Multiple sclerosis (MS) is the most common disabling neurological disease of young adults. People with MS fall frequently. These falls result in injuries and are associated with loss of confidence and independence, social isolation, curtailment of activities, increased risk for more falls, use of more healthcare services, and poorer quality of life. Given the high prevalence, serious consequences, and significant impact of falls in people with MS, interventions that reduce the risk of falls in this population are essential. In addition, one of the significant challenges in managing falls is having an accurate, efficient and user-friendly method for detecting and counting falls and determining the circumstances of falls that does not rely on patient recall and reporting. Studies suggest that group exercise and education programs may help prevent falls in people with MS but none have been evaluated in a randomized controlled trial. Comprehensive fall prevention programs with exercise and education prevent falls in older adults. The proposed study will evaluate, in a randomized controlled trial, the impact of a comprehensive group fall-prevention program called Free From Falls (FFF) that includes both exercise and education, on falls and their consequences in people with MS. Devices have recently been developed to automatically detect falls using body worn sensors with gyroscopes and accelerometers and to localize these falls with access points installed in the home and GPS transceivers outdoors. This study, where subjects are likely to fall frequently, provides the ideal opportunity for optimizing this technology. The goals of this research are to evaluate a comprehensive fall prevention program and a comprehensive automated fall detection system in people with MS. The objectives of this research are, in people with MS: First, to assess, in a randomized controlled trial, the impact of the FFF program on falls and fall-related injuries; second, to assess the impact of the FFF program on confidence, social satisfaction and participation, and quality of life; third, to determine if an automated fall detection system using accelerometers, gyroscopes and radio-frequency transceivers, accurately detects, counts and captures the location of falls. We will conduct a two group randomized trial at VA Portland Health Care System to compare the effectiveness of the FFF program to a stand of care control condition. FFF consists of 8 weekly 2-hour sessions with 1 hour of education and 1 hour of exercise. The control condition will consist of providing participants with a fall prevention educational brochure and informing their provider that they are falling frequently. The primary outcome will be prospectively counted falls and fall-related injuries during and for 6 months following the program and patient reported outcomes of balance confidence, social satisfaction and participation and quality of life at the beginning and end of the program and 3 and 6 months following the program. A subset of subjects in both intervention allocations will also use the MobileRF monitoring system, which includes a wrist worn sensor containing gyroscopes, accelerometers, a radio-frequency and a GPS transmitter and access points installed in the home, to automatically detect, count and localize falls in and outside the home. We expect to find a greater reduction in falls and fall related injuries and greater improvements in balance confidence, social satisfaction and participation, and quality of life in subjects randomized to th FFF program than in subjects randomized to the standard of care condition. In addition, the MobileRF system will accurately detect, count and localize falls compared with self report and will be more reliable and more immediate. The proposed work will definitively determine if the FFF program prevents falls in people with MS and will validate the MobileRF fall detection system while also being a first step in validating a comprehensive fall prevention and detection program for other Veterans at risk for falls.

Public Health Relevance

Multiple sclerosis (MS) is the most common progressive neurological disorder of young adults, affecting over 400,000 Americans and over 16,000 Veterans, approximately 6,000 of whom are service connected for MS. People with MS fall frequently, resulting in injuries and reduced balance confidence, social satisfaction, participation, and quality of life. Reducing the risk of flls in this population is essential. In addition, a significant challenge in managing falls is monitorig falls accurately and efficiently. The proposed study is a randomized controlled trial designed to definitively determine if a comprehensive fall prevention program that includes exercise and education is more effective than current standard of care in preventing falls and fall- related injuries and in improving balance confidence, social satisfaction and participation, and quality of life in people with MS. This study will also develop an accurate automated method for detecting, counting and localizing falls in and outside the home in people with MS.

National Institute of Health (NIH)
Veterans Affairs (VA)
Non-HHS Research Projects (I01)
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Portland VA Medical Center
United States
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Shastry, Mahesh C; Asgari, Meysam; Wan, Eric A et al. (2016) Context-aware fall detection using inertial sensors and time-of-flight transceivers. Conf Proc IEEE Eng Med Biol Soc 2016:570-573