Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review
  • Published:

Clinical nutrition and patient care

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

European Journal of Clinical Nutrition volume 67, pages 555–557 (2013)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

Similar content being viewed by others

References

  1. Heymsfield SB, Martin-Nguyen A, Fong TM, Gallagher D, Pietrobelli A . Body circumferences: clinical implications emerging from a new geometric model. Nutr Metab (Lond) 2008; 5: 24 PMCID: 2569934.

    Article  Google Scholar 

  2. Wishnofsky N . Caloric equivalents of gained or lost weight. Metabolism 1952; 1: 554–555.

    CAS  PubMed  Google Scholar 

  3. Van Horn L . Calories count: but can consumers count on them? JAMA 2011; 306: 315–316.

    Article  CAS  Google Scholar 

  4. Sazonova NA, Browning R, Sazonov ES . Prediction of bodyweight and energy expenditure using point pressure and foot acceleration measurements. Open Biomed Eng J 2011; 5: 110–115. PMCID: 3257550.

    Article  Google Scholar 

  5. van Hees VT, Renstrom F, Wright A, Gradmark A, Catt M, Chen KY et al. Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. PLoS One 2011; 6: e22922 PMCID: 3146494.

    Article  CAS  Google Scholar 

  6. Chen KY, Sun M . Improving energy expenditure estimation by using a triaxial accelerometer. J Appl Physiol 1997; 83: 2112–2122.

    Article  CAS  Google Scholar 

  7. Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL et al. Quantification of the effect of energy imbalance on bodyweight. Lancet 2011; 378: 826–837.

    Article  Google Scholar 

  8. Thomas DM, Schoeller DA, Redman LA, Martin CK, Levine JA, Heymsfield SB . A computational model to determine energy intake during weight loss. Am J Clin Nutr 2010; 92: 1326–1331. PMCID: 2980958. PMCID: 2980958.

    Article  CAS  Google Scholar 

  9. Cawley J, Meyerhoefer C . The medical care costs of obesity: an instrumental variables approach. J Health Econ 2012; 31: 219–230.

    Article  Google Scholar 

  10. Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B et al. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012; 42: 563–570.

    Article  Google Scholar 

  11. Fawcett TW, Higginson AD . Heavy use of equations impedes communication among biologists. Proc Natl Acad Sci USA. 2012; 109: 11735–11739.

    Article  CAS  Google Scholar 

  12. Thomas DM et al. A simple model predicting individual weight change in humans. J Biol Dyn 2011; 5: 579–599.

    Article  Google Scholar 

  13. Martin CK, Miller AC, Thomas DM, Stewart TMC, Han H Feasibility and preliminary efficacy of SmartLoss, a smartphone-based weight loss intervention. Under Review 2012.

  14. Thomas DM, Navarro-Barrientos JE, Rivera DE, Heymsfield SB, Bredlau C, Redman LM et al. Dynamic energy-balance model predicting gestational weight gain. Am J Clin Nutr 2012; 95: 115–122. PMCID: 3238455.

    Article  CAS  Google Scholar 

  15. Martin CK, Chellino A, Correa JB, Johnson WD, Church TS . Efficacy of an e-Health intervention at promoting weight loss through remote delivery of services: Preliminary results from a randomized controlled trial. Obesity Reviews 2011; 11 (Suppl. 1) T3:PO.59.

  16. Livingston EH, Kohlstadt I . Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals. Obes Res 2005; 13: 1255–1262.

    Article  Google Scholar 

  17. Harris JA, Benedict FGA . Biometric Study of Human Basal Metabolism. Proc Natl Acad Sci USA. 1918; 4: 370–373. PMCID: 1091498.

    Article  CAS  Google Scholar 

  18. Mifflin MD, Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO . A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990; 51: 241–247.

    Article  CAS  Google Scholar 

  19. Schofield WN . Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985; 39 (Suppl 1), 5–41.

    PubMed  Google Scholar 

  20. Muller MJ, Bosy-Westphal A, Klaus S, Kreymann G, Luhrmann PM, Neuhauser-Berthold M et al. World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure. Am J Clin Nutr 2004; 80: 1379–1390.

    Article  Google Scholar 

  21. Engert A, Haverkamp H, Kobe C, Markova J, Renner C, Ho A et al. Reduced-intensity chemotherapy and PET-guided radiotherapy in patients with advanced stage Hodgkin's lymphoma (HD15 trial): a randomised, open-label, phase 3 non-inferiority trial. Lancet 2012; 379: 1791–1799.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

I would like to thank Dr Steven Heymsfield, Dr Corby Martin and Dr Leanne Redman for their helpful discussions in preparing this article. This research was supported in part by National Institutes of Health grants R15 DK090739 and U01DK094418.

Author information

Authors and Affiliations

  1. Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ, USA

    D M Thomas

Authors
  1. D M Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D M Thomas.

Ethics declarations

Competing interests

Diana Thomas is a consultant for Jenny Craig.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Thomas, D. Modeling in clinical nutrition: does it add to patient care?. Eur J Clin Nutr 67, 555–557 (2013). https://doi.org/10.1038/ejcn.2013.16

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ejcn.2013.16

Keywords

This article is cited by

Search

Advanced search

Quick links