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The growth velocity prediction using the sampled-transfer-function

J S Huang, C J Burstone

    Growth
    |June 1, 1977
    PubMed
    Summary
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    This study explored using a sampled-transfer-function to predict peak height velocity in humans. The developed model accurately predicted individual growth spurts, outperforming average value predictions.

    Area of Science:

    • Human growth and development
    • Biomedical engineering
    • System theory applications

    Background:

    • Predicting adolescent growth spurts is crucial for understanding human development.
    • Existing methods for predicting peak height velocity have limitations.
    • System theory offers novel approaches to biological modeling.

    Purpose of the Study:

    • To investigate the feasibility of employing a sampled-transfer-function for predicting human peak height velocity.
    • To develop and validate a computational model for individual growth prediction.
    • To assess the accuracy of the transfer-function model against actual growth data.

    Main Methods:

    • Established a sampled-transfer-function model for longitudinal growth data.
    • Utilized system theory's input-output concept and the z-transform.

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  • Developed a computer program to predict circumpubertal height velocities from prepubertal data.
  • Compared model predictions with actual growth data from 25 males in the Denver growth sample.

    • Main Results:

    • The sampled-transfer-function model demonstrated feasibility in predicting peak height velocity.
    • Predictions generated by the transfer-function model were significantly more accurate than those using mean values.
    • The model successfully predicted individual circumpubertal height velocities.

    Conclusions:

    • The sampled-transfer-function is a viable tool for predicting peak height velocity in human growth studies.
    • This systems-based approach offers improved accuracy for individual growth prediction.
    • Further research can refine this model for clinical and research applications in pediatric endocrinology and growth assessment.