). If a significant difference between groups was detected with the ANOVA, a
Holm-Sidak post hoc test was performed to compare each mean with the other means to isolate
the groups from each other. Effect size between conditions regarding all parameters was
assessed using Cohen's d.
