| Summary: | Background: Tinnitus is a phantom auditory perception, that is, the perception of sound in the absence of an external stimuli. It is estimated that 10 – 15% of the adult population experience tinnitus at some point during their life. Current treatment options focus on diverting attention away from the tinnitus through sound therapy and counselling to facilitate habituation. Advancements in technology have shifted the focus to more personalised therapies leading to the development of digital therapeutics. One such prototype app-based therapy developed at The University of Auckland and has been shown to be more effective than available masking apps. The use of smartphones has broadened the range of sound delivery methods available. However, the effect of different delivery methods on the effectiveness of masking has not been explored. Additionally, previous studies have demonstrated the spatially tailored masking to be more effective than standard masking noise. Thus, this project aimed to investigate the potential effects of delivery method on the effectiveness of tinnitus masking by spatially tailored and non-spatial masking noise. Methods: Participants (n=19) received either a spatial masker or non-spatial masker delivered through hearing aids fit to their hearing thresholds and through off-the-shelf earphones. The minimum masking level (MML) and desired masking level (DML) were measured for each transducer. Real-ear measures were also conducted at the measured MMLs and DMLs to contextualise the subjective measure of MML and DML as an objective measure of sound pressure level at the ear drum. Results: Overall, we found that white noise delivered through earphones were more effective at completely masking tinnitus compared to hearing aids due to lower MMLs. However, no difference was observed when comparing DMLs. With regards to spatial masking, we found no difference in masking effectiveness between the spatially tailored and non-spatial masker when delivered through either transducer. Summary: Although MMLs ...
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