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.

Keywords: crowdsensing; heterogeneity; personalized treatment; smart device; tinnitus.

Figure 1

Amount of variance explained in the linear regression models by each predictor across all different 25 treatments.

Figure 2

Mean amount of variance explained by type of predictor. Error bars represent standard deviation. “Personal Characteristics” contains the predictors Age, Gender, and Tinnitus Onset. “Tinnitus Characteristics” contains the predictors Tinnitus Frequency, Laterality of Hearing Loss, Perception of Tinnitus, Reactiveness to Noise, Hearing Loss, Laterality of Tinnitus, Hyperacusis, and Jaw/Neck Problems. “Treatment Characteristics” contains the predictor Treatment Duration.

Figure 3

Amount of variance explained by the Elastic Net model with all the 13 predictors added simultaneously. HBOT, Hyperbaric Oxygen Therapy; TRT, Tinnitus Retraining Therapy; TMS, Transcranial Magnetic Stimulation; CBT, Cognitive Behavior Therapy.

Figure 4

Mean treatment outcomes on a 1–5 Likert scale clustered by treatment duration.

Figure 5

Mean treatment outcomes on a 1–5 Likert scale clustered by treatments with an acoustic component (yellow) and without an acoustic component (blue). Error bar accounts for the standard deviation across all 25 treatments. TRT, Tinnitus Retraining Therapy; CBT, Cognitive Behavior Therapy; TMS, Transcranial Magnetic Stimulation; HBOT, Hyperbaric Oxygen Therapy.