Journal of Aging and Physical Activity, 2015, 23, 346 -351 http://dx.doi.org/10.1123/japa.2013-0268 © 2015 Human Kinetics, Inc.
ORIGINAL RESEARCH
The Test-Retest Reliability of Indirect Calorimetry Measures of Energy Expenditure During Overground Walking in Older Adults With Mobility Limitations David M. Wert, Jessie M. VanSwearingen, Subashan Perera, and Jennifer S. Brach The purpose of this study was to assess the relative and absolute reliability of metabolic measures of energy expenditure and gait speed during overground walking in older adults with mobility limitations. Thirty-three (mean age [SD] = 76.4 [6.6] years; 66% female) older adults with slow gait participated. Measures of energy expenditure and gait speed were recorded during two 6-min bouts of overground walking (1 week apart) at a self-selected “usual” walking pace. The relative reliability for all variables was excellent: ICC = .81–.91. Mean differences for five of the six outcome variables was less than or equal to the respected SEM, while all six mean differences fell below the calculated MDC95. Clinicians and researchers can be confident that metabolic measures of energy expenditure and gait speed in older adults with slow walking speeds can be reliably assessed during overground walking, providing an alternative to traditional treadmill assessments. Keywords: reliability, indirect calorimetry, older adults
Once common only to the world of sport and athletic performance, the metabolic assessment of task-related movement efficiency (i.e., energy cost of performance) is now commonly used to describe walking in older adults with and without mobility limitations (Christiansen, Schenkman, McFann, Wolfe, & Kohrt, 2009; Dawes et al., 2004; Malatesta et al., 2003; VanSwearingen et al., 2009; Waters & Lunsford, 1985; Wert, Brach, Perera, & VanSwearingen, 2013a). For both athletes and older adults, the physiological performance information is sought for essentially the same questions: How much energy for activity can be produced? and What is the energy used (cost) for the task performance? The metabolic measures collected during such assessments are indicators of the person’s task-specific motor efficiency or skill, which may be used both to recognize decline in health status and walking ability in older adults and as outcomes of targeted interventions for mobility disability. Metabolic measures of walking have traditionally been recorded during treadmill walking in laboratory settings. Because of the laboratory setting (i.e., stationary metabolic cart and treadmill walking), metabolic measures may not represent the metabolic demands of walking for older adults under less controlled and more natural conditions, like walking as they live (Parvataneni, Ploeg, Olney, & Brouwer, 2009). The more recent development of portable gas analysis measurement devices means the metabolic measures can be recorded during overground walking in more naturalistic environments. The ability to record metabolic measures during overground walking may be particularly important for older adults with mobility limitations. Differences in treadmill walking compared with overground walking in older adults with mobility limitations have been demonstrated; the treadmill reduces abnormal gait variability, usual walking speed on the treadmill is slower than overground, and walking on the treadmill reduces hesitancy and increases energy cost of walking (Malatesta, et al., 2003; Parvataneni et al., 2009; Warabi, Kato, Kiriyama, Yoshida, & Kobayashi, 2005). Wert, VanSwearingen, and Brach are with the Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA. Perera is with the Division of Geriatric Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA. Address author correspondence to David M. Wert at [email protected]. 346
Little is known about the reliability of metabolic measures derived from portable gas analysis in overground walking. A primary factor in metabolic gas analysis of task performance is the maintenance of physiological steady state. Gas-analysis-based metabolic measures of energy expenditure are based on principles of submaximal oxygen consumption kinetics and physiological steady state (McArdle, Katch, & Katch, 1986). Physiological steady state is the condition during submaximal exercise (task) performance at a constant workload, where the energy produced by oxidative metabolism is the energy used for the task performance. Under nonsteady-state conditions and inconsistency of workload, oxygen consumed does not represent the energy used for the performance. During treadmill walking, constant workload can be assured by setting a constant speed. Overground, the constant speed of walking (i.e., workload) is based on the consistency of the person walking. While most adults walk at a constant speed overground in usual environments, consistency may be more of a problem in older adults with mobility limitations. Literature is scant on published reports of reliability of metabolic measures assessed during overground walking, especially in vulnerable populations where consistency of workload (gait speed) may be an issue. Portable gas-analysis-derived metabolic measures of walking have been reported specifically for adults with neurologic disorders of gait across a range of ages (Dawes, et al., 2004; Eng, Dawson, & Chu, 2004; Stookey et al., 2013). However, the reliability of the measurement specifically in older adults and those without specific neurologic involvement is not well reported. This gap in the literature is of concern as older adults with slow gait are frequently referred for physical therapy assessments and gait-related interventions, and are at greater risk for future decline in mobility. Therefore, the purpose of this study was to assess the test-retest reliability of metabolic measures of energy expenditure (e.g., oxygen and carbon dioxide consumption, rate of oxygen consumption, respiratory exchange ratio, and the energy cost of walking) and gait speed during overground walking in a sample of older adults (65 years and older) with mobility limitation (slow gait). Our interest in this population is twofold: (1) energy expenditure may be a major factor in mobility and physical function
Test-Retest Reliability of Overground Metabolic Measures 347
decline in older adults with slow gait speed and (2) measures of energy expenditure (e.g., oxygen consumption, energy cost of walking) can be important to monitor progress and outcomes of rehabilitation interventions.
All participants provided written informed consent before study participation. Study protocol was approved by the university’s Institutional Review Board for Human Subjects Research.
Procedures
Methods Study Design In this study we assessed the test-retest reliability of metabolic measures derived from indirect calorimetry methods of measuring energy expenditure during overground walking. The measures were recorded over two clinic visits separated by approximately one week.
