Ignite Creation Date:
2025-12-24 @ 2:45 PM
Ignite Modification Date:
2026-01-01 @ 12:04 AM
Study NCT ID:
NCT06644859
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-10-16
First Post:
2024-10-06
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
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
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 17466}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2024-08-06', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-10', 'completionDateStruct': {'date': '2030-08', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-10-14', 'studyFirstSubmitDate': '2024-10-06', 'studyFirstSubmitQcDate': '2024-10-14', 'lastUpdatePostDateStruct': {'date': '2024-10-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-10-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2030-08-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Identification of High-Risk "Signatures" for Fall Prevention', 'timeFrame': 'Based on 1-year, 5-year, and 10-year fall risk prediction models', 'description': '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.'}], 'primaryOutcomes': [{'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.', 'description': '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.'}, {'measure': 'Association of Cadence with Risk of Injurious Falls (AIM1)', 'timeFrame': 'Injurious falls within 1 year after baseline assessment using time-to-event analyses.', 'description': '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.'}, {'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.', 'description': '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.'}, {'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', 'description': '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.'}, {'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.', 'description': '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.'}, {'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.', 'description': '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.'}], 'secondaryOutcomes': [{'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.', 'description': "This outcome will assess whether participants' self-reported exercise history is associated with gait speed (measured in meters per second) derived from accelerometer data."}, {'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', 'description': "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."}, {'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', 'description': "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."}, {'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', 'description': "his outcome will evaluate whether participants' self-reported exercise history is associated with activity fragmentation (measured by the fragmentation index) derived from accelerometer data."}, {'measure': 'Association of Gait Speed with Risk of Injurious Falls (Over 5 Years)', 'timeFrame': '5 years after baseline.', 'description': '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.'}, {'measure': 'Association of Gait Variability with Risk of Injurious Falls (Over 5 Years)', 'timeFrame': '5 years after baseline.', 'description': '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.'}, {'measure': 'Association of Overall Activity Levels with Risk of Injurious Falls (Over 5 Years)', 'timeFrame': '5 years after baseline.', 'description': '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.'}, {'measure': 'Association of Activity Fragmentation with Risk of Injurious Falls (Over 5 Years)', 'timeFrame': '5 years after baseline.', 'description': '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.'}]}, 'oversightModule': {'isUsExport': True, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['gait', 'risk of injurious falls', 'gait measures'], 'conditions': ['Women', 'Age ≥45', 'After Menopause or Without Intention of Pregnancy']}, 'descriptionModule': {'briefSummary': "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:\n\nCan 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."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '45 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': "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).", 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* after menopause or without intention of pregnancy\n\nExclusion Criteria:\n\n* history of CHD, cerebrovascular disease, cancer (except non-melanoma skin cancer), or other serious illness;\n* history of serious side effects to study treatments;\n* taking aspirin, drugs containing aspirin, or non-steroidal anti-inflammatory drugs \\> once a week, or ready to give up the use of these drugs;\n* taking anticoagulants or corticosteroids;\n* Taking vitamin A, E or ß-carotene supplements \\> once a week.'}, 'identificationModule': {'nctId': 'NCT06644859', 'acronym': 'WHS', 'briefTitle': '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', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Tel-Aviv Sourasky Medical Center'}, 'officialTitle': 'Data Analysis to Evaluate Which Specific Gait Measures Are Associated with Risk of Injurious Falls', 'orgStudyIdInfo': {'id': 'TLV-0054-24'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'WHS', 'description': "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", 'interventionNames': ['Device: Daily Activity Patterns Using Wearable Tri-Axial Sensors']}], 'interventions': [{'name': 'Daily Activity Patterns Using Wearable Tri-Axial Sensors', 'type': 'DEVICE', 'description': "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.", 'armGroupLabels': ['WHS']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Tel Aviv', 'state': 'Israel', 'country': 'Israel', 'facility': 'Tel Aviv Medical Center', 'geoPoint': {'lat': 32.08088, 'lon': 34.78057}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tel-Aviv Sourasky Medical Center', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'SPONSOR'}}}}