An interventional study of Metrology of motor behavior in Aging Disorder and Parkinson Disease, sponsored by Central Hospital, Nancy, France. Status unknown at 1 site in France. Open to participants aged 65 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2019-04-16.
Sponsored by Central Hospital, Nancy, France · Not applicable, Interventional, and Prevention
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.
4,487 studies on the registry are indexed under Parkinson Disease; 1,082 are open to participants now.
This study's planned enrollment of 116 is above the median of 40 across 3,294 interventional studies indexed under Parkinson Disease.
Browse Parkinson Disease studies →Central Hospital, Nancy, France is the lead sponsor of 778 studies on the registry; 183 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Non-faller elderly
Fallers elderly
Non-faller Patients with Parkinson's disease
Exclusion Criteria:
Other: Metrology of motor behavior
Other: Metrology of motor behavior
Other: Metrology of motor behavior
Biomechanical, physiological, psychological and behavioral analyses
Timed Up & Go in virtual reality (VR)
Time
Time frame: Baseline
Timed Up & Go (non VR condition)
Time
Time frame: Baseline
Validation of the TUG in VR condition
Sensitivity and specificity of the TUG and TUG VR conditions
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
Measurement of full body motion (coordinates on x, y, z axis) in function of the time during the virtual reality tasks
Time frame: Baseline
Kinetics analysis
Measurement of plantar pressure evolution (force in Newton) in function of the time during the virtual reality tasks
Time frame: Baseline
Physiological analysis 1
Measurement of heart pace evolution (bpm) in function of the time during the virtual reality tasks
Time frame: Baseline
Physiological analysis 2
Measurement of breathing evolution (frequence) in function of the time during the virtual reality tasks
Time frame: Baseline
Physiological analysis 3
Measurement of galvanic skin response evolution (µSiemens) in function of the time during the virtual reality tasks
Time frame: 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)
Time frame: Baseline
Psychology analysis 1
Measurement of the fear of falling (Fall Efficacy Scale-International from Tinetti with a score from 16 to 64)
Time frame: 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)
Time frame: 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).
Time frame: 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".
Time frame: up to 3 years
Plan to share: No
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This study is status unknown, as verified in Apr 2019. You cannot join it, but the record below documents what was studied.
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Central Hospital, Nancy, France