Investigating the Provision and Context of Use of Hearing Aid Listening Programs From Real-world Data: Observational Study.

BACKGROUND: Listening programs enable hearing aid (HA) users to change device settings for specific listening situations and thereby personalize their listening experience. However, investigations into real-world use of such listening programs to support clinical decisions and evaluate the success of HA treatment are lacking. OBJECTIVE: We aimed to investigate the provision of listening programs among a large group of in-market HA users and the context in which the programs are typically used. METHODS: First, we analyzed how many and which programs were provided to 32,336 in-market HA users. Second, we explored 332,271 program selections from 1312 selected users to investigate the sound environments in which specific programs were used and whether such environments reflect the listening intent conveyed by the name of the used program. Our analysis was based on real-world longitudinal data logged by smartphone-connected HAs. RESULTS: In our sample, 57.71% (18,663/32,336) of the HA users had programs for specific listening situations, which is a higher proportion than previously reported, most likely because of the inclusion criteria. On the basis of association rule mining, we identified a primary additional listening program, Speech in Noise, which is frequent among users and often provided when other additional programs are also provided. We also identified 2 secondary additional programs (Comfort and Music), which are frequent among users who get ≥3 programs and usually provided in combination with Speech in Noise. In addition, 2 programs (TV and Remote Mic) were related to the use of external accessories and not found to be associated with other programs. On average, users selected Speech in Noise, Comfort, and Music in louder, noisier, and less-modulated (all P<.01) environments compared with the environment in which they selected the default program, General. The difference from the sound environment in which they selected General was significantly larger in the minutes following program selection than in the minutes preceding it. CONCLUSIONS: This study provides a deeper insight into the provision of listening programs on a large scale and demonstrates that additional listening programs are used as intended and according to the sound environment conveyed by the program name.

Evaluating Real-World Benefits of Hearing Aids With Deep Neural Network-Based Noise Reduction: An Ecological Momentary Assessment Study.

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 were 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.

The adaptation of noise-induced temporary hearing threshold shift predictive models for modelling the public health policy.

OBJECTIVES: It has been shown that monitoring temporary threshold shift (TTS) after exposure to noise may have a predictive value for susceptibility of developing permanent noise-induced hearing loss. The aim of this study is to present the assumptions of the TTS predictive model after its verification in normal hearing subjects along with demonstrating the usage of this model for the purposes of public health policy. MATERIAL AND METHODS: The existing computational predictive TTS models were adapted and validated in a group of 18 bartenders exposed to noise at the workplace. The performance of adapted TTS predictive model was assessed by receiver operating characteristic (ROC) analysis. The demonstration example of the usage of this model for estimating the risk of TTS in general unscreened population after exposure to loud music in discotheque bars or music clubs is provided. RESULTS: The adapted TTS predictive model shows a satisfactory agreement in distributions of actual and predicted TTS values and good correlations between these values in examined bartenders measured at 4 kHz, and as a mean at speech frequencies (0.5-4 kHz). An optimal cut-off level for recognizing the TTS events, ca. 75% of young people (aged ca. 35 years) may experience TTS >5 dB, while <10% may exhibit TTS of 15-18 dB. CONCLUSIONS: The final TTS predictive model proposed in this study needs to be validated in larger groups of subjects exposed to noise. Actual prediction of TTS episodes in general populations may become a helpful tool in creating the hearing protection public health policy. Int J Occup Med Environ Health. 2023;36(1):125-38.

Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data.

Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.

Assessing Real-Life Benefit From Hearing-Aid Noise Management: SSQ12 Questionnaire Versus Ecological Momentary Assessment With Acoustic Data-Logging.

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 (NMON) 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 NMON. The EMA scores, together with the logged acoustic data, revealed that this benefit was driven by NMON 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.

Real-World Hearing Aid Usage Patterns and Smartphone Connectivity.

Data for monitoring individual hearing aid usage has historically been limited to retrospective questionnaires or data logged intrinsically in the hearing aid cumulatively over time (e. g., days or more). This limits the investigation of longitudinal interactions between hearing aid use and environmental or behavioral factors. Recently it has become possible to analyze remotely logged hearing aid data from in-market and smartphone compatible hearing aids. This can provide access to novel insights about individual hearing aid usage patterns and their association to environmental factors. Here, we use remotely logged longitudinal data from 64 hearing aid users to establish basic norms regarding smartphone connectivity (i.e., comparing remotely logged data with cumulative true hearing aid on-time) and to assess whether such data can provide representative information about ecological usage patterns. The remotely logged data consists of minute-by-minute timestamped logs of cumulative hearing aid on-time and characteristics of the momentary acoustic environment. Using K-means clustering, we demonstrate that hourly hearing aid usage patterns (i.e., usage as minutes/hour) across participants are separated by four clusters that account for almost 50% of the day-to-day variation. The clusters indicate that hearing aids are worn either sparsely throughout the day; early morning to afternoon; from noon to late evening; or across the day from morning to late evening. Using linear mixed-effects regression modeling, we document significant associations between daily signal-to-noise, sound intensity, and sound diversity with hearing aid usage. Participants encounter louder, noisier, and more diverse sound environments the longer the hearing aids are worn. Finally, we find that remote logging via smartphones underestimates the daily hearing aid usage with a pooled median of 1.25 h, suggesting an overall connectivity of 85%. The 1.25 h difference is constant across days varying in total hearing aid on-time, and across participants varying in average daily hearing aid-on-time, and it does not depend on the identified patterns of daily hearing aid usage. In sum, remote data logging with hearing aids has high representativeness and face-validity, and can offer ecologically true information about individual usage patterns and the interaction between usage and everyday contexts.

Clustering Users Based on Hearing Aid Use: An Exploratory Analysis of Real-World Data.

While the assessment of hearing aid use has traditionally relied on subjective self-reported measures, smartphone-connected hearing aids enable objective data logging from a large number of users. Objective data logging allows to overcome the inaccuracy of self-reported measures. Moreover, data logging enables assessing hearing aid use with a greater temporal resolution and longitudinally, making it possible to investigate hourly patterns of use and to account for the day-to-day variability. This study aims to explore patterns of hearing aid use throughout the day and assess whether clusters of users with similar use patterns can be identified. We did so by analyzing objective hearing aid use data logged from 15,905 real-world users over a 4-month period. Firstly, we investigated the daily amount of hearing aid use and its within-user and between-user variability. We found that users, on average, used the hearing aids for 10.01 h/day, exhibiting a substantial between-user (SD = 2.76 h) and within-user (SD = 3.88 h) variability. Secondly, we examined hearing aid use hourly patterns by clustering 453,612 logged days into typical days of hearing aid use. We identified three typical days of hearing aid use: full day (44% of days), afternoon (27%), and sporadic evening (26%) day of hearing aid use. Thirdly, we explored the usage patterns of the hearing aid users by clustering the users based on the proportion of time spent in each of the typical days of hearing aid use. We found three distinct user groups, each characterized by a predominant (i.e., experienced ~60% of the time) typical day of hearing aid use. Notably, the largest user group (49%) of users predominantly had full days of hearing aid use. Finally, we validated the user clustering by training a supervised classification ensemble to predict the cluster to which each user belonged. The high accuracy achieved by the supervised classifier ensemble (~86%) indicated valid user clustering and showed that such a classifier can be successfully used to group new hearing aid users in the future. This study provides a deeper insight into the adoption of hearing care treatments and paves the way for more personalized solutions.

Investigating Real-World Benefits of High-Frequency Gain in Bone-Anchored Users with Ecological Momentary Assessment and Real-Time Data Logging.

PURPOSE: To compare listening ability (speech reception thresholds) and real-life listening experience in users with a percutaneous bone conduction device (BCD) with two listening programs differing only in high-frequency gain. In situ real-life experiences were recorded with ecological momentary assessment (EMA) techniques combined with real-time acoustical data logging and standard retrospective questionnaires. METHODS: Nineteen experienced BCD users participated in this study. They all used a Ponto 4 BCD from Oticon Medical during a 4-week trial period. Environmental data and device parameters (i.e., device usage and volume control) were logged in real-time on an iPhone via a custom iOS research app. At the end of the trial period, subjects filled in APHAB, SSQ, and preference questionnaires. Listening abilities with the two programs were evaluated with speech reception threshold tests. RESULTS: The APHAB and SSQ questionnaires did not reveal any differences between the two listening programs. The EMAs revealed group-level effects, indicating that in speech and noisy listening environments, subjects preferred the default listening program, and found the program with additional high-frequency gain too loud. This finding was corroborated by the volume log-subjects avoided the higher volume control setting and reacted more to changes in environmental sound pressure levels when using the high-frequency gain program. Finally, day-to-day changes in EMAs revealed acclimatization effects in the listening experience for ratings of "sound quality" and "program suitability" of the BCD, but not for ratings of "loudness perception" and "speech understanding". The acclimatization effect did not differ among the listening programs. CONCLUSION: Adding custom high-frequency amplification to the BCD target-gain prescription improves speech reception in laboratory tests under quiet conditions, but results in poorer real-life listening experiences due to loudness.