Understanding tinnitus symptom dynamics and clinical improvement through intensive longitudinal data

Area of Science:

• Audiology
• Digital Health
• Clinical Research

Background:

• Intensive longitudinal sampling captures real-time data in natural settings.
• Traditional methods for subjective data collection have limitations.
• Digital health tools offer potential for enhanced patient monitoring.

Purpose of the Study:

• To evaluate the feasibility of using daily self-reported app data to assess clinical improvement in tinnitus patients.
• To determine if app-based data can predict treatment response.
• To identify specific symptom changes associated with clinical improvement.

Main Methods:

• Analysis of app data from a multi-center randomized clinical trial.
• Time-series feature extraction and ordinal regression with elastic net regularization.
• Prediction of clinical improvement using the Clinical Global Impression-Improvement scale (CGI-I).

Main Results:

• A model with 50% app compliance (N=129, 8480 entries) showed good fit (McFadden R2=0.82), indicating generalizability.
• Clinical improvement correlated with decreased tinnitus thoughts, jaw tension, and loudness.
• Increased happiness and changes in variability of tinnitus loudness/distress also indicated improvement.

Conclusions:

• Daily self-reported tinnitus symptom data is sensitive to treatment response.
• App-based data provides valuable insights into specific symptom changes during tinnitus treatment.
• This approach enhances the assessment of clinical improvement in tinnitus management.