The reasons why stroke patients expend so much ... - Gait & Posture

Gait & Posture 36 (2012) 409–413

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The reasons why stroke patients expend so much energy to walk slowly G. Stoquart a,b,*, C. Detrembleur a, T.M. Lejeune a,b a b

Universite´ Catholique de Louvain, Institute of Neuroscience (IoNS), Belgium Universite´ Catholique de Louvain, Cliniques Universitaires Saint-Luc, Belgium

A R T I C L E I N F O

A B S T R A C T

Article history: Received 29 August 2011 Received in revised form 6 March 2012 Accepted 27 March 2012

Background: The energy consumed per covered distance (C) is increased in hemiparetic stroke adults during walking. Objective: To ascertain if increased C in stroke patients is a result of increased mechanical work, of decreased efficiency of work production by muscles or of slow walking speed. Methods: C and mechanical work were computed in 20 patients walking on a force measuring treadmill at speeds ranging from 1 km h1 to their own maximum speed (WSMAX). Works done by healthy and pathological limbs were computed separately. Results: For hemiparetic patients, C was around 1.7 times greater than normal. When these patients had a slower WSMAX, they had greater C and mechanical work (r = 0.44 and 0.57, respectively). The increased C was related to the external work performed to lift the center of body mass when the healthy limb was supporting the body weight (r = 0.77). Conclusions: The increase of C in stroke patients is more pronounced when WSMAX is slow. Moreover, this increase is related to increased mechanical work done by muscles and is not related to slow walking speed or decreased efficiency. As in healthy subjects, C and external work presented optimum speeds, indicating a preserved pendular mechanism of walking. ß 2012 Elsevier B.V. All rights reserved.

Keywords: Stroke Walking Energetics Mechanics Treadmill test

1. Introduction Spastic hemiparetic stroke patients expend up to two times more energy to walk than healthy subjects, and that limits their activities and participation. Though it is obvious that those patients walk at slow speeds (for review, Waters [1]), the relation between energy consumption and walking speed remains partly unknown. In healthy subjects, since the energy consumed per unit of time increases as a quadratic function of walking speed (power, W kg1), the energy per covered distance (energy cost, C, J kg1 m1) fits a well-known U-shaped curve [2]. This curve presents minimum values around spontaneous speeds (4.5 km h1). When speed varies from this optimum, C progressively increases [3]. In stroke patients walking at various speeds on a treadmill, Reisman et al. [4] have shown that energy cost of stroke patients decreased with increasing walking speed. Average maximum walking speed of all patients was 68.2 28.9% higher than spontaneous one. It corroborates the hypothesis of authors [5] who believe that increased energy cost in stroke patients is explained by their very slow walking speed [1]. A normal energy consumption

* Corresponding author at: Cliniques Universitaires Saint-Luc, 10 Avenue Hippocrate, B-1200 Brussels, Belgium. Tel.: +32 2 764 16 49; fax: +32 2 764 90 63. E-mail address: [email protected] (G. Stoquart). 0966-6362/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gaitpost.2012.03.019

would then be reached if patients were able to walk as fast as healthy subjects. However, in addition to slow walking speed, the increased energy cost could be related either to the increased mechanical work done by muscles, or the decreased efficiency of production of this work. Detrembleur et al. [6] found, in nine stroke patients compared to controls walking at same speeds, that increased energy cost was proportional to increased mechanical work. Therefore, the aim of the present study is two-fold: first, to study more deeply the relationships between energetics and mechanics to understand the origin of increased C in stroke patients; second, to evaluate the effects of walking speed on mechanics and energetics. 2. Methods 2.1. Study population Twenty post-stroke chronic (more than 6 months) hemiparetic patients (age: 50 12 years; weight 78 18 kg; height: 173 11 cm) were enrolled in the present study. Inclusion criteria included the ability to walk independently on a treadmill without any assistive device, at a minimum of two different speeds (1 km h1), for a time allowing metabolic measurement (around 3 min at each speed). After giving their informed consent, all patients participated freely in the study, which was approved by the ethical board of the university. A clinical examination was carried out by the same examiner, including Stroke Impairment Assessment Set (SIAS, 0-76) [7], and the 10-m walking test on level ground to evaluate their spontaneous walking speed (WSspont). Patients’ results were compared to reference data from our lab, obtained in 12 healthy subjects [8]

