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

Parkinson Disease Clinical Trial

— PrévSim  

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.

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT03848897
• Other study ID #: 2018-A02637-48
• Status: Not yet recruiting
• Phase: N/A
• Start date: April 30, 2019
• Est. completion date: June 30, 2022

Study information

• Verified date: April 2019
• Source: Central Hospital, Nancy, France
• Contact: Philippe Perrin, MD PhD Prof
• Phone: +33383154650
• Email: philippe.perrin[@]univ-lorraine.fr
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 116
• Est. completion date: June 30, 2022
• Est. primary completion date: June 30, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 65 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

• Aging Disorder
• Parkinson Disease

Intervention

Other:
• Metrology of motor behavior
Biomechanical, physiological, psychological and behavioral analyses

Locations

Country	Name	City	State
France	University Hospital of Nancy	Vandœuvre-lès-Nancy

Sponsors (4)

Lead Sponsor	Collaborator
Central Hospital, Nancy, France	OHS - Office d'Hygiène Sociale, ONPA - Office Nancéien des Personnes Agées, University of Lorraine

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Timed Up & Go in virtual reality (VR)	Time	Baseline
Secondary	Timed Up & Go (non VR condition)	Time	Baseline
Secondary	Validation of the TUG in VR condition	Sensitivity and specificity of the TUG and TUG VR conditions	1 year follow-up
Secondary	Correlation between TUG and TUG VR times and fall follow-up		1 year follow-up
Secondary	Kinematics analysis	Measurement of full body motion (coordinates on x, y, z axis) in function of the time during the virtual reality tasks	Baseline
Secondary	Kinetics analysis	Measurement of plantar pressure evolution (force in Newton) in function of the time during the virtual reality tasks	Baseline
Secondary	Physiological analysis 1	Measurement of heart pace evolution (bpm) in function of the time during the virtual reality tasks	Baseline
Secondary	Physiological analysis 2	Measurement of breathing evolution (frequence) in function of the time during the virtual reality tasks	Baseline
Secondary	Physiological analysis 3	Measurement of galvanic skin response evolution (µSiemens) in function of the time during the virtual reality tasks	Baseline
Secondary	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
Secondary	Psychology analysis 1	Measurement of the fear of falling (Fall Efficacy Scale-International from Tinetti with a score from 16 to 64)	Baseline
Secondary	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
Secondary	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
Secondary	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