ALTHOUGH IT HAS BEEN SHOWN that the energy demands

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1 136 ORIGINAL ARTICLE Energy Demands of Walking in Persons With Postpoliomyelitis Syndrome: Relationship With Muscle Strength and Reproducibility Merel-Anne Brehm, MSc, Frans Nollet, MD, PhD, Jaap Harlaar, PhD ABSTRACT. Brehm M-A, Nollet F, Harlaar J. Energy demands of walking in persons with postpoliomyelitis syndrome: relationship with muscle strength and reproducibility. Arch Phys Med Rehabil 2006;87:136-. Objectives: To describe the energy demands of walking in subjects with postpoliomyelitis syndrome () in comparison with the demands in healthy subjects, and to assess the reproducibility of walking energy measurements. Design: Four repeated measurements with a 1-week interval between each measurement. Setting: Outpatient clinic of a university hospital. Participants: Fourteen subjects with and 14 age- and sex-matched healthy subjects. Interventions: Not applicable. Main Outcome Measures: Walking speed and energy cost of walking. The correlation parameter was lower-extremity muscle strength sum (MSS). The reproducibility parameters were standard error (SE) of measurement and smallest detectable difference (SDD). Results: Walking speed in subjects with (61.8m/min) was significantly lower ( 28%) and energy cost (4.8J kg 1 m 1 ) was significantly higher (%) than in healthy subjects. MSS correlated strongly with energy cost (r.84, P.000), explaining 71% of the variance. The SE of measurement of energy cost measurements ranged between 1.7% and 3.4% for subjects and between 1.2% and 2.4% for healthy subjects. The SDD ranged between 4.6% and 9.4% for subjects and between 3.3% and 6.6% for healthy subjects, depending on the number of repeated measurements that were considered. Conclusions: Energy cost of walking in subjects with is strongly related to the extent of muscle weakness in the lower extremities. Although variability was greater for subjects than for healthy subjects, reproducibility of energy cost measurements was high. Therefore, metabolic assessment of energy cost of walking is a sensitive tool that can reveal clinically relevant changes even in the condition of a person with. Key Words: Blood gas analysis; Energy expenditure; Muscle weakness; Postpoliomyelitis syndrome; Rehabilitation; Reproducibility of results; Walking. From the Department of Rehabilitation Medicine, VU University Medical Center, Amsterdam (Brehm, Harlaar); and Department of Rehabilitation, Academic Medical Center, University of Amsterdam, Amsterdam (Nollet), The Netherlands. Supported by the Anna Fund and ZonMw. No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the authors or upon any organization with which the authors are associated. Correspondence to Merel-Anne Brehm, MSc, Dept of Rehabilitation Medicine, VU University Medical Center, De Boelenlaan 1117, 1081 HV Amsterdam, The Netherlands, m.brehm@vumc.nl /06/ $32.00/0 doi: /j.apmr by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation ALTHOUGH IT HAS BEEN SHOWN that the energy demands of walking are elevated for many patient groups, 1-6 those demands in adults with a history of poliomyelitis have not been fully described. In our review of the few studies that have focused on the topic, we found that they either concentrated on the cardiorespiratory responses of training programs, 7,8 on walking energy in general, 9 or on the energy demands of walking in children with polio. 10 No studies have described the energy demands of walking in adults with polio and compared them with the demands of matched healthy subjects. Such information may be relevant because it has been proposed that late onset neuromuscular symptoms, which are referred to as postpoliomyelitis syndrome (), might partly be explained by severely reduced work capacity and the increased energy demands of performing sustained submaximal exercise, 8,11 thereby predisposing people with to premature fatigue in carrying out activities of daily living (ADLs). Nollet et al 11 reported that energy consumption during submaximal cycling was significantly higher in subjects with polio than in healthy subjects because of reduced muscle capacity. However, as they proposed in their conclusion, results from cycling cannot simply be generalized to walking, due to task-specific muscle loads. Walking energy can be estimated with fully automated portable gas analysis systems that measure both oxygen uptake and carbon dioxide production. From these parameters, walking energy can be calculated, thereby taking into account the relative contributions of carbohydrates and lipids. 