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(age: 23 2 years; weight: 58 10 kg; height: 167 7 cm), knowing that age is not a major determinant of walking energetics [9]. 2.2. Gait analysis Gait was assessed by a three-dimensional analysis, including simultaneous mechanical and energetic measurements. Data were acquired simultaneously on a force measuring treadmill (Mercury LTmed, HP Cosmos, Germany) [10]. Total positive mechanical work done by muscles during walking, Wtot, is computed as:

W tot ¼ W int þ W ext where Wint is the internal work, i.e. mechanical work performed to move body segments relative to the center of body mass (COMb), and Wext is the external work, i.e. mechanical work performed to move the COMb relative to the surroundings. Wtot was computed following the method described by Willems et al. [11], and adapted to pathological walking [12]. Wint was computed from the kinematics (Elite system, BTS, Italy). Wext was computed from measurement of 3D ground reaction forces (GRF) recorded by four strain gauges located under each corner of the treadmill [10]. Positive works done by muscles during walking to accelerate the COMb mainly in forward direction (Wk), and to increase potential energy of the COMb (Wp), were respectively computed from kinetic and potential energy variations of the COMb, during one gait cycle. Wext was computed from variations of the sum of these two energy curves. The recovery, R, quantifies the amount of energy-saving transfer between potential and kinetic energies, reflecting the efficiency of pendulum-like mechanism of walking. It was calculated as: R ¼ 100

W k þ W p W ext Wk þ W p

Wext was divided in two parts: the one done by the healthy limb (Wext-HL) and the one done by the pathological limb (Wext-PL). Wext-HL corresponds to the work performed during the second single stance when the healthy foot was the sole on the ground, and during the first double stance when the healthy foot was in the rear and pushed the body up and forward. Wext-PL corresponds to the work performed during the first single stance and the second double stance. Metabolic cost of walking was determined by patient’s oxygen consumption (VO2) and carbon dioxide production (VCO2) measured during steady state periods of 2 min, at each speed. The energy expended above resting value was divided by walking speed to obtain the net energy cost of walking (C, J kg1 m1). Respiratory exchange ratio (RER), computed as the ratio between VCO2 and VO2, was always less than 1. 2.3. Protocol All patients were accustomed to the treadmill prior to the study. Then the study started, all patients walking at 1 km h1. Speed was then increased by steps of 0.5 or 1 km h1, up to the maximum speed (WSMAX) that the patient could reach. They walked for at least 2 min in metabolic steady state at each speed. Kinematic, mechanical and energetic data were acquired at the same time. Acquisitions stopped either because of breathlessness, fatigue or leg pain [13], or because RER became greater than 1. 2.4. Statistical analysis Because the range of walking speeds explored was different in each patient and different from healthy subjects (see Section 3), variables were difficult to compare between patients and healthy subjects and to submit to statistical analysis. To overcome this problem, each variable (X) was transformed in Xi, which corresponds

Fig. 1. Method for computation of Xi. The variable (X, in J kg1 m1) is presented as a function of walking speed (in km h1). The white curve and the gray area represent the mean value of X for the 12 healthy subjects and one standard deviation around this mean value, from 1 to 4 km h1. The blue curve represents X of one stroke patient. The hatched area, i.e. the area under the curve of healthy subjects R WSMAX X HS , and the cyan area, i.e. the area under the curve of the patient. R1 WSMAX X P , were divided by the range of speed of the patient (WSMAX 1). The 1 former was then subtracted from the latter to compute Xi. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

to the ‘‘mean’’ increase in one patient in comparison to healthy subjects mean value, through all walking speeds assessed in this patient (Fig. 1). It was computed as: R WSMAX R WSMAX XP X HS 1 1 ¼ X Pi X HSi WSMAX 1 WSMAX 1 R WS R WS where XP ( 1 MAX X P : blue area) and XHS ( 1 MAX X HS : hatched area) were the values of X recorded at each explored speed in each patient and in all healthy subjects, respectively. XPi and XHSi were the mean values for each patient and for the 12 healthy subjects over the range of speeds explored (WSMAX 1) for each patient. If the mean value of X of a patient and of healthy subjects were identical over the range of speeds explored for the patient, then Xi = 0. Negative and positive values of Xi corresponded, respectively, to lower and greater patients’ values in comparison to healthy subjects mean value. A paired t-test between XPi and XHSi allowed determining if the difference was significant between patients and healthy subjects. Pearson correlations were computed to explore relationships between mechanics, energetics and WSMAX. Pearson and Spearman correlations were computed to explore relationships between WSMAX and WSspont, and between WSMAX and SIAS, respectively.

