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

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.

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.