Cargando…

Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring

OBJECTIVES: There are concerns regarding the accuracy of step count in Parkinson’s disease (PD) when wearable sensors are used. In this study, it was predicted that providing the normal rhythmicity of walking was maintained, the autocorrelation function used to measure step count would provide relat...

Descripción completa

Detalles Bibliográficos
Autores principales: Shokouhi, Navid, Khodakarami, Hamid, Fernando, Chathurini, Osborn, Sarah, Horne, Malcolm
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244695/
https://www.ncbi.nlm.nih.gov/pubmed/35783129
http://dx.doi.org/10.3389/fnagi.2022.904895
_version_ 1784738585161760768
author Shokouhi, Navid
Khodakarami, Hamid
Fernando, Chathurini
Osborn, Sarah
Horne, Malcolm
author_facet Shokouhi, Navid
Khodakarami, Hamid
Fernando, Chathurini
Osborn, Sarah
Horne, Malcolm
author_sort Shokouhi, Navid
collection PubMed
description OBJECTIVES: There are concerns regarding the accuracy of step count in Parkinson’s disease (PD) when wearable sensors are used. In this study, it was predicted that providing the normal rhythmicity of walking was maintained, the autocorrelation function used to measure step count would provide relatively low errors in step count. MATERIALS AND METHODS: A total of 21 normal walkers (10 without PD) and 27 abnormal walkers were videoed while wearing a sensor [Parkinson’s KinetiGraph (PKG)]. Median step count error rates were observed to be <3% in normal walkers but ≥3% in abnormal walkers. The simultaneous accelerometry data and data from a 6-day PKG were examined and revealed that the 5th percentile of the spectral entropy distribution, among 10-s walking epochs (obtained separately), predicted whether subjects had low error rate on step count with reference to the manual step count from the video recording. Subjects with low error rates had lower Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) scores and UPDRS III Q10–14 scores than the high error rate counterparts who also had high freezing of gait scores (i.e., freezing of gait questionnaire). RESULTS: Periods when walking occurred were identified in a 6-day PKG from 190 non-PD subjects aged over 60, and 155 people with PD were examined and the 5th percentile of the spectral entropy distribution, among 10-s walking epochs, was extracted. A total of 84% of controls and 72% of people with PD had low predicted error rates. People with PD with low bradykinesia scores (measured by the PKG) had step counts similar to controls, whereas those with high bradykinesia scores had step counts similar to those with high error rates. On subsequent PKGs, step counts increased when bradykinesia was reduced by treatment and decreased when bradykinesia increased. Among both control and people with PD, low error rates were associated with those who spent considerable time making walks of more than 1-min duration. CONCLUSION: Using a measure of the loss of rhythmicity in walking appears to be a useful method for detecting the likelihood of error in step count. Bradykinesia in subjects with low predicted error in their step count is related to overall step count but when the predicted error is high, the step count should be assessed with caution.
format Online
Article
Text
id pubmed-9244695
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92446952022-07-01 Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring Shokouhi, Navid Khodakarami, Hamid Fernando, Chathurini Osborn, Sarah Horne, Malcolm Front Aging Neurosci Aging Neuroscience OBJECTIVES: There are concerns regarding the accuracy of step count in Parkinson’s disease (PD) when wearable sensors are used. In this study, it was predicted that providing the normal rhythmicity of walking was maintained, the autocorrelation function used to measure step count would provide relatively low errors in step count. MATERIALS AND METHODS: A total of 21 normal walkers (10 without PD) and 27 abnormal walkers were videoed while wearing a sensor [Parkinson’s KinetiGraph (PKG)]. Median step count error rates were observed to be <3% in normal walkers but ≥3% in abnormal walkers. The simultaneous accelerometry data and data from a 6-day PKG were examined and revealed that the 5th percentile of the spectral entropy distribution, among 10-s walking epochs (obtained separately), predicted whether subjects had low error rate on step count with reference to the manual step count from the video recording. Subjects with low error rates had lower Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) scores and UPDRS III Q10–14 scores than the high error rate counterparts who also had high freezing of gait scores (i.e., freezing of gait questionnaire). RESULTS: Periods when walking occurred were identified in a 6-day PKG from 190 non-PD subjects aged over 60, and 155 people with PD were examined and the 5th percentile of the spectral entropy distribution, among 10-s walking epochs, was extracted. A total of 84% of controls and 72% of people with PD had low predicted error rates. People with PD with low bradykinesia scores (measured by the PKG) had step counts similar to controls, whereas those with high bradykinesia scores had step counts similar to those with high error rates. On subsequent PKGs, step counts increased when bradykinesia was reduced by treatment and decreased when bradykinesia increased. Among both control and people with PD, low error rates were associated with those who spent considerable time making walks of more than 1-min duration. CONCLUSION: Using a measure of the loss of rhythmicity in walking appears to be a useful method for detecting the likelihood of error in step count. Bradykinesia in subjects with low predicted error in their step count is related to overall step count but when the predicted error is high, the step count should be assessed with caution. Frontiers Media S.A. 2022-06-16 /pmc/articles/PMC9244695/ /pubmed/35783129 http://dx.doi.org/10.3389/fnagi.2022.904895 Text en Copyright © 2022 Shokouhi, Khodakarami, Fernando, Osborn and Horne. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
Shokouhi, Navid
Khodakarami, Hamid
Fernando, Chathurini
Osborn, Sarah
Horne, Malcolm
Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring
title Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring
title_full Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring
title_fullStr Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring
title_full_unstemmed Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring
title_short Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring
title_sort accuracy of step count estimations in parkinson’s disease can be predicted using ambulatory monitoring
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244695/
https://www.ncbi.nlm.nih.gov/pubmed/35783129
http://dx.doi.org/10.3389/fnagi.2022.904895
work_keys_str_mv AT shokouhinavid accuracyofstepcountestimationsinparkinsonsdiseasecanbepredictedusingambulatorymonitoring
AT khodakaramihamid accuracyofstepcountestimationsinparkinsonsdiseasecanbepredictedusingambulatorymonitoring
AT fernandochathurini accuracyofstepcountestimationsinparkinsonsdiseasecanbepredictedusingambulatorymonitoring
AT osbornsarah accuracyofstepcountestimationsinparkinsonsdiseasecanbepredictedusingambulatorymonitoring
AT hornemalcolm accuracyofstepcountestimationsinparkinsonsdiseasecanbepredictedusingambulatorymonitoring