Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video

Fall Injury Clinical Trial

Official title:

Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video: A Randomized Control Trial

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT03685240
• Other study ID #: SY-NIA-GX001
• Status: Not yet recruiting
• Phase: N/A
• Start date: October 31, 2023
• Est. completion date: December 31, 2023

Study information

• Verified date: May 2022
• Source: SafelyYou
• Contact: Glen Xiong, MD
• Phone: 415-579-3630
• Email: gxiong[@]safely-you.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of the research is to study a new safety monitoring system developed by SafelyYou to help care for a loved one with dementia. The goal is to provide better support for unwitnessed falls.

The SafelyYou system is based on AI-enabled cameras which detect fall related events and upload video only when these events are detected. The addition of a Human in the Loop (HIL) will alert the facility staff when an event is detected by the system.

Description:

This process enables staff to know about falls without requiring residents wear a device and to see how falls occur for residents that cannot advocate for themselves while still protecting resident privacy by only uploading video when safety critical events are detected. Seeing how the resident went to the ground (1) prevents the need for emergency room visits when residents intentionally moved to the ground without risk and (2) allows the care team to determine what caused an event like a fall and what changes can be made to reduce risk.

PRELIMINARY EVIDENCE. The proposed study follows a series of pilots. In pilot 1, we showed the technical feasibility of detecting falls from video with 200 falls acted out by healthy subjects. In pilot 2, in a 40-resident facility, we demonstrated the acceptance of privacy-safety tradeoffs and showed a reduction of total facility falls by 80% by providing the system for 10 repeat fallers. In pilot 3, we addressed repeatability of fall reduction in a cohort of 87 residents with ADRD in 11 facilities of three partner networks. In pilot 4 (NIH SBIR Phase I), we demonstrated that falls can be detected reliably in real-time within the partner facilities. We detected 93% of the falls; reduced the time on the ground by 42%; showed that when video was available, the likelihood of EMS visit was reduced by 50%; and reduced total facility falls by 38%.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 460
• Est. completion date: December 31, 2023
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

The study population includes residents of care facilities that are a high fall risk with a particular focus on care facilities with high populations of individuals with Alzheimer's disease and related dementias. There are no gender, race, ethnicity, language or literacy requirements for participation and all residents are eligible.

Inclusion criteria - Living at a participating skilled nursing facility or equivalent, CCRC,

Exclusion criteria

• 18 years old or younger

Related Conditions & MeSH terms

• Alzheimer Disease
• Alzheimer's Disease and Related Dementia
• Fall Injury

Intervention

Behavioral:
• SafelyYou Fall Prevention System
Technology + Quality Assurance Services Provided by SafelyYou

Locations

Country	Name	City	State
United States	SafelyYou	San Francisco	California

Sponsors (2)

Lead Sponsor	Collaborator
SafelyYou	National Institute on Aging (NIA)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Enrollment rate	Detection of falls will be performed with blurred video, hence with increased privacy. Expected outcome will be the change in enrollment rate compared to previous feasibility studies (i.e. impacted rate of positive responses to recruitment efforts within facilities).	Data on enrollment will be recorded during recruitment in year 1 and assessed at the end of year 1
Primary	Fall rate due to sit to stand transition detection	Care staff will be alerted as soon as the transition is detected (intervention of the front line staff). This may produce an immediate reduction in falls due to this type of transition.	Data will be collected during year 1 and assessed at the end of year 1.
Primary	Fall rate due to gait change detection	As the system learns to may produce an immediate impact on the fall rate by intervention of the front-line staff when the change is detected.	Data will be collected through year 1 and assessed at the end of year 1.