NCT06644859

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

Data Analysis to Evaluate Which Specific Gait Measures Are Associated with Risk of Injurious Falls

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 17,466 (actual)
• Sponsor: Tel-Aviv Sourasky Medical Center · Other Government
• Sex: Female
• Age: 45 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
DEVICE	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.

Timeline

• Start date: 2024-08-06
• Primary completion: 2030-08-01
• Completion: 2030-08-01
• First posted: 2024-10-16
• Last updated: 2024-10-16

Locations

1 site across 1 country: Israel

Regulatory

• FDA-regulated device study