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Early Detection of Infection Using the Fitbit in Pediatric Surgical Patients
Sponsor: Ann & Robert H Lurie Children's Hospital of Chicago
Summary
The purpose of this study is to analyze Fitbit data to predict infection after surgery for complicated appendicitis and the effect this prediction has on clinician decision making.
Official title: Using the Fitbit for Early Detection of Infection and Reduction of Healthcare Utilization After Discharge in Pediatric Surgical Patients
Key Details
Gender
All
Age Range
3 Years - 18 Years
Study Type
INTERVENTIONAL
Enrollment
500
Start Date
2025-01-07
Completion Date
2027-07-31
Last Updated
2026-03-19
Healthy Volunteers
No
Interventions
Infection-Prediction Algorithm
This machine learning algorithm will be developed(Aim1a) and validated(Aim 1b) using the participant Fitbit data and survey results collected during Aim 1. In Aim 2 the algorithm will be used in real time to predict postoperative infection.
Locations (4)
Ann & Robert H. Lurie Children's Hospital of Chicago
Chicago, Illinois, United States
Northwestern University (Feinberg School of Medicine, Shirley Ryan AbilityLab)
Chicago, Illinois, United States
Loyola University Medical Center
Maywood, Illinois, United States
Northwestern Medicine Central DuPage Hospital
Winfield, Illinois, United States