Skip to main content
NCBI home page
Wiley - PMC COVID-19 Collection logoLink to Wiley - PMC COVID-19 Collection
. 2022 Nov 12:10.1111/jcap.12401. Online ahead of print. doi: 10.1111/jcap.12401

Factors related to self‐reported smartphone addiction among Brazilian adolescents in the face of the COVID‐19 pandemic: A mixed‐method study

Bruna H B M de Freitas 1,✉, Maria Aparecida M Gaíva 1, Paula M J Diogo 2, Juliano Bortolini 3
PMCID: PMC9877641  PMID: 36371611

Abstract

Problem

(1) To identify the factors associated with self‐reported smartphone addiction (SRSA) among adolescents in the face of the COVID‐19 pandemic; and (2) to analyze the adolescents' perception of these factors related to SRSA.

Methods

A mixed‐method study with a sequential explanatory design, carried out with Brazilian adolescents aged between 15 and 18 years old.

Findings

The prevalence of SRSA was 56.37%, and the variables that remained in the final model of association were as follows: public schools; longer smartphone use during the COVID‐19 pandemic; number of hours connected to the smartphone; preference for sleeping during the day; use of the device immediately after waking up, smartphone use after 9 p.m., amount of sleep less than 8 h a day; and smartphone use during meals. Sequentially, after analyzing the data obtained in the focus groups, it was possible to describe how adolescents perceive the intensification of smartphone uses, its repercussions, and activities carried out on it during the pandemic.

Conclusions

The pandemic had repercussions on the behavior established with the smartphone, such as time and period of use, being associated with the SRSA. In addition, it was found that such conditions also affect the adolescents' sleep quality, diet, and studies.

Keywords: adolescents' behavior, Covid‐19 pandemic, smartphone addiction

1. INTRODUCTION

Smartphone addiction is conceptualized by many scholars as the maladaptive or obsessive‐compulsive use of smartphones (Yu & Sussman, 2020), involving interaction between man and the device, causing neglect in other life areas (Griffiths, 1996). However, some researchers in the field argue that current knowledge still does not support the claim that this term is the most appropriate, and propose the use of alternative ones, such as self‐reported smartphone addiction (SRSA) when it comes to studies that use self‐reporting instruments (Panova & Carbonell, 2018). Therefore, this term, SRSA, will be adopted in this study.

Use of the device by human beings has been increasingly precocious, even in early childhood (Rich et al., 2019). In adolescence, individuals spend hours on their smartphones, developing a range of activities, among which games and social networks stand out. In addition, this pandemic has kept much of humanity indoors, replacing physical contact with virtual electronic connections, increasing the adolescents' screen time. Although for the vast majority its moderate use is adaptable and harmless, some vulnerable individuals are likely to develop harmful use patterns (Király et al., 2020).

Habitual smartphone use is associated with SRSA, and the greater the number of hours connected to it, the greater the probability of the individual developing it (Cha & Seo, 2018; Santana‐Vega et al., 2019). During the COVID‐19 pandemic, adolescents spent more than 5 h a day online (Duan et al., 2020), which can be a potential risk factor for addiction. Due to neurological immaturity, they can become more dependent on instant rewards associated with smartphones, as opposed to natural and/or delayed rewards that come from interactions with friends and family members, or with their hobbies (Chen et al., 2016; S. G. Kim et al., 2019). No group has been as affected in the emotional, cognitive and behavioral dimensions by smartphone use as children and adolescents (Rich et al., 2019), even more so since the pandemic.

Investigation of the phenomenon in Brazil is still incipient, especially in light of the COVID‐19 pandemic. In addition, there are few studies that investigate the adolescents' perception about the factors associated with SRSA through mixed methods. The hypothesis for this research is that the behavior established with the smartphone during the COVID‐19 pandemic, such as the time and modes of use of the device, is related to SRSA. These findings are essential to understand reality and to trace paths to ensure health in the process of growing up in the smartphone age. Therefore, this study intended to: (1) To identify the factors associated with SRSA among adolescents in the face of the COVID‐19 pandemic; and (2) To analyze the adolescents' perception of these factors related to SRSA.

2. METHODS

This was a mixed‐method study with a sequential and explanatory design, guided by the Mixed methods appraisal tool (Hong et al., 2018). In the first phase quantitative (QUAN) an observational cross‐sectional study was carried out and, in the second, an exploratory research with a qualitative (qual) approach (Creswell, 2012). The representative diagram of the research is shown in Figure 1.

Figure 1.

Figure 1

Open in a new tab

Diagram representing the study design. QUAN, quantitative.

The research participants were adolescents from 21 public schools and four privates from Cuiabá, Mato Grosso, Brazil. As an eligibility criterion, participants were adolescents aged between 15 and 18 years, enrolled in one of the participating schools and who had a smartphone with internet access. The sample size of the quantitative study was calculated, resulting in an expected minimum sample of 377 individuals (287 from public schools and 90 from private educational institutions). Although 495 young adolescents participated in the study, 16 answers were excluded due to duplicity. Therefore, the final sample consisted of 479 subjects (27.1% higher than expected).

Data collection took place via an electronic form, which contained a questionnaire with 25 items of Sociodigital variables and the 26 items of the Smartphone Addiction Inventory (SPAI‐BR) instrument. The SPAI‐BR items are presented in a dichotomous version (yes and no), in which the adolescent marks one of the alternatives. These items are arranged in four domains: compulsive behavior, functional impairment, withdrawal, and tolerance (Khoury et al., 2017). In 2021, the scale was validated with Brazilian adolescents with good internal consistency (α = 0.88) and a cutoff point of 10 (for answers yes) was determined because it achieved the best sensitivity (79.87) and specificity (78.15) (Andrade et al., 2021). This cutoff point was therefore adopted in this research.

The students were invited to participate in the study through group messages on instant messaging apps administered by the schools or through email messages directed to the adolescents' parents or guardians. The invitation to participate in the research, sent by the school management, contained information and clarifications regarding the research and the access link to the electronic form, as well as the contact corresponding to the researcher. The quantitative data collection period was from April to July 2021.

Then, the descriptive analysis of the variables under study was performed. The unadjusted odds ratio (ORunad) was calculated through simple logistic regression separately associating the SRSA response variable for each of the independent variables: age, gender, teaching network, longer smartphone time during the pandemic, smartphone connection time during the pandemic (in hours), sleep preference during the pandemic, smartphone use immediately after waking up during the pandemic, smartphone use after 9 p.m., daily sleep time, smartphone use at school, smartphone use during meals, and social media as main activity.

In addition to the ORunad, the adjusted odds ratio (ORa) was calculated from the multiple logistic regression model obtained by the backward method, with permanence of the variable in the model if its significance was less than 5%. All independent variables of this model were the same as those of the simple logistic regression model. Subsequently, the likelihood ratio test was performed, as well as the R 2 R2Nagelkerke and the Hosmer and Lemeshow test measures to verify adequacy of the model. The measure of internal consistency of the SPAI‐BR for the data of this research resulted in Cronbach's α equal to 0.88, showing good internal consistency. All statistical analyses were performed in the R software.

Sequentially, data collection (qual) was carried out through five focus groups (FGs) in July 2021 with 16 adolescents who had participated in the first phase (QUAN). These youth were intentionally classified as smartphone addiction via their scores on the SPAI‐BR. Recruitment was terminated due to reaching sufficient information power to meet the objective proposed. This is considered a pragmatic model for evaluating and defining participant thresholds in qualitative studies. It is a manageable strategy, in which some dimensions are considered for the establishment of participants, such as: study objective, sample specificity, established theory, quality of dialogue, and analysis strategy (Malterud et al., 2016).

The FGs was carried out by a nurse (moderator) and two undergraduate Nursing students (reporters). The meetings were held by means of a video call in an instant messaging app, consisting of the following key moments: opening of the session, welcoming of the participants, clarification on the dynamics of participatory discussion, definition of the setting, debate, synthesis, and closure of the session (Kinalski et al., 2017). Each FG lasted between 51 and 106 min across the groups.

To respond to the objective of this study, a semistructured script for the FGs was elaborated from the following guiding questions: “Tell me about your relationship with the smartphone these days,” “Did you start to stay connected for longer periods of time after the pandemic started?,” and “What do you think contributes to intensive smartphone use?.” The meetings were transcribed in full, later organized by codes and submitted to deductive content analysis, with the preestablished Sociodigital variables as initial analysis categories (Bardin, 2016). The analysis process was supported by the Atlas.ti 9.1.5.0 software. The participants' statements were presented with the fictitious name chosen by them, their age and their SPAI‐BR score.

After collecting and analyzing the quantitative and qualitative data, in clear and separate stages, data were combined together with the discussion, through the connection and joint assessment of the quantitative and qualitative results interpreted.

This study was approved by the Ethics and Research Committee with Human Beings from the Health Area of the Federal University of Mato Grosso under Opinion No. 4,661,013. There was authorization and support from the participating schools, from the parents or guardians for participants under 18 years of age via the Free and Informed Consent Form, and from the participants through their signing of the Free and Informed Assent form.

3. RESULTS

3.1. Quantitative phase

A total of 479 adolescents participated in the study and their Sociodigital characteristics are shown in Table 1. The prevalence of SRSA among young adolescents was 56.37%.

Table 1.

Sociodigital characteristics of the adolescents participating in the survey (N = 479)

Variables N (%) Mean ± SD
Age 16.03 (±1.01)
Gender
Female 355 (74.11%)
Male 124 (25.89%)
Teaching network
Private 91 (19.00%)
Public 388 (81.00%)
Year of study
High school 1st year 209 (43.63%)
High school 2nd year 136 (28.39%)
High school 3rd year 134 (27.97%)
Age at smartphone use initiation 10.63 (±2.43)
Age at first smartphone 11.01 (±2.12)
Longer smartphone time during the pandemic
No 48 (10.02%)
Yes 431 (89.98%)
Smartphone connection time during the pandemic (in hours) 10.42 (±5.81)
Sleep preference during the pandemic 404 (84.34%)
During the night 75 (15.66%)
During the day
Smartphone use immediately after waking up during the pandemic
No 107 (22.38%)
Yes 371 (77.62%)
Daily smartphone use end time during the pandemic
Evening until 6 p.m. 18 (3.76%)
Between 6 p.m. and 9 p.m. 66 (13.78%)
From 9 p.m. until midnight 253 (52.82%)
After midnight 142 (29.65%)
Daily sleep time (h/day)
≥8 243 (50.13%)
<8 236 (49.27%)
Smartphone use at school
No 76 (15.87%)
Yes 403 (84.13%)
Smartphone use during meals
No 163 (34.03%)
Yes 316 (65.97%)
Internet access network
Only via mobile data 38 (7.93%)
Only via Wi‐Fi 196 (40.92%)
Wi‐Fi and mobile data 245 (51.15%)
Main smartphone activity during the pandemic
WhatsApp 107 (22.34%)
TikTok 86 (17.95%)
Instagram 84 (17.54%)
YouTube 66 (13.78%)
Music apps 42 (8.77%)
Games 32 (6.68%)
Studies/readings 25 (5.22%)
Facebook 13 (2.71)
Audio/video call 13 (2.71%)
Twitter 4 (0.84%)
Pornography 2 (0.42%)
Others 5 (1.05%)
Self‐reported smartphone addiction
No 209 (43.63%)
Yes 270 (56.37%)
Open in a new tab

