- Email: [email protected]
Influence of visual cues on gait in Parkinson's disease during treadmill walking at multiple velocities
Journal of the Neurological Sciences 314 (2012) 78–82
Contents lists available at SciVerse ScienceDirect
Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns
Influence of visual cues on gait in Parkinson's disease during treadmill walking at multiple velocities☆ F. Luessi ⁎, L.K. Mueller, M. Breimhorst, T. Vogt Department of Neurology, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
a r t i c l e
i n f o
Article history: Received 30 August 2011 Accepted 24 October 2011 Available online 17 November 2011 Keywords: Gait Parkinson's disease Treadmill Visual cue
a b s t r a c t Objective: To evaluate the interaction of different treadmill-induced gait velocities and visual cues on the gait performance in Parkinson's disease (PD). Background: External cuing has been reported to facilitate hypokinetic gait patterns in PD. Methods: 19 PD-patients and 17 controls volunteered for the study. Gait analyses were conducted using dynamic pressure sensors integrated in a treadmill at a given velocity of 1, 2, 3 or 4 km/h. For each velocity, measurements were performed under three conditions. The first condition was without visual cuing, the remaining two consisted of visual cuing e.g. white stripes put on the treadmill belt 25 or 50 cm apart. Results: Visual cuing lowered the cadence and increased stride length and stride time while maintaining gait velocity in both PD-patients and controls. A significant interaction between this effect of visual cuing and gait velocity was observed. Visual cuing demonstrated a clear velocity-dependency with less influence on cadence, stride length, stride time and coefficient of variation in stride time at higher velocities. At lower velocities visual cuing was more effective in reducing gait variability as assessed by the coefficient of variation in stride time in PD-patients than in controls. Conclusion: The current experiment shows that the gait patterns of PD-patients are not rigidly coupled to gait velocity and can be manipulated with visual cuing techniques. Our results suggest that visual cuing can improve the efficacy of treadmill training. Due to an interaction between the effect of visual cuing and gait velocity, the application of visual cues could enhance the efficacy of treadmill training particularly at lower velocities. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Gait disorders are among the most disabling symptoms experienced by Parkinson's disease (PD) patients. PD-patients demonstrate a shuffling gait pattern with a shortened stride length, a reduced overall velocity, limited natural arm swing and difficulty in initiating or altering their gait patterns [1–5]. While several studies have demonstrated that cadence (steps per min) remains intact in PD [2,4,6–8], others have detected a reduction in cadence [1,5,9,10]. PD-patients also suffer from a high coefficient of variation for the stride time which is associated with an increased risk of suffering from falls [7,11,12]. Additionally, the coefficient of variation of stride length was found to be increased [1,6]. Difficulties in initiating gait and in changing from a steady gait pattern have been attributed to deficits in the motor planning process [13].
☆ Financial disclosure related to research covered in this article: The authors have no financial disclosures to make. ⁎ Corresponding author at: Department of Neurology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckstr. 1, D-55101 Mainz, Germany. Tel.: + 49 6131 17 5278; fax: + 49 6131 17 47 5278. E-mail address: [email protected] (F. Luessi). 0022-510X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jns.2011.10.027
External cuing defined as application of temporal or spatial stimuli has also been reported to facilitate locomotor activity in PD since 1942 [14]. The first detailed analysis of external cuing on gait was performed by Martin in 1967 [15]. Since this time, a number of subsequent studies have shown that the utilization of appropriate external cues can improve the spatiotemporal gait pattern in PD patients [9,10]. Although such improvements have been demonstrated with the use of auditory [9] and somatosensory cues [16] the most successful type of external cues appear to be visual [17]. Visual stimulation has traditionally been provided by means of a series of stripes laid out on the floor for the patients to walk over. This kind of stimulation has been shown to improve stride length [2–4,6,10,18], gait velocity [3,4,6,10] and the coefficient of variation of stride length [6]. In most of the studies using visual stimulation, gait changes have been investigated during experimental settings at the patients' self-selected preferred gait velocities [19]. However, since variables such as stride length and cadence change as a function of gait velocity [20], appropriate protocols which experimentally control gait velocity as well as external cuing are required to analyze differences in gait parameters between PD-patients and healthy subjects. Thus, the aim of our study was twofold: (1) to investigate the effects of experimentally controlled velocities on the gait parameters and
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
(2) to examine whether visual cuing by transverse stripes at different distances improves performance of gait across different velocities. The results of this study could optimize the application of visual cuing strategies in gait rehabilitation for Parkinson's disease. 2. Methods
79
its surface at the time of analysis. During the treadmill protocol the following variables were experimentally manipulated: visual cuing and gait velocity. Visual cuing was provided by means of transverse stripes made of white cloth tape (3 cm in width) fixed 25 or 50 cm apart on the dark gray treadmill belt. The subjects walked on the treadmill under the following three conditions in a randomized order to control for series effects:
