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Variability in Stuttering Severity and Other Symptoms Across Three Fluency States.
Edward C Brown1, Tricia Hedinger1, Tim Saltuklaroglu1
1The Department of Audiology and Speech Pathology, University of Tennessee Health Science Center, Knoxville, TN.
Adults who stutter (AWS) show significant variability in stuttering symptoms and reactions across different fluency states. Even in naturally fluent moments, many symptoms persist, impacting communication.
Area of Science:
- Speech and Language Pathology
- Psycholinguistics
- Communication Sciences
Background:
- Stuttering is a complex disorder characterized by disruptions in speech fluency.
- Understanding variability in stuttering symptoms is crucial for effective intervention.
- Previous research has not fully quantified symptom variability across distinct fluency states in adults who stutter (AWS).
Purpose of the Study:
- To quantify intra-individual variability in external stuttering severity, associated symptoms, and listener reactions among adults who stutter (AWS).
- To compare these variables across three distinct fluency states: least fluent (LF), most naturally fluent (MNF), and using fluency shaping (FS) techniques.
Main Methods:
- 130 adults who stutter (AWS) completed an online survey.
- Participants rated stuttering severity using a 9-point scale for LF, MNF, and FS states.
- 14 additional questions assessed stuttering symptoms and listener reactions for each state.
Main Results:
- High intra- and interindividual variability in stuttering symptoms and reactions were observed.
- Least fluent (LF) states were associated with the most negative ratings for 13 of 14 symptoms/reactions.
- Most naturally fluent (MNF) states yielded the most positive ratings, though many symptoms persisted.
Conclusions:
- Findings reveal significant intra-individual variability in stuttering symptoms related to external stuttering severity.
- Fluency shaping (FS) techniques showed intermediate effects, with both costs and benefits.
- The persistent nature of some symptoms even in fluent states has important clinical implications for AWS.
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