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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Daily Activity Patterns Using Wearable Tri-Axial Sensors | 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. |
| 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. |
| 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. |
| 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 |
Inclusion Criteria:
Exclusion Criteria:
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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).
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tel Aviv Medical Center | Tel Aviv | Israel | Israel |
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| 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. |
| 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. |