Advancing hearing research with the NAL ecologically momentary assessment platform for real-world insights

N. Chong-White, Joseph Tagudin, Jorge Mejia
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Abstract

Ecological Momentary Assessment (EMA) is an invaluable tool for assessing people's behaviours and experiences in their natural surroundings. We have developed a smartphone-based EMA tool, called NEMA, that has contributed to over 10 research studies by providing meaningful real-world insights into how individuals with hearing loss interact with various hearing device technologies. By providing real-world perspectives that complement traditional lab-based tests, NEMA offers a more comprehensive understanding of everyday hearing and communication challenges. Its advanced features include cloud-connectivity, customisable surveys, cross-device acoustic feature measurements from the environment, and a back-end platform enabling real-time data monitoring and visualization. To derive vital insights from this rich data stream, advanced analytical methodologies are applied. However, there exist limitations with smartphone-based EMAs that can influence their overall effectiveness. We identify these constraints and discuss strategies to alleviate participant burden and increase task relevance. These initiatives aim to enhance compliance and diversify data collected. Current work involves integrating smartwatches and additional sensor technology into our next generation NEMA platform. This progression aims to facilitate a more comprehensive capture of diverse data sets, ultimately leading to a deeper understanding of individual auditory experiences.
利用 NAL 生态瞬间评估平台推进听力研究,深入了解真实世界
生态瞬间评估(EMA)是评估人们在自然环境中的行为和体验的重要工具。我们开发了一种基于智能手机的 EMA 工具,名为 NEMA,它为 10 多项研究做出了贡献,为听力损失患者如何与各种听力设备技术互动提供了有意义的现实世界见解。通过提供真实世界的视角来补充传统的实验室测试,NEMA 可以更全面地了解日常听力和交流方面的挑战。其先进的功能包括云连接、可定制的调查、从环境中进行跨设备声学特征测量,以及实现实时数据监控和可视化的后端平台。为了从这些丰富的数据流中获得重要的见解,我们采用了先进的分析方法。然而,基于智能手机的 EMA 存在一些局限性,会影响其整体效果。我们找出了这些限制因素,并讨论了减轻参与者负担和提高任务相关性的策略。这些措施旨在提高依从性并使收集的数据多样化。目前的工作包括将智能手表和其他传感器技术整合到我们的下一代 NEMA 平台中。这一进展旨在促进更全面地捕捉各种数据集,最终加深对个人听觉体验的理解。
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