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Gait Speed Characteristics and Their Spatiotemporal Determinants in Nursing Home Residents: A Cross-Sectional Study

Fien, Samantha BExSc1; Henwood, Timothy PhD1,2; Climstein, Mike PhD3,4; Rathbone, Evelyne MSc1; Keogh, Justin William Leslie PhD1,5,6

Author Information

1Faculty of Health Science and Medicine, Bond University, Robina, Australia.

2Southern Cross Care, North Plympton, Australia.

3Exercise, Health and Performance Faculty Research Group, The University of Sydney, Sydney, Australia.

4Water-Based Research Unit, Faculty of Health Sciences, Bond University, Gold Coast, Australia.

5Human Potential Centre, AUT University, Auckland, New Zealand.

6Cluster for Health Improvement, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs, Australia.

Address correspondence to: Samantha Fien, BExSc, Faculty of Health Science and Medicine, Bond University, Robina, QLD 4229, Australia ([email protected]).

The authors declare no conflicts of interest.Bill Andrews was the Decision Editor.

Journal of Geriatric Physical Therapy: July/September 2019 - Volume 42 - Issue 3 - p E148-E154
doi: 10.1519/JPT.0000000000000160
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Abstract

INTRODUCTION

Walking is a key physical performance task for people of all ages, including older adults. The majority of older adults, especially those living in nursing home (residential aged care) settings, have decreased physical activity1 and poor physical function as indicated by their reduced gait speed, muscle strength, and balance.2 Older adults with slower gait speeds are at higher risk of disability, cognitive impairment, institutionalization, falls, and mortality.3 While a variety of gait speed thresholds exist, healthy community-dwelling older adults tend to experience poorer health when their habitual gait speed is less than 0.8 m/s,3,4 whereas for nursing home residents older than 80 years, a threshold of less than 0.5 m/s has been proposed.4,5 A recent systematic review, which included 34 studies quantifying the gait speed of residents living in nursing homes, reported a mean habitual pace gait speed of 0.48 m/s (95% confidence interval [CI], 0.40-0.55).6 Low gait speeds suggest that most nursing home residents are limited in mobility and independence, have decreased stability, and are at increased risk for many other age-related conditions.6

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. While McGough et al7 and Keogh et al8 have reported that measures of physical function, balance, lifetime physical activity levels, and sitting time correlate with gait speed in these less functioning older cohorts, no studies have quantified the spatiotemporal determinants (eg, step length and step rate) that determine gait speed in a nursing home population.9Figure 1 presents a pictorial representation of the relationship between selected spatiotemporal parameters and gait speed. When compared with younger adults, older adults walk more slowly; have a shorter step length and a broader support base than their younger counterparts, with these differences more pronounced in older adults living in a nursing home and/or with a high risk of falls.10–13

F1
Figure 1.:
Deterministic model of gait speed outlining the direct relationships between the spatiotemporal determinants and gait speed.

A greater understanding of nursing home residents' gait speed and spatiotemporal determinants may assist health professionals to identify nursing home residents at high risk of adverse events and allow for a more specific-tailored physical therapy and rehabilitation program for each individual.11 We would argue this is very important, as indicated by a systematic review that found exercise-related improvements in older adults' gait speed are typically smaller in magnitude and more variable than the improvements in muscular strength.14 Specifically, semiregular monitoring of gait speed and spatiotemporal parameters could assist appropriate health professionals to prescribe resistance, balance, and gait exercises that target their clients' major spatiotemporal limitations. The collection of these gait spatiotemporal data may also allow the exercise therapist to provide their client task-relevant augmented feedback (eg, visual cues, instant, or delayed feedback) during these exercises to improve the transfer of training to their habitual walking performance.15,16

This study's aims were to objectively quantify the spatiotemporal determinants and gait speed of nursing home residents and to gain some insight into whether these spatiotemporal parameters may predict their gait speed.

