Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents With Alzheimer Disease/ADRD - Feasibility Study
Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents With Alzheimer Disease/ADRD
Study Overview
Status
Status
Conditions
Conditions
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Rhode Island
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Providence, Rhode Island, United States, 02903
- Steere House Nursing & Rehabilitation Center
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Nursing home resident living with dementia
Exclusion Criteria:
- 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
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
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A robot will patrol the rooms of nursing home residents to detect falls
Nursing home residents in a long-term-care memory unit
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Feasibility of a robot to detect falls in a nursing home
Time Frame: during three months
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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.
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during three months
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Nursing staff satisfaction with the robot detection of falls
Time Frame: during three months
|
75% of nursing staff said that the fall detection system improved their ability to provide safe care
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during three months
|
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The robot was able to detect falls in the nursing home before the nursing staff
Time Frame: During three months
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That in more than 30% of falls the robot detected the falls before the nursing staff.
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During three months
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Lynn McNicoll, BS, MDCM, Brown University Health
Publications and helpful links
General Publications
- Malinsky Y,McNicoll L,Gravenstein S
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- VMRFalls1
- 1R43AG082599-01 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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