The short hyperacusis questionnaire: A tool for the identification and measurement of hyperacusis in the Italian tinnitus population
AbstractThe aim of this study was to explore the collapsibility of the Italian version of Khalfa’s hyperacusis questionnaire (HQ). We identified the more statistically significant items of the HQ and created the short hyperacusis questionnaire (SHQ). We recruited 117 consecutive outpatients with a primary complaint of tinnitus at least from 3 months. All patients filled in the complete Italian version of the HQ and underwent an audiological examination including uncomfortable loudness levels (ULLs). A logistic model was carried out getting odds ratios (ORs) estimates of hyperacusis according to the items responses. To create the SHQ, we selected six items that were the only ones to present a statistically significant ORs value different from 1. The internal consistency of the SHQ was assessed by means of Cronbach α index. A ROC analysis was performed and an optimal cut-off point was found using the Youden index. Our analysis showed a Cronbach α of 0.67. The area under the ROC curve (AUC), expression of the overall performance of the SHQ versus the ULLs test, was statistically significant (P<0.05). We found a cut-off of 0.24 as indicative of hyperacusis (sensitivity (Se) = 78.79%, specificity (Sp) = 42.50%). SHQ could be useful only in the initial screening of individuals with hyperacusis. We suggest further studies for the validation of a new questionnaire on hyperacusis.
Appendix (in Italian): 181
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2017 Federica Tortorella, Silva Pavaci, Alessandra Barbara Fioretti, Francesco Masedu, Maria Lauriello, Alberto Eibenstein
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.