Objectives – Effects of endurance training in multiple sclerosis (MS) patients complaining of motor fatigue. Materials and methods – Thirty MS patients complaining of fatigue with low to moderate disabilities randomly allocated to the intervention (thrice weekly 45‐min intervals of endurance exercise) or control treatment (three 45‐min episodes of stretching, balance training and coordination), both as ‘add‐on’ therapy for 3 weeks during inpatient rehabilitation. Results – Maximal walking distance before intervention averaged 1043 ± 568 and 1163 ± 750 m in the two groups. The intervention group increased its maximal walking distance by 650 ± 474 m. The control group extended its walking distance by 96 ± 70 m. Conclusions – The present data confirm a strong effect of endurance exercise on maximal walking distance. Remarkably, there were no parallel improvements on the Modified Fatigue Impact Scale, the Beck Depression Inventory and the Hamburg Quality of Life Questionnaire for MS.
Fatigue is a common and frequently disabling symptom of multiple sclerosis (MS). The aim of this study was to develop the Fatigue index Kliniken Schmieder (FKS) for detecting motor fatigue in patients with MS using kinematic gait analysis. The FKS relies on the chaos theoretical term "attractor", which, if unchanged, is a necessary and sufficient indicator of a stable dynamical system. We measured the acceleration of the feet at the beginning of and shortly before stopping a treadmill walking task in 20 healthy subjects and 40 patients with multiple sclerosis. The attractor and movement variability were calculated. In the absence of muscular exhaustion a significant difference in the attractor and movement variability between the two time points demonstrates altered motor control indicating fatigue. Subjects were classified using the FKS. All healthy subjects had normal FKS and thus no fatigue. 29 patients with MS were classified into a fatigue group and 11 patients into a non-fatigue group. This classification agreed with the physician's observation and video analyses in up to 97 % of cases. The FKS did not correlate significantly with the overall and motor dimensions of the fatigue questionnaire scores in patients with MS and motor fatigue. The common concept of fatigue as overall subjective sensation of exhaustion can be affected by conditions including depression, sleep disorder and others. FKS constitutes a robust and objective measure of changes in motor performance. Therefore, the FKS allows correct identification of motor fatigue even in cases where common comorbidities mask motor fatigue.
BackgroundFatigue is a frequent and serious symptom in patients with Multiple Sclerosis (MS). However, to date there are only few methods for the objective assessment of fatigue. The aim of this study was to develop a method for the objective assessment of motor fatigue using kinematic gait analysis based on treadmill walking and an infrared-guided system.Patients and methodsFourteen patients with clinically definite MS participated in this study. Fatigue was defined according to the Fatigue Scale for Motor and Cognition (FSMC). Patients underwent a physical exertion test involving walking at their pre-determined patient-specific preferred walking speed until they reached complete exhaustion. Gait was recorded using a video camera, a three line-scanning camera system with 11 infrared sensors. Step length, width and height, maximum circumduction with the right and left leg, maximum knee flexion angle of the right and left leg, and trunk sway were measured and compared using paired t-tests (α = 0.005). In addition, variability in these parameters during one-minute intervals was examined. The fatigue index was defined as the number of significant mean and SD changes from the beginning to the end of the exertion test relative to the total number of gait kinematic parameters.ResultsClearly, for some patients the mean gait parameters were more affected than the variability of their movements while other patients had smaller differences in mean gait parameters with greater increases in variability. Finally, for other patients gait changes with physical exertion manifested both in changes in mean gait parameters and in altered variability. The variability and fatigue indices correlated significantly with the motoric but not with the cognitive dimension of the FSMC score (R = -0.602 and R = -0.592, respectively; P < 0.026).ConclusionsChanges in gait patterns following a physical exertion test in patients with MS suffering from motor fatigue can be measured objectively. These changes in gait patterns can be described using the motor fatigue index and represent an objective measure to assess motor fatigue in MS patients. The results of this study have important implications for the assessments and treatment evaluations of fatigue in MS.
In this paper we introduce a new method to expressly use live/corporeal data in quantifying differences of time series data with an underlying limit cycle attractor; and apply it using an example of gait data. Our intention is to identify gait pattern differences between diverse situations and classify them on group and individual subject levels. First we approximated the limit cycle attractors, from which three measures were calculated: δM amounts to the difference between two attractors (a measure for the differences of two movements), δD computes the difference between the two associated deviations of the state vector away from the attractor (a measure for the change in movement variation), and δF, a combination of the previous two, is an index of the change. As an application we quantified these measures for walking on a treadmill under three different conditions: normal walking, dual task walking, and walking with additional weights at the ankle. The new method was able to successfully differentiate between the three walking conditions. Day to day repeatability, studied with repeated trials approximately one week apart, indicated excellent reliability for δM (ICCave > 0.73 with no differences across days; p > 0.05) and good reliability for δD (ICCave = 0.414 to 0.610 with no differences across days; p > 0.05). Based on the ability to detect differences in varying gait conditions and the good repeatability of the measures across days, the new method is recommended as an alternative to expensive and time consuming techniques of gait classification assessment. In particular, the new method is an easy to use diagnostic tool to quantify clinical changes in neurological patients.
Various publications discuss the discrepancies of running in triathlons and stand-alone runs. However, those methods, such as analysing step-characteristics or ground-contact time, lack the ability to quantitatively discriminate between subtle running differences. The attractor method can be applied to overcome those shortcomings. The purpose was to detect differences in athletes' running patterns (δM) and movement precision (δD) by comparing a 5,000 m run after a prior cycling session (TRun) with an isolated run over the same distance (IRun). Participants completed the conditions on a track and a stationary trainer, allowing the use of their personal bike to simulate an Olympic triathlon. During each run, three-dimensional acceleration data, using sensors attached to the ankles, were collected. Results showed that both conditions lead to elevated attractor parameters (δM and δD) over the initial five minutes before the athletes found their rhythm. This generates a new perspective because independent of running after a bike session or without preload, an athlete needs certain time to adjust to the running movement. Coaches must consider this factor as another tool to fine-tune pacing and performance. Moreover, the attractor method is a novel approach to gain deeper insight into human cyclic motions in athletic contexts.
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