Clinical trial 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
Official title: Data Analysis to Evaluate Which Specific Gait Measures Are Associated with Risk of Injurious Falls
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Women
Age ≥45
After Menopause or Without Intention of Pregnancy
Keywords: gait
risk of injurious falls
gait measures
Lead sponsor: Tel-Aviv Sourasky Medical Center
Sponsor class: OTHER_GOV
First posted: 2024-10-16
Enrollment: 17466
Sex: FEMALE
Minimum age (years): 45
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
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).

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.


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 \&gt; 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 \&gt; once a week.

Primary outcomes:
measure: Association of Gait Speed with Risk of Injurious Falls (AIM1)
timeFrame: njurious falls within 1 year after baseline assessment using time-to-event analyses.
measure: Association of Cadence with Risk of Injurious Falls (AIM1)
timeFrame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.
measure: Association of Gait Variability with Risk of Injurious Falls (AIM1)
timeFrame: Time Frame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.
measure: Association of Overall Activity Levels with Risk of Injurious Falls (AIM2)
timeFrame: Injurious falls within 1 year after baseline assessment using time-to-event analyses
measure: Association of Activity Fragmentation with Risk of Injurious Falls (AIM2)
timeFrame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.
measure: Combined DLG and DLPA Measure for Predicting Risk of Injurious Falls (AIM3)
timeFrame: Time Frame: Injurious falls within 1 year after baseline assessment, using combined predictive models.
Secondary outcomes:
measure: Association of Self-Reported Exercise History with Gait Speed
timeFrame: 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.
measure: Association of Self-Reported Exercise History with Gait Variability
timeFrame: 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
measure: Association of Self-Reported Exercise History with Overall Activity Levels
timeFrame: 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
measure: Association of Self-Reported Exercise History with Activity Fragmentation
timeFrame: 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
measure: Association of Gait Speed with Risk of Injurious Falls (Over 5 Years)
timeFrame: 5 years after baseline.
measure: Association of Gait Variability with Risk of Injurious Falls (Over 5 Years)
timeFrame: 5 years after baseline.
measure: Association of Overall Activity Levels with Risk of Injurious Falls (Over 5 Years)
timeFrame: 5 years after baseline.
measure: Association of Activity Fragmentation with Risk of Injurious Falls (Over 5 Years)
timeFrame: 5 years after baseline.
Other outcomes:
measure: Identification of High-Risk "Signatures" for Fall Prevention
timeFrame: Based on 1-year, 5-year, and 10-year fall risk prediction models
Locations:
city: Tel Aviv
country: Israel
facility: Tel Aviv Medical Center
Study officials:
References: