To develop and validate a noninvasive mobility sensor to automatically and continuously detect and measure patient mobility in the ICU.
Prospective, observational study.
Surgical ICU at an academic hospital.
Three hundred sixty-two hours of sensor color and depth image data were recorded and curated into 109 segments, each containing 1,000 images, from eight patients.
None.
Three Microsoft Kinect sensors (Microsoft, Beijing, China) were deployed in one ICU room to collect continuous patient mobility data. We developed software that automatically analyzes the sensor data to measure mobility and assign the highest level within a time period. To characterize the highest mobility level, a validated 11-point mobility scale was collapsed into four categories: nothing in bed, in-bed activity, out-of-bed activity, and walking. Of the 109 sensor segments, the noninvasive mobility sensor was developed using 26 of these from three ICU patients and validated on 83 remaining segments from five different patients. Three physicians annotated each segment for the highest mobility level. The weighted Kappa (κ) statistic for agreement between automated noninvasive mobility sensor output versus manual physician annotation was 0.86 (95% CI, 0.72–1.00). Disagreement primarily occurred in the “nothing in bed” versus “in-bed activity” categories because “the sensor assessed movement continuously,” which was significantly more sensitive to motion than physician annotations using a discrete manual scale.
Noninvasive mobility sensor is a novel and feasible method for automating evaluation of ICU patient mobility.
1Department of Computer Science, Johns Hopkins University, Baltimore, MD.
2Department of Anesthesia and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
3Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, MD.
4Johns Hopkins University School of Medicine, Baltimore, MD.
5Outcomes after Critical Illness and Surgery Group, John Hopkins University School of Medicine, Baltimore, MD.
6Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
7John Hopkins Hospital, Baltimore, MD.
8Department of Physical Medicine and Rehabilitation, John Hopkins University School of Medicine, Baltimore, MD.
9Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.
This work was performed at the School of Medicine and Department of Computer Science, Johns Hopkins University, Baltimore, MD.
Drs. Ma and Rawat are co-first authors and contributed equally to this work.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).
Supported, in part, by the Gordon and Betty Moore Foundation. This work was also supported by a Patient-Oriented Mentored Career Development Award (K23) from the National, Heart, Blood, and Lung Institute.
Dr. Rawat’s institution received funding from the K23 from the National, Heart, Blood and Lung Institute. Dr. Reiter’s institution received funding from the Gordon and Betty Moore Foundation. Dr. Shrock’s institution received funding from the Moore Foundation. Dr. Needham’s institution received funding from National Institutes of Health, Agency for Healthcare Research and Quality, National Health and Medical Research Council (Australia), and Gordon and Betty Moore Foundation (all peer-reviewed grants). He disclosed off-label use of existing sensor technology for monitoring mobility in the ICU. Dr. Saria received support for article research from the Gordon and Betty Moore Foundation. The remaining authors have disclosed that they do not have any potential conflicts of interest.
For information regarding this article, E-mail: suchi.saria@gmail.com