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Participants Individuals were included in the study if they were 65 years of age or older, reported the ability to ambulate for up to 6 min continuously without the assistance of a device or another person, and had a self-selected usual gait speed between 48–60 m/min (assessed using a computerized walkway). Older adults were not eligible if they: (1) were hospitalized for a serious or life-threatening illness within 6 months of the study screening, (2) had an unstable cardiac condition, (3) required the use of supplemental oxygen during rest or with activity, or (4) had a diagnosed neurological disorder that would influence their walking or movement (e.g., Parkinson’s, multiple sclerosis). Eighty-two older adults were screened for eligibility to participate; 40 met the study inclusion/exclusion criteria and passed the clinic screen for gait speed. Seven individuals had incomplete metabolic data for at least one of the walking trials (six individuals for trial 1 and one individual for trial 2) due to portable gas analysis equipment malfunction. Thirty-three of the 40 older adults (mean age [SD] = 76.4 [6.6] years; 66% female] had complete test-retest metabolic data for both testing sessions, and were included in the analyses for this study (Figure 1). No differences in baseline measures were observed between individuals with incomplete data and those included in the study.
Figure 1 — Study recruitment flowchart.
Upon verification of appropriate vital signs (systolic blood pressure < 200 mgHg, diastolic blood pressure < 100 mgHg, and resting heart rate < 100 beats/min), older adults were fitted with a portable gas analysis system and provided instructions for completing the walking session. All participants were allowed to practice walking with the portable device before completing the recorded walking trial. Participants were instructed to walk continuously around a 150 ft. flat indoor walking track at their self-selected usual walking pace for a minimum of 4 min, and up to 6 min if they felt capable (all 33 participants completed 6 min for both walking trials). Participants were encouraged to limit talking during the walking session in an effort to reduce the potential impact it may have on the metabolic variables. Participants repeated the above procedures approximately one week following the initial session. All measures were recorded by the same investigator for both testing sessions.
Outcome Measures Portable Metabolic Measures of Energy Expenditure. All
measures of energy expenditure were collected during a period of steady state for each participant. At physiologic steady state, the cardiopulmonary system delivers sufficient oxygenated blood to the active tissues to support oxidative metabolism to generate the energy needed for the activity. Thus, (at steady state) the amount of oxygen used represents the amount of energy used to complete the activity. The plateau of the rate of oxygen consumption represents the physiological steady state (McArdle et al., 1986). To confirm the period of steady state in the oxygen consumption record, various methods have been applied, including acceptable variations in oxygen consumption, time from onset of submaximal exercise, and physiological indicators of aerobic metabolism (McArdle et al., 1986; VanSwearingen et al., 2009; Waters, 1992). Some researchers will define an acceptable percent variation in the rate of oxygen consumption to confirm steady state for a duration of the plateau period (Matarese, 1997). Others define a magnitude range for the variability of oxygen consumption, such as less than the magnitude of resting energy expenditure (e.g., < 3.5 ml/kg∙min–1), to be accepted as within steady state (VanSwearingen et al., 2009; Waters, 1992). Physiological steady state is also recognized by a combination of measures: (1) the period of steady state is typically achieved 2–3 min after the onset of submaximal, constant workload activity (McArdle et al., 1986; Waters, 1992); and (2) the mean respiratory exchange ratio (RER) for the measurement period is < 1.0, and/or is not consistently rising (McArdle et al., 1986; Waters, 1992). The rate of oxygen consumption at steady state for usual walking is about 12 ml/ kg∙min–1 for adults (Waters, 1992). The Medgraphics VO2000 portable gas analysis system (MGC Diagnostics, St. Paul, MN) was used to collect metabolic variables at physiologic steady state during bouts of overground walking. The neoprene face mask was secured over the nose and mouth, and was fitted with a pneumotach attached by umbilical tubing to the gas analyzer component of the VO2000. The VO2000 gas analyzer component and battery module are carried in pockets on the front and back of a nylon shoulder harness, and together weigh approximately 1.8 kg. At the completion of the overground
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348 Wert et al.
walk, the gas analysis recording is downloaded to the Medgraphics BreezeSuite software for the processing and report of the indirect calorimetry measures of energy expenditure. Rate of oxygen and carbon dioxide consumption, rate of oxygen consumption corrected for body weight, and RER were derived for each walking session. The VO2000 uses a breath-by-breath measure of gas exchange; data are recorded and stored as the three-breath average. Mean values for all metabolic measures were averaged from the steady-state recordings (range 1–3 min; mean = 2.0 min for both walking trials). As previously mentioned, energy cost of walking (ml/kg/m) was derived by standardizing (dividing) the mean oxygen consumption (ml/kg/min) recorded during physiological steady state by mean gait speed (m/min) during the walk (see gait speed measurement description below). Portable gas analysis devices have been shown to be a valid and reliable tool for assessing metabolic variables during walking (Anderson & University, 2006; Hannink et al., 2010; McLaughlin, King, Howley, Bassett, & Ainsworth, 2001; Schrack, Simonsick, & Ferrucci, 2010), and have been shown to have little impact on metabolic values compared with traditional stationary methods of data collection (Bales et al., 2001; Gault, Clements, & Willems, 2009). Gait Speed: For Eligibility and During Metabolic Measurement. To determine eligibility based on slow gait speed (≤ 1.0
m/s), participants completed two practice trials walking across a computerized walkway. Upon completion of the practice trials, a third trial was performed and gait speed was recorded to determine study eligibility. Gait speed for the 6-min walking trials was determined over a measured distance at two positions along the straight sections of the indoor walking track. The time to traverse a 4-m distance (positioned within each of the two straightaways of the indoor track) was manually recorded with a stopwatch. The recording of gait speed was initiated when any part of the participant’s foot crossed the tape marking the beginning of the 4-m distance, and recording was stopped when any portion of the participant’s foot crossed the marking for the end of the 4-m distance. Participants were not made aware of the 4-m markings. Gait speed was derived by dividing the 4-m distance by the time to complete the distance to yield the units of meters/second. The total number of gait speeds recorded for each participant varied depending on walking speed, stopwatch failure, and mis-timed starts/stops; the total number of gait speed recordings per individual/session ranged from 2–15, with the median being 6 recordings/individual per session. Consistency of overground gait speed has been previously reported (ICC = .84) (Evans, Goldie, & Hill, 1997; Fulk & Echternach, 2008; Wert, Brach, Perera, & VanSwearingen, 2013b). The individual responsible for gait speed data collection was a licensed physical therapist with 12 years of clinical experience working with and assessing such measures in older adults.
