Obesity and psychology: a bibliometric analysis of half a century
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,065 | 0,086 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».