Critical IllnessNCT05127265
Pervasive Sensing and AI in Intelligent ICU
An observational study of Critical Illness and Pain, run by University of Florida at 1 site in United States.
Trial summary
Important information related to the visual assessment of patients, such as facial expressions, head and extremity movements, posture, and mobility are captured sporadically by overburdened nurses, or are not captured at all. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Critical Illness, Pain, Delirium
- What is being tested?
- Video Monitoring, Accelerometer Monitoring and Noise Level Monitoring
- How many people
- 400 participants
- Where
- 1 siteUnited States
- Study dates
- 24 May 2021to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Critically ill adults, aged 18 and over, admitted to UF Health Shands Gainesville ICU wards
Who may be able to join
- aged 18 or older
- admitted to UF Health Shands Gainesville ICU ward
- expected to remain in ICU ward for at least 24 hours at time of screening
Who cannot join
- under the age of 18
- on any contact/isolation precautions
- expected to transfer or discharge from the ICU in 24 hours or less
- unable to provide self-consent or has no available proxy/LAR
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of Florida Health Shands Hospital
Gainesville, Florida, 32610
Andrea Davidson, BS, 352-294-8723, adavidson@ufl.edu
Azra Bihorac, MD, MS
About this study
Official title: Pervasive Sensing and Artificial Intelligence in Intelligent ICU Subtitles: -Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making -ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
Important information related to the visual assessment of patients, such as facial expressions, head and extremity movements, posture, and mobility are captured sporadically by overburdened nurses, or are not captured at all. Consequently, these important visual cues, although associated with critical indices such as physical functioning, pain, delirious state, and impending clinical deterioration, often cannot be incorporated into clinical status. The overall objectives of this project are to sense, quantify, and communicate patients' clinical conditions in an autonomous and precise manner, and develop a pervasive intelligent sensing system that combines deep learning algorithms with continuous data from inertial, color, and depth image sensors for autonomous visual assessment of critically ill patients. The central hypothesis is that deep learning models will be superior to existing acuity clinical scores by predicting acuity in a dynamic, precise, and interpretable manner, using autonomous assessment of pain, emotional distress, and physical function, together with clinical and physiologic data.
Read the detailed description
The under-assessment of pain is one of the primary barriers to the adequate treatment of pain in critically ill patients, and is associated with many negative outcomes such as chronic pain after discharge, prolonged mechanical ventilation, longer ICU stay, and increased mortality risk. Many ICU patients cannot self-report their pain intensity due to their clinical condition, ventilation devices, and altered consciousness. The monitoring of patients' pain status is yet another task for over-worked nurses, and due to pain's subjective nature, those assessments may vary among care staff. These challenges point to a critical need for developing objective and autonomous pain recognition systems. Delirium is another common complication of patient hospitalization, which is characterized by changes in cognition, activity level, consciousness, and alertness and has rates of up to 80% in surgical patients. The risk factors that have been associated with delirium include age, preexisting cognitive dysfunction, vision and hearing impairment, severe illness, dehydration, electrolyte abnormalities, overmedication, alcohol abuse, and disruptions in sleep patterns. Estimates show that about one third of delirium cases can benefit from drug and non-drug prevention and intervention. However, detecting and predicting pain and delirium is still very limited in practice.
The aim of this study is to evaluate the ability of the investigators' proposed model to leverage accelerometer, environmental, circadian rhythm biomarkers, and video data in autonomously quantifying pain, characterizing functional activities, and delirium status. The Autonomous Delirium Monitoring and Adaptive Prevention (ADAPT) system will use novel pervasive sensing and deep learning techniques to autonomously quantify patients' mobility and circadian dyssynchrony in terms of nightly disruptions, light intensity, and sound pressure level. This will allow for the integration of these risk factors into a dynamic model for predicting delirium trajectories. Commercially available cameras will be used to monitor patients' facial expressions and contextualize patients' actions by providing imaging data to provide additional patient movement information. Commercially available environmental sensors will be used to provide data on illumination, decibel level, and air quality. Patient blood samples will help determine their circadian rhythm and compare and validate the pervasive sensing system's capabilities of autonomously monitoring circadian dyssynchrony. Electronic health record data will also be collected.
