Goals and significance: After standardized gait and balance assessments (SGB), elderly residents in Assisted Living Facilities (ALF) will have the velocity, direction and duration of their daytime movements automatically tracked by a movement tracking system (MTS) in common areas of congregate living settings for 12 months. Prospective and retrospective fall histories will be evaluated for their relationship to SGB and to a measure of movement variability called """"""""tortuosity,"""""""" derived from MTS data. Two goals are to determine if information generated by MTS can be a proxy for SGB and if predictions of a fall improve as changes in MTS occur closer in time to falls. The relevance to the call for application is that the MTS may provide a direct, automatic means to relate health and medication changes to falls. A third goal is to explore how best to present MTS information to administrators and health care providers. Methods: Location changes of ambulatory ALF residents within a monitored common living area will be measured to an accuracy of 20 cm using Ubisense Ultra Wideband radio transponders (MTS) during normal waking hours for 12 months. When in motion, resident locations are recorded every 0.43s, with respect to a fixed origin. The Mean Fractal D estimator measures the """"""""tortuosity"""""""" (deviation from a straight line of travel) of each episode. SGB measures include the variability of step and stride lengths and velocity of gait. Balance will be assessed by one and two legged stances and by requiring participants to walk while performing simultaneous tasks such as rhythmic head movements. The SGB measures selected are the most predictive of falls. In addition to history of falls, covariates include an assessment of subject cognitiabilities, medication use and limitations in function that may affect falls. Results and discussion: Multiple regression will be used to identify variance shared by SGB and path tortuosity derived from MTS. SGB and MTS measures will be evaluated for their ability to predict past and future falls recorded during the study interval. Within subject inter-day Fractal D variability will be numerically assessed using change point analyses and represented graphically. Preliminary studies indicate: 1) there will be at least one fall in approximately 50% of the participants over the 12 months, and 2) that tortuosity by fallers is greater prior to a fall in comparison to comparable non-fallers. Keywords: everyday movements, gait, balance, falls, falls risk, Ultrawideband location aware data, frail elderly.

Public Health Relevance

Relevance: This proposal is in response to funding opportunity: PAR-08-269 Exploratory and Developmental Grant to Improve Health Care Quality through Health Information Technology (IT) (R21). It is most relevant to the second of the three areas addressed in the call for proposals. It proposes a procedure for the automatic prediction of falls, one of the most expensive and deleterious events facing elderly Americans. Falls in the elderly cost the American economy in excess of $19 billion per year (Stevens, 2006), some of which is attributable to medication errors arising from transitions between formal and ambulatory care settings such as assisted living facilities which have few or no medically trained personnel. Our Health Information Technology approach combines readily available sensor technology and higher order mathematics to the description of movement variability in elderly persons for the purpose of better estimating short and long term risk of falls. Through better longitudinal monitoring of everyday movements, the short term adverse effects of medication changes and acute illnesses on fall risk may be improved.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HS018205-01
Application #
7773960
Study Section
Health Care Technology and Decision Science (HTDS)
Program Officer
Chaney, Kevin J
Project Start
2009-09-30
Project End
2011-09-29
Budget Start
2009-09-30
Budget End
2010-09-29
Support Year
1
Fiscal Year
2009
Total Cost
Indirect Cost
Name
University of South Florida
Department
Other Health Professions
Type
Organized Research Units
DUNS #
069687242
City
Tampa
State
FL
Country
United States
Zip Code
33612