ArticleJMIR mHealth and uHealth2022
Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis.
Article in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gait in depression: a bibliometric analysis and knowledge mapping of research trends over the past 20 years.Frontiers in psychiatry · 2025Pooled it
- Using wearable data to detect depression severity across clinical and non-clinical samples.Scientific reports · 2026Article
- Normative Data for Single- and Dual-Task Tandem Gait Performance in Collegiate Athletes.Sports medicine (Auckland, N.Z.) · 2026Article
- Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China.BMC geriatrics · 2025Article
- Lactobacillus fermentum ATCC 9338 Supplementation Prevents Depressive-Like Behaviors Through Glucocorticoid Receptor and N-Methyl-D-aspartate2b in Chronic Unpredictable Mild Stress Mouse Model.Molecular neurobiology · 2025Article
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
- Exploring the transformative impact of traditional Chinese medicine on depression: Insights from animal models.World journal of psychiatry · 2024Review
- Associations of depressive symptoms with lower extremity function and balance in Korean older adults.Epidemiology and health · 2024Article
- Walking Speed and Risk of Falling Patients Operated for Selected Malignant Tumors.Healthcare (Basel, Switzerland) · 2023Article
- Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.Journal of medical Internet research · 2023Article
- Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
backgroundGait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored.
objectiveThe 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.
methodsWe 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.
resultsHigher 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 (R
conclusionsThis 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.
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