Literature DB >> 30718220

Associations between daily-living physical activity and laboratory-based assessments of motor severity in patients with falls and Parkinson's disease.

Irina Galperin1, Inbar Hillel1, Silvia Del Din2, Esther M J Bekkers3, Alice Nieuwboer3, Giovanni Abbruzzese4, Laura Avanzino5, Freek Nieuwhof6, Bastiaan R Bloem7, Lynn Rochester8, Ugo Della Croce9, Andrea Cereatti10, Nir Giladi11, Anat Mirelman11, Jeffrey M Hausdorff12.   

Abstract

INTRODUCTION: Recent work suggests that wearables can augment conventional measures of Parkinson's disease (PD). We evaluated the relationship between conventional measures of disease and motor severity (e.g., MDS-UPDRS part III), laboratory-based measures of gait and balance, and daily-living physical activity measures in patients with PD.
METHODS: Data from 125 patients (age: 71.7 ± 6.5 years, Hoehn and Yahr: 1-3, 60.5% men) were analyzed. The MDS-UPDRS-part III was used as the gold standard of motor symptom severity. Gait and balance were quantified in the laboratory. Daily-living gait and physical activity metrics were extracted from an accelerometer worn on the lower back for 7 days.
RESULTS: In multivariate analyses, daily-living physical activity and gait metrics, laboratory-based balance, demographics and subject characteristics together explained 46% of the variance in MDS-UPDRS-part III scores. Daily-living measures accounted for 62% of the explained variance, laboratory measures 30%, and demographics and subject characteristics 7% of the explained variance. Conversely, demographics and subject characteristics, laboratory-based measures of gait symmetry, and motor symptom severity together explained less than 30% of the variance in total daily-living physical activity. MDS-UPDRS-part III scores accounted for 13% of the explained variance, i.e., <4% of all the variance in total daily-living activity.
CONCLUSIONS: Our findings suggest that conventional measures of motor symptom severity do not strongly reflect daily-living activity and that daily-living measures apparently provide important information that is not captured in a conventional one-time, laboratory assessment of gait, balance or the MDS-UPDRS. To provide a more complete evaluation, wearable devices should be considered.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Accelerometers; Daily-living activity; Digital health; Inertial measurement units; Parkinson's disease; Wearable device

Mesh:

Year:  2019        PMID: 30718220     DOI: 10.1016/j.parkreldis.2019.01.022

Source DB:  PubMed          Journal:  Parkinsonism Relat Disord        ISSN: 1353-8020            Impact factor:   4.891


  24 in total

Review 1.  Clinical and methodological challenges for assessing freezing of gait: Future perspectives.

Authors:  Martina Mancini; Bastiaan R Bloem; Fay B Horak; Simon J G Lewis; Alice Nieuwboer; Jorik Nonnekes
Journal:  Mov Disord       Date:  2019-05-02       Impact factor: 10.338

2.  Multimodal Mobility Assessment Predicts Fall Frequency and Severity in Cerebellar Ataxia.

Authors:  Roman Schniepp; Anna Huppert; Julian Decker; Fabian Schenkel; Marianne Dieterich; Thomas Brandt; Max Wuehr
Journal:  Cerebellum       Date:  2022-02-04       Impact factor: 3.847

3.  Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors.

Authors:  Delaram Safarpour; Marian L Dale; Vrutangkumar V Shah; Lauren Talman; Patricia Carlson-Kuhta; Fay B Horak; Martina Mancini
Journal:  Gait Posture       Date:  2021-10-26       Impact factor: 2.840

4.  Objective Measurement of Walking Activity Using Wearable Technologies in People with Parkinson Disease: A Systematic Review Protocol.

Authors:  Thomas Carlin; Clint Hansen; Nicolas Vuillerme
Journal:  Biomed Hub       Date:  2021-07-13

Review 5.  Objective Measurement of Walking Activity Using Wearable Technologies in People with Parkinson Disease: A Systematic Review.

Authors:  Mathias Baptiste Correno; Clint Hansen; Thomas Carlin; Nicolas Vuillerme
Journal:  Sensors (Basel)       Date:  2022-06-16       Impact factor: 3.847

6.  Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.

Authors:  Pin-Wei Chen; Nathan A Baune; Igor Zwir; Jiayu Wang; Victoria Swamidass; Alex W K Wong
Journal:  Int J Environ Res Public Health       Date:  2021-02-09       Impact factor: 3.390

7.  Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study.

Authors:  Annemarie Smid; Jan Willem J Elting; J Marc C van Dijk; Bert Otten; D L Marinus Oterdoom; Katalin Tamasi; Tjitske Heida; Teus van Laar; Gea Drost
Journal:  J Clin Med       Date:  2022-04-19       Impact factor: 4.241

8.  Sensor-Based and Patient-Based Assessment of Daily-Living Physical Activity in People with Parkinson's Disease: Do Motor Subtypes Play a Role?

Authors:  Irina Galperin; Talia Herman; Mira Assad; Natalie Ganz; Anat Mirelman; Nir Giladi; Jeffrey M Hausdorff
Journal:  Sensors (Basel)       Date:  2020-12-08       Impact factor: 3.576

9.  Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device.

Authors:  Nikhil Mahadevan; Charmaine Demanuele; Hao Zhang; Dmitri Volfson; Bryan Ho; Michael Kelley Erb; Shyamal Patel
Journal:  NPJ Digit Med       Date:  2020-01-15

Review 10.  Sensor-to-Segment Calibration Methodologies for Lower-Body Kinematic Analysis with Inertial Sensors: A Systematic Review.

Authors:  Léonie Pacher; Christian Chatellier; Rodolphe Vauzelle; Laetitia Fradet
Journal:  Sensors (Basel)       Date:  2020-06-11       Impact factor: 3.576

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.