PDF.pdf (1.13 MB)
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
journal contribution
posted on 2023-06-10, 04:50 authored by Yuezhou Zhang, Ams A Folarin, Shaoxiong Sun, Nicholas Cummins, Srinivasan Vairavan, Linglong Qian, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Heet Sankesara, Faith Matcham, Katie M White, Carolin OetzmannBackground 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.
History
Publication status
- Published
File Version
- Published version
Journal
JMIR mHealth and uHealthISSN
2291-5222Publisher
JMIR PublicationsExternal DOI
Issue
10Volume
10Page range
e40667 1-15Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-09-27First Open Access (FOA) Date
2022-09-27First Compliant Deposit (FCD) Date
2022-09-26Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC