Literature DB >> 23403881

Modeling in clinical nutrition: does it add to patient care?

D M Thomas1.   

Abstract

BACKGROUND/
OBJECTIVES: Remarkable improvements in mathematical methodology combined with knowledge and data on the response of the human body to changes in nutrition, activity and environment have led to a rapid expansion of mathematical models that predict, describe and aggregate conclusions in nutrition. Although mathematical models in nutrition have made significant advances in predictive accuracy and physiological descriptions, these advances have compromised model simplicity, introducing obstacles to their widespread application and contribution to clinical care. The challenge of model complexity is moderated by delivery through well-designed software. SUBJECTS/
METHODS: We reviewed several recent and novel web-based mathematical models related to nutrition and describe the successful application of a dynamic mathematical model to patient care implemented through counseling software in a recent weight-loss intervention. To illustrate the power of model transfer through software, we designed a Visual Basic macro within Microsoft Excel to deliver predictions from six well-established and validated resting energy expenditure formulas in children and adults.
RESULTS: The six resting energy expenditure models that were deployed using the Visual Basic Application developer ranged in technical complexity requiring decision trees, calculation of nonlinear terms or inclusion of multiple covariates. The developed software allows users to select specific models and desired units. After input of individual height, weight, age and sex data through a user form, individuals can effortlessly view predictions.
CONCLUSIONS: Advances in web-based and widely accessible software provide the capacity to deliver more accurate and physiologically realistic nutrition-related models, and ultimately translating model results to patient care.

Entities:  

Mesh:

Year:  2013        PMID: 23403881     DOI: 10.1038/ejcn.2013.16

Source DB:  PubMed          Journal:  Eur J Clin Nutr        ISSN: 0954-3007            Impact factor:   4.016


  5 in total

Review 1.  Predictive Mathematical Models of Weight Loss.

Authors:  Diana M Thomas; Michael Scioletti; Steven B Heymsfield
Journal:  Curr Diab Rep       Date:  2019-08-31       Impact factor: 4.810

2.  Obesity Tissue: Composition, Energy Expenditure, and Energy Content in Adult Humans.

Authors:  Phoenix Hwaung; Anja Bosy-Westphal; Manfred J Muller; Corinna Geisler; Moonseong Heo; Diana M Thomas; Samantha Kennedy; Steven B Heymsfield
Journal:  Obesity (Silver Spring)       Date:  2019-07-19       Impact factor: 5.002

Review 3.  Why are there race/ethnic differences in adult body mass index-adiposity relationships? A quantitative critical review.

Authors:  S B Heymsfield; C M Peterson; D M Thomas; M Heo; J M Schuna
Journal:  Obes Rev       Date:  2015-12-11       Impact factor: 9.213

4.  The Behavioral Intervention with Technology for E-Weight Loss Study (BITES): Incorporating Energy Balance Models and the Bite Counter into an Online Behavioral Weight Loss Program.

Authors:  Carly M Goldstein; Stephanie P Goldstein; Diana M Thomas; Adam Hoover; Dale S Bond; J Graham Thomas
Journal:  J Technol Behav Sci       Date:  2020-11-27

5.  Dispatch from the field: is mathematical modeling applicable to obesity treatment in the real world?

Authors:  Ignatius Brady; Kevin D Hall
Journal:  Obesity (Silver Spring)       Date:  2014-06-04       Impact factor: 5.002

  5 in total

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