Evidence map›Paper›PMID 23403881›Full record

ReviewEuropean journal of clinical nutrition2013

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

D M Thomas

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of clinical nutrition, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Predictive Mathematical Models of Weight Loss.Current diabetes reports · 2019
    Review
  4. Why are there race/ethnic differences in adult body mass index-adiposity relationships? A quantitative critical review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2016
    Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

D M ThomasCenter for Quantitative Obesity Research, Montclair State University, Montclair, NJ, USA. thomasdia@mail.montclair.edu

Funding

Expecting Success: Personalized management of body weight during pregnancyU01DK094418 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI REDMAN, LEANNE MAREE · 2011 to 2015
$2.8M
A Computational Model to Determine Energy Intake During Weight LossR15DK090739 · NIDDK · MONTCLAIR STATE UNIVERSITY · PI THOMAS, DIANA MARIA · 2011 to 2011
$303k
NIDDK NIH HHS R15 DK090739NIDDK NIH HHS U01DK094418
6 · The paper itself

Abstract

BACKGROUND/

objectivesRemarkable 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/

methodsWe 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.

resultsThe 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.

conclusionsAdvances 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.

Indexed as

Basal MetabolismModels, BiologicalModels, TheoreticalNutritional SciencesObesityQuality of Health CareSoftwareHumansInternetPatientsWeight Loss

Identifiers

PMID23403881

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.