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Greene, Barry R.
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Greene, Barry R.
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Greene, Barry R.
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Now showing 1 - 5 of 5
- PublicationShort Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis(MDPI, 2020-09-20)
; ; ; ; Background: Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gaitdata collected in non-controlled settings is often fragmented and may not provide enough information forreliable measures. We evaluate a novel approach, extracting pre-defined numbers of gait cycles from the fulllength of a walking task, and their effects on the reliability of spatiotemporal gait parameters. Methods: The present study evaluates intra-session reliability of spatiotemporal gait parameters for short bouts of gaitdata extracted from the full length of the walking tasks to 1) determine the effects of the length of the walkingtask on the reliability of calculated measures and 2) identify spatiotemporal gait parameters that can providereliable measures for gait assessments and reference data in different settings. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS rage 0 to 4.5) executed two trials,walking 20m each, with inertial sensors attached to their right and left shanks. Previously published algorithms were applied to identify gait events from the medio-lateral angular velocity. Short bouts of gait data wereextracted from each trial, with lengths varying from 3 to 9 gait cycles. Twenty-one measures of spatiotemporalgait parameters were calculated. Intraclass correlation coefficients (ICCs) were calculated to evaluate how the degree of agreement between the two trials of each participant varied with the number of gait cycles included inthe analysis. Results: Spatiotemporal gait parameters calculated as the mean across included gait cycles reach excellent reliabilityfrom three gait cycles. Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, reach good reliability from six gait cycles and should be further explored for persons with MS, whilestride time asymmetry and step time asymmetry do not seem to provide reliable measures and should bereported carefully. Conclusion: Short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using wearable devices in non-controlled environments, to support monitoring of symptoms of persons with neurological diseases.180 - PublicationSensor-based Assessment of Falls Risk of the Timed Up and Go in Real-World Settings(Oxford University Press, 2019-11-08)
; ; ; ; Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings.230 - PublicationLongitudinal assessment of falls in patients with Parkinson’s disease using inertial sensors and the Timed Up and Go testObjective: To examine the predictive validity of a TUG test for falls risk, quantified using body-worn sensors (QTUG) in people with Parkinsons Disease (PD). We also sought to examine the inter-session reliability of QTUG sensor measures and their association with the Unified Parkinsons Disease Rating Scale (UPDRS) motor score. Approach: A six-month longitudinal study of 15 patients with Parkinsons disease. Participants were asked to complete a weekly diary recording any falls activity for six months following baseline assessment. Participants were assessed monthly, using a Timed Up and Go test, quantified using body-worn sensors, placed on each leg below the knee. Main results: The results suggest that the QTUG falls risk estimate recorded at baseline is 73.33% (44.90, 92.21)accurate in predicting falls within 90 days, while the Timed Up and Go time at baseline was 46.67% (21.27, 73.41)accurate. The Timed Up and Go time and QTUG falls risk estimate were strongly correlated with UPDRS motor score. Fifty-two of 59 inertial sensor parameters exhibited excellent inter-session reliability, five exhibited moderate reliability, while two parameters exhibited poor reliability. Significance: The results suggest that QTUG is a reliable tool for the assessment of gait and mobility in Parkinsons disease and, furthermore, that it may have utility in predicting falls in patients with Parkinsons disease.
308 - PublicationInvestigating normal day to day variations in postural control in a healthy young population (age 18-40) using wii balance boards(BMJ Publishing Group, 2015-10)
; ; ; ; ; ; Objective me asurements of postural control are frequently used to examine the causes of, features associated with, and therapeutic interventions for ankle instability. However, researchers have typically used single-session measures to represent postural control at one point in time. Recent studies in a healthy elderly population demonstrate significant variations in day-to-day postural control and suggest that single-session measurement may not truly reflect postural control capabilities. An investigation into patterns of day-to-day variation in postural control in a younger population are warranted.319 - PublicationDigital assessment of falls risk, frailty, and mobility impairment using wearable sensors(Springer, 2019-12-11)
; ; ; ; Falls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the “first fall”, delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.314Scopus© Citations 29