Xi ¼

3. Results The maximal walking speed on the treadmill (WSMAX) and the spontaneous walking speed adopted overground (WSspont) varied from patient to patient, ranging from 1.7 to 5.5 km h1 and from 2.1 to 5.1 km h1, respectively. However, both speeds were well correlated (r = 0.81, p < 0.0001), and mean WSMAX (3.5 1.1 km h1) and WSspont (3.4 0.8 km h1) were similar. They

Table 1 Results in energy cost and mechanical work.

C Wtot Wext Wint Wp Wk Wext-HL Wext-PL R

J kg1 m1 J kg1 m1 J kg1 m1 J kg1 m1 J kg1 m1 J kg1 m1 J kg1 m1 J kg1 m1 %

Patients

Healthy subjects

WSMAX

Ci

4.3 1.2* 0.69 0.13* 0.43 0.12* 0.28 0.05* 0.55 0.11* 0.18 0.06 (p = 0.283) 0.35 0.13* 0.33 0.14* 45 6 (p = 0.306)

2.3 0.2 0.47 0.02 0.28 0.00 0.18 0.02 0.35 0.02 0.19 0.05 0.21 0.01 0.21 0.01 44 11

r = 0.44, p = 0.05 r = 0.57, p = 0.008 r = 0.62, p = 0.004 r = 0.09, p = 0.7 r = 0.49, p = 0.03 r = 0.21, p = 0.37 r = 0.40, p = 0.03 r = 0.50, p = 0.03 r = 0.42, p = 0.07

r = 0.77, p < 0.001 r = 0.69, p < 0.001 r = 0.06, p = 0.78 r = 0.66, p = 0.001 r = 0.102, p = 0.668 r = 0.77, p < 0.001 r = 0.01, p = 0.97 r = 0.50, p = 0.024

Data in columns ‘‘Patients’’ and ‘‘Healthy subjects’’ were computed as XPi and XHSi, respectively. The column ‘‘WSMAX’’ shows correlation coefficient values (r) and p-values for the relationship between WSMAX and each variable, when computed as Xi (see Section 2). The column ‘‘Ci’’ shows r and p-values for the relationship between Ci and each variable, when computed as Xi. * Indicates data statistically different between patients and healthy subjects (p < 0.001 for all data).


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were lower than usual spontaneous walking speeds in healthy subjects (4.5 km h1). Neurological impairments correlated significantly with WSMAX (r = 0.63, p = 0.003) and WSspont (r = 0.62, p = 0.002). The median value [range] of SIAS was 58 [36–73]. Mean results (sd) for all variables, computed as XPi and XHSi (see Section 2), are presented in Table 1. Energy cost and representative data (C, Wtot, Wp, and Wext-HL) are also presented in Fig. 2A as a function of walking speed. Most mechanical and energetic data were statistically greater in patients than in healthy subjects (p < 0.001). Only Wk and R were not increased in comparison to normal data (p = 0.283 and 0.306, respectively). Mean Ci of all patients corresponded to 2.0 J kg1 m1, indicating that C was on average 2.0 J kg1 m1 greater in patients than in healthy subjects, what corresponds to 1.7 times normal values. Mean Wtot-i and Wext-HLi were 0.22 and 0.14 J kg1 m1. Mean Wint-i and Wext-PLi were 0.10 and 0.12 J kg1 m1, respectively. In Fig. 2B, all variables decreased with increasing WSMAX, indicating that faster walking patients had mechanical and energetic data (including Wext) closer to normal than slower walking patients. A regression line was fitted through data and confirmed a significant but moderate relationship for all variables. In Fig. 3, Ci increased as a function of Wtot-i, Wp-i and Wext-HLi. It means that increased C was mainly explained by an increase of mechanical work mainly done by the healthy leg, to lift the COMb. On the contrary, Ci was not explained by Wint-i, by Wk-i, and by Wext-PLi. R was only slightly related to Ci.

