Abstract
Individuals have different preferences for setting hearing aid (HA) algorithms that reduce ambient noise but introduce signal distortions. "Noise haters" prefer greater noise reduction, even at the expense of signal quality. "Distortion haters" accept higher noise levels to avoid signal distortion. These preferences have so far been assumed to be stable over time, and individuals were classified on the basis of these stable, trait scores. However, the question remains as to how stable individual listening preferences are and whether day-to-day state-related variability needs to be considered as further criterion for classification. We designed a mobile task to measure noise-distortion preferences over 2 weeks in an ecological momentary assessment study with N = 185 (106 f, M(age) = 63.1, SD(age) = 6.5) individuals. Latent State-Trait Autoregressive (LST-AR) modeling was used to assess stability and dynamics of individual listening preferences for signals simulating the effects of noise reduction algorithms, presented in a web browser app. The analysis revealed a significant amount of state-related variance. The model has been extended to mixture LST-AR model for data-driven classification, taking into account state and trait components of listening preferences. In addition to successful identification of noise haters, distortion haters and a third intermediate class based on longitudinal, outside-of-the-lab data, we further differentiated individuals with different degrees of variability in listening preferences. Individualization of HA fitting could be improved by assessing individual preferences along the noise-distortion trade-off, and the day-to-day variability of these preferences needs to be taken into account for some individuals more than others.