Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis
Year: 2022
Type: article
Study design: Observational Study
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.
Source: JMIR mhealth and uhealth
DOI: 10.2196/40667
Authors: Yuezhou Zhang, Amos 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 Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Sara Simblett, Aki Rintala, David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W J H Penninx, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard J B Dobson
Institutions: King's College London, University College London Hospitals NHS Foundation Trust, South London and Maudsley NHS Foundation Trust, UCL Biomedical Research Centre, Health Data Research UK, NIHR Maudsley Biomedical Research Centre, University College London, University of Sussex, Amsterdam Public Health, Amsterdam UMC Location Vrije Universiteit Amsterdam, Centro de Investigación Biomédica en Red de Salud Mental, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, LAB University of Applied Sciences, KU Leuven, Northwestern University, Lundbeck (Denmark)
Language: English
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,850
Funders: NHS Blood and Transplant, South London and Maudsley NHS Foundation Trust, Wellcome Trust, King's College Hospital NHS Foundation Trust, Scottish Government, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, King's College London, University College London, European Commission, Public Health Agency, King's Health Partners, Innovative Medicines Initiative, Chief Scientist Office, Scottish Government Health and Social Care Directorate, NIHR BioResource, NIHR Maudsley Biomedical Research Centre, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division, National Institute for Health Research Applied Research Collaboration South London, Health Data Research UK
Awards: G1175529158, G1356971313, Remote Assessment of Disease and Relapse in Central Nervous System Disorders, G2259971274, Big Data 4 Better Hearts - Sofia ref.: 116074, NIHR Senior Investigator Award, G434337789, DTP Bid Led by King's College London, NIHR Senior Investigator Award, G7376789991, G8085324931
OpenAlex ID: W4293794945