4. Discussion

Fig. 2. Main results. Each of the four panels A presents the data of patients and healthy subjects for one of the following variables: C, Wtot, Wp, and Wext-HL, as a function of the walking speed. The white curve and the gray area represent the mean value of the 12 healthy subjects and one standard deviation around this mean value, respectively. The curve and the area were fitted through values of all subjects, from 1 to 6 km h1. Each blue curve represents the data of one stroke patient. Each curve was fitted through all data recorded at all explored walking speeds for the specific patient. A faster WSMAX is denoted by a darker curve. The panels B present the data of patients for all variables (Xi) as a function of WSMAX. Each black circle corresponds to the data of one patient. A regression line was computed to describe

The present study confirms that stroke patients expend more energy to walk than healthy subjects. The increased C was not explained by their slow walking speed. Indeed, C was greater in patients than in healthy subjects walking at a comparable slow speed. This change of C was related to an increase of the mechanical work, mainly done by the healthy leg to lift the center of body mass, but poorly related to the efficiency of work production, confirming previous results [6,14]. In stroke patients, many studies have documented the increased C, but few have found relationships between C and other gait variables. C is often related to neurological impairments in stroke patients or to spontaneous or maximal walking speeds [1]. Increased mechanical work could be explained by typical joint deformities (equinus, hip flessum, . . .) or altered pattern of movement (hip hiking, stiff-knee gait, . . .). In the present study, Ci was only poorly correlated to SIAS (r = 0.26, p = 0.27), what could be due to high and similar values of SIAS, which were explained by strict inclusion criteria. Despite the fact that other authors have studied mechanical work in various diseases and in healthy subjects (for example [15,16]), this is the first time, to our knowledge, that the total mechanical work done by muscles was measured at several speeds in stroke patients. Allen et al. [17] showed that plantarflexor moment and knee extensor moment were enhanced in the healthy leg of stroke patients. These moments can possibly lead to an elevation of the COMb. In myelomeningocele patients, McDowell et al. [18] showed a good relationship between energy cost and mechanical work estimated from vertical excursion of the sacrum, also studied by Bennett et al. [19]. In amputees, Tesio et al. [12] found an increase of mechanical work done by the healthy leg in comparison to the pathological one.

the relationship between each variable and the WSMAX. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 3. Relationship between Ci and mechanical variables. The three panels present the data of all patients for Ci as a function of each mechanical variable. Each black circle corresponds to the data of one patient. A regression line was computed to describe the relationship between each variable and Ci.

Interestingly, the knowledge of this increase could lead to specific treatments aiming at reducing mechanical work. For example, Massaad et al. [20] trained stroke patients to decrease vertical displacements of their COMb, which resulted in lower mechanical work and energy cost. In hemiparetic patients walking with stiff-knee [21,22], C and mechanical work were lowered after botulinum toxin injections decreasing the stiff-knee. Treadmill walking is comparable to overground walking in stroke patients, on a kinematic and electromyographic point of view [23]. In the present study, seven patients with slow WSMAX did not reach their WSspont. Similarly, Eng et al. [24] found that comfortable walking speed on treadmill was only 60% of WSspont in 12 stroke patients. In the present study, although patients were accustomed to the assessment [25], it could be due to fatigue induced by the duration of exercise (up to 25 min). WSMAX could also be limited by the fitness of patients. Because of increased C, patients had to decrease their walking speed to maintain their energy expenditure at levels adapted to their fitness. The maximal oxygen consumption (VO2-max) was not measured. However, O2 power at WSMAX, when expressed per unit of time (14.5 3.0 ml kg1 min1), was close to low VO2-max values found in stroke patients by several authors [26–28]. This decreased exercise capacity has many possible origins (for review, Ivey [26]). Exercise programs have become recommended among stroke patients [29]: if their cardio-vascular fitness increased, patients with slow WSMAX would probably attain higher walking speeds. Thirteen patients with medium to fast WSMAX presented optimum speeds at which C and Wext were minimized. In ten of these patients, optimum speeds were similar to WSspont, as in healthy subjects [3,30]. Reisman et al. [4] found a similar trend in 11 (among 16) stroke patients walking on a treadmill. However, she did not find minimum values around optimal speeds. 5. Conclusion The energy cost increase is mainly due to mechanical work done by the healthy limb, mainly to lift the COMb. Future studies should examine kinematics and kinetics to explain the mechanical work increase. This could allow targeted treatment to improve energy cost of stroke patients, and then their walking ability. Acknowledgments This study was sponsored by the Association Nationale d’Aide aux personnes Handicapeés (ANAH-Rotary Belgium and Luxemburg), the Fondation du patrimoine (Universite´ catholique de Louvain, Brussels) and the Fonds National de la Recherche Scientifique (FNRS).

Conflict of interest statement Authors disclose any financial and personal relationship with other people that could inappropriately influence their work.

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