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Ernsting, C., Dombrowski, S.U., Oedekoven, M., Lo, J., Kanzler, M., Kuhlmey, A. and Gellert, P. (2017) Using Smartphones and Health Apps to Change and Manage Health Behaviors: A Population-Based Survey. Journal of Medical Internet Research, 19, e6838.
https://doi.org/10.2196/jmir.6838

has been cited by the following article:

• TITLE: New Mathematical Modelling on BMR and Weight Prediction for Ghanaians

  AUTHORS: Ian Yaw Asare, Ezer Osei Yeboah-Boateng

  KEYWORDS: Basal Metabolic Rate (BMR), Lean Body Mass (LBM), Weight, Calories

  JOURNAL NAME: E-Health Telecommunication Systems and Networks, Vol.12 No.4, December 6, 2023

  ABSTRACT: Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of individuals. Many scholastic projects have led to the establishment of mathematical models and inventions that measure the BMR and other body composition parameters. However, existing computations have limitations as they do not offer accurate results for Ghanaians. Aim: The purpose of the study was to model BMR metrics that are most suitable for Ghanaians and to investigate the effect of caloric difference on weight, Lean Body Mass (LBM) and % fat composition that can be implemented with Information Technology. Research Methods and Procedures: This was an experimental study that adopted a quantitative approach. BMR and body composition were measured in a sample of 242 Ghanaian adults (141 males and 101 females) from 19 to 30 years of age. Body composition was measured using bioelectrical impendence analysis (BIA) in all participants. Each participant was under study for 7 days. A simple linear regression model was used to examine associations between BMR/calorie intake and total body weight and LBM. Results: There was a significant statistical relation between BMR and LBM and between BMR and weight of both men and women. Equations for BMR and weight were established for males and females. Furthermore, caloric intake differences affected changes in total weight as well as differences in % fat composition. Caloric intake however did not affect the difference in LBM. Conclusion: Caloric difference had an impact on total body weight and Lean Body Mass. The model derived from the study predicts weight change and BMR of Ghanaians from 19 to 30 years of age. It is termed the Health and Age Monitoring System (HAMS).