Technological progress is one of the hallmarks of the new millennium. The use of smartphones has increased substantially over time, in part due to the many apps that these devices can support (Andrew, 2018). Although smartphones can be very useful and benefit the lives of many people, they can also bring problems such as “smartphone addiction” (Alosaimi, Alyahya, Alshahwan, Al Mahyijari, & Shaik, 2016; Boumosleh & Jaalouk, 2017). The term “addiction” is used when a person’s obsession with a certain activity is problematic to one’s daily life; smartphone addiction has patterns that are similar to substance dependency (Kwon, Kim, Cho, & Yang, 2013). For example, smartphone restriction can cause withdrawal symptoms (Eide, Aarestad, Andreassen, Bilder, & Pallesen, 2018).
Currently, the accuracy of the term “smartphone addiction” is being questioned. Panova and Carbonell (2018) explained that smartphone addiction is not really an addiction because crucial characteristics of an addiction are not achieved in the smartphone addiction construct. Examples of the lacking characteristics include the following: (1) the absence of severe physical consequences—one important characteristic of an addiction—as smartphone users have at most wrist and neck pain; (2) salience—the concept that the activity of addiction becomes the most relevant activity of the addicted—may not be true in smartphone addiction because smartphones mediate the social, professional, and personal lives of the user; (3) lack of longitudinal studies that confirm stability of the addiction as well as relapses, which are important aspects of addiction; and (4) smartphone addiction can be better explained by other conditions, such as an insecure attachment style, reassurance behavior, and other conditions, while a true addiction is not better explained by another condition. Panova and Carbonell (2018) also suggest that the smartphone may not be the addiction element, but rather the object related to an addiction; they compare the smartphone with the glass in alcohol addiction or the needle in heroin addiction. Further, Veissière and Stendel (2018) contend that “smartphone addiction” is not a real addiction, but simply a human desire to connect with other humans. As a result, Panova and Carbonell (2018) suggest abandoning the terminology of “smartphone addiction” and using “problematic smartphone use” instead, at least until there is more evidence to confirm the existence of a smartphone addiction.
Montag, Wegmann, Sariyska, Demetrovics, and Brand (2019) proposed that problematic smartphone use is essentially a type of Internet use disorder. In this sense, Internet use disorder should be divided into two areas: predominantly mobile and predominantly non-mobile. This new categorization may help problematic smartphone use to be recognized in the new International Classification of Diseases, under the umbrella of Internet use disorder, and accept the idea that the smartphone is just a way to use Internet, and not the problem itself.
A model was created to explain the interaction between several individual characteristics with Internet use disorder. The model is called “I-PACE” and is an acrogram for Interaction of Person-Affect-Cognition-Execution. This model accounts for biopsychological and social features of the person (e.g., genetics, personality, psychopathologies, motives to use), affect and cognition (e.g., attention, mood regulation, coping), and executive functions (e.g., inhibitory control, working memory (Brand, Young, Laier, Wölfling, & Potenza, 2016; Brand et al., 2019)).
Several studies have investigated the reasons for and the impact of excessive smartphone use, as well as potential risk factors for this behavior (e.g., Oviedo-Trespalacios, Nandavar, Newton, Demant, & Phillips, 2019; Lee, Kim, & Choi, 2017). In Australia, Oviedo-Trespalacios et al. (2019) found that there are more cases of problematic smartphone use now than the number of mobile phone users in 2005, particularly in 18–25-year-olds. In a study involving Korean adolescents, Lee and Lee (2017) reported that being female, focusing too much on the device, and having conflicts in real life due to excessive and ubiquitous smartphone use were risk factors for problematic smartphone use, while use of the device for learning was a protective factor. Others have also reported that women present more problematic smartphone use than men (Kwon, Kim, et al., 2013; Lapointe, Boudreau-Pinsonneault, & Vaghefi, 2013). Family income as a possible risk factor for problematic smartphone use has also been investigated by several researchers, but most research found no significant association (Aljomaa, Al Qudah, Albursan, Bakhiet, & Abduljabbar, 2016; Alhassan et al., 2018; Cha & Seo, 2018), with one exception (Aktürk, Budak, Gültekin, & Özdemir, 2018), although this study was not specifically designed to investigate this association. Moreover, Sanal and Ozer (2017) reported no correlation between university students’ major and problematic smartphone use.
The relationship between problematic smartphone use and psychological dysfunctions, including loneliness, has also been investigated. A recent study (Aktürk et al., 2018) reported an association between loneliness and problematic smartphone use among high school students, and other studies (Bian & Leung, 2014; Darcin, Noyan, Nurmedov, Yilmaz, & Dilbaz, 2015) have found this association in university students. However, other studies found no connection between loneliness and problematic smartphone use among university students (Darcin et al., 2016; Aktürk et al., 2018), requiring further research. Problematic smartphone use has been linked to anxiety, depression, low conscientiousness, and high neuroticism (Elhai et al., 2019; Peterka-Bonetta, Sindermann, Elhai, & Montag, 2019). Problematic smartphone use has also been connected with deficits in inhibitory control (Chen, Liang, Mai, Zhong, & Qu, 2016), attention, numerical processing, and the excitability of the right prefrontal cortex (Hadar et al., 2017). Additionally, Fransson, Chóliz, and Håkansson (2018) found that smartphone use and problem gambling are sometimes related, although further studies are needed.
