Prediction of Gait Speed by Spatiotemporal Parameters in Residential Aged Care Residents

Justin Keogh, Samantha Fien, Timothy Henwood, Mike Climstein

Research output: Contribution to journalMeeting AbstractResearchpeer-review

Abstract

The majority of older Australians, especially residential aged care (RAC) adults, have decreased physical activity, leading to poor physicalfunction such as reduced gait speed, strength, and balance (Peel et al., 2013). Specifically, individuals with slower gait speeds are at higher risk ofdisability, cognitive impairment, institutionalisation, falls, and mortality (Abellan Van Kan et al., 2009). The aim was to describe gait spatiotemporalcharacteristics and determine if the spatiotemporal gait parameters (e.g., step length, step rate) could predict gait speed in RAC adults. Methods: A totalof 100 older RAC adults (85.6 ± 6.7 years, range 66–99 years, 66 females) provided informed consent. Participants completed three trials of their habitualgait speed over the 3.66-m long Gaitmat II pressure mat system. The Gaitmat II allowed calculation of gait speed as well as many spatiotemporal gaitparameters including step length, stride length, support base, step time, swing time, stance time, single support time, and double support time. Thesespatiotemporal parameters were input into univariable and multivariable regression analyses to predict gait speed. Results: The multivariable linearregression involving all independent secondary spatiotemporal outcomes identified the following factors (stride length, support base, and step time) thatpredicted walking speed (r2 = .89). Stride length was the strongest predictor, with each 0.1 m increase in stride length resulting in an average 0.09 (95%CI 0.06–0.13) m/s faster preferred gait speed. Conclusion: While more research is required, preliminary evidence suggests that three spatiotemporalparameters (stride length, support base, and step time) predict gait speed in RAC residents. Therefore, interventions focusing on improving these threespatiotemporal parameters may increase gait speed in this population. References: Peel, N.M., Kuys, S.S., & Klein, K. (2013). Gait speed as a measurein geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci. 68(1):39–46. Abellan Van Kan, G., Rolland, Y., Andrieu,S., Bauer, J., Beauchet, O., Bonnefoy, M., et al. (2009). Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older peoplean international academy on nutrition and aging (IANA) task force. JNHA: Clinical Neurosciences. 13:881–889
Original languageEnglish
Pages (from-to)S36-S36
Number of pages1
JournalJournal of Aging and Physical Activity
Volume24
DOIs
Publication statusPublished - Jun 2016

Cite this

Keogh, Justin ; Fien, Samantha ; Henwood, Timothy ; Climstein, Mike. / Prediction of Gait Speed by Spatiotemporal Parameters in Residential Aged Care Residents. In: Journal of Aging and Physical Activity. 2016 ; Vol. 24. pp. S36-S36.
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abstract = "The majority of older Australians, especially residential aged care (RAC) adults, have decreased physical activity, leading to poor physicalfunction such as reduced gait speed, strength, and balance (Peel et al., 2013). Specifically, individuals with slower gait speeds are at higher risk ofdisability, cognitive impairment, institutionalisation, falls, and mortality (Abellan Van Kan et al., 2009). The aim was to describe gait spatiotemporalcharacteristics and determine if the spatiotemporal gait parameters (e.g., step length, step rate) could predict gait speed in RAC adults. Methods: A totalof 100 older RAC adults (85.6 ± 6.7 years, range 66–99 years, 66 females) provided informed consent. Participants completed three trials of their habitualgait speed over the 3.66-m long Gaitmat II pressure mat system. The Gaitmat II allowed calculation of gait speed as well as many spatiotemporal gaitparameters including step length, stride length, support base, step time, swing time, stance time, single support time, and double support time. Thesespatiotemporal parameters were input into univariable and multivariable regression analyses to predict gait speed. Results: The multivariable linearregression involving all independent secondary spatiotemporal outcomes identified the following factors (stride length, support base, and step time) thatpredicted walking speed (r2 = .89). Stride length was the strongest predictor, with each 0.1 m increase in stride length resulting in an average 0.09 (95{\%}CI 0.06–0.13) m/s faster preferred gait speed. Conclusion: While more research is required, preliminary evidence suggests that three spatiotemporalparameters (stride length, support base, and step time) predict gait speed in RAC residents. Therefore, interventions focusing on improving these threespatiotemporal parameters may increase gait speed in this population. References: Peel, N.M., Kuys, S.S., & Klein, K. (2013). Gait speed as a measurein geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci. 68(1):39–46. Abellan Van Kan, G., Rolland, Y., Andrieu,S., Bauer, J., Beauchet, O., Bonnefoy, M., et al. (2009). Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older peoplean international academy on nutrition and aging (IANA) task force. JNHA: Clinical Neurosciences. 13:881–889",
author = "Justin Keogh and Samantha Fien and Timothy Henwood and Mike Climstein",
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Prediction of Gait Speed by Spatiotemporal Parameters in Residential Aged Care Residents. / Keogh, Justin; Fien, Samantha; Henwood, Timothy; Climstein, Mike.

