title: A multifactorial falls risk prediction model for hospitalized older adults.
abstract: Ageing population worldwide has grown fast with more cases of chronic illnesses and co-morbidity, involving higher healthcare costs. Falls are one of the leading causes of unintentional injury-related deaths in older adults. The aim of this study was to develop a robust multifactorial model toward the falls risk prediction. The proposed model employs real-time vital signs, motion data, falls history and muscle strength. Moreover, it identifies high-risk individuals for the development falls in their activity of daily living (ADL). The falls risk prediction model has been tested at a controlled-environment in hospital with 30 patients and compared with the results from the Morse fall scale. The simulated results show the proposed algorithm achieved an accuracy of 98%, sensitivity of 96% and specificity of 100% among a total of 80 intentional falls and 40 ADLs. The ultimate aim of this study is to extend the application to elderly home care and monitoring.
authors.forename: Hamid
authors.initials: H
authors.lastname: GholamHosseini
authors.forename: Mirza Mansoor
authors.initials: MM
authors.lastname: Baig
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Connolly
authors.forename: Maria
authors.initials: M
authors.lastname: Lindén
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D012307:Risk Factors