The risk of falling increases with age, occurring each year in approximately 28-35% of people over 65 years of age and increasing to 32-42% for people 70 years and over. Finding effective methods capable of detecting a manifestation of impaired balance when there are no apparent signs of instability can thus improve the quality of life of billions of people, reducing their fall risk, helping them prevent injuries, and maintain independence. One of the fundamental limitations is that postural instability is rarely identified before a fall occurs, and only then does a patient receive appropriate attention for improving balance. Such an advancement would help to limit falls by indicating a need for preventative interventions such as rehabilitation or the use of assistive technologies. The long-term goal of this work is twofold. The first is to determine if unique instability predicted by mathematical models of human balance when neuromuscular control changes exist experimentally. The second is to determine if subtle manipulation in virtual reality can be used to detect balance impairment and the mechanisms behind such deficiencies. These goals are tested in two Specific Aims.
In Specific Aim 1, we will determine the perception of magnified and delayed postural deviations through virtual reality will lead to a unique oscillatory instability for healthy participants with no previous signs of postural instability. This will provide an understanding of the relationship increased neuromuscular feedback gain and delay have on postural instability, allowing insight into the unique instability encounter through these mechanisms.
In Specific Aim 2, we will determine if the perception of attenuated postural deviations through virtual reality will lead to a leaning instability for healthy participants with no previous signs of postural instability. This will provide an understanding of the relationship decrease neuromuscular feedback gain have on postural instability, allowing insight into the unique instability encountered through this mechanism. Investigation of these Specific Aims will allow us to determine if the amount of manipulation to the perception of postural deviation in virtual reality to induce instability correlates with clinical assessment methods. The underlying hypothesis of this Specific Aim is that individuals with a low fall-risk will require the largest amount of manipulation in VR with the opposite being true for individuals with a high fall-risk and will directly correlate with fall-risk as assessed by the Berg Balance Scale and the Timed Up and Go test. This proposed work will lead to an increased fundamental understanding of the mechanisms responsible for postural instability and their relationship to factors such as age, sex, height, weight, fall-risk, fall history, and fear of falling. An additional benefit is the development of an object method for assessing balance and provides critical insights into deficient mechanisms of postural control, a significant advancement towards improved methods of fall prevention. Identifying an individual's proximity to postural instability could lead to personalized training, more efficiently addressing their specific needs. The development of virtual reality strategies to assess and rehabilitate postural instability could help to reduce the substantial economic cost associated with falls.
The proposal will lead to a fundamental understanding of the underlying control of upright balance and the mechanisms leading to different types of instabilities through the aging process. The proposal will develop a new innovative method for assessing fall-risk with an ability to identify the specific mechanism(s) responsible for balance impairment, a critical need for the appropriate rehabilitation of balance control that is currently lacking in the field. This will motivate the development of new personalized training and rehabilitative methods for improving balance control by targeting an individual's specific needs and manipulating the virtual environment to create accurate and efficient recovery.
