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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