Physical Exercise in the Context of Air Pollution: An Emerging Research Topic
Notice bibliographique
Résumé
Physical exercise (PE) brings physiological benefits to human health; paradoxically, exposure to air pollution (AP) is harmful. Hence, the combined effects of AP and PE are interesting issues worth exploring. The objective of this study is to review literature involved in AP-PE fields to perform a knowledge-map analysis and explore the collaborations, current hotspots, physiological applications, and future perspectives. Herein, cluster, co-citation, and co-occurrence analysis were applied using CiteSpace and VOSviewer software. The results demonstrated that AP-PE domains have been springing up and in rapid growth since the 21st century. Subsequently, active countries and institutions were identified, and the productive institutions were mainly located in USA, China, UK, Spain, and Canada. Developed countries seemed to be the major promoters. Additionally, subject analysis found that environmental science, public health, and sports medicine were the core subjects, and multidimensional communications were forming. Thereafter, a holistic presentation of reference co-citation clusters was conducted to discover the research topics and trace the development focuses. Youth, elite athletes, and rural population were regarded as the noteworthy subjects. Commuter exposure and moderate aerobic exercise represented the common research context and exercise strategy, respectively. Simultaneously, the research hotspots and application fields were elaborated by keyword co-occurrence distribution. It was noted that physiological adaptations including respiratory, cardiovascular, metabolic, and mental health were the major themes; oxidative stress and inflammatory response were the mostly referred mechanisms. Finally, several challenges were proposed, which are beneficial to promote the development of the research field. Molecular mechanisms and specific pathways are still unknown and the equilibrium points and dose-effect relationships remain to be further explored. We are highly confident that this study provides a unique perspective to systematically and comprehensively review the pieces of AP-PE research and its related physiological mechanisms for future investigations.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».