Effect of Haptic Cueing on Long-Range Autocorrelations in Parkinson's Disease Gait Variability

Effect of Rhythmic Haptic Stimulations on Long-Range Autocorrelations in Parkinson's Disease Gait Variability : A Comparison With Auditory Stimulations

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.

Study Overview

• Status: Completed
• Conditions: Parkinson Disease; Gait, Unsteady
• Intervention / Treatment: Other: No cueing versus Haptic cueing versus Auditory cueing during parkinson's disease gait

Detailed 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.

• Study Type: Interventional
• Enrollment (Actual): 10
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Belgium
  • Brussel, Belgium, 1200
    • Cliniques Universitaires Saint Luc
  • Brussels, Belgium, 1200
    • Cliniques Universitaires Saint-Luc

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Idiopathic PD according to the UK Brain Bank Criteria
• modified Hoehn & Yahr scale <= 3
• Mini-Mental State Examination > 24/30
• Ability to walk 256 consecutive strides

Exclusion Criteria:

• Other pathology interacting with gait
• Significant hearing problems not allowing to hear the auditory cueing
• Significant somatosensory problems not allowing to feel the haptic cueing on the skin

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: PD patients; See inclusion criteria in the right section. See procedures in the the right section.

Other: No cueing versus Haptic cueing versus Auditory cueing during parkinson's disease gait; Patients walked in three conditions in a randomized order. During control condition, patients walked without cueing on a rectangular track of 63.2 meters at their comfortable gait speed. During Auditory Cueing Condition (ACC) patients walked on the same track using auditory cueing by the mean of a smartphone app called Soundbrenner thath deliverered rhythmic auditory stimulations through earphones paced 10% faster than each patient's preferred step frequence. During Haptic Cueing Condition (HCC) patients walked on the same 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Long-Range Autocorrelations	Long-Range Autocorrelations computation using the evenly spaced version of the Detrended Fluctuations Analysis	Change from baseline in long-range autocorrelations during each intervention condition (3 x 10 min walking)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Coefficient of variation of stride duration	[SD/mean stride duration] * 100	Change from baseline in coefficient of variation during each intervention condition (3 x 10 min walking)
Mean Gait Speed	otal walking distance (m)/ Acquisition duration (s)	Change from baseline in mean gait speed during each intervention condition (3 x 10 min walking)
Step Length	Gait speed (m/s)*60/Gait cadence (steps/min)	Change from baseline in step length during each intervention condition (3 x 10 min walking)
Gait Cadence	Total number of steps (#)/Acquisition duration (min)	Change from baseline in gait cadence during each intervention condition (3 x 10 min walking)

Collaborators and Investigators

• Sponsor: Cliniques universitaires Saint-Luc- Université Catholique de Louvain
• Investigators: Principal Investigator: Thierry Lejeune, PhD, Cliniques Universitaires Saint-Luc

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2020
• Primary Completion (Actual): March 15, 2020
• Study Completion (Actual): March 15, 2020

Study Registration Dates

• First Submitted: November 18, 2021
• First Submitted That Met QC Criteria: March 16, 2023
• First Posted (Actual): March 30, 2023

Study Record Updates

• Last Update Posted (Actual): March 30, 2023
• Last Update Submitted That Met QC Criteria: March 16, 2023
• Last Verified: March 1, 2023

More Information

Terms related to this study

• Keywords: Gait; Parkinson's Disease; Cueing; Long-Range Autocorrelations; Rhytmic stimulations
• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurologic Manifestations; Parkinsonian Disorders; Basal Ganglia Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases; Parkinson Disease; Gait Disorders, Neurologic
• Other Study ID Numbers: NMSK - Lheureux 03

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: IPD could be shared if asked. Anonymized data are available.
• IPD Sharing Time Frame: No particular time frame
• IPD Sharing Access Criteria: If asked : alexis.lheureux@uclouvain.be
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No