2.1. Subjects Nineteen patients (13 men and 6 women) with idiopathic Parkinson's disease, as defined by the UK Brain Bank Criteria [21], were recruited from the outpatient clinic of the Department of Neurology of the University Medical Center Mainz. The age of the PD-patients was 60.6 years (±9.4 years) [mean ± standard deviation] with a range from 45 to 78 years. The patients had an average height of 1.70 m (±0.08 m) and an average weight of 76.2 kg ± 16.5 kg. The median of the Hoehn and Yahr (H&Y) rating scale [22] was 2.0 (range 1.0 to 3.0), the motor component (Part III) of Unified Parkinson's Disease Rating Scale (UPDRS) was 21.0 ± 7.7 (range 11 to 34) [23]. The average disease duration was 3.8 years (±2.4 years). All testing was carried out while the patients were in the ON state of medication. Details of the patients are shown in Table 1. Seventeen controls (10 men and 7 women) volunteered for the study. The age of the control group was 63.1 years (±7.3 years) ranging from 58 to 74 years. The control subjects' average height was 1.68 ± 0.07 m, the average weight was 76.8 ± 12.7 kg. They were recruited from the community; in addition spouses from the patient group participated. All participants were required to have normal or corrected-tonormal vision and no cardiovascular limitations impairing walking ability or efficiency. Furthermore, patients and controls with any orthopedic limitations or dyskinesias affecting gait were excluded from participation in the study. All participants gave their written informed consent according to the declaration of Helsinki (1964), before entering the study. The study was approved by the local ethics committee. 2.2. Protocol Subjects were familiarized with walking on a motorized medical treadmill (zebris FDM-T Gait analysis system, zebris Medical GmbH, Isny, Germany) at 2 km/h for at least 5 min and felt comfortable on
1. No visual cuing (no stripes) 2. Visual cuing with transverse stripes 25 cm apart (narrow stripes) 3. Visual cuing with transverse stripes 50 cm apart (wide stripes) Within each condition, the velocity of the treadmill was set in a randomized sequence to 1, 2, 3 or 4 km/h to avoid ordering effect. Each velocity level lasted about one minute, of which the last 30 s was used to collect data for gait analysis. Subjects were instructed to “walk on the treadmill by stepping over the lines” [6]. While walking, the subjects gently held the side bars of the treadmill for safety. 2.3. Apparatus The computerized gait analysis system was based on motorized treadmill ergometer with an integrated, calibrated measuring sensor matrix (zebris FDM-T, zebris Medical GmbH, Isny, Germany). On the treading area of 1.5 × 0.5 m the sensor unit comprised 5370 pressure/force sensors used at a sample frequency of 120 Hz. Measurements were transferred to a personal computer for further analysis. The gait parameters measured for each condition of gait included: average stride length (in cm), average stride time (in s), and average cadence (in steps per min). Variability measures to assess intra-individual spread in the sequence were quantified using the coefficient of variation e.g. stride time variability = standard deviation / average stride time [24]. 2.4. Statistical analysis Analysis of variances (ANOVA) with repeated measurement on the within-subject factors “condition” (no stripes, narrow stripes, wide stripes) and “velocity” (1 km/h, 2 km/h, 3 km/h, 4 km/h), and the between-subjects factor group (PD-patients vs. control group) was applied. Normality was checked using Kolmogorov–Smirnov-
Table 1 Characteristics of Parkinson's disease patients. Patient number
Age (year)
Sex
Height (m)
Weight (kg)
Disease duration (year)
UPDRS Part III/ (108)
H&Y
Medication per day (mg)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Mean SD
45 75 67 51 56 72 68 67 56 56 71 52 58 46 59 57 58 78 59 60.6 9.4
F M M F F M M M M M M M F F F M M M M
1.60 1.79 1.86 1.73 1.68 1.65 1.68 1.75 1.75 1.69 1.76 1.71 1.57 1.62 1.60 1.80 1.75 1.63 1.67 1.70 0.08
53 76 80 100 67 65 74 77 72 75 107 75 65 55 68 120 77 70 72 76.2 16.5
1 7 3 4 4 6 5 9 2 1 2 1 5 2 3 4 4 8 1 3.8 2.4
11 23 27 16 23 31 34 32 14 13 20 15 26 11 12 20 25 31 15 21.0 7.7
1.0 2.5 2.5 2.0 2.0 3.0 3.0 3.0 1.0 2.0 2.0 2.0 2.5 1.0 1.0 2.0 2.5 3.0 1.5 2.0
Levodopa/Carbidopa 450/125.5 Levodopa/Carbidopa 450/125.5, Entacapone 800 Ropinirole 3 Pramipexole 0.7, Amantadine 200 Pramipexole 2.8 Levodopa/Benserazide 600/150 Levodopa/Benserazide 500/125 Levodopa/Carbidopa 550/137.5, Cabergoline 8
a
Median of the sample.
Levodopa/Carbidopa 300/75 Levodopa/Carbidopa 300/75, Entacapone 600, Pramipexole 1.05 Ropinirole 7.5, Amantadine 200 Pramipexole 2.1 Pramipexole 2.1 Levodopa/Carbidopa 300/75, Pramipexole 1.05 Levodopa/Benserazide 500/125, Ropinirole 6 Peripedil 100 a
80
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
test. Levene's-test was applied to check for homogeneity of variances. Greenhouse–Geisser epsilon was used to correct possible lacks of sphericity. Bonferroni-correction was used for pair-wise comparison. All data are presented as mean and standard error of mean (SEM). Significance was considered as p b .05. All statistical analyses were performed using SPSS version 15.0 (SPSS, Chicago, IL).
3. Results 3.1. Stride length and coefficient of variation in stride length PD-patients differed significantly from the control group in both stride length (F 1/34 = 9.86, p = .003) and coefficient of variation in stride length (F 1/34 = 9.28, p = .004), indicating a decreased stride length (82.65 cm) and increased coefficient of variation in stride length (0.044) in PD-patients compared to the control group (stride length: 94.68 cm, coefficient: 0.030). Regardless of group differences, stride length increased with higher velocities (1 km/h: 53.26 cm, 2 km/h: 83.41 cm, 3 km/h: 102.22 cm, 4 km/h: 115.75 cm) (F 3/102 = 1111.05, p b .001) whereas the coefficient of variation in stride length decreased with increasing velocity (1 km/h: 0.067, 2 km/h: 0.034, 3 km/h: 0.024, 4 km/h: 0.021) (F 3/102 = 48.06, p b .001). Furthermore, stride length was increased by narrow stripes (89.63 cm, p b .001) and wide stripes (91.43 cm, p b .001) compared to no stripes (84.93 cm) (F 2/68 = 18.39, p b .001). Also for the coefficient of variation in stride length, narrow stripes (0.032, p = .002) differed significantly from the condition of no stripes (0.043) (F 2/68 = 18.39, p b .001). For stride length (F 6/204 = 9.43, p b .001) a significant interaction of velocity ∗ condition was observed but not for the coefficient of variation in stride length (see Fig. 1 A and C for details). In contrast to our hypothesis, no interaction between group and condition and velocity was observed.
