A corrected version of the Timed-25 Foot Walk Test with a dynamic start to capture the maximum ambulation speed in multiple sclerosis patients

Phan-Ba, Rémy; Calay, Philippe; Grodent, Patrick; Delrue, Gaël; Lommers, Émilie; Delvaux, Valérie; Moonen, Gustave; Nagels, Guy; Belachew, Shibeshih

doi:10.3233/nre-2012-0754

Cited by 20 publications

(8 citation statements)

References 26 publications

(32 reference statements)

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“…In order to quantify ambulation fatigability over a demanding distance of effort, we proposed to integrate the fastest and the lowest measurable walking speeds over the different tested walking paradigms. The T25FW + MWS was previously confirmed to be a valid test to approach the fastest MWS of MS patients on a very short distance regardless of their acceleration capacity [10] . On the other hand, the mean finishing pace during the last 100 m of the T500MW (T400–500MW) appeared to be the lowest measurable speed over this fatigue inducing longer distance ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 93%

“…pMS and healthy controls performed a multimodal walking assessment that comprised 4 tests, in the following order : the Timed 25-Foot Walk Test (T25FW, performed twice), a corrected version of the T25FW with a dynamic start (T25FW + , performed twice [10] ), the Timed 100-Meter Walk Test (T100MW [7] ), and the Timed 500-Meter Walk Test (T500MW). A period of rest of 15 minutes was allowed between each test to minimize interference due to potential test-related fatigue, and all demanding physical activities (such as rehabilitation sessions) were suspended in the last 24 hours prior to the assessment.…”

Section: Methodsmentioning

confidence: 99%

“…The T25FW was performed according to the published standardized instructions [4] , [5] . The T25FW + was also strictly following the guidelines of the T25FW [4] , [5] , except that the subjects were allowed to take a 3 meters run-up before the starting line [10] . This run-up was clearly demarcated on the ground.…”

Section: Methodsmentioning

confidence: 99%

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Motor Fatigue Measurement by Distance-Induced Slow Down of Walking Speed in Multiple Sclerosis

et al. 2012

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Background and rationaleMotor fatigue and ambulation impairment are prominent clinical features of people with multiple sclerosis (pMS). We hypothesized that a multimodal and comparative assessment of walking speed on short and long distance would allow a better delineation and quantification of gait fatigability in pMS. Our objectives were to compare 4 walking paradigms: the timed 25-foot walk (T25FW), a corrected version of the T25FW with dynamic start (T25FW+), the timed 100-meter walk (T100MW) and the timed 500-meter walk (T500MW).MethodsThirty controls and 81 pMS performed the 4 walking tests in a single study visit.ResultsThe 4 walking tests were performed with a slower WS in pMS compared to controls even in subgroups with minimal disability. The finishing speed of the last 100-meter of the T500MW was the slowest measurable WS whereas the T25FW+ provided the fastest measurable WS. The ratio between such slowest and fastest WS (Deceleration Index, DI) was significantly lower only in pMS with EDSS 4.0–6.0, a pyramidal or cerebellar functional system score reaching 3 or a maximum reported walking distance ≤4000 m.ConclusionThe motor fatigue which triggers gait deceleration over a sustained effort in pMS can be measured by the WS ratio between performances on a very short distance and the finishing pace on a longer more demanding task. The absolute walking speed is abnormal early in MS whatever the distance of effort when patients are unaware of ambulation impairment. In contrast, the DI-measured ambulation fatigability appears to take place later in the disease course.

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Section: Resultsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

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Motor Fatigue Measurement by Distance-Induced Slow Down of Walking Speed in Multiple Sclerosis

et al. 2012

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“…Firstly, classification with high accuracy was possible using only data gathered from an instrumented walkway system [ 14 , 15 ]. At present, clinicians, and patients use a wide variety of walking tests (Timed 25 Foot Walk Test, Six-Minute Walk Test, Dynamic Gait Index, 12-Item Walking scale, and others) to identify gait problems [ 20 – 22 ]. The machine learning process described in this paper may be useful to automatically distinguish gait problems.…”

Section: Discussionmentioning

confidence: 99%

Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway

Combden

Jiang

et al. 2022

BioMed Eng OnLine

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Background Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance. Results Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%. Conclusions The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).