METHODS

Recruitment and Study Design

The study employed a cross-sectional design, with data collected over an 8-month period across 3 nursing home facilities in South East Queensland, Australia. The facilities were from different providers that were either part of a small chain of nursing homes or a not-for-profit organization. The flow of recruitment to assessment is represented in Figure 2. Facilities were approached via e-mail and telephone follow-up seeking an expression of interest for participation. Following an expression of interest, nursing homes were visited and the study explained to the service manager. Once the service manager approved the participation of their nursing home in the project, eligible participants were identified at a meeting between the project lead researcher and the service manager, head registered nurse and lifestyle coordinator. Ethical approval for this study was attained from the University Human Ethics Research Committee (RO 1823) and gatekeeper's approval obtained through the nursing homes.

F2
Figure 2.:
Consort flow diagram of the recruitment process within the nursing home facilities.

Based on study's eligibility, participants were eligible for inclusion if they were (a) 65 years and older, (b) residing in a nursing home facility, (c) ambulate independently with or without a walking aid, and (d) could provide informed consent. The exclusion criteria included (a) end-stage terminal and/or life expectancy of less than 6 months (ethical reasons), (b) 2-person transfer or increased fall risk during ambulation (as assessed by the nursing home staff), (c) unable to communicate or follow instructions (personal needs beyond the scope of this project), and (d) behaviors that would endanger the participant or research staff.

All participants were approached personally about participation and given the opportunity to ask questions or raise concerns about the study. Following this discussion and reading of the participant information sheet, participants provided their informed consent if they wished to participate. A total of 100 participants took part in the study, with the primary investigator responsible for observing and administering all of the assessments.

Primary Outcome Measures: Gait Speed

Gait speed was assessed using a computer-interfaced electronic system (model GaitMat II, EQInc), which required participants to walk across a level pressure mat system that was 3.66-m (11.91 ft) long.17 The concurrent validity of the spatiotemporal determinants of gait recorded with the GaitMat II is extremely high when compared with the criterion method of 3-D motion capture (R = 0.99).18,19 The Gait Mat II was chosen due to it being much more feasible to use in nursing homes than 3-D motion capture. The GaitMat II system automatically measured gait speed then spatiotemporal determinants, with these data automatically stored in a Microsoft Excel spreadsheet.

Participants completed the trials at their habitual gait speed in their regular footwear. The following instructions were provided, “Walk towards the end of the room in the centre of the mat at a pace that is comfortable for you.” All measures were initiated from a standing start 2 m (6.56 ft) from the GaitMat II platform to reduce the effect that acceleration or deceleration may have on the outcomes.20,21 The average gait speed (m/s) from 3 attempts was used for data analysis. Participants were allowed as much rest as required between attempts, with rest periods typically being up to 1 minute.

Secondary Outcome Measures

A full spectrum of spatiotemporal gait determinants outputted was recorded. These spatiotemporal gait determinants included step length, stride length, support base, step time, swing time, stance time, single support time, and double support time and are defined by the GaitMat II manual, found in Table 1.

Table 1. - Spatiotemporal Gait Determinants and Definitions as Defined by the GaitMat II Manual39
Spatiotemporal Gait Determinants Definitions
Step length The distance from the first switch closure of one footprint to that of the footprint on the contralateral side
Stride length The distance from the first switch closure of one footprint to the next footprint on the ipsilateral side
Support base The medial lateral distance across the mat to the innermost switch closure for one footprint from the innermost switch closure of the previous footprint on the contralateral side
Step time The time to the earliest switch closure of a footfall from the earliest switch closure of the previous footfall on the contralateral side
Swing time The time to the earliest switch closure of a footfall from the latest switch opening of the previous footfall on the ipsilateral side
Stance time The time to the latest switch opening of a footfall from the earliest switch closure of the same footfall
Single support time The time to the earliest switch closure of the next footfall on the contralateral side from the latest switch opening of the previous footfall on the contralateral side
Double support time The time to the latest switch opening of the previous footfall on the contralateral side from the earliest switch closure of a footfall

In addition, handgrip strength, the Mini-Cog test,22 and a simple 5-item questionnaire (SARC-F)23,24 were collected for the purpose of cohort characteristics description. Nursing home facility records provided other relevant descriptors including the number of medical conditions and medications.

