January 6, 2018 | Author: Anonymous | Category: Math, Statistics And Probability, Statistics
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Gait Parameters with and without Balance-Based Torso Weighting (BBTW) in People with Multiple Sclerosis (PwMS) Anna-Maria 1Kinesiology

1 Gorgas,

Department SFSU,

Introduction ● Up to 85% of PwMS experience balance and walking impairments related to muscle weakness, ataxia, or spasticity ● These impairments can cause frequent falls and limitations in activities and participation in daily life.1 ● Evidence suggests that sensori-motor control is a crucial parameter for maintaining stability during gait. ● The purpose of this study was to highlight effects of BBTW on spatio-temporal gait parameters in PwMS.

Gail L.


2 Widener,

2 Gibson-Horn,


Merritt University,


Diane D.

Program in Physical Therapy UCSF/SFSU


● Data collection and analysis: ● Participants walked across a 26-foot GaitRite instrumented gait mat ● The gait mat is computerized with sensors arranged in a grid-like pattern to identify the pressure applied by each foot as it steps ● The software program calculates multiple spatiotemporal parameters of the person’s gait, averaged across all steps for a particular trial

● The increase in velocity in our sample was not considered clinically significant at less than 3%. ● The change confirms previous studies recording immediate velocity improvement with BBTW in PwMS with more significant gait impairments (average unweighted fast-walk velocity 110 cm/sec).2-4 ● Our data provide insight regarding the gait parameters that change along with velocity (SLS, DLS) in PwMS even when they have minimal gait dysfunction.

● Dependent variables ● Parameters of interest included velocity, cadence, step length, between-foot support base, and the percentage of the gait cycle spent in single and double limb support

● The present study approach differs from previous research in the following points: ● Demanded walking velocity5-6 ● The sample’s average age and age range5-6 ● Investigation of effects of an intervention (BBTW) on spatio-temporal gait parameters

● Statistical Analysis ● Comparisons of the means and standard deviations for each variable based on the averaged values of the trials without weights vs. with weights. ● Paired t-tests to evaluate differences in gait parameters between the 2 conditions. Alpha value was set at .05.

BalanceWear Vest

3 Allen


Methods ● Subjects: 18 volunteers (16 females, 2 males) Age in years, mean (SD), range Years with diagnosis, mean (SD) EDSS score equivalent Number (%) claiming falls in the past 6 months Number (%) female

People with MS (n=14)

Healthy controls (n=4)

52 (13), 25-68

53 (10), 41-54

11.3 (8.8)


range 2-6, 10 with EDSS ≤3


6 (42.9)


13 (92.9)

3 (75)

● Procedure ● Medical questionnaire prior to data recording ● Recent fall history ● Experienced MS symptoms ● Walking trials ● Instruction: “Walk as fast as you can safely” before each trial ● Three trials without weight applied to the vest ● Weighting procedure used the BBTW protocol2-4 ● Three trials with specifically adjusted light weights ● Healthy controls performed additional trials after the fast walk trials in each condition – asked to attain the averaged velocity walked by the matched PwMS.

● The evidence indicates that BBTW can affect gait parameters in PwMS; these parameters may be associated with improved balance.

Results ● In PwMS mean velocity and percentage of gait cycle in single (SLS) and double (DLS) limb support showed statistically significant results: ● mean (SD) velocity of 182.3 (27.6) cm/sec for the weighted trials and 177.5 (26.4) cm/sec for the non-weighted trials. ● SLS averaged 41.2% (1.4) with weights and 40.8% (1.4) without weights. ● DLS averaged 17.2 (2.9) with versus 17.9 (2.9) without weights ● Cadence, step length, and step width showed no significant difference between the two conditions ● Without weights, none of the variables were significantly different between PwMS and controls at matched velocities. Variable (PwMS)

Mean ± SD


Walking w/o weights

Walking with weights

Velocity (cm/s)

177.5 ± 29.2

182.3 ± 28.0

˂ 0.05

Cadence (steps/min)

144.3 ± 15.0

145.9 ± 14.9


Step length (cm)

72.7 ± 6.2

73.5 ± 7.3


Base of support (cm)

11.1 ± 2.9

10.3 ± 2.0


Single support (%GC)

40.8 ± 1.4

41.2 ± 1.4

˂ 0.02

Double support (%GC)

17.9 ± 2.8

17.2 ± 2.8

˂ 0.02

Acknowledgement: This study was supported by Award Number R15HD066397 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institutes of Child Health and Human Development or the National Institutes of Health.

References 1. Cromwell, R.L., and Newton, R.A. (2004). Relationship between balance and gait stability in healthy older adults. Journal of Aging and Physical Activity, 11: 90-100 2. Gibson-Horn, C. (2008). Balance-Based Torso Weighting in a person with ataxia and multiple sclerosis: A case report. Journal of Neurologic Physical Therapy, 32: 139-146 3. Widener, G.L., Allen, D.D., and Gibson-Horn, C. (2009). BalancedBased Torso-Weighting may enhance balance in persons with multiple sclerosis: Preliminary evidence. Arch Phys Med Rehabil, 90: 602-609 4. Widener, G.L., Allen, D.D., and Gibson-Horn, C. (2009). Randomized clinical trial of Balance-Based Torso Weighting for improving upright mobility in people with multiple sclerosis. Neurorehabil Neural Repair, 23: 784-791 5. Givon, U., Zeilig, G., and Achiron, A. (2008). Gait analysis in multiple sclerosis: Characterization of temporal-spatial parameters using GAITRite functional ambulation system. Gait & Posture, 29: 138-142 6. Sosnoff, J.J., Weikert, M., Dlugonski, D., Smith, D., and Motl,R. (2011). Quantifying gait impairment in multiple sclerosis using GAITRiteTM technology. Gait & Posture, 34: 145-147

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