Research Report Does Upper Limb Coordination Predict Walking Speed in Older Adults? A Cross-Sectional Study John H. Hollman, PT, PhD, Katherine C. Beed, DPT, Ryan J. Buus, DPT, Kenzie L. Schleicher, DPT, Desiree J. Lanzino, PT, PhD ABSTRACT Background and Purpose: Walking speed is a measure of physical function in older adults. Older adults are sometimes nonambulatory, however, and proxy measures for walking speed may be indicated. Since limb coordination tests can be conducted in non–weight-bearing positions, they may provide that capability. The purpose of this study was to examine the relationship between timed limb coordination and preferred and maximum walking speed, controlling for other known determinants of walking speed. Methods: A total of 84 healthy adults (60 women and 24 men) older than 60 years participated. Preferred and maximum walking speed were measured during 10-Meter Walk Tests. Upper limb coordination performance was measured during a timed 5-repetition finger-to-nose test. Other variables measured included isometric knee extension strength, cognition (Montreal Cognitive Assessment), limits of stability (Functional Reach Test), the number of comorbidities (Functional Comorbidity Index), age, height, and sex. Multiple regression and partial correlation analyses (α = .05) were used to identify which variables predicted preferred and maximum walking speed, controlling for all other variables. Results: Participants’ mean preferred walking speed was 129 (24) cm·s−1, and mean maximum walking speed was 176 (37) cm·s−1. Finger-to-nose coordination performance, 4.8 (1.3) seconds, correlated negatively with preferred (r = −0.403) and maximum (r = −0.429) walking speed. Those bivariate correlation coefficients, however, were attenuated by other variables in the regression models (partial r = −0.031, P = .786, and partial r = −0.075, P = .513, for preferred and maximum walking speed, respectively). Variance in age, comorbidities, functional reach, knee extension strength, and height accounted for 55.4% of the variance in preferred walking speed. Variance in knee extension strength, cognition, functional reach, age, and comorbidities accounted Program in Physical Therapy, Department of Physical Medicine & Rehabilitation, Mayo Clinic College of Medicine, Rochester, Minnesota. The authors declare that they have no financial, consultant, institutional, or other relationships that may lead to bias in this study or create a conflict of interest. Address correspondence to: John H. Hollman, PT, PhD, Program in Physical Therapy, Department of Physical Medicine & Rehabilitation, Mayo Clinic College of Medicine, Rochester, MN ([email protected]). Richard Bohannon was the Decision Editor. DOI: 10.1519/JPT.0b013e3182abe793 106
for 63.5% of the variance in maximum walking speed. After removing knee extension strength and functional reach from the models—those variables that may be difficult or contraindicated to measure in some patient populations—finger-tonose coordination was not a statistically significant predictor of preferred walking speed. Variance in age, comorbidities, cognition, height, and finger-to-nose coordination accounted for 55.9% of the variance in maximum walking speed. The change in R2 attributed to finger-to-nose coordination performance, however, was only 2.9%. Discussion: While knee extension strength, functional reach, comorbidities, and age were most predictive of walking speed, after removing knee extension strength and functional reach from the regression models, finger-to-nose coordination remained a potentially modifiable marker of neuromuscular control that only weakly predicted maximum walking speed in older adults. Conclusions: The timed finger-to-nose test would not appear to be a valid proxy for walking speed when weight-bearing clinical examination procedures are contraindicated. Key Words: aging, coordination impairment, muscle strength, walking (J Geriatr Phys Ther 2014;37:106-115.)
