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Modeling and Predicting Hearing Aid Outcome

Department of Speech and Hearing Sciences, Indiana University, Bloomington, IN 47405-7002

Following a brief tutorial on the application of factor analysis to hearing aid outcome measures, three studies of hearing aid outcome measures in elderly adults are presented and analyzed. Two of the studies were completed at Indiana University (IU-1 and IU-2), and one was a collaborative multisite study by the Veterans Administration and the National Institute of Deafness and other Communication Disorders (NIDCD/VA). IU-1 measured hearing aid outcome in 173 elderly wearers of single-channel, linear, in-the-ear hearing aids with output limiting compression, whereas IU-2 obtained the same extensive set of outcome measures from 53 elderly wearers of two-channel, wide-dynamic-range compression, in-the-canal hearing aids. In the NIDCD/VA study, 333 to 338 participants wore three single-channel circuits in succession, with each circuit housed within an in-the-ear shell. The three circuits included in that study and in this analysis were: (1) linear with peak clipping, (2) linear with output-limiting compression, and (3) single-channel, wide-dynamic-range compression. Evaluation of the many outcome measures completed in each study using principal components factor analysis revealed that from three (both IU studies) to five (NIDCD/VA study) principal components captured the individual differences in hearing aid outcome. This was independent of hearing aid type (in-the-ear or in-the-canal) and circuitry. Subsequent multiple regression analyses of individual differences in performance along each dimension of hearing aid outcome revealed that these individual differences could be accounted for reasonably well by various prefit variables for some dimensions of outcome, but not others. In general, measures of speech recognition performance were well accounted for by prefit measures, with the best predictors being hearing loss, cognitive performance, and age. Measures of hearing aid usage were less well accounted for by prefit measures, with the most accurate predictor of current hearing aid use being prior hearing aid use. The outcome dimension accounted for most poorly was that associated with hearing aid satisfaction, with subjective measures of aided sound quality being the best predictor of performance along this dimension of hearing aid outcome. Additional multicenter, large-scale studies are needed to develop more complete models of hearing aid outcome and to identify the variables that influence various aspects of hearing aid outcome. It is only through this additional research that it will be possible to optimize outcome for hearing aid wearers.

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