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Updated: Jun 23, 2026

Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Comparison of regression models for the analysis of fall risk factors in older veterans
Dawn P Gill1, Guang Yong Zou, Gareth R Jones
1Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada. dpgill2@u.washington.edu
Purpose:
To compare the performance of eight regression models for analyzing risk of falling, focusing on the effect of physical inactivity in older veterans.
Methods:
This study uses data from a fall risk factor screening and modification trial in community-dwelling Canadian male veterans of World War II or the Korean War, with falls ascertained prospectively using calendars and physical activity (PA) measured at baseline with a single global question. The effect of PA on falling was assessed using eight different multivariable regression models, with three models treating falling as a non-recurrent event whereas the other five models regard falls as recurrent events.
Results:
Recurrent event models showed that male veterans who reported being less active than their peers were 1.42 (1.02-1.97) to 2.46 (1.18-5.14) times more likely to fall than those who reported being about as or more active than their peers (n = 270; mean age +/- SD = 81.1 +/- 4.0 years). None of the non-recurrent event models detected a statistically significant association between PA and falls.
Conclusions:
Risk of falling may be better analyzed using regression models for recurrent events. These results have important implications for the collection and analysis of fall outcome data.
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