Intelligent Intensive Care Unit
| Status: | Recruiting |
|---|---|
| Conditions: | Neurology, Psychiatric |
| Therapuetic Areas: | Neurology, Psychiatry / Psychology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 2/20/2019 |
| Start Date: | February 2016 |
| End Date: | July 2020 |
| Contact: | Sandhya A Chheda, JD |
| Email: | sandhya.chheda@medicine.ufl.edu |
| Phone: | 352-273-8820 |
Motion Analysis of Delirium in Intensive Care Units (ICUs)
Delirium, as a common complication of hospitalization, poses significant health problems in
hospitalized patients. Though about a third of delirium cases can benefit from intervention,
detecting and predicting delirium is still very limited in practice. A common
characterization of delirium is changes in activity level, causing patients to become
hyperactive or hypoactive which is manifested in facial expressions and total body movements.
This pilot study is designed to test the feasibility of a delirium detection system using
movement data obtained from 3-axis wearable accelerometers and commercially available camera
and facial recognition video system, in conjunction with electronics medical record (EMR)
data, to analyze the relation of whole-body movement as well as facial expressions with
delirium.
hospitalized patients. Though about a third of delirium cases can benefit from intervention,
detecting and predicting delirium is still very limited in practice. A common
characterization of delirium is changes in activity level, causing patients to become
hyperactive or hypoactive which is manifested in facial expressions and total body movements.
This pilot study is designed to test the feasibility of a delirium detection system using
movement data obtained from 3-axis wearable accelerometers and commercially available camera
and facial recognition video system, in conjunction with electronics medical record (EMR)
data, to analyze the relation of whole-body movement as well as facial expressions with
delirium.
The aim of the study is to assess the potential of motion and facial expression data for
detecting delirium in ICU patients by comparing motion and facial expression patterns in
delirium and control group. In this study, the investigators will use ActiGraph accelerometer
to record each subject's movement patterns. Also, a processed video using a commercially
available camera interfaces with specialized program to identify patient facial expressions
and movement patterns. A total of 40 participants will be enrolled with delirium and 20
patients without delirium will be used as control group. Motion profiles will be compared in
the motorically defined subgroups (hyperactive, hypoactive, normal) based on accelerometer
and facial recognition data. Then differences in facial expression, number of changes in
postures and percentage of time spent moving between motorically defined subgroups and in
delirium and control group. EMR data will also be used to assess the feasibility of detecting
delirium by additionally including information on related risk factors.
detecting delirium in ICU patients by comparing motion and facial expression patterns in
delirium and control group. In this study, the investigators will use ActiGraph accelerometer
to record each subject's movement patterns. Also, a processed video using a commercially
available camera interfaces with specialized program to identify patient facial expressions
and movement patterns. A total of 40 participants will be enrolled with delirium and 20
patients without delirium will be used as control group. Motion profiles will be compared in
the motorically defined subgroups (hyperactive, hypoactive, normal) based on accelerometer
and facial recognition data. Then differences in facial expression, number of changes in
postures and percentage of time spent moving between motorically defined subgroups and in
delirium and control group. EMR data will also be used to assess the feasibility of detecting
delirium by additionally including information on related risk factors.
Inclusion Criteria (ICU Patients):
- Intensive care unit patient
- 18 years of age or older
Exclusion Criteria (ICU Patients):
- Anticipated intensive care unit stay less than one day
- Less than 18 years of age
- Inability to wear a motion sensor watch (ActiGraph)
Inclusion Criteria (Healthy Controls):
- 18 years of age or older.
- sleeps in home environment
Exclusion Criteria (Healthy Controls):
- does not sleep in home environment
- Less than 18 years of age
- Inability to wear a motion sensor watch (ActiGraph)
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