Aging DisorderPrévSimNCT03848897
Contribution of Virtual Reality and Modelling in Falling Risk Assessment in Elderly and Parkinson's Disease Patients
A study testing Metrology of motor behavior in people with Aging Disorder and Parkinson Disease, run by Central Hospital, Nancy, France at 1 site in France.
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. Read more about this study
- Who is it for?
- Ages 65 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Aging Disorder, Parkinson Disease
- What is being tested?
- Metrology of motor behavior
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 116 participants
- Where
- 1 siteFrance
- Study dates
- 30 Apr 2019to 30 Jun 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 to 80 years
- All sexes
- Healthy volunteers accepted
- Older adult
Who may be able to join
- Non-faller elderly
- Male and female
- Age between 65 and 80 years old
- Autonomous
- Reporting no fall in the last 12 months
- Fallers elderly
Show all 17Show fewer
- 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
Who cannot join
- 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,
Show all 12Show fewer
- 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
Trial locations and contacts
1 site in 1 country.
France1 site
University Hospital of Nancy
Vandœuvre-lès-Nancy, 54500
Philippe Perrin, MD PhD Prof, 383154650, philippe.perrin@univ-lorraine.fr
About this study
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.
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.
Read more
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.
- Conditions
- Aging Disorder, Parkinson Disease
- Being tested
- Metrology of motor behavior
- Keywords
- Virtual reality, Human metrology, Motor behavior, Modeling, Ageing, Parkinson's disease
- Medical subject headings
- Parkinson Disease, Motor Activity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Metrology of motor behaviorOther
Biomechanical, physiological, psychological and behavioral analyses
| Study group | Role | What they receive |
|---|---|---|
| Non falling elderly | Experimental | Not described Receives: Other: Metrology of motor behavior |
| Falling elderly | Experimental | Not described Receives: Other: Metrology of motor behavior |
| Non falling patients with Parkinson's disease | Experimental | Not described Receives: Other: Metrology of motor behavior |
What is being measured
The primary outcome is the main question the study is designed to answer.
Timed Up & Go in virtual reality (VR)
Time
Measured: Baseline
Secondary outcomes (13)
Timed Up & Go (non VR condition)
Time
Measured: Baseline
Validation of the TUG in VR condition
Sensitivity and specificity of the TUG and TUG VR conditions
Measured: 1 year follow-up
Correlation between TUG and TUG VR times and fall follow-up
Measured: 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
Measured: Baseline
Kinetics analysis
Measurement of plantar pressure evolution (force in Newton) in function of the time during the virtual reality tasks
Measured: Baseline
Physiological analysis 1
Measurement of heart pace evolution (bpm) in function of the time during the virtual reality tasks
Measured: Baseline
Physiological analysis 2
Measurement of breathing evolution (frequence) in function of the time during the virtual reality tasks
Measured: Baseline
Physiological analysis 3
Measurement of galvanic skin response evolution (µSiemens) in function of the time during the virtual reality tasks
Measured: 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)
Measured: Baseline
Psychology analysis 1
Measurement of the fear of falling (Fall Efficacy Scale-International from Tinetti with a score from 16 to 64)
Measured: 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)
Measured: 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).
Measured: 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".
Measured: up to 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2019
- Study start30 Apr 2019 (estimated)
- Primary completion30 Jun 2020 (estimated)
- Study completion30 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Aging DisorderStudies of the same condition, open and under way.
- More trials run by Central Hospital, Nancy, FranceThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 116 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Central Hospital, Nancy, France
- Sponsor type
- University, hospital or other
- Collaborators
- OHS - Office d'Hygiène Sociale, ONPA - Office Nancéien des Personnes Agées, University of Lorraine
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03848897
- Sponsor's protocol ID
- 2018-A02637-48
- Acronym
- PrévSim
- Status last verified
- Apr 2019
- First submitted
- 30 Jan 2019
- Last updated
- 16 Apr 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Apr 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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.
- ClinicalTrials.gov ID
- NCT03848897
- Acronym / sponsor ID
- PrévSim / 2018-A02637-48
- Conditions
- Aging Disorder, Parkinson Disease
- Keywords
- Virtual reality, Human metrology, Motor behavior, Modeling, Ageing, Parkinson's disease
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 116 participants (estimated)
- Lead sponsor
- Central Hospital, Nancy, FranceUniversity, hospital or other
- Collaborators
- OHS - Office d'Hygiène Sociale, ONPA - Office Nancéien des Personnes Agées, University of Lorraine
- Study start
- 30 Apr 2019 (estimated)
- Primary completion
- 30 Jun 2020 (estimated)
- Study completion
- 30 Jun 2022 (estimated)
- First posted
- 21 Feb 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Prevention
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Non falling elderly | Experimental | Other: Metrology of motor behavior |
| Falling elderly | Experimental | Other: Metrology of motor behavior |
| Non falling patients with Parkinson's disease | Experimental | Other: Metrology of motor behavior |
Who may qualify
- Ages
- 65 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
Key inclusion criteria (17 listed)
- Non-faller elderly
- Male and female
- Age between 65 and 80 years old
- Autonomous
- Reporting no fall in the last 12 months
Key exclusion criteria (12 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Metrology of motor behaviorBiomechanical, physiological, psychological and behavioral analyses | Other | Falling elderly, Non falling elderly, Non falling patients with Parkinson's disease |
Primary outcome
Timed Up & Go in virtual reality (VR)
Time frame: Baseline
Time
13 secondary outcomes
Timed Up & Go (non VR condition)
Time frame: Baseline
Validation of the TUG in VR condition
Time frame: 1 year follow-up
Correlation between TUG and TUG VR times and fall follow-up
Time frame: 1 year follow-up
Kinematics analysis
Time frame: Baseline
Kinetics analysis
Time frame: Baseline
Physiological analysis 1
Time frame: Baseline
Physiological analysis 2
Time frame: Baseline
Physiological analysis 3
Time frame: Baseline
Visual attention analysis
Time frame: Baseline
Psychology analysis 1
Time frame: Baseline
Psychology analysis 2
Time frame: Baseline
Psychology analysis 3
Time frame: Baseline
Automated learning and falling risk estimation
Time frame: up to 3 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Apr 2019.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Metrology of motor behavior in people with Aging Disorder and Parkinson Disease, run by Central Hospital, Nancy, France at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 17 inclusion and 12 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 16 Apr 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Philippe Perrin, MD PhD Prof (+33383154650, philippe.perrin@univ-lorraine.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Timed Up & Go in virtual reality (VR) (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Apr 2019.