Quantifying Mobility in the ICU: Comparison of Electronic Health Record Documentation and Accelerometer-Based Sensors to Clinician-Annotated Video.

early mobility electronic health records fitness trackers informatics intensive care units

Journal

Critical care explorations
ISSN: 2639-8028
Titre abrégé: Crit Care Explor
Pays: United States
ID NLM: 101746347

Informations de publication

Date de publication:
Apr 2020
Historique:
entrez: 20 5 2020
pubmed: 20 5 2020
medline: 20 5 2020
Statut: epublish

Résumé

To compare the accuracy of electronic health record clinician documentation and accelerometer-based sensors with a gold standard dataset derived from clinician-annotated video to quantify early mobility activities in adult ICU patients. Prospective, observational study. Medical ICU at an academic hospital. Adult ICU patients (n = 30) were each continuously monitored over a median of 24.4 hours, yielding 711.5 hours of video, electronic health record, and sensor data. None. Electronic health record documentation estimated ambulation (intraclass correlation coefficient, 0.89; 95% CI, 0.78-0.95), sitting out-of-bed (intraclass correlation coefficient, 0.85; 95% CI, 0.72-0.93), and turning events (intraclass correlation coefficient, 0.87; 95% CI, 0.75-0.94) with excellent agreement but underestimated the number of standing, transferring, and pregait activities performed per patient. The accelerometer-based sensor had excellent agreement with video annotation for estimating duration of time spent supine (intraclass correlation coefficient, 0.99; CI, 0.97-0.99) and sitting/standing upright (intraclass correlation coefficient, 0.92; CI, 0.82-0.96) but overestimated ambulation time. Our results show that electronic health record documentation and sensor-based technologies accurately capture distinct but complimentary metrics for ICU mobility measurement. Innovations in artifact detection, standardization of clinically relevant mobility definitions, and electronic health record documentation enhancements may enable further use of these technologies to drive critical care research and technology leveraged data-driven ICU models of care.

Identifiants

pubmed: 32426733
doi: 10.1097/CCE.0000000000000091
pmc: PMC7188433
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e0091

Subventions

Organisme : NHLBI NIH HHS
ID : T32 HL007013
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000002
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001860
Pays : United States

Informations de copyright

Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.

Déclaration de conflit d'intérêts

The authors have disclosed that they do not have any conflicts of interest.

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Auteurs

Sarina Fazio (S)

Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, University of California, Davis, Sacramento, CA.
Betty Irene Moore School of Nursing, University of California, Davis, Sacramento, CA.
Betty Irene Moore School of Nursing, University of California, Davis, Sacramento, CA.

Amy Doroy (A)

Medical ICU, UC Davis Medical Center, UC Davis Health, Sacramento, CA.

Natalie Da Marto (N)

Medical ICU, UC Davis Medical Center, UC Davis Health, Sacramento, CA.

Sandra Taylor (S)

Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Sacramento, CA.

Nicholas Anderson (N)

Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Sacramento, CA.

Heather M Young (HM)

Betty Irene Moore School of Nursing, University of California, Davis, Sacramento, CA.

Jason Y Adams (JY)

Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, University of California, Davis, Sacramento, CA.

Classifications MeSH