This tutorial presents a step-by-step guide on how to use an adult-oriented wearable (Fitbit) to collect and analyze activity and cardiovascular data in a pediatric population of school-aged children with obesity. After outlining the current landscape of commercial wearables for adults and children, the tutorial illustrates the distinct considerations required for accurate pediatric monitoring, especially for cardiovascular metrics and derived features. The text provides a clinical application, highlighting how data from these devices were gathered and integrated with standard clinical measurements (ie, 1 week of monitoring with the wearable compared with the 6-minute walk test). The tutorial also discusses potential correlations, which should be interpreted as exploratory, given the small sample size (n=16), as well as limitations and future perspectives on using wearables for long-term pediatric monitoring of school-aged children, aiming to inform clinicians, researchers, and other stakeholders about the additional considerations that are needed to use wearables designed for adults to monitor this age group.
Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time Data Transformation
Background: Artificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). However, AI models typically exhibit reduced accuracy and