3.2. Stride time and coefficient of variation in stride time Stride time (F 1/34 = 7.71, p = .009) and coefficient of stride time (F 1/34 = 7.98, p = .008) significantly differed between PD-patients (stride time: 1.34 s, coefficient: 0.039) and control group (stride time: 1.54 s, coefficient: 0.022). Independent of group differences, higher velocities were associated with decreased stride times (1 km/h = 2.03 s, 2 km/h = 1.44 s, 3 km/h = 1.22 s, 4 km/h = 1.08 s) (F 3/102 = 146.39, p b .001) and decreased coefficient of variation in stride time (1 km/h: 0.062, 2 km/h: 0.026, 3 km/h: 0.020, 4 km/h: 0.016) (F 3/102 = 15.94, p b .001). The factor “condition” significantly influenced both stride time (F 2/68 = 22.72, p b .001) and coefficient of variation in stride time (F 2/68 = 4.14, p = .033). Pair-wise comparisons indicated that stride time differed between narrow stripes (1.45 s, p b .001) and wide stripes (1.51 s, p b .001) compared to no stripes (1.35 s) whereas narrow stripes significantly differed from wide stripes (p= .019). Stride time (F 6/204 = 22.55, p b .001) (see Fig. 1 B for details) and coefficient of variation in stride time (F 6/204 = 3.49, p = .040) were additionally affected by the interaction velocity ∗ condition. The latter interaction was furthermore explained by a significant triple interaction group ∗ velocity ∗ condition which was observed (F 6/204 = 3.79, p = .031) (see Fig. 1 D for details). Beyond that, a significant effect of group ∗ velocity for stride time (F 3/102 = 4.34, p = .043) but not for the coefficient of stride time was obtained (see Fig. 1 B for details). 3.3. Cadence PD-patients (48.55 steps/min) showed increased cadence compared to the control group (42.59 steps/min) (F 1/34 = 9.83, p = .004). Regardless of group differences, cadence was significantly influenced by velocity (F 3/102 = 418.53, p b .001), indicating that this parameter increased with increasing velocities (1 km/h: 32.44 steps/min, 2 km/h: 43.13 steps/min, 3 km/h: 50.37 steps/min, 4 km/h: 56.34 steps/min).
Fig. 1. Stride length (A), stride time (B), coefficient of variation in stride length (C), coefficient of variation in stride time (D) and cadence (E) under three conditions (no stripes, narrow stripes, wide stripes) across the different velocities in controls and PD-patients. A significant interaction velocity ∗ condition was observed in cadence, stride length, stride time and coefficient of variation in stride time. A significant triple interaction group ∗ velocity ∗ condition was observed in coefficient of variation in stride time. Thus, at lower velocities visual cuing was significantly more effective in reducing gait variability in PD-patients as compared to controls.
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
The effect of “condition” (F 2/68 = 17.56, p b .001) furthermore showed that the cadence was different between narrow (44.97 steps/min, p = .002) and wide stripes (43.83 steps/min, p b .001) compared to no stripes (47.91 steps/min). Moreover, a significant effect of velocity ∗ condition (F 6/204 = 9.48, p b .001) was observed (see Fig. 1 E for details). No further interactions were obtained. 4. Discussion The present study was performed to examine the influence of experimentally controlled velocities and visual cuing on the spatial and temporal parameters of gait in PD. To our knowledge, this is the first study that has used transverse stripes fixed on a treadmill belt while walking on a treadmill across multiple controlled gait velocities. The main findings were that visual cuing provided by transverse stripes on the treadmill belt can be used to lower the cadence and increasing the stride length and the stride time while maintaining gait velocity in both PD patients and controls. Furthermore, we found an interaction between visual cuing and gait velocity e.g. the effect of visual cuing on stride length, stride time and cadence demonstrated a clear velocity-dependency with proportionally less influence at higher velocities. The results of treadmill walking at different velocities without application of visual cues show that PD patients walk with a shorter stride length, a decreased stride time and an increased cadence and an increased variability of gait compared to healthy controls, which is consistent with the literature [3,4,6]. Furthermore, walking at higher velocities resulted in reduced gait variability as measured by the coefficients of variation in stride length and stride time. In agreement with other reports, visual cues provided by transverse stripes fixed on a treadmill belt while walking on a treadmill significantly improved gait performance by increasing stride length and stride time and reducing cadence compared to an uncued condition [2,25,26]. This effect occurred in both PD-patients and controls. The effect of visual cuing demonstrated a clear velocitydependency with proportionally less influence on gait parameters at higher velocities. Variability of gait, a known predictor of falls, was assessed by coefficients of variation in stride length and stride time [7,11,17,27]. Visual cuing led to a significant reduction of the coefficient of variation in stride time. This effect also displayed a velocity-dependency and was more pronounced at lower velocities. Interestingly, a significant triple interaction group ∗ velocity ∗ condition was observed for the coefficient of variation of stride time. Thus, at lower velocities visual cuing was significantly more effective in reducing gait variability in PD-patients as compared to controls. It has been suggested that the effect of visual cues provided by transverse stripes is caused by the fact that the approaching stripes have certain dynamic properties that encourage stepping over or on the stripes through activation of alternative visuomotor circuits [10]. Among cortical motor areas, dysfunction of the supplementary motor area (SMA), strongly influenced by the basal ganglia, plays a substantial role in the pathophysiology of PD [28]. By contrast, the premotor cortex (PMC), mainly regulated by cerebellar inputs, is overactive during visual-cued movements in PD patients, probably to compensate for the impaired SMA function [29,30]. Thus, visual stimuli can bypass the damaged basal ganglia and allow an intact cerebellar circuit to be used for visuomotor control [10]. A possible explanation for the interaction between the effect of visual cuing and gait velocity is that walking at higher velocities is more demanding and focuses one's attention to the walking pattern itself, which allows a partial correction of impaired automaticity of locomotion in PD by intentional modulation of gait. In addition, it has been hypothesized that the treadmill acts as an external cue [31,32], which might compete with the effect of visual cuing at higher velocities.
