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

Objective: To 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 design: Prospective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.

Setting: Tertiary referral center.

Patients: Two hundred fifty participants aged 55-100 years who were at risk for falls.

Interventions: Fall 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 measures: Fall risk categorizations based on 4-Stage Balance, Timed Up and Go (TUG), and 30-Second Chair Stand tests obtained from IMU-HAs and clinicians.

Results: Interrater 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.

Conclusions: These 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.