Aging Disorder, Parkinson Disease
Conditions
Keywords
Virtual reality, Human metrology, Motor behavior, Modeling, Ageing, Parkinson's disease
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.
Interventions
OTHERMetrology of motor behavior
Biomechanical, physiological, psychological and behavioral analyses
Sponsors
OHS - Office d'Hygiène Sociale
ONPA - Office Nancéien des Personnes Agées
University of Lorraine
Central Hospital, Nancy, France
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
65 Years to 80 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Timed Up & Go in virtual reality (VR) | Baseline | Time |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Validation of the TUG in VR condition | 1 year follow-up | Sensitivity and specificity of the TUG and TUG VR conditions |
| Correlation between TUG and TUG VR times and fall follow-up | 1 year follow-up | — |
| Kinematics analysis | Baseline | Measurement of full body motion (coordinates on x, y, z axis) in function of the time during the virtual reality tasks |
| Kinetics analysis | Baseline | Measurement of plantar pressure evolution (force in Newton) in function of the time during the virtual reality tasks |
| Physiological analysis 1 | Baseline | Measurement of heart pace evolution (bpm) in function of the time during the virtual reality tasks |
| Physiological analysis 2 | Baseline | Measurement of breathing evolution (frequence) in function of the time during the virtual reality tasks |
| Timed Up & Go (non VR condition) | Baseline | Time |
| Visual attention analysis | Baseline | Measurement of the gaze focused on virtual objects parameters (number of gazed on each object and time spend focused on the said object) |
| Psychology analysis 1 | Baseline | Measurement of the fear of falling (Fall Efficacy Scale-International from Tinetti with a score from 16 to 64) |
| Psychology analysis 2 | Baseline | Measurement of the fear of falling (Activities specific Balance Confidence - Scale from Powell & Myers with a score from 0 to 45) |
| Psychology analysis 3 | Baseline | 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). |
| Automated learning and falling risk estimation | up to 3 years | 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. |
| Physiological analysis 3 | Baseline | Measurement of galvanic skin response evolution (µSiemens) in function of the time during the virtual reality tasks |
Countries
France
Contacts
Primary ContactPhilippe Perrin, MD PhD Prof
Outcome results
None listed