Using Fitbit to detect infections in children after appendectomy
Using the Fitbit for Early Detection of Infection and Reduction of Healthcare Utilization After Discharge in Pediatric Surgical Patients
NA · Ann & Robert H Lurie Children's Hospital of Chicago · NCT06395636
This study tests if using Fitbit data can help doctors spot infections early in children recovering from appendix surgery.
Quick facts
| Phase | NA |
|---|---|
| Study type | Interventional |
| Enrollment | 500 (estimated) |
| Ages | 3 Years to 18 Years |
| Sex | All |
| Sponsor | Ann & Robert H Lurie Children's Hospital of Chicago (other) |
| Locations | 4 sites (Chicago, Illinois and 3 other locations) |
| Trial ID | NCT06395636 on ClinicalTrials.gov |
What this trial studies
This study investigates the use of Fitbit data to predict postoperative infections in pediatric patients who have undergone laparoscopic appendectomy for complicated appendicitis. By analyzing near-real-time data on heart rate, physical activity, and sleep patterns, the study aims to enhance clinician decision-making and improve patient outcomes. Machine learning algorithms will be employed to interpret the large volumes of data generated by the Fitbit, allowing for timely interventions when physiological changes indicate potential infections. The goal is to monitor recovery more effectively and reduce postoperative healthcare utilization.
Who should consider this trial
Good fit: Ideal candidates for this study are children aged 3-18 years who have recently undergone laparoscopic appendectomy for complicated appendicitis.
Not a fit: Patients who are non-ambulatory, have significant pre-existing mobility limitations, or have other comorbidities affecting recovery may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to earlier detection of infections, improving recovery times and reducing complications for pediatric surgical patients.
How similar studies have performed: While the use of consumer wearables in clinical settings is emerging, this specific application of Fitbit data for postoperative infection detection in pediatric patients is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * children aged 3-18 years * must be post-surgical laparoscopic appendectomy for complicated appendicitis (Appendicitis is categorized as complicated if perforation, phlegmon, or abscess was present at surgery.) Exclusion Criteria: * children who are non-ambulatory or have any pre-existing mobility limitations * children who have a doctor-ordered physical activity limit \>48 hours post-surgery * children who have a comorbidity which will impact a patient's recovery * children and/or parents who do not speak English or Spanish (Translation services beyond Spanish will not be available at this time)
Where this trial is running
Chicago, Illinois and 3 other locations
- Ann & Robert H. Lurie Children's Hospital of Chicago — Chicago, Illinois, United States (RECRUITING)
- Northwestern University (Feinberg School of Medicine, Shirley Ryan AbilityLab) — Chicago, Illinois, United States (NOT_YET_RECRUITING)
- Loyola University Medical Center — Maywood, Illinois, United States (NOT_YET_RECRUITING)
- Northwestern Medicine Central DuPage Hospital — Winfield, Illinois, United States (RECRUITING)
Study contacts
- Principal investigator: Fizan Abdullah, MD, PhD — Ann & Robert H Lurie Children's Hospital of Chicago
- Study coordinator: Fizan Abdullah, MD, PhD
- Email: fabdullah@luriechildrens.org
- Phone: 312-227-4210
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Appendectomy, Appendicitis, Appendicitis Acute, consumer wearables, machine learning, ML, Fitbit, infection