Use of Hearing Aids Embedded with Inertial Sensors and Artificial Intelligence to Identify Patients at Risk for Falling

ObjectiveTo compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.Study DesignProspective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.SettingTertiary referral center.PatientsTwo hundred fifty participants aged 55-100 years who were at risk for falls.InterventionsFall risk was categorized using the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) test battery consisting of the 4-Stage Balance, Timed Up and Go (TUG), and 30-Second Chair Stand tests. Performance was scored using bilateral IMU-HAs and compared to scores by clinicians blinded to the hearing aid measures.Main Outcome MeasuresFall risk categorizations based on 4-Stage Balance, Timed Up and Go (TUG), and 30-Second Chair Stand tests obtained from IMU-HAs and clinicians.ResultsInterrater reliability was excellent across all clinicians. The 4-Stage Balance and TUG showed no statistically significant differences between clinician and HAs. However, the IMU-HAs failed to record a response in 12% of TUG trials. For the 30-Second Chair Stand test, there was a significant difference of nearly one stand count, which would have altered fall risk classification in 21% of participants.ConclusionsThese results suggest that fall risk as determined by the STEADI tests was in most instances similar for IMU-HAs and trained observers; however, differences were observed in certain situations, suggesting improvements are needed in the algorithm to maximize accurate fall risk categorization.