Literature DB >> 34207565

Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease.

Marta Francisca Corrà1,2, Arash Atrsaei3, Ana Sardoreira2, Clint Hansen4, Kamiar Aminian3, Manuel Correia1,2, Nuno Vila-Chã2, Walter Maetzler4, Luís Maia1,2,5.   

Abstract

Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.

Entities:  

Keywords:  Parkinson’s disease; gait speed; human gait; lab vs. home; medication states; remote patient monitoring; wearable sensors

Year:  2021        PMID: 34207565     DOI: 10.3390/s21123974

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  4 in total

1.  Accuracy of Step Count Estimations in Parkinson's Disease Can Be Predicted Using Ambulatory Monitoring.

Authors:  Navid Shokouhi; Hamid Khodakarami; Chathurini Fernando; Sarah Osborn; Malcolm Horne
Journal:  Front Aging Neurosci       Date:  2022-06-16       Impact factor: 5.702

2.  Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function.

Authors:  Maximilien Burq; Erin Rainaldi; King Chung Ho; Chen Chen; Bastiaan R Bloem; Luc J W Evers; Rick C Helmich; Lance Myers; William J Marks; Ritu Kapur
Journal:  NPJ Digit Med       Date:  2022-05-23

3.  A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts.

Authors:  Robbin Romijnders; Elke Warmerdam; Clint Hansen; Gerhard Schmidt; Walter Maetzler
Journal:  Sensors (Basel)       Date:  2022-05-19       Impact factor: 3.847

4.  The Forward and Lateral Tilt Angle of the Neck and Trunk Measured by Three-Dimensional Gait and Motion Analysis as a Candidate for a Severity Index in Patients with Parkinson's Disease.

Authors:  Hirofumi Matsumoto; Makoto Shiraishi; Ariaki Higashi; Sakae Hino; Mayumi Kaburagi; Heisuke Mizukami; Futaba Maki; Junji Yamauchi; Kenichiro Tanabe; Tomoo Sato; Yoshihisa Yamano
Journal:  Neurol Int       Date:  2022-09-13
  4 in total

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