Toward Personalized Tinnitus Treatment: An Exploratory Study Based on Internet Crowdsensing

Abstract

Introduction: Chronic tinnitus is a condition estimated to affect 10–15% of the population. No treatment has shown efficacy in randomized clinical trials to reliably and effectively suppress the phantom perceptions, and little is known why patients react differently to the same treatments. Tinnitus heterogeneity may play a central role in treatment response, but no study has tried to capture tinnitus heterogeneity in terms of treatment response. Research Goals: To test if the individualized treatment response can be predicted using personal, tinnitus, and treatment characteristics. Methods: A survey conducted by the web platform Tinnitus Hub collected data of 5017 tinnitus bearers. The participants reported which treatments they tried and the outcome of the given treatment. Demographic and tinnitus characteristics, alongside with treatment duration were used as predictors of treatment outcomes in both an univariate as well as a multivariate regression setup. First, simple linear regressions were used with each of the 13 predictors on all of 25 treatment outcomes to predict how much variance could be explained by each predictor individually. Then, all 13 predictors were added together in the elastic net regression to predict treatment outcomes. Results: Individual predictors from the linear regression models explained on average 2% of the variance of treatment outcome. “Duration of treatment” was the predictor that explained, on average, most of the variance, 6.8%. When combining all the predictors in the elastic net, the model could explain on average 16% of the deviance of treatment outcomes. Discussion: By demonstrating that different aspects predict response to various treatments, our results support the notion that tinnitus heterogeneity influences the observed variability in treatment response. Moreover, the data suggest the potential of personalized tinnitus treatment based on demographic and clinical characteristics.

Neuroanatomical Alterations in Patients With Tinnitus Before and After Sound Therapy: A Voxel-Based Morphometry Study

“…There is a pressing need to standardize the use of VBM when evaluating tinnitus patients (Scott-Wittenborn et al, 2017). We should pay further attention to tinnitus heterogeneity, which could be expressed in terms of treatment response (Simoes et al, 2019). Research using machine learning may be helpful.…”

Methodological Aspects of Randomized Controlled Trials for Tinnitus: A Systematic Review and How a Decision Support System Could Overcome Barriers

“…This could be attributed to statistical variance, but it could also be claimed that some therapeutic interventions could potentially be beneficial in a specific subgroup of patients with identifiable characteristics. Very few studies, however, attempt to create and identify a certain profile correlating with treatment response and the main research question is limited to whether an intervention is effective or not in a group of patients, rather than which factors influence treatment response [ 6 , 7 ].…”

Insomnia Associated with Tinnitus and Gender Differences

“…The prevalence of tinnitus has been estimated between 10 and 15% in the adult population. From those, 20% will require clinical intervention and 80% are not particularly bothered by tinnitus [ 3 , 4 ]. Previous studies have shown that chronic tinnitus may be accompanied by comorbidities such as anxiety, depression and insomnia, and can have a negative impact on sleep quality, in a sense that the increased severity of sleep disruption is associated with increased tinnitus severity [ 5 , 6 , 7 , 8 , 9 , 10 ].…”