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20212023
most citedPredicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

78 citations · 144 across the 4 of their papers we have counts for

collaborators

4 papers

stat.AP2023

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +24

Objective: This study aimed to explore the associations between depression severity and wearable-measured circadian rhythms, accounting for seasonal impacts and quantifying seasona…

q-bio.QM2022★ 45 cited

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Shaoxiong Sun, Amos A. Folarin, Yuezhou Zhang +28

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an accepta…

q-bio.QM2022★ 21 cited

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

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +27

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of dai…

stat.ML2021★ 78 cited

Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +21

The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluet…