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Fall in Nursing HomeNCT07451223

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

An observational study of Fall in Nursing Home, run by Vigorous Mind, Inc. at 1 site in United States.

Terminated

Stopped early. The study ended before it was planned to and will not resume.

Official record
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Trial 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… Read more about this study

Who is it for?
Ages 65 years and older
Conditions the study is for
Fall in Nursing Home
How many people
40 participants
Where
1 siteUnited States
Study dates
31 Jul 2025to 31 Aug 2025
How long will I take part?
1 Day
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 65 years and older
  • All sexes
  • No healthy volunteers
  • Older adult

Study population: Nursing home residents living with dementia

Who may be able to join

  • Nursing home resident living with dementia

Who cannot join

  • 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

Trial locations and contacts

1 site in 1 country.

United States1 site
  • Steere House Nursing & Rehabilitation Center

    Providence, Rhode Island, 02903

Other trials in United States

About this study

Official title: Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents With Alzheimer Disease/ADRD

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.

Read the detailed description

Intervention Protocol Prior to sending the robot on patrol to detect whether a resident is attempting to get up from the chair or bed or if a resident fell, we will get residents used to the robot. We will do this through the morning musical wake up tour playing "Oh What a Beautiful Morning" for a month. We will explain to the residents that the robot will come to "visit" them on a regular basis to see how they are doing and demonstrate how the robot comes and observes them. Using an infra-red camera and artificial intelligence, the robot can find where a human is located in the room and in what position. If it visits a private room and the robot "sees" that a resident is not on the floor or is on her/his wheel chair or couch or in bed and is not attempting to get up or out of bed or stand up, it will just turn around and leave the room without interrupting the resident. If it detects that a human is on the floor, it will say "This is temi the robot, I am getting help". It will turn on ambient light, alert staff and turn on the robot video camera to facilitate communication between the staff and the resident so staff can assess the situation and take proper action. If it detects that there is more than one human in the room and no human is on the floor, it will leave the room. If there is another human in the room and one human is on the floor, it will alert staff. If the second person in the room is a staff member, s/he will be able to click a button on the robot screen to send it to continue the patrol and it will leave the room and continue the patrol. If the room is a semiprivate room and there are two residents living there, the robot will check on one resident and if s/he is not at risk it will turn around and check the status of the second resident. If both are resting, the robot will leave the room without interrupting the residents. If the robot detects that a resident is attempting to get up or out of bed it will say: "this is temi the robot, please sit, I am getting help". It will turn on ambient light and its video camera and alert staff. If it detects that a resident is on the floor, it will perform the procedure described above. If a staff member is already in a semi-private room where there was a fall, s/he will be able to send the robot to continue the patrol or task as described in the private room. All falls reported by the robot will have an image captured so that the fall can be verified by research staff. Falls reported by the staff and robot will be tabulated according to their concordance: 1) Robot correctly detected and reported but staff did not record a fall; 2) staff reported fall but robot did not; 3) robot correctly and staff reported a fall; and, 4) robot reports fall: no image in robot recording to validate that a fall occurred and staff does not report a fall.

Keywords
Using a robot for fall detection in nursing homes

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Study groups in NCT07451223
Study groupRoleWhat they receive
A robot will patrol the rooms of nursing home residents to detect fallsNursing home residents in a long-term-care memory unit

What is being measured

The primary outcome is the main question the study is designed to answer.

  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.

    Measured: during three months

Secondary outcomes (2)
  1. Nursing staff satisfaction with the robot detection of falls

    75% of nursing staff said that the fall detection system improved their ability to provide safe care

    Measured: during three months

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

    That in more than 30% of falls the robot detected the falls before the nursing staff.

    Measured: During three months

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted5 Mar 2026
  2. Study start31 Jul 2025
  3. Primary completion31 Jul 2025
  4. Study completion31 Aug 2025

Why it stopped: The robot had technical problems which couldn't be fixed and the study could not begin.

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Other
Time perspective
Other
Enrolment
40 participants (actual)
Target duration
1 Day
IDs, dates and publications
ClinicalTrials.gov ID
NCT07451223
Sponsor's protocol ID
VMRFalls1
Other IDs
1R43AG082599-01
Status last verified
Feb 2026
First submitted
27 Feb 2026
Last updated
5 Mar 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No: No IPD is collected

Publications

  • 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.

    PubMed 39949787

Questions about this trial

What is this clinical trial studying?

An observational study of Fall in Nursing Home, run by Vigorous Mind, Inc. at 1 site in United States.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 1 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: stopped early, as of the sponsor's last update on 5 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

The study was stopped early (reason given: The robot had technical problems which couldn't be fixed and the study could not begin.). Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Feasibility of a robot to detect falls in a nursing home (time frame: during three months). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07451223, which also lists 1 linked publication.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Mar 2026.

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