One important element related to smartphone use is the fear of missing out (Elhai et al., 2019; Elhai, Yang, Fang, Bai, & Hall, 2020; Oberst, Wegmann, Stodt, Brand, & Chamarro, 2017; Wegmann, Oberst, Stodt, & Brand, 2017). Montag, Lachmann, Herrlich, and Zweig (2019) suggest that smartphone apps have a list of mechanisms that makes users use their apps even more. For social media, one of the most important mechanisms is using the fear of missing out. In this way, a user would be afraid of missing a friend’s reply to a message, thus checking the app more often. Another possibility would be observing their friends using an app and having fun, creating a social pressure for them to use it as well.
Differences in the characteristics of users of different models of smartphones (e.g., iPhone vs. Samsung; iOS vs. Android) have also been investigated, with conflicting results. While Shaw, Ellis, Kendrick, Ziegler, and Wiseman (2016) reported that iPhone users were more likely to be women, to be younger, and to view their smartphones as status symbols, Götz, Stieger, and Reips (2017) found few differences in personality between iOS and Android users. A survey involving 200 Stanford University students who used an iPhone reported that 10% of the participants demonstrated problematic smartphone use, 34% were likely to develop problematic use of the device, 69% stated that they were more likely to forget their wallet than their iPhone, and 41% said that it would be “a tragedy” if they lost their smartphone (Hope, 2010).
There has also been research on the social networks accessed by smartphone users because of concerns about their problematic use of networks (Kuss & Griffiths, 2017), especially for Instagram users (Huang & Su, 2018; Kicaburun & Griffiths, 2018). Instagram users state that they are motivated to access this network because they want to view posts and become involved in social interaction (Huang & Su, 2018). Kicaburun and Griffiths (2018) found a negative correlation between self-liking and Instagram problematic use and reported that users who spend more daily time on the Internet were the ones with the most problematic use. However, time spent on a smartphone should not be a criterion of problematic smartphone use, given that other motivations (e.g., work-related) can increase smartphone use (Billieux et al., 2015; King, Herd, & Delfabbro, 2018). Similarly, this caution should also be applied to using the number of messages as criterion (Panova & Carbonell, 2018). Several studies have examined the negative effects of the overuse of social networks and found that overuse is linked with depression, difficulty communicating face-to-face, need for immediate rewards, neglect of offline relationships, problems in professional contexts, and loneliness (for a review, see Kuss & Griffiths, 2017). Indeed, loneliness has been associated with social network addiction (De Cock et al., 2014). Similarly, Primack et al. (2017) found that individuals who were in the highest quartile of social media use were twice as likely to feel socially isolated.
Although accessing social networks has been singled out as the most used function in smartphones (Haug et al., 2015), few studies have investigated the relationship between problematic smartphone use and the importance of social networks. Most studies have only investigated whether individuals use social networks, and if so, the daily amount of time that they spend on social networks (Arnavut, Nutri, & Direktor, 2018; Gezgin, 2018). Nevertheless, Jeong, Kim, Yum, and Hwang (2016) researched if games or social networks predicted greater problematic smartphone use in participants and found that, although games and social networks were both predictors, the stronger predictor was social networks. Salehan and Negahban (2013) also found that social networks predicted problematic smartphone use. Further, these authors demonstrated that social network intensity and network size are important factors for predicting problematic smartphone use. Additionally, Sha, Sariyska, Riedl, Lachmann, and Montag (2019) demonstrated that there is a specific relation between problematic smartphone use and Whatsapp and Facebook use disorders. These authors argue that the problematic use of smartphones is more strongly related with Whatsapp use disorder than with Facebook use disorder and that this association is more likely to be present in women. Lastly, Sha et al. (2019) affirm that this relation is mediated through the fear of missing out.
Other studies have tried to predict problematic smartphone use (e.g., Bian & Leung, 2014; Kim et al., 2016; Lachmann, Duke, Sariyska, & Montag, 2019; Peterka-Bonetta et al., 2019). Kim et al. (2016) used logistical regression to demonstrate that demographic variables (gender, age, education level, occupation, marital status), smartphone use (weekday average usage hours, weekend average usage hours), and personality factors (behavioral inhibition system, behavioral activation system, impulsivity, self-control) could predict problematic smartphone use predisposition. Bian and Leung (2014) used multiple linear regression to predict problematic smartphone use by age, gender, grade, family monthly income, loneliness, shyness, and smartphone usage (e.g., information seeking, utility, fun seeking, sociability); they found that these variables predicted 20% of problematic smartphone use. Loneliness and shyness were the most predictive variables, while smartphone usage was the least. Peterka-Bonetta et al. (2019) used a hierarchical regression to predict problematic smartphone use with the following predictors: age, gender, the big five personality traits, anxiety, and impulsivity. They found that demographics predicted 5.23% of the smartphone use, the big five predicted 7.17%, and anxiety and impulsivity predicted 4.18%, totaling a prediction of 16.58% of the variance of the smartphone use. Furthermore, Lachmann et al. (2019) used a hierarchical regression to predict “problematic digital use,” a composite score between problematic Internet use and problematic smartphone use. Their final model had age, gender, self-directedness, and the big five personality traits as the predictors. Specifically, the demographic variables accounted for 2.6% of the variance in the problematic digital use, self-directness accounted for 15.6%, and the big five accounted for 5.0%, totaling for 23.2% of the variance explained.
