Mapping Hot Spots and Global Research Trends in Exergaming Between 1997 and 2024: Bibliometric Analysis
Notice bibliographique
Résumé
Background: Exergaming, the combination of exercise and gaming, has emerged as an important area in physical activity (PA) research. By leveraging advances in video game technology, exergaming supports both physical and mental health. This growing interest in exergaming has increasingly attracted researchers over the years. Examining the development of exergaming research with a bibliometric approach is thought to offer valuable perspectives to researchers by revealing the trends and main contributions in the field. Objective: This study aims to identify the most researched concepts and topics in the field of exergaming; track the changes of trending topics over the years; identify the most influential journals as well as the authors who have contributed the most to the field; identify groundbreaking studies and neglected topics that shape future work; and reveal the countries, institutions, and collaborations that have contributed the most to the field. It also aims to identify research gaps in the field of exergaming and provide important recommendations for future research. Methods: A bibliometric analysis covering studies between 1997 and 2024 was conducted using the Web of Science database. The R-based Bibliometrix package and the Biblioshiny web interface were used for data analysis and visualization. The analysis included original research papers and reviews. These analyses provided insights into research trends, citation metrics, and thematic developments. Results: A total of 1626 studies were analyzed, and the results indicated a steep rise in exergaming research since 2015, peaking in the years 2020-2021. Major high-impact journals publishing in this area include Games for Health Journal and International Journal of Environmental Research and Public Health. Researchers who have contributed significantly and enriched the knowledge base of the exergaming field included Gao Zan, Eling de Bruin, and Zeng Nan. The most cited studies were classified into 2 different clusters, namely, cluster 1 that focuses on the concepts of PA, exercise, energy expenditure, and children, while cluster 2 focuses on rehabilitation, balance, adults, and aging. Medicine, information technology, and intention are some of the emerging themes. From a research productivity perspective, there is an undisputed front-runner, the United States, but substantial contributions have definitely come from either the Swiss Federal Institute of Technology or the Karolinska Institute. Conclusions: Despite significant growth in exergaming research over the last decade, research gaps remain, particularly in understanding how exergaming can be effectively integrated into long-term PA promotion and broader health outcomes. These gaps were identified by the absence or low representation of relevant keywords (eg, "cost-effectiveness," "community-based intervention," and "long-term health outcomes") in thematic mapping and keyword trend analyses and limited citation density in these areas. Future work should explore these issues more systematically to advance the field.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,215 | 0,290 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».