Data Management and Statistical Analysis

All data were initially checked for normality prior to analysis. As data were normally distributed, descriptive statistics are presented as mean and standard deviations for continuous variables. A one-way analysis of variance and post hoc Tukey and Scheffe tests were performed to investigate between nursing home differences. Linear regression analyses were performed to gain insight into the potential determinants of gait speed (ie, gait spatiotemporal determinants) in residents. Univariate analyses of all gait spatiotemporal determinants were employed to identify possible determinants of gait speed (2-tailed). Factors with a significance P ≤ .10 determined from simple linear regression analyses were included in the multiple linear regression model. This multivariable model determined which combination of variables best-predicted gait speed in residents. The 95% CI was included for the coefficients in the multivariable model. All data were analyzed using SPSS statistic software (version 22), with statistical significance set at P < .05 a priori.

RESULTS

Participants

One hundred of 166 (60.24%) invited, eligible residents were recruited to the study. No significant differences were observed between all variables for nursing home cohorts in this study (P > .05), thus data combined into one group for analysis. Cohort data are present in Table 2. The average age of the 100 residents was 85.7 (7.1) years, with a mean gait speed of 0.63 (0.19) m/s, an average of 11.0 (4.9) medical conditions and 14.0 (5.8) prescribed medications. No significant differences were found with gait speed and spatiotemporal parameters, handgrip strength, sarcopenia status, and medications and chronic diseases across males and females. However, males were significantly younger (P = .038) and had a lower mini-Cog assessment (P = .002) in comparison to females.

Table 2. - Characteristics of the Cohort of 100 Nursing Home Residents
Parameter Group Mean (SD) Females (n = 67) Mean (SD) Males (n = 33) Mean (SD)
Age, y 85.7 (7.1) 86.1 (6.6)a 85.0 (8.1)
Handgrip strength, kg 11.1 (4.9) 10.7 (4.2) 11.7 (6.2)
Mini-COG, n 1.2 (0.4) 1.8 (0.4)a 1.3 (0.5)
SARC-F, n 5.5 (3.3) 4.9 (3.3) 6.6 (3.1)
Medical conditions, n 11.0 (4.9) 11.4 (4.9) 10.2 (4.9)
Medications, n 14.0 (5.8) 13.8 (6.1) 14.4 (5.3)
Gait speed, m/s 0.6 (0.2) 0.7 (0.2) 0.6 (0.2)
Step length, m 0.4 (0.1) 0.4 (0.1) 0.4 (0.1)
Stride length, m 0.8 (0.1) 0.8 (0.2) 0.8 (0.1)
Support base, m 0.1 (0.1) 0.2 (0.1) 0.1 (0.1)
Step time, s 0.7 (0.1) 0.6 (0.1) 0.7 (0.1)
Swing time, s 0.4 (0.1) 0.4 (0.1) 0.4 (0.1)
Stance time, s 0.9 (0.2) 0.9 (0.2) 1.0 (0.2)
Single support time, s 0.4 (0.1) 0.4 (0.1) 0.4 (0.1)
Double support time, s 0.2 (0.1) 0.2 (0.1) 0.3 (0.1)
Abbreviations: Mini-COG, mini-cognitive test; SARC-F, simple 5-item questionnaire; SD, standard deviation.
aStatistical significance P < .05.

The majority of participants (79%, n = 79/100) presented with below normal habitual gait speeds (<0.80 m/s), while 26% (n = 26/100) ambulated at below the mean reported for nursing facilities residents (<0.48 m/s, 95% CI, 0.40-0.55).6

Results of the univariable linear regression analyses identified 3 spatiotemporal factors as being predictive of gait speed: stride length (P < .001), support base (P < .001), and step time (P = .002) (see Table 3). Of these factors, stride length contributed to the largest change in gait speed, with each 0.1-m increase in stride length resulting in an average 0.09 m/s (95% CI, 0.06 - 0.13) faster habitual gait speed.

Table 3. - Univariable and Multivariable Linear Regression Model of the Spatiotemporal Predictors for Habitual Gait Speed in 100 Residents Living in Nursing Homesa
Factor Univariable Multivariable
Coefficient (95% Confidence Interval) P Value Coefficient (95% Confidence Interval) P Value
Stride length, m 0.9 (0.6 to 1.3) <.001 0.8 (0.7 to 0.9) <.001
Support base, m −0.5 (−0.8 to −0.3) <.001 −0.4 (−0.7 to −0.2) <.001
Step time, s −0.7 (−1.1 to −0.3) .002 −0.9 (−1.0 to −0.8) <.001
aR2 of multiple regression equals 0.892. All results significant P < .05.

The multivariable linear regression model that included stride length, support base, and step time predicted 89% (R2 = 0.89) of the variation in gait speed (see Table 3). Specifically, step time contributed to the largest change in gait speed, with every 0.1-s decrease in step time resulting in a mean increase in gait speed of 0.09 m/s (95% CI, 0.08-0.10). A 0.1-m increase in stride length was also associated with a mean increase of 0.08 m/s (95% CI, 0.07-0.09) in gait speed. The third determinant identified in the multivariable regression, support base, appeared to have a smaller effect on gait speed, with a 0.1-m decrease in support base resulting in a mean gait speed increase of 0.04 m/s (95% CI, 0.02-0.07).

DISCUSSION

This study demonstrated that nursing home residents who can self-ambulate with or without a walking aid still walk at a gait speed (0.63 ± 0.19 m/s) and possess spatiotemporal parameters that place them at high risk of falls and other adverse age-related events.3,11,12 A total of 79 participants presented with below normal habitual gait speeds (<0.80 m/s), which is a threshold defined to screen for sarcopenia in older adults 80 years and older.25 A total of 26 participants also walked at a threshold below 0.48 m/s, which a meta-analysis of 48 studies found to be the mean gait speed for older adults in nursing homes.6

Results of the regression analyses also indicated that nursing home residents who ambulated at a slower habitual gait speed were more likely to have an increased step time, shorter stride length, and a wider support base than their more ambulatory counterparts. While the finding that some spatiotemporal parameters do predict gait speed is not overly surprising, to our knowledge this is the first study to investigate the potential for spatiotemporal determinants to determine gait speed in the nursing home setting. The importance of the spatiotemporal parameters in determining gait speed also appears consistent with Sterke et al11 and Taylor et al,12 who demonstrated that slower older walkers with increased fall risk had shorter stride lengths, longer double support times, and a wider support base when compared with age-matched individuals with no fall history.

The significant ability of spatiotemporal determinants such as step time, stride length, and support base to determine gait speed in the current study (R2 = 0.89) and falls in previous studies11,12 would appear to reflect a variety of biomechanical concepts. For example, the ability of step time and stride length to determine gait speed may be explained by the impulse-momentum relationship and/or the nursing home residents' reduced ability to maintain balance during the gait cycle. It is fair to conclude that reduced lower body muscle strength and power may mean that the nursing home residents require greater single/double support times to produce the necessary impulse (force multiplied by time) to propel their body forward during the gait cycle.26 Their reduced force production ability and greater stance time would then contribute to a reduced stride length, increased step time, and ultimately a reduced habitual gait speed. Poor stride length may also be suggestive of shuffling gait and low plantar flexor and hip flexor strength.26 Therefore, certain exercises such as calf raises and leg raises coupled with gait training may need to be incorporated in resistance training programs to improve gait speed in nursing home residents.21

The clinical significance of this study is that gait speed characteristics and spatiotemporal determinants are becoming more easily measured and analyzed in nursing home settings. Health professionals can then use these individualized gait data to identify residents at risk of adverse events and intervene where appropriate by providing an individualized exercise intervention for each resident. In doing so, residents are likely to benefit more from these exercise programs, as they are better tailored to the specific spatiotemporal parameters underlying the participants' poor gait speed and/or fall risk. Such an approach may also improve exercise adherence, as these programs can better concentrate on improving gait performance in activities of daily living.27

Collectively, the manner in which the nursing home residents walk (as described by their spatiotemporal determinants) has major implications to exercise therapy and rehabilitative approaches to improving gait speed in this cohort. With the clinical implications being that if health professionals can continually monitor and assess gait speed and spatiotemporal determinants, we may be able to decrease or prolong the amount of residents who are induced into the vicious cycle of reduced physical activity and decreased mobility and physical performance that have a direct effect on their health and survival.

However, it is also possible that the tendency for the slower nursing home residents to have shorter stride lengths, increased step times, and wider support bases may be indicative of a compensation for their reduced strength and dynamic stability. Longer strides and a narrower support base increase the distance that the center of mass travels outside the anterior-posterior and medial-lateral bases of support, respectively.26 As Sherrington et al28 have reported that exercise programs that do not sufficiently challenge balance may actually increase rather than decrease the risk of falling of nursing home residents, we would recommend that nursing home residents with short strides and wide support bases focus initially on improving their static and dynamic balance. Once balance has been improved in the anterior-posterior and medial-lateral directions, these residents may further prioritize resistance and gait retraining to safely improve their gait speed and overall mobility.

Given the interplay between decreasing mobility and increasing disability, the monitoring of gait speed by health professionals (and if possible the primary spatiotemporal determinants) on at least an annual basis in the nursing home setting has been recommended.8,29 For those nursing home residents identified with poor and slowing mobility, systematic review evidence suggests that regular progressive resistance and balance training can improve their habitual gait speed by 0.07 m/s (95% CI, 0.02-0.11) when compared with nonexercising controls.30 While these reported improvements in gait speed are positive, there may be 2 potential criticisms of the studies reviewed in this meta-analysis (and the wider literature). The first is that the studies have typically used quite generic exercise prescriptions that focus on improving muscular hypertrophy and strength in a variety of muscle groups. Based on emerging evidence that reduced gait speed in older adults is primarily a result of reduced ankle plantar flexor rather than hip or knee extensor moment and muscle power,26 a greater focus on increasing the muscle strength and power of the plantar flexors compared with the traditional focus on the knee and hip extensors may be warranted. In addition, the majority of studies in this area that have included balance training primarily used static balance tasks that require the older adults to hold a position for a period of 10 to 20 seconds (eg, 2-ft stands on unstable surface or with eyes closed or semitandem/tandem stance). Based on our results, we suggest that dynamic balance ability, which would appear more closely related to the balance requirements of human gait, be taught by health professionals on a weekly basis. Therefore, nursing home residents may obtain greater gait speed benefit from performing dynamic balance tasks (eg, stepping and perturbation response) than static balance tasks.31–33

The Gait Mat II provided a feasible, reliable, and valid tool to measure gait speed and spatiotemporal determinants in nursing home adults.2,18,19 New equipment has been developed since the data collection of this study. One suggestion for future studies would be to use inertial sensors,34 which may be a more portable and affordable gait assessment. Such advancements in inertial sensor technology would more easily allow health professionals to routinely monitor gait speed and spatiotemporal parameters in the nursing home setting, which may further increase allowing the development of targeted exercise programs for each nursing home resident.

Participation selection bias is a limitation that may have influenced our findings, as the inclusion criteria deemed that a participant should have the ability to walk with or without an aid. A total of 55% of residents in the nursing home facilities were ineligible because of the inability to mobilize or because they were deemed too high a risk to participate. Because of this bias in selecting individuals who were ambulant (with or without assistive devices), the gait speed and spatiotemporal parameters obtained in this study may not be generalized to all nursing home residents. Nevertheless, the participants in the current study were still below the cutoff for physical performance and at risk of further decreased disability, cognitive decline, and mortality,35 with every 0.1-m/s reduction in gait speed equating to a 10% decrease in older adults' ability to perform activities of daily living.36 We also acknowledge that static or dynamic balance ability was not directly assessed in this study. Therefore, while our proposition of poor dynamic balance contributing to the reduced gait speed of nursing home participants has some experimental support,37,38 we cannot explicitly state that is the case with our participants.

CONCLUSIONS

This is the first study to investigate gait speed characteristics and spatiotemporal determinants in the nursing home setting. While our cross-sectional study suggests that step time, stride length, and support base are highly predictive of gait speed in nursing home residents, longitudinal research is required to determine whether changes in these 3 spatiotemporal determinants may be predictive of changes in gait speed. If these longitudinal relationships between gait speed and spatiotemporal determinants can be found, health professionals may be better able to alter aspects of their exercise prescription and augmented feedback approach to improve outcomes for nursing home residents.