12 Several systems that are currently available are valid for determining energy consumption during exercise However, information on the reproducibility of portable metabolic systems is limited. 18 Most studies have focused on laboratory-based systems 19,20 and portable systems of an early generation 21,22 ; little is known about the reproducibility of the currently available portable systems. 23,24 There have been no studies of the dayto-day reproducibility of submaximal walking energy measured with an up-to-date portable gas analysis system in subjects with polio or with other disorders. Such information is required in order to consider the clinical applicability of these measurements in evaluating patients and/or the effects of interventions. Our objective in this study was 2-fold: to describe the energy demands of walking in subjects with by comparing them with age- and sex-matched healthy subjects, and to assess the reproducibility of the walking energy measurements. METHODS Participants The study sample included 14 adults with, defined according to the March of Dimes Foundation criteria, 25 and 14

2 ENERGY DEMANDS OF WALKING POSTPOLIO, Brehm 137 Table 1: Clinical Condition of Subjects With Subject MSS* MSA* Walking Distance (m) Walking Devices KAFO L Cane Cane AFO BL Cane KAFO L AFO R Cane None m None None KAFO R KAFO R cane BL KAFO R cane Abbreviations: AFO, ankle-foot orthosis; BL, bilateral; KAFO, kneeankle-foot orthosis; L, left; MSA, muscle strength asymmetry; MSS, muscle strength sum; R, right. *MSS range, 0 32; MSA range, 0 1. Self-reported maximal walking distance classified into 4 categories: 250m; m; m; 1000m. healthy adults. Participants with (6 men, 8 women) were recruited from the outpatient clinic of the Department of Rehabilitation Medicine at the VU University Medical Center in Amsterdam. The inclusion criteria were: (1) a history of poliomyelitis with residual muscle weakness in at least 1 leg; (2) ability to walk for at least 4 minutes at a self-selected, comfortable walking speed (with or without walking aids); (3) age between 18 and 70 years; and (4) no existent impaired pulmonary or cardiac disease, as confirmed by medical examination. They ranged in age from 36 to 67 years (mean, 55y), their body mass ranged from 53 to 95kg (mean, 72kg), and their body mass index (BMI) ranged from 20 to 28kg/m 2 (mean, 24.5kg/ m 2 ). The 14 healthy subjects (6 men, 8 women) were employees of the outpatient clinic. The activity levels of these subjects were moderate, meaning that they engaged in some form of physical exercise once or twice per week. The healthy subjects were matched to the subjects with regard to age, sex, body mass, and height. Their ages ranged from 31 to 64 years (mean, 51y), their body mass ranged from 53 to 95kg (mean, 74kg), and their BMI ranged from 19 to 29kg/m 2 (mean, 24.1 kg/m 2 ). Subject characteristics did not differ between the groups. The clinical condition of the subjects, with respect to muscle strength in the lower extremities, self-reported maximal walking capacity, and walking devices are described in table 1. Muscle strength for hip flexors, hip extensors, hip abductors, hip adductors, knee flexors, knee extensors, dorsiflexors, and plantarflexors was assessed by manual testing, according to the Medical Research Council scale. 