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Gait is an essential manifestation of depression. Laboratory gaitcharacteristics have been found to be closely associated with depression.However, the gait characteristics of daily walking in real-world scenarios andtheir relationships with depression are yet to be fully explored. This studyaimed to explore associations between depression symptom severity anddaily-life gait characteristics derived from acceleration signals in real-worldsettings. In this study, we used two ambulatory datasets: a public dataset with71 elder adults' 3-day acceleration signals collected by a wearable device, anda subset of an EU longitudinal depression study with 215 participants and theirphone-collected acceleration signals (average 463 hours per participant). Wedetected participants' gait cycles and force from acceleration signals andextracted 20 statistics-based daily-life gait features to describe thedistribution and variance of gait cadence and force over a long-term periodcorresponding to the self-reported depression score. The gait cadence of fastersteps (75th percentile) over a long-term period has a significant negativeassociation with the depression symptom severity of this period in bothdatasets. Daily-life gait features could significantly improve the goodness offit of evaluating depression severity relative to laboratory gait patterns anddemographics, which was assessed by likelihood-ratio tests in both datasets.This study indicated that the significant links between daily-life walkingcharacteristics and depression symptom severity could be captured by bothwearable devices and mobile phones. The gait cadence of faster steps indaily-life walking has the potential to be a biomarker for evaluatingdepression severity, which may contribute to clinical tools to remotely monitormental health in real-world settings.

jmir mhealth and uhealth

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