Medicine & Science in Sports & Exercise:
CLINICAL SCIENCES: Clinically Relevant
Repeated Treadmill Walks Affect Physiologic Responses in Children with Cerebral Palsy
MALTAIS, DÉSIRÉE1; BAR-OR, ODED1; PIERRYNOWSKI, MICHAEL2; GALEA, VICTORIA2
1Children’s Exercise and Nutrition Centre, Department of Pediatrics, McMaster University, Hamilton, CANADA; and
2School of Rehabilitation Science, McMaster University, Hamilton, CANADA
Address for correspondence: Désirée Maltais, Children’s Exercise and Nutrition Centre, Department of Pediatrics, Chedoke Hospital Division, Hamilton, ON L8N 3Z5, Canada; E-mail: firstname.lastname@example.org.
Submitted for publication February 2003.
Accepted for publication June 2003.
MALTAIS, D., O. BAR-OR, M. PIERRYNOWSKI, and V. GALEA. Repeated Treadmill Walks Affect Physiologic Responses in Children with Cerebral Palsy. Med. Sci. Sports Exerc., Vol. 35, No. 10, pp. 1653–1661, 2003.
Purpose: To determine whether physiologic responses during treadmill walking in children with cerebral palsy (CP) are affected by repeated walking bouts on different days, and whether effects are different at different speeds.
Methods: Three girls and five boys (9.2–15.7 yr, 23.3–64.4 kg) with mild CP received 12–15 min of treadmill walking practice and had their fastest walking speed (FWS) determined during an introductory visit. During each of three subsequent visits (day 1, day 2, day 3), subjects walked for 3 min at 60, 75, and 90% FWS. Resting physiologic measures were taken on day 1.
Results: From day 1 to day 3, net ventilation (V̇E), and net heart rate (HR) at 90% FWS decreased by 3.6 L·min−1 and 8 beats·min−1, respectively. There were no differences between day 1 and day 2 or day 1 and day 3 for any other physiologic variable at any speed. Day 3 was less than day 2 for net HR (60% FWS) and, independent of speed, net V̇O2 (per kilogram of body mass and per stride) and net energy expenditure (kJ·min−1). Between-day reliability (R) of physiologic responses was ≥ 0.95, except respiratory rate (R = 0.75). Intrasubject, between-day variability for the V̇O2 measures was 7.6–12.9%.
Conclusion: Because there were no day 1 to day 3 reductions in metabolic variables, day 1 to day 3 reductions at 90% FWS in net HR may reflect decreased emotional stress over time and reductions in net V̇E, an uncoupling of V̇O2 and V̇E. Despite between-day differences, reliable net physiologic and stable net metabolic variables may be collected in subjects with mild CP after one treadmill walking practice session.
Cerebral palsy (CP) is a “group of nonprogressive, but often changing, motor impairment syndromes secondary to lesions or anomalies of the brain arising early in development” (19). It occurs 2–2.5 times per 1000 live births (23). A decrease in walking proficiency and economy (5,29) is a main physical disability of spastic diplegic and hemiplegic CP, which are the most common subtypes (23). High, lower-limb antagonist muscle coactivation (29) and mechanical power (28) are associated with this low economy. Interventions to improve walking proficiency also increase walking economy; for example, orthopedic surgery (6) and bracing (15). Studies investigating mechanisms (28,29) and interventions primarily (6,15) used a treadmill walking protocol, which allows speed to be more precisely controlled than during over ground walking and electromyographic, biomechanic, and physiologic variables to be more easily collected simultaneously. Control of walking speed may be especially relevant in intervention studies. The effect of hinged ankle foot orthoses on the oxygen cost of walking, for example, is speed dependent (15).
To ensure the data from a treadmill walking protocol do not simply reflect an adaptation to the test, it has traditionally been assumed that subjects should be habituated to walking on the treadmill before testing. In healthy 7- to 11-yr-old children, who presumably have a mature gait pattern (25), Frost et al. (8) found no significant between-trial or between-day differences in oxygen uptake (V̇O2), heart rate (HR), or kinematic variables at various walking and running speeds after 15–20 s of treadmill walking practice. This suggests that, as a group, children whose gait pattern has matured require very little time to habituate to walking on the treadmill. In younger, healthy 6-yr-old children, however, whose gait pattern is likely not mature (25), V̇O2 during treadmill walking (1.34 m·s−1) even after 5 min of practice was significantly higher for the first trial than for trials 2 and 3 (within-day), although the difference was small (0.2 mL·kg−1·min−1) (27). Those under 7 yr, perhaps due to their immature motor abilities related to walking (25), appear to require more treadmill walking practice before there are no significant differences in V̇O2 between the walks.