81
Surprisingly, the effect of visual cuing failed to reach a significant difference between PD patients and controls in the majority of the gait parameters. This is in line with previous studies which have already observed considerable effects of visual cuing with transverse stripes in the control population [6,26,30]. One potential limitation of this study was that the subjects walked on a motorized treadmill. Compared to overground walking, treadmill walking induces walking with slightly shorter stride lengths and slightly higher cadence [33]. This effect has been reported to be somewhat stronger in PD-patients [34]. Thus, the typical differences between PD-patients and controls during overground walking become even more pronounced. However, as all subjects were tested under the same experimental conditions, the observed differences between cuing conditions, gait velocities and groups are expected to remain during overground walking. 5. Conclusions The current experiment shows that the gait patterns of PD-patients are not rigidly coupled to gait velocity and can be manipulated with visual cuing techniques using alternate sensory motor pathways. An interaction of visual cuing and gait velocity was found as the influence of visual cues on gait parameters was proportionally more pronounced at lower velocities. Recent studies demonstrated that treadmill training is efficacious and safe for improving gait and mobility in PD [35]. Our study supports the idea that visual cuing applied by transverse stripes fixed on a treadmill belt could enhance the efficacy of treadmill training particularly at lower velocities. Conflict of interest Nothing to report. References [1] Blin O, Ferrandez AM, Serratrice G. Quantitative analysis of gait in Parkinson patients: increase variability of stride length. J Neurol Sci 1990;1:91–7. [2] Morris ME, Iansek R, Matyas TA, Summers JJ. Ability to modulate walking cadence remains intact in Parkinson's disease. J Neurol Neurosurg Psychiatry 1994;57: 1532–4. [3] Morris ME, Iansek R, Matyas TA, Summers JJ. Stride length regulation in Parkinson's disease. Normalization strategies and underlying mechanisms. Brain 1996;119:551–68. [4] Morris ME, Iansek R, McGinley J, Matyas T, Huxham F. Three-dimensional gait biomechanics in Parkinson's disease: evidence for centrally mediated amplitude regulation disorder. Mov Disord 2005;20:40–50. [5] Willems AM, Nieuwboer A, Chavret F, Desloovere K, Dom R, Rochester L, et al. The use of rhythmic auditory cues to influence gait in patients with Parkinson's disease, the differential effect for freezers and non-freezers, an explorative study. Disabil Rehabil 2006;28:721–8. [6] Lewis GN, Byblow WD, Walt SE. Stride length regulation in Parkinson's disease: the use of extrinsic, visual cues. Brain 2000;123:2077–90. [7] Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL. Gait variations of gait cycle timing in Parkinson's disease and Huntington's disease. Mov Disord 1998;13:428–37. [8] Frenkel-Toledo S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM. Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently. J Neuroeng Rehabil 2005;2:23–9. [9] Thaut MH, McIntosh GC, Rice RR, Miller RA, Rathbun J, Brault JM. Rhythmic auditory stimulation in gait training for Parkinson's disease patients. Mov Disord 1996;11:193–200. [10] Azulay JP, Mesure S, Amblard B, Blin O, Sangla I, Pouget J. Visual control of locomotion in Parkinson's disease. Brain 1999;122:111–20. [11] Nakamura T, Meguro K, Saski H. Relationship between falls and stride length variability in senile dementia of the Alzheimer type. Gerontology 1996;42: 108–13. [12] Schaafsma JD, Giladi N, Balash Y, Bartels AL, Gurevich T, Hausdorff JM. Gait dynamics in Parkinson's disease: relationship to Parkinsonian features, falls and response to levodopa. J Neurol Sci 2003;212:47–53. [13] Chan CWY. Could Parkinsonian akinesia be attributable to a disturbance in the motor preparatory process? Brain Res 1986;386:183–96. [14] Von Wilzenben HD. Locomotion in the treatment of post encephalitic Parkinson's disease. New York: Grune and Stratten; 1942. p. 135–8.
82
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
[15] McIntosh GC, Brown SH, Rice RR, Thaut MH. Rhythmic auditorymotor facilitation of gait patterns in patients with Parkinson's disease. J Neurol Neurosurg Psychiatry 1997;62:22–6. [16] Nieuwboer A, Kwakkel G, Rochester L, Jones D, van Wegen E, Willems AM, et al. Cuing training in the home improves gait-related mobility in Parkinson's disease: the RESCUE trial. J Neurol Neurosurg Psychiatry 2007;78:134–40. [17] Morris ME, Iansek R, Matyas TA, Summers JJ. The pathogenesis of gait hypokinesia in Parkinson's disease. Brain 1994;117:1169–81. [18] Suteerawattananon M, Morris GS, Etnyre BR, Jankovic J, Protas EJ. Effects of visual and auditory cues on gait in individuals with Parkinson's disease. J Neurol Sci 2004;219:63–9. [19] Zijlstra W, Rutgers AWF, Van Weerden TW. Voluntary and involuntary adaptation of gait in Parkinson's disease. Gait Posture 1998;7:53–63. [20] Kirtley C, Whittle MW, Jefferson RJ. Influence of walking speed on gait parameters. J Biomed Eng 1985;7:282–8. [21] Hughes AJ, Daniel SE, Ben Shlomo Y, Lees AJ. The accuracy of diagnosis of Parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125:861–70. [22] Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology 1967;17:427–42. [23] Fahn S, Elton R. Members of the UPDRS development committee. Unified Parkinson's disease rating scale. In: Fahn S, editor. Recent developments in Parkinson's disease. Florham Park, NJ: Macmillan Healthcare Information; 1987. p. 153–63. [24] Gabell A, Nayak US. The effect of age on the variability in gait. J Gerontol 1984;39: 662–6. [25] Bagley S, Kelly B, Tunnicliffe N, Turnbull GI, Walker JM. The effect of visual cues on the gait of independently mobile Parkinson's disease patients. Physiotherapy 1991;77:415–20. [26] Azulay JP, Van Den Brand C, Mestre D, Blin O, Sangla I, Pouget J, et al. Gait analysis in Parkinson's disease: effect of levodopa and visual stimulation. Rev Neurol (Paris) 1996;152:128–34.
[27] Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in communityliving older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001;82: 1050–6. [28] Samuel M, Ceballos-Baumann AO, Turjanski N, Boecker H, Gorospe A, Linazasoro A, et al. Pallidotomy in Parkinson's disease increases supplementary motor area and prefrontal activation during performance of volitional movements: an H2(15)O PET study. Brain 1997;120:1301–13. [29] Samuel M, Ceballos-Baumann AO, Blin J, Uema T, Boecker H, Passingham RE, et al. Evidence for lateral premotor and parietal overactivity in Parkinson's disease during sequential and bimanual movements: a PET study. Brain 1997;120: 963–76. [30] Hanakawa T, Fukuyama H, Katsumi Y, Honda M, Shibasaki H. Enhanced lateral premotor activity during paradoxical gait in Parkinson's disease. Neurology 1999;45:329–36. [31] Herman T, Giladi N, Gruendlinger, Hausdorff JM. Six weeks of intensive treadmill training improves gait and quality of life in patients with Parkinson's disease: a pilot study. Arch Phys Med Rehabil 2007;88:1154–8. [32] Frenkel-Toledo S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM. Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease. Mov Disord 2005;20:1109–14. [33] Murray MP, Spurr GB, Sepic SB, Gardner GM, Mollinger LA. Treadmill vs. floor walking: kinematics, electromyogram, and heart rate. Eur J Appl Physiol 1995;59:87–91. [34] Zijlsstra W, Rutgers AW, Van Weerden TW. Voluntary and involuntary adaptation of gait in Parkinson's disease. Gait Posture 1998;7:53–63. [35] Herman T, Giladi N, Hausdorff JM. Treadmill training for the treatment of gait disturbances in people with Parkinson's disease: a mini-review. J Neural Transm 2009;116:307–18.
