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.
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
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.
- Conditions
- Fall in Nursing Home
- 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 group | Role | What they receive |
|---|---|---|
| A robot will patrol the rooms of nursing home residents to detect falls | Nursing 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.
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)
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
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.
- First posted5 Mar 2026
- Study start31 Jul 2025
- Primary completion31 Jul 2025
- Study completion31 Aug 2025
Why it stopped: The robot had technical problems which couldn't be fixed and the study could not begin.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Fall in Nursing HomeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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
Sponsor and investigators
- Lead sponsor
- Vigorous Mind, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Principal investigator, Yuval Malinsky, Vigorous Mind, Inc.
- Investigators
- Lynn McNicoll, BS, MDCM, Principal investigator, Brown University Health
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
Study at a glance
From the registry record, last updated by the sponsor on 5 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents With Alzheimer Disease/ADRD
- ClinicalTrials.gov ID
- NCT07451223
- Acronym / sponsor ID
- VMRFalls1
- Conditions
- Fall in Nursing Home
- Keywords
- Using a robot for fall detection in nursing homes
- Study type
- Observational
- Recruitment status
- TerminatedReason stopped: The robot had technical problems which couldn't be fixed and the study could not begin.
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Vigorous Mind, Inc.Industry (pharma, biotech, devices)
- Study start
- 31 Jul 2025 (actual)
- Primary completion
- 31 Jul 2025 (actual)
- Study completion
- 31 Aug 2025 (actual)
- First posted
- 5 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Observational model
- Other
- Time perspective
- Other
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| A robot will patrol the rooms of nursing home residents to detect falls | – | –Nursing home residents in a long-term-care memory unit |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- Study population
- Nursing home residents living with dementia
Key inclusion criteria (1 listed)
- Nursing home resident living with dementia
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Feasibility of a robot to detect falls in a nursing home
Time frame: during three months
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.
2 secondary outcomes
Nursing staff satisfaction with the robot detection of falls
Time frame: during three months
The robot was able to detect falls in the nursing home before the nursing staff
Time frame: During three months
Results and safety
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.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Mar 2026.
- Background: PubMed 39949787Malinsky 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.
- Individual participant dataSharing planned: No. No IPD is collected
Related trials for Fall in Nursing Home
Enrolling by invitation
Preventing Loss of Independence Through Exercise in Community Living Centers
Dementia, Cognitive Dysfunction
Not yet recruitingObservational study
THE EFFECTS OF MULTIPLE DRUG USE ON BALANCE, FALL RISK, AND COGNITIVE STATUS
Polypharmacy (Individuals Treated With 4 or More Medications)
Recruiting
Multicomponent Exercise Program on Physical Function, Cognition and Falls Risk Among Older Adults Living in Nursing Homes (MOVE4CARE)
Frailty, Dementia
Enrolling by invitation
Prevention of Injury in Skilled Nursing Facilities Through Optimizing Medications
Accidental Falls/Prevention and Control, Osteoporosis
Recruiting
Evaluation of a Foot Health Program Incorporating Minimalist Footwear on Fall Risk Factors in Older People Living in Senior Nursing Homes: a Pilot Study.
Fall Prevention
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Lynn McNicoll, BS, MDCM principal investigator
Brown University Health
1 site in 1 country: see every site and its status.
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.