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 WJH; Narayan, Vaibhav A; Annas, Peter; Hotopf, Matthew; Dobson, Richard JB

doi:10.2196/40667

Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis

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 WJH ; Narayan, Vaibhav A ; Annas, Peter ; Hotopf, Matthew ; Dobson, Richard JB

Keywords:

Science & Technology, Life Sciences & Biomedicine, Health Care Sciences & Services, Medical Informatics, depression, gait, mobile health, mHealth, acceleration signals, monitoring, wearable devices, mobile phones, mental health, PRIMARY-CARE, PSYCHOMOTOR SYMPTOMS, PATTERNS, QUALITY, OLDER, INDIVIDUALS, RECOGNITION, DISPARITIES, DISORDERS, ANXIETY, Acceleration, Aged, Depression, Gait, Humans, Retrospective Studies, Walking, RADAR-CNS Consortium, 1117 Public Health and Health Services, 4203 Health services and systems, 4206 Public health, 4601 Applied computing

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.