The factors associated with SRSA by simple logistic regression can be seen in Table 2. In addition, Table 2 also shows the variables that remained in the final model after multiple logistic regression, namely: public schools (ORa = 1.94; 95% CI = 1.15−3.33); longer smartphone use during the COVID‐19 pandemic (ORa = 3.19; 95% CI = 1.49−7.17); number of hours connected to the smartphone (ORa = 1.06; 95% CI = 1.02−1.10); preference for sleeping during the day (ORa = 2.30; 95% CI = 1.20−4.58); use of the device immediately after waking up (ORa = 2.13; 95% CI = 1.26−3.62); smartphone use after 9 p.m. (ORa = 1.57; 95% CI = 1.18−2.11); amount of sleep less than 8 h a day (ORa = 1.55; 95% CI = 1.01−2.40); and smartphone use during meals (ORa = 2.41; 95% CI = 1.54−3.79).

Table 2.

Factors associated with SRSA, with the respective odds ratios and confidence intervals

Variables SRSA Logistic regression
No Yes ORunad 95% CI ORa 95% CI
N (%) N (%)
Age group (years old)
15−16 141 (67.46%) 178 (65.93%) 1
17−18 68 (32.54%) 91 (34.07%) 1.07 0.73−1.57
Gender
Female 64 (30.62%) 60 (22.22%) 1
Male 145 (69.38%) 210 (77.78%) 1.54a 1.03−2.33
Teaching network
Private 51 (24.40%) 40 (14.81%) 1 1
Public 158 (75.60%) 230 (85.19%) 1.86a 1.17−2.94 1.94a 1.15−3.33
Longer smartphone time during the pandemic
No 36 (17.22%) 12 (4.44%) 1 1
Yes 173 (82.78%) 258 (95.56%) 4.47a 2.26−8.84 3.19a 1.49−7.17
Smartphone connection time during the pandemic (in hours) 1.12a 1.08−1.16 1.06a 1.02−1.10
Sleep preference during the pandemic
During the night 193 (92.34%) 211 (78.15%) 1 1
During the day 16 (7.66%) 59 (21.85%) 3.37a 1.88−6.06 2.30a 1.20−4.58
Smartphone use immediately after waking up during the pandemic
No 75 (35.89%) 33 (12.22%) 1 1
Yes 134 (64.11%) 237 (87.78%) 4.02a 2.53−6.37 2.13a 1.26−3.62
Smartphone use after 9 p.m.
No 52 (24.88%) 32 (11.85%) 1 1
Yes 157 (75.12%) 238 (88.15%) 2.46a 1.52−4.00 1.57a 1.18−2.11
Daily sleep time (h/day)
≥8 120 (57.42%) 123 (45.56%) 1 1
<8 89 (42.58%) 147 (54.44%) 1.61a 1.12−2.32 1.55a 1.01−2.40
Smartphone use at school
No 48 (22.97%) 28 (10.37%) 1
Yes 161 (77.03%) 242 (89.63%) 2.58a 1.55−4.28
Smartphone use during meals
No 107 (21.20%) 56 (20.74%) 1 1
Yes 102 (48.80%) 214 (79.26%) 4.01a 2.69−5.98 2.41a 1.54−3.79
Social media as main activity
No 63 (30.14%) 57 (21.11%) 1
Yes 146 (69.86%) 213 (78.89%) 1.61a 1.06−2.44
Open in a new tab

Abbreviations: CI, confidence interval; ORa, adjusted odds ratio;  ORunad, unadjusted odds ratio; SRSA, self‐reported smartphone addiction.

a

p < 0.05; likelihood ratio test χ 2(8) = 126.72 (p < 0.01); Nagelkerke R 2 = 0.31; Hosmer and Lemeshow test = 13.52 (p = 0.09).

3.2. Qualitative phase

3.2.1. Intensification of smartphone use during the COVID‐19 pandemic and its repercussions on sleep, diet, and studies

The findings in the adolescents' testimonies point to an increase in smartphone use time in the COVID‐19 pandemic. They emphasized that the consequences of this period, such as closure of schools, adoption of emergency remote teaching (ERT), need for physical isolation and, consequently, reduction in leisure activities, made them spend more hours connected to their devices in a search to pass the time and be entertained. In addition to that, they miss the device when they do not connect to it and realize that this use has become harmful:

After the beginning of the pandemic I started to fiddle a lot with it, more than usual, everywhere I go I need to have the cell phone. […] I spend all day on the cell phone, and when I'm not with it, I miss it, it's strange, very strange (Emily, 17, SPAI‐BR 14).

I think school requires a lot of time […], not counting the time you spend doing the activities (Luiza, 16, SPAI‐BR 19).

It's just that there wasn't much to do, right? Couldn't go out. I wanted to go around the corner, I couldn't, so the way was to stay more on the cell phone (Alexandre, 16, SPAI‐BR 11).

Participants told us they even stay late at night connected to their smartphones and sometimes change their circadian rhythm. As a result, they spend the night awake on their devices and sleep during the day. In this sense, sleep quality is impaired, with presence of sleepiness and tiredness in the morning and even use of sleep medications. Some adolescents admitted that they are seeking sleep regulation and changes in habits, going to bed earlier and avoiding using the device at dawn:

I usually go to sleep at 1 a.m., after 2 hours fiddling with the cell phone (Liana, 17, SPAI‐BR 19).

In the morning I feel sleepy, I feel very sleepy, but at dawn I'm not sleepy at all, you know? Exchanging day for night. So that's basically it! (Emily, 17, SPAI‐BR 14).

So, I take sleeping pills, right? […] (Nayane, 17, SPAI‐BR 22).

In addition to taking their smartphones to bed and using them during the night, sometimes the first action immediately after waking up is to pick them up, as evidenced in the following statements:

As soon as I wake up, until… (Emily, 17, SPAI‐BR 14).

When I wake up, I use the phone, I need to solve something from school, I need the phone, there's nowhere else to run, everything is there, then you end up becoming more dependent in the end (Luiza, 16, SPAI‐BR 19).

The adolescents mention that they eat their meals connected to the smartphones and that they find it difficult to let go of them, even to eat. During meals, they like to access their social networks, watch movies and series, mainly by looking for company. They feel that this habit exerts an impact on food quality, causing them to be less attentive during the meal, eating less or more than they should:

I eat while fiddling with the cell phone, I know it's a horrible habit, but I do it (Isis, 15, SPAI‐BR 19).

[…] food doesn't kill hunger because you didn't pay attention to the food, right? Then I eat more (Mary, 17, SPAI‐BR 13).

[…], I think that it's a feeling of not wanting to be alone. […] sometimes I eat too much, sometimes I eat too little, I really kind of lose track of it (Pedro, 18, SPAI‐BR 19).

It was noticed that smartphones are used for school classes due to closure of schools and adoption of ERT by the institutions researched. However, this use during classes is not restricted to school activities; for example, many adolescents access their social networks during ERT. Participants were aware indiscriminate smartphone use during classes as a distraction caused a functional impairment in studies. Before the pandemic, this use was limited to school breaks and now there has been loss of control over this behavior:

In relation to studies, you keep fiddling with the cell phone and then you have a bad conscience. It seems that the cell phone calls us kind of, the cell phone is far away over there, we're trying to pay attention, but then we feel like picking it up and when we do, we don't let go of it anymore (Mary, 17, SPAI‐BR 13).

It does hinder studying, yes, but it's my refuge. Sometimes, when it's a class that, kind of, is very boring or a class of a subject that I already know, I take out my cell phone (Pedro agreed with a piece of jewelry) […] (Apolo, 17, SPAI‐BR 12).

[…] when it was in person I took my cell phone and used it in the breaks […] (Júlia, 15, SPAI‐BR 16).

