Growth Characteristics of Age and Gender-based Anthropometric Data from Human Assisted Remote Healthcare System

Abstract

Growth monitoring and promotion of optimal growth are essential components of primary health care. The most popular approach to this topic has been developed and utilized for decades by the CDC (Center for Disease Control and Prevention) in the United States, resulting in its well-known clinical growth pattern charts for boys and girls. This metric comprises a series of percentile curves that illustrate the distribution of selected body measurements, by age. The results show a clear uptrend of three traditional measures: height, weight, and BMI. The chart also shows a trend with the corresponding 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentile data variations. Unfortunately, the CDC metric system only addresses ages 2–20 years. Apparently, no other studies show correspondingly systematic growth characteristic patterns for humans more than 20 years old. Our Portable Health Clinic system has for many years been archiving remote health care data records collected from different age and socioeconomic levels in many locations throughout Bangladesh. This data provides an important resource with which to study the age-related evolving nature of anthropometric data. We aim to see if there are any significant clinical growth patterns, specifically regarding height, weight, BMI, waist, and hip for humans over the age of 20 years. We could not determine a clear indication in terms of a specific age where the significant change from growth to decline occurs.

Authors and Affiliations

Mehdi Hasan, Rafiqul Islam, Fumuhiko Yokota, Mariko Nishikitani, Akira Fukuda, Ashir Ahmed

Keywords

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  • EP ID EP499684
  • DOI 10.14569/IJACSA.2019.0100379
  • Views 106
  • Downloads 0

How To Cite

Mehdi Hasan, Rafiqul Islam, Fumuhiko Yokota, Mariko Nishikitani, Akira Fukuda, Ashir Ahmed (2019). Growth Characteristics of Age and Gender-based Anthropometric Data from Human Assisted Remote Healthcare System. International Journal of Advanced Computer Science & Applications, 10(3), 615-619. https://europub.co.uk/articles/-A-499684