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

NCT ID: NCT06644859

Last Updated: 2024-10-16

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 17466 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-08-06
• Study Completion Date: 2030-08-31

Brief Summary

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.

Conditions

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

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

WHS

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

Daily Activity Patterns Using Wearable Tri-Axial Sensors

Intervention Type: DEVICE

This intervention uniquely focuses on the prediction of injurious falls by combining daily life gait (DLG) measures (e.g., gait speed, cadence, variability) with daily life physical activity (DLPA) measures (e.g., activity levels, activity fragmentation). Unlike other studies, this analysis leverages data from a large cohort of older women (n=17,466) enrolled in the Women's Health Study (WHS), where participants wore a tri-axial accelerometer for 1 week. Additionally, the study links accelerometer data to long-term health outcomes, specifically fall-related injuries from Centers for Medicare \& Medicaid Services (CMS) records. This is the first study to explore whether combining DLG and DLPA measures, derived from wearable technology, can predict fall-related injuries in an aging population, applying advanced machine learning techniques to this large, anonymized dataset.

Interventions

Daily Activity Patterns Using Wearable Tri-Axial Sensors

This intervention uniquely focuses on the prediction of injurious falls by combining daily life gait (DLG) measures (e.g., gait speed, cadence, variability) with daily life physical activity (DLPA) measures (e.g., activity levels, activity fragmentation). Unlike other studies, this analysis leverages data from a large cohort of older women (n=17,466) enrolled in the Women's Health Study (WHS), where participants wore a tri-axial accelerometer for 1 week. Additionally, the study links accelerometer data to long-term health outcomes, specifically fall-related injuries from Centers for Medicare \& Medicaid Services (CMS) records. This is the first study to explore whether combining DLG and DLPA measures, derived from wearable technology, can predict fall-related injuries in an aging population, applying advanced machine learning techniques to this large, anonymized dataset.

Intervention Type: DEVICE

Eligibility Criteria

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.

• Minimum Eligible Age: 45 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Tel-Aviv Sourasky Medical Center

OTHER_GOV

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Tel Aviv Medical Center

Tel Aviv, Israel, Israel

Countries

Israel

Other Identifiers

TLV-0054-24

Identifier Type: -

Identifier Source: org_study_id