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“…The frequency of sampling data is 50 Hz (Antwi-Afari et al, 2020a). Temporal gait parameters (e.g., gait speed, cadence, double support time, step duration, stance duration, and swing duration) and dynamic stability [the anteroposterior and mediolateral center of pressure (CoP) displacements, and mean sway velocity in the anteroposterior and mediolateral directions] were collected (Oerbekke et al, 2017, Phan-Ba et al, 2012. Participants were asked to walk at the usual speed along an 8meter walkway as a trial before the actual data collection.…”

Section: Participantsmentioning

confidence: 99%

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Anwer

Antwi‐Afari

et al. 2021

ECAM

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PurposeSince construction workers often need to carry various types of loads in their daily routine, they are at risk of sustaining musculoskeletal injuries. Additionally, carrying a load during walking may disturb their walking balance and lead to fall injuries among construction workers. Different load carrying techniques may also cause different extents of physical exertion. Therefore, the purpose of this paper is to examine the effects of different load-carrying techniques on gait parameters, dynamic balance, and physiological parameters in asymptomatic individuals on both stable and unstable surfaces.Design/methodology/approachFifteen asymptomatic male participants (mean age: 31.5 ± 2.6 years) walked along an 8-m walkway on flat and foam surfaces with and without a load thrice using three different techniques (e.g. load carriage on the head, on the dominant shoulder, and in both hands). Temporal gait parameters (e.g. gait speed, cadence, and double support time), gait symmetry (e.g. step time, stance time, and swing time symmetry), and dynamic balance parameters [e.g. anteroposterior and mediolateral center of pressure (CoP) displacement, and CoP velocity] were evaluated. Additionally, the heart rate (HR) and electrodermal activity (EDA) was assessed to estimate physiological parameters.FindingsThe gait speed was significantly higher when the load was carried in both hands compared to other techniques (Hand load, 1.02 ms vs Head load, 0.82 ms vs Shoulder load, 0.78 ms). Stride frequency was significantly decreased during load carrying on the head than the load in both hands (46.5 vs 51.7 strides/m). Step, stance, and swing time symmetry were significantly poorer during load carrying on the shoulder than the load in both hands (Step time symmetry ration, 1.10 vs 1.04; Stance time symmetry ratio, 1.11 vs 1.05; Swing time symmetry ratio, 1.11 vs 1.04). The anteroposterior (Shoulder load, 17.47 mm vs Head load, 21.10 mm vs Hand load, −5.10 mm) and mediolateral CoP displacements (Shoulder load, −0.57 mm vs Head load, −1.53 mm vs Hand load, −3.37 ms) significantly increased during load carrying on the shoulder or head compared to a load in both hands. The HR (Head load, 85.2 beats/m vs Shoulder load, 77.5 beats/m vs No load, 69.5 beats/m) and EDA (Hand load, 14.0 µS vs Head load, 14.3 µS vs Shoulder load, 14.1 µS vs No load, 9.0 µS) were significantly larger during load carrying than no load.Research limitations/implicationsThe findings suggest that carrying loads in both hands yields better gait symmetry and dynamic balance than carrying loads on the dominant shoulder or head. Construction managers/instructors should recommend construction workers to carry loads in both hands to improve their gait symmetry and dynamic balance and to lower their risk of falls.Practical implicationsThe potential changes in gait and balance parameters during various load carrying methods will aid the assessment of fall risk in construction workers during loaded walking. Wearable insole sensors that monitor gait and balance in real-time would enable safety managers to identify workers who are at risk of falling during load carriage due to various reasons (e.g. physical exertion, improper carrying techniques, fatigue). Such technology can also empower them to take the necessary steps to prevent falls.Originality/valueThis is the first study to use wearable insole sensors and a photoplethysmography device to assess the impacts of various load carrying approaches on gait parameters, dynamic balance, and physiological measures (i.e. HR and EDA) while walking on stable and unstable terrains.

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A corrected version of the Timed-25 Foot Walk Test with a dynamic start to capture the maximum ambulation speed in multiple sclerosis patients

Cited by 20 publications

References 26 publications

Motor Fatigue Measurement by Distance-Induced Slow Down of Walking Speed in Multiple Sclerosis

Motor Fatigue Measurement by Distance-Induced Slow Down of Walking Speed in Multiple Sclerosis

Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

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