In children and adolescents with CP, who also have an immature walking pattern (12), walking practice sessions of 15–20 min before testing have been reported in the literature (9,15,28,29), although other studies (6,21) have not stated if or how much walking practice preceded testing. After 5 min of treadmill walking practice, Keefer et al. (11) found no significant between-trial (within-day) differences in V̇O2 (trials 1 and 2 = 6.6 mL·kg−1·min−1, trial 3 = 6.2 mL·kg−1·min−1, P < 0.05) for 6- to 15-yr-olds with spastic hemiplegic CP walking on the treadmill at 0.67 m·s−1. It is unknown whether, after one practice session, V̇O2 would remain stable, or if it would further be reduced due to habituation, if subjects were tested on different days. It is also unknown whether any effects on V̇O2 of repeated walking bouts would be different at different speeds. Such information would be useful for researchers using a treadmill protocol to assess the effect on walking economy of various interventions. Because the increase in walking economy when wearing orthoses (compared with wearing only shoes) in children with mild CP is greater at faster compared with slower speeds (15), and because there was no within-day improvement in walking economy (after one practice session) for those with CP with repeated walking bouts at a slow speed (11), improvements in walking economy due to repeated waking bouts on different days in this population may be greater at faster speeds.
The purpose of this study was to determine whether after one 12–15 min practice session: i) metabolic and cardiorespiratory responses during treadmill walking in children and adolescents with spastic CP are affected by repeated walking bouts on different days and ii) these responses are different at different speeds. We hypothesized that after 12–15 min of treadmill walking practice, there would be no between-day differences in physiologic responses during walking at a relatively slow speed but that at faster speeds the responses would be lower on day 2 and day 3 compared with day 1.
Five boys and three girls, 9.2–15.7 yr, with mild spastic CP (as determined by the Gross Motor Function Classification System (20)) participated in the study (Tables 1 and 2). No subject had previous experience walking on a treadmill without handrail use. Six subjects regularly used treadmills 1–2 per week while holding on to the handrails, which reflects the current practice in our area, in that treadmills are frequently used for rehabilitation. No one had undergone orthopedic surgery or taken medication to reduce spasticity within the preceding year. No subject wore braces and all walked without assistive devices. The degree of soft tissue contracture was similar in all subjects. They all had at least 105° of hip flexion and lacked no more than 20° of hip extension. Passive range of motion at the knee was full (tested with the hip extended), but subjects on average had about a 35° contracture (lack of knee extension with the hip at 90°) of the hamstrings muscle group (bilaterally). Ankle dorsiflexion (knee extended) was at least neutral (0°). Subjects were otherwise healthy, involved in 2–3 h·wk−1 of recreational physical activity, outside of school hours (i.e., soccer, cycling, basketball, swimming), and on no medication that would affect the variables measured in the study. They refrained from caffeine for 3 h, eating for 2 h, and heavy exercise for 8 h before coming to the lab for each visit. Written, informed consent was obtained from subjects at or over the age of 14 yr. For subjects under 14 yr, written, informed consent was obtained from parents preceded by ascent from the child. The study was approved by the McMaster University Research Ethics Board. Subjects were recruited through the local children’s rehabilitation center.
The participants visited the Children’s Exercise and Nutrition Center on four occasions. During the first introductory visit, they were oriented to the laboratory and all equipment to be used in the study. Their fastest treadmill walking speed (FWS) was determined, as described below. Subjects returned for three subsequent visits. During each visit, they walked on the treadmill three times, for 3 min each time, at 60, 75, and 90% of their previously determined FWS. The order of speeds was randomized. There was a maximum of 2 wk between the introductory and the first of the three treadmill walking visits. These subsequent three visits took place within 10.3 ± 5.5 d.
Visit 1 (introductory visit).
The subjects completed questionnaires about physical activity (modified from Bar-Or (1)), health status, and diet (time, content, and amount of the last meal and snack), with the assistance of a parent if needed. Pubertal stage (pubic hair for boys, breast development for girls) was self-determined, based on photographs (16) according to the criteria of Tanner (26). Total body length was estimated from arm span (Stanley metal tape measure, Canadian Tire Corp., Hamilton, ON) because not all subjects could stand erect. Body adiposity was estimated by summing the medians of three skinfold measurements taken at the biceps, triceps, subscapular, and suprailiac sites on the dominant side. Body mass (Mott Electronic Scale, UMC1000, accuracy ± 20 g; Ancaster Scale Co. Ltd., Brantford, ON) was measured after subjects emptied their bladder. To subsequently calculate nude body weight, clothes, including shoes, were also weighed (Accuba Scale, 1200, accuracy ± 0.20 g). All children wore gym shorts, a T-shirt, socks, and running shoes. The same shoes were worn for all visits. Topographic distribution of spasticity was based on the classification of Minear (18). One person (DM) determined the severity of gross motor involvement using the Gross Motor Function Classification System (20), a five-level grading system, where level I refers to those with the mildest involvement. The degree of lower-limb spasticity was assessed using the modified Ashworth Scale (2), a five-level scale (0 = no spasticity; 4 = rigidity) that is feasible and commonly used in this population (3,24). Lower-limb passive range of motion (to screen for contractures) was assessed by goniometry using standardized techniques modified from McDowell et al. (17). Gross motor function was measured using the walking, running, and jumping component of the Gross Motor Function Measure (22). This specific component relates to walking proficiency in children with CP (7). Walking proficiency was also determined by measuring the comfortable and fast walking speed on level ground (30-m walkway) using the median of a triplicate measurement. The order of ground walking speeds was randomized. Subjects rested in the sitting position between each trial until HR (Polar Vantage XL, Polar CIC, Port Washington, NY) was within 10% of its preexercise value.
After the descriptive measurements, they were taught how to walk on a recently calibrated treadmill (Woodway Desmo M TreadErgometer, Woodway USA, Waukesha, WI) without holding on to the handrails. Their FWS on the treadmill, defined as the fastest speed maintained for 3 min without loss of double-limb support, was determined through a three-stage protocol, with each stage lasting 3 min. The starting speed for this procedure was the subject’s subjectively determined comfortable treadmill walking speed. They rested in the sitting position between walks until HR was within 10% of its preexercise value. In total, the subjects walked for about 12–15 min on the treadmill.
Visits 2–4 (treadmill walking visits, days 1–3).
Preexercise metabolic rate was measured in the sitting position for 5 min (after subjects rested for 15 min) to allow for subsequent calculation of the net metabolic cost of walking. This was done during visit 2 only, to minimize the time burden on the subjects. Metabolic and respiratory data were measured with the child connected to an open circuit system by a mouthpiece (Vmax29 SensorMedics Corp., Yorba Linda, CA). HR was also monitored continuously by the Polar HR monitor and stored in the receiver as 5-s averages. Metabolic variables, V̇O2 and carbon dioxide output (V̇CO2), and respiratory variables, minute ventilation (V̇E) and respiratory rate (RR), were recorded at 20-s intervals. The respiratory exchange ratio (RER), used for determination of energy expenditure, was calculated automatically from the V̇CO2 and V̇O2 data and also stored at 20-s intervals. Just before each data collection, gas flow was calibrated with a 3-L syringe. The O2 and CO2 analyzers were also calibrated at that time using gases of known concentration (O2 = 16.00, 20.94, 26.00%; CO2 = 4.00, 0.05%; Balance N2). After collection of preexercise values, subjects walked on the treadmill for 3 min at 60, 75, and 90% of their previously determined treadmill FWS (0.67 ± 0.35 m·s−1; 0.83 ± 0.44 m·s−1; 1.01 ± 0.54 m·s−1, respectively) while connected to the metabolic cart and wearing the HR monitor. Ventilatory expired gas and HR data were collected as during the preexercise measures. Subjects rested in the sitting position between walks until HR was within 10% of its preexercise value or remained steady and was no longer decreasing. To subsequently calculate the energy cost of walking per stride, the children wore custom-made foot switches. For each foot, switches were positioned and taped in place on the sole of the shoe at the heel and first, third, and fifth metatarsal heads. The four leads from each foot converged into a single lead, which was plugged into a small junction box attached to an elastic belt worn by the subject. A single lead from the junction box connected the subject to an A/D conversion system (CODAS, Dataq Instruments Inc., OH), which sampled the signal at 1000 Hz.
Calculations and Data Reduction
To determine whether steady state was reached, the mean V̇O2 from the second and third minutes of each treadmill walk were compared. Steady state was defined as a difference in V̇O2 of < 2 mL·kg−1·min−1 between minutes 2 and 3. V̇E (L·min−1), RR (breaths·min−1), RER, and HR (beats·min−1) for each trial were determined by averaging these data over this same 1-min period of steady state (third minute of each walk) as chosen for V̇O2. Sitting metabolic, respiratory, and HR values were calculated by averaging the three consecutively lowest 20-s values from that 5-min sitting collection period. The net values for V̇O2, V̇E, RR, RER, and HR for each treadmill walk were calculated by subtracting the sitting values from the walking values. V̇O2 per kilogram body mass (V̇O2-kg) for each walk was calculated by dividing net V̇O2 by the total body mass of the subject, including clothes and equipment. The O2 cost per stride (V̇O2-stride, mL·stride−1) of each trial was calculated by dividing net V̇O2 during walking by the mean stride rate of the of walk (strides·min−1). Stride rate was determined from the heel switch data using custom software (written by one of the authors, MRP). To calculate net energy expenditure (EE, kJ·min−1) during walking, the sitting and steady state V̇O2 from each walk were first converted to kilojoules using the following equation:MATH* Regression equation created from the data of Lusk (14).
