3 Clinical Trials for Various Conditions
Chronic stroke is the leading cause of long-term disability in the United States. Post-stroke health is negatively impacted by two interrelated factors-a substantial risk of falls and limited walking activity. The risk of falling is a barrier to walking activity, with falls self-efficacy mediating the relationship between impaired physical capacity and limited activity. The ability to recover from a fall (i.e. arrest a fall before impact) is a logical, yet untested rehabilitation target to enable walking activity through sustained benefits to falls self-efficacy. Our aim is to demonstrate that fall-recovery training is feasible in stroke survivors with low falls self-efficacy. Five participants will undergo an adapted version of fall-recovery training. We will gather evidence of the implementation, adaptation, and limited efficacy of this intervention in affecting falls self-efficacy and walking activity.
The objectives of this proposal are to evaluate the eSTEPS CDS (eSTEPS) in a cluster randomized controlled trial. The intervention includes the following: 1) A machine learning-based fall injury risk screening algorithm to improve traditional fall risk screening. 2) Provider BPA and/or Care Gap and Smart Set to provide CDS that helps primary care providers develop a tailored fall prevention exercise plan in the context of a Medicare Wellness Visit and 3) eSTEPS Patient App and exercise tools to provide older patients continued access to their interactive, tailored exercise plan.
This study evaluates the effectiveness of a web-based fall prevention program called Free From Falls Online (FFFO) on people with multiple sclerosis.