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Physical Activity and Exercise: Text Mining Analysis

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Physical Activity and Exercise: Text Mining Analysis

dc.contributor.author	Pans Sancho, Miquel Àngel
dc.contributor.author	Madera Gil, Joaquín
dc.contributor.author	González Moreno, Luis Millán
dc.contributor.author	Pellicer Chenoll, María Teresa
dc.date.accessioned	2021-09-14T11:04:25Z
dc.date.available	2021-09-14T11:04:25Z
dc.date.issued	2021
dc.identifier.citation	Pans Sancho, Miquel Àngel Madera Gil, Joaquín González Moreno, Luis Millán Pellicer Chenoll, María Teresa 2021 Physical Activity and Exercise: Text Mining Analysis International Journal Of Environmental Research And Public Health 18 18 9642
dc.identifier.uri	https://hdl.handle.net/10550/80301
dc.description.abstract	It is currently difficult to have a global state of the art vision of certain scientific topics. In the field of physical activity (PA) and exercise, this is due to information overload. The present study aims to provide a solution by analysing a large mass of scientific articles using text mining (TM). The purpose was to analyse what is being investigated in the PA health field on young people from primary, secondary and higher education. Titles and abstracts published in the Web of Science (WOS) database were analysed using TM on 24 November 2020, and after removing duplicates, 85,368 remained. The results show 9960 (unique) words and the most frequently used bi-grams and tri-grams. A co-occurrence network was also generated. 'Health' was the first term of importance and the most repeated bi-grams and tri-grams were 'body_mass' and 'body_mass_index'. The analyses of the 20 topics identified focused on health-related terms, the social sphere, sports performance and research processes. It also found that the terms health and exercise have become more important in recent years.
dc.relation.ispartof	International Journal Of Environmental Research And Public Health, 2021, vol. 18, num. 18, p. 9642
dc.subject	Esports
dc.subject	Educació
dc.subject	Lingüística
dc.title	Physical Activity and Exercise: Text Mining Analysis
dc.type	journal article	es_ES
dc.date.updated	2021-09-14T11:04:26Z
dc.identifier.doi	10.3390/ijerph18189642
dc.identifier.idgrec	147828
dc.rights.accessRights	open access	es_ES