** Conversion factor for determining energy in kilojoules from kilocalories.
Net EE was determined by subtracting sitting EE from walking EE.
Between-day and between-speed differences in metabolic and cardiorespiratory variables and the effect of the treadmill belt speed on the pattern of between-day differences was determined using a two-way, repeated measures ANOVA. Tukey’s HSD post hoc test was used to identify pairs that were significantly different. Alpha was set at 0.05. Between-day reliability of these same variables across all speeds was measured by calculating the intraclass correlation coefficient (R). Intrasubject (between-day) variability was determined by calculating the coefficient of variation (CV, %), at each speed, for each variable. Post hoc power calculations were performed in Minitab for Windows, Release 13.1 (Minitab Inc., State College, PA).
When mean V̇O2 from minute 2 and minute 3 of each walk were compared to determine whether a steady state was achieved, there were no differences ≥ 2 mL·kg−1·min−1 in the group data. For individual subjects, steady state was achieved in 81% of the treadmill walks.
Independent of treadmill belt speed, net V̇O2-kg was lower on day 3 than on day 2 (P = 0.049). There was no significant difference between day 2 and day 1, or between day 3 and day 1 at any speed (Table 3). Net V̇O2-kg was higher when subjects walked at 90 than when they walked at either 60 (P = 0.0006) or 75% FWS (P = 0.008). There was no significant difference between 60 and 75% FWS (Table 3). Between-day reliability of net V̇O2-kg was high (R, Table 4). Intrasubject, between-day variability was lowest when subjects walked at 90% FWS and highest when they walked at 75% FWS (CV, Table 4). Net V̇O2-stride showed the same pattern as net V̇O2-kg. Irrespective of speed, day 3 values were lower than on day 2 (P = 0.02), but not significantly different than on day 1, nor were they different on day 2 compared with day 1 (Table 3). Net V̇O2-stride was higher when subjects walked at 90 than when they walked either 60 (P = 0.02) or 75% FWS (P = 0.04). There was no significant difference between 60 and 75% FWS (Table 3). Between-day reliability of net V̇O2 stride was high (R, Table 4). Intrasubject, between-day variability was lowest when subjects walked at 90% FWS and highest when they walked at 75% FWS (CV, Table 4). Net EE (independent of speed) was also lower on day 3 compared with day 2 (P = 0.04), with no significant difference between day 2 and day 1, nor between day 3 and day 1 (Table 3). When subjects walked at 90% FWS, net EE was higher than when they walked at 60% FWS (P = 0.01). Net EE at 75% FWS was not significantly different than at 60 or 90% FWS (Table 3). Between-day reliability of net EE was high (R, Table 4). Intrasubject, between-day variability was lowest when subjects walked at 90% FWS and highest when they walked at 75% FWS (CV, Table 4).
Net V̇E was lower on day 3 than on day 1 (P = 0.04) and day 2 (P = 0.03), but only when subjects walked at 90% FWS (Fig. 1). When subjects walked at 90% FWS, net V̇E was higher than when they walked at 60% FWS (P = 0.01). There were no differences in net V̇E at 75% compared with 60 or 90% FWS (Fig. 1). Between-day reliability of net V̇E was high (R, Table 4). Intrasubject, between-day variability was lowest when subjects walked at 90% FWS and highest when they walked at 60% FWS (CV, Table 4). Repeated treadmill walks on different days did not affect net RR at any speed (Table 3). When subjects walked at 90% FWS net RR was higher than when they walked at 60 (P = 0.0005) and 75% FWS (P = 0.02). There was no significant difference when subjects walked at 75 and 60% FWS (Table 3). Between-day reliability of net RR was moderate (R, Table 4). Intrasubject, between-day variability was overall high, but lowest when subjects walked at 60% FWS and highest when they walked at 75% FWS (CV, Table 4).
When subjects walked at 90% FWS, net HR was lower on day 3 than on day 1 (P = 0.0004, Fig. 2), but day 2 values were not significantly different from those on day 1 or day 3 (Fig. 2). When they walked at 60% FWS, net HR was lower on day 3 than on day 2 (P = 0.03), whereas day 1 values were not significantly different from day 2 or day 3 values (Fig. 2). Net HR was higher when subjects walked at 90 than at 75 (P = 0.0003) or 60% FWS (P = 0.004). There was no significant difference between 60 and 75% FWS. Between-day reliability was high (R, Table 4). Intrasubject, between-day variability was also high, but lowest when subjects walked at 75% FWS and highest when they walked at 60% FWS (CV, Table 4).
