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Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis
BACKGROUND: Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. OBJECTIVE: The aim of this study was to explore associations between depression symptom severity and daily-life gait...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579931/ https://www.ncbi.nlm.nih.gov/pubmed/36194451 http://dx.doi.org/10.2196/40667 |
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author | Zhang, Yuezhou Folarin, Amos A Sun, Shaoxiong Cummins, Nicholas Vairavan, Srinivasan Qian, Linglong Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Stewart, Callum Laiou, Petroula Sankesara, Heet Matcham, Faith White, Katie M Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Rintala, Aki Mohr, David C Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W J H Narayan, Vaibhav A Annas, Peter Hotopf, Matthew Dobson, Richard J B |
author_facet | Zhang, Yuezhou Folarin, Amos A Sun, Shaoxiong Cummins, Nicholas Vairavan, Srinivasan Qian, Linglong Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Stewart, Callum Laiou, Petroula Sankesara, Heet Matcham, Faith White, Katie M Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Rintala, Aki Mohr, David C Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W J H Narayan, Vaibhav A Annas, Peter Hotopf, Matthew Dobson, Richard J B |
author_sort | Zhang, Yuezhou |
collection | PubMed |
description | BACKGROUND: Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. OBJECTIVE: The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. METHODS: We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. RESULTS: Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R(2)=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R(2)=0.06). CONCLUSIONS: This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings. |
format | Online Article Text |
id | pubmed-9579931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-95799312022-10-20 Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis Zhang, Yuezhou Folarin, Amos A Sun, Shaoxiong Cummins, Nicholas Vairavan, Srinivasan Qian, Linglong Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Stewart, Callum Laiou, Petroula Sankesara, Heet Matcham, Faith White, Katie M Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Rintala, Aki Mohr, David C Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W J H Narayan, Vaibhav A Annas, Peter Hotopf, Matthew Dobson, Richard J B JMIR Mhealth Uhealth Original Paper BACKGROUND: Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. OBJECTIVE: The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. METHODS: We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. RESULTS: Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R(2)=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R(2)=0.06). CONCLUSIONS: This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings. JMIR Publications 2022-10-04 /pmc/articles/PMC9579931/ /pubmed/36194451 http://dx.doi.org/10.2196/40667 Text en ©Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Srinivasan Vairavan, Linglong Qian, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Heet Sankesara, Faith Matcham, Katie M White, Carolin Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Sara Simblett, Aki Rintala, David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W J H Penninx, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard J B Dobson, RADAR-CNS Consortium. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.10.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Zhang, Yuezhou Folarin, Amos A Sun, Shaoxiong Cummins, Nicholas Vairavan, Srinivasan Qian, Linglong Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Stewart, Callum Laiou, Petroula Sankesara, Heet Matcham, Faith White, Katie M Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Rintala, Aki Mohr, David C Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W J H Narayan, Vaibhav A Annas, Peter Hotopf, Matthew Dobson, Richard J B Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
title | Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
title_full | Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
title_fullStr | Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
title_full_unstemmed | Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
title_short | Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
title_sort | associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings: retrospective analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579931/ https://www.ncbi.nlm.nih.gov/pubmed/36194451 http://dx.doi.org/10.2196/40667 |
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