Evaluating the Accuracy of Step Tracking and Fall Detection in the Starkey Livio Artificial Intelligence Hearing Aids: A Pilot Study.
Mohamed Rahme, Paula Folkeard, Susan Scollie
https://pubmed.ncbi.nlm.nih.gov/33284647Actively Recruiting
Phase Not Applicable
Age: 55Years +
All Genders
Healthy Volunteers
ID05365646
Fall Risk Assessment and Speech Intelligibility Enhancement Using In-ear Device
Led by Stanford University · Updated on 2026-04-28
350
Participants Needed
1
Research Sites
N/A
Total Duration
On this page
Sponsors
S
Stanford University
Lead Sponsor
S
Starkey Laboratories, Inc
Collaborating Sponsor
AI-Summary
What this Trial Is About
Researchers are studying the use of hearing aids equipped with embedded sensors and artificial intelligence to assess the risk of falling and to improve speech understanding in quiet and noisy environments. This study includes adults aged 55 and older who use hearing aids and aims to reduce falls and enhance hearing quality by tracking physical activity and social engagement. The research is led by Stanford University and involves a non-randomized, interventional design focused on fall prevention and hearing improvement. Participants will wear special hearing aids that monitor their movement and signal if they are at risk of falling. The study examines fall risk assessment and speech intelligibility enhancement using these in-ear devices. Both study groups receive the same intervention with devices that detect motion and assist in evaluating balance and hearing performance. During the study, participants will undergo several tests such as the CDC's STEADI protocol for fall risk, Timed Up and Go Test, Chair Stand Test, Balance Test, and various speech perception tests. They will also complete questionnaires about their listening effort, hearing lifestyle, balance confidence, and user experience. Hearing aid data and health information will be collected. The total study duration and follow-up periods are not specified, but assessments typically take between 2 and 15 minutes each.
CONDITIONS
Brief Title
Fall Risk Assessment and Speech Intelligibility Enhancement Using In-ear Device
Who Can Participate
Age: 55Years +
All Genders
Healthy Volunteers
Eligibility Criteria
You may qualify if you...
- Age 55 years or older
- Currently use hearing aids in both ears
- Able to give consent in English
- Have access to a phone capable of running the study's mobile app
- Show signs of fall risk based on screening questions
- Able to walk without using a wheelchair
You will not qualify if you...
- Use of a wheelchair or mobility restrictions preventing functional assessments
- Severe movement disorders like advanced Parkinson's disease
- Severe cognitive disorders such as advanced dementia impacting assessment ability
- Inability to understand, consent, or complete study requirements including verbal instructions
- Dependence on a wheelchair or non-ambulatory status
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Implementation
Duration - Duration of device usage as per study protocol
Participants wear study hearing aids equipped with embedded sensors and artificial intelligence to assess movement, fall risk, and speech intelligibility.
Visits as needed to monitor device use and collect data
Surveillance
Duration - Duration of device usage as per study protocol
Participants are observed while wearing the hearing aids to track movement and fall risk using embedded sensors.
Data collection visits as scheduled
Trial Site Locations
Total: 1 location
1
Stanford Ear Institute
Palo Alto, California, United States, 94303
Actively Recruiting
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How is the study designed?
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
NON_RANDOMIZED
Model
PARALLEL
Primary Purpose
SCREENING
Number of Arms
2
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Published Research Related To This Trial
Preliminary Examination of the Accuracy of a Fall Detection Device Embedded into Hearing Instruments.
Justin R Burwinkel, Buye Xu, Jeff Crukley
https://pubmed.ncbi.nlm.nih.gov/31914373