Idiopathic Fallers
Conditions
Brief summary
The study is aimed to assess a new system for the automatic detection, quantification and treatment of Near Fall (NF) episodes in healthy older adults with a history of falls. The system is comprized of a treadmill and a virtual reality simulation which provides a motor-cognitive challenge to provoke NF. The challenges provided by the system are individualized and using machine learning algorithms will enable the identification and detection of NF under different conditions and allow for the most suitable treatment.
Interventions
DEVICEnew system to detect Near Falls
We will invite healthy older adults with a history of falls to try the new system and assess whether the new system can induce, detect and quantify Near Fall episodes
Sponsors
Tel-Aviv Sourasky Medical Center
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
60 Years to 85 Years
Healthy volunteers
Yes
Inclusion criteria
1. Healthy older adults with a history of falls or complaints of gait instability 2. Able to walk independently for at least 10 minutes
Exclusion criteria
1. Systemic chronic or acute pathologies: 1. Ischemic heart disease 2. Orthopedic or Rheumatic diseases 3. Severe vision problems 4. Neurological disease: PD, AD, CVA 2. Patients who underwent brain surgery in the last 6 months prior to the study
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| usability of the system to detect Near Falls | one year |
Secondary
| Measure | Time frame |
|---|---|
| Near Fall severity | one year |
Countries
Israel
Contacts
Primary ContactNir Giladi, MD
Backup ContactAnat Mirelman, PhD
Outcome results
None listed