  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.