26 From these strength measurements, 2 parameters were determined: a muscle strength sum (MSS), and a muscle strength asymmetry (MSA). Both parameters were calculated according to the method described by Nollet et al. 27 The VU University Medical Ethics Committee approved the study, and written informed consent was obtained from all participants. Equipment We used a lightweight portable gas analysis system (Vmax- ST), a based on breath-by-breath technology, to determine the energy demands of walking. The system is composed of a facemask, a Triple volume transducer, a gas-sample line, and a battery-operated unit (650g) that is worn on the shoulders. To maintain the highest possible accuracy, a periodic calibration of the analyzers with certified calibration gases was performed in accordance with the manufacturer s instructions. 17 Procedures Each measurement consisted of a resting test followed by a walking test. Subjects were first seated in a comfortable chair, and the equipment and the facemask were put on. The fitting of the facemask was carefully inspected for leakage. The resting test, which consisted of sitting quietly for 10 minutes, was then started. Subjects were given specific instructions to not be distracted, or to talk or laugh, and to fidget as little as possible. This test was followed by the walking test, which consisted of walking for 4 to 5 minutes on an indoor oval track with a length of 50m. Subjects were asked to walk at their usual, self-selected, comfortable speed. During the resting test and the walking test, there were breath-bybreath registrations of oxygen uptake (V O2 ) and carbon dioxide production (V CO2 ). The measurements were completed 4 times on 4 different days (repetitions) with a 1-week interval. Data Analysis Walking speed was expressed in meters per minute and was calculated as the distance walked over the last 2 minutes of the test, divided by 2. For each walking test, the mean V O2 and V CO2 were determined by averaging all breath-by-breath values for the last 2 steady-state minutes of the test. Respiratory exchange ratios (RERs) were calculated as the quotient of V CO2 and V O2.V O2 and RER values were then used to compute the energy demands of walking. The following body mass normalized parameters were calculated. Gross energy consumption, defined as the total energy used per unit of time, was calculated in J kg 1 min 1, according to the Garby and Astrup method. 12 Gross energy cost, defined as the total energy used per unit of distance, was calculated in J kg 1 m 1 by dividing energy consumption during walking by walking speed. Based on the normal distribution of the data, we used parametric statistics. Systematic differences between groups were analyzed with Student t tests. For the subjects with, we calculated Pearson product-moment correlations for walking Table 2: Mean Walking Parameter Values Over 4 Repetitions Parameter Subjects* (n 14) Healthy Subjects (n 14) Difference P Speed (m/min) (8.0) 20.2 ( 28).000 Energy consumption (J kg 1 min 1 ) (41.1) 25 (9.0).002 Energy cost (J kg 1 m 1 ) (0.28) 1.6 ().000 *Values are mean over 4 repetitions standard deviation. Difference is absolute and (percentage); percentage expressed as ([ control]/[control ]/2) 100%. Significantly different.

3 138 ENERGY DEMANDS OF WALKING POSTPOLIO, Brehm A 100 B 7 walking speed and energy cost measurements. Multiple regression analysis showed that MSA was not an independent contributor to either walking speed or energy cost (P.363). speed (m/min) walking muscle strength sum healthy speed and energy cost with MSS and MSA. Stepwise multiple regressions were applied to determine their combined effect on walking speed and energy cost. A factor with a P value below.05 was entered in the regression model, whereas it was removed with a P value above.10. Reproducibility was analyzed with the generalizability analysis. 