Contents lists available at SciVerse ScienceDirect
Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns
Influence of visual cues on gait in Parkinson's disease during treadmill walking at multiple velocities☆ F. Luessi ⁎, L.K. Mueller, M. Breimhorst, T. Vogt Department of Neurology, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
a r t i c l e
i n f o
Article history: Received 30 August 2011 Accepted 24 October 2011 Available online 17 November 2011 Keywords: Gait Parkinson's disease Treadmill Visual cue
a b s t r a c t Objective: To evaluate the interaction of different treadmill-induced gait velocities and visual cues on the gait performance in Parkinson's disease (PD). Background: External cuing has been reported to facilitate hypokinetic gait patterns in PD. Methods: 19 PD-patients and 17 controls volunteered for the study. Gait analyses were conducted using dynamic pressure sensors integrated in a treadmill at a given velocity of 1, 2, 3 or 4 km/h. For each velocity, measurements were performed under three conditions. The first condition was without visual cuing, the remaining two consisted of visual cuing e.g. white stripes put on the treadmill belt 25 or 50 cm apart. Results: Visual cuing lowered the cadence and increased stride length and stride time while maintaining gait velocity in both PD-patients and controls. A significant interaction between this effect of visual cuing and gait velocity was observed. Visual cuing demonstrated a clear velocity-dependency with less influence on cadence, stride length, stride time and coefficient of variation in stride time at higher velocities. At lower velocities visual cuing was more effective in reducing gait variability as assessed by the coefficient of variation in stride time in PD-patients than in controls. Conclusion: The current experiment shows that the gait patterns of PD-patients are not rigidly coupled to gait velocity and can be manipulated with visual cuing techniques. Our results suggest that visual cuing can improve the efficacy of treadmill training. Due to an interaction between the effect of visual cuing and gait velocity, the application of visual cues could enhance the efficacy of treadmill training particularly at lower velocities. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Gait disorders are among the most disabling symptoms experienced by Parkinson's disease (PD) patients. PD-patients demonstrate a shuffling gait pattern with a shortened stride length, a reduced overall velocity, limited natural arm swing and difficulty in initiating or altering their gait patterns [1–5]. While several studies have demonstrated that cadence (steps per min) remains intact in PD [2,4,6–8], others have detected a reduction in cadence [1,5,9,10]. PD-patients also suffer from a high coefficient of variation for the stride time which is associated with an increased risk of suffering from falls [7,11,12]. Additionally, the coefficient of variation of stride length was found to be increased [1,6]. Difficulties in initiating gait and in changing from a steady gait pattern have been attributed to deficits in the motor planning process [13].
☆ Financial disclosure related to research covered in this article: The authors have no financial disclosures to make. ⁎ Corresponding author at: Department of Neurology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckstr. 1, D-55101 Mainz, Germany. Tel.: + 49 6131 17 5278; fax: + 49 6131 17 47 5278. E-mail address: [email protected] (F. Luessi). 0022-510X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jns.2011.10.027
External cuing defined as application of temporal or spatial stimuli has also been reported to facilitate locomotor activity in PD since 1942 [14]. The first detailed analysis of external cuing on gait was performed by Martin in 1967 [15]. Since this time, a number of subsequent studies have shown that the utilization of appropriate external cues can improve the spatiotemporal gait pattern in PD patients [9,10]. Although such improvements have been demonstrated with the use of auditory [9] and somatosensory cues [16] the most successful type of external cues appear to be visual [17]. Visual stimulation has traditionally been provided by means of a series of stripes laid out on the floor for the patients to walk over. This kind of stimulation has been shown to improve stride length [2–4,6,10,18], gait velocity [3,4,6,10] and the coefficient of variation of stride length [6]. In most of the studies using visual stimulation, gait changes have been investigated during experimental settings at the patients' self-selected preferred gait velocities [19]. However, since variables such as stride length and cadence change as a function of gait velocity [20], appropriate protocols which experimentally control gait velocity as well as external cuing are required to analyze differences in gait parameters between PD-patients and healthy subjects. Thus, the aim of our study was twofold: (1) to investigate the effects of experimentally controlled velocities on the gait parameters and
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
(2) to examine whether visual cuing by transverse stripes at different distances improves performance of gait across different velocities. The results of this study could optimize the application of visual cuing strategies in gait rehabilitation for Parkinson's disease. 2. Methods
79
its surface at the time of analysis. During the treadmill protocol the following variables were experimentally manipulated: visual cuing and gait velocity. Visual cuing was provided by means of transverse stripes made of white cloth tape (3 cm in width) fixed 25 or 50 cm apart on the dark gray treadmill belt. The subjects walked on the treadmill under the following three conditions in a randomized order to control for series effects:
