Zhang, Yuezhou;
Folarin, Amos A;
Sun, Shaoxiong;
Cummins, Nicholas;
Vairavan, Srinivasan;
Qian, Linglong;
Ranjan, Yatharth;
... RADAR-CNS Consortium; + view all
(2022)
Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis.
JMIR mHealth and uHealth
, 10
(10)
, Article e40667. 10.2196/40667.
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Abstract
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 (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=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.
| Type: | Article |
|---|---|
| Title: | Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis |
| Location: | Canada |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.2196/40667 |
| Publisher version: | https://doi.org/10.2196/40667 |
| Language: | English |
| Additional information: | 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. |
| Keywords: | acceleration signals, depression, gait, mHealth, mental health, mobile health, mobile phones, monitoring, wearable devices, Acceleration, Aged, Depression, Gait, Humans, Retrospective Studies, Walking |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157268 |
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