WomenWHSNCT06644859
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
An observational study of Women and Age ≥45, run by Tel-Aviv Sourasky Medical Center at 1 site in Israel.
Trial 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… Read more about this study
- Who is it for?
- Ages 45 years and older, women only
- Conditions the study is for
- Women, Age ≥45, After Menopause or Without Intention of Pregnancy
- What is being tested?
- Daily Activity Patterns Using Wearable Tri-Axial Sensors
- Type of treatment
- Medical device
- How many people
- 17,466 participants
- Where
- 1 siteIsrael
- Study dates
- 6 Aug 2024to Aug 2030
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 45 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
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).
Who may be able to join
- after menopause or without intention of pregnancy
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
- Being tested
- Daily Activity Patterns Using Wearable Tri-Axial Sensors
- Keywords
- gait, risk of injurious falls, gait measures
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Daily Activity Patterns Using Wearable Tri-Axial SensorsDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Device: Daily Activity Patterns Using Wearable Tri-Axial Sensors |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Time Frame: Injurious falls within 1 year after baseline assessment, using combined predictive models.
Secondary outcomes (8)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 5 years after baseline.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Oct 2024
- Study start6 Aug 2024
- Primary completion1 Aug 2030 (estimated)
- Study completionAug 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for WomenStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 17,466 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tel-Aviv Sourasky Medical Center
- Sponsor type
- Other government
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06644859
- Sponsor's protocol ID
- TLV-0054-24
- Acronym
- WHS
- Status last verified
- Oct 2024
- First submitted
- 6 Oct 2024
- Last updated
- 16 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Data Analysis to Evaluate Which Specific Gait Measures Are Associated with Risk of Injurious Falls
- ClinicalTrials.gov ID
- NCT06644859
- Acronym / sponsor ID
- WHS / TLV-0054-24
- Keywords
- gait, risk of injurious falls, gait measures
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 17,466 participants (actual)
- Lead sponsor
- Tel-Aviv Sourasky Medical CenterOther government
- Study start
- 6 Aug 2024 (actual)
- Primary completion
- 1 Aug 2030 (estimated)
- Study completion
- Aug 2030 (estimated)
- First posted
- 16 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| WHS | – | Device: Daily Activity Patterns Using Wearable Tri-Axial SensorsA 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 |
Who may qualify
- Ages
- 45 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (1 listed)
- after menopause or without intention of pregnancy
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Daily Activity Patterns Using Wearable Tri-Axial SensorsThis 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. | Device | WHS |
Primary outcome
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.
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.
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.
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.
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.
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.
8 secondary outcomes
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.
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
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
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
Association of Gait Speed with Risk of Injurious Falls (Over 5 Years)
Time frame: 5 years after baseline.
Association of Gait Variability with Risk of Injurious Falls (Over 5 Years)
Time frame: 5 years after baseline.
Association of Overall Activity Levels with Risk of Injurious Falls (Over 5 Years)
Time frame: 5 years after baseline.
Association of Activity Fragmentation with Risk of Injurious Falls (Over 5 Years)
Time frame: 5 years after baseline.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Oct 2024.
- Individual participant dataSharing planned: No
Related trials for Women
Not yet recruitingPhase 1
Study of ACC-1898 in Adult Participants With Advanced Solid Tumors
Hepatocellular Carcinoma, Renal Cell Carcinoma
Recruiting
Supervised Endoscopic Tele-controlled Intelligent Lithotripsy
Renal Stone
Not yet recruiting
MYTP01 for Immune-Mediated Diseases
IgA Nephropathy, Kidney Transplant Recipients
Active, not recruitingPhase 2
Study of Senaparib in Combination With Temozolomide in ARID1A Mutation Associated Ovarian Cancer
Ovary Cancer, Fallopian Tube Cancer
Recruiting
Methylation Profiling in Self-samples for Identification of (Epi)Genetic Biomarkers for Gynecological Cancers
Gynaecological Cancers, Ovarian Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Women and Age ≥45, run by Tel-Aviv Sourasky Medical Center at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 16 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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 lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Oct 2024.