Recruiting

Fitbit

Sponsor:

Ann & Robert H Lurie Children's Hospital of Chicago

Code:

NCT06395636

Conditions

Appendectomy

Appendicitis

Appendicitis Acute

Eligibility Criteria

Sex: All

Age: 3 - 18

Healthy Volunteers: Not accepted

Interventions

Infection-Prediction Algorithm

Study Details

Brief 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.

Conditions

Appendectomy

Appendicitis

Appendicitis Acute

Study ID

NCT06395636

Start date

Jan 7, 2025

Status verified date

May, 2026

Completion date

Jun 30, 2027

Anticipated

Primary completion date

Jun 30, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 3 - 18

Healthy Volunteers: Not accepted

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)

Study Design

Enrollment

500 participants

Anticipated

Allocation

Non randomized

Intervention Model

Sequential

Primary purpose

Diagnostic

Interventions and Outcome Measures

Arms

no intervention: Aim 1 - Validation

1a. Development and Internal validation

  • analyze Fitbit data (PA, HR, sleep) by applying ML methods to create an infection algorithm indicating onset of infection.

1b. External Validation
  • Once the ML classifier has been internally validated (using Lurie Children's data only) for its ability to detect the presence or absence of postoperative infection using LOSO cross-validation, where each subject is iteratively held out from the training data and used as a test set. External validation will involve applying this classifier to a newer cohort at LCH and cohorts at Loyola University Hospital and CDH and evaluating its performance.

experimental: Aim 2 - Implementation of Algorithm

2a. Exploratory \& Inductive analysis

  • one transcript will be coded to generate initial themes, using qualitative analytic software 2b. Time to first contact with the healthcare system \& Healthcare use
  • Cox regression model will be used to model the time to first contact, adjusted for covariates
  • All comparisons between the two groups will be tested using a chi-square test. Cost will be modeled as a continuous variable and is expected to be skewed, as is typical of cost data. We will use a general linear model (GLM) to model cost outcomes.

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.

Primary outcome measure

  • Trends in Participant Fitbit Data (Physical Activity, Heart Rate, Sleep) during the Recovery Period post Complicated Appendectomy [ Time Frame: Fitbit data metrics will be collected for 30 days starting at date of enrollment. ]

Central Contacts and Locations

Central contacts

Fizan Abdullah, MD, PhD

312-227-4210fabdullah@luriechildrens.org

Arianna Edobor, CRC

312-227-2118idetect@luriechildrens.org

Locations

Ann & Robert H. Lurie Children's Hospital of Chicago

Recruiting

Chicago, Illinois, United States, 60611

Contacts

Fizan Abdullah, MD, PhD

312-227-4210FAbdullah@luriechildrens.org

Arianna Edobor, BS

312-227-2118aedobor@luriechildrens.org

Principal Investigator:

Fizan Abdullah, MD, PhD

Northwestern Medicine Central DuPage Hospital

Recruiting

Winfield, Illinois, United States, 60190

Contacts

Clinical Research Coordinator

312-227-2118aedobor@luriechildrens.org

More Information

Sponsor

Ann & Robert H Lurie Children's Hospital of Chicago

Last update posted

May 13, 2026

Last verified

May, 2026

Keywords

  • consumer wearables
  • machine learning
  • ML
  • Fitbit
  • infection
  • detection
  • algorithm
  • prediction

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by Ann & Robert H Lurie Children's Hospital of Chicago on 2026-05-13.