INTRODUCTION Walking performance is a hallmark of physical function. One’s preferred walking speed, for example, reflects quality of life and health status and predicts cognitive decline and remaining life expectancy in older adults.1-5 In addition to preferred walking speed, maximum walking speed may also be important. Maximum walking speed declines more rapidly than preferred walking speed in older adults, due in part to age-related changes in neuromuscular control.6,7 Therefore, assessing maximum walking speed may provide advantages over preferred walking speed for detecting functional decline. Regardless of whether one measures preferred or maximum walking speed, walking speed is touted as a “vital sign” for function.8 Oftentimes, however, older adults may temporarily be nonambulatory (eg, postsurgical patients) and proxy measures for walking speed may be indicated. Several factors, both nonmodifiable and modifiable, influence walking speed, and therefore, those proxy measures must be considered carefully when one interprets walking speed as a biomarker for quality of life, health status, Volume 37 • Number 3 • July-September 2014
Copyright © 2014 The Academy of Geriatric Physical Therapy of the American Physical Therapy Association. Unauthorized reproduction of this article is prohibited.
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or physical function. Nonmodifiable factors include age, sex, and height.9,10 Modifiable factors include lower limb strength, cognition, and balance.11-14 It is important to understand which modifiable factors are associated with walking speed, because those can potentially be targeted through intervention. Less well-understood is how limb coordination, defined as the ability to execute accurate and controlled movements at various speeds,15 influences walking speed. Similar to gait characteristics, normally coordinated limb movements are characterized in part by rhythmic muscle contractions and relaxations that promote easy reversal between opposing muscle groups.16 Limb coordination is assessed in part through tests that examine a person’s ability to accurately and quickly perform alternate or reciprocal motions and movement synergies. When assessed quantitatively in healthy individuals, performance on several timed limb coordination tests has been correlated with age, sex, and height,17 similar to relationships observed with walking speed. Since limb coordination tests can be conducted in non–weight-bearing positions, these may serve as a proxy for the measurement of walking speed in individuals who are temporarily nonambulatory. Hollman et al,18 controlling for the effects of sex, age, and height, reported that timed upper limb coordination tests in older adults were more strongly associated with preferred (r = −0.396) and maximum walking speed (r = −0.356) than was a test of lower limb coordination, timed heel-on-shin performance (r = −0.228 and r = −0.288, respectively). It is unclear, however, whether timed upper limb coordination predicts walking speed more than and beyond that which can be accounted for by other variables such as lower limb strength and postural stability, both of which may be difficult to assess in many postsurgical patient populations. The purpose of this study was, therefore, to examine the extent to which timed upper limb coordination performance predicts preferred and maximum walking speed, independent of other variables identified as determinants of walking speed.
METHODS Study Design and Participants We conducted this cross-sectional, exploratory study to examine the relationship between coordination performance and walking speed, controlling for age, height, knee extensor strength, postural stability, and cognitive abilities. Flyers were posted in the community to recruit healthy adults older than 60 years to participate. On the basis of a power analysis, 81 participants were sufficient to detect a change in R2 of 0.10 or higher for any given variable entered into a regression model at α = .05 at a desired statistical power of 0.80. Eligible participants were English-speaking, communitydwelling individuals older than 60 years who were indepenJournal of GERIATRIC Physical Therapy
dent in activities of daily living and community ambulators, which we operationally defined as having a score of 13 or greater on the Rivermead Mobility Index. While the Rivermead Mobility Index was originally developed to assess functional mobility following traumatic brain injury and stroke,19 it has been validated as an index of functional mobility in other patient populations20 and can be used as an index of community ambulation capacity in older adults. Exclusion criteria included any medically diagnosed neurologic pathology or cardiovascular pathology leading to neurologic symptoms, or a history of surgery within 12 months of the test date (eg, hip or knee arthroplasty, back surgery), which may have impacted one’s ability to ambulate independently. The Mayo Foundation institutional review board approved the protocol, and all participants provided written informed consent.
