Zhang, Yuezhou, Folarin, Ams 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 and Oetzmann, Carolin (2022) Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings. JMIR mHealth and uHealth, 10 (10). e40667 1-15. ISSN 2291-5222

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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 are yet to be fully explored.
Objective: This study aimed 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 datasets (N=71 and N=215) whose acceleration signals were 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-effect models were used to explore the associations between daily-life gait features and depression symptom severity measured by GDS-15 and 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 found to be significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both datasets. The linear regression model with long-term daily-life gait features ( =0.30) fitted depression scores significantly better (LR test: P value = .001) than the model with only laboratory gait features ( =0.06).

Conclusion
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.

Item Type:	Article
Keywords:	Depression, Gait, Mobile health (mHealth), mHealth, Acceleration signals, Monitoring, Wearable devices, Mobile phones
Schools and Departments:	School of Psychology > Psychology
SWORD Depositor:	Mx Elements Account
Depositing User:	Mx Elements Account
Date Deposited:	27 Sep 2022 10:38
Last Modified:	19 Oct 2022 15:00
URI:	http://sro.sussex.ac.uk/id/eprint/108152

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