TY  - JOUR
AU  - Zhang, Yuezhou
AU  - Folarin, Amos A
AU  - Sun, Shaoxiong
AU  - Cummins, Nicholas
AU  - Vairavan, Srinivasan
AU  - Qian, Linglong
AU  - Ranjan, Yatharth
AU  - Rashid, Zulqarnain
AU  - Conde, Pauline
AU  - Stewart, Callum
AU  - Laiou, Petroula
AU  - Sankesara, Heet
AU  - Matcham, Faith
AU  - White, Katie M
AU  - Oetzmann, Carolin
AU  - Ivan, Alina
AU  - Lamers, Femke
AU  - Siddi, Sara
AU  - Simblett, Sara
AU  - Rintala, Aki
AU  - Mohr, David C
AU  - Myin-Germeys, Inez
AU  - Wykes, Til
AU  - Haro, Josep Maria
AU  - Penninx, Brenda W J H
AU  - Narayan, Vaibhav A
AU  - Annas, Peter
AU  - Hotopf, Matthew
AU  - Dobson, Richard J B
PY  - 2022
DA  - 2022/10/4
TI  - Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis
JO  - JMIR Mhealth Uhealth
SP  - e40667
VL  - 10
IS  - 10
KW  - depression
KW  - gait
KW  - mobile health
KW  - mHealth
KW  - acceleration signals
KW  - monitoring
KW  - wearable devices
KW  - mobile phones
KW  - mental health
AB  - 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. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2022/10/e40667
UR  - https://doi.org/10.2196/40667
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36194451
DO  - 10.2196/40667
ID  - info:doi/10.2196/40667
ER  -