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

TL;DR

This study investigates how daily-life gait characteristics, derived from acceleration signals via wearable devices and smartphones, are associated with depression severity, highlighting the potential of gait cadence as a remote biomarker for depression monitoring.

Contribution

It is the first to analyze long-term daily-life gait features from real-world data in relation to depression severity, using both wearable and mobile phone accelerometers.

Findings

Faster gait cadence (75th percentile) negatively correlates with depression severity.

Daily-life gait features improve depression severity assessment over laboratory measures.

Gait characteristics can be captured reliably by wearable devices and smartphones.

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.