Personalizing over-the-counter hearing aids using pairwise comparisons.

Over-the-counter hearing aids enable more affordable and accessible hearing health care by shifting the burden of configuring the device from trained audiologists to end-users. A critical challenge is to provide users with an easy-to-use method for personalizing the many parameters which control sound amplification based on their preferences. This paper presents a novel approach to fitting hearing aids that provides a higher degree of personalization than existing methods by using user feedback more efficiently. Our approach divides the fitting problem into two parts. First, we discretize an initial 24-dimensional space of possible configurations into a small number of presets. Presets are constructed to ensure that they can meet the hearing needs of a large fraction of Americans with mild-to-moderate hearing loss. Then, an online agent learns the best preset by asking a sequence of pairwise comparisons. This learning problem is an instance of the multi-armed bandit problem. We performed a 35-user study to understand the factors that affect user preferences and evaluate the efficacy of multi-armed bandit algorithms. Most notably, we identified a new relationship between a user's preference and presets: a user's preference can be represented as one or more preference points in the initial configuration space with stronger preferences expressed for nearby presets (as measured by the Euclidean distance). Based on this observation, we have developed a Two-Phase Personalizing algorithm that significantly reduces the number of comparisons required to identify a user's preferred preset. Simulation results indicate that the proposed algorithm can find the best configuration with a median of 25 comparisons, reducing by half the comparisons required by the best baseline. These results indicate that it is feasible to configure over-the-counter hearing aids using a small number of pairwise comparisons without the help of professionals.

Why Ecological Momentary Assessment Surveys Go Incomplete: When It Happens and How It Impacts Data.

BACKGROUND: Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. PURPOSE: This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. RESEARCH DESIGN: An observational study. STUDY SAMPLE: Ten adults hearing aid (HA) users. DATA COLLECTION AND ANALYSIS: Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. RESULTS: Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. CONCLUSION: The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

Common Configurations of Real-Ear Aided Response Targets Prescribed by NAL-NL2 for Older Adults With Mild-to-Moderate Hearing Loss.

Purpose This study investigates common real-ear aided response (REAR) configurations prescribed by the NAL-NL2 algorithm for older adults with hearing loss. Method A data set that is representative of the older adult U.S. population with mild-to-moderate sensorineural hearing loss was constructed from the audiometric data of 934 adults (aged 55-85 years) from the National Health and Nutrition Examination Survey years 1999-2012. Two clustering approaches were implemented to generate common REAR configurations for eight frequencies (0.25, 0.5, 1, 2, 3, 4, 6, and 8 kHz) at three input levels (55, 65, and 75 dB SPL). (a) In the REAR-based clustering approach, the National Health and Nutrition Examination Survey audiograms were first converted to REAR targets and then clustered to generate common REAR configurations. (b) In the audiogram-based clustering approach, the audiograms were first clustered into common hearing loss profiles and then converted to REAR configurations. The trade-off between the number of available REAR configurations and the percentage of the U.S. population whose hearing loss could be fit by at least one of them (i.e., percent coverage) was evaluated. Hearing loss fit was defined as less than ± 5-dB difference between an individual's REAR targets and those of the clustered REAR configuration. Results Percent coverage increases with the number of available REAR configurations, with four configurations resulting in 75% population coverage. Overall, REAR-based clustering yielded 5 percentage points better coverage on average compared to audiogram-based clustering. Conclusions The common REAR configurations can be used for programming the gain frequency responses in preconfigured over-the-counter hearing aids and provide clinically appropriate amplification settings for older adults with mild-to-moderate hearing loss.