Data Analysis to Evaluate Which Specific Gait Measures Are Associated with Risk of Injurious Falls Evaluating Gait Measures Associated with the Risk of Injurious Falls Through Data Analysis (WHS)

Tel Aviv Sourasky Medical Center

Status: Active, not recruiting

Conditions: After Menopause or Without Intention of Pregnancy; Age ≥45; Women

Treatments: Device: Daily Activity Patterns Using Wearable Tri-Axial Sensors

Study type: Observational

Funder types: Other

Identifiers

NCT06644859

TLV-0054-24

Details and patient eligibility

About

The goal of this study is to understand if specific gait and activity measures can help predict injurious falls in older women. The main questions it aims to answer are:

Can combining daily gait (DLG) and daily physical activity (DLPA) measures more accurately predict the risk of injurious falls? How effective is wearable technology and machine learning in analyzing these activity measures for fall prediction? Researchers will analyze data from the Women's Health Study (WHS), using wearable technology to track daily walking patterns and physical activity, and apply machine learning to assess the likelihood of harmful falls.

Enrollment: 17,466 patients

Sex: Female

Ages: 45+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• after menopause or without intention of pregnancy

Exclusion criteria

• history of CHD, cerebrovascular disease, cancer (except non-melanoma skin cancer), or other serious illness;
• history of serious side effects to study treatments;
• taking aspirin, drugs containing aspirin, or non-steroidal anti-inflammatory drugs > once a week, or ready to give up the use of these drugs;
• taking anticoagulants or corticosteroids;
• Taking vitamin A, E or ß-carotene supplements > once a week.

Trial design

17,466 participants in 1 patient group

WHS

Description:

A large existing and anonymized dataset of older women enrolled in the Women's Health Study From 2011 to 2015, 17,466 women wore a triaxial accelerometer during waking hours for a week

Treatment:

Device: Daily Activity Patterns Using Wearable Tri-Axial Sensors