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Frontiers in Physiology|April 24, 2023
Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosisSpyridon Kontaxis, Estela Laporta, Esther Garcia, et al.Journal of Affective Disorders|March 29, 2024
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic modelYuezhou Zhang, Amos A Folarin, Judith Dineley, et al.JACC. Advances|January 30, 2026
Automated Abdominal Aortic Calcification Scores and Atherosclerotic Cardiovascular Disease in the UK Biobank Imaging StudyMarc Sim, James Webster, Cassandra Smith, et al.Iscience|June 13, 2024
Machine-learning-based integrative -'omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunctionFatima Zulqarnain, Xueheng Zhao, Kenneth D R Setchell, et al.Journal of Affective Disorders|August 20, 2023
Multilingual markers of depression in remotely collected speech samples: A preliminary analysisNicholas Cummins, Judith Dineley, Pauline Conde, et al.Pattern Recognition|November 1, 2021
Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoderShuo Liu, Jing Han, Estela Laporta Puyal, et al.NPJ Digital Medicine|February 22, 2023
Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression studyYuezhou Zhang, Abhishek Pratap, Amos A Folarin, et al.JMIR Mental Health|March 11, 2022
Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling StudyYuezhou Zhang, Amos A Folarin, Shaoxiong Sun, et al.JMIR Mhealth and Uhealth|October 4, 2022
Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective AnalysisYuezhou Zhang, Amos A Folarin, Shaoxiong Sun, et al.Journal of Medical Internet Research|August 14, 2023
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective AnalysisShaoxiong Sun, Amos A Folarin, Yuezhou Zhang, et al.Pageof 53