Effects of a novel perturbation balance training program on laboratory and real-world trip-induced losses of balance and falls among balance-impaired older adults
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The long-term goal of this project is to develop, validate, and implement an effective, practical, and low-cost balance training exercise that will reduce older adults’ fall risk. Falls are the leading cause of injuries and injury-related deaths among older adults over the age of 65 in the United States. General exercise and balance training has many health benefits but does not consistently reduce fall risk unless it sufficiently challenges balance and the ability to avoid falling after a loss of balance. Given that over 50% of falls among community-dwelling older adults are due to losses of balance after tripping, the ability to quickly react to a trip with an effective balance recovery response is critical to reduce these falls. Perturbation balance training is an emerging and promising fall prevention paradigm that aims to improve this balance recovery ability by repeatedly exposing individuals to simulated trips in a safe, controlled manner. However, current state-of-the-art of this training requires a resource intensive specialized treadmill that presents a major barrier to wider adoption. To address this barrier, the so-called step training under development here is an innovative form of perturbation balance training that eliminates the need for a specialized treadmill. In a randomized controlled trial, we will compare the effects of three interventions: the step training under development here, traditional perturbation balance training requiring a specialized treadmill, and an active control group receiving an exercise intervention shown to reduce fall risk. Another key and innovative aspect of this trial is our use of wearable sensors to study how balance improvements from the different perturbation balance training paradigms carryover to real-world losses of balance and fall reduction. No studies to date have investigated these details to clarify the mechanisms by which perturbation training reduces real-world fall risk. Lastly, the proposed work will develop a novel statistical analysis that simultaneously accounts for real-world losses of balance, balance recoveries, and falls and the risk factors associated with each to maximize insight from these data. Overall, the results of this project will 1) evaluate a new low-technology version of PBT that has greater potential for wider scalability; 2) expand the evaluation of PBT to include real- world losses of balance and their underlying mechanisms and risk factors.