- Conditions
- Critical Illness, Pain, Delirium, Confusion
- Being tested
- Video Monitoring, Accelerometer Monitoring, Noise Level Monitoring, Light Level Monitoring, Air Quality Monitoring
- Keywords
- Critical Illness, Pain, Delirium, Confusion, Patient Acuity, ICU, Acute Illness
- Medical subject headings
- Critical Illness, Pain, Delirium, Confusion
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Video MonitoringOther
continuous video monitoring
Accelerometer MonitoringOther
continuous accelerometer monitoring of patient movements
Noise Level MonitoringOther
continuous environmental noise monitoring
Light Level MonitoringOther
continuous environmental light monitoring
Air Quality MonitoringOther
continuous environmental air quality monitoring
EKG MonitoringOther
continuous EKG monitoring
Vitals MonitoringOther
continuous vitals monitoring (heart rate, oxygen saturation)
Biosample CollectionOther
blood and urine samples collected once on Day 1 and once on Day 2
Delirium Motor Subtyping Scale 4 (DMSS-4)Other
done daily on delirious patients to subtype delirium
| Study group | Role | What they receive |
|---|---|---|
| adult ICU patients | adult patients aged 18 or older admitted to University of Florida Health Shands Gainesville ICU wards Receives: Other: Video Monitoring; Other: Accelerometer Monitoring; Other: Noise Level Monitoring; Other: Light Level Monitoring; Other: Air Quality Monitoring; Other: EKG Monitoring; Other: Vitals Monitoring; Other: Biosample Collection; Other: Delirium Motor Subtyping Scale 4 (DMSS-4) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Algorithmic Activity Labeling
The algorithm's output will report on which activity the patient is performing in the corresponding image data.
Measured: Image frames collected continuously for up to 7 days maximum.
Algorithmic Pain Labeling
The algorithm's output will report on whether the patient is experiencing pain in the corresponding image data.
Measured: Image frames collected continuously for up to 7 days maximum.
Decibel Levels
Determine relative decibel (noise loudness) levels in study patient's ICU room to alert for abnormalities in decibel level (noisiness of environment).
Measured: Noise sensor data collected continuously for up to 7 days maximum.
Lux Levels
Determine relative lux (light illumination) levels in study patient's ICU room to alert for abnormalities in illumination level.
Measured: Light sensor data collected continuously for up to 7 days maximum.
Air Quality
Determines relative air quality pollution levels in study patient's ICU room to alert for abnormalities in room air quality.
Measured: Air quality sensor data collected continuously for up to 7 days maximum.
Circadian Dyssynchrony Index
Blood and urine samples will be collected and processed to determine the presence of dyssynchrony in a subject's internal circadian clock.
Measured: Change in internal circadian profile from Day 1 to Day 2.
Algorithmic Delirium Recognition Profile
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.
Measured: Data collected for up to 7 days maximum.
Delirium Motor Subtyping Scale 4 (DMSS-4)
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'.
Measured: Changes from baseline up to a maximum of 7 days
Secondary outcomes (1)
Mortality
Status of alive or deceased
Measured: From baseline (study enrollment) up to a maximum of 7 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Nov 2021
- Study start24 May 2021
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
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 Critical IllnessStudies of the same condition, open and under way.
- More trials run by University of FloridaThe sponsor's other registered studies.
- 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Florida
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of Neurological Disorders and Stroke (NINDS), National Institute for Biomedical Imaging and Bioengineering (NIBIB)
- Responsible party
- Sponsor
- Investigators
- Azra Bihorac, MD, MS, Principal investigator, University of Florida
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05127265
- Sponsor's protocol ID
- IRB-202101013
- Other IDs
- R01NS120924, R01EB029699
- Status last verified
- Jul 2026
- First submitted
- 4 Nov 2021
- Last updated
- 6 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M, Bihorac E, Ozrazgat-Baslanti T, Tighe PJ, Bihorac A, Rashidi P. Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning. Sci Rep. 2019 May 29;9(1):8020. doi: 10.1038/s41598-019-44004-w.
PubMed 31142754
Study at a glance
From the registry record, last updated by the sponsor on 6 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pervasive Sensing and Artificial Intelligence in Intelligent ICU Subtitles: -Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making -ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
- ClinicalTrials.gov ID
- NCT05127265
- Acronym / sponsor ID
- IRB-202101013
- Conditions
- Critical Illness, Pain, Delirium, Confusion
- Keywords
- Critical Illness, Pain, Delirium, Confusion, Patient Acuity, ICU, Acute Illness
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- University of FloridaUniversity, hospital or other
- Collaborators
- National Institute of Neurological Disorders and Stroke (NINDS), National Institute for Biomedical Imaging and Bioengineering (NIBIB)
- Study start
- 24 May 2021 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 19 Nov 2021 (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.
Important information related to the visual assessment of patients, such as facial expressions, head and extremity movements, posture, and mobility are captured sporadically by overburdened nurses, or are not captured at all. Consequently, these important visual cues, although associated with critical indices such as physical functioning, pain, delirious state, and impending clinical deterioration, often cannot be incorporated into clinical status. The overall objectives of this project are to sense, quantify, and communicate patients' clinical conditions in an autonomous and precise manner, and develop a pervasive intelligent sensing system that combines deep learning algorithms with continuous data from inertial, color, and depth image sensors for autonomous visual assessment of critically ill patients. The central hypothesis is that deep learning models will be superior to existing acuity clinical scores by predicting acuity in a dynamic, precise, and interpretable manner, using autonomous assessment of pain, emotional distress, and physical function, together with clinical and physiologic data.
