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| Name | Class |
|---|---|
| Owlytics Healthcare | UNKNOWN |
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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.
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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Using Digital wearable system for fall detection | Experimental | 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). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Digital wearable system | Behavioral | Owlytics Healthcare's system is dedicated to improving the lives of all seniors by using the predictive power of data analytics. |
|
| 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 |
| 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 |
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Inclusion criteria:
Exclusion criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Marina Brozol, Ms | Contact | +972-3-6947513 | marinab@tlvmc.gov.il |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Tel Aviv Sourasky Medical Center | Recruiting | Tel Aviv | 64239 | Israel |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 11494184 | Background | Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001 Aug;82(8):1050-6. doi: 10.1053/apmr.2001.24893. | |
| 16719031 | Background | Mackenzie L, Byles J, D'Este C. Validation of self-reported fall events in intervention studies. Clin Rehabil. 2006 Apr;20(4):331-9. doi: 10.1191/0269215506cr947oa. |
| Label | URL |
|---|---|
| Owlytics Healthcare - Personalized Health Detection App | View source |
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Timed Up & Go test scores (will be compared to baseline performance).
| One week post monitoring period |