The impact of using the Ida “My Hearing Explained” tool on audiologists’ language and patient understanding of hearing test results:a comparison with standard audiogram explanations

Objective: Explore the impact of Ida’s “My Hearing Explained” (MHE) tool on audiologists’ language and patients’ understanding/interpretation of hearing test results.Design: Audiologists were video-recorded in two sequential conditions: 1) giving standard audiogram explanations to 13 patients and, 2) following discretionary self-training, giving explanations using the MHE tool (nine patients). Outcomes of interest were audiologists’ language complexity, use of jargon, and audiologist-patient interactivity. Semi-structured patient interviews, conducted 1-7 days after appointments, were analysed using inductive qualitative content analysis. Patient recall was verified.Study Sample: Four audiologists from one United Kingdom audiology service, and 22 patients (mean age 63.5 yrs) participated.Results: In comparison to standard audiogram explanations, audiologists’ language was simpler and audiologist-patient interactivity greater with the MHE tool. Interview data analysis revealed differences between explanation types within the themes of “Understanding” and “Interpretation.” 54% (standard audiogram) and 22% (MHE tool) of patients expressed a desire for takeaway information. 31% (standard audiogram) and 67% (MHE tool) of patients reported their explanation helped them relay their results to others. Four patients (one receiving the MHE tool) incorrectly recalled information, suggesting inadequate understanding in these cases.Conclusions: The MHE tool has potential for improving the accessibility and comprehensibility of hearing test results

Threshold Equalizing Noise Test Reveals Suprathreshold Loss of Hearing Function, Even in the "Normal" Audiogram Range

Objectives: The threshold equalizing noise (TEN(HL)) is a clinically administered test to detect cochlear “dead regions” (i.e., regions of loss of inner hair cell [IHC] connectivity), using a “pass/fail” criterion based on the degree of elevation of a masked threshold in a tone-detection task. With sensorineural hearing loss, some elevation of the masked threshold is commonly observed but usually insufficient to create a “fail” diagnosis. The experiment reported here investigated whether the gray area between pass and fail contained information that correlated with factors such as age or cumulative high-level noise exposure (>100 dBA sound pressure levels), possibly indicative of damage to cochlear structures other than the more commonly implicated outer hair cells. Design: One hundred and twelve participants (71 female) who underwent audiometric screening for a sensorineural hearing loss, classified as either normal or mild, were recruited. Their age range was 32 to 74 years. They were administered the TEN test at four frequencies, 0.75, 1, 3, and 4 kHz, and at two sensation levels, 12 and 24 dB above their pure-tone absolute threshold at each frequency. The test frequencies were chosen to lie either distinctly away from, or within, the 2 to 6 kHz region where noise-induced hearing loss is first clinically observed as a notch in the audiogram. Cumulative noise exposure was assessed by the Noise Exposure Structured Interview (NESI). Elements of the NESI also permitted participant stratification by music experience. Results: Across all frequencies and testing levels, a strong positive correlation was observed between elevation of TEN threshold and absolute threshold. These correlations were little-changed even after noise exposure and music experience were factored out. The correlations were observed even within the range of “normal” hearing (absolute thresholds ≤15 dB HL). Conclusions: Using a clinical test, sensorineural hearing deficits were observable even within the range of clinically “normal” hearing. Results from the TEN test residing between “pass” and “fail” are dominated by processes not related to IHCs. The TEN test for IHC-related function should therefore only be considered for its originally designed function, to generate a binary decision, either pass or fail

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 (HA) are compared. HA1 used traditional noise reduction; HA2 used DNN-based noise reduction. Participants reported listening experiences several times a day while ambient sound pressure level (SPLs), signal-to-noise ratio (SNRs) and hearing-aid volume adjustments were recorded. 40 experienced hearing-aid users completed a total of 3614 EMAs and recorded 6812 hours 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. <br/

Predicting Individual Hearing Aid Preference from Self-Reported Listening Experiences in Daily Life

Objectives: The study compared the utility of two approaches for collecting real-world listening experiences to predict hearing-aid preference: a retrospective questionnaire (Speech, Spatial and Qualities of Hearing Scale, SSQ) and in-situ Ecological Momentary Assessment (EMA). The rationale being that each approach likely provides different and yet complementary information. Additionally, it was examined how self-reported listening activity and hearing-aid data-logging can augment EMAs for individualized and contextualized hearing outcome assessments. Design: Experienced hearing aid users (N = 40) with mild-to-moderate symmetrical sensorineural hearing loss completed the SSQ questionnaire and gave repeated EMAs for two wear periods of 2-weeks each with two different hearing-aid models that differed mainly in their noise reduction technology. The EMAs were linked to a self-reported listening activity and sound environment parameters (from hearing-aid data-logging) recorded at the time of EMA completion. Wear order was randomized by hearing-aid model. Linear mixed-effects models and Random Forest models with 5-fold cross validation were used to assess the statistical associations between listening experiences and end-of-trial preferences, and to evaluate how accurately EMAs predicted preference within individuals. Results: Only 6 of the 49 SSQ items significantly discriminated between responses made for the end-of-trial preferred versus non-preferred hearing-aid model. For the EMAs, questions related to perception of the sound from the hearing aids were all significantly associated with preference, and these associations were strongest in EMAs completed in sound environments with predominantly low SNR and listening activities related to television, people talking, non-specific listening, and music listening. Mean differences in listening experiences from SSQ and EMA correctly predicted preference in 71.8% and 72.5% of included participants, respectively. However, a prognostic classification of single EMAs into end-of-trial preference with a Random Forest model achieved a 95.2% accuracy when contextual information was included. Conclusions: SSQ and EMA predicted preference equally well when considering mean differences, however, EMAs had a high prognostic classifications accuracy due to the repeated-measures nature, which make them ideal for individualized hearing outcome investigations, especially when responses are combined with contextual information about the sound environment. <br/

Real-world benefits of DNN-based noise reduction (Christensen et al., 2024)

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. The first hearing aid (HA1) used traditional noise reduction; the second hearing aid (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 S1. Additional information on study design and sound data.Christensen, J. H., Whiston, H., Lough, M., Gil-Carvajal, J. C., Rumley, J., & Saunders, G. H. (2024). Evaluating real-world benefits of hearing aids with deep neural network–based noise reduction: An ecological momentary assessment study. American Journal of Audiology, 33(1), 242–253. https://doi.org/10.1044/2023_AJA-23-00149</p