Within-Person Symptom Dynamics in Serious Mental Illness: Insights from Daily Self-reports and Audio Journals

Understanding how clinical symptoms fluctuate within individuals is crucial for developing personalized treatment, yet such dynamic processes are understudied. This study examined how daily self-reports and speech features from audio journals predict monthly, clinician-rated symptoms in a transdiagnostic outpatient sample with affective and psychotic disorders. We identified a seven-factor within-person clinical symptom structure, a more granular perspective than previous between-person models. Bayesian multilevel models revealed that daily fluctuations in both self-reported experiences and objective speech features from audio journals were significantly associated with changes in clinical symptoms. Importantly, model comparisons showed that the optimal temporal window for aggregating daily data varied by symptom domain and data modality. These findings demonstrate the utility of daily self-reports and audio journals for dynamic symptom tracking, and showcase a data-driven approach to investigating the optimal length of daily history for developing personalized monitoring tools in psychiatry.