Modelling Missteps to Improve Fall Risk Assessment.

Tel Aviv Sourasky Medical Center

Status: Enrolling

Conditions: Fall Patients

Treatments: Behavioral: Digital wearable system

Study type: Interventional

Funder types: Other

Identifiers

NCT04324333

TASMC-18-NG-0646-CTIL

Details and patient eligibility

About

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.

Full 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.

Enrollment: 200 estimated patients

Sex: All

Ages: 65 to 90 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Prevention

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

---

200 participants in 1 patient group

Using Digital wearable system for fall detection

Experimental group

Description:

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).

Treatment:

Behavioral: Digital wearable system