Previously submitted to: JMIR Mental Health (no longer under consideration since Nov 21, 2023)
Date Submitted: Nov 21, 2023
Open Peer Review Period: Nov 21, 2023 - Nov 21, 2023
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Multi-Classification of Social Anxiety Disorder Symptom Severity Using Digital Phenotype and Feature Representation Learning
ABSTRACT
Background:
Social Anxiety Disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships, leading to a decreased quality of life. This disruption often extends to a delayed onset of diagnosis and medical treatment. While digital phenotypes derived from daily behavioral patterns are known to effectively classify various mental states, the key predictive digital phenotypes for the diverse symptoms of SAD, particularly by severity, have not been explored.
Objective:
This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of various clinical symptoms associated with SAD. Furthermore, it sought to elucidate how the main predictive digital phenotypes differed depending on the severity of these symptoms.
Methods:
We collected 511 behavioral and physiological data sets over a 2-week period from 27 SAD and 31 healthy controls using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with 3 social anxiety-specific and 9 additional psychological scales. For each symptom, we developed individual classifiers to predict its severity and applied integrated gradients to identify the critical predictive features.
Results:
Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84%-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom type and levels of severity.
Conclusions:
This research demonstrates that leveraging digital phenotypes through feature representation learning can effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of the disorder. These findings underscore the potential utility of digital phenotypes in informing clinical management.
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