Evidence map›Paper›PMID 25080458›Full record

Trial reportThe American journal of clinical nutrition2014

Effect of dietary adherence on the body weight plateau: a mathematical model incorporating intermittent compliance with energy intake prescription.

Diana M Thomas, Corby K Martin, Leanne M Redman, Steven B Heymsfield, Steven Lettieri, James A Levine, Claude Bouchard, Dale A Schoeller

Abstract readComparative StudyRandomized Controlled TrialTwin Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in The American journal of clinical nutrition, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
–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

34 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. International Society of Sports Nutrition position stand: diets for body weight reduction.Journal of the International Society of Sports Nutrition · 2026
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Understanding the Impact of Social Networks on the Spread of Obesity.International journal of environmental research and public health · 2023
    Article
  15. Article
  16. Observational
  17. Article
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Diana M ThomasFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
Corby K MartinFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
Leanne M RedmanFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
Steven B HeymsfieldFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
Steven LettieriFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
James A LevineFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
Claude BouchardFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).
Dale A SchoellerFrom the Center for Quantitative Obesity Research, Montclair State University, Montclair, NJ (DMT); the Pennington Biomedical Research Center, Baton Rouge, LA (CKM, LMR, SBH, and CB); the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA (SL); Obesity Solutions, Mayo Clinic, Arizona State University, Phoenix, AZ (JAL); and Nutritional Sciences, University of Wisconsin, Madison, WI (DAS).

Funding

Research BaseP30DK072476 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI Peter Todd Katzmarzyk · 2005 to 2026
$26.5M
Metabolic Adaptations to Two Year Caloric RestrictionU01AG020478 · NIA · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI RAVUSSIN, ERIC · 2002 to 2008
$13.4M
Examination of mechanisms (E-MECHANIC) of exercise-induced weight compensationR01HL102166 · NHLBI · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI CHURCH, TIMOTHY STEPHEN, MARTIN, CORBY K · 2010 to 2014
$3.4M
Expecting Success: Personalized management of body weight during pregnancyU01DK094418 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI REDMAN, LEANNE MAREE · 2011 to 2015
$2.8M
Remote food photography for the real-time measurement of children's food intakeR01DK089051 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI MARTIN, CORBY K · 2011 to 2014
$1.5M
Effects of weight and insulin sensitivity on reproductive function in PCOSR00HD060762 · NICHD · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI REDMAN, LEANNE MAREE · 2011 to 2013
$717k
A Computational Model to Determine Energy Intake During Weight LossR15DK090739 · NIDDK · MONTCLAIR STATE UNIVERSITY · PI THOMAS, DIANA MARIA · 2011 to 2011
$303k
NHLBI NIH HHS HL102166NIA NIH HHS U01 AG20478NICHD NIH HHS R00HD060762NIDDK NIH HHS DK089051NIDDK NIH HHS P30 DK072476NIDDK NIH HHS R15 DK090739-01NIDDK NIH HHS U01 DK094418NIDDK NIH HHS U01DK094418
6 · The paper itself

Abstract

backgroundClinical weight loss in individuals typically stabilizes at 6 mo. However, validated models of dynamic energy balance have consistently shown weight plateaus between 1 and 2 y. The cause for this discrepancy is unclear.

objectiveWe developed 2 mathematical models on the basis of the first law of thermodynamics to investigate plausible explanations for reaching an early weight plateau at 6 mo.

designThe first model was an energy-expenditure adaptation model and was applied to determine the degree of metabolic adaptation required to generate this plateau. The second model was an intermittent lack-of-adherence model formulated by using a randomly fluctuating energy intake term accounting for intermittent noncompliance in dietary intake to reach this plateau. To set model variables, validate models, and compare free-living weight-loss patterns to in-residence supervised programs, we applied the following 4 different studies: The US NHANES 1999-2004, Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) weight-loss study, the Bouchard Twin overfeeding study, and the Minnesota Starvation Experiment.

resultsThe metabolic adaptation model increased final weight but did not affect the predicted plateau time point. The intermittent lack-of-adherence model generated oscillating weight graphs that have been frequently observed in weight-loss studies. The model showed that a 6-mo weight-loss plateau can be attained despite what can be considered as high diet adherence. The model was programmed as a downloadable application.

conclusionsAn intermittent lack of diet adherence, not metabolic adaptation, is a major contributor to the frequently observed early weight-loss plateau. The new weight-loss prediction software, which incorporates an intermittent lack of adherence, can be used to guide and inform patients on realistic levels of adherence on the basis of patient lifestyle.

Indexed as

Diet, ReducingEnergy IntakeModels, BiologicalPatient ComplianceAdultCaloric RestrictionCanadaCohort StudiesEnergy MetabolismFemaleHumansHyperphagiaMaleNutrition SurveysObesityOverweight

Identifiers

PMID25080458
PMCPMC4135489

What OpenQuestion holds

Textmetadata
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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.