Dataset Information

Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed.

ABSTRACT: While balance and gait limitations are hallmarks of multiple sclerosis (MS), standard stopwatch-timed measures practical for use in the clinic are insensitive in minimally affected patients. This prevents early detection and intervention for mobility problems. The study sought to determine if body-worn sensors could detect differences in balance and gait between people with MS with normal walking speeds and healthy controls. Thirty-one MS and twenty-eight age- and sex-matched control subjects were tested using body-worn sensors both during quiet stance and gait (Timed Up and Go test, TUG). Results were compared to stopwatch-timed measures. Stopwatch durations of the TUG and Timed 25 Foot Walk tests were not significantly different between groups. However, during quiet stance with eyes closed, people with MS had significantly greater sway acceleration amplitude than controls (p=0.02). During gait, people with MS had greater trunk angular range of motion in roll (medio-lateral flexion, p=0.017) and yaw (axial rotation, p=0.026) planes. Turning duration through 180° was also longer in MS (p=0.031). Thus, body-worn motion sensors detected mobility differences between MS and healthy controls when traditional timed tests could not. This portable technology provides objective and quantitative mobility data previously not obtainable in the clinic, and may prove a useful outcome measure for early mobility changes in MS.

SUBMITTER: Spain RI

PROVIDER: S-EPMC3614340 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed.

Spain R I RI St George R J RJ Salarian A A Mancini M M Wagner J M JM Horak F B FB Bourdette D D

Gait & posture 20120125 4

While balance and gait limitations are hallmarks of multiple sclerosis (MS), standard stopwatch-timed measures practical for use in the clinic are insensitive in minimally affected patients. This prevents early detection and intervention for mobility problems. The study sought to determine if body-worn sensors could detect differences in balance and gait between people with MS with normal walking speeds and healthy controls. Thirty-one MS and twenty-eight age- and sex-matched control subjects we ...[more]

PMID: 22277368

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Project description:BackgroundAxial spondyloarthritis (axSpA) can lead to spinal mobility restrictions associated with restricted lower limb ranges of motion, thoracic kyphosis, spinopelvic ankylosis, or decrease in muscle strength. It is well known that these factors can have consequences on spatiotemporal gait parameters during walking. However, no study has assessed spatiotemporal gait parameters in patients with axSpA. Divergent results have been obtained in the studies assessing spatiotemporal gait parameters in ankylosing spondylitis, a subgroup of axSpA, which could be partly explained by self-reported pain intensity scores at time of assessment. Inertial measurement units (IMUs) are increasingly popular and may facilitate gait assessment in clinical practice.ObjectiveThis study compared spatiotemporal gait parameters assessed with foot-worn IMUs in patients with axSpA and matched healthy individuals without and with pain intensity score as a covariate.MethodsA total of 30 patients with axSpA and 30 age- and sex-matched healthy controls performed a 10-m walk test at comfortable speed. Various spatiotemporal gait parameters were computed from foot-worn inertial sensors including gait speed in ms-1 (mean walking velocity), cadence in steps/minute (number of steps in a minute), stride length in m (distance between 2 consecutive footprints of the same foot on the ground), swing time in percentage (portion of the cycle during which the foot is in the air), stance time in percentage (portion of the cycle during which part of the foot touches the ground), and double support time in percentage (portion of the cycle where both feet touch the ground).ResultsAge, height, and weight were not significantly different between groups. Self-reported pain intensity was significantly higher in patients with axSpA than healthy controls (P<.001). Independent sample t tests indicated that patients with axSpA presented lower gait speed (P<.001) and cadence (P=.004), shorter stride length (P<.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than healthy controls. When using pain intensity as a covariate, spatiotemporal gait parameters were still significant with patients with axSpA exhibiting lower gait speed (P<.001), shorter stride length (P=.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than matched healthy controls. Interestingly, there were no longer statistically significant between-group differences observed for the cadence (P=.17).ConclusionsGait was significantly altered in patients with axSpA with reduced speed, cadence, stride length, and swing time and increased double support and stance time. Taken together, these changes in spatiotemporal gait parameters could be interpreted as the adoption of a so-called cautious gait pattern in patients with axSpA. Among factors that may influence gait in patients with axSpA, patient self-reported pain intensity could play a role. Finally, IMUs allowed computation of spatiotemporal gait parameters and are usable to assess gait in patients with axSpA in clinical routine.Trial registrationClinicalTrials.gov NCT03761212; https://clinicaltrials.gov/ct2/show/NCT03761212.International registered report identifier (irrid)RR2-10.1007/s00296-019-04396-4.

Project description:BackgroundMobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices.MethodsA total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA.ResultsMean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6-2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01).ConclusionBioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.

Dataset Information

Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed.

Publications

Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed.

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