To the best of the authors’ knowledge, this is the first study to examine the effect of repeated, multi-day, multi-speed treadmill sessions on physiologic variables in children and adolescents with mild CP. The authors’ hypothesized that there would be no between-day differences when subjects walked on the treadmill at a relatively slow speed but that at faster speeds their physiologic strain would be lower on day 2 and day 3 compared with day 1. When subjects walked at the fastest speed (90% FWS), there was a decrease from day 1 to day 3 in net V̇E (3.6 L·min−1;Fig. 1) and net HR (8 beat·min−1;Fig. 2), thereby partly confirming our hypothesis that between-day differences would be found at the faster speed but not at the slower speeds. With the exception of net HR (where there was only a trend for this same pattern at 60% FWS;Fig. 2), there were no other significant differences from day 1 to day 3 in any variable, at any speed.
The present study had adequate statistical power (0.80) to detect minimum between-day differences in net V̇E at any speed of about 3.5 L·min−1. Figure 1 shows there is no reduction in net V̇E over successive days that was missed due to low statistical power. Our failure to find a significant difference in net V̇E between 75 and 90% FWS independent of day, however, is likely due to low statistical power. There was adequate power to determine minimum between-speed differences of about 9.9 L·min−1, and the difference in this case was 8.3 L·min−1. We found a reduction in the net V̇E with repeated treadmill walks at a relatively high speed but not at the lower speeds. In the absence of a similar pattern in V̇O2, this suggests that at the higher exercise intensity (90% FWS), V̇E, and V̇O2 were no longer tightly coupled, perhaps because the subjects were working above their ventilatory threshold. Because the subjects did not perform a progressive exercise test to peak V̇O2, however, ventilatory threshold cannot be accurately determined for these subjects. We previously reported a similar finding with this population (15). The group mean net V̇E in the present study (Fig. 1) was similar to that of the subjects in this previous study (net V̇E at 90% FWS = 22 L·min−1).
The study had adequate statistical power (0.80) to detect minimum between-day differences in net HR at any speed of about 5 beat·min−1. Figure 2 shows that no new between-day patterns in HR were missed. Had statistical power been higher, it would have been more clearly shown that when subjects walked at 90% FWS, net HR decreased with each visit (day) and when they walked at 60% FWS, net HR was lower on day 3 compared with both day 1 and day 2. Our failure to find a significant difference in net HR between 60 and 75% FWS independent of testing day, however, is likely due to low statistical power. There was adequate power to determine minimum between-speed differences of about 11 beat·min−1, and the difference in this case was 8 beat·min−1. We found net HR was reduced with repeated treadmill walks on different days at 90% FWS (and with a trend for the same finding at 60% FWS). Although not specifically measured in this study, and in the absence of a similar pattern for the V̇O2 variables, it is possible that anxiety (due to walking relatively fast and relatively slow), which can increase HR (10,13), may have decreased over time as subjects became more familiar with treadmill walking without holding on to the handrails and with the testing environment and procedures. Although subjects were introduced to all equipment and procedures during the introductory visit, anxiety-related increases in HR cannot be ruled out. It is possible that anxiety during treadmill walking is relevant to HR with this population, who have difficulty walking, and not to healthy children where this phenomenon is not observed (8,30). Furthermore, the extent of walking impairment appears to affect HR more than V̇O2 in this population, which again suggests that factors other than exercise intensity elevate HR in those with CP. Compared with the present group (Fig. 2), while walking at the same relative intensity (90% FWS), children and adolescents with CP who have more difficulty walking (lower walking-related gross motor function scores and slower ground walking speeds than the present group) demonstrated 51% higher net HR but only 13% higher net V̇O2-kg (15). In the future, a measure of anxiety could prove useful to more clearly determine the cause of the between-day differences in HR. It is possible that more practice time walking on the treadmill is needed to reduce anxiety-related between-day differences in HR.
We had adequate statistical power (0.80) to detect minimum between-day differences in net V̇O2-kg at any speed of about 1.2 mL·kg−1·min−1, which was the difference between day 3 and day 2, independent of speed. With a larger sample, the 1.1 mL·kg−1·min−1 increase from day 1 to day 2, irrespective of speed would likely have also been significant. Our failure to find a significant difference in net V̇O2-kg between 60 and 75% FWS, independent of testing day is probably due to low statistical power, as there was adequate power to determine a minimum between-speed difference of about 4.4 mL·kg−1·min−1, and the difference in this case was 1.2 mL·kg−1·min−1. It is difficult to determine from the results of the present study why net V̇O2-kg was higher on day 2. Subjects may have walked differently on day 2 (and hence were less economical) than on day 1 or day 3, although a post hoc analysis of stride data (not shown) revealed no significant differences in stride length or rate among the three days. The between-day differences in net V̇O2-kg are small, however, compared with the differences among subjects, and thus between-day reliability is high (Table 4). A previous treadmill walking study with 6-yr-old healthy children (27) showed that, after 5 min of treadmill walking practice, within-day net V̇O2-kg was higher for the first than for the second and third trial. In this previous study, however, only one speed was used (1.34 m·s−1), which was about 30% faster than the group mean speed for 90% FWS (1.01 m·s−1). Thus, it is possible that we would have found speed-related differences in the pattern of the V̇O2 response to repeated treadmill walks had subjects been able to walk at a faster speed and therefore at a higher absolute exercise intensity. In other words, more practice time may be needed for very, very mild subjects with CP who, like young healthy children, have an immature gait pattern but are able to walk faster than the present subjects with CP. When 6- to 15-yr-old children with mild spastic hemiplegic CP walked at 0.67 m·s−1, the same speed as the group mean speed for our slowest speed, for example, there were no within-day differences in net V̇O2-kg, which also suggests that a clear reduction in net V̇O2 with repeated treadmill walks is not seen with slower speeds in CP. When subjects walked on the treadmill at 90% FWS, mean net V̇O2-kg for the group (Table 3) was similar to that reported in the literature (16–21 mL·kg−1·min−1) for children and adolescents with mild CP (15,29).
