Abstract
Swallowing dysfunction, or dysphagia, is a serious condition that can result from any structural or neurological impairment (such as stroke, neurodegenerative disease or brain injury) that affects the swallowing mechanism. The gold-standard method of instrumental swallowing assessment is an x-ray examination known as the videofluoroscopic swallowing study, which involves radiation exposure. Consequently, there is interest in exploring the potential of less invasive methods, with lesser risks of biohazard, to accurately detect swallowing abnormalities. Accelerometry is one such technique, which measures the epidermal vibration signals on a patient's neck during swallowing. Determining the utility of accelerometry signals for detecting dysphagia requires an understanding of the physiological source of the vibrations that are measured on the neck during swallowing. The purpose of the current study was to determine the extent to which movement of the hyoid bone and larynx contributes to the vibration signal that is registered during swallowing accelerometry. This question was explored by mapping the movement trajectories of the hyoid bone and the arytenoid cartilages from lateral videofluoroscopy recordings collected during thin liquid swallowing, and comparing these trajectories to time-linked signals obtained from a dual-axis accelerometer placed on the neck, just anterior to the cricoid cartilage. Participants for this study included 43 adult patients referred for videofluoroscopic swallowing studies to characterize the nature and severity of suspected neurogenic dysphagia. A software program was created to allow frame-by-frame tracking of structural movement on the videofluoroscopy recordings. These movement data were then compared to the integrated acceleration data using multiple linear regressions. The results concur with previous studies, implicating hyolaryngeal excursion as the primary physiological source of swallowing accelerometry signals, with both the hyoid and the larynx contributing approximately equal amounts to the explained variance of the dependent variable, the integrated accelerometry signal.
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