Demographics and Comorbidity. The older adults self-reported basic demographic information (age, sex, marital status, and education) and comorbidity status using the Comorbidity Index (Rigler, Studenski, Wallace, Reker, & Duncan, 2002). The Comorbidity Index includes questions about 18 different categories of health conditions; the responses condense to represent general health status in eight basic health domains.
standard deviation) were used to describe our sample of older adults. Student’s dependent t test was used to assess differences between variables for the two walking sessions. A significant difference was defined as p < .05. Intraclass correlation coefficient (ICC [two-way mixed effects model, absolute agreement]) was computed to assess the relative reliability of each variable. Excellent reliability was defined as an ICC > .75 (Rosner, 1995). Absolute reliability for each outcome measure was determined by deriving the standard error of the measure (SEM), calculated as: SEM = SD (√1–ICC). The SEM was then used to calculate the minimal detectable change (MDC) score at a 95% confidence level, calculated as: MDC = SEM × √2 × 1.96. The MDC provides a range of values within which a truly unchanged participant’s score is expected to remain over repeat testing, at the 95% confidence level (Stratford, 2004).
Results Characteristics of the sample of community-dwelling older adults are summarized in Table 1. Overall, the older adults studied walked slowly compared with average adult walking speed (0.93 m/s vs. 1.25 m/s) and had little comorbidity. The percentages of specific comorbidities represented in our sample (n = 33) were: 85% musculoskeletal, 71% visual, 41% cancer, 35% general, 15% respiratory, 11% cardiac, 9% diabetes, and 0% neurological. The results of the relative test-retest reliability for the indirect calorimetry measures of energy expenditure and gait speed are summarized in Table 2. All five metabolic variables and gait speed had excellent reliability (ICCs > .75) (Rosner, 1995); oxygen consumption and carbon dioxide production showed the highest relative reliability (ICC = .91 for both variables). Mean values for oxygen consumption (gross and body weight corrected) and energy cost of walking did not differ between visits (p > .05); while RER (mean difference = –.03, p = .02) and carbon dioxide production (mean difference = –39.72 ml/kg∙min–1, p = .03) showed statistically significant differences between walking sessions. Similarly, a statistical difference was also observed for gait speed (mean difference = –0.031 m/s, p = .05), although the mean difference was less than a small clinically meaningful difference (0.05 m/s) (Perera, Mody, Woodman, & Studenski, 2006). To be sure that the number of gait speed recordings did not impact the mean gait speed difference between sessions, gait speed was stratified based on the number of recorded gait speeds (≤ 6 recordings and > 6 recordings), and the mean difference in gait speed was then determined. With stratification by number of recorded gait speeds, differences in gait speed between walking sessions remained below a small meaningful change (mean difference = 0.015 m/s and –0.035 m/s, respectively). The confidence intervals for the relative reliability, as well as the absolute reliability and MDC for all of the outcome measures are also presented in Table 2. Table 1 Characteristics of Study Sample (n = 33) Characteristics
Mean (SD)
Age (years)
76.4 (6.6)
Sex (n, % female)
22 (67)
Data Analysis
Gait speed (m/s)
0.93 (0.05)
Analyses were conducted using SPSS Statistics software, version 20 (IBM, Armonk, NY). Basic descriptive statistics (mean and
Body mass index
29.9 (7.2)
Comorbidity (0–8)
2.7 (1.4)
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Table 2 Values, Differences, Relative Reliability, Absolute Reliability, and Minimal Detectable Change of Metabolic Measures of Energy Expenditure and Gait Speed During Overground Walking in Older Adults With Slow Gait Variables Oxygen consumption (ml/min) Oxygen consumption (ml/kg/min) Carbon dioxide production (ml/min)
Visit 1
Visit 2
Mean Difference (p)
ICC
95% CI
SEM
MDC
813.96 (199.79)
833.28 (206.50)
–19.32 (.35)
.91
(0.82–0.96)
34.71
96.21
10.12 (1.80)
10.37 (1.81)
–.25 (.30)
.84
(0.67–0.92)
0.54
1.50
676.25 (165.60)
715.98 (182.80)
–39.72 (.03)
.91
(0.80–0.96)
29.22
81.00
Respiratory exchange ratio
0.84 (0.083)
0.86 (0.084)
–.02 (.02)
.83
(0.63–0.92)
0.03
0.07
Energy cost of walking (ml/kg/m)
0.172 (0.027)
0.172 (0.029)
.0003 (.94)
.81
(0.62–0.91)
0.01
0.02
0.98 (0.14)
1.01 (0.12)
–.03 (.05)
.88
(0.77–0.94)
0.03
0.24
Gait speed (m/s)
Abbreviations: ICC = intraclass correlation coefficient; CI = confidence interval; SEM = standard error of measurement; MDC = minimal detectable change (at 95% CI). Note. Values (mean, SD); differences (mean, significance); relative reliability (ICC); absolute reliability (SEM); and minimal detectable change (MDC95).
