Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10495
Title: Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese
Authors: Chen, Jie 
Chan, Ngan Yin 
Li, Chun Tung 
Chan, Joey W. Y. 
Liu, Yaping 
Li, Shirley Xin 
Chau, Steven W. H. 
Prof. LEUNG Kwong Sak 
Heng, Pheng-Ann 
Lee, Tatia M. C. 
Li, Tim M. H. 
Wing, Yun-Kwok 
Issue Date: 2024
Source: Translational Psychiatry, 2024, vol. 14, article no. 150.
Journal: Translational Psychiatry 
Abstract: There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.
Description: Open access
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10495
ISSN: 2158-3188
DOI: 10.1038/s41398-024-02873-4
Appears in Collections:Applied Data Science - Publication

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