Study Results
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Basic Information
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TERMINATED
5 participants
OBSERVATIONAL
2012-12-31
2014-03-31
Brief Summary
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The data collected from the bed sensor will be processed offline and separately from the PHI to do proof of concept evaluation for the use of machine learning technology to predict bed exits 1 to 5 minutes ahead of time.
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Detailed Description
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The investigators believe that patients' heart rate or breathing changes before they leave bed. They may also start moving within the bed. This is a brief study with nursing home patient participants. Our primary outcome of interest is bed-exits, and up to 10 participants at a time will be monitored for an average of 6 weeks (less than their anticipated stay) until which time that 250 bed exits have been recorded. Nearly all participants will have physical and/or mental impairments and will be at high risk for falling.
The investigators will use an investigational device to watch over the patient using a pad under the mattress. This monitor is called the "Early-Sense 5". The system works like a microphone for very low sounds. It changes heart, lungs, and movement vibrations into tiny electrical signals. A wire carries these signals to a control box.
The information collected in the box will be stored and checked later. We will use five different math descriptions for recognizing patterns. One or more of these may be useful to give a 1 - 5 minute early warning that the patient is about to exit the bed.
The plan is to determine whether patterns of differences in three areas (heart rate, breathing rate, and body movement) can be recognized and depended on to warn us about bed-exits or attempted bed-exits.
There are four study targets. The first is to develop five possible mathematical descriptions. The second is to use the rest of the information to test which of the descriptions have meaningful ability to predict that a patient is about to get out of bed. The third is to show that warning times are one to five minutes. The fourth is to test the best mathematical descriptions for false alarms and true fall prevention.
How doable Phase I is will depend on how well we can predict that a patient is about to get out of bed. If we can identify a pattern easily, then Phase II research will be put forward.
This study is supported by the National Institute on Aging (SBIR-I).
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Bed Exit
Participants will be up to 60 ambulatory Budd Terrace residents.
Inclusion/Exclusion:
Inclusion:
1. Ambulatory patient able to leave the bed.
2. Willingness to consent and participate in a 30-night study
Exclusion:
1. Lack of capacity to consent, without an identifiable surrogate.
2. Terminal Prognosis
3. Unstable health, as determined by the principal investigator, medical doctor, or registered nurse.
Pressure sensitive pad
The proposed research uses an investigational device from EarlySense consisting of a pressure sensitive piezoelectric pad 350 mm x 226 mm x 12 mm or a little less than 9 by 13 inches and under a half inch thick connected to a cord resembling a phone cord to a controller 10.3 by 10.5 by 5.5 inches which in turn plugs into a standard electrical outlet. The power cord is modu¬lar, so it is possible to select a cord that is long enough without having excessive extra length. The con¬nec¬tion between the pad and the monitor has a quick release like a modular telephone.
This system is designed to very unobtrusively collect heartbeat patterns, respiratory patterns, motion in bed, and bed-exit data with no risk or inconvenience to the patient.
Interventions
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Pressure sensitive pad
The proposed research uses an investigational device from EarlySense consisting of a pressure sensitive piezoelectric pad 350 mm x 226 mm x 12 mm or a little less than 9 by 13 inches and under a half inch thick connected to a cord resembling a phone cord to a controller 10.3 by 10.5 by 5.5 inches which in turn plugs into a standard electrical outlet. The power cord is modu¬lar, so it is possible to select a cord that is long enough without having excessive extra length. The con¬nec¬tion between the pad and the monitor has a quick release like a modular telephone.
This system is designed to very unobtrusively collect heartbeat patterns, respiratory patterns, motion in bed, and bed-exit data with no risk or inconvenience to the patient.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
2. Willingness to consent and participate in a 30-night study
Exclusion:
1. Lack of capacity to consent, without an identifiable surrogate.
2. Terminal Prognosis
3. Unstable health, as determined by the principal investigator, medical doctor, or registered nurse.
18 Years
ALL
Yes
Sponsors
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National Institute on Aging (NIA)
NIH
Atlanta VA Medical Center
FED
Responsible Party
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Theodore Johnson II, M.D., M.P.H.
Co-Investigator
Principal Investigators
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Thomas Whalen
Role: PRINCIPAL_INVESTIGATOR
CDIC, Inc
Locations
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Budd Terrace
Atlanta, Georgia, United States
Countries
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References
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Vandenberg AE, van Beijnum BJ, Overdevest VGP, Capezuti E, Johnson TM 2nd. US and Dutch nurse experiences with fall prevention technology within nursing home environment and workflow: A qualitative study. Geriatr Nurs. 2017 Jul-Aug;38(4):276-282. doi: 10.1016/j.gerinurse.2016.11.005. Epub 2016 Dec 10.
Johnson TM, Capezuti E, Whalen T, Vandenberg AE, Taylor T, Cohen M. Predicting resident bed exits in long-term care. Journal of the American Geriatrics Society. 2016; 64(S1): S183.
Other Identifiers
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0664699330000
Identifier Type: REGISTRY
Identifier Source: secondary_id
1R21EB015943-01
Identifier Type: -
Identifier Source: org_study_id
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