A lifetime asymptotic growth curve for human height

Area of Science:

• Human biology
• Biometrics
• Growth modeling

Background:

• Accurate modeling of human growth is crucial for understanding developmental trajectories.
• Existing asymptotic growth curves have limitations in fitting diverse age ranges.
• Longitudinal growth data analysis requires robust and precise mathematical models.

Purpose of the Study:

• To introduce and evaluate a new seven-parameter asymptotic growth curve.
• To compare the performance of the new curve against established models.
• To assess the curve's efficacy in fitting longitudinal height data across a wide age spectrum.

Main Methods:

• Application of a novel seven-parameter asymptotic growth curve to longitudinal height data.
• Comparison of residual sums of squares (RSS) against existing five-parameter (Preece and Baines, 1978) and six-parameter (Shohoji and Sasaki, 1987) curves.
• Analysis of data from 13 boys and 14 girls aged 1 month to 19 years.

Main Results:

• The new seven-parameter curve yielded significantly lower residual sums of squares.
• RSS were 7.5 times lower than the Preece and Baines curve.
• RSS were 2.4 times lower than the Shohoji and Sasaki curve.
• The new curve fits infant and older child data with comparable accuracy.
• The curve is expressed with respect to total age and passes through the origin.

Conclusions:

• The proposed seven-parameter asymptotic growth curve offers superior fit and accuracy for longitudinal human height data.
• This model provides a more effective tool for pediatric growth analysis than current methods.
• The curve's ability to fit data across all ages, including infancy, marks a significant advancement in growth modeling.