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Discussion The test-retest reliability results of this study indicate metabolic measures and gait speed recorded during more natural and less controlled conditions (e.g., overground walking) can be reliably determined in older adults with slow gait. The assessment of metabolic measures of energy expenditure need no longer be restricted to only laboratory settings and treadmill testing for older adults, environments where both gait characteristics and metabolic variables are known to be influenced (Frenkel-Toledo et al., 2005; Parvataneni et al., 2009; Warabi et al., 2005). While using portable gas analysis equipment during overground walking, we found the metabolic measures to be reliable by the ICC for between test sessions. However, differences in the reliability of the individual measures and some mean differences observed do deserve some attention.
Relative Reliability We were able to compare our study results to studies which similarly assessed the relative reliability of metabolic variables during overground gait in vulnerable populations, or among persons expected to have greater potential for inconsistent walking or behavior (e.g., those with stroke and traumatic brain injury) (Dawes et al., 2004; Eng et al., 2004; Stookey et al., 2013). The reliability of indirect calorimetry measures of energy expenditure observed in our study (ICCs = .80–.91) approximated or surpassed reliability values reported by other studies assessing similar metabolic variables during overground walking in vulnerable populations (ICCs = .42–.97) (Dawes et al., 2004; Eng et al., 2004; Stookey et al., 2013). Similarly, the relative reliability of overground gait speed for our study (ICC = .89) approximated values reported by Dawes and colleagues in their study examining test-retest reliability of oxygen consumption during self-selected walking in individuals recovering from brain injury (ICC = .70) and in healthy controls (ICC = .89) (Dawes et al., 2004). The differences in the reported relative reliability of the overground assessment of metabolic measures and gait speed between our work and the studies cited above likely lie in the variety of underlying diagnoses and wide range of physical impairments affecting walking inherent in the various study samples. A high level of day-to-day variability in behavior is certainly expected in individuals with neurological impairments compared with those without neurological insult (Dawes et al., 2004). The chronicity of disease and stage of rehabilitation may also have some bearing on day-to-day performance. While Stookey et al. (2013) included only those participants who were at least six months post stroke occurrence, Dawes et al. (2004) only report selecting individuals
with acquired brain injury with residual gait abnormalities who were participating in physiotherapy—chronicity of diagnosis wasn’t mentioned. Individuals in the more acute stages of recovery would be expected to exhibit more variability in behavior and function, which may have contributed to the lower ICCs reported by Dawes et al. (2004) than the values reported by Stookey et al. (2013) and our study.
Absolute Reliability and Minimal Detectable Change The absolute reliability (SEM) of the metabolic variables and gait speed helps to estimate the precision of the values on repeated testing (Rosner, 1995). The SEM and MDC95 are clinically useful as they are expressed in the same units as the original measure. Values for the SEM and MDC95 are reported in Table 2. Using one SEM, a range of values can be created for each variable in which, 68% of the time, the true score for each variable can be expected to lie between. For example, an older adult with a value of 676 ml/min for carbon dioxide production will have scores between 647–705 ml/ min 68% of the time. In addition, while we know of no “adequate” level of absolute reliability, the SEM derived in our study allows for comparison between the mean differences observed for each outcome variable and the amount of difference that can be attributed to error for each measure, with regard to our specific older adult population. As seen in Table 2, five out of six mean differences were less than or equal to the error of the measure for each respective variable—therefore, we suggest the observed differences may be due to error and not “true” differences. Clinically, the MDC95 allows clinicians and researchers to be confident (95% of the time) that a true difference or change has occurred when a value is greater than the MDC95. Thus, if an older adult had a mean carbon dioxide production level of 676 ml/ min, our MDC95 value suggests that a value outside the range of 595–757 ml/min would indicate a change in performance (95% of the time). This would suggest that although a statistical difference was found for carbon dioxide production between walking trials in our older adults, the mean difference (albeit greater than the measurement error) is likely to reflect “normal” variability of this metabolic measure in our sample, but (95% of the time), not likely to be a true change in carbon dioxide production.
Strengths One of the major strengths of this study is that we were able to demonstrate acceptable reliability of metabolic and gait speed measures during overground assessment in older adults with
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mobility limitation, a highly relevant subsample of older adults who are at greater risk for future decline in physical function and falls.
Limitations
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The findings of relative and absolute reliability are specific to our sample of older adults who have slower gait speed compared with age-related norms (per selection criteria), a low number of comorbid conditions, and were independent community-dwelling individuals. As such, our findings are best generalized to similar subpopulations of older adults. We also recognize that the absolute reliability (SEM) and MDC95 are representative of our specific study sample; additional studies assessing reliability would begin to provide additional support for the generalizability of metabolic and gait speed values across the broader population of older adults.
Clinical Relevance Metabolic-related variables, such as energy cost of walking, have been shown to be related to altered gait mechanics and lower reports of physical function in some populations of older adults. While the assessment of such variables can provide researchers and clinicians with meaningful information to guide the development of gait-related interventions, the manner in which metabolic variables are assessed may impact the accuracy of the values that are obtained. While walking on a treadmill ensures a consistent and well-controlled environment (workload), the treadmill has also been shown to alter specific gait characteristics and, as such, can alter subsequent metabolic values (Schrack, Simonsick, & Ferrucci, 2010; Warabi et al., 2005). Demonstrating the ability to reliably record metabolic values during less controlled overground settings supports the assessment of gait efficiency measures in more natural and routine settings, thus eliminating the influences presented when using a treadmill. Clinicians and researchers can be confident that assessments of efficiency of movement in older adults with slow gait can be completed reliable during overground walking, providing an alternative to traditional treadmill assessments. Future research should continue to focus on determining the appropriateness of such measurement methods across additional populations with various gait, cognitive, and physical limitations (e.g., Parkinson’s, Alzheimer’s).
