Clinical trial NCT03685240: Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video
Official title: Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video: A Randomized Control Trial
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Alzheimer's Disease and Related Dementia
Fall Injury
Lead sponsor: SafelyYou
Sponsor class: INDUSTRY
Collaborators: name: National Institute on Aging (NIA)
First posted: 2018-09-26
Enrollment: 460
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SUPPORTIVE_CARE

Brief 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.

Detailed 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%.

Eligibility criteria: 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

Primary outcomes:
measure: Enrollment rate
timeFrame: Data on enrollment will be recorded during recruitment in year 1 and assessed at the end of year 1
measure: Fall rate due to sit to stand transition detection
timeFrame: Data will be collected during year 1 and assessed at the end of year 1.
measure: Fall rate due to gait change detection
timeFrame: Data will be collected through year 1 and assessed at the end of year 1.
Secondary outcomes:
Other outcomes:
Locations:
city: San Francisco
country: United States
facility: SafelyYou
state: California
Study officials:
References: