Obesity and psychology: a bibliometric analysis of half a century
Bibliographic record
Abstract
Background: The interaction mechanisms between obesity and psychological factors are intricate and bidirectional. Psychological issues can prompt unhealthy eating behaviors, impede weight management efforts, and elevate the risk of obesity. This study employs bibliometric approaches to conduct a comprehensive analysis of the knowledge structure, research hotspots, and development trends in the field of obesity and psychology, offering valuable references for future research in this area. Methods: This study draws on the Web of Science Core Collection (WoSCC) database, with "obesity" and "psychology" serving as the primary search terms. Leveraging CiteSpace (version 6.3.R1) and VOSviewer (version 1.6.20) software, bibliometric analyses were conducted on various indicators, including the number of publications, publication volume, authors, journals, references, countries, institutions, and keywords. Through co-citation analysis and keyword co-occurrence analysis, the research hotspots and developmental trajectories in this field were revealed. Results: Based on the inclusion and exclusion criteria, a total of 2,753 relevant articles were ultimately included in this study. The results indicate that since the 21st century, there has been a significant surge in the number of publications in the field of obesity and psychology. Developed countries like the United States, Canada, the United Kingdom, and Australia are at the forefront of this field. Leading research institutions include Yale University, University College London, and the University of Pennsylvania. Among the authors, GRILO CM has the highest publication output. Research hotspot keywords primarily include "depression," "stress," "emotional eating," "bariatric surgery," "intervention," "weight stigma," and "self-regulation." Current research trends reveal a marked regional imbalance in international collaboration in the field of obesity and psychology. In particular, there exists a notable absence of substantive cooperation between developed and developing countries. Research hotspots mainly center around the following aspects: Firstly, it focuses on the prevalence of common psychological distress symptoms, including depression, anxiety, and stress, within the obese population and the implications these symptoms have for health. Secondly, mental health issues like binge eating and emotional eating play a pivotal role in the onset and maintenance of obesity. Thirdly, psychosocial factors like health-related quality of life and weight stigma are at the core of obesity intervention and have potential impacts on behavioral change. Meanwhile, researchers are increasingly concentrating on the individualized mental health requirements of obese populations, emphasizing the importance of evidence-based psychological interventions in the management of obesity. These research hotspots not only enhance our understanding of the complex relationship between obesity and mental health but also provide crucial theoretical foundations and practical insights for future research directions. Conclusion: This study employs bibliometric approaches to conduct a comprehensive and in-depth analysis of research trends and developments in the field of obesity and psychology. The research reveals the current status and characteristics of this field from multiple perspectives, offering scientific backing for researchers to identify potential collaborators, pinpoint hotspot issues, and keep abreast of the latest developments. Looking forward to the future, related research can further expand data sources, diversify research viewpoints, and delve more profoundly into the complex relationship between obesity and mental health.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.230 | 0.298 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".