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ORIGINAL RESEARCH
The Test-Retest Reliability of Indirect Calorimetry Measures of Energy Expenditure During Overground Walking in Older Adults With Mobility Limitations David M. Wert, Jessie M. VanSwearingen, Subashan Perera, and Jennifer S. Brach The purpose of this study was to assess the relative and absolute reliability of metabolic measures of energy expenditure and gait speed during overground walking in older adults with mobility limitations. Thirty-three (mean age [SD] = 76.4 [6.6] years; 66% female) older adults with slow gait participated. Measures of energy expenditure and gait speed were recorded during two 6-min bouts of overground walking (1 week apart) at a self-selected “usual” walking pace. The relative reliability for all variables was excellent: ICC = .81–.91. Mean differences for five of the six outcome variables was less than or equal to the respected SEM, while all six mean differences fell below the calculated MDC95. Clinicians and researchers can be confident that metabolic measures of energy expenditure and gait speed in older adults with slow walking speeds can be reliably assessed during overground walking, providing an alternative to traditional treadmill assessments. Keywords: reliability, indirect calorimetry, older adults
Once common only to the world of sport and athletic performance, the metabolic assessment of task-related movement efficiency (i.e., energy cost of performance) is now commonly used to describe walking in older adults with and without mobility limitations (Christiansen, Schenkman, McFann, Wolfe, & Kohrt, 2009; Dawes et al., 2004; Malatesta et al., 2003; VanSwearingen et al., 2009; Waters & Lunsford, 1985; Wert, Brach, Perera, & VanSwearingen, 2013a). For both athletes and older adults, the physiological performance information is sought for essentially the same questions: How much energy for activity can be produced? and What is the energy used (cost) for the task performance? The metabolic measures collected during such assessments are indicators of the person’s task-specific motor efficiency or skill, which may be used both to recognize decline in health status and walking ability in older adults and as outcomes of targeted interventions for mobility disability. Metabolic measures of walking have traditionally been recorded during treadmill walking in laboratory settings. Because of the laboratory setting (i.e., stationary metabolic cart and treadmill walking), metabolic measures may not represent the metabolic demands of walking for older adults under less controlled and more natural conditions, like walking as they live (Parvataneni, Ploeg, Olney, & Brouwer, 2009). The more recent development of portable gas analysis measurement devices means the metabolic measures can be recorded during overground walking in more naturalistic environments. The ability to record metabolic measures during overground walking may be particularly important for older adults with mobility limitations. Differences in treadmill walking compared with overground walking in older adults with mobility limitations have been demonstrated; the treadmill reduces abnormal gait variability, usual walking speed on the treadmill is slower than overground, and walking on the treadmill reduces hesitancy and increases energy cost of walking (Malatesta, et al., 2003; Parvataneni et al., 2009; Warabi, Kato, Kiriyama, Yoshida, & Kobayashi, 2005). Wert, VanSwearingen, and Brach are with the Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA. Perera is with the Division of Geriatric Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA. Address author correspondence to David M. Wert at [email protected]. 346
Little is known about the reliability of metabolic measures derived from portable gas analysis in overground walking. A primary factor in metabolic gas analysis of task performance is the maintenance of physiological steady state. Gas-analysis-based metabolic measures of energy expenditure are based on principles of submaximal oxygen consumption kinetics and physiological steady state (McArdle, Katch, & Katch, 1986). Physiological steady state is the condition during submaximal exercise (task) performance at a constant workload, where the energy produced by oxidative metabolism is the energy used for the task performance. Under nonsteady-state conditions and inconsistency of workload, oxygen consumed does not represent the energy used for the performance. During treadmill walking, constant workload can be assured by setting a constant speed. Overground, the constant speed of walking (i.e., workload) is based on the consistency of the person walking. While most adults walk at a constant speed overground in usual environments, consistency may be more of a problem in older adults with mobility limitations. Literature is scant on published reports of reliability of metabolic measures assessed during overground walking, especially in vulnerable populations where consistency of workload (gait speed) may be an issue. Portable gas-analysis-derived metabolic measures of walking have been reported specifically for adults with neurologic disorders of gait across a range of ages (Dawes, et al., 2004; Eng, Dawson, & Chu, 2004; Stookey et al., 2013). However, the reliability of the measurement specifically in older adults and those without specific neurologic involvement is not well reported. This gap in the literature is of concern as older adults with slow gait are frequently referred for physical therapy assessments and gait-related interventions, and are at greater risk for future decline in mobility. Therefore, the purpose of this study was to assess the test-retest reliability of metabolic measures of energy expenditure (e.g., oxygen and carbon dioxide consumption, rate of oxygen consumption, respiratory exchange ratio, and the energy cost of walking) and gait speed during overground walking in a sample of older adults (65 years and older) with mobility limitation (slow gait). Our interest in this population is twofold: (1) energy expenditure may be a major factor in mobility and physical function
Test-Retest Reliability of Overground Metabolic Measures 347
decline in older adults with slow gait speed and (2) measures of energy expenditure (e.g., oxygen consumption, energy cost of walking) can be important to monitor progress and outcomes of rehabilitation interventions.
All participants provided written informed consent before study participation. Study protocol was approved by the university’s Institutional Review Board for Human Subjects Research.
Procedures
Methods Study Design In this study we assessed the test-retest reliability of metabolic measures derived from indirect calorimetry methods of measuring energy expenditure during overground walking. The measures were recorded over two clinic visits separated by approximately one week.
