Hearing aids have stored aggregate data about the sound environments for nearly two decades, and meanwhile the roll-out and refinement of Bluetooth technology enabled the connection of hearing aids to phones so that detailed data can be read out during use. For some in the hearing society it may seem remarkable that hearing aids do not already collect and store massive amounts data. However, the limited access to power inside hearing aids has prevented this. Moreover, the perceived and experienced challenges with using Bluetooth among the elderly population has also contributed to delaying the introduction of widespread collection of real-world data from hearing aids. Modern hearing aids do store aggregated data about the use and sound environments and allow for more detailed data collection with time stamps that link the different data types in time with connected smartphones.
Traditional hearing rehabilitation combines the users recall of their everyday sound environments and their corresponding perceived benefit with hearing aids with the hearing care professionals’ knowledge and ability to infer insights into what would be a successful adjustment of hearing aids for the hearing aid user in order to adjust their hearing aids in the clinic. Likewise, the overall assessment of different hearing compensation interventions relied on listening tests conducted in the lab, combined with questionnaires, and preferences known from cross over studies. While these methods brought the field forward, they fail to address todays’ demand for individualized hearing solutions that provide benefit in most situations due to the complex interaction between the thousands of parameters in hearing aids and the many different needs for various situations and listening in individual users. Lately remote care allows for in situ adjustments which still requires appointments for every adjustment.
Real world data from hearing aids should help the hearing aid user and the HCP to achieve higher quality personalized rehabilitation based on utilizing patterns in real world data. These patterns are generally now known at this point, but we expect to see applications of contextual usage patterns, user segmentation, integration of additional sensors, behavioral analysis, self-managed solutions, ecological momentary assessments, questionnaires, focus groups, counseling tools, and evidence gathering to propel the field forward within the next few years. In parallel to addressing individual benefits, real world data from hearing aids are also expected to improve the ability to document benefits of use of hearing aids and rank hearing aid interventions according to their outcome and efficiency.
We welcome manuscripts that demonstrate tangible insights from gathering of real-world data with hearing aids based on available data or benchmark data. These insights can cover the whole span covering individual patterns, patterns based on segmentation of hearing aid users, towards public health. Moreover, we explicitly welcome manuscripts that go beyond the technical side and include social, psychological, economic, and health sciences within the are of hearing loss and hearing rehabilitation. Hearing care is a global challenge, and the rehabilitation follows different pathways with varying availability of hearing care professionals. We therefore also welcome manuscripts that demonstrate insights based on Personal Sound Amplifiers and Over-the-Counter hearing aids.
Hearing aids have stored aggregate data about the sound environments for nearly two decades, and meanwhile the roll-out and refinement of Bluetooth technology enabled the connection of hearing aids to phones so that detailed data can be read out during use. For some in the hearing society it may seem remarkable that hearing aids do not already collect and store massive amounts data. However, the limited access to power inside hearing aids has prevented this. Moreover, the perceived and experienced challenges with using Bluetooth among the elderly population has also contributed to delaying the introduction of widespread collection of real-world data from hearing aids. Modern hearing aids do store aggregated data about the use and sound environments and allow for more detailed data collection with time stamps that link the different data types in time with connected smartphones.
Traditional hearing rehabilitation combines the users recall of their everyday sound environments and their corresponding perceived benefit with hearing aids with the hearing care professionals’ knowledge and ability to infer insights into what would be a successful adjustment of hearing aids for the hearing aid user in order to adjust their hearing aids in the clinic. Likewise, the overall assessment of different hearing compensation interventions relied on listening tests conducted in the lab, combined with questionnaires, and preferences known from cross over studies. While these methods brought the field forward, they fail to address todays’ demand for individualized hearing solutions that provide benefit in most situations due to the complex interaction between the thousands of parameters in hearing aids and the many different needs for various situations and listening in individual users. Lately remote care allows for in situ adjustments which still requires appointments for every adjustment.
Real world data from hearing aids should help the hearing aid user and the HCP to achieve higher quality personalized rehabilitation based on utilizing patterns in real world data. These patterns are generally now known at this point, but we expect to see applications of contextual usage patterns, user segmentation, integration of additional sensors, behavioral analysis, self-managed solutions, ecological momentary assessments, questionnaires, focus groups, counseling tools, and evidence gathering to propel the field forward within the next few years. In parallel to addressing individual benefits, real world data from hearing aids are also expected to improve the ability to document benefits of use of hearing aids and rank hearing aid interventions according to their outcome and efficiency.
We welcome manuscripts that demonstrate tangible insights from gathering of real-world data with hearing aids based on available data or benchmark data. These insights can cover the whole span covering individual patterns, patterns based on segmentation of hearing aid users, towards public health. Moreover, we explicitly welcome manuscripts that go beyond the technical side and include social, psychological, economic, and health sciences within the are of hearing loss and hearing rehabilitation. Hearing care is a global challenge, and the rehabilitation follows different pathways with varying availability of hearing care professionals. We therefore also welcome manuscripts that demonstrate insights based on Personal Sound Amplifiers and Over-the-Counter hearing aids.