Procedures Participants who met enrollment criteria and provided informed consent were administered a battery of tests that represent potential determinants of walking speed. On the basis of our interpretation of multiple studies,11-14 the primary determinants of walking speed in healthy older adults appear to be age, sex, height, knee extension strength, cognition, and postural stability. The participants’ age, sex, height, and weight were recorded. Investigators then administered sequentially to each participant a survey to quantify comorbidities, a cognitive assessment, a test of isometric knee extension strength, a test of postural stability, a timed upper limb coordination test, and walking tests to determine preferred and maximum walking speeds. Physical functions like walking can be influenced by the number of comorbid diseases with which a person presents. To examine the associations between multiple clinical tests and walking speed while controlling for comorbidities, we quantified the number of comorbidities with the Functional Comorbidity Index (FCI). The FCI was developed with physical function as the outcome of interest and incorporates a list of 18 diagnoses that correlate with declining function; 1 point is assigned per diagnosis, and the points are summed, yielding a score between 0 and 18.21 We assessed cognition with the Montreal Cognitive Assessment (MoCA), a 30-point test that assesses several cognitive domains and is validated as a screening tool for mild cognitive impairment in older adults.22 The test-retest reliability coefficient of the MoCA is 0.92 and the internal consistency of its items, measured with Cronbach α, is 0.83.22 We quantified isometric knee extension strength of participants’ dominant lower extremities using a MicroFET2 handheld dynamometer (Hoggan Health Industries, Salt Lake City, Utah). Participants were seated in a chair with their knees flexed approximately 90°. The dynamometer was stabilized at the anterior distal leg, approximately 107
Copyright © 2014 The Academy of Geriatric Physical Therapy of the American Physical Therapy Association. Unauthorized reproduction of this article is prohibited.
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5 cm proximal to the ankle joint, with a fixation belt and an examiner’s hand. Participants maximally contracted the extensors for 5 seconds. The maximum isometric force produced over 3 repetitions was included in subsequent analyses. A 30-second rest period was provided between repetitions. The method for collecting knee extension strength data was comparable to methods in which the testretest reliability coefficient exceeded 0.90 in older adults.23 We assessed postural stability with the Functional Reach Test. As per methods described by Duncan et al,24 participants stood adjacent to a wall with an attached meter stick, elevated the arm nearest the wall to 90° of shoulder flexion, and reached as far anteriorly as possible without taking a step, as directed by the examiner. The difference between the ending and starting positions of the tip of the middle finger was recorded as the functional reach distance. The maximum of 3 trials was included in subsequent analyses. The Functional Reach Test correlates highly with laboratory measures of center of pressure excursion, indicating its validity as a test of limits of stability, and is conducted with a test-retest reliability coefficient that exceeds 0.80.24 We assessed upper limb coordination with the timed finger-to-nose test. As per methods described by Lanzino et al,17 the participant alternately touched the tip of an examiner’s finger—held at eye level at approximately the participant’s arm reach—and the participant’s nose as quickly and accurately as possible. After 1 practice trial, the time required to complete 5 cycles of the finger-to-nose movement was measured with a stopwatch and recorded for analysis. Since timed limb coordination performance does not differ between dominant and nondominant limbs,17 we collected data from participants’ dominant arms only. Lanzino et al25 reported that timed measurements on the test have an interrater reliability coefficient of 0.92. Last, preferred and maximum walking speeds were measured over the inner 6 m of a 10-m walkway. Participants were permitted to wear their preferred footwear. In the preferred walking speed trials, participants were instructed to “walk down the path at your normal speed as though you were walking on the sidewalk outside.” In the maximum walking speed trials, they were instructed to “walk down the path as quickly as you can, without running.” Times were recorded with a TracTronix TF 100 infrared dualbeam timing system (TracTronix, Lenexa, Kansas). The mean walking speeds over 2 walks in each condition were included in subsequent analyses. Steffen et al26 reported that comparable methods for measuring walking speed have test-retest reliability coefficients that exceed 0.95.
