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)

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Women; Age ≥45; After Menopause or Without Intention of Pregnancy
• Intervention / Treatment: Device: Daily Activity Patterns Using Wearable Tri-Axial Sensors

• Study Type: Observational
• Enrollment (Actual): 17466

Contacts and Locations

Study Locations

• Israel
  • Tel Aviv, Israel
    • Tel Aviv Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

he study population consists of a large cohort of 17,466 older women enrolled in the Women's Health Study (WHS), a long-term observational study. These women were initially recruited between 1992 and 1995 for a randomized clinical trial of aspirin and vitamin E for the primary prevention of cardiovascular disease and cancer. The current analysis focuses on a subset of participants who, between 2011 and 2015, wore a tri-axial accelerometer during waking hours for one week to capture measures of daily life gait (DLG) and daily life physical activity (DLPA).

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Association of Gait Speed with Risk of Injurious Falls (AIM1)	The study will evaluate the association between gait speed (measured in meters per second) and the risk of injurious falls within one year following the accelerometer assessment.	njurious falls within 1 year after baseline assessment using time-to-event analyses.
Association of Cadence with Risk of Injurious Falls (AIM1)	The study will assess the association between cadence (measured in steps per minute) and the risk of injurious falls within one year following the accelerometer assessment.	Injurious falls within 1 year after baseline assessment using time-to-event analyses.
Association of Gait Variability with Risk of Injurious Falls (AIM1)	The study will assess the association between gait variability (measured as the standard deviation of step times) and the risk of injurious falls within one year following the accelerometer assessment.	Time Frame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.
Association of Overall Activity Levels with Risk of Injurious Falls (AIM2)	The study will evaluate the association between overall activity levels (measured in average accelerometer counts per minute) and the risk of injurious falls within one year following the baseline assessment.	Injurious falls within 1 year after baseline assessment using time-to-event analyses
Association of Activity Fragmentation with Risk of Injurious Falls (AIM2)	The study will assess the association between activity fragmentation (measured by the fragmentation index) and the risk of injurious falls within one year following the baseline assessment.	Injurious falls within 1 year after baseline assessment using time-to-event analyses.
Combined DLG and DLPA Measure for Predicting Risk of Injurious Falls (AIM3)	his outcome will evaluate a single combined score derived from both daily life gait (DLG) and daily life physical activity (DLPA) measures to assess the association with the risk of injurious falls. The combined score will be created incorporating DLG measures (e.g., gait speed, variability) and DLPA measures (e.g., overall activity levels, fragmentation) into a unified predictor.	Time Frame: Injurious falls within 1 year after baseline assessment, using combined predictive models.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Association of Self-Reported Exercise History with Gait Speed	This outcome will assess whether participants' self-reported exercise history is associated with gait speed (measured in meters per second) derived from accelerometer data.	Assessed at baseline (self-reported exercise history) and at the time of accelerometer data collection, with data analyzed within 1 year of the baseline assessment.
Association of Self-Reported Exercise History with Gait Variability	This outcome will evaluate whether participants' self-reported exercise history is associated with gait variability (measured as the standard deviation of step times) derived from accelerometer data.	Assessed at baseline (self-reported exercise history) and at the time of accelerometer data collection, with data analyzed within 1 year of the baseline assessment
Association of Self-Reported Exercise History with Overall Activity Levels	This outcome will assess whether participants' self-reported exercise history is associated with overall activity levels (measured in accelerometer counts per minute) derived from accelerometer data.	Assessed at baseline (self-reported exercise history) and at the time of accelerometer data collection, with data analyzed within 1 year of the baseline assessment
Association of Self-Reported Exercise History with Activity Fragmentation	his outcome will evaluate whether participants' self-reported exercise history is associated with activity fragmentation (measured by the fragmentation index) derived from accelerometer data.	Assessed at baseline (self-reported exercise history) and at the time of accelerometer data collection, with data analyzed within 1 year of the baseline assessment
Association of Gait Speed with Risk of Injurious Falls (Over 5 Years)	This outcome will assess whether gait speed (measured in meters per second) is associated with the risk of injurious falls over a 5-year follow-up period.	5 years after baseline.
Association of Gait Variability with Risk of Injurious Falls (Over 5 Years)	This outcome will assess whether gait variability (measured as the standard deviation of step times) is associated with the risk of injurious falls over a 5-year follow-up period.	5 years after baseline.
Association of Overall Activity Levels with Risk of Injurious Falls (Over 5 Years)	This outcome will evaluate whether overall activity levels (measured in accelerometer counts per minute) are associated with the risk of injurious falls over a 5-year follow-up period.	5 years after baseline.
Association of Activity Fragmentation with Risk of Injurious Falls (Over 5 Years)	This outcome will assess whether activity fragmentation (measured by the fragmentation index) is associated with the risk of injurious falls over a 5-year follow-up period.	5 years after baseline.

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Identification of High-Risk "Signatures" for Fall Prevention	Using machine learning and statistical techniques, the study will identify potential "signatures" combining DLG and DLPA measures to identify older adults at high risk of injurious falls. These signatures could inform early fall prevention strategies.	Based on 1-year, 5-year, and 10-year fall risk prediction models

Collaborators and Investigators

• Sponsor: Tel-Aviv Sourasky Medical Center

Study record dates

Study Major Dates

• Study Start (Actual): August 6, 2024
• Primary Completion (Estimated): August 1, 2030
• Study Completion (Estimated): August 1, 2030

Study Registration Dates

• First Submitted: October 6, 2024
• First Submitted That Met QC Criteria: October 14, 2024
• First Posted (Actual): October 16, 2024

Study Record Updates

• Last Update Posted (Actual): October 16, 2024
• Last Update Submitted That Met QC Criteria: October 14, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Keywords: gait; risk of injurious falls; gait measures
• Other Study ID Numbers: TLV-0054-24

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes