[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100546492":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":25,"centralContacts":35,"locations":44,"responsibleParty":104,"collaborators":106,"id":115,"slug":116,"hasResults":117,"nctId":118,"briefTitle":119,"officialTitle":120,"acronym":121,"eligibilityCriteria":122,"healthyVolunteers":117,"sex":123,"minAge":124,"maxAge":125,"enrollmentInfo":126,"targetDuration":12,"studyType":129,"phases":130,"briefSummary":132,"conditions":133,"keywords":137,"overallStatus":47,"whyStopped":12,"lastUpdateSubmitDate":146,"lastUpdatePostDateStruct":147,"startDateStruct":150,"completionDateStruct":152,"leadSponsor":154,"locationsCount":155},{"fullName":5,"class":6},"Ann & Robert H Lurie Children's Hospital of Chicago","OTHER",[8,13],{"label":9,"type":10,"description":11,"interventionNames":12},"Aim 1 - Validation","NO_INTERVENTION","1a. Development and Internal validation\n\n* analyze Fitbit data (PA, HR, sleep) by applying ML methods to create an infection algorithm indicating onset of infection.\n\n  1b. External Validation\n* 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.",null,{"label":14,"type":15,"description":16,"interventionNames":17},"Aim 2 - Implementation of Algorithm","EXPERIMENTAL","2a. Exploratory \\& Inductive analysis\n\n* one transcript will be coded to generate initial themes, using qualitative analytic software 2b. Time to first contact with the healthcare system \\& Healthcare use\n* Cox regression model will be used to model the time to first contact, adjusted for covariates\n* 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.",[18],"Device: Infection-Prediction Algorithm",[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":12},"DEVICE","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.",[14],[26,29,32],{"name":27,"affiliation":5,"role":28},"Fizan Abdullah, MD, PhD","PRINCIPAL_INVESTIGATOR",{"name":30,"affiliation":31,"role":28},"Hassan Ghomrawi, PhD, MPH","University of Alabama at Birmingham",{"name":33,"affiliation":34,"role":28},"Arun Jayaraman, PT, PhD","Shirley Ryan AbilityLab",[36,40],{"name":27,"role":37,"phone":38,"phoneExt":12,"email":39},"CONTACT","312-227-4210","fabdullah@luriechildrens.org",{"name":41,"role":37,"phone":42,"phoneExt":12,"email":43},"Arianna Edobor, CRC","312-227-2118","idetect@luriechildrens.org",[45,66,77,91],{"facility":46,"status":47,"city":48,"state":49,"zip":50,"country":51,"countryCode":52,"cosmosGeoPoint":53,"geoPoint":58,"contacts":59},"Ann & Robert H. Lurie Children's Hospital of Chicago","RECRUITING","Chicago","Illinois","60611","United States","US",{"type":54,"coordinates":55},"Point",[56,57],-87.65005,41.85003,{"lat":57,"lon":56},[60,62,65],{"name":27,"role":37,"phone":38,"phoneExt":12,"email":61},"FAbdullah@luriechildrens.org",{"name":63,"role":37,"phone":42,"phoneExt":12,"email":64},"Arianna Edobor, BS","aedobor@luriechildrens.org",{"name":27,"role":28,"phone":12,"phoneExt":12,"email":12},{"facility":67,"status":68,"city":48,"state":49,"zip":50,"country":51,"countryCode":52,"cosmosGeoPoint":69,"geoPoint":71,"contacts":72},"Northwestern University (Feinberg School of Medicine, Shirley Ryan AbilityLab)","NOT_YET_RECRUITING",{"type":54,"coordinates":70},[56,57],{"lat":57,"lon":56},[73,74,76],{"name":27,"role":37,"phone":38,"phoneExt":12,"email":39},{"name":75,"role":37,"phone":42,"phoneExt":12,"email":64},"Clinical Research Coordinator",{"name":27,"role":28,"phone":12,"phoneExt":12,"email":12},{"facility":78,"status":68,"city":79,"state":49,"zip":80,"country":51,"countryCode":52,"cosmosGeoPoint":81,"geoPoint":85,"contacts":86},"Loyola University Medical Center","Maywood","60153",{"type":54,"coordinates":82},[83,84],-87.84312,41.8792,{"lat":84,"lon":83},[87,88],{"name":75,"role":37,"phone":42,"phoneExt":12,"email":64},{"name":89,"role":90,"phone":12,"phoneExt":12,"email":12},"Steven A De Jong, MD","SUB_INVESTIGATOR",{"facility":92,"status":47,"city":93,"state":49,"zip":94,"country":51,"countryCode":52,"cosmosGeoPoint":95,"geoPoint":99,"contacts":100},"Northwestern Medicine Central DuPage Hospital","Winfield","60190",{"type":54,"coordinates":96},[97,98],-88.1609,41.8617,{"lat":98,"lon":97},[101,102],{"name":75,"role":37,"phone":42,"phoneExt":12,"email":64},{"name":103,"role":90,"phone":12,"phoneExt":12,"email":12},"Guillermo Ares, MD",{"type":28,"investigatorFullName":27,"investigatorTitle":105,"investigatorAffiliation":5,"oldNameTitle":12,"oldOrganization":12},"Fizan Abdullah M.D., Ph.D",[107,109,111,113],{"name":108,"class":6},"Northwestern University",{"name":110,"class":6},"Central DuPage Hospital",{"name":112,"class":6},"University of Chicago",{"name":114,"class":6},"Loyola University Chicago","100546492","early-detection-of-infection-using-the-fitbit-in-pediatric-surgical-patients-100546492",false,"NCT06395636","Early Detection of Infection Using the Fitbit in Pediatric Surgical Patients","Using the Fitbit for Early Detection of Infection and Reduction of Healthcare Utilization After Discharge in Pediatric Surgical Patients","i-DETECT","Inclusion Criteria:\n\n* children aged 3-18 years\n* must be post-surgical laparoscopic appendectomy for complicated appendicitis (Appendicitis is categorized as complicated if perforation, phlegmon, or abscess was present at surgery.)\n\nExclusion Criteria:\n\n* children who are non-ambulatory or have any pre-existing mobility limitations\n* children who have a doctor-ordered physical activity limit \\>48 hours post-surgery\n* children who have a comorbidity which will impact a patient's recovery\n* children and\u002For parents who do not speak English or Spanish (Translation services beyond Spanish will not be available at this time)","ALL","3 Years","18 Years",{"count":127,"type":128},500,"ESTIMATED","INTERVENTIONAL",[131],"NA","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.",[134,135,136],"Appendectomy","Appendicitis","Appendicitis Acute",[138,139,140,141,142,143,144,145],"consumer wearables","machine learning","ML","Fitbit","infection","detection","algorithm","prediction","2026-05-11",{"date":148,"type":149},"2026-05-13","ACTUAL",{"date":151,"type":149},"2025-01-07",{"date":153,"type":128},"2027-06-30",{"name":5,"class":6},4]