extension strength, functional reach, MoCA scores, FCI scores, age, and height. Two stepwise multiple regression models (αentry= .05 and αremoval= .10) along with partial correlation coefficients were used to identify which variables predicted preferred and maximum walking speed, controlling for all other variables. In the first regression model, all 7 predictor variables were entered. In the second model, knee extension strength and functional reach—those variables that either require full weight bearing to complete or may be difficult or contraindicated to measure in postsurgical patient populations—were removed. IBM SPSS (IBM Corporation, Armonk, NY) 21.0 software was used for all analyses. Assumptions for conducting multiple regression analyses were examined. With the exception of MoCA scores, all other variables were normally distributed (Kolmogorov-Smirnov tests with P > .05). Each predictor variable was linearly associated with preferred and maximum walking speed. Plots of standardized residuals and standardized predicted values indicated the assumptions of homoscedasticity were not violated. Variance inflation factors were less than 2.0 for each variable included in the regressions, suggesting that multicollinearity was not problematic.
RESULTS Participant Characteristics and Descriptive Data Demographic data are provided in Table 1. The sample included 84 participants, 60 women (71%) and 24 men (29%), who ranged in age from 60 to 92 years, 75 (9) years, and were predominantly white/Caucasian (99%). Most participants had no history of falls (74%), and their body mass index, 25.8 (4.3) kg·m−2, indicated that most were in the healthy weight to overweight categories. Descriptive data for each variable are provided in Table 2. Preferred walking speed was 129 (24) cm·s−1, and maximum walking speed was 176 (37) cm·s−1. Preferred walking speed (Figure 1) was positively correlated with functional reach performance (r = 0.529), knee extension strength (r = 0.496), and MoCA scores (r = 0.478) and negatively correlated with FCI scores (r = −0.432), age (r = −0.554), and timed finger-to-nose performance (r = −0.403). Maximum walking speed (Figure 2) was positively correlated with knee extension strength (r = 0.635), functional reach performance (r = 0.533), MoCA scores (r = 0.477), and height (r = 0.199) and negatively correlated with age (r = −0.575), FCI scores (r = −0.433), and timed finger-to-nose performance (r = −0.429).
Regression on Preferred Walking Speed Data Analysis Descriptive statistics, mean (SD) were calculated for each variable. Pearson product-moment correlation coefficients (r) were used to examine associations among preferred and maximum walking speed with timed finger-to-nose performance, knee 108
The first regression model (Table 3) indicated that variance in 5 variables—age, FCI scores, functional reach, knee extension strength, and height—accounted for 55.4% of the variance in preferred walking speed (R2 = 0.554, P < .001). Controlling for all other variables, preferred Volume 37 • Number 3 • July-September 2014
Copyright © 2014 The Academy of Geriatric Physical Therapy of the American Physical Therapy Association. Unauthorized reproduction of this article is prohibited.
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Table 1. Demographic Characteristics of Participants Demographic Characteristic
correlated with age (partial r = −0.379, P = .001), FCI scores (partial r = −0.364, P = .001), and height (r = −0.238, P = .034). The bivariate correlation between preferred walking speed and finger-to-nose performance (Figure 1; r = −0.403) was attenuated (partial r = −0.031, P = .786). On the basis of these findings, preferred walking speed was best predicted with the following regression equation:
n
%
Male
24
28.6
Female
60
71.4
60-69
27
32.1
70-79
24
28.6
80-89
30
35.7
3
3.6
for 63.5% of the variance in maximum walking speed. After removing knee extension strength and functional reach from the models—those variables that may be difficult or contraindicated to measure in some patient populations—finger-tonose coordination was not a statistically significant predictor of preferred walking speed. Variance in age, comorbidities, cognition, height, and finger-to-nose coordination accounted for 55.9% of the variance in maximum walking speed. The change in R2 attributed to finger-to-nose coordination performance, however, was only 2.9%. Discussion: While knee extension strength, functional reach, comorbidities, and age were most predictive of walking speed, after removing knee extension strength and functional reach from the regression models, finger-to-nose coordination remained a potentially modifiable marker of neuromuscular control that only weakly predicted maximum walking speed in older adults. Conclusions: The timed finger-to-nose test would not appear to be a valid proxy for walking speed when weight-bearing clinical examination procedures are contraindicated. Key Words: aging, coordination impairment, muscle strength, walking (J Geriatr Phys Ther 2014;37:106-115.)
INTRODUCTION Walking performance is a hallmark of physical function. One’s preferred walking speed, for example, reflects quality of life and health status and predicts cognitive decline and remaining life expectancy in older adults.1-5 In addition to preferred walking speed, maximum walking speed may also be important. Maximum walking speed declines more rapidly than preferred walking speed in older adults, due in part to age-related changes in neuromuscular control.6,7 Therefore, assessing maximum walking speed may provide advantages over preferred walking speed for detecting functional decline. Regardless of whether one measures preferred or maximum walking speed, walking speed is touted as a “vital sign” for function.8 Oftentimes, however, older adults may temporarily be nonambulatory (eg, postsurgical patients) and proxy measures for walking speed may be indicated. Several factors, both nonmodifiable and modifiable, influence walking speed, and therefore, those proxy measures must be considered carefully when one interprets walking speed as a biomarker for quality of life, health status, Volume 37 • Number 3 • July-September 2014
Copyright © 2014 The Academy of Geriatric Physical Therapy of the American Physical Therapy Association. Unauthorized reproduction of this article is prohibited.
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or physical function. Nonmodifiable factors include age, sex, and height.9,10 Modifiable factors include lower limb strength, cognition, and balance.11-14 It is important to understand which modifiable factors are associated with walking speed, because those can potentially be targeted through intervention. Less well-understood is how limb coordination, defined as the ability to execute accurate and controlled movements at various speeds,15 influences walking speed. Similar to gait characteristics, normally coordinated limb movements are characterized in part by rhythmic muscle contractions and relaxations that promote easy reversal between opposing muscle groups.16 Limb coordination is assessed in part through tests that examine a person’s ability to accurately and quickly perform alternate or reciprocal motions and movement synergies. When assessed quantitatively in healthy individuals, performance on several timed limb coordination tests has been correlated with age, sex, and height,17 similar to relationships observed with walking speed. Since limb coordination tests can be conducted in non–weight-bearing positions, these may serve as a proxy for the measurement of walking speed in individuals who are temporarily nonambulatory. Hollman et al,18 controlling for the effects of sex, age, and height, reported that timed upper limb coordination tests in older adults were more strongly associated with preferred (r = −0.396) and maximum walking speed (r = −0.356) than was a test of lower limb coordination, timed heel-on-shin performance (r = −0.228 and r = −0.288, respectively). It is unclear, however, whether timed upper limb coordination predicts walking speed more than and beyond that which can be accounted for by other variables such as lower limb strength and postural stability, both of which may be difficult to assess in many postsurgical patient populations. The purpose of this study was, therefore, to examine the extent to which timed upper limb coordination performance predicts preferred and maximum walking speed, independent of other variables identified as determinants of walking speed.
METHODS Study Design and Participants We conducted this cross-sectional, exploratory study to examine the relationship between coordination performance and walking speed, controlling for age, height, knee extensor strength, postural stability, and cognitive abilities. Flyers were posted in the community to recruit healthy adults older than 60 years to participate. On the basis of a power analysis, 81 participants were sufficient to detect a change in R2 of 0.10 or higher for any given variable entered into a regression model at α = .05 at a desired statistical power of 0.80. Eligible participants were English-speaking, communitydwelling individuals older than 60 years who were indepenJournal of GERIATRIC Physical Therapy
dent in activities of daily living and community ambulators, which we operationally defined as having a score of 13 or greater on the Rivermead Mobility Index. While the Rivermead Mobility Index was originally developed to assess functional mobility following traumatic brain injury and stroke,19 it has been validated as an index of functional mobility in other patient populations20 and can be used as an index of community ambulation capacity in older adults. Exclusion criteria included any medically diagnosed neurologic pathology or cardiovascular pathology leading to neurologic symptoms, or a history of surgery within 12 months of the test date (eg, hip or knee arthroplasty, back surgery), which may have impacted one’s ability to ambulate independently. The Mayo Foundation institutional review board approved the protocol, and all participants provided written informed consent.
