# Identification of preschool children's physical fitness phenotypes and their association with multidimensional body shape indicators: a data-driven approach with risk prediction modeling

## TL;DR

This study identifies distinct physical fitness profiles in preschool children and develops a body shape-based screening tool to predict low fitness, improving on traditional BMI measures.

## Contribution

A data-driven method identifies fitness profiles and a new composite index for predicting low fitness in preschoolers using multidimensional body shape indicators.

## Key findings

- Three distinct physical fitness profiles (low, moderate, high) were identified in preschool children.
- A composite body shape index using waist circumference and pelvic width outperformed BMI in predicting low fitness.
- The index showed strong predictive performance with an AUC of 0.779 and consistent results in sex-stratified analyses.

## Abstract

Traditional physical fitness assessments in young children often rely on single indicators, which fail to capture integrated ability patterns. The common use of body mass index (BMI) to link morphology and fitness has limited discriminant validity. This study aimed to identify naturally emerging physical fitness profiles among preschool children in Macao using a data-driven approach and to develop a screening tool based on multi-dimensional body shape indicators—beyond BMI—for identifying children at risk of low physical fitness.

The sample comprised 3,180 children aged 3–6 years from the Macao China Physical Fitness Surveillance and Physical Activity Survey (2010, 2015, 2020). K-means clustering was performed on six standardized physical fitness test scores to identify intrinsic fitness profiles. One-way ANOVA was used to compare body shape indicators across clusters. Using the low-fitness cluster as the dependent variable, logistic regression identified key morphological predictors, from which a composite discriminant index was constructed. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis.

Cluster analysis revealed three distinct physical fitness profiles: low, moderate, and high fitness. ANOVA showed significant gradient differences across clusters for all body shape indicators (height, weight, circumferences, etc.). Effect sizes (Cohen's f) ranged from 0.025 to 0.190. Logistic regression identified four core predictors: height, weight, waist circumference, and pelvic width. The composite body shape index demonstrated good discriminative ability for low fitness risk, with an area under the curve (AUC) of 0.779 (95% CI: 0.742–0.800). At the optimal cut-off, sensitivity was 77.7%, and specificity was 66.8%. The index showed consistent performance in sex-stratified analyses (Boys: AUC = 0.87; Girls: AUC = 0.88).

This study confirms that preschool children's physical fitness develops in distinct, internally coherent profiles, closely associated with body morphology. The composite index based on waist circumference and pelvic width—among other measures—proves more effective than BMI alone in identifying children at risk of low fitness. It offers a practical tool for rapid, early screening during routine health checks and supports targeted physical activity interventions.