Tinnitus Subtyping with Subgrouping Within Group Iterative Multiple Model Estimation: An Ecological Momentary Assessment Study
Authors: Simoes Jorge , Bulla Jan , Neff Patrick , Pryss Rüdiger , Marcrum Steven C. , Langguth Berthold , Schlee Winfried 
Published: February 2022

Background

Tinnitus is a heterogeneous condition associated with moderate to severe disability, but the reasons why only a subset of individuals is burdened by the condition are not fully clear. Ecological momentary assessment (EMA) allows a better understanding of tinnitus by allowing individualized models and by capturing the fluctuations of tinnitus symptoms and other behavioral dynamics as they occur, and therefore minimizing the risk of recollection bias. The TrackYourTinnitus (TYT) mobile app provides a platform for collecting ecologically valid time series data from tinnitus users and can be used to address questions like how mood, concentration, tinnitus distress, or loudness relate over time. Whether any of those variables have an influence over the next day, that is, whether any of these variables are auto- or cross-correlated, is still unanswered.

Objectives

Assess whether behavioral and symptom-related data from tinnitus users from the TYT app auto- and cross-correlate in different time lags, both within and between individuals.

Methods

Anonymized data was collected from 278 users of the iOS or Android TYT apps between 2014 and 2020. Tinnitus-related distress, tinnitus loudness, concentration level, overall mood, emotional arousal, and overall stress level were assessed using a 10-point visual analog scale via a daily survey. Auto- and cross-correlations were calculated for participants who used the app for at least 10 consecutive days. Lagged cross-correlation was used to investigate the dynamics of each of these variables over time at the group level, followed by linear regression with elastic net regularization for each user. Additionally, subgrouping within group iterative multiple model estimation (S-GIMME) was used to model the behavioral dynamics at the group, subgroup, and individual levels with data collected from 32 users.

Results

No autocorrelation or cross-correlation was observed at the group level between the variables assessed. However, application of the regression models with elastic net regularization identified individualized predictors of tinnitus loudness and distress for most participants, with the models including contemporaneous and lagged information from the previous day. The finding that a subset of users experienced lagged and contemporaneous dynamics was corroborated by the models from S-GIMME. The models had adequate fits, with both contemporaneous and lagged coefficients obtained for most individuals. Two subgroups were identified, the first consisting of users where both contemporaneous and lagged effects were observed, and a second subgroup consisting of users whose dynamics were mainly of contemporaneous effects.

Discussion

We showed that tinnitus loudness and tinnitus distress are affected by the contemporaneous and lagged dynamics of behavioral and emotional processes measured through EMA. These effects were seen at the group, subgroup, and individual levels. The relevance EMA and the implications of the insights derived from it for tinnitus care are discussed, especially considering current trends towards the individualization of tinnitus care.