Procedures Participants who met enrollment criteria and provided informed consent were administered a battery of tests that represent potential determinants of walking speed. On the basis of our interpretation of multiple studies,11-14 the primary determinants of walking speed in healthy older adults appear to be age, sex, height, knee extension strength, cognition, and postural stability. The participants’ age, sex, height, and weight were recorded. Investigators then administered sequentially to each participant a survey to quantify comorbidities, a cognitive assessment, a test of isometric knee extension strength, a test of postural stability, a timed upper limb coordination test, and walking tests to determine preferred and maximum walking speeds. Physical functions like walking can be influenced by the number of comorbid diseases with which a person presents. To examine the associations between multiple clinical tests and walking speed while controlling for comorbidities, we quantified the number of comorbidities with the Functional Comorbidity Index (FCI). The FCI was developed with physical function as the outcome of interest and incorporates a list of 18 diagnoses that correlate with declining function; 1 point is assigned per diagnosis, and the points are summed, yielding a score between 0 and 18.21 We assessed cognition with the Montreal Cognitive Assessment (MoCA), a 30-point test that assesses several cognitive domains and is validated as a screening tool for mild cognitive impairment in older adults.22 The test-retest reliability coefficient of the MoCA is 0.92 and the internal consistency of its items, measured with Cronbach α, is 0.83.22 We quantified isometric knee extension strength of participants’ dominant lower extremities using a MicroFET2 handheld dynamometer (Hoggan Health Industries, Salt Lake City, Utah). Participants were seated in a chair with their knees flexed approximately 90°. The dynamometer was stabilized at the anterior distal leg, approximately 107
Copyright © 2014 The Academy of Geriatric Physical Therapy of the American Physical Therapy Association. Unauthorized reproduction of this article is prohibited.
JGPT-D-13-00050R1_LR 107
13/06/14 4:38 AM
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5 cm proximal to the ankle joint, with a fixation belt and an examiner’s hand. Participants maximally contracted the extensors for 5 seconds. The maximum isometric force produced over 3 repetitions was included in subsequent analyses. A 30-second rest period was provided between repetitions. The method for collecting knee extension strength data was comparable to methods in which the testretest reliability coefficient exceeded 0.90 in older adults.23 We assessed postural stability with the Functional Reach Test. As per methods described by Duncan et al,24 participants stood adjacent to a wall with an attached meter stick, elevated the arm nearest the wall to 90° of shoulder flexion, and reached as far anteriorly as possible without taking a step, as directed by the examiner. The difference between the ending and starting positions of the tip of the middle finger was recorded as the functional reach distance. The maximum of 3 trials was included in subsequent analyses. The Functional Reach Test correlates highly with laboratory measures of center of pressure excursion, indicating its validity as a test of limits of stability, and is conducted with a test-retest reliability coefficient that exceeds 0.80.24 We assessed upper limb coordination with the timed finger-to-nose test. As per methods described by Lanzino et al,17 the participant alternately touched the tip of an examiner’s finger—held at eye level at approximately the participant’s arm reach—and the participant’s nose as quickly and accurately as possible. After 1 practice trial, the time required to complete 5 cycles of the finger-to-nose movement was measured with a stopwatch and recorded for analysis. Since timed limb coordination performance does not differ between dominant and nondominant limbs,17 we collected data from participants’ dominant arms only. Lanzino et al25 reported that timed measurements on the test have an interrater reliability coefficient of 0.92. Last, preferred and maximum walking speeds were measured over the inner 6 m of a 10-m walkway. Participants were permitted to wear their preferred footwear. In the preferred walking speed trials, participants were instructed to “walk down the path at your normal speed as though you were walking on the sidewalk outside.” In the maximum walking speed trials, they were instructed to “walk down the path as quickly as you can, without running.” Times were recorded with a TracTronix TF 100 infrared dualbeam timing system (TracTronix, Lenexa, Kansas). The mean walking speeds over 2 walks in each condition were included in subsequent analyses. Steffen et al26 reported that comparable methods for measuring walking speed have test-retest reliability coefficients that exceed 0.95.
