Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson’s disease and matched controls during daily living

Shah, Vrutangkumar V.; McNames, James; Mancini, Martina; Carlson‐Kuhta, Patricia; Spain, Rebecca; Nutt, John G.; El‐Gohary, Mahmoud; Curtze, Carolin; Horak, Fay B.

doi:10.1007/s00415-020-09696-5

Cited by 61 publications

(50 citation statements)

References 48 publications

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“…At-home gait speed differed significantly between age groups Participants were asked to continuously wear an accelerometer (GeneActiv) attached to the lumbar region with an elastic belt for a period of approximately one week (range = [6][7][8][9][10][11][12][13][14][15] days, mean ± SD = 8.72 ± 1.88 days; younger group = 8.61 ± 1.73 days; older group = 8.84 ± 2.05 days. There were no group differences between the number of walking bouts of younger and older groups during the at-home monitoring period (p = 0.8).…”

Section: Resultsmentioning

confidence: 99%

“…We recruited 65 participants in total, 33 healthy young participants (age = 29.2 ± 4.6 years, 17F, body mass index (BMI) = 23.4 ± 2.6) and 32 healthy older participants (age = 72.3 ± 5.8 years, 16F, BMI = 24.5 ± 2.6, Table 1) to take part in two instrumented in-lab assessments each lasting around two hours in duration about 7-14 days apart, and an at-home portion in between the two visits (range = [6][7][8][9][10][11][12][13][14][15] days, mean ± SD = 8.72 ± 1.88 days; younger group = 8.61 ± 1.73 days; older group = 8.84 ± 2.05 days). Throughout this manuscript at-home activity monitoring refers to monitoring all activity outside the laboratory; i.e., real-world environment or daily life.…”

Section: Subjects and Proceduresmentioning

confidence: 99%

“…In addition to assessing the accuracy with which they can measure spatial and temporal aspects of gait, it is also necessary to evaluate the sensitivity of measures derived during daily life for detecting clinically meaningful changes. Shah et al showed that measures of quantity were able to better discriminate between patients with multiple sclerosis and controls, whereas measures of quality were more discriminative for patients with Parkinson's disease (PD) and controls 13 .…”

Section: Introductionmentioning

confidence: 99%

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Age and environment-related differences in gait in healthy adults using wearables

et al. 2020

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Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18–40 years) and older (n = 32, 65–85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.

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Section: Resultsmentioning

confidence: 99%

Section: Subjects and Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Age and environment-related differences in gait in healthy adults using wearables

et al. 2020

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“…Recently, the use of wearable technology has made it feasible to quantify gait quality outside the laboratory during daily life [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Although the use of wearable technology is increasing in daily life environments, this is still a relatively new field of research in which algorithms and data analysis methods are under continuous development [ 11 , 20 , 21 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Bout Length on Gait Measures in People with and without Parkinson’s Disease during Daily Life

Shah

McNames

Harker

et al. 2020

Sensors

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Although the use of wearable technology to characterize gait disorders in daily life is increasing, there is no consensus on which specific gait bout length should be used to characterize gait. Clinical trialists using daily life gait quality as study outcomes need to understand how gait bout length affects the sensitivity and specificity of measures to discriminate pathological gait as well as the reliability of gait measures across gait bout lengths. We investigated whether Parkinson’s disease (PD) affects how gait characteristics change as bout length changes, and how gait bout length affects the reliability and discriminative ability of gait measures to identify gait impairments in people with PD compared to neurotypical Old Adults (OA). We recruited 29 people with PD and 20 neurotypical OA of similar age for this study. Subjects wore 3 inertial sensors, one on each foot and one over the lumbar spine all day, for 7 days. To investigate which gait bout lengths should be included to extract gait measures, we determined the range of gait bout lengths available across all subjects. To investigate if the effect of bout length on each gait measure is similar or not between subjects with PD and OA, we used a growth curve analysis. For reliability and discriminative ability of each gait measure as a function of gait bout length, we used the intraclass correlation coefficient (ICC) and area under the curve (AUC), respectively. Ninety percent of subjects walked with a bout length of less than 53 strides during the week, and the majority (>50%) of gait bouts consisted of less than 12 strides. Although bout length affected all gait measures, the effects depended on the specific measure and sometimes differed for PD versus OA. Specifically, people with PD did not increase/decrease cadence and swing duration with bout length in the same way as OA. ICC and AUC characteristics tended to be larger for shorter than longer gait bouts. Our findings suggest that PD interferes with the scaling of cadence and swing duration with gait bout length. Whereas control subjects gradually increased cadence and decreased swing duration as bout length increased, participants with PD started with higher than normal cadence and shorter than normal stride duration for the smallest bouts, and cadence and stride duration changed little as bout length increased, so differences between PD and OA disappeared for the longer bout lengths. Gait measures extracted from shorter bouts are more common, more reliable, and more discriminative, suggesting that shorter gait bouts should be used to extract potential digital biomarkers for people with PD.

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“…Subjects wore three inertial sensors (same as Study I) only one on each foot and one over the lumbar area for a week of continuous monitoring for at least 8 h/day, details in Shah et al [37]. The Opal is lightweight (22 g), has a battery life of 16 h, and includes 8 GB of storage, which can record over 30 days of data.…”

Section: Procedures For Study IImentioning

confidence: 99%

Measuring freezing of gait during daily-life: an open-source, wearable sensors approach

Mancini

Shah

Stuart

et al. 2021

J NeuroEngineering Rehabil

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119

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Background Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson’s disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II). Methods In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2–5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability. Results We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes. Conclusion Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD.

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Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson’s disease and matched controls during daily living

Cited by 61 publications

References 48 publications

Age and environment-related differences in gait in healthy adults using wearables

Age and environment-related differences in gait in healthy adults using wearables

Effect of Bout Length on Gait Measures in People with and without Parkinson’s Disease during Daily Life

Measuring freezing of gait during daily-life: an open-source, wearable sensors approach

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