ArticleJournal of the Association for Research in Otolaryngology : JARO2026
Rapid Characterization of Middle-Ear Muscle Reflexes Using Swept Elicitors.
Article in Journal of the Association for Research in Otolaryngology : JARO, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeWe describe a novel paradigm for evoking and measuring middle ear muscle reflex (MEMR), in which a train of broadband clicks act as probes, while a broadband noise elicitor is continuously swept in both ascending and descending sound levels. A new measure, total change, incorporates both magnitude and phase to quantify MEMR in a way that promotes meaningful averaging across a wide range of sound levels and frequencies. The aims of the study were to assess the retest reliability of the new swept-elicitor MEMR paradigm, to compare results with those obtained using traditional discrete-elicitor stimuli, and to preliminarily examine correlations with speech-in-noise performance.
methodsMEMR was measured in 38 young, normal-hearing participants (24 female, 14 male) using both the novel swept paradigm and a more conventional paradigm with elicitor noises that were discretely varied in level. Key measures of MEMR dynamics were obtained from the swept elicitor paradigm, including maximum total change, onset and offset thresholds, hysteresis, and reflex delay. Intraclass correlation coefficients (ICCs) were used to assess repeatability, and robust linear regression was used to examine correlations with QuickSIN performance.
resultsThe swept MEMR paradigm demonstrated excellent repeatability, with ICC values exceeding 0.90 for all extracted measures. MEMR thresholds from the swept elicitor correlated moderately with speech-in-noise performance.
conclusionsOur new MEMR paradigm provides fast, repeatable measurements. Several measures of MEMR dynamics can be obtained, improving upon traditional measurement approaches. Results suggest a possible link between MEMR dynamics and speech-in-noise performance.
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