Evidence map›Paper›PMID 29792067›Full record

Trial reportJournal of diabetes science and technology2018

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

Stephanie P Goldstein, Fengqing Zhang, John G Thomas, Meghan L Butryn, James D Herbert, Evan M Forman

Abstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Trial
  2. Observational
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

6 authors.

Stephanie P Goldstein1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.
Fengqing Zhang2 Department of Psychology, College of Arts and Sciences, Drexel University, Philadelphia, PA, USA.
John G Thomas3 Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, The Miriam Hospital Weight Control and Diabetes Research Center, Providence, RI, USA.
Meghan L Butryn1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.
James D Herbert4 President's Office, University of New England, Biddeford, ME, USA.
Evan M Forman1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndividuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction.

methodsThe current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers.

resultsWEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance.

conclusionsThe predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.

Indexed as

AlgorithmsMachine LearningPatient ComplianceAdultDiet, ReducingEcological Momentary AssessmentFemaleHumansMaleMobile ApplicationsObesityOverweightSmartphoneWeight Reduction Programsdietecological momentary assessmentmachine learningobesity

Identifiers

PMID29792067
PMCPMC6134608

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.