Post hoc sample size calculations for the other metabolic variables, V̇O2-stride and EE, showed the same results as for V̇O2-kg, i.e., the sample of eight subjects was sufficient to detect the main between-day difference (higher values on day 2 than on day 3 with no difference between day 1 and day 3;Table 3). As with V̇O2-kg, it is likely that there was insufficient power to detect the speed-related differences that were not significant (Table 3). Net V̇O2-stride showed the same pattern as net V̇O2-kg, possibly because there were no significant between-day differences in stride rate (not shown). We expected that stride rates would decrease over repeated walks on the treadmill as subjects became more familiar with treadmill walking and were able to take longer steps, but this was not the case. Our subjects, like healthy children (8), did not show any between-day differences in stride rate. EE showed the same pattern as V̇O2-kg, possibly because there were no significant interday differences in RER (not shown). We attempted to control fuel source somewhat by having the subjects fast (with the exception of water) for 2 h before coming to the lab (no caffeine for the preceding 3 h). We also tested each subject at the same time of day for the three visits. The last meal before testing, according to the information given to us by the subject or parent, was usually similar in content from testing day to testing day.
Between-day differences in all physiologic variables were relatively small compared with the intersubject differences, and thus between-day reliability of all variables, irrespective of speed, was high, with the exception of RR, where reliability was moderate (Table 4). In other words, day-to-day intrasubject differences in these measures (with the exception of RR) have minimal effect on the ability of these measures to reliably differentiate between subjects. Our between-day reliability (Table 4) for net V̇O2-kg was greater than the within-day reliability reported in the literature (0.78) for children and adolescents with spastic hemiplegic CP (11). This is perhaps because our subjects received more walking practice (12–15 min compared with 5 min). It is also possible that the subjects themselves were more similar to each other in the previous study (all had hemiplegic CP) and, thus, compared with the present study, between-trial differences in V̇O2 were relatively greater than the between-subject differences.
Intrasubject, between-day variability was less for the metabolic variables at 90% FWS than at the slower speeds (Table 4). In a clinical setting, where V̇O2 during treadmill walking might be tested for an individual before and after an intervention, it would perhaps be prudent to test such individuals at close to their fastest treadmill walking speed, rather than at slower speeds, especially if the expected difference in V̇O2 due to the intervention might be small. Net V̇E showed higher intrasubject, between-day variability than the V̇O2-related measures (Table 4). It may not be the most suitable physiologic outcome measure in a clinical setting. Although net HR and net RR both showed higher intrasubject, between-day variability than the other measures, this is mostly a “mathematical artifact” due to low values for both net HR and net RR. When absolute HR and RR are considered, the CV is much lower (about 4% for HR and 9–10% for RR, across the three speeds). Our mean intrasubject variability for net V̇O2-kg during treadmill walking was similar to that reported in the literature for this population (8.4 ± 8.5%) (11). Our mean intrasubject variability for absolute V̇O2-kg, 5.7–8.1%, is lower than what is reported for over ground walking (4) (17% when speed is not controlled for, 13% when speed is controlled for and V̇O2 is calculated per meter walked). The higher variability for over ground walking may reflect the greater motor impairment of the subjects in that study in that some of them used braces and walking aids. It is also possible that with speed (and the width of the walking track) more precisely controlled with treadmill walking, subjects walk more similarly from trial to trial on the treadmill than over ground. If this is true, then a treadmill walking protocol might be more appropriate than an overground one for assessment of an intervention, especially if small, but clinically relevant differences are expected. Further research is needed to determine whether physiologic responses during treadmill walking are more reliable and less variable than during over ground walking in those with mild CP.