28,29 As a measure of reproducibility, the intraclass correlation coefficient (ICC) was calculated. Furthermore, the standard error (SE) of measurement, reflecting the variability of measurements due to repetition and random error, and the smallest detectable difference (SDD), reflecting the smallest change that can be detected in a subject, were calculated. Based on the method described by Roebroeck et al, 28 reproducibility parameters were calculated for different study designs (ie, different numbers of measurement repetitions). SPSS b for Windows was used for the statistical analysis. The level of significance for all statistical tests was set at P less than.05. RESULTS muscle strength sum Walking Demands A significant difference was found between the 2 groups for all walking parameters. Walking speed was 28% lower, and energy consumption, and energy cost were, respectively, 9% and % higher for the subjects with than for the healthy subjects (table 2). For the subjects, both MSS and MSA correlated significantly with walking speed (r.77, P.001; r.56, P.036, respectively) and energy cost (r 84, P.000; r 56, P.036, respectively) (fig 1). Lower-extremity MSS explained 59% of the variance in walking speed and 71% of the variance in energy cost measurements. MSA accounted for 32% of the variance in both (J/kg/m) cost energy healthy Fig 1. Correlations (A) between MSS and walking speed (R 2.59), and (B) between MSS and energy cost (R 2.71). Legend:, subjects with (n 14); x, healthy subjects (n 14). Lines are regression lines for subjects. Reproducibility The measurements of walking parameters were more variable for the subjects than for the healthy subjects, that is, smaller ICC values and greater SE of measurement and SDD values for the subjects. The healthy subjects did not show much difference in reproducibility for the different parameters of walking speed, energy consumption, and energy cost, where in subjects reproducibility of energy cost was better than reproducibility of energy consumption or walking speed (see tables 3 and 4 for subjects and healthy subjects, respectively). The tables also show the effect of increasing the number of measurement repetitions on reproducibility. In healthy subjects, SE of measurement and SDD values were below 2.4% and 6.6%, respectively, for a design with 1 repetition, and reduced to approximately half their size when the number of repetitions was increased to 4 (see table 4). For the subjects, the SE of measurement and SDD values were below 5.7% and 16.0%, respectively, for a design with 1 repetition, and also reduced to half their size in case of 4 repetitions (see table 3). DISCUSSION In this study, subjects with used considerably more energy for walking, % per distance covered, than age- and sex-matched healthy subjects. Similar results were found in the only study of the energy demands of walking in children with polio. 10 In a group of 8 children with polio residuals, comfortable walking speed appeared to be 32% lower, and energy consumption and energy cost were, respectively, 7% lower and 50% higher, in comparison with healthy children. Studies that assessed the energy demands of walking in subjects with other disorders, such as cerebrovascular lesions, multiple sclerosis, osteoarthritis, or amputations, 1-6 found similar increments in walking demands: a slight elevation in energy consumption, a much lower walking speed and, consequently, a moderate to severe increase in energy cost. The differences in walking demands are greatest when expressed as the energy used per unit of distance, which is in line with the review by Fisher and Gullickson, 1 in which the biomechanics and determinants of gait were described and the energy demands of walking in health and disability were summarized. The 6 major determinants of gait are pelvic rotation, pelvic tilt, lateral displacement of the pelvis, knee flexion, and foot and knee mechanisms. Fisher 1 argued that in people with walking disabilities, these determinants are changed or lost. This increases the vertical excursion of the center of body mass. 31 In response, patients decrease their walking speed to avoid an increase in energy consumption. As a result, there is Table 3: Reproducibility for Subjects, Depending on the Number of Measurement Repetitions Parameter ICC SE of Measurement* SDD* No. of repetitions Speed (m/min) (4.5) 1.9 (3.2) 1.4 (2.2) 7.5 (13) 5.3 (9) 3.8 (6.1) Energy consumption (J kg 1 min 1 ) (5.7) 11.1 (3.9) 7.8 (2.7) 43.4 (16).7 (11) 21.7 (7.5) Energy cost (J kg 1 m 1 ) (3.4) (2.4) (1.7) (9.4) (6.6) (4.6) *SE of measurement and SDD values are absolute and (percentage). Percentage expressed as (absolute/mean of measurements taken) 100%.