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Participants Individuals were included in the study if they were 65 years of age or older, reported the ability to ambulate for up to 6 min continuously without the assistance of a device or another person, and had a self-selected usual gait speed between 48–60 m/min (assessed using a computerized walkway). Older adults were not eligible if they: (1) were hospitalized for a serious or life-threatening illness within 6 months of the study screening, (2) had an unstable cardiac condition, (3) required the use of supplemental oxygen during rest or with activity, or (4) had a diagnosed neurological disorder that would influence their walking or movement (e.g., Parkinson’s, multiple sclerosis). Eighty-two older adults were screened for eligibility to participate; 40 met the study inclusion/exclusion criteria and passed the clinic screen for gait speed. Seven individuals had incomplete metabolic data for at least one of the walking trials (six individuals for trial 1 and one individual for trial 2) due to portable gas analysis equipment malfunction. Thirty-three of the 40 older adults (mean age [SD] = 76.4 [6.6] years; 66% female] had complete test-retest metabolic data for both testing sessions, and were included in the analyses for this study (Figure 1). No differences in baseline measures were observed between individuals with incomplete data and those included in the study.
Figure 1 — Study recruitment flowchart.
Upon verification of appropriate vital signs (systolic blood pressure < 200 mgHg, diastolic blood pressure < 100 mgHg, and resting heart rate < 100 beats/min), older adults were fitted with a portable gas analysis system and provided instructions for completing the walking session. All participants were allowed to practice walking with the portable device before completing the recorded walking trial. Participants were instructed to walk continuously around a 150 ft. flat indoor walking track at their self-selected usual walking pace for a minimum of 4 min, and up to 6 min if they felt capable (all 33 participants completed 6 min for both walking trials). Participants were encouraged to limit talking during the walking session in an effort to reduce the potential impact it may have on the metabolic variables. Participants repeated the above procedures approximately one week following the initial session. All measures were recorded by the same investigator for both testing sessions.
Outcome Measures Portable Metabolic Measures of Energy Expenditure. All
measures of energy expenditure were collected during a period of steady state for each participant. At physiologic steady state, the cardiopulmonary system delivers sufficient oxygenated blood to the active tissues to support oxidative metabolism to generate the energy needed for the activity. Thus, (at steady state) the amount of oxygen used represents the amount of energy used to complete the activity. The plateau of the rate of oxygen consumption represents the physiological steady state (McArdle et al., 1986). To confirm the period of steady state in the oxygen consumption record, various methods have been applied, including acceptable variations in oxygen consumption, time from onset of submaximal exercise, and physiological indicators of aerobic metabolism (McArdle et al., 1986; VanSwearingen et al., 2009; Waters, 1992). Some researchers will define an acceptable percent variation in the rate of oxygen consumption to confirm steady state for a duration of the plateau period (Matarese, 1997). Others define a magnitude range for the variability of oxygen consumption, such as less than the magnitude of resting energy expenditure (e.g., < 3.5 ml/kg∙min–1), to be accepted as within steady state (VanSwearingen et al., 2009; Waters, 1992). Physiological steady state is also recognized by a combination of measures: (1) the period of steady state is typically achieved 2–3 min after the onset of submaximal, constant workload activity (McArdle et al., 1986; Waters, 1992); and (2) the mean respiratory exchange ratio (RER) for the measurement period is < 1.0, and/or is not consistently rising (McArdle et al., 1986; Waters, 1992). The rate of oxygen consumption at steady state for usual walking is about 12 ml/ kg∙min–1 for adults (Waters, 1992). The Medgraphics VO2000 portable gas analysis system (MGC Diagnostics, St. Paul, MN) was used to collect metabolic variables at physiologic steady state during bouts of overground walking. The neoprene face mask was secured over the nose and mouth, and was fitted with a pneumotach attached by umbilical tubing to the gas analyzer component of the VO2000. The VO2000 gas analyzer component and battery module are carried in pockets on the front and back of a nylon shoulder harness, and together weigh approximately 1.8 kg. At the completion of the overground
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348 Wert et al.
walk, the gas analysis recording is downloaded to the Medgraphics BreezeSuite software for the processing and report of the indirect calorimetry measures of energy expenditure. Rate of oxygen and carbon dioxide consumption, rate of oxygen consumption corrected for body weight, and RER were derived for each walking session. The VO2000 uses a breath-by-breath measure of gas exchange; data are recorded and stored as the three-breath average. Mean values for all metabolic measures were averaged from the steady-state recordings (range 1–3 min; mean = 2.0 min for both walking trials). As previously mentioned, energy cost of walking (ml/kg/m) was derived by standardizing (dividing) the mean oxygen consumption (ml/kg/min) recorded during physiological steady state by mean gait speed (m/min) during the walk (see gait speed measurement description below). Portable gas analysis devices have been shown to be a valid and reliable tool for assessing metabolic variables during walking (Anderson & University, 2006; Hannink et al., 2010; McLaughlin, King, Howley, Bassett, & Ainsworth, 2001; Schrack, Simonsick, & Ferrucci, 2010), and have been shown to have little impact on metabolic values compared with traditional stationary methods of data collection (Bales et al., 2001; Gault, Clements, & Willems, 2009). Gait Speed: For Eligibility and During Metabolic Measurement. To determine eligibility based on slow gait speed (≤ 1.0
m/s), participants completed two practice trials walking across a computerized walkway. Upon completion of the practice trials, a third trial was performed and gait speed was recorded to determine study eligibility. Gait speed for the 6-min walking trials was determined over a measured distance at two positions along the straight sections of the indoor walking track. The time to traverse a 4-m distance (positioned within each of the two straightaways of the indoor track) was manually recorded with a stopwatch. The recording of gait speed was initiated when any part of the participant’s foot crossed the tape marking the beginning of the 4-m distance, and recording was stopped when any portion of the participant’s foot crossed the marking for the end of the 4-m distance. Participants were not made aware of the 4-m markings. Gait speed was derived by dividing the 4-m distance by the time to complete the distance to yield the units of meters/second. The total number of gait speeds recorded for each participant varied depending on walking speed, stopwatch failure, and mis-timed starts/stops; the total number of gait speed recordings per individual/session ranged from 2–15, with the median being 6 recordings/individual per session. Consistency of overground gait speed has been previously reported (ICC = .84) (Evans, Goldie, & Hill, 1997; Fulk & Echternach, 2008; Wert, Brach, Perera, & VanSwearingen, 2013b). The individual responsible for gait speed data collection was a licensed physical therapist with 12 years of clinical experience working with and assessing such measures in older adults.
