@Article{info:doi/10.2196/40667,
author="Zhang, Yuezhou
and Folarin, Amos A
and Sun, Shaoxiong
and Cummins, Nicholas
and Vairavan, Srinivasan
and Qian, Linglong
and Ranjan, Yatharth
and Rashid, Zulqarnain
and Conde, Pauline
and Stewart, Callum
and Laiou, Petroula
and Sankesara, Heet
and Matcham, Faith
and White, Katie M
and Oetzmann, Carolin
and Ivan, Alina
and Lamers, Femke
and Siddi, Sara
and Simblett, Sara
and Rintala, Aki
and Mohr, David C
and Myin-Germeys, Inez
and Wykes, Til
and Haro, Josep Maria
and Penninx, Brenda W J H
and Narayan, Vaibhav A
and Annas, Peter
and Hotopf, Matthew
and Dobson, Richard J B",
title="Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis",
journal="JMIR Mhealth Uhealth",
year="2022",
month="Oct",
day="4",
volume="10",
number="10",
pages="e40667",
keywords="depression; gait; mobile health; mHealth; acceleration signals; monitoring; wearable devices; mobile phones; mental health",
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. ",
issn="2291-5222",
doi="10.2196/40667",
url="https://mhealth.jmir.org/2022/10/e40667",
url="https://doi.org/10.2196/40667",
url="http://www.ncbi.nlm.nih.gov/pubmed/36194451"
}