JMIR Publications

ABSTRACT

Background:

Digital mental health applications enable high-frequency behavioral monitoring and scalable interventions. Journaling provides a therapeutically grounded and intrinsically engaging activity for many users. AI-based text analysis enables privacy-preserving phenotyping of clinically relevant patterns in naturalistic writing, including emotional distress and behavioral risk (e.g., indicators of intent, planning, or preparatory actions for harm to self or others).

Objective:

This study evaluated whether a mobile journaling platform could reduce anxiety and depression symptoms while supporting longitudinal behavioral phenotyping and real-time risk monitoring.

Methods:

We evaluated the platform in an 8-week randomized controlled trial of young adults with mild-to-moderate anxiety and depression symptoms (N=507). Clinical outcomes were assessed at the 8-week endpoint and 1-month follow-up. In parallel, behavioral phenotyping analyses examined text-based risk signals, self-reported affective states, circadian variation, short-term persistence of mood and risk states, and temporal patterns preceding high-risk journal entries. Key behavioral dynamics were tested for replication in an independent general population dataset (N=16,630).

Results:

Journaling produced modest reductions in anxiety relative to controls at the 8-week endpoint and 1-month follow-up (d = 0.16–0.19). Effects were small and did not remain significant after correction for multiple comparisons; complementary Bayesian models nonetheless provided moderate-to-strong directional evidence (90–97%) supporting a modest anxiety reduction. In parallel, behavioral phenotyping analyses showed that high-risk journal entries were more common among younger users (OR = 0.77 per year of age, p = 0.007). Text-based risk signals and self-reported energy exhibited significant circadian variation (e.g., risk probability was highest during late-night and overnight hours). Within-person analyses demonstrated strong short-term persistence in mood and risk states, with calm/relaxed showing the highest persistence and anxious/agitated exhibiting the lowest persistence. High-risk journal entries clustered temporally and were preceded by sustained low valence and energy. Although affective volatility was associated with acute declines within the same affective dimension (pleasantness or energy), it was not associated with escalation to high-risk states. Key behavioral dynamics observed in the trial were replicated in an independent general population dataset.

Conclusions:

Privacy-preserving digital journaling can support scalable longitudinal behavioral phenotyping and real-time detection of distress and risk signals, while providing evidence of modest, anxiety-specific clinical benefit. These findings highlight the potential of AI-enabled journaling platforms to combine low-intensity intervention with clinically relevant behavioral measurement. Clinical Trial: ClinicalTrials.gov ID: NCT07126275