Demographics and Comorbidity. The older adults self-reported basic demographic information (age, sex, marital status, and education) and comorbidity status using the Comorbidity Index (Rigler, Studenski, Wallace, Reker, & Duncan, 2002). The Comorbidity Index includes questions about 18 different categories of health conditions; the responses condense to represent general health status in eight basic health domains.
standard deviation) were used to describe our sample of older adults. Student’s dependent t test was used to assess differences between variables for the two walking sessions. A significant difference was defined as p < .05. Intraclass correlation coefficient (ICC [two-way mixed effects model, absolute agreement]) was computed to assess the relative reliability of each variable. Excellent reliability was defined as an ICC > .75 (Rosner, 1995). Absolute reliability for each outcome measure was determined by deriving the standard error of the measure (SEM), calculated as: SEM = SD (√1–ICC). The SEM was then used to calculate the minimal detectable change (MDC) score at a 95% confidence level, calculated as: MDC = SEM × √2 × 1.96. The MDC provides a range of values within which a truly unchanged participant’s score is expected to remain over repeat testing, at the 95% confidence level (Stratford, 2004).
Results Characteristics of the sample of community-dwelling older adults are summarized in Table 1. Overall, the older adults studied walked slowly compared with average adult walking speed (0.93 m/s vs. 1.25 m/s) and had little comorbidity. The percentages of specific comorbidities represented in our sample (n = 33) were: 85% musculoskeletal, 71% visual, 41% cancer, 35% general, 15% respiratory, 11% cardiac, 9% diabetes, and 0% neurological. The results of the relative test-retest reliability for the indirect calorimetry measures of energy expenditure and gait speed are summarized in Table 2. All five metabolic variables and gait speed had excellent reliability (ICCs > .75) (Rosner, 1995); oxygen consumption and carbon dioxide production showed the highest relative reliability (ICC = .91 for both variables). Mean values for oxygen consumption (gross and body weight corrected) and energy cost of walking did not differ between visits (p > .05); while RER (mean difference = –.03, p = .02) and carbon dioxide production (mean difference = –39.72 ml/kg∙min–1, p = .03) showed statistically significant differences between walking sessions. Similarly, a statistical difference was also observed for gait speed (mean difference = –0.031 m/s, p = .05), although the mean difference was less than a small clinically meaningful difference (0.05 m/s) (Perera, Mody, Woodman, & Studenski, 2006). To be sure that the number of gait speed recordings did not impact the mean gait speed difference between sessions, gait speed was stratified based on the number of recorded gait speeds (≤ 6 recordings and > 6 recordings), and the mean difference in gait speed was then determined. With stratification by number of recorded gait speeds, differences in gait speed between walking sessions remained below a small meaningful change (mean difference = 0.015 m/s and –0.035 m/s, respectively). The confidence intervals for the relative reliability, as well as the absolute reliability and MDC for all of the outcome measures are also presented in Table 2. Table 1 Characteristics of Study Sample (n = 33) Characteristics
Mean (SD)
Age (years)
76.4 (6.6)
Sex (n, % female)
22 (67)
Data Analysis
Gait speed (m/s)
0.93 (0.05)
Analyses were conducted using SPSS Statistics software, version 20 (IBM, Armonk, NY). Basic descriptive statistics (mean and
Body mass index
29.9 (7.2)
Comorbidity (0–8)
2.7 (1.4)
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Table 2 Values, Differences, Relative Reliability, Absolute Reliability, and Minimal Detectable Change of Metabolic Measures of Energy Expenditure and Gait Speed During Overground Walking in Older Adults With Slow Gait Variables Oxygen consumption (ml/min) Oxygen consumption (ml/kg/min) Carbon dioxide production (ml/min)
Visit 1
Visit 2
Mean Difference (p)
ICC
95% CI
SEM
MDC
813.96 (199.79)
833.28 (206.50)
–19.32 (.35)
.91
(0.82–0.96)
34.71
96.21
10.12 (1.80)
10.37 (1.81)
–.25 (.30)
.84
(0.67–0.92)
0.54
1.50
676.25 (165.60)
715.98 (182.80)
–39.72 (.03)
.91
(0.80–0.96)
29.22
81.00
Respiratory exchange ratio
0.84 (0.083)
0.86 (0.084)
–.02 (.02)
.83
(0.63–0.92)
0.03
0.07
Energy cost of walking (ml/kg/m)
0.172 (0.027)
0.172 (0.029)
.0003 (.94)
.81
(0.62–0.91)
0.01
0.02
0.98 (0.14)
1.01 (0.12)
–.03 (.05)
.88
(0.77–0.94)
0.03
0.24
Gait speed (m/s)
Abbreviations: ICC = intraclass correlation coefficient; CI = confidence interval; SEM = standard error of measurement; MDC = minimal detectable change (at 95% CI). Note. Values (mean, SD); differences (mean, significance); relative reliability (ICC); absolute reliability (SEM); and minimal detectable change (MDC95).
