SLPOtherJournal of voice : official journal of the Voice Foundation2026

Clinician Perceptions of Phonatory-Respiratory Kinematic Patterns in Primary Muscle Tension Dysphonia.

Denise Kim, Elizabeth D Young, Robert A Morrison and 2 others

PMID 41539905

WHAT IT FOUND

Expert clinicians watching videos of people with muscle tension dysphonia identified respiratory patterns like neck tension and shallow breathing, but could not reliably diagnose the condition.

Visual cues alone are insufficient for diagnosis and require objective measurement.

Key findings

01Blinded speech-language pathologists correctly diagnosed muscle tension dysphonia from video only 60% of the time, which is only slightly better than chance.

02While patients received significantly higher severity ratings for respiratory patterns, the effect size was moderate and negative, suggesting the statistical difference may not reflect a meaningful clinical distinction.

03Qualitative analysis revealed that clinicians' interpretations of respiratory cues varied widely, with significant uncertainty and ambiguity regarding what constitutes normal versus dysfunctional breathing.

STILL TO COME

How it was doneWhat they foundWhat it means for SLPs

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What it does not show

The sample size was small (46 participants) and the study was underpowered (power 0.66). Raters relied on video recordings rather than in-person observation, potentially missing subtle cues. There was no standardized scoring rubric, leading to variability in how clinicians interpreted severity. The study did not compare perceptual ratings against objective kinematic ground truth (e.g., motion capture).

Declared interests

The authors declared no conflicts of interest.

The easy way to misread this

Do not interpret the significantly higher severity scores in the pMTD group as evidence that visual respiratory assessment is a valid diagnostic tool. The diagnostic accuracy was near chance (60%), and the authors explicitly state that the statistical difference in severity scores may not reflect a meaningful clinical distinction.

Summarised by AI from the full paper, without a clinician reviewing it. Check it against the source before it changes what you do. Read it on PubMed →