Only 81% of individual trials reached the steady state criteria in this study, which is similar to that (82%) reported for healthy children (8). It is difficult to say, but possible, that interindividual variability was increased due to difficulties with achieving steady state in 19% of the trials. Previous research, however, has shown that group V̇O2 values are not significantly different between minutes 2, 3, or 4 during treadmill walking in this population (29). It is also possible that patterns in the data were obscured due to the subjects having resting measures taken only on day 1. We elected to have only one resting measurement session for each subject to decrease the time commitment for subjects (this study was part of a larger study and subjects were making a total of seven visits to the lab, three after this phase of the study was complete). We also elected to have only one resting measurement session to decrease the between-day variability in the net physiologic measures due to the increased susceptibility of anxiety-related differences in resting measures. Because our RER measures were not different among the days, it is likely that there were no between-day differences in V̇O2 due to fuel or diet differences and thus the resting metabolic measures, if not affected by factors such as anxiety, would not have differed greatly among the testing days.
It is difficult to determine from our results or from the literature what the optimum protocol is to ensure that subjects with CP are habituated to treadmill walking. It appears that as little as 5 min of treadmill walking practice is needed to obtain reliable V̇O2 data if the walking speed is slow (11). Based on our results, after 12–15 min of walking practice, reliable and stable metabolic data may be collected at various speeds. Both comfortable and fast ground walking speeds (Table 2) were, however, on average faster for the group than any of the treadmill walking speeds. The fastest treadmill speed (90% FWS) was closest to the comfortable ground walking speed (mean intrasubject difference = 0.25 ± 0.47 m·s−1). It has previously been shown that the comfortable walking speed on the treadmill is slower than the comfortable walking speed on the ground for children with mild spastic hemiplegic CP (9). The subjects in the previous study received a similar amount of treadmill walking practice to the present subjects. Thus, perhaps more practice would result in increases in the comfortable and fastest walking speeds on the treadmill and therefore decrease the differences between the comfortable and fast walking speeds on the ground and treadmill.
In conclusion, this is the first study to examine in children and adolescents with mild spastic CP, the effect on physiologic responses of repeated treadmill walks on different days, and whether the effects are different at different speeds. Irrespective of speed, in this sample, net metabolic responses on day 1 did not differ from those on day 3. Net V̇E and net HR were both lower on day 3 compared with day 1, but only when subjects walked at the fastest speed (90% FWS). Because metabolic responses did not show the same pattern as these cardiorespiratory responses, it is possible that the reduction over time in net HR was due to a reduction in anxiety rather than an improvement in walking economy per se and the reductions in net V̇E, to an uncoupling of V̇O2 and V̇E. Thus, these two variables may not be the most appropriate to measure if researchers wish to use a faster walking speed and only 12–15 min of treadmill walking practice. Researchers interested in mechanisms and interventions related to walking economy in subjects with mild spastic CP may be able to collect reliable net physiologic and stable net metabolic variables, especially those related to V̇O2, after one treadmill walking practice session. More research is needed to determine whether physiologic responses during treadmill walking are more reliable and less variable than during over ground walking in those with mild CP. More research is also needed to determine the optimal treadmill walking habituation protocol in this population and whether more treadmill walking practice would result in greater similarity between comfortable and fast walking speeds on the ground and on the treadmill. A larger sample, of subjects with mild CP that included subjects who walk faster and slower than the present group, would also increase the generalizability of our findings, which at present likely relate best to those whose walking speeds are similar to the present group.
We would like to thank the volunteers and their families for their participation in this study. We would also like to thank N. Huybrechts, B. Smith, and B. Timmons for assistance with data collection and M. L. Schmuck for assistance with data analysis.
This study was supported by a grant from the Bloorview Children’s Hospital Foundation.
1. B ar- O r, O. Pediatric Sports Medicine for the Practitioner: From Physiologic Principals to Clinic Applications.
New York: Springer Verlag, 1983, pp. 343–348.
2. Bohannon, R. W., and M. B. Smith. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys. Ther. 67: 206–207, 1987.
3. Booth, C. M., M. J. Cortina-Borja, and T. N. Theologis. Collagen accumulation in muscles of children with cerebral palsy and correlation with severity of spasticity. Dev. Med. Child Neurol. 43: 314–320, 2001.
4. Bowen, T. R., N. Lennon, P. Castagno, F. Miller, and J. Richards. Variability of energy-consumption measures in children with cerebral palsy. J. Pediatr. Orthop. 18: 738–742, 1998.
5. Campbell, J., and J. Ball. Energetics of walking in cerebral palsy. Orthop. Clin. North Am. 9: 374–377, 1978.
6. Dahlbäck, G. O., and R. Norlin. The effect of corrective surgery on energy expenditure during ambulation in children with cerebral palsy. Eur. J. Appl. Physiol. 54: 67–70, 1985.
7. Damiano, D. L., and M. F. Abel. Relation of gait analysis to gross motor function in cerebral palsy. Dev. Med. Child Neurol. 38: 389–396, 1996.
