Modelling Missteps to Improve Fall Risk Assessment.

NCT ID: NCT04324333

Last Updated: 2020-03-31

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 200 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-09-09
• Study Completion Date: 2024-12-31

Brief Summary

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

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:

1. First session in Gait Lab (CMCM) for an overall assessment of subject health (see below).

2. Using the system ("monitoring ADL period") in daily life for 4 months (system: Owlytics Healthcare's app+wearable wristband & insoles).

3. 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.

Conditions

Fall Patients

Keywords

Falls; Falls Prevention in Older Age; Automated detection of missteps; Fall Risk

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

Using Digital wearable system for fall detection

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).

Group Type: EXPERIMENTAL

Digital wearable system

Intervention Type: BEHAVIORAL

Owlytics Healthcare's system is dedicated to improving the lives of all seniors by using the predictive power of data analytics.

Interventions

Digital wearable system

Owlytics Healthcare's system is dedicated to improving the lives of all seniors by using the predictive power of data analytics.

Intervention Type: BEHAVIORAL

Other Intervention Names

Digital wearable system (Owlytics Healthcare's app)

Eligibility Criteria

Inclusion Criteria

1. 65-90 years old;

2. Ambulatory without help from another person (with or without use of walking aid);

3. Able to follow simple directions (Mini Mental State Examination score >21);

4. Community-living or assisted living housing for elderly.

Exclusion Criteria

1. 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.);

2. 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;

3. 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.

• Minimum Eligible Age: 65 Years
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Owlytics Healthcare

UNKNOWN

Sponsor Role: collaborator

Tel-Aviv Sourasky Medical Center

OTHER_GOV

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

The Tel Aviv Sourasky Medical Center

Tel Aviv, , Israel

Site Status: RECRUITING

Countries

Israel

Central Contacts

Marina Brozol, Ms

Role: CONTACT

Phone: +972-3-6947513

Email: marinab@tlvmc.gov.il

Facility Contacts

Jeffrey M Hausdorff, Prof.

Role: primary

Marina Brozgol

Role: backup

References

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.

Reference Type: BACKGROUND

PMID: 11494184 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 16719031 (View on PubMed)

Related Links

https://www.owlytics.com/

Owlytics Healthcare - Personalized Health Detection App

Other Identifiers

TASMC-18-NG-0646-CTIL

Identifier Type: -

Identifier Source: org_study_id