Abstract
Background: Preliminary studies have shown that respiratory– swallow training (RST) is a successful treatment for oropharyngeal head and neck cancer patients with refractory dysphagia. Refining the RST protocol with automated analysis software to provide real-time performance feedback has the potential to improve accessibility, reproducibility, and translation to diverse clinical settings. Method: An automated software program for data acquisition and analysis developed to detect swallows, determine respiratory phase, calculate lung volume at the onset of the swallow, and provide real-time performance feedback was tested for feasibility in a small cohort of healthy adults. Outcome Measures: Percent difference in swallow detection and accuracy of real-time performance feedback of respiratory phase and lung volume at swallowing onset between the automated software and the manual gold standard method were determined. Results: The automated software program accurately detected the onset of the swallow on 91% of the swallows completed during the training trials. Feedback of respiratory phase and lung volume was accurate on 94% of the trials in which the swallow was accurately detected. Conclusions: This novel, automated, and real-time RST software successfully detected the onset of the swallow, respiratory phase, and lung volume at swallow onset and provided appropriate real-time performance feedback with a high degree of accuracy in healthy adults. The software has the potential to improve the accessibility, efficiency, and translation of RST to diverse patient populations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1012-1021 |
| Number of pages | 10 |
| Journal | American journal of speech-language pathology |
| Volume | 29 |
| Issue number | 2S |
| DOIs | |
| State | Published - Jul 2020 |
Bibliographical note
Funding Information:Funding for this project was provided by Northwestern University.
Publisher Copyright:
© 2020 American Speech-Language-Hearing Association.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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