1. Gait Speed and the Spatio-Temporal Determinants of Residents in Nursing Homes
Samantha Fien1, Timothy Henwood2,1, Mike Climstein3,1, Justin Keogh1,4,5
1 Bond University, Robina, Australia
2 Southern Cross Care, South Australia, Australia
3 University of Sydney, Sydney, Australia
4University of the Sunshine Coast, Sippy Downs, Australia
5AUT University, Auckland, New Zealand
Background:
Chronic diseases are the leading cause of illness, disability and death in Australia, accounting for 90% of all deaths in 2011 (1). One of the most common behavioural risk factors for older Australians is physical inactivity, especially nursing home residents whom perform little physical activity. This contributes to their poor physical function and performance such as reduced gait speed, strength and balance (2). Currently, little is known in relation to the physical determinants or risk factors for low gait speed in low-functioning older adults and those living in nursing home facilities. The aim of this study was to objectively quantify the gait speed and spatio-temporal determinants of gait among nursing home residents and to investigate if the spatio-temporal determinants may predict their gait speed.
Conclusion:
Preliminary evidence suggests that three spatio-temporal parameters (stride length, support base and step time) predict gait speed in nursing home residents. A greater understanding of nursing home residents’ gait spatio-temporal determinants may assist allied health professionals develop more effective exercise prescriptions for enhancing gait speed in this population. As well as enhance the ability of clinicians to supply task relevant augmented feedback during the exercise sessions to their patients (3). By improving their gait speed, nursing home residents may become more physically active, which may improve their overall health, quality of life and minimise the progression of chronic disease.
Methods:
A total of 100 nursing home residents (85.6  6.7 years, range years, 66 females) provided informed consent. Participants completed three trials of their gait speed over the 3.66 m long GaitMat II pressure mat system. The GaitMat II allowed calculation of gait speed as well as many spatio-temporal gait parameters (such as step length, support base and step time), which were inputted into univariable and multivariable regression analyses to predict gait speed.
Figure 1: GaitMat II foot contacts of a 85 year old male.
Results:
The nursing home residents were characterised by a mean habitual gait speed of 0.63  0.19 m/s, stride length of 0.83  0.15 m/s, support base of 0.15  0.06 m/s and step time of 0.66  0.12 s. Multivariable linear regression revealed stride length, support base and step time predicted gait speed (R2 = 0.89, p<0.05). Step time contributed to the largest change in gait speed with each 0.1 s decrease resulting in a 0.09 m/s (95% CI 0.08 – 0.10) faster habitual gait speed.
References:
1. AIHW. Key indicators of progress for chronic disease and associated determinants: data report. Cat. No. PHE 142. Canberra: AIHW.
2. Peel, N. Kuys, S. S., & Klein, K. (2013). Gait speed as a measure in geriatric assessment in clinical settings: a systematic review. Journal of Gerontology Series A: Biological Sciences and Medical Sciences, 68(1), doi: /gerona/gls174
3. Sterke, C. S., van Beeck, E. F., Loonman, C. W. N., Kressig, R. W., & van der Cammen, T. J. M. (2012). An electronic walkway can predict short-term fall risk in nursing home residents with dementia. Gait and Posture, 36,