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Comparison of Automated Activity Recognition to Provider Observations of Patient Mobility in the ICU*

Rawat, Nishi MD1,2; Rao, Vishal BE3; Peven, Michael BS, MSE3; Shrock, Christine AB4; Reiter, Austin PhD3; Saria, Suchi PhD2,3,5; Ali, Haider PhD3

doi: 10.1097/CCM.0000000000003852
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Objectives: To compare noninvasive mobility sensor patient motion signature to direct observations by physicians and nurses.

Design: Prospective, observational study.

Setting: Academic hospital surgical ICU.

Patients and Measurements: A total of 2,426 1-minute clips from six ICU patients (development dataset) and 4,824 1-minute clips from five patients (test dataset).

Interventions: None.

Main Results: Noninvasive mobility sensor achieved a minute-level accuracy of 94.2% (2,138/2,272) and an hour-level accuracy of 81.4% (70/86).

Conclusions: The automated noninvasive mobility sensor system represents a significant departure from current manual measurement and reporting used in clinical care, lowering the burden of measurement and documentation on caregivers.

1Department of Anesthesia and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.

2Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, MD.

3Department of Computer Science, Johns Hopkins University, Baltimore, MD.

4Johns Hopkins University School of Medicine, Baltimore, MD.

5Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.

*See also p. 1274.

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).

Drs. Rawat’s and Rao’s institutions received funding from the National Heart, Lung, and Blood Institute. Drs. Rawat, Shrock, Reiter, Saria, and Ali received support for article research from the National Institutes of Health (NIH). Dr. Reiter’s institution received funding from the NIH and the Gordon and Betty Moore Foundation. Dr. Saria received funding from Bayesian Health and PatientPing and received support for article research from the Gordon and Betty Moore Foundation. Dr. Ali’s institution received funding from the NIH. Dr. Peven disclosed that he does not have any potential conflicts of interest.

For information regarding this article, E-mail: nrawat1@jhmi.edu

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