King's College London

Research portal

Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Research output: Working paper/PreprintPreprint

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 & 10 more David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda WJH Penninx, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard JB Dobson, RADAR-CNS consortium

Original languageEnglish
Published29 Jan 2022

Documents

  • 2201.12644v1

    2201.12644v1.pdf, 929 KB, application/pdf

    Uploaded date:31 Aug 2022

Links

King's Authors

Abstract

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of daily walking in real-world scenarios and their relationships with depression are yet to be fully explored. This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. In this study, we used two ambulatory datasets: a public dataset with 71 elder adults' 3-day acceleration signals collected by a wearable device, and a subset of an EU longitudinal depression study with 215 participants and their phone-collected acceleration signals (average 463 hours per participant). We detected participants' gait cycles and force from acceleration signals and extracted 20 statistics-based daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period corresponding to the self-reported depression score. The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets. Daily-life gait features could significantly improve the goodness of fit of evaluating depression severity relative to laboratory gait patterns and demographics, which was assessed by likelihood-ratio tests in both datasets. 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 gait cadence of faster steps in daily-life walking has the potential to be a biomarker for evaluating depression severity, which may contribute to clinical tools to remotely monitor mental health in real-world settings.

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454