Effectiveness of Motivational Interviewing in Managing Overweight and Obesity
Bibliographic record
Abstract
LettersSeptember 2022Effectiveness of Motivational Interviewing in Managing Overweight and ObesityAnda I. Dragomir, PhD, Brigitte Voisard, BA, Vincent Gosselin Boucher, PhD, Geneviève Szczepanik, PhD, Simon L. Bacon, PhD, Kim L. Lavoie, PhDAnda I. Dragomir, PhDCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and Concordia University, Montreal, Quebec, CanadaSearch for more papers by this author, Brigitte Voisard, BACentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and Université du Québec à Montréal, Montreal, Quebec, CanadaSearch for more papers by this author, Vincent Gosselin Boucher, PhDCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and University of British Columbia, Montreal, Quebec, and Vancouver, British Columbia, CanadaSearch for more papers by this author, Geneviève Szczepanik, PhDCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal, Montreal, Quebec, CanadaSearch for more papers by this author, Simon L. Bacon, PhDCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and Concordia University, Montreal, Quebec, CanadaSearch for more papers by this author, Kim L. Lavoie, PhDCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and Université du Québec à Montréal, Montreal, Quebec, CanadaSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L22-0237 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR: We read Michalopoulou and colleagues' review (1) with interest. Unfortunately, their conclusions may be misleading because of methodological shortcomings and important errors of interpretation of the reviewed literature.The authors declare that the fidelity of the delivery of MI was generally “satisfactory” without explaining how it was measured or providing objective outcomes. This raises 2 issues: a high likelihood of reporting bias and an even higher likelihood that MI was not delivered as intended. Indeed, many interventionists were students, and there was no mention of assessing competency in MI after training. The authors consequently may have arrived ...References1. Michalopoulou M, Ferrey AE, Harmer G, et al. Effectiveness of motivational interviewing in managing overweight and obesity. A systematic review and meta-analysis. Ann Intern Med. 2022;175:838-50. [PMID: 35344379] doi:10.7326/M21-3128 LinkGoogle Scholar2. Freedland KE, King AC, Ambrosius WT, et al; National Institutes of Health Office of Behavioral and Social Sciences Research Expert Panel on Comparator Selection in Behavioral and Social Science Clinical Trials. The selection of comparators for randomized controlled trials of health-related behavioral interventions: recommendations of an NIH expert panel. J Clin Epidemiol. 2019;110:74-81. [PMID: 30826377] doi:10.1016/j.jclinepi.2019.02.011 CrossrefMedlineGoogle Scholar3. Nobles JD, Perez A, Skelton JA, et al. The engagement pathway: a conceptual framework of engagement-related terms in weight management. Obes Res Clin Pract. 2018 Mar-Apr;12:133-8. [PMID: 29396232] doi:10.1016/j.orcp.2017.12.005 CrossrefMedlineGoogle Scholar4. Rubino F, Puhl RM, Cummings DE, et al. Joint international consensus statement for ending stigma of obesity. Nat Med. 2020;26:485-97. [PMID: 32127716] doi:10.1038/s41591-020-0803-x CrossrefMedlineGoogle Scholar5. Rollnick S, Miller WR, Butler CC. Motivational Interviewing in Health Care: Helping Patients Change Behavior. Guilford Pr; 2008 Google Scholar6. Miller WR, Rollnick S. Ten things that motivational interviewing is not. Behav Cogn Psychother. 2009;37:129-40. [PMID: 19364414] doi:10.1017/S1352465809005128 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Anda I. Dragomir, PhD; Brigitte Voisard, BA; Vincent Gosselin Boucher, PhD; Geneviève Szczepanik, PhD; Simon L. Bacon, PhD; Kim L. Lavoie, PhDAffiliations: Centre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and Concordia University, Montreal, Quebec, CanadaCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and Université du Québec à Montréal, Montreal, Quebec, CanadaCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal and University of British Columbia, Montreal, Quebec, and Vancouver, British Columbia, CanadaCentre Intégré Universitaire de santé et services sociaux du Nord-de-l’Ile-de-Montréal, Montreal, Quebec, CanadaDisclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L22-0237. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoEffectiveness of Motivational Interviewing in Managing Overweight and Obesity Moscho Michalopoulou , Anne E. Ferrey , Georgina Harmer , Lucy Goddard , Maryam Kebbe , Annika Theodoulou , Susan A. Jebb , and Paul Aveyard Effectiveness of Motivational Interviewing in Managing Overweight and Obesity Moscho Michalopoulou , Susan A. Jebb , Paul Aveyard Effectiveness of Motivational Interviewing in Managing Overweight and Obesity Jonathan Smith Metrics September 2022Volume 175, Issue 9Page: W104-W105KeywordsAdolescentsBehaviorClinical trialsExerciseMotivationObesityOverweightRandomized trialsSystematic reviewsWeight loss ePublished: 20 September 2022 Issue Published: September 2022 Copyright & PermissionsCopyright © 2022 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".