Detailed description
The under-assessment of pain is one of the primary barriers to the adequate treatment of pain in critically ill patients, and is associated with many negative outcomes such as chronic pain after discharge, prolonged mechanical ventilation, longer ICU stay, and increased mortality risk. Many ICU patients cannot self-report their pain intensity due to their clinical condition, ventilation devices, and altered consciousness. The monitoring of patients' pain status is yet another task for over-worked nurses, and due to pain's subjective nature, those assessments may vary among care staff. These challenges point to a critical need for developing objective and autonomous pain recognition systems. Delirium is another common complication of patient hospitalization, which is characterized by changes in cognition, activity level, consciousness, and alertness and has rates of up to 80% in surgical patients. The risk factors that have been associated with delirium include age, preexisting cognitive dysfunction, vision and hearing impairment, severe illness, dehydration, electrolyte abnormalities, overmedication, alcohol abuse, and disruptions in sleep patterns. Estimates show that about one third of delirium cases can benefit from drug and non-drug prevention and intervention. However, detecting and predicting pain and delirium is still very limited in practice.
The aim of this study is to evaluate the ability of the investigators' proposed model to leverage accelerometer, environmental, circadian rhythm biomarkers, and video data in autonomously quantifying pain, characterizing functional activities, and delirium status. The Autonomous Delirium Monitoring and Adaptive Prevention (ADAPT) system will use novel pervasive sensing and deep learning techniques to autonomously quantify patients' mobility and circadian dyssynchrony in terms of nightly disruptions, light intensity, and sound pressure level. This will allow for the integration of these risk factors into a dynamic model for predicting delirium trajectories. Commercially available cameras will be used to monitor patients' facial expressions and contextualize patients' actions by providing imaging data to provide additional patient movement information. Commercially available environmental sensors will be used to provide data on illumination, decibel level, and air quality. Patient blood samples will help determine their circadian rhythm and compare and validate the pervasive sensing system's capabilities of autonomously monitoring circadian dyssynchrony. Electronic health record data will also be collected.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| adult ICU patients | – | Other: Video Monitoring, Other: Accelerometer Monitoring, Other: Noise Level Monitoring, Other: Light Level Monitoring, Other: Air Quality Monitoring, Other: EKG Monitoring, Other: Vitals Monitoring, Other: Biosample Collection, Other: Delirium Motor Subtyping Scale 4 (DMSS-4)adult patients aged 18 or older admitted to University of Florida Health Shands Gainesville ICU wards |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Critically ill adults, aged 18 and over, admitted to UF Health Shands Gainesville ICU wards
Key inclusion criteria (3 listed)
- aged 18 or older
- admitted to UF Health Shands Gainesville ICU ward
- expected to remain in ICU ward for at least 24 hours at time of screening
Key exclusion criteria (4 listed)
- under the age of 18
- on any contact/isolation precautions
- expected to transfer or discharge from the ICU in 24 hours or less
- unable to provide self-consent or has no available proxy/LAR
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Video Monitoringcontinuous video monitoring | Other | adult ICU patients |
| Accelerometer Monitoringcontinuous accelerometer monitoring of patient movements | Other | adult ICU patients |
| Noise Level Monitoringcontinuous environmental noise monitoring | Other | adult ICU patients |
| Light Level Monitoringcontinuous environmental light monitoring | Other | adult ICU patients |
| Air Quality Monitoringcontinuous environmental air quality monitoring | Other | adult ICU patients |
| EKG Monitoringcontinuous EKG monitoring | Other | adult ICU patients |
| Vitals Monitoringcontinuous vitals monitoring (heart rate, oxygen saturation) | Other | adult ICU patients |
| Biosample Collectionblood and urine samples collected once on Day 1 and once on Day 2 | Other | adult ICU patients |
| Delirium Motor Subtyping Scale 4 (DMSS-4)done daily on delirious patients to subtype delirium | Other | adult ICU patients |
Primary outcome
Algorithmic Activity Labeling
Time frame: Image 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 Labeling
Time frame: Image 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 Levels
Time frame: Noise 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 Levels
Time frame: Light 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 Quality
Time frame: Air 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 Index
Time frame: Change 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 Profile
Time frame: Data 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)
Time frame: 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'.
1 secondary outcome
Mortality
Time frame: From baseline (study enrollment) up to a maximum of 7 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jul 2026.
- Background: PubMed 31142754Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M, Bihorac E, Ozrazgat-Baslanti T, Tighe PJ, Bihorac A, Rashidi P. Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning. Sci Rep. 2019 May 29;9(1):8020. doi: 10.1038/s41598-019-44004-w.
- Individual participant dataSharing planned: No
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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.
Azra Bihorac, MD, MS principal investigator
University of Florida
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 Critical Illness and Pain, run by University of Florida 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 3 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 6 Jul 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Andrea E Davidson, BS (352-294-8723, adavidson@ufl.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Algorithmic Activity Labeling (time frame: Image frames collected continuously for up to 7 days maximum.). The study lists 8 primary outcomes in all. 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 NCT05127265, 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 6 Jul 2026.