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20212023
most citedChallenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

45 citations · 66 across the 3 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

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…