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Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects

BACKGROUND: Abnormalities in motor activity represent a central feature in major depressive disorder. However, measurement issues are poorly understood, limiting the use of objective measurement of motor activity for diagnostics and treatment monitoring. METHODS: To improve measurement issues, espec...

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Autores principales: Reichert, Markus, Lutz, Alexander, Deuschle, Michael, Gilles, Maria, Hill, Holger, Limberger, Matthias F., Ebner-Priemer, Ulrich W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401688/
https://www.ncbi.nlm.nih.gov/pubmed/25885258
http://dx.doi.org/10.1371/journal.pone.0124231
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author Reichert, Markus
Lutz, Alexander
Deuschle, Michael
Gilles, Maria
Hill, Holger
Limberger, Matthias F.
Ebner-Priemer, Ulrich W.
author_facet Reichert, Markus
Lutz, Alexander
Deuschle, Michael
Gilles, Maria
Hill, Holger
Limberger, Matthias F.
Ebner-Priemer, Ulrich W.
author_sort Reichert, Markus
collection PubMed
description BACKGROUND: Abnormalities in motor activity represent a central feature in major depressive disorder. However, measurement issues are poorly understood, limiting the use of objective measurement of motor activity for diagnostics and treatment monitoring. METHODS: To improve measurement issues, especially sensor placement, analytic strategies and diurnal effects, we assessed motor activity in depressed patients at the beginning (MD; n=27) and after anti-depressive treatment (MD-post; n=18) as well as in healthy controls (HC; n=16) using wrist- and chest-worn accelerometers. We performed multiple analyses regarding sensor placements, extracted features, diurnal variation, motion patterns and posture to clarify which parameters are most powerful in distinguishing patients from controls and monitoring treatment effects. RESULTS: Whereas most feature-placement combinations revealed significant differences between groups, acceleration (wrist) distinguished MD from HC (d=1.39) best. Frequency (vertical axis chest) additionally differentiated groups in a logistic regression model (R2=0.54). Accordingly, both amplitude (d=1.16) and frequency (d=1.04) showed alterations, indicating reduced and decelerated motor activity. Differences between MD and HC in gestures (d=0.97) and walking (d=1.53) were found by data analysis from the wrist sensor. Comparison of motor activity at the beginning and after MD-treatment largely confirms our findings. LIMITATIONS: Sample size was small, but sufficient for the given effect sizes. Comparison of depressed in-patients with non-hospitalized controls might have limited motor activity differences between groups. CONCLUSIONS: Measurement of wrist-acceleration can be recommended as a basic technique to capture motor activity in depressed patients as it records whole body movement and gestures. Detailed analyses showed differences in amplitude and frequency denoting that depressed patients walked less and slower.
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spelling pubmed-44016882015-04-21 Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects Reichert, Markus Lutz, Alexander Deuschle, Michael Gilles, Maria Hill, Holger Limberger, Matthias F. Ebner-Priemer, Ulrich W. PLoS One Research Article BACKGROUND: Abnormalities in motor activity represent a central feature in major depressive disorder. However, measurement issues are poorly understood, limiting the use of objective measurement of motor activity for diagnostics and treatment monitoring. METHODS: To improve measurement issues, especially sensor placement, analytic strategies and diurnal effects, we assessed motor activity in depressed patients at the beginning (MD; n=27) and after anti-depressive treatment (MD-post; n=18) as well as in healthy controls (HC; n=16) using wrist- and chest-worn accelerometers. We performed multiple analyses regarding sensor placements, extracted features, diurnal variation, motion patterns and posture to clarify which parameters are most powerful in distinguishing patients from controls and monitoring treatment effects. RESULTS: Whereas most feature-placement combinations revealed significant differences between groups, acceleration (wrist) distinguished MD from HC (d=1.39) best. Frequency (vertical axis chest) additionally differentiated groups in a logistic regression model (R2=0.54). Accordingly, both amplitude (d=1.16) and frequency (d=1.04) showed alterations, indicating reduced and decelerated motor activity. Differences between MD and HC in gestures (d=0.97) and walking (d=1.53) were found by data analysis from the wrist sensor. Comparison of motor activity at the beginning and after MD-treatment largely confirms our findings. LIMITATIONS: Sample size was small, but sufficient for the given effect sizes. Comparison of depressed in-patients with non-hospitalized controls might have limited motor activity differences between groups. CONCLUSIONS: Measurement of wrist-acceleration can be recommended as a basic technique to capture motor activity in depressed patients as it records whole body movement and gestures. Detailed analyses showed differences in amplitude and frequency denoting that depressed patients walked less and slower. Public Library of Science 2015-04-17 /pmc/articles/PMC4401688/ /pubmed/25885258 http://dx.doi.org/10.1371/journal.pone.0124231 Text en © 2015 Reichert et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Reichert, Markus
Lutz, Alexander
Deuschle, Michael
Gilles, Maria
Hill, Holger
Limberger, Matthias F.
Ebner-Priemer, Ulrich W.
Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects
title Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects
title_full Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects
title_fullStr Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects
title_full_unstemmed Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects
title_short Improving Motor Activity Assessment in Depression: Which Sensor Placement, Analytic Strategy and Diurnal Time Frame Are Most Powerful in Distinguishing Patients from Controls and Monitoring Treatment Effects
title_sort improving motor activity assessment in depression: which sensor placement, analytic strategy and diurnal time frame are most powerful in distinguishing patients from controls and monitoring treatment effects
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401688/
https://www.ncbi.nlm.nih.gov/pubmed/25885258
http://dx.doi.org/10.1371/journal.pone.0124231
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