2.1. Subjects Nineteen patients (13 men and 6 women) with idiopathic Parkinson's disease, as defined by the UK Brain Bank Criteria [21], were recruited from the outpatient clinic of the Department of Neurology of the University Medical Center Mainz. The age of the PD-patients was 60.6 years (±9.4 years) [mean ± standard deviation] with a range from 45 to 78 years. The patients had an average height of 1.70 m (±0.08 m) and an average weight of 76.2 kg ± 16.5 kg. The median of the Hoehn and Yahr (H&Y) rating scale [22] was 2.0 (range 1.0 to 3.0), the motor component (Part III) of Unified Parkinson's Disease Rating Scale (UPDRS) was 21.0 ± 7.7 (range 11 to 34) [23]. The average disease duration was 3.8 years (±2.4 years). All testing was carried out while the patients were in the ON state of medication. Details of the patients are shown in Table 1. Seventeen controls (10 men and 7 women) volunteered for the study. The age of the control group was 63.1 years (±7.3 years) ranging from 58 to 74 years. The control subjects' average height was 1.68 ± 0.07 m, the average weight was 76.8 ± 12.7 kg. They were recruited from the community; in addition spouses from the patient group participated. All participants were required to have normal or corrected-tonormal vision and no cardiovascular limitations impairing walking ability or efficiency. Furthermore, patients and controls with any orthopedic limitations or dyskinesias affecting gait were excluded from participation in the study. All participants gave their written informed consent according to the declaration of Helsinki (1964), before entering the study. The study was approved by the local ethics committee. 2.2. Protocol Subjects were familiarized with walking on a motorized medical treadmill (zebris FDM-T Gait analysis system, zebris Medical GmbH, Isny, Germany) at 2 km/h for at least 5 min and felt comfortable on
1. No visual cuing (no stripes) 2. Visual cuing with transverse stripes 25 cm apart (narrow stripes) 3. Visual cuing with transverse stripes 50 cm apart (wide stripes) Within each condition, the velocity of the treadmill was set in a randomized sequence to 1, 2, 3 or 4 km/h to avoid ordering effect. Each velocity level lasted about one minute, of which the last 30 s was used to collect data for gait analysis. Subjects were instructed to “walk on the treadmill by stepping over the lines” [6]. While walking, the subjects gently held the side bars of the treadmill for safety. 2.3. Apparatus The computerized gait analysis system was based on motorized treadmill ergometer with an integrated, calibrated measuring sensor matrix (zebris FDM-T, zebris Medical GmbH, Isny, Germany). On the treading area of 1.5 × 0.5 m the sensor unit comprised 5370 pressure/force sensors used at a sample frequency of 120 Hz. Measurements were transferred to a personal computer for further analysis. The gait parameters measured for each condition of gait included: average stride length (in cm), average stride time (in s), and average cadence (in steps per min). Variability measures to assess intra-individual spread in the sequence were quantified using the coefficient of variation e.g. stride time variability = standard deviation / average stride time [24]. 2.4. Statistical analysis Analysis of variances (ANOVA) with repeated measurement on the within-subject factors “condition” (no stripes, narrow stripes, wide stripes) and “velocity” (1 km/h, 2 km/h, 3 km/h, 4 km/h), and the between-subjects factor group (PD-patients vs. control group) was applied. Normality was checked using Kolmogorov–Smirnov-
Table 1 Characteristics of Parkinson's disease patients. Patient number
Age (year)
Sex
Height (m)
Weight (kg)
Disease duration (year)
UPDRS Part III/ (108)
H&Y
Medication per day (mg)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Mean SD
45 75 67 51 56 72 68 67 56 56 71 52 58 46 59 57 58 78 59 60.6 9.4
F M M F F M M M M M M M F F F M M M M
1.60 1.79 1.86 1.73 1.68 1.65 1.68 1.75 1.75 1.69 1.76 1.71 1.57 1.62 1.60 1.80 1.75 1.63 1.67 1.70 0.08
53 76 80 100 67 65 74 77 72 75 107 75 65 55 68 120 77 70 72 76.2 16.5
1 7 3 4 4 6 5 9 2 1 2 1 5 2 3 4 4 8 1 3.8 2.4
11 23 27 16 23 31 34 32 14 13 20 15 26 11 12 20 25 31 15 21.0 7.7
1.0 2.5 2.5 2.0 2.0 3.0 3.0 3.0 1.0 2.0 2.0 2.0 2.5 1.0 1.0 2.0 2.5 3.0 1.5 2.0
Levodopa/Carbidopa 450/125.5 Levodopa/Carbidopa 450/125.5, Entacapone 800 Ropinirole 3 Pramipexole 0.7, Amantadine 200 Pramipexole 2.8 Levodopa/Benserazide 600/150 Levodopa/Benserazide 500/125 Levodopa/Carbidopa 550/137.5, Cabergoline 8
a
Median of the sample.
Levodopa/Carbidopa 300/75 Levodopa/Carbidopa 300/75, Entacapone 600, Pramipexole 1.05 Ropinirole 7.5, Amantadine 200 Pramipexole 2.1 Pramipexole 2.1 Levodopa/Carbidopa 300/75, Pramipexole 1.05 Levodopa/Benserazide 500/125, Ropinirole 6 Peripedil 100 a
80
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
test. Levene's-test was applied to check for homogeneity of variances. Greenhouse–Geisser epsilon was used to correct possible lacks of sphericity. Bonferroni-correction was used for pair-wise comparison. All data are presented as mean and standard error of mean (SEM). Significance was considered as p b .05. All statistical analyses were performed using SPSS version 15.0 (SPSS, Chicago, IL).
3. Results 3.1. Stride length and coefficient of variation in stride length PD-patients differed significantly from the control group in both stride length (F 1/34 = 9.86, p = .003) and coefficient of variation in stride length (F 1/34 = 9.28, p = .004), indicating a decreased stride length (82.65 cm) and increased coefficient of variation in stride length (0.044) in PD-patients compared to the control group (stride length: 94.68 cm, coefficient: 0.030). Regardless of group differences, stride length increased with higher velocities (1 km/h: 53.26 cm, 2 km/h: 83.41 cm, 3 km/h: 102.22 cm, 4 km/h: 115.75 cm) (F 3/102 = 1111.05, p b .001) whereas the coefficient of variation in stride length decreased with increasing velocity (1 km/h: 0.067, 2 km/h: 0.034, 3 km/h: 0.024, 4 km/h: 0.021) (F 3/102 = 48.06, p b .001). Furthermore, stride length was increased by narrow stripes (89.63 cm, p b .001) and wide stripes (91.43 cm, p b .001) compared to no stripes (84.93 cm) (F 2/68 = 18.39, p b .001). Also for the coefficient of variation in stride length, narrow stripes (0.032, p = .002) differed significantly from the condition of no stripes (0.043) (F 2/68 = 18.39, p b .001). For stride length (F 6/204 = 9.43, p b .001) a significant interaction of velocity ∗ condition was observed but not for the coefficient of variation in stride length (see Fig. 1 A and C for details). In contrast to our hypothesis, no interaction between group and condition and velocity was observed.
