Gathering Ecological Data to Assess Real-life Benefits of Cochlear Implants

Authors: Lelia A Erscoi1*; Yue Zhang1; Manuel Segovia-Martinez1

1Oticon Medical

Cochlear implant (CI) can restore sensation of hearing and near perfect speech communication to many patients with severe-to-profound sensorineural hearing loss in quiet environments. However, there are big individual variabilities observed in speech and quality of life outcomes following cochlear implantation. Specifically, most of the clinical assessments for patients are conducted in an isolated lab or clinic, using unrealistic sound stimuli. Therefore, results generated from these tests cannot uncover fully the challenges and difficulties CI users are experiencing. This discrepancy might be able to explain the lack of association between real-life quality of life measurement and standard clinical assessment. Oticon Medical Field Research Platform (OMFRP) introduces a new way to investigate the realistic usage of medical devices for CI users. It is a standalone iPhone application connected with the hearing devices via Bluetooth connection to control device-related settings (e.g., programs, sound level, data logging, acoustic scene classification). The application interacts with the user by collecting ecological momentary assessment (EMA) questionnaires at certain intervals. To understand better the validity and sensitivity of this method in assessing CI device usage, we are designing a clinical protocol to compare the in-lab results and field data on two CI features: noise reduction and NofM. During the in-lab session, participants will perform a series of speech-in-noise tasks with simultaneous pupillometry and post-task subjective effort rating, to obtain CI speech recognition and listening effort. Then the participants will take OMFRP back to their daily life and fill out EMA assessment on self-rated communication success and effort/fatigue level. Furthermore, we can also utilize the device’s own data logging and OMFRP’s automatic scene classification to understand how different communication scenarios affect user speech recognition and perceived difficulty.