Observational Study of Pervasive Sensing and AI in the ICU
This study is looking at how continuous monitoring with video, accelerometers (devices that measure movement), and sensors for noise and light levels can help understand patients' conditions in the Intensive Care Unit (ICU). Researchers want to see if these tools can accurately identify things like pain and confusion (delirium) by analyzing images and environmental data. You might be able to join if you are 18 or older, admitted to the UF Health Shands Gainesville ICU, and expected to stay for at least 24 hours. The study aims to see if these monitoring methods can accurately label patient activity and pain, and measure noise levels continuously for up to 7 days. The current recruitment status is unclear.
- Study design
- This is an observational study involving 400 participants. It uses continuous monitoring with video, accelerometers, and environmental sensors.
- What's involved
- Not specified in the trial record.
- Compensation
- Not stated in the trial record.
- Follow-up
- The primary endpoints are measured continuously for up to 7 days maximum.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Pervasive Sensing and AI in Intelligent ICU
At a glance
Conditions
Where it's being run
1 sites across 1 statesStudy leadership
- Azra Bihorac, MD, MS · PRINCIPAL_INVESTIGATOR · University of Florida
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Algorithmic Activity LabelingImage frames collected continuously for up to 7 days maximum.
The algorithm's output will report on which activity the patient is performing in the corresponding image data.
- Algorithmic Pain LabelingImage frames collected continuously for up to 7 days maximum.
The algorithm's output will report on whether the patient is experiencing pain in the corresponding image data.
- Decibel LevelsNoise sensor data collected continuously for up to 7 days maximum.
Determine relative decibel (noise loudness) levels in study patient's ICU room to alert for abnormalities in decibel level (noisiness of environment).
- Lux LevelsLight sensor data collected continuously for up to 7 days maximum.
Determine relative lux (light illumination) levels in study patient's ICU room to alert for abnormalities in illumination level.
- Air QualityAir quality sensor data collected continuously for up to 7 days maximum.
Determines relative air quality pollution levels in study patient's ICU room to alert for abnormalities in room air quality.
- Circadian Dyssynchrony IndexChange in internal circadian profile from Day 1 to Day 2.
Blood and urine samples will be collected and processed to determine the presence of dyssynchrony in a subject's internal circadian clock.
- Algorithmic Delirium Recognition ProfileData collected for up to 7 days maximum.
The algorithm's output will report on whether patient is likely to be delirious or at-risk of delirium based on activity, facial expression, and circadian dyssynchrony index data collected from study devices and biosamples.
- Delirium Motor Subtyping Scale 4 (DMSS-4)Changes from baseline up to a maximum of 7 days
Determines which subtype of delirium a subject is experiencing. This subtyping scale has 13 symptom items (5 hyperactive and 8 hypoactive) derived from the 30-item Delirium Motor Checklist. To subtype a delirious subject, at least 2 symptoms are required to be present from either the hyperactive or hypoactive checklist to meet the subtyping criteria for 'hyperactive delirium' or 'hypoactive delirium'. Patients who meet both hyperactive and hypoactive criteria are determined as 'mixed subtype', while patients meeting neither hyperactive or hypoactive criteria are labeled as 'no subtype'.