3.2. Stride time and coefficient of variation in stride time Stride time (F 1/34 = 7.71, p = .009) and coefficient of stride time (F 1/34 = 7.98, p = .008) significantly differed between PD-patients (stride time: 1.34 s, coefficient: 0.039) and control group (stride time: 1.54 s, coefficient: 0.022). Independent of group differences, higher velocities were associated with decreased stride times (1 km/h = 2.03 s, 2 km/h = 1.44 s, 3 km/h = 1.22 s, 4 km/h = 1.08 s) (F 3/102 = 146.39, p b .001) and decreased coefficient of variation in stride time (1 km/h: 0.062, 2 km/h: 0.026, 3 km/h: 0.020, 4 km/h: 0.016) (F 3/102 = 15.94, p b .001). The factor “condition” significantly influenced both stride time (F 2/68 = 22.72, p b .001) and coefficient of variation in stride time (F 2/68 = 4.14, p = .033). Pair-wise comparisons indicated that stride time differed between narrow stripes (1.45 s, p b .001) and wide stripes (1.51 s, p b .001) compared to no stripes (1.35 s) whereas narrow stripes significantly differed from wide stripes (p= .019). Stride time (F 6/204 = 22.55, p b .001) (see Fig. 1 B for details) and coefficient of variation in stride time (F 6/204 = 3.49, p = .040) were additionally affected by the interaction velocity ∗ condition. The latter interaction was furthermore explained by a significant triple interaction group ∗ velocity ∗ condition which was observed (F 6/204 = 3.79, p = .031) (see Fig. 1 D for details). Beyond that, a significant effect of group ∗ velocity for stride time (F 3/102 = 4.34, p = .043) but not for the coefficient of stride time was obtained (see Fig. 1 B for details). 3.3. Cadence PD-patients (48.55 steps/min) showed increased cadence compared to the control group (42.59 steps/min) (F 1/34 = 9.83, p = .004). Regardless of group differences, cadence was significantly influenced by velocity (F 3/102 = 418.53, p b .001), indicating that this parameter increased with increasing velocities (1 km/h: 32.44 steps/min, 2 km/h: 43.13 steps/min, 3 km/h: 50.37 steps/min, 4 km/h: 56.34 steps/min).
Fig. 1. Stride length (A), stride time (B), coefficient of variation in stride length (C), coefficient of variation in stride time (D) and cadence (E) under three conditions (no stripes, narrow stripes, wide stripes) across the different velocities in controls and PD-patients. A significant interaction velocity ∗ condition was observed in cadence, stride length, stride time and coefficient of variation in stride time. A significant triple interaction group ∗ velocity ∗ condition was observed in coefficient of variation in stride time. Thus, at lower velocities visual cuing was significantly more effective in reducing gait variability in PD-patients as compared to controls.
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
The effect of “condition” (F 2/68 = 17.56, p b .001) furthermore showed that the cadence was different between narrow (44.97 steps/min, p = .002) and wide stripes (43.83 steps/min, p b .001) compared to no stripes (47.91 steps/min). Moreover, a significant effect of velocity ∗ condition (F 6/204 = 9.48, p b .001) was observed (see Fig. 1 E for details). No further interactions were obtained. 4. Discussion The present study was performed to examine the influence of experimentally controlled velocities and visual cuing on the spatial and temporal parameters of gait in PD. To our knowledge, this is the first study that has used transverse stripes fixed on a treadmill belt while walking on a treadmill across multiple controlled gait velocities. The main findings were that visual cuing provided by transverse stripes on the treadmill belt can be used to lower the cadence and increasing the stride length and the stride time while maintaining gait velocity in both PD patients and controls. Furthermore, we found an interaction between visual cuing and gait velocity e.g. the effect of visual cuing on stride length, stride time and cadence demonstrated a clear velocity-dependency with proportionally less influence at higher velocities. The results of treadmill walking at different velocities without application of visual cues show that PD patients walk with a shorter stride length, a decreased stride time and an increased cadence and an increased variability of gait compared to healthy controls, which is consistent with the literature [3,4,6]. Furthermore, walking at higher velocities resulted in reduced gait variability as measured by the coefficients of variation in stride length and stride time. In agreement with other reports, visual cues provided by transverse stripes fixed on a treadmill belt while walking on a treadmill significantly improved gait performance by increasing stride length and stride time and reducing cadence compared to an uncued condition [2,25,26]. This effect occurred in both PD-patients and controls. The effect of visual cuing demonstrated a clear velocitydependency with proportionally less influence on gait parameters at higher velocities. Variability of gait, a known predictor of falls, was assessed by coefficients of variation in stride length and stride time [7,11,17,27]. Visual cuing led to a significant reduction of the coefficient of variation in stride time. This effect also displayed a velocity-dependency and was more pronounced at lower velocities. Interestingly, a significant triple interaction group ∗ velocity ∗ condition was observed for the coefficient of variation of stride time. Thus, at lower velocities visual cuing was significantly more effective in reducing gait variability in PD-patients as compared to controls. It has been suggested that the effect of visual cues provided by transverse stripes is caused by the fact that the approaching stripes have certain dynamic properties that encourage stepping over or on the stripes through activation of alternative visuomotor circuits [10]. Among cortical motor areas, dysfunction of the supplementary motor area (SMA), strongly influenced by the basal ganglia, plays a substantial role in the pathophysiology of PD [28]. By contrast, the premotor cortex (PMC), mainly regulated by cerebellar inputs, is overactive during visual-cued movements in PD patients, probably to compensate for the impaired SMA function [29,30]. Thus, visual stimuli can bypass the damaged basal ganglia and allow an intact cerebellar circuit to be used for visuomotor control [10]. A possible explanation for the interaction between the effect of visual cuing and gait velocity is that walking at higher velocities is more demanding and focuses one's attention to the walking pattern itself, which allows a partial correction of impaired automaticity of locomotion in PD by intentional modulation of gait. In addition, it has been hypothesized that the treadmill acts as an external cue [31,32], which might compete with the effect of visual cuing at higher velocities.