ACKNOWLEDGMENTS

The authors would like to thank the management, staff, and residents of the nursing home settings for their assistance and participation in this project.

REFERENCES

1. Reid N, Eakin E, Henwood T, et al. Objectively measured activity patterns among adults in residential aged care. Int J Environ Res Public Health. 2013;10(12):6783–6798.
2. Kuys SS, Peel NM, Klein K, Slater A, Hubbard RE. Gait speed in ambulant older people in long term care: a systematic review and meta-analysis. J Am Med Dir Assoc. 2014;15(3):194–200.
3. Abellan van Kan G, Rolland Y, Andrieu S, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. J Nutr Health Aging. 2009;13(10):881–889.
4. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–423.
5. Weidung B, Bostrom G, Toots A, et al. Blood pressure, gait speed, and mortality in very old individuals: a population-based cohort study. J Am Med Dir Assoc. 2015;16(3):208–214.
6. Peel NM, Kuys SS, Klein K. Gait speed as a measure in geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci. 2013;68(1):39–46.
7. McGough EL, Logsdon RG, Kelly VE, Teri L. Functional mobility limitations and falls in assisted living residents with dementia. J Geriatr Phys Ther. 2012;195(1):78–86.
8. Keogh JW, Senior H, Beller EM, Henwood T. Prevalence and risk factors for low habitual walking speed in nursing home residents: an observational study. Arch Phys Med Rehabil. 2015;96(11):1993–1999.
9. Espy DD, Yang F, Bhatt T, Pai YC. Independent influence of gait speed and step length on stability and fall risk. Gait Posture. 2010;32(3):378–382.
10. Hausdorff JM, Edelberg HK, Mitchell SL, Goldberger AL, Wei JY. Increased gait unsteadiness in community-dwelling elderly fallers. Arch Phys Med Rehabil. 1997;78(3):278–283.
11. Sterke CS, van Beeck EF, Loonman CWN, Kressig RW, van der Cammen TJM. An electronic walkway can predict short-term fall risk in nursing home residents with dementia. Gait Posture. 2012;36(1):95–101.
12. Taylor ME, Delbaere K, Mikolaizak AS, Lord SR, Close JCT. Gait parameter risk factors for falls under simple and dual task conditions in cognitively impaired older people. Gait Posture. 2013;37(1):126–130.
13. Agner S, Bernet J, Brulhart Y, Radlinger L, Rogan S. Spatiotemporal gait parameters during dual task walking in need of care elderly and young adults: a cross-sectional study. Z Gerontol Geriatric. 2015;48(8):740–746.
14. Valenzuela T. Efficacy of progressive resistance training interventions in older adults in nursing homes: a systematic review. J Am Med Dir Assoc. 2012;13(5):418–428.
15. Begg RK, Tirosh O, Said CM, et al. Gait training with real-time augmented toe-ground clearance information decreases tripping risk in older adults and a person with chronic stroke. Front Hum Neurosci. 2014;8:243.
16. Shen X, Mak MKY. Balance and gait training with augmented feedback improves balance confidence in people with Parkinson's disease. Neurorehab Neural Repair. 2014;28(6):524–535.
17. McDonough AL, Batavia M, Chen FC, Kwon S, Ziai J. The validity and reliability of the GAITRite system's measurements: a preliminary evaluation. Arch Phys Med Rehabil. 2001;82(3):419–425.
18. Huang W-N, VanSwearingen JM, Brach JS. Gait variability in older adults: observational rating validated by comparison with a computerized walkway gold standard. Phys Ther. 2008;88(10):1146–1153.
19. Barker S, Craik R, Freedman W, et al. Accuracy, reliability, and validity of a spatiotemporal gait analysis system. Med Eng Phys. 2006;28(5):460–467.
20. Kressig RW, Gregor RJ, Oliver A, et al. Temporal and spatial features of gait in older adults transitioning to frailty. Gait Posture. 2004;20(1):30–35.
