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.