An observational study in Critical Illness, Pain and Delirium, sponsored by University of Florida. Recruiting at 1 site in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-07-06.
Sponsored by University of Florida · Observational
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.
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.
10,054 studies on the registry are indexed under Pain; 1,649 are open to participants now.
This study's planned enrollment of 400 is above the median of 100 across 1,252 observational studies indexed under Pain.
Browse Pain studies →University of Florida is the lead sponsor of 1,287 studies on the registry; 199 are open to participants now.
Of its 180 completed or terminated interventional studies of FDA-regulated products, 143 (79%) have results posted.
Compared within Pain, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Critical Illness. The counts are of registry records indexed under each term, refreshed daily. How we classify →
Critically ill adults, aged 18 and over, admitted to UF Health Shands Gainesville ICU wards
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
adult patients aged 18 or older admitted to University of Florida Health Shands Gainesville ICU wards
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)
continuous video monitoring
continuous accelerometer monitoring of patient movements
continuous environmental noise monitoring
continuous environmental light monitoring
continuous environmental air quality monitoring
continuous EKG monitoring
continuous vitals monitoring (heart rate, oxygen saturation)
blood and urine samples collected once on Day 1 and once on Day 2
done daily on delirious patients to subtype delirium
Algorithmic Activity Labeling
The algorithm's output will report on which activity the patient is performing in the corresponding image data.
Time frame: 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.
Time frame: 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).
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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'.
Time frame: Changes from baseline up to a maximum of 7 days
Mortality
Status of alive or deceased
Time frame: From baseline (study enrollment) up to a maximum of 7 days
Plan to share: No
Started May 2021 (actual). Primary completion is estimated for Dec 2026, 67 months after the start.
Of 504 non-industry observational studies under Pain, half reached primary completion within 18 months of starting (the median), counting those still running; a quarter within 8 and three quarters within 42. This study's planned 67 months is above that.
Counting every sponsor, half of 519 studies reached it within 18 months; a quarter within 8 and three quarters within 41.
How these figures are worked out, and which studies are left out of them.
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