Using the fitbit for early detection of infection and reduction of healthcare utilization after discharge in pediatric surgical patients
Active Project Grant Awarded Last action
- Award ID
- R01NR020918
- Recipient
-
Loyola University Of Chicago UEI RFRPFMNR8LA5
- Awarding agency
-
National Institute of Nursing Research
National Institutes of Health, Department of Health and Human Services
- Obligated
- $6,290,449
Award facts
- Assistance listing
- 93.361 Nursing Research
- Completion date 65% complete
- Originally
- Place of performance
- Maywood, IL 60153, USA
Award details come from USAspending.gov.
Description
- Abstract
- Pediatric appendectomy, the most prevalent inpatient procedure in children, is associated with significant burden to the patient, their parents, healthcare systems and third party payors. After discharge, monitoring by parents consists only of such "proxy" subjective assessments, which have been reported as inaccurate, and resulted in both increased complications (e.g., readmissions), and wasted healthcare resources (e.g., potentially avoidable emergency department (ED) visits after surgery). Advances in consumer wearable devices ("cwds") that passively and non-invasively monitor physical activity (PA), heart rate (HR), and sleep are ushering in a new ERA of symptoms science, particularly after surgery. Their expanding capability to generate continuous, valid, objective, and actionable measures in near-real time in children, provide opportunities to detect altered post-operative recovery patterns early, and therefore improve the precision and timeliness of any necessary clinical interventions. The proposed study will use a CWD, the fitbit inspire 2, and will apply machine learning methods to the fitbit data (physical activity, HR, and sleep) to create clinically meaningful alerts for early detection of postoperative infection. During hospitalization and continuing after discharge, a fitbit inspire 2, a widely-used, commercially wearable device well-tolerated by young children (3- 18 years old) will be used to measure step counts, sleep, and HR. The proposal has 2 aims. Aim 1 develops and validates machine learning algorithm for infection using the fitbit. Aim 2 prospectively feeds near-real time fitbit data on postoperative appendectomy patients to clinicians, and examines their effect on clinical decision making, time to first contact with the healthcare system, and on overall healthcare use patterns. The proposal is aligned with ninr's research priorities. Methods developed from this work will pave the way to develop similar algorithms for other patient populations needing a proxy, as well as to characterize other surgeries and, should improve overall postoperative management for all surgical patients.
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| Original award | $849,863 |
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Subawards
| Subrecipient | Amount | Awarded |
|---|---|---|
| University Of Alabama At Birmingham | $42,994 | |
| ANN & Robert H Lurie Childrens Hospital Of Chicago | $263,043 | |
| Northwestern University | $19,487 | |
| Rehabilitation Institute Of Chicago | $211,880 | |
| Loyola University Of Chicago | $65,382 | |
| The University Of Chicago | $84,854 | |
| Rehabilitation Institute Of Chicago | $539,555 | |
| University Of Alabama At Birmingham | $106,643 | |
| Northwestern University | $0 | |
| Northwestern University | $80,650 | |
| University Of North Texas | $13,616 | |
| Rehabilitation Institute Of Chicago | $273,067 |
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Quick answers
Who received award R01NR020918?
R01NR020918 was awarded to Loyola University Of Chicago by National Institute of Nursing Research (National Institutes of Health) on September 22, 2023.
How much is award R01NR020918 worth?
$6,290,449 has been obligated on R01NR020918.
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