Similarly, Mitchell and Hussain (2018) used multiple linear regression and found that age and personality/psychological traits (impulsiveness, extraversion, excessive reassurance, and depression) could predict problematic smartphone use; they found that the variance of these variables explained 23% of problematic smartphone use variance. Furthermore, Lee and Lee (2017) demonstrated that demographic variables (i.e., gender), attachment variables (i.e., attachment to parents), and school life motivations (i.e., obtaining infotainment, gaining peer acceptance, finding new people) are predictors of proneness to problematic smartphone use in middle and high school students; these variables predicted 27.1% of the variation of proneness to problematic smartphone use. Moreover, Durak (2018) found that demographic variables (i.e., gender, age, educational level), variables related to parents (i.e., mother’s education level), information technology usage variables (i.e., Internet usage experience, daily Internet usage time), smartphone usage variables (i.e., smartphone control frequency, daily smartphone usage time, smartphone usage experience, smartphone usage purpose), and school achievement variables (i.e., mathematics achievement, science achievement, language lesson achievement, social sciences achievement, information technology achievement) could predict problematic smartphone use; these variables explained almost 50% of the problematic smartphone use variance in secondary and high school students in Turkey.
Notably, there is common ground in these regression studies. Participant age and sex were used as variables in almost every study. Additionally, economic status was measured by family monthly income, mother’s education level, education level, and occupation. Some studies focused on psychological aspects such as loneliness, shyness, personality traits, and attachment styles (e.g., Lachmann et al., 2019; Peterka-Bonetta et al., 2019). These variables are all in accordance with the I-PACE model (Brand et al., 2016; 2019), specifically the person variables. Additionally, studies (e.g., Durak, 2018) have considered the content participants access on their smartphones (i.e., smartphone usage, smartphone usage experience, smartphone usage purpose) and time spent on smartphones; however, time spent is not a good measure because it does not provide information about what the participant is doing during that time (Panova & Carbonell, 2018).
Although previous studies identified some problematic smartphone use predictors, most of them studied elementary and high school students (e.g., Lee & Lee, 2017; Durak, 2018). Additionally, only a few models tried to relate social apps (social network apps and messaging apps), despite that they are an important aspect of problematic smartphone use. Finally, to the best of our knowledge, no study created models using the smartphone model as a predictor, even though this may be an important variable for problematic smartphone use (Hope, 2010). Therefore, the objective of this study was to identify predictors of problematic smartphone use in university students among demographic characteristics, loneliness, social app use, and smartphone model.
To reach this objective, we selected variables that have already been studied in the literature and added other variables that have not been studied but may be related to smartphone use. The variables are age, gender, family income, university major, smartphone model/operating system, social networks used, the importance attributed to smartphone social apps, and loneliness. The relevance of this type of study is due to a greater need to understand the relationship between problematic smartphones use and problematic social network use. As most recent theories point out (Montag et al., 2019), the two concepts probably overlap within the problematic Internet use, in which case it is necessary to raise more evidence, especially in cultures that escape the rich and industrialized context, as is the case in most research reported. Furthermore, it is important to understand how the use of smartphones can be healthy and productive.
Based on previous research, we hypothesized the following:
- 1.
Age was negatively correlated with smartphone use (Bian & Leung, 2014; Kim et al., 2016).
- 2.
Women would have a higher score on problematic smartphone use (Lee et al., 2017).
- 3.
No relation would be found between family incomes and smartphone use (Aktürk et al., 2018; Alhassan et al., 2018; Aljomaa et al., 2016; Cha & Seo, 2018).
- 4.
No relation would be found between university majors and smartphone use (Sanal & Ozer, 2017).
- 5.
Users of iPhone/iOS would demonstrate more problematic smartphone use (Hope, 2010).
- 6.
More use of different social network apps was related with higher smartphone usage (Arnavut et al., 2018; Gezgin, 2018; Haug et al., 2015; Kircaburun & Griffiths, 2018).
- 7.
The importance attributed to the social networks was directly correlated with problematic smartphone use (Arnavut et al., 2018; Gezgin, 2018; Haug et al., 2015).
- 8.
And, loneliness will be positively correlated with problematic smartphone use (Bian & Leung, 2014; Darci et al., 2015).
This is one of the first studies on smartphone use originating in Brazil as well as South America. Given that this is a problem that has gained popularity due to its importance, data from this region of the world are missing, which makes this study highly relevant.