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NCT Number: 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

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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This study is active but is not currently recruiting participants.

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Key information

Conditions

Women After Menopause or Without Intention of Pregnancy Age ≥45

Age range

45 year and older

Sex eligibility

Female

Study type

Observational

Primary location

Tel Aviv Medical Center

Tel Aviv, Israel

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

  1. Association of Gait Speed with Risk of Injurious Falls (AIM1)

    Time frame: njurious falls within 1 year after baseline assessment using time-to-event analyses.

    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.

  2. Association of Cadence with Risk of Injurious Falls (AIM1)

    Time frame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.

    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.

  3. Association of Gait Variability with Risk of Injurious Falls (AIM1)

    Time frame: Time Frame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.

    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.

  4. Association of Overall Activity Levels with Risk of Injurious Falls (AIM2)

    Time frame: Injurious falls within 1 year after baseline assessment using time-to-event analyses

    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.

  5. Association of Activity Fragmentation with Risk of Injurious Falls (AIM2)

    Time frame: Injurious falls within 1 year after baseline assessment using time-to-event analyses.

    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.

  6. Combined DLG and DLPA Measure for Predicting Risk of Injurious Falls (AIM3)

    Time frame: Time Frame: Injurious falls within 1 year after baseline assessment, using combined predictive models.

    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.

Secondary outcomes

  1. Association of Self-Reported Exercise History with Gait Speed

    Time frame: 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.

    This outcome will assess whether participants' self-reported exercise history is associated with gait speed (measured in meters per second) derived from accelerometer data.

  2. Association of Self-Reported Exercise History with Gait Variability

    Time frame: 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

    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.

  3. Association of Self-Reported Exercise History with Overall Activity Levels

    Time frame: 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

    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.

  4. Association of Self-Reported Exercise History with Activity Fragmentation

    Time frame: 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

    his outcome will evaluate whether participants' self-reported exercise history is associated with activity fragmentation (measured by the fragmentation index) derived from accelerometer data.

  5. Association of Gait Speed with Risk of Injurious Falls (Over 5 Years)

    Time frame: 5 years after baseline.

    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.

  6. Association of Gait Variability with Risk of Injurious Falls (Over 5 Years)

    Time frame: 5 years after baseline.

    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.

  7. Association of Overall Activity Levels with Risk of Injurious Falls (Over 5 Years)

    Time frame: 5 years after baseline.

    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.

  8. Association of Activity Fragmentation with Risk of Injurious Falls (Over 5 Years)

    Time frame: 5 years after baseline.

    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.

Other outcomes

  1. Identification of High-Risk "Signatures" for Fall Prevention

    Time frame: Based on 1-year, 5-year, and 10-year fall risk prediction models

    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.

Sponsors and collaborators

Lead sponsor

Tel-Aviv Sourasky Medical Center

Other Gov

Registry information

Official study title

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

Acronym: WHS

Important dates

Study start
2024
Primary completion
2030
Study completion
2030
First posted
Oct 16, 2024
Registry last updated
Oct 16, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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