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从日常生活中自我报告的听力体验中预测个体的助听器偏好
Jeppe H Christensen1, Johanne Rumley2, Juan Camilo Gil-Carvajal2
1Eriksholm Research Centre, Oticon A/S, Snekkersten, Denmark.
Ear and hearing
|May 24, 2024
概括
生态瞬间评估 (EMA) 与上下文数据相结合,可以准确预测助听器偏好. 这种方法提供了一个比传统问卷更个性化的方法来评估助听器的结果.
科学领域:
- 听力学 听力学是指听力学.
- 听力科学 听力科学
- 人与计算机的交互
背景情况:
- 助听器偏好对于用户满意度和有效康复至关重要.
- 传统的回顾性问卷可能无法捕捉现实世界听力体验的细微差别.
- 在现场的方法,如生态瞬间评估 (EMA),为收集动态用户反提供了一个有希望的替代方案.
研究的目的:
- 为了比较追溯问卷 (语音,空间和听力素质量表 (SSQ)) 和现场EMA的实用性,用于预测助听器偏好.
- 调查如何自我报告的听力活动和助听器数据记录可以增强EMA的个性化听力结果评估.
- 评估EMA在预测个人助听器偏好方面的预测准确度.
主要方法:
- 40名经验丰富的助听器使用者与轻度至中度的感觉神经神经听力损失参与.
- 参与者完成了SSQ,并接受了两次为期2周的试验,使用不同的助听器模型.
- 收集了重复的EMA,与听力活动和听力助听器数据记录的声音环境数据相关联.
主要成果:
- 在49个SSQ项目中,只有6个有显著差异,优选的助听器和非优选的助听器.
- 欧洲药物管理局的回复,特别是关于在低信号对噪声比 (SNR) 环境中的声音感知,与偏好有很强的联系.
- 在SSQ和EMA的平均差异预测了大约72%的参与者的偏好.
- 随机森林模型使用EMA与上下文信息在预测偏好方面实现了93.8%的准确性.
结论:
- 虽然SSQ和EMA基于平均差异显示出类似的预测能力,但EMA的重复测量性质和上下文数据集成提供了更高的预测准确度.
- 对于个性化听力结果评估和预测助听器偏好而言,EMA,特别是当它与上下文信息相辅相成长时,是理想的选择.
- 这种方法提供了对助听器用户体验的更细致和个性化的理解.
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