Project Summary/Abstract Voice disorders have been estimated to affect approximately 30% of the adult population in the United States at some point in their lives with the most prevalent being associated with vocal hyperfunction. Phonotraumatic vocal hyperfunction (PVH) is associated with the formation of benign vocal fold lesions (e.g., nodules and polyps). Vocal hyperfunction is also associated with dysphonia that occurs in the absence of concurrent vocal fold trauma or other structural/neurological abnormalities (e.g., muscle tension dysphonia), which is referred to as non- phonotraumatic vocal hyperfunction (NPVH). Unfortunately, the prevention, diagnosis, and treatment of these hyperfunctional voice disorders continue to be hampered by limited knowledge of the etiological and pathophysiological mechanisms that underlie specific voice disorders within the broad range of those associated with vocal hyperfunction. This project continues to elucidate multiple factors/mechanisms that are assumed to play important roles in causing and maintaining hyperfunctional voice disorders using a combination of data from ambulatory voice monitoring and other important sources: reflux status, personality, vocal reactivity to environmental sound levels, responses to voice therapy and ambulatory biofeedback, assessment of auditory-motor deficits, and model- generated estimates of phonatory physiological parameters that are difficult/impossible to measure clinically. Research during this second grant cycle will focus on the interrelated goals of further improving the detection of PVH and NPVH and identifying clinically meaningful subgroups (phenotypes) within these two broad categories. Detection will be improved by developing two new ambulatory measures, energy dissipation dose (EDD), which is expected to be more sensitive to mild (early-stage) phonotrauma, and a time series voicing-resting ratio (VRR) that will provide objective insight into how the relationship between vocal load and recovery is associated with phonotrauma and vocal fatigue. A combination of advanced statistical and machine learning approaches will be applied to our growing comprehensive/multidimensional voice database to optimize the discovery of PVH and NPVH subgroups based on differences in objective measures of daily (ambulatory) vocal behavior and phonatory pathophysiology. The subgroups will be further differentiated by additional measures that reflect deeper insights into pathophysiological mechanisms (e.g., intrinsic laryngeal muscle activation levels, vocal fold collision pressure, etc.) and potential predisposing factors (e.g., personality, reflux, etc.). Achieving the goals of this research program will lead to improved prevention, differential diagnosis, and more focused and efficient treatment of these highly prevalent voice disorders.