Clinical trial NCT03848897: Contribution of Virtual Reality and Modelling in Falling Risk Assessment in Elderly and Parkinson's Disease Patients
Official title: 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.
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Aging Disorder
Parkinson Disease
Keywords: Virtual reality
Human metrology
Motor behavior
Modeling
Ageing
Parkinson's disease
Lead sponsor: Central Hospital, Nancy, France
Sponsor class: OTHER
Collaborators: name: OHS - Office d'Hygiène Sociale
name: ONPA - Office Nancéien des Personnes Agées
name: University of Lorraine
First posted: 2019-02-21
Enrollment: 116
Sex: ALL
Minimum age (years): 65
Maximum age (years): 80
Standard ages: OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief summary: 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.


Eligibility criteria: 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

Primary outcomes:
measure: Timed Up & Go in virtual reality (VR)
timeFrame: Baseline
Secondary outcomes:
measure: Timed Up & Go (non VR condition)
timeFrame: Baseline
measure: Validation of the TUG in VR condition
timeFrame: 1 year follow-up
measure: Correlation between TUG and TUG VR times and fall follow-up
timeFrame: 1 year follow-up
measure: Kinematics analysis
timeFrame: Baseline
measure: Kinetics analysis
timeFrame: Baseline
measure: Physiological analysis 1
timeFrame: Baseline
measure: Physiological analysis 2
timeFrame: Baseline
measure: Physiological analysis 3
timeFrame: Baseline
measure: Visual attention analysis
timeFrame: Baseline
measure: Psychology analysis 1
timeFrame: Baseline
measure: Psychology analysis 2
timeFrame: Baseline
measure: Psychology analysis 3
timeFrame: Baseline
measure: Automated learning and falling risk estimation
timeFrame: up to 3 years
Other outcomes:
Locations:
city: Vandœuvre-lès-Nancy
country: France
facility: University Hospital of Nancy
Study officials:
References: