Characterizing Variability in Hearing Aid Outcomes Among Older Adults

项目摘要 数字助听器中使用的先进信号处理算法改进了普通助听器 利益和满足感。然而，对助听器的反应是高度不同的，一些人报告说 比其他人受益多得多。这种差异在年长的听众中最为明显。一个重要的问题是 要在以下方面取得助听器的成功，需要哪些级别的高级助听器处理 单独的听众。每种形式的非线性信号处理都有自己的一套改进的权衡 可听度与信号处理引起的信号修改增加的对比。然而，没有 在临床配件中确定谁将从加工中受益的有效程序或如何 处理应针对单个监听者进行调整。这项研究的长期目标是使用证据- 以临床测试为基础，指导选择最适合个别老年人的信号处理 成年人在自己的听力环境中佩戴助听器。拟议的续签申请将移动 分三步走向临床应用。第一个具体目标是描述响应于 老年人的信号处理，并确定哪些患者因素与 临床条件，并使用这些数据来修改我们的拟合指标，以便更准确地应用于临床 配件。听力专家将使用当前的最佳实践(即标准 护理)在我们的现场诊所。信号处理将被量化并与辅助可理解性的测量相关， 质量和喜好。此数据集将允许我们扩展基于模型的方法，以包括完整的 为具有多种听力损失配置的患者提供连续的信号处理。在第二个 具体目标是，临床工具包将在基于计算机的应用程序中实施，该应用程序可以指导 听觉学家根据个体听者的特征进行信号处理的拟合和调整。 应用合作伙伴将与我们合作开发必要的软件和硬件(低噪声探头 麦克风系统)能够测量助听器输出并将这些值输入计算机 申请。第三个具体目标是通过比较患者群体来验证该工具集的临床应用 使用工具包与那些使用当前护理标准的人相匹配。将收集有关患者结果的数据， 临床影响(调整助听器所需的就诊次数)和听力专家的反馈 职业自信。还将收集临床医生的反馈，以完善临床实施 应用，并提高其可用性。假设助听器配件是使用临床完成的 工具集将带来更好的患者结果、更少的配戴后就诊次数和更高的临床医生信心 与标准护理助听器配件相比。综上所述，本项目中解决的问题将 全面评估助听器在真实听力中的处理效果 环境，同时考虑影响信号处理响应的特定能力。

Project abstract The advanced signal-processing algorithms used in digital hearing aids have improved average hearing aid benefit and satisfaction. However, response to hearing aids is highly variable, with some individuals reporting much more benefit than others. This variability is most evident among older listeners. An important issue is what levels of advanced hearing aid processing are necessary to achieve success with hearing aids in individual listeners. Every form of nonlinear signal processing has its own set of trade-offs of improved audibility versus increased modification of the signal caused by the signal processing. However, there are no effective procedures for determining in clinical fittings who will benefit from the processing or how the processing should be adjusted for the individual listener. The long term goal of this research is use evidence- based clinical tests to guide the selection of signal processing that is most appropriate for individual older adults wearing hearing aids in their own listening environments. The proposed renewal application moves this work toward clinical application in three steps. The first specific aim is to characterize variability in response to signal manipulation among older adults and determine what patient factors are related to that variability under clinical conditions, and to use those data to modify our fitting metrics for more accurate application to clinical fittings. Data will be collected for hearing aids fit by audiologists using current best practice (i.e., standard of care) in our site clinics. Signal manipulation will be quantified and related to measures of aided intelligibility, quality, and preference. This dataset will allow us to extend our model-based approach to include a full continuum of signal processing for patients with a wide range of hearing loss configurations. In the second specific aim, the clinical toolset will be implemented in a computer-based application that can guide audiologists in the fitting and adjustment of signal processing based on individual listener characteristics. Application partners will work with us to develop the necessary software and hardware (a low-noise probe microphone system) capable of measuring hearing aid output and inputting those values to the computer application. The third specific aim is to validate clinical use of the toolset by comparing a population of patients fit with the toolset to those fit using current standard of care. Data will be collected on patient outcomes, clinical impact (number of visits needed to adjust the hearing aids) and audiologist feedback regarding professional confidence. Clinician feedback will also be collected to refine implementation of the clinical application and improve its usability. It is hypothesized that hearing aid fittings completed using the clinical toolset will result in better patient outcomes, fewer post-fitting visits and a higher level of clinician confidence compared to standard-of-care hearing aid fittings. Taken together, the questions addressed in this project will provide a comprehensive assessment of the effects of hearing aid processing in realistic listening environments, while considering specific abilities that affect response to signal processing.