8. Frost, G., O. Bar-Or, J. Dowling, and C. White. Habituation of children to treadmill walking and running: metabolic and kinematic criteria. Pediatr. Exerc. Sci. 7: 162–175, 1995.
9. Jeng, S.-F., K. G. Holt, L. Fetters, and C. Certo. Self-optimization in nondisabled children and children with cerebral palsy. J. Mot. Behav. 28: 15–27, 1996.
10. Katz, K., R. Fogelman, J. Attias, E. Baron, and M. Soudry. Anxiety reaction in children during removal of their plaster cast with a saw. J. Bone Joint Surg. Br. 83: 388–390, 2001.
11. Keefer, D. J., K. Apperson, S. Mcgreal, W. Tseh, J. L. Caputo, and D. W. Morgan. Within-day stability of walking oxygen uptake in children with cerebral palsy. Med. Sci. Sports 34: S291, 2002.
12. Leonard, C. T., H. Hirschfeld, and H. Forssberg. The development of independent walking in children with cerebral palsy. Dev. Med. Child Neurol. 33: 567–577, 1991.
13. Lumley, M. A., B. G. Melamed, and L. A. Abeles. Predicting children’s presurgical anxiety and subsequent behavior changes. J. Pediatr. Psychol. 18: 481–497, 1993.
14. L usk, G. The Elements of the Science of Nutrition.
Philadelphia: WB Saunders, 1928, pp. 61–74.
15. Maltais, D., O. Bar-Or, V. Galea, and M. Pierrynowski. Use of orthoses lowers the O2
cost of walking in children with spastic cerebral palsy. Med. Sci. Sports Exerc. 33: 320–325, 2001.
16. Matsudo, S. M., and K. R. Matsudo. Physician assessment of sexual maturation in Brazilian boys and girls: concordance and reproducibility. Am. J. Hum. Biol. 6: 451–455, 1994.
17. Mcdowell, B. C., V. Hewitt, A. Nurse, T. Weston, and R. Baker. The variability of goniometric measurements in ambulatory children with spastic cerebral palsy. Gait Posture 12: 114–121, 2000.
18. Minear, W. L. A classification of cerebral palsy. Pediatrics 18: 841–841, 1956.
19. Mutch, L., E. Alberman, B. Hagberg, K. Kodama, and M. V. Perat. Cerebral palsy epidemiology: where are we now and where are we going? Dev. Med. Child Neurol. 34: 547–551, 1992.
20. Palisano, R., P. Rosenbaum, S. Walter, D. Russell, E. Wood, and B. Galuppi. Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev. Med. Child Neurol. 39: 214–223, 1997.
21. Rose, J., J. G. Gamble, J. Medeiros, A. Burgos, and W. L. Haskell. Energy cost of walking in normal children and in those with cerebral palsy: comparison of heart rate and oxygen uptake. J. Pediatr. Orthop. 9: 276–279, 1989.
22. Russell, D., P. Rosenbaum, C. Gowland, et al. Gross Motor Function Measure Manual.
Hamilton, Canada: Neurodevelopmental Clinical Research Unit, McMaster University, 1993, pp. 1–112.
23. Stanley, F., E. Blair, and E. Alberman. Cerebral Palsies: Epidemiology and Causal Pathways. London: MacKeith Press, 2000, pp.14–39.
24. Suputtitada, A. Managing spasticity in pediatric cerebral palsy using a very low dose of botulinum toxin type A: preliminary report. Am. J. Phys. Med. Rehabil. 79: 320–326, 2000.
25. Sutherland, D. H., R. Olshen, L. Cooper, and S. L. Woo. The development of mature gait. J. Bone Joint Surg. (Am.) 62: 336–353, 1980.
26. Tanner, J. M. Growth in Adolescence. Oxford: Blackwell Scientific, 1962, pp. 32–37.
27. Tseh, W., J. L. Caputo, I. S. Craig, D. J. Keefer, P. E. Martin, and D. W. Morgan. Metabolic accommodation of young children to treadmill walking. Gait Posture 12: 139–142, 2000.
28. Unnithan, V., J. Dowling, G. Frost, and O. Bar-Or. Role of mechanical power estimates in the O2
cost of walking in children with cerebral palsy. Med. Sci. Sports Exerc. 31: 1703–1706, 1999.
29. Unnithan, V. B., J. J. Dowling, G. Frost, and O. Bar-Or. Role of cocontraction in the O2
cost of walking in children with cerebral palsy. Med. Sci. Sports Exerc. 28: 1498–1504, 1996.
30. Unnithan, V. B., L. A. Murray, J. A. Timmons, D. Buchanan, and J. Y. Paton. Reproducibility of cardiorespiratory measurements during submaximal and maximal running in children. Br. J. Sports Med. 29: 66–71, 1995.
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