In: Journal of Aging and Physical Activity, Vol. 24, 06.2016, p. S36-S36.

Research output: Contribution to journalMeeting AbstractResearchpeer-review

TY - JOUR

T1 - Prediction of Gait Speed by Spatiotemporal Parameters in Residential Aged Care Residents

AU - Keogh, Justin

AU - Fien, Samantha

AU - Henwood, Timothy

AU - Climstein, Mike

PY - 2016/6

Y1 - 2016/6

N2 - The majority of older Australians, especially residential aged care (RAC) adults, have decreased physical activity, leading to poor physicalfunction such as reduced gait speed, strength, and balance (Peel et al., 2013). Specifically, individuals with slower gait speeds are at higher risk ofdisability, cognitive impairment, institutionalisation, falls, and mortality (Abellan Van Kan et al., 2009). The aim was to describe gait spatiotemporalcharacteristics and determine if the spatiotemporal gait parameters (e.g., step length, step rate) could predict gait speed in RAC adults. Methods: A totalof 100 older RAC adults (85.6 ± 6.7 years, range 66–99 years, 66 females) provided informed consent. Participants completed three trials of their habitualgait speed over the 3.66-m long Gaitmat II pressure mat system. The Gaitmat II allowed calculation of gait speed as well as many spatiotemporal gaitparameters including step length, stride length, support base, step time, swing time, stance time, single support time, and double support time. Thesespatiotemporal parameters were input into univariable and multivariable regression analyses to predict gait speed. Results: The multivariable linearregression involving all independent secondary spatiotemporal outcomes identified the following factors (stride length, support base, and step time) thatpredicted walking speed (r2 = .89). Stride length was the strongest predictor, with each 0.1 m increase in stride length resulting in an average 0.09 (95%CI 0.06–0.13) m/s faster preferred gait speed. Conclusion: While more research is required, preliminary evidence suggests that three spatiotemporalparameters (stride length, support base, and step time) predict gait speed in RAC residents. Therefore, interventions focusing on improving these threespatiotemporal parameters may increase gait speed in this population. References: Peel, N.M., Kuys, S.S., & Klein, K. (2013). Gait speed as a measurein geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci. 68(1):39–46. Abellan Van Kan, G., Rolland, Y., Andrieu,S., Bauer, J., Beauchet, O., Bonnefoy, M., et al. (2009). Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older peoplean international academy on nutrition and aging (IANA) task force. JNHA: Clinical Neurosciences. 13:881–889

AB - The majority of older Australians, especially residential aged care (RAC) adults, have decreased physical activity, leading to poor physicalfunction such as reduced gait speed, strength, and balance (Peel et al., 2013). Specifically, individuals with slower gait speeds are at higher risk ofdisability, cognitive impairment, institutionalisation, falls, and mortality (Abellan Van Kan et al., 2009). The aim was to describe gait spatiotemporalcharacteristics and determine if the spatiotemporal gait parameters (e.g., step length, step rate) could predict gait speed in RAC adults. Methods: A totalof 100 older RAC adults (85.6 ± 6.7 years, range 66–99 years, 66 females) provided informed consent. Participants completed three trials of their habitualgait speed over the 3.66-m long Gaitmat II pressure mat system. The Gaitmat II allowed calculation of gait speed as well as many spatiotemporal gaitparameters including step length, stride length, support base, step time, swing time, stance time, single support time, and double support time. Thesespatiotemporal parameters were input into univariable and multivariable regression analyses to predict gait speed. Results: The multivariable linearregression involving all independent secondary spatiotemporal outcomes identified the following factors (stride length, support base, and step time) thatpredicted walking speed (r2 = .89). Stride length was the strongest predictor, with each 0.1 m increase in stride length resulting in an average 0.09 (95%CI 0.06–0.13) m/s faster preferred gait speed. Conclusion: While more research is required, preliminary evidence suggests that three spatiotemporalparameters (stride length, support base, and step time) predict gait speed in RAC residents. Therefore, interventions focusing on improving these threespatiotemporal parameters may increase gait speed in this population. References: Peel, N.M., Kuys, S.S., & Klein, K. (2013). Gait speed as a measurein geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci. 68(1):39–46. Abellan Van Kan, G., Rolland, Y., Andrieu,S., Bauer, J., Beauchet, O., Bonnefoy, M., et al. (2009). Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older peoplean international academy on nutrition and aging (IANA) task force. JNHA: Clinical Neurosciences. 13:881–889

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DO - 10.1123/japa.24.s1.s48

M3 - Meeting Abstract

VL - 24

SP - S36-S36

JO - Journal of Aging and Physical Activity

JF - Journal of Aging and Physical Activity

SN - 1063-8652

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