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Behavioural Intervention

Fitbit Data for Detecting Infections After Appendicitis Surgery (i-DETECT Trial)

N/A

Waitlist Available

Led By Hassan Ghomrawi, PhD, MPH

Research Sponsored by Ann & Robert H Lurie Children's Hospital of Chicago

Eligibility Criteria Checklist

Specific guidelines that determine who can or cannot participate in a clinical trial

Must have

Children aged 3-18 years

Must have undergone post-surgical laparoscopic appendectomy for complicated appendicitis (Appendicitis is categorized as complicated if perforation, phlegmon, or abscess was present at surgery)

Must not have

Children who are non-ambulatory or have any pre-existing mobility limitations

Children and/or parents who do not speak English or Spanish (Translation services beyond Spanish will not be available at this time)

Timeline

Screening 3 weeks

Treatment Varies

Follow Up for 30 days starting at day of participant enrollment

Awards & highlights

No Placebo-Only Group

Summary

This trial aims to use Fitbit data to predict infections after surgery for complicated appendicitis and see how this prediction impacts doctors' decisions. "This trial aims to predict infections after surgery for complicated append

Who is the study for?

This trial is for pediatric patients who have undergone surgery for complicated appendicitis. Specific eligibility criteria are not provided, but typically participants would be children with recent appendectomies.

What is being tested?

The study is testing an infection-prediction algorithm using data from Fitbits to foresee infections after appendectomy in young patients and how these predictions influence doctors' decisions.

What are the potential side effects?

Since the intervention involves data analysis rather than a physical treatment, there are no direct side effects associated with this trial.

Eligibility Criteria

Inclusion Criteria

You may be eligible if you check “Yes” for the criteria below

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I am between 3 and 18 years old.

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I had surgery to remove my appendix due to severe infection or rupture.

Exclusion Criteria

You may be eligible for the trial if you check “No” for criteria below:

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My child cannot walk or has difficulty moving around.

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I (or my child) do not speak English or Spanish.

Timeline

Screening ~ 3 weeks3 visits

Treatment ~ Varies

Follow Up ~ fitbit data metrics will be collected for 30 days starting at date of enrollment.

Screening ~ 3 weeks

Treatment ~ Varies

Follow Up ~fitbit data metrics will be collected for 30 days starting at date of enrollment.

This trial's timeline: 3 weeks for screening, Varies for treatment, and fitbit data metrics will be collected for 30 days starting at date of enrollment. for reporting.

Treatment Details

Study Objectives

Study objectives can provide a clearer picture of what you can expect from a treatment.

Primary study objectives

Trends in Participant Fitbit Data (Physical Activity, Heart Rate, Sleep) during the Recovery Period post Complicated Appendectomy

Secondary study objectives

Change in Clinician Decision Making from Algorithm Results

Healthcare Utilizations during Recovery Period

Number of Reported Symptoms and Complications during Recovery

Awards & Highlights

No Placebo-Only Group

All patients enrolled in this study will receive some form of active treatment.

Trial Design

2Treatment groups

Experimental Treatment

Active Control

Group I: Aim 2 - Implementation of AlgorithmExperimental Treatment1 Intervention

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.

Group II: Aim 1 - ValidationActive Control1 Intervention

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.

0 / 4Eligibility criteria met