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Discussion The test-retest reliability results of this study indicate metabolic measures and gait speed recorded during more natural and less controlled conditions (e.g., overground walking) can be reliably determined in older adults with slow gait. The assessment of metabolic measures of energy expenditure need no longer be restricted to only laboratory settings and treadmill testing for older adults, environments where both gait characteristics and metabolic variables are known to be influenced (Frenkel-Toledo et al., 2005; Parvataneni et al., 2009; Warabi et al., 2005). While using portable gas analysis equipment during overground walking, we found the metabolic measures to be reliable by the ICC for between test sessions. However, differences in the reliability of the individual measures and some mean differences observed do deserve some attention.
Relative Reliability We were able to compare our study results to studies which similarly assessed the relative reliability of metabolic variables during overground gait in vulnerable populations, or among persons expected to have greater potential for inconsistent walking or behavior (e.g., those with stroke and traumatic brain injury) (Dawes et al., 2004; Eng et al., 2004; Stookey et al., 2013). The reliability of indirect calorimetry measures of energy expenditure observed in our study (ICCs = .80–.91) approximated or surpassed reliability values reported by other studies assessing similar metabolic variables during overground walking in vulnerable populations (ICCs = .42–.97) (Dawes et al., 2004; Eng et al., 2004; Stookey et al., 2013). Similarly, the relative reliability of overground gait speed for our study (ICC = .89) approximated values reported by Dawes and colleagues in their study examining test-retest reliability of oxygen consumption during self-selected walking in individuals recovering from brain injury (ICC = .70) and in healthy controls (ICC = .89) (Dawes et al., 2004). The differences in the reported relative reliability of the overground assessment of metabolic measures and gait speed between our work and the studies cited above likely lie in the variety of underlying diagnoses and wide range of physical impairments affecting walking inherent in the various study samples. A high level of day-to-day variability in behavior is certainly expected in individuals with neurological impairments compared with those without neurological insult (Dawes et al., 2004). The chronicity of disease and stage of rehabilitation may also have some bearing on day-to-day performance. While Stookey et al. (2013) included only those participants who were at least six months post stroke occurrence, Dawes et al. (2004) only report selecting individuals
with acquired brain injury with residual gait abnormalities who were participating in physiotherapy—chronicity of diagnosis wasn’t mentioned. Individuals in the more acute stages of recovery would be expected to exhibit more variability in behavior and function, which may have contributed to the lower ICCs reported by Dawes et al. (2004) than the values reported by Stookey et al. (2013) and our study.
Absolute Reliability and Minimal Detectable Change The absolute reliability (SEM) of the metabolic variables and gait speed helps to estimate the precision of the values on repeated testing (Rosner, 1995). The SEM and MDC95 are clinically useful as they are expressed in the same units as the original measure. Values for the SEM and MDC95 are reported in Table 2. Using one SEM, a range of values can be created for each variable in which, 68% of the time, the true score for each variable can be expected to lie between. For example, an older adult with a value of 676 ml/min for carbon dioxide production will have scores between 647–705 ml/ min 68% of the time. In addition, while we know of no “adequate” level of absolute reliability, the SEM derived in our study allows for comparison between the mean differences observed for each outcome variable and the amount of difference that can be attributed to error for each measure, with regard to our specific older adult population. As seen in Table 2, five out of six mean differences were less than or equal to the error of the measure for each respective variable—therefore, we suggest the observed differences may be due to error and not “true” differences. Clinically, the MDC95 allows clinicians and researchers to be confident (95% of the time) that a true difference or change has occurred when a value is greater than the MDC95. Thus, if an older adult had a mean carbon dioxide production level of 676 ml/ min, our MDC95 value suggests that a value outside the range of 595–757 ml/min would indicate a change in performance (95% of the time). This would suggest that although a statistical difference was found for carbon dioxide production between walking trials in our older adults, the mean difference (albeit greater than the measurement error) is likely to reflect “normal” variability of this metabolic measure in our sample, but (95% of the time), not likely to be a true change in carbon dioxide production.
Strengths One of the major strengths of this study is that we were able to demonstrate acceptable reliability of metabolic and gait speed measures during overground assessment in older adults with
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mobility limitation, a highly relevant subsample of older adults who are at greater risk for future decline in physical function and falls.
Limitations
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The findings of relative and absolute reliability are specific to our sample of older adults who have slower gait speed compared with age-related norms (per selection criteria), a low number of comorbid conditions, and were independent community-dwelling individuals. As such, our findings are best generalized to similar subpopulations of older adults. We also recognize that the absolute reliability (SEM) and MDC95 are representative of our specific study sample; additional studies assessing reliability would begin to provide additional support for the generalizability of metabolic and gait speed values across the broader population of older adults.
Clinical Relevance Metabolic-related variables, such as energy cost of walking, have been shown to be related to altered gait mechanics and lower reports of physical function in some populations of older adults. While the assessment of such variables can provide researchers and clinicians with meaningful information to guide the development of gait-related interventions, the manner in which metabolic variables are assessed may impact the accuracy of the values that are obtained. While walking on a treadmill ensures a consistent and well-controlled environment (workload), the treadmill has also been shown to alter specific gait characteristics and, as such, can alter subsequent metabolic values (Schrack, Simonsick, & Ferrucci, 2010; Warabi et al., 2005). Demonstrating the ability to reliably record metabolic values during less controlled overground settings supports the assessment of gait efficiency measures in more natural and routine settings, thus eliminating the influences presented when using a treadmill. Clinicians and researchers can be confident that assessments of efficiency of movement in older adults with slow gait can be completed reliable during overground walking, providing an alternative to traditional treadmill assessments. Future research should continue to focus on determining the appropriateness of such measurement methods across additional populations with various gait, cognitive, and physical limitations (e.g., Parkinson’s, Alzheimer’s).
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