JMIR Publications

ABSTRACT

Background:

Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health through passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, several limitations leave such data’s practical utility uncertain. Most investigations have been cross-sectional and depended solely on self-report measures of psychiatric symptoms, and knowledge is limited regarding the influence of demographic differences and how predictive findings should be interpreted.

Objective:

This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes.

Methods:

We collected two weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale (HAM-D) and Hamilton Anxiety Rating Scale (HAM-A), respectively. Behavioral features, including physical activity, app use, and screen usage metrics, were extracted and encoded using a deep learning-based autoencoder. Latent representations—reduced using principal component analysis (PCA)—were utilized to train a random forest classifier to predict changes in HAM scores (increase, decrease, or unchanged). Model performance was evaluated using repeated 5-fold cross-validation, while subgroup analyses were conducted by sex and age. Additionally, unsupervised clustering (t-distributed stochastic neighbor embedding followed by K-means) was applied to latent features to further identify explanatory behavioral subtypes. Subsequently, we compared clusters in terms of changes in HAM-A/HAM-D scores and behavioral characteristics, including total usage levels, temporal usage distribution, and behavioral regularity (entropy).

Results:

The model exhibited moderate performance in predicting changes in HAM-D (mean accuracy = 0.70, mean area under the receiver operating characteristic curve [AUC] = 0.74) and HAM-A (mean accuracy = 0.65, mean AUC = 0.69) scores. Performance varied by demographics, with reduced accuracy among younger adults and females. Clustering revealed four distinct behavioral trajectories according to smartphone usage patterns. One cluster, characterized by structured and daytime-concentrated usage with lower temporal entropy, demonstrated significantly greater improvement in depressive symptoms than the others. By contrast, clusters with lower and irregular usage patterns were associated with either minimal improvement or worsening of symptoms.

Conclusions:

Smartphone-derived behavioral data modestly predict short-term changes in mental health symptoms and uncover distinct usage patterns linked to symptom trajectories. This study’s findings support digital phenotyping’s potential in identifying behaviorally grounded mental health subtypes and informing personalized, data-driven interventions.