Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents With Alzheimer Disease/ADRD - Feasibility Study

NCT07451223 | Terminated

• 2 other identifiers: VMRFalls11R43AG082599-01
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

This study is a feasibility study to prove that an autonomously navigating robot can patrol nursing home rooms day and night and detect if a resident has fallen, turn on ambient light at night and a video camera and allow nursing staff to view and assess the situation through the robot video camera and communicate with the resident through the robot screen.

Conditions

Fall in Nursing Home

Keywords

Using a robot for fall detection in nursing homes

Outcome Measures

Primary Outcomes (1)

• Feasibility of a robot to detect falls in a nursing home

  The Primary Outcome Measure is that 95% of the time the robot correctly detected that a resident fell, was able to turn on ambient light, alert nursing staff, turn on the video camera and facilitate communication between the resident and nursing staff.

  during three months

Secondary Outcomes (2)

• Nursing staff satisfaction with the robot detection of falls

  during three months

• The robot was able to detect falls in the nursing home before the nursing staff

  During three months

Study Arms (1)

A robot will patrol the rooms of nursing home residents to detect falls

Nursing home residents in a long-term-care memory unit

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Nursing home residents living with dementia

You may qualify if:

• Nursing home resident living with dementia

You may not qualify if:

• Residents on isolation precautions (e.g. Clostridoides difficile, COVID 19, MRSA)
• Actively dying resident
• Resident and/or family decline
• Functional or structural quadriplegia with inability to mobilize with little to no risk of falling
• Residents become agitated when the robot engages with them

Sponsors & Collaborators

• Vigorous Mind, Inc.: lead

Study Sites (1)

Steere House Nursing & Rehabilitation Center

Providence, Rhode Island, 02903, United States

Location

Related Publications (1)

• Malinsky Y, McNicoll L, Gravenstein S. Robots in Nursing Homes: Helping Nurses Detect and Prevent Falls. Adv Geriatr Med Res. 2024;7(1):e250001. doi: 10.20900/agmr20250001. Epub 2025 Jan 3.

  PMID: 39949787 BACKGROUND

Study Officials

• Lynn McNicoll, BS, MDCM

  Brown University Health

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Target Duration: 1 Day
• Sponsor Type: INDUSTRY
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: CEO

Study Record Dates

• First Submitted: February 27, 2026
• First Posted: March 5, 2026
• Study Start: July 31, 2025
• Primary Completion: July 31, 2025
• Study Completion: August 31, 2025
• Last Updated: March 5, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)