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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Identifier: NCT03685240
Recruitment Status : Not yet recruiting
First Posted : September 26, 2018
Last Update Posted : May 9, 2022
National Institute on Aging (NIA)
Information provided by (Responsible Party):

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.

Condition or disease Intervention/treatment Phase
Alzheimer's Disease and Related Dementia Fall Injury Behavioral: SafelyYou Fall Prevention System Not Applicable

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%.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 460 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double (Participant, Care Provider)
Primary Purpose: Supportive Care
Official Title: Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video: A Randomized Control Trial
Estimated Study Start Date : October 31, 2023
Estimated Primary Completion Date : December 31, 2023
Estimated Study Completion Date : December 31, 2023

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Falls Memory

Arm Intervention/treatment
Experimental: Intervention
AI-enabled camera fall detection with Human-in-the-Loop (HIP) review
Behavioral: SafelyYou Fall Prevention System
Technology + Quality Assurance Services Provided by SafelyYou

No Intervention: Control
No camera detection

Primary Outcome Measures :
  1. Enrollment rate [ Time Frame: Data on enrollment will be recorded during recruitment in year 1 and assessed at the end of year 1 ]
    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).

  2. Fall rate due to sit to stand transition detection [ Time Frame: Data will be collected during year 1 and assessed at the end of year 1. ]
    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.

  3. Fall rate due to gait change detection [ Time Frame: Data will be collected through year 1 and assessed at the end of year 1. ]
    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.

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT03685240

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Contact: Glen Xiong, MD 415-579-3630 ext 115

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United States, California
San Francisco, California, United States, 94107
Contact: Jason Panganiban    415-579-3630   
Contact: Glen Xiong    4155793630 ext 115   
Sponsors and Collaborators
National Institute on Aging (NIA)
  Study Documents (Full-Text)

Documents provided by SafelyYou:
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Responsible Party: SafelyYou Identifier: NCT03685240    
Other Study ID Numbers: SY-NIA-GX001
First Posted: September 26, 2018    Key Record Dates
Last Update Posted: May 9, 2022
Last Verified: May 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Alzheimer Disease
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Neurodegenerative Diseases
Neurocognitive Disorders
Mental Disorders