TeenGrowth: Individualized Estimations of Weight-Related Risk and Recovery Metrics for Young People With Eating Disorders
Abstract
OBJECTIVE: While weight restoration and/or stabilization is crucial for successful treatment and sustained recovery from restrictive eating disorders (EDs), it is often challenging to define an individual's expected healthy body weight. This paper introduces the TeenGrowth package and its web-based application, designed to calculate and forecast predicted body mass index (BMI) and weight across adolescence.
METHOD: TeenGrowth includes functions for data cleaning, predicted BMI z-score and BMI calculations, and growth forecasting. The accompanying Shiny web application provides a user-friendly interface, enabling the identification of predicted weights for individuals. Through a series of 30 computer-simulated datasets for 1100 individuals (1000 "healthy" and 100 "ED"), the package's options for predictive models are evaluated.
RESULTS: Simulation results highlight the potential for use in ED screening and treatment and guide users on modeling options. Prediction of adolescent BMI was more accurate for TeenGrowth models, specifically mean pre-ED BMIz, most recent pre-ED BMIz, or the combination of these metrics (median BMI error for these methods across all simulations = 0.69) when compared to predictions at the 50th percentile of population-based norms (median BMI error = 2.15). Aggregated across simulation approaches, results further support optimal accuracy in identifying ED cases when using mean, most recent, or mean + most recent methods (mean ED case classification…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.