Purpose: The purpose of this study was to investigate real-life benefit from directional microphone and noise reduction (“noise management” [NM]) processing using retrospective self-reports and smartphone-based ecological momentary assessments (EMAs) combined with logging of the acoustic environments. Method: A single-blinded, counterbalanced crossover design was used. Eleven hearing-impaired adults were bilaterally fitted with behind-the-ear devices with NM either activated (NM _ON ) or deactivated. For the retrospective self-reports, the short scale of the Speech, Spatial, and Qualities Hearing Scale questionnaire (SSQ12) was applied. For the EMAs, smartphone-based self-reports combined with hearing aid (HA)–based classifications of the listening environments (“soundscapes”) experienced by the participants was used. To explore potential associations with the real-life data, two laboratory measures of aided speech recognition in noise were administered. Results: The soundscapes in which the participants submitted their EMAs were representative of the soundscapes they experienced during normal HA use and of the soundscapes reported in the literature for older HA users. The SSQ12 and EMA scores both showed an overall benefit from NM _ON . The EMA scores, together with the logged acoustic data, revealed that this benefit was driven by NM _ON being preferred particularly in listening environments classified as “speech” or “speech in noise.” The laboratory measures of aided speech recognition in noise were unable to predict the real-life data. Conclusions: EMA combined with acoustic data-logging is suited for more targeted evaluations of real-life HA benefit. Advanced NM settings can provide subjective user benefits in specific listening situations.

We investigate the short-term association between multidimensional acoustic characteristics of everyday ambient sound and continuous mean heart rate. We used in-market data from hearing aid users who logged ambient acoustics via smartphone-connected hearing aids and continuous mean heart rate in 5 min intervals from their own wearables. We find that acoustic characteristics explain approximately 4% of the fluctuation in mean heart rate throughout the day. Specifically, increases in ambient sound pressure intensity are significantly related to increases in mean heart rate, corroborating prior laboratory and short-term real-world data. In addition, increases in ambient sound quality—that is, more favourable signal to noise ratios—are associated with decreases in mean heart rate. Our findings document a previously unrecognized mixed influence of everyday sounds on cardiovascular stress, and that the relationship is more complex than is seen from an examination of sound intensity alone. Thus, our findings highlight the relevance of ambient environmental sound in models of human ecophysiology.

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies.

Purpose: Noise reduction technologies in hearing aids provide benefits under controlled conditions. However, differences in their real-life effectiveness are not established. We propose that a deep neural network (DNN)–based noise reduction system trained on naturalistic sound environments will provide different real-life benefits compared to traditional systems. Method: Real-life listening experiences collected with Ecological Momentary Assessments (EMAs) of participants who used two premium models of hearing aid are compared. One hearing aid model (HA1) used traditional noise reduction; the other hearing aid model (HA2) used DNN-based noise reduction. Participants reported listening experiences several times a day while ambient SPL, SNR, and hearing aid volume adjustments were recorded. Forty experienced hearing aid users completed a total of 3,614 EMAs and recorded 6,812 hr of sound data across two 14-day wear periods. Results: Linear mixed-effects analysis document that participants' assessments of ambient noisiness were positively associated with SPL and negatively associated with SNR but are not otherwise affected by hearing aid model. Likewise, mean satisfaction with the two models did not differ. However, individual satisfaction ratings for HA1 were dependent on ambient SNR, which was not the case for HA2. Conclusions: Hearing aids with DNN-based noise reduction resulted in consistent sound satisfaction regardless of the level of background noise compared to hearing aids implementing noise reduction based on traditional statistical models. While the two hearing aid models also differed on other parameters (e.g., shape), these differences are unlikely to explain the difference in how background noise impacts sound satisfaction with the aids. Supplemental Material: https://doi.org/10.23641/asha.25114526

Fully Synthetic Longitudinal Real-World Data From Hearing Aid Wearers for Public Health Policy Modeling