Title page for ETD etd-01222004-210953

Type of Document Master's Thesis
Author Bishop, Keith Allan
URN etd-01222004-210953
Title Predictor Variables Related To Falls In A Long-Term Care Environment
Degree Master of Science
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Lockhart, Thurmon E. Committee Chair
Kleiner, Brian M. Committee Member
Roberto, Karen A. Committee Member
  • fall risk assessment
  • risk factors
  • screening tests
  • older persons
  • nursing assessment/standards
  • aging
  • long-term care
Date of Defense 2003-07-30
Availability unrestricted
Although a great deal is known about the etiology of falls in elderly individuals, fall accidents continue to represent a significant burden to elders residing in long-term care facilities. It has been stated that 75% of deaths due to falls in the United States occur in the 13% of the population age 65 and over. The first objective of the study was to identify which fall-predictor variables acknowledged in the research literature are associated with increased fall frequency with the older population. Identifying specific predictor variables related to a high occurrence of falls in long-term care setting can assist in the redesign of tools and programs aimed to recognize fall risk, and prevent fall-related accidents and fatalities in the geriatric population. The second objective of the study was to identify which combination of predictor variables could better predict the frequency of falls.

A history of falls variable was the only predictive variable that differed significantly between groups of residents who had sustained subsequent falls and those who had not. Other variables including age, mental status, day number of stay, elimination, visual impairment, confinement, blood pressure drop, gait and balance, and medication were found to not be statistically significant between groups of fallers and non-fallers. In this setting, the current design of the tool had limited accuracy and exhibited an inability to effectively discriminate between resident populations at risk of falling and those not at risk of falling. Consequently, the current fall risk assessment tool is not adequate for assessing fall risk in this clinical setting.

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