Understanding tinnitus symptom dynamics and clinical improvement through intensive longitudinal data

Abstract Intensive longitudinal sampling enhances subjective data collection by capturing real-time, dynamic inputs in natural settings, complementing traditional methods. This study evaluates the feasibility of using daily self-reported app data to assess clinical improvement among tinnitus patients undergoing treatment. App data from a multi-center randomized clinical trial were analysed using time-series feature extraction and nested cross-validated ordinal regression with elastic net regulation to predict clinical improvement based on the Clinical Global Impression—Improvement scale (CGI-I). With 50% app compliance (N = 129, 8480 entries), the model demonstrated good fit to the test data (McFadden R2 = 0.82) suggesting its generalizability. Clinical improvement was associated with linear declines in tinnitus-related thoughts, jaw tension, tinnitus loudness, increases in happiness, and variability changes in tinnitus loudness and distress. These findings suggest that daily self-reported data on tinnitus symptoms is sensitive to treatment response and provides insights into specific symptom changes that occur during treatment.