Dataset Information

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

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.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC9579931 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Zhang Yuezhou Y Folarin Amos A AA Sun Shaoxiong S Cummins Nicholas N Vairavan Srinivasan S Qian Linglong L Ranjan Yatharth Y Rashid Zulqarnain Z Conde Pauline P Stewart Callum C Laiou Petroula P Sankesara Heet H Matcham Faith F White Katie M KM Oetzmann Carolin C Ivan Alina A Lamers Femke F Siddi Sara S Simblett Sara S Rintala Aki A Mohr David C DC Myin-Germeys Inez I Wykes Til T Haro Josep Maria JM Penninx Brenda W J H BWJH Narayan Vaibhav A VA Annas Peter P Hotopf Matthew M Dobson Richard J B RJB

JMIR mHealth and uHealth 20221004 10

<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable ...[more]

PMID: 36194451

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Project description:BackgroundAn automated web-based assessment and monitoring system ( www.psynary.com ) has been developed to assist non-specialist clinicians in managing common mood and anxiety disorders. Psynary promotes the use of standardised outcome measures to assess symptom severity and optimise treatments with the aim of improving outcomes and enabling faster recovery. This paper analyses the results from two parallel studies in New Zealand and Japan (OptiMA-1 NZ and Japan) to assess the validity of the R8 Depression scale, one of the system's core outcome measures.MethodsClinical samples were recruited from a public secondary care and a private psychiatry clinic. Participants completed the outcome measures for the study via the online Psynary system. The R8 Depression scale is a 30-item questionnaire which includes all symptom domains covered in the ICD-10 classification of depression. The Patient Health Questionnaire (PHQ-9) was completed at the same time points as the R8 Depression, with a smaller sample also completing a paper-based Quick Inventory of Depressive Symptomatology (QIDS-SR16). Internal validity was quantified via Cronbach's alpha and Guttman lower bounds method. External validation against the PHQ-9 and QIDS used the Pearson's and Kendall's correlation coefficients. Severity categories were set using a multivariate regression model.Results270 patients participated in the study and completed a maximum of 1 baseline and 5 reviews within a 90-day period, giving a total of 1124 assessments with the PHQ-9 also being completed in 1053 of these assessments. R8 Depression normative data was also collected from 204 non-clinical volunteers with 187 of these also completing the PHQ9. Internal reliability scores were all higher than 0.9 (n = 1328). There was overall good external validity when comparing the R8 Depression to the PHQ-9, with a correlation of 0.91 for the combined normative and clinical samples (n = 1240).ConclusionsThe R8 Depression has been developed as a patient-rated outcome measure for depression for administration on an online system called "Psynary". It has high internal and external validity against current widely used scales. Further work is underway to determine the sensitivity to change of the R8 Depression.

Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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