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 accuracy = 0.86) as compared to the use of a population-based metric-85% of the 50th percentile BMI (mean classification accuracy = 0.61).

Discussion: The introduction of TeenGrowth represents a first step towards setting reproducible, personalized predicted body weights for young people.