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Local dynamic stability during gait for predicting falls in elderly people: A one-year prospective study.

ABSTRACT: Computing the local dynamic stability using accelerometer data from inertial sensors has recently been proposed as a gait measure which may be able to identify elderly people at fall risk. However, the assumptions supporting this potential were concluded as most studies implement a retrospective fall history observation. The aim of this study was to evaluate the potential of local dynamic stability for fall risk prediction in a cohort of subjects over the age of 60 years using a prospective fall occurrence observation. A total of 131 elderly subjects voluntarily participated in this study. The baseline measurement included gait stability assessment using inertial sensors and clinical examination by Tinetti Balance Assessment Tool. After the baseline measurement, subjects were observed for a period of one year for fall occurrence. Our results demonstrated poor multiple falls predictive ability of trunk local dynamic stability (AUC = 0.673). The predictive ability improved when the local dynamic stability was combined with clinical measures, a combination of trunk medial-lateral local dynamic stability and Tinetti total score being the best predictor (AUC = 0.755). Together, the present findings suggest that the medial-lateral local dynamic stability during gait combined with a clinical score is a potential fall risk assessment measure in the elderly population.

SUBMITTER: Bizovska L

PROVIDER: S-EPMC5944953 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCurrently, there are several studies showing that wearable inertial sensors are highly sensitive in the detection of gait disturbances in people with multiple sclerosis (PwMS), showing excellent reliability within one or 7-14 days. However, it is not known how stable these gait parameters remain over a longer period of time. This is surprising, because many treatments last longer than two weeks. Thus, the purpose of the current study was to examine gait parameters obtained by means of wearable inertial sensors during a 6-min walk and to reassess these parameters after a period of one year.MethodsFifty PwMS (without a relapse or a recent change in the Expanded Disability Status Scale (EDSS) or treatment) and 20 healthy participants were examined at two assessment points (interval between assessments: 14.4 ± 6.6 months). At each assessment point, all participants had to complete a 6-min walking test, an observer-rater test (Berg Balance Scale, BBS) and a Timed-up and Go Test (TUG). To measure mean gait parameters (i.e. walking speed, stride length, stride time, the duration of the stance and swing phase and minimum toe-to-floor distance), as well as the intraindividual standard deviation of each mean gait parameter, wearable inertial sensors were utilized.ResultsWe found that even after one year all mean gait parameters showed excellent Intraclass Correlation Coefficients (ICC between 0.75 and 0.95) in PwMS. Looking at MS subgroups, the ICCs were slightly higher in MS subgroup 2 (EDSS 2.0-5.0) than those in MS subgroup 1 (EDSS 0.0-1.5) and healthy controls. Compared to the mean gait parameters, parameters of gait variability showed only good-to-fair ICC values in PwMS. Concerning BBS and TUG, the ICC values after one year were close to the ICC values of the measured mean gait parameters.ConclusionsDue to the excellent stability of mean gait parameters after one year, these sensor-based gait parameters can be identified as clinically relevant markers to evaluate treatment effects over a longer (several months) period of time in MS.

Project description:BackgroundFalls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12?months following rehabilitation discharge.MethodsA prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63?years; median 24?days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars.ResultsOver 12?months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P?<?0.001-0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P?<?0.040; IQR-odds ratio(OR)?=?1.37-7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P?=?0.001-0.018; IQR-OR?=?5.28-10.29).ConclusionsReduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables. Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.

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Local dynamic stability during gait for predicting falls in elderly people: A one-year prospective study.

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