JMIR Publications

ABSTRACT

Background:

Chronic tinnitus is an increasing world-wide health concern, causing a significant burden to the health care system each year. The pandemic of Covid-19 has seen a further increase in reported cases. For those with tinnitus, symptoms are exacerbated due to social isolation and the elevated level of anxiety and depression caused by quarantine and lockdown. Although it has been reported that tinnitus patients can suffer changes in cognitive capabilities, changes in adaptive learning via decision-making tasks for tinnitus population has not yet been investigated.

Objective:

In this study, we aim to assess state- and trait-related impairments in adaptive learning ability on the probabilistic learning task among tinnitus population. Given that performance in such tasks can be quantified through machine learning methods in terms of a small set of neural-informed model parameters, such approaches are promising in terms of assessment of tinnitus severity. We will first examine baseline differences in characterisation of decision-making under contingency volatility between healthy individuals and people suffering from tinnitus in terms of differences in the parameters of computational models in a cross-sectional experiment. We will also investigate if these computational markers that capture characteristics of decision-making can be used to understand the cognitive impact of tinnitus symptoms fluctuations through a longitudinal experimental design.

Methods:

We have developed a mobile app named AthenaCX to deliver e-consent, baseline tinnitus and psychological assessments and regular Ecological Momentary Assessments (EMA) of perceived tinnitus loudness and a web-based aversive version of a probabilistic decision-making task, which can be triggered based on the participants’ responses to the EMA surveys. Computational models will be developed to fit participants’ choice data in the task and the cognitive parameters will be estimated to characterize participants’ current ability to adapt learning to the change of the simulated environment at each session when the task is triggered. Linear regression will be conducted to evaluate the impacts of baseline tinnitus severity, along with psychological assessments on adapting performance of decision-making. Repeated measures linear regression will be used to examine model-derived parameters of decision-making in measuring real-time perceived tinnitus loudness fluctuations.