Modelling Missteps to Improve Fall Risk Assessment.

Study Overview
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Brief Summary
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The long-term goals of the project are: 1) Preventing falls before they occur, by significantly improving our ability to monitor fall risk and develop early and sensitive markers for this risk, based on tripping and near falls and other physiological signs, 2) automatically diagnosing falls within seconds from the time of the incident, without the need for an emergency / distress button or making a phone call.

Detailed Description
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All subjects will be asked to come to the Center for the Study of Movement, Cognition and Mobility (CMCM), where they will undergo baseline testing. This initial evaluation is designed 1) to assess each subject's mobility, fall risk and related functions, and 2) to obtain more specific information that will be used to inform and update the model of falls and missteps detection. The study is divided into 3 sections: First session in Gait Lab (CMCM) for an overall assessment of subject health (see below). Using the system (monitoring ADL period) in daily life for 4 months (system: Owlytics Healthcare's app+wearable wristband & insoles). A concluding session where the mobility tests performed at the beginning of the study are repeated to assess the changes that occurred during the a period in which the monitoring system is used. During the first session medical data will be recorded, such as demographics (age, gender, years of education, etc.), habits (physical activity, leisure activities, dietary habits), daily life activities, health-related behaviors (e.g., alcohol consumption and smoking history) and so. Medical examination will include standardized walking tests (usual-walking and dual-task walking), eye examination, hearing test, balance tests, etc. In addition, to assess cognitive abilities standard Neuropsychological Battery will be used. At the end of the session, the participant will be asked to place a small accelerometer (AX6 - 6-Axis Logging Accelerometer) to measure daily activity for 7 days. The device will be attached to the lower back using a medical patch. The sensor is lightweight, non-invasive and does not endanger subject's health in any way. The second part of the study (or monitoring ADL period) - after the initial assessment, the research coordinator will instruct the subject to use the system. As mentioned, the system is given for 4 months. The participant will be requested to complete a fall log for tracking (via mail, e-mail, phone call or fax). If the system detects a fall or tripping event, one of the research team will contact the participant to verify the incident and get information about its circumstances (e.g., what the subject did at that time) and the consequences (e.g., does this require medical attention). Any health changes will also be documented during the follow-up period. Part Three - repeats the tests to assess the changes that occurred during the monitoring period.

Official Title
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Modelling Missteps to Improve Fall Risk Assessment.

Conditions
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Fall Patients

Intervention / Treatment
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* Behavioral: Digital wearable system


Participation Criteria
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Eligibility Criteria
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Inclusion criteria: 65-90 years old; Ambulatory without help from another person (with or without use of walking aid); Able to follow simple directions (Mini Mental State Examination score >21); Community-living or assisted living housing for elderly. Exclusion criteria: Subjects who will not be able to wear the devices for more than a 1 week period during the 4 months following their baseline evaluation (planning on traveling out of town, etc.); Patients who are not able to deal with the device and do not family member or therapist who is willing to help with the system; A state of health that does not allow participation in research and testing, or who has not agreed to participate in the study, or is unable to understand and follow simple instructions.

Ages Eligible for Study
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Minimum Age: 65 Years
Maximum Age: 90 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Using Digital wearable system for fall detection<br>Digital wearable system (Owlytics Healthcare's app) enables a 24/7 health-tracking service, collecting personal health data from wearable wristbands and insoles. The data is analyzed by machine-learning algorithms that can detect abnormal physiological patterns. This allows the prediction and prevention of potentially harmful health events (such as falls). | Behavioral: Digital wearable system<br>* Owlytics Healthcare's system is dedicated to improving the lives of all seniors by using the predictive power of data analytics.<br>* Other names: Digital wearable system (Owlytics Healthcare's app);|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Physical Activity | The International Physical Activity Questionnaires (IPAQ-short). It will quantify the health-related physical activity. | One week post monitoring period | 
| Community ambulation | A body-worn small lightweight device (6-Axis Logging Accelerometer) that will be worn by the subject for 7 days to monitor ADL. | One week post monitoring period | 
| The frequency of falling | The subjects are asked to fill in monthly Fall log (Frequency and circumstances of falls if occurred) | One week post monitoring period | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in endurance | This measure will be assessed using the 2 minute walk test. The distance walked during 2 minutes will be compared to baseline performance. | One week post monitoring period | 
| Improve in motor function | Timed Up & Go test scores (will be compared to baseline performance). | One week post monitoring period | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Falls, Falls Prevention in Older Age, Automated detection of missteps, Fall Risk