JMIR Publications

ABSTRACT

Background:

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.

Objective:

This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals gathered by wearables and mobile phones in real-world settings.

Methods:

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 raw acceleration signals and extracted twelve 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.

Results:

The higher depression symptom severity was found to be significantly associated with lower gait cadence of high-performance walking (faster walking segments) over a long-term period. Long-term daily-life gait features could significantly improve the goodness of fit of predicting depression severity relative to laboratory gait patterns and demographics.

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 gait cadence of high-performance walking has the potential to be an indicator for monitoring depression, which may contribute to developing clinical tools to remotely monitor mental health in real-world settings.