extension strength, functional reach, MoCA scores, FCI scores, age, and height. Two stepwise multiple regression models (αentry= .05 and αremoval= .10) along with partial correlation coefficients were used to identify which variables predicted preferred and maximum walking speed, controlling for all other variables. In the first regression model, all 7 predictor variables were entered. In the second model, knee extension strength and functional reach—those variables that either require full weight bearing to complete or may be difficult or contraindicated to measure in postsurgical patient populations—were removed. IBM SPSS (IBM Corporation, Armonk, NY) 21.0 software was used for all analyses. Assumptions for conducting multiple regression analyses were examined. With the exception of MoCA scores, all other variables were normally distributed (Kolmogorov-Smirnov tests with P > .05). Each predictor variable was linearly associated with preferred and maximum walking speed. Plots of standardized residuals and standardized predicted values indicated the assumptions of homoscedasticity were not violated. Variance inflation factors were less than 2.0 for each variable included in the regressions, suggesting that multicollinearity was not problematic.
RESULTS Participant Characteristics and Descriptive Data Demographic data are provided in Table 1. The sample included 84 participants, 60 women (71%) and 24 men (29%), who ranged in age from 60 to 92 years, 75 (9) years, and were predominantly white/Caucasian (99%). Most participants had no history of falls (74%), and their body mass index, 25.8 (4.3) kg·m−2, indicated that most were in the healthy weight to overweight categories. Descriptive data for each variable are provided in Table 2. Preferred walking speed was 129 (24) cm·s−1, and maximum walking speed was 176 (37) cm·s−1. Preferred walking speed (Figure 1) was positively correlated with functional reach performance (r = 0.529), knee extension strength (r = 0.496), and MoCA scores (r = 0.478) and negatively correlated with FCI scores (r = −0.432), age (r = −0.554), and timed finger-to-nose performance (r = −0.403). Maximum walking speed (Figure 2) was positively correlated with knee extension strength (r = 0.635), functional reach performance (r = 0.533), MoCA scores (r = 0.477), and height (r = 0.199) and negatively correlated with age (r = −0.575), FCI scores (r = −0.433), and timed finger-to-nose performance (r = −0.429).
Regression on Preferred Walking Speed Data Analysis Descriptive statistics, mean (SD) were calculated for each variable. Pearson product-moment correlation coefficients (r) were used to examine associations among preferred and maximum walking speed with timed finger-to-nose performance, knee 108
The first regression model (Table 3) indicated that variance in 5 variables—age, FCI scores, functional reach, knee extension strength, and height—accounted for 55.4% of the variance in preferred walking speed (R2 = 0.554, P < .001). Controlling for all other variables, preferred Volume 37 • Number 3 • July-September 2014
Copyright © 2014 The Academy of Geriatric Physical Therapy of the American Physical Therapy Association. Unauthorized reproduction of this article is prohibited.
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Table 1. Demographic Characteristics of Participants Demographic Characteristic
correlated with age (partial r = −0.379, P = .001), FCI scores (partial r = −0.364, P = .001), and height (r = −0.238, P = .034). The bivariate correlation between preferred walking speed and finger-to-nose performance (Figure 1; r = −0.403) was attenuated (partial r = −0.031, P = .786). On the basis of these findings, preferred walking speed was best predicted with the following regression equation:
n
%
Male
24
28.6
Female
60
71.4
60-69
27
32.1
70-79
24
28.6
80-89
30
35.7
3
3.6