21. Fien S, Henwood T, Climstein M, Keogh JWL. Feasibility and benefits of group based exercise in residential aged care adults: a pilot study for the GrACE programme. PeerJ. 2016;4:e2018.
22. Borson S, Scanlan JM, Watanabe J, Tu SP, Lessig M. Improving identification of cognitive impairment in primary care. Int J Geriatr Psychiatry. 2006;21(4):349–355.
23. Malmstrom TK, Morley JE. SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc. 2013;14(8):1–2.
24. Cao L, Chen S, Zou C, et al. A pilot study of the SARC-F scale on screening sarcopenia and physical disability in the Chinese older people. J Nutr Health Aging. 2014;18(3):277–283.
25. Lourenco RA, Perez-Zepeda M, Gutierrez-Robeldo L, Garcia-Garcia FJ, Manas LR. Performance of the European Working Group on Sarcopenia in Older People algorithm in screening older adults for muscle mass assessment. Age Ageing. 2015;44:334–338.
26. Kulmala JP, Korhonen MT, Kuitunen S, et al. Which muscles compromise human locomotor performance with age? J R Soc Interface. 2014;11(100):20140858.
27. Henwood T, Neville C, Baguley C, Clifton K, Beattie E. Physical and functional implications of aquatic exercise for nursing home residents with dementia. Geriatr Nurs. 2015;36(1):35–39.
28. Sherrington C, Tiedemann A, Fairhall N, Close JCT, Lord SR. Exercise to prevent falls in older adults: an updated meta-analysis and best practice recommendations. NSW Public Health Bull. 2011;22(4):78–83.
29. Fien S, Henwood T, Climstein M, Keogh JWL. Clinical importance of assessing walking speed in older adults in general practice. Aust Fam Physician. 2016;45(4):250–251.
30. Chou CH, Hwang CL, Wu YT. Effect of exercise on physical function, daily activities, and quality of life in the frail older adults: a meta analysis. Arch Phys Med Rehabil. 2012;93(2):237–244.
31. Nnodim JO, Strasburg D, Nabozny M, et al. Dynamic balance and stepping versus Tai Chi training to improve balance and stepping in at-risk older adults. J Am Geriatr Soc. 2006;54(12):1825–1831.
32. Granacher U, Zahner L, Gollhofer A. Strength, power, and postural control in seniors: considerations for functional adaptations and for fall prevention. Eur J Sport Sci. 2008;8(6):325–340.
33. Carty CP, Cronin NJ, Lichtwark GA, Mills PM, Barrett RS. Mechanisms of adaptation from a multiple to a single step recovery strategy following repeated exposure to forward loss of balance in older adults. PLoS One. 2012;7(3):e33591.
34. Greene BR, Redmond SJ, Caulfield B. Fall risk assessment through automatic combination of clinical fall risk factors and body-worn sensor data. IEEE J Biomed Health Inform. 2016;21(3):725–731.
35. Houles M, Abellan Van Kan G, Rolland Y, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people [French] La vitesse de marche comme critere de fragilite chez la personne agee vivant au domicile. Cah l'Annee Gerontol. 2010;2(1):13–23.
36. Hausdorff J. Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. Hum Mov Sci. 2007;26(4):557–589.
37. Gusi N, Carmelo Adsuar J, Corzo H, del Pozo-Cruz B, Olivares PR, Parraca JA. Balance training reduces fear of falling and improves dynamic balance and isometric strength in institutionalised older people: a randomised trial.J Physiother. 2012;58(2):97–104.
38. Mohammadi M, Kaldirimci M, Ebrahim Kazemi S, Mizrak O, Tugrulhansam C. The effect of Pilates exercise on gait speed and strength of lower limb in elderly male. Adv Appl Sci Res. 2015;6(7):1–6.
39. Walsh J. GaitMat II User's Manual. EQ, Inc Chalfont, PA. Revision 3.2.1.
    Keywords:

    gait speed; health professionals; nursing home; spatiotemporal determinants

    © 2017 Academy of Geriatric Physical Therapy, APTA.