ReviewObesity science & practice2025
Motivational Interviewing (MI) in Obesity Care: Cultivating Person-Centered and Supportive Clinical Conversations to Reduce Stigma: A Narrative Review.
Review in Obesity science & practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Generative AI-Assisted Simulation Training Is Associated with Higher Post-Intervention Diagnostic Communication Scores Across Type 2 Diabetes, Obesity, and Breast Cancer Scenarios.Healthcare (Basel, Switzerland) · 2026Article
- Supporting and Non-Stigmatizing Communication in the Process of Weight Change: The Role of Motivational Interviewing.Nutrients · 2026Review
- Integrating Obesity Care into Chronic Disease Management in Catalonia: Progress and Challenges in Health System Approaches.Current obesity reports · 2025Review
- Perceptions, attitudes, and practices influencing management of obesity in Ireland-The Awareness, Care and Treatment in Obesity maNagement, (ACTION) Ireland study.Obesity pillars · 2025Article
- Integrative Strategies in Primary Care: Addressing Recurrent Weight Gain Post-Metabolic and Bariatric Surgery.Obesity science & practice · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients perceive high levels of weight prejudice, stigma, and discrimination within health systems, affecting their ability to manage their obesity and related chronic conditions. Scientific and patient obesity associations worldwide have prioritized the reduction of weight stigma to improve patient experiences in health systems and overall health outcomes. Since a significant proportion of the population is now living with multiple chronic diseases related to obesity, healthcare systems must shift toward multi-disease management frameworks incorporating person-centered and non-stigmatizing clinical conversations. Motivational Interviewing (MI) has the potential to transform clinical interactions by using non-stigmatizing language, communication, and practices. Studies using MI in obesity management have solely focused on weight loss outcomes, while other patient experience related outcomes would also be relevant to evaluate. Methods: A narrative review was undertaken to critically analyze the potential impact of MI on obesity and chronic disease management practices and experiences. Findings: An analysis and contextualization of the MI theoretical framework for obesity management, based on the philosophy of motivational spirit, was reviewed, assessing micro skills or strategies. Conclusion: MI may assist healthcare professionals conduct non-stigmatizing clinical conversations in accordance with basic principles of collaborative therapeutic alliances. A proposal for research considerations that can help illuminate the potential for of MI in obesity management is also outlined.
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Registered trials
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.