Predictors of problematic smartphone use among university students

Laurence, Paulo Guirro; Busin, Yuri; Lima, Helena Scoz da Cunha; Macedo, Elizeu Coutinho de

doi:10.1186/s41155-020-00147-8

Cited by 23 publications

(22 citation statements)

References 55 publications

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“…Another reason for the difference may be the distinct socioeconomic indicators, which impact the profile of smartphone use. Some authors have identified an association between income and higher frequency of Internet use (Laurence et al 2020;Pawlowska et al 2015;Wu et al 2016). In Brazil, the country experienced intense Internet penetration also in more isolated regions, bringing Internet access to many people through smartphones (ICS 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Excessive use of smartphones can lead to addictive behaviour and has been associated with a higher frequency of psychological problems (Alhassan et al 2018;Laurence et al 2020;Lee et al 2020), lower quality of life and physical problems (Horwood and Anglim 2019), especially in the musculoskeletal system (Akodu et al 2018), and reduced sleep quality (Wang et al 2019). Although the current version of the American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM-5) only includes Internet-gaming disorder in Section III, as a behavioural addiction (APA 2013), smartphone addiction is associated with intensive use of the Internet and has been increasingly studied because of its impact on the population, especially among those younger one.…”

Section: Introductionmentioning

confidence: 99%

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Validity and Reliability of the Brazilian Version of the Smartphone Addiction Scale-Long Version (SAS-LV)

et al. 2020

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This study performed a cross-cultural adaptation and identified validity evidences of the smartphone addiction scale-long version (SAS-LV) in a sample of Brazilian university students (n = 504; M age = 21.7; SD = 3.03) and adults (n = 578; M age = 37.8; SD = 11.8). The adaptation of the SAS-LV was performed based on specific protocols, with the participation of experts in the field. The factorial structure was evaluated by using confirmatory factor analysis (CFA), principal component analysis (PCA), and network analysis (NA). The CFA did not detect adequate parameters, but the PCA presented a five-factor solution, explaining 55.2% of the variance. All items from the original version were retained because they had a factor load above 0.3. The SAS-LV presented good reliability indexes (α = .93). The NA indicated that the instrument had good psychometric properties for the two populations studied. The Portuguese version of the SAS-LV presented good validity indexes and can be used as a screening tool for smartphone addiction. Este estudo realizou a adaptação transcultural e identificou evidências de validade da smartphone addiction scale-long version (SAS-LV) para uma amostra de universitários (n = 504; M idade = 21,7; DP = 3,03) e adultos (n = 578; M idade = 37,8; DP = 11,8) brasileiros. A adaptação da SAS-LV foi realizada a partir de protocolos específicos, com a participação de especialistas da área. A estrutura fatorial foi avaliada pela análise fatorial confirmatória (AFC), análise de componentes principais (ACP) e análise de rede (AR). A AFC não apresentou parâmetros adequados, mas a ACP apresentou uma solução com cinco fatores, explicando 55,2% da variância. Todos os itens da versão original foram mantidos porque apresentaram carga fatorial acima de 0,3. A SAS-LV apresentou bons índices de confiabilidade (α = 0,93). A AR indicou um bom ajuste psicométrico do instrumento para as duas populações estudadas. A versão em português da SAS-LV apresentou bons índices de validade, podendo ser utilizada como instrumento de rastreio para a dependência de smartphone.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validity and Reliability of the Brazilian Version of the Smartphone Addiction Scale-Long Version (SAS-LV)

et al. 2020

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“…Although the number of samples meets the planned number of studies, the number of samples is still small, compared with other studies. It is important to acknowledge that our sample was small, generating smaller power in our results (Laurence et al, 2020). Secondly, when we define emoji with an opposite meaning, we mainly consider a specific meaning, context, and language culture.…”

Section: Discussionmentioning

confidence: 99%

A study on the embarrassment of senders who missend emojis with opposite meanings on social apps: taking WeChat as an example

Liu

Hsu

Zhang

et al. 2020

Psicol. Refl. Crít.

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With the increasing popularity of social apps, sending emojis has become a very common way of expressing one's emotions. However, situations often arise when people send the wrong emoji by mistake, or sometimes even an emoji with an opposite meaning, which can cause embarrassment to the sender. Taking WeChat as an example, which is widely used in Chinese communities, this study summarizes 10 types of dialogue situations in which the meaning of an emoji is wrongly sent and 12 types of emotional components that are related to embarrassment. The purpose of this study was to analyze the extent to which the 12 emotional components that are associated with embarrassment actually explain what embarrassment is, as well as the different degrees of embarrassment among the different genders and age groups. The results showed that (1) among the emotional components of embarrassment, shame has the highest explanation degree for embarrassment; (2) males are more likely to be affected by embarrassment than females; and (3) users aged 18-25 and 26-30 years are more likely to be affected by embarrassment than those aged between 31 and 40 when they mistakenly send WeChat emojis. This study provides a reference value for their sustainable psychological impact on social app users.

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“…Other works accessed do not allow similar comparisons. However, a study conducted at Mackenzie Presbyterian University, in the City of Sao Paulo (Brazil), showed that 124 (48.2%) students from the faculty of humanities, 43 (16.7%) from natural sciences, 7 (2.7%) from applied and formal sciences, and 83 (32.3%) from others [40] have some level of smartphone dependency. The department with the highest proportion showing signs to smartphone dependency was the health school (n = 259; 77.8%), mostly belonging to the medical academic program (n = 47; 83.9%).…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

Giraldo-Jiménez

Gaviria-Chavarro

Urrutia-Valdés

et al. 2021

Preprint

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Background: Machine learning techniques allow highly accurate prediction of different tasks by measuring the event probabilities. This research proposes a prediction model for dependency on smartphones based on machine learning techniques. Methods: We performed an analytical observational study with a retrospective case–control approach; the different classification methods used were decision tree, random forest, logistic regression, and support vector machine. The sample demographic included 1228 students from a private university in Cali. The tests were 1) smartphone dependency assessment and 2) the Nordic musculoskeletal symptoms questionnaire. Results: It was found that some of the variables related to smartphone dependency are academic curriculum, school, marital status, socioeconomic status, rules, discussions, and discrimination. Conclusions: The support vector machine model evidences highest prediction precision for smartphone dependency, obtained through the stratified-k-fold cross-validation technique.

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Predictors of problematic smartphone use among university students

Cited by 23 publications

References 55 publications

Validity and Reliability of the Brazilian Version of the Smartphone Addiction Scale-Long Version (SAS-LV)

Validity and Reliability of the Brazilian Version of the Smartphone Addiction Scale-Long Version (SAS-LV)

A study on the embarrassment of senders who missend emojis with opposite meanings on social apps: taking WeChat as an example

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

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