4 ENERGY DEMANDS OF WALKING POSTPOLIO, Brehm 139 Table 4: Reproducibility for Healthy Subjects, Depending on the Number of Measurement Repetitions Parameter ICC SE of Measurement* SDD* No. of repetitions Speed (m/min) (1.9) 0.89 (1.1) 0.77 (0.9) 4. (5.3) 2.46 (3.0) 2.13 (2.6) Energy consumption (J kg 1 min 1 ) (2.3) 3.44 (1.3) 2.98 (1.1) (6.3) 9.53 (3.6) 8.26 (3.1) Energy cost (J kg 1 m 1 ) (2.4) (1.4) (1.2) (6.6) (3.8) (3.3) *SE of measurement and SDD values are absolute and (percentage). Percentage expressed as (absolute/mean of measurements taken) 100%. an increase in energy demands for walking a set distance, which makes walking less efficient in terms of energy cost. The reduced walking efficiency in subjects was strongly associated with the degree of lower-extremity muscle weakness. MSS, a lower-extremity muscle strength index, correlated with comfortable walking speed, and accounted for 59% of the variance. This is consistent with the results of the study by Siegel et al, 32 who found a comparable association in children with idiopathic inflammatory myopathies. MSS correlated even better with energy cost, accounting for 71% of its variance. Although MSS is an indicator of the severity of polio, this percentage is surprisingly high, considering that most subjects used orthoses and/or crutches to compensate for paresis. These devices influence energy costs of walking because of their weight and properties. It is, however, difficult to separate these effects from severity of paresis, as many people cannot walk without them. Orthotic and walking devices may also account in part for the unexplained variance in energy cost. However, from a clinical perspective, it is important to realize that the severity of paresis is apparently a strong indicator of the increased energy cost of walking in patients. The observation that subjects with polio residuals with more severe paresis are less able to meet ADL demands and perceive more limitations in physical functioning 33 may, to a certain extent, be due to the higher energy demands of walking. It has been reported that oxygen consumption was elevated in subjects with polio who performed a cycling exercise, 11 mainly in association with reduced leg muscle capacity. Oxygen consumption during cycling increased significantly with increasing strength asymmetry. Our study found that energy cost of walking was associated with muscle strength asymmetry. However, MSA was not a significant contributor. This dissimilarity may be due to differences between walking and cycling that cause a difference in muscle loads for the 2 activities. Additionally, it is conceivable that the walking devices used, such as braces and canes, partly compensated for strength asymmetry during walking. Nevertheless, both our study and that of Nollet et al 11 provide important information about the physical strain of performing submaximal activities in relation to the severity of polio paresis, especially in that the extent of the paresis appeared to be a determinant of change in physical functioning over time. 33 Limitations of this study are its small sample size and our inclusion criteria. Subjects were selected for their ability to walk for at least 4 minutes with or without walking aids. Therefore, selection bias toward less severely affected subjects may be assumed. However, given the severity of polio residuals and the use of assistive devices in our sample, we believe that this bias is limited, and that our sample was a good representation of people with postpolio who are capable of walking. Nevertheless, we do not advocate that our results should be generalized to all people with polio. The reproducibility of the walking energy measurements obtained from respiratory gas analysis with the portable VmaxST system was high. When comparing reproducibility between the 2 groups, the variability of measurements was greater for subjects with than for healthy subjects. This implies less sensitivity of walking measurements to detect change in the group. Applying more repetitions in a study design diminishes variability, thereby making measurements more sensitive to change. We showed that by increasing the number of repetitions, the SDD for the walking parameters (ie, the smallest change that can be identified) can be reduced to levels well below what is considered to be clinically relevant. McLaughlin et al 15 reported that a difference up to 10% during exercise is physiologically insignificant for most purposes. It appears that a simple design with only 2 repetitions guarantees sufficient sensitivity to determine clinically relevant changes in the energy cost of walking in subjects with. CONCLUSIONS Energy cost of walking in subjects with was significantly higher than in healthy subjects. Walking efficiency decreased with the severity of paresis of the legs. Future research into maintaining function in people with should focus on stabilizing or decreasing the energy demands of physical activities by means of exercise programs and/or improvements in walking devices (eg, leg braces). Measuring metabolism with portable devices provides a sensitive tool with which to detect clinically relevant changes in energy costs of walking. Acknowledgment: We thank Dirk Knol for his assistance with the statistical analyses. References 1. Fisher SV, Gullickson G. Energy cost of ambulation in health and disability: a literature review. 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