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Predictors of problematic smartphone use among university students
Predictors of problematic smartphone use have been found mainly in studies on elementary and high school students. Few studies have focused on predictors related to social network and messaging apps or smartphone model. Thus, the objective of our study was to identify predictors of problematic smart...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237596/ https://www.ncbi.nlm.nih.gov/pubmed/32430727 http://dx.doi.org/10.1186/s41155-020-00147-8 |
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author | Laurence, Paulo Guirro Busin, Yuri da Cunha Lima, Helena Scoz Macedo, Elizeu Coutinho |
author_facet | Laurence, Paulo Guirro Busin, Yuri da Cunha Lima, Helena Scoz Macedo, Elizeu Coutinho |
author_sort | Laurence, Paulo Guirro |
collection | PubMed |
description | Predictors of problematic smartphone use have been found mainly in studies on elementary and high school students. Few studies have focused on predictors related to social network and messaging apps or smartphone model. Thus, the objective of our study was to identify predictors of problematic smartphone use related to demographic characteristics, loneliness, social app use, and smartphone model among university students. This cross-sectional study involved 257 Brazilian university students who answered a smartphone addiction scale, a questionnaire about smartphone usage patterns, and the Brazilian version of the UCLA-R loneliness scale. Women, iPhone owners, and users of Instagram and Snapchat had significantly higher smartphone addiction scores. We found correlations between scores for the Brazilian version of smartphone addiction scale and the importance attributed to WhatsApp, Facebook, Instagram, and Snapchat, and the Brazilian version of the UCLA-R loneliness scale. Our hierarchical regression model predicted 32.2% of the scores of the Brazilian version of the smartphone addiction scale, with the greatest increase in predictive capability by the step that added smartphone social app importance, followed by the step that added loneliness. Adding the smartphone model produced the smallest increase in predictive capability. The theoretical and practical implications of these results are discussed. |
format | Online Article Text |
id | pubmed-7237596 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-72375962020-05-27 Predictors of problematic smartphone use among university students Laurence, Paulo Guirro Busin, Yuri da Cunha Lima, Helena Scoz Macedo, Elizeu Coutinho Psicol Reflex Crit Research Predictors of problematic smartphone use have been found mainly in studies on elementary and high school students. Few studies have focused on predictors related to social network and messaging apps or smartphone model. Thus, the objective of our study was to identify predictors of problematic smartphone use related to demographic characteristics, loneliness, social app use, and smartphone model among university students. This cross-sectional study involved 257 Brazilian university students who answered a smartphone addiction scale, a questionnaire about smartphone usage patterns, and the Brazilian version of the UCLA-R loneliness scale. Women, iPhone owners, and users of Instagram and Snapchat had significantly higher smartphone addiction scores. We found correlations between scores for the Brazilian version of smartphone addiction scale and the importance attributed to WhatsApp, Facebook, Instagram, and Snapchat, and the Brazilian version of the UCLA-R loneliness scale. Our hierarchical regression model predicted 32.2% of the scores of the Brazilian version of the smartphone addiction scale, with the greatest increase in predictive capability by the step that added smartphone social app importance, followed by the step that added loneliness. Adding the smartphone model produced the smallest increase in predictive capability. The theoretical and practical implications of these results are discussed. Springer International Publishing 2020-05-19 /pmc/articles/PMC7237596/ /pubmed/32430727 http://dx.doi.org/10.1186/s41155-020-00147-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Laurence, Paulo Guirro Busin, Yuri da Cunha Lima, Helena Scoz Macedo, Elizeu Coutinho Predictors of problematic smartphone use among university students |
title | Predictors of problematic smartphone use among university students |
title_full | Predictors of problematic smartphone use among university students |
title_fullStr | Predictors of problematic smartphone use among university students |
title_full_unstemmed | Predictors of problematic smartphone use among university students |
title_short | Predictors of problematic smartphone use among university students |
title_sort | predictors of problematic smartphone use among university students |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237596/ https://www.ncbi.nlm.nih.gov/pubmed/32430727 http://dx.doi.org/10.1186/s41155-020-00147-8 |
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