Detalhes bibliográficos
| Ano de defesa: |
2023 |
| Autor(a) principal: |
BEZERRA, Sonny Állan Silva
 |
| Orientador(a): |
SALVADOR, Emanuel Péricles
|
| Banca de defesa: |
SALVADOR, Emanuel Péricles
,
SANTOS, Davi Viana dos
,
SOUZA, Bruno Feres de
,
REIS, Andréa Dias
,
BRAZ JUNIOR, Geraldo
|
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
|
| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUACAO EM EDUCAÇÃO FÍSICA
|
| Departamento: |
DEPARTAMENTO DE EDUCAÇÃO FÍSICA/CCBS
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
https://tedebc.ufma.br/jspui/handle/tede/4545
|
Resumo: |
Objective: this research aimed to describe the state of the art of the use of data obtained in social media in the surveillance of outcomes related to physical activity and sedentary lifestyle. Methodology: this is a systematic review of articles published between 2012 and 2022 and present in the PubMed/MEDLINE, Embase and El Compendex databases, analyzed by a team composed of a principal researcher, 2 auxiliary researchers and 2 coordinating researchers with extensive experience. The searches were made between May 19, 2022 and June 14, 2022 and the process followed the stages of systematic search, filtering of articles by title and abstract, selection of articles after full reading, data extraction and analysis of the risk of bias. The review included original peer-reviewed articles of an observational nature, where physical activity or sedentary lifestyle was one of the primary or secondary outcomes and the data used were obtained on social media. Results: A total of 6447 records were found and 20 publications were included in the review. Twitter was the social media investigated in 17 articles. Content analysis was used in all articles and in 13 publications analysis techniques and sentiment classification were identified. Data geoprocessing was observed in 14 studies and machine learning was used in nine studies to collect and analyze data. Most of the studies presented moderate-good quality, with an average of eight points on a scale from zero to 10. Six studies evaluated the feasibility of using data and techniques for processing or extracting these data, all with positive results and superior or superior methodological quality. equal to average. Conclusion:The use of data from social media to investigate outcomes related to physical activity is feasible and potentially useful in monitoring behaviors and feelings. However, presented sociodemographic limitations may compromise the external validity of the results and ethical issues related to the use of this type of data still need to be discussed. |
