Contribution of Virtual Reality and Modelling in Falling Risk Assessment in Elderly and Parkinson's Disease Patients (PrévSim)

Immersive Virtual Reality Using a Head Mounted Display and Modelling Using Machine Learning Algorithms to Assess Risk of Falling in the Elderly and Patients With Parkinson's Disease.

The process of ageing affects at the same time the sensory, cognitive and driving functions. Furthermore, ageing is often accompanied by pathologies increasing the effects of the senescence. An ageing subject will have then more difficulties in maintaining balance control and will have a falling risk with sometimes critical consequences for the quality of life.

The risk of fall is estimated by tests at the same time of current life and with scores of sensitivity and specificity which must be improved. In a review including 25 studies (2 314 subjects), show a sensitivity of 32 % and a specificity of 73 % on the test "Timed Up and Go" (TUG) with a threshold at 13.5 seconds.

In addition, the fall occurs in a multifactorial context when a subject interacts with his environment. It therefore seems essential to test balance control or falling risk of individuals as close as possible to the situations of daily life. This research, based on the TUG, will aim to assess the neuro-psycho-motor behavior of subjects in situations close to daily life using a Virtual Reality (VR) and Human Metrology platform.

The results could ultimately lead to increased sensitivity and specificity in assessing the risk of falling with a TUG performed in VR, compared to the classic TUG, which is commonly used by healthcare professionals and thus allow for earlier or more appropriate management of the subject in preventing the risk of falling. This could allow healthcare professionals to better understand the risk of falling and thus guide medical recommendations and prescribing, particularly in terms of appropriate physical activity programs.

Study Overview

• Status: Unknown
• Conditions: Parkinson Disease; Aging Disorder
• Intervention / Treatment: Other: Metrology of motor behavior

• Study Type: Interventional
• Enrollment (Anticipated): 116
• Phase: Not Applicable

Contacts and Locations

Study Locations

• France
  • Vandœuvre-lès-Nancy, France, 54500
    • University Hospital of Nancy

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 65 years to 80 years (Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

Inclusion Criteria:

Non-faller elderly

• Male and female
• Age between 65 and 80 years old
• Autonomous
• Reporting no fall in the last 12 months

Fallers elderly

• Male and female
• Age between 65 and 80 years old
• Autonomous
• Reporting at least 1 fall in the last 12 months

Non-faller Patients with Parkinson's disease

• Male and female
• Age between 65 and 80 years old
• Autonomous
• Reporting no fall in the last 12 months
• Dopa-sensitive
• In ON period of treatment of Parkinson's disease

Exclusion Criteria:

• Hearing loss preventing understanding of the instructions and listening to the sound message
• Visual acuity not compatible with the test procedure in virtual reality
• Inability to move without assistance
• Not understanding written and oral French, illiteracy, dementia
• Treatment including psychotropic drugs
• Person in emergency situation,
• Major person subject to a legal protection measure (guardianship, curator, safeguard of justice),
• Major person unable to express his consent,
• Hospitalized person,
• Person deprived of liberty by a judicial or administrative decision, the persons being the object of psychiatric care by virtue of articles L. 3212-1 and L. 3213-1 of the french Code of Public Health,
• Person likely, in the opinion of the investigator, not to be cooperating or respectful of the obligations inherent to participation in the study
• Person with a predisposition to epilepsy

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Non falling elderly	Other: Metrology of motor behavior; Biomechanical, physiological, psychological and behavioral analyses
Experimental: Falling elderly	Other: Metrology of motor behavior; Biomechanical, physiological, psychological and behavioral analyses
Experimental: Non falling patients with Parkinson's disease	Other: Metrology of motor behavior; Biomechanical, physiological, psychological and behavioral analyses

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Timed Up & Go in virtual reality (VR)	Time	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Timed Up & Go (non VR condition)	Time	Baseline
Validation of the TUG in VR condition	Sensitivity and specificity of the TUG and TUG VR conditions	1 year follow-up
Correlation between TUG and TUG VR times and fall follow-up		1 year follow-up
Kinematics analysis	Measurement of full body motion (coordinates on x, y, z axis) in function of the time during the virtual reality tasks	Baseline
Kinetics analysis	Measurement of plantar pressure evolution (force in Newton) in function of the time during the virtual reality tasks	Baseline
Physiological analysis 1	Measurement of heart pace evolution (bpm) in function of the time during the virtual reality tasks	Baseline
Physiological analysis 2	Measurement of breathing evolution (frequence) in function of the time during the virtual reality tasks	Baseline
Physiological analysis 3	Measurement of galvanic skin response evolution (µSiemens) in function of the time during the virtual reality tasks	Baseline
Visual attention analysis	Measurement of the gaze focused on virtual objects parameters (number of gazed on each object and time spend focused on the said object)	Baseline
Psychology analysis 1	Measurement of the fear of falling (Fall Efficacy Scale-International from Tinetti with a score from 16 to 64)	Baseline
Psychology analysis 2	Measurement of the fear of falling (Activities specific Balance Confidence - Scale from Powell & Myers with a score from 0 to 45)	Baseline
Psychology analysis 3	Measurement of the coping strategies (Ways of Coping Checklist from Folkman & Lazarus with scores from 1 to 5 for the remembered stress situation subjective evaluation, a score from 10 to 40 for the Problem item, a score from 9 to 36 for the Emotion item and a score from 8 to 32 for the encourgament item).	Baseline
Automated learning and falling risk estimation	Supervised learning with Support Vector Machine, Decision tree, Linear discriminant. Using machine learning algorithms is not a measurement but data processing compiling all the data from measurement and comparing them to the number of fall during the year follow up. Machine learning algorithms will learn from these data to classify any new participant into a profile "with a low risk of fall", "with a high risk of fall" or "without a risk of fall".	up to 3 years

Collaborators and Investigators

• Sponsor: Central Hospital, Nancy, France
• Collaborators: University of Lorraine; OHS - Office d'Hygiène Sociale; ONPA - Office Nancéien des Personnes Agées

Study record dates

Study Major Dates

• Study Start (Anticipated): April 30, 2019
• Primary Completion (Anticipated): June 30, 2020
• Study Completion (Anticipated): June 30, 2022

Study Registration Dates

• First Submitted: January 30, 2019
• First Submitted That Met QC Criteria: February 19, 2019
• First Posted (Actual): February 21, 2019

Study Record Updates

• Last Update Posted (Actual): April 16, 2019
• Last Update Submitted That Met QC Criteria: April 12, 2019
• Last Verified: April 1, 2019

More Information

Terms related to this study

• Keywords: Ageing; Parkinson's disease; Virtual reality; Modeling; Human metrology; Motor behavior
• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Parkinsonian Disorders; Basal Ganglia Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases; Parkinson Disease
• Other Study ID Numbers: 2018-A02637-48

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

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