Digital Voice Journaling for Depressive Symptoms in Youth: Predicting Symptom Changes Using Machine Learning

Given the existing evidence supporting the benefits of journaling, digital voice journaling may represent a promising approach for alleviating depressive symptoms. This study examined changes in depressive symptoms associated with its use, identified linguistic and acoustic correlates of symptom improvement, and developed machine learning models to predict subsequent improvement.

The study employed a pretest-posttest design. Young adults aged 18-24 years with depressive symptoms (17-item Hamilton Depression Rating Scale [HAMD-17] ≥ 8) completed a seven-day digital voice journaling with twice-daily prompts, followed by a repeated HAMD-17 assessment. Linguistic and acoustic features extracted from the recordings were used to develop 12 machine learning models through five-fold cross-validation, for predicting symptom remission (post-journaling HAMD-17 < 8) and improvement (reduction in severity category), and model performance is evaluated using pooled AUC.

Among the 68 participants (age = 20.78 ± 1.98 years; 85.29% female), a mean decrease of 4.54 points in HAMD-17 scores was found with a moderate effect size (Cohen's d = 0.78). Improvement and remission were observed in 61.76% (n = 42) and 52.94% (n = 36) of participants, respectively. Multiple linguistic and acoustic features, including achievement-related words, function words, F2 formant frequency, and MFCC 2, were significantly associated with these outcomes. The highest-performing models achieved AUCs of 0.86 (95% CI: 0.76-0.94) for improvement and 0.88 (95% CI: 0.79-0.95) for remission.

Depressive symptoms decreased during the seven-day voice journaling period, although the uncontrolled design limits causal interpretation of this change. Furthermore, linguistic and acoustic features may help identify individuals who are more likely to experience symptom improvement or remission.