81
Surprisingly, the effect of visual cuing failed to reach a significant difference between PD patients and controls in the majority of the gait parameters. This is in line with previous studies which have already observed considerable effects of visual cuing with transverse stripes in the control population [6,26,30]. One potential limitation of this study was that the subjects walked on a motorized treadmill. Compared to overground walking, treadmill walking induces walking with slightly shorter stride lengths and slightly higher cadence [33]. This effect has been reported to be somewhat stronger in PD-patients [34]. Thus, the typical differences between PD-patients and controls during overground walking become even more pronounced. However, as all subjects were tested under the same experimental conditions, the observed differences between cuing conditions, gait velocities and groups are expected to remain during overground walking. 5. Conclusions The current experiment shows that the gait patterns of PD-patients are not rigidly coupled to gait velocity and can be manipulated with visual cuing techniques using alternate sensory motor pathways. An interaction of visual cuing and gait velocity was found as the influence of visual cues on gait parameters was proportionally more pronounced at lower velocities. Recent studies demonstrated that treadmill training is efficacious and safe for improving gait and mobility in PD [35]. Our study supports the idea that visual cuing applied by transverse stripes fixed on a treadmill belt could enhance the efficacy of treadmill training particularly at lower velocities. Conflict of interest Nothing to report. References [1] Blin O, Ferrandez AM, Serratrice G. Quantitative analysis of gait in Parkinson patients: increase variability of stride length. J Neurol Sci 1990;1:91–7. [2] Morris ME, Iansek R, Matyas TA, Summers JJ. Ability to modulate walking cadence remains intact in Parkinson's disease. J Neurol Neurosurg Psychiatry 1994;57: 1532–4. [3] Morris ME, Iansek R, Matyas TA, Summers JJ. Stride length regulation in Parkinson's disease. Normalization strategies and underlying mechanisms. Brain 1996;119:551–68. [4] Morris ME, Iansek R, McGinley J, Matyas T, Huxham F. Three-dimensional gait biomechanics in Parkinson's disease: evidence for centrally mediated amplitude regulation disorder. Mov Disord 2005;20:40–50. [5] Willems AM, Nieuwboer A, Chavret F, Desloovere K, Dom R, Rochester L, et al. The use of rhythmic auditory cues to influence gait in patients with Parkinson's disease, the differential effect for freezers and non-freezers, an explorative study. Disabil Rehabil 2006;28:721–8. [6] Lewis GN, Byblow WD, Walt SE. Stride length regulation in Parkinson's disease: the use of extrinsic, visual cues. Brain 2000;123:2077–90. [7] Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL. Gait variations of gait cycle timing in Parkinson's disease and Huntington's disease. Mov Disord 1998;13:428–37. [8] Frenkel-Toledo S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM. Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently. J Neuroeng Rehabil 2005;2:23–9. [9] Thaut MH, McIntosh GC, Rice RR, Miller RA, Rathbun J, Brault JM. Rhythmic auditory stimulation in gait training for Parkinson's disease patients. Mov Disord 1996;11:193–200. [10] Azulay JP, Mesure S, Amblard B, Blin O, Sangla I, Pouget J. Visual control of locomotion in Parkinson's disease. Brain 1999;122:111–20. [11] Nakamura T, Meguro K, Saski H. Relationship between falls and stride length variability in senile dementia of the Alzheimer type. Gerontology 1996;42: 108–13. [12] Schaafsma JD, Giladi N, Balash Y, Bartels AL, Gurevich T, Hausdorff JM. Gait dynamics in Parkinson's disease: relationship to Parkinsonian features, falls and response to levodopa. J Neurol Sci 2003;212:47–53. [13] Chan CWY. Could Parkinsonian akinesia be attributable to a disturbance in the motor preparatory process? Brain Res 1986;386:183–96. [14] Von Wilzenben HD. Locomotion in the treatment of post encephalitic Parkinson's disease. New York: Grune and Stratten; 1942. p. 135–8.
82
F. Luessi et al. / Journal of the Neurological Sciences 314 (2012) 78–82
[15] McIntosh GC, Brown SH, Rice RR, Thaut MH. Rhythmic auditorymotor facilitation of gait patterns in patients with Parkinson's disease. J Neurol Neurosurg Psychiatry 1997;62:22–6. [16] Nieuwboer A, Kwakkel G, Rochester L, Jones D, van Wegen E, Willems AM, et al. Cuing training in the home improves gait-related mobility in Parkinson's disease: the RESCUE trial. J Neurol Neurosurg Psychiatry 2007;78:134–40. [17] Morris ME, Iansek R, Matyas TA, Summers JJ. The pathogenesis of gait hypokinesia in Parkinson's disease. Brain 1994;117:1169–81. [18] Suteerawattananon M, Morris GS, Etnyre BR, Jankovic J, Protas EJ. Effects of visual and auditory cues on gait in individuals with Parkinson's disease. J Neurol Sci 2004;219:63–9. [19] Zijlstra W, Rutgers AWF, Van Weerden TW. Voluntary and involuntary adaptation of gait in Parkinson's disease. Gait Posture 1998;7:53–63. [20] Kirtley C, Whittle MW, Jefferson RJ. Influence of walking speed on gait parameters. J Biomed Eng 1985;7:282–8. [21] Hughes AJ, Daniel SE, Ben Shlomo Y, Lees AJ. The accuracy of diagnosis of Parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125:861–70. [22] Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology 1967;17:427–42. [23] Fahn S, Elton R. Members of the UPDRS development committee. Unified Parkinson's disease rating scale. In: Fahn S, editor. Recent developments in Parkinson's disease. Florham Park, NJ: Macmillan Healthcare Information; 1987. p. 153–63. [24] Gabell A, Nayak US. The effect of age on the variability in gait. J Gerontol 1984;39: 662–6. [25] Bagley S, Kelly B, Tunnicliffe N, Turnbull GI, Walker JM. The effect of visual cues on the gait of independently mobile Parkinson's disease patients. Physiotherapy 1991;77:415–20. [26] Azulay JP, Van Den Brand C, Mestre D, Blin O, Sangla I, Pouget J, et al. Gait analysis in Parkinson's disease: effect of levodopa and visual stimulation. Rev Neurol (Paris) 1996;152:128–34.
[27] Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in communityliving older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001;82: 1050–6. [28] Samuel M, Ceballos-Baumann AO, Turjanski N, Boecker H, Gorospe A, Linazasoro A, et al. Pallidotomy in Parkinson's disease increases supplementary motor area and prefrontal activation during performance of volitional movements: an H2(15)O PET study. Brain 1997;120:1301–13. [29] Samuel M, Ceballos-Baumann AO, Blin J, Uema T, Boecker H, Passingham RE, et al. Evidence for lateral premotor and parietal overactivity in Parkinson's disease during sequential and bimanual movements: a PET study. Brain 1997;120: 963–76. [30] Hanakawa T, Fukuyama H, Katsumi Y, Honda M, Shibasaki H. Enhanced lateral premotor activity during paradoxical gait in Parkinson's disease. Neurology 1999;45:329–36. [31] Herman T, Giladi N, Gruendlinger, Hausdorff JM. Six weeks of intensive treadmill training improves gait and quality of life in patients with Parkinson's disease: a pilot study. Arch Phys Med Rehabil 2007;88:1154–8. [32] Frenkel-Toledo S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM. Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease. Mov Disord 2005;20:1109–14. [33] Murray MP, Spurr GB, Sepic SB, Gardner GM, Mollinger LA. Treadmill vs. floor walking: kinematics, electromyogram, and heart rate. Eur J Appl Physiol 1995;59:87–91. [34] Zijlsstra W, Rutgers AW, Van Weerden TW. Voluntary and involuntary adaptation of gait in Parkinson's disease. Gait Posture 1998;7:53–63. [35] Herman T, Giladi N, Hausdorff JM. Treadmill training for the treatment of gait disturbances in people with Parkinson's disease: a mini-review. J Neural Transm 2009;116:307–18.