3.2.2. Activities in the smartphone during the COVID‐19 pandemic

During the FGs, the adolescents cited several activities they performed on their smartphones during the pandemic, such as engaging in social networking apps, movies and series, games, instant messaging, and educational and work‐related options. According to them, there is a constant search for activities that interest them, regardless of the app used, as everything they need is on the device. Furthermore, they feel stuck with the smartphone in the face of so many features which keep them more and more connected because they show new possibilities for distraction:

I watch a lot of series, animé, I watch a lot of things. I watch for entertainment, then when I finish the series or movie, something, then I go to Instagram and see more about what I watched… then time passes and I really waste time, then I go to Instagram, Pinterest too, TikTok, several apps, right? […] I go into a video, then I see another related and then it's kind of arresting you […] (Jaqueline, 16, SPAI‐BR 13).

I like to watch series, I like watching videos on TikTok, I think it's a form of entertainment, we can't go out, we're doing nothing, then the cell phone is a way, kind of, for you to have fun, right? (Emily, 17, SPAI‐BR 14).

Everything I need is on the cell phone […] (Liana, 17, SPAI‐BR 19).

In addition to seeking entertainment during the pandemic, the participants sought to remain active on the social networks and tuned in to recent events. In addition, they search incessantly for different activities as an avoidance strategy, escape from reality, filling the void resulting from free time and lack of schedules, as well as relief from boredom. The adolescents acknowledged that sometimes the smartphone was no longer a useful tool and becomes harmful to health and life:

We're bored, and on the smartphone, we can talk to other people or see other things we like. Really entertaining (Jaqueline, 16, SPAI‐BR 13).

I stay on the cell phone because that way, there are movies, series that we can watch and it'll distance us from these issues as well, from bad thoughts as well (Mary, 17, SPAI‐BR 13).

We see what we want, right?, if we want to get away from reality a little […], we go looking for something that makes you happy […], a series, something and you stay there in your little world with the good things that only you like (Emily, 17, SPAI‐BR 14).

4. DISCUSSION

This study revealed that most of the adolescents increased their smartphone connection time after the beginning of the COVID‐19 pandemic and that this was associated with SRSA. Furthermore, in the quantitative phase the pandemic was associated with longer periods of time connected to it, preference for sleeping during the day, use of the device immediately after waking up, and smartphone use after 9 p.m. Participants told us other ramifications of excessive smartphone use including sleeping less than 8 h a day and use during meals. These data confirmed the research hypothesis that the behavior established with the smartphone during the COVID‐19 pandemic was related to SRSA particularly in the amount of time spent and modes of use of the device. The qualitative phase, in turn, contributed to our understanding that for them, the consequences of the pandemic, such as closure of schools and physical isolation, exerted a negative influence on the relationship established with the device. In this scenario, a relentless search began to fill time and be entertained through the device in a parallel virtual reality, customized to attract them with content of personal interest.

Technology has been playing a crucial role during the quarantine for young people, who have increased their daily use of smartphones to stay connected with the rest of the world (Salzano et al., 2021). By spending more time on the screens, the adolescents become subjected to harmful smartphone use (J. Yang, Fu et al., 2020), as habitual use is associated with SRSA and the greater the number of use hours, the greater the probability of the individual developing it (Santana‐Vega et al., 2019).

Most of the adolescents investigated access their smartphones as soon as they wake up and use them after 9 p.m., changing their circadian rhythm. Use immediately after waking up, preference for sleeping during the day and amount of sleep less than 8 h a day were associated with SRSA cases among the adolescents in the quantitative phase. In its turn, the qualitative sequential analysis also revealed that the adolescents perceive the harms in sleep and are willing to regulate it and change such habits in search of better quality of life, although they present difficulties to do so.

It is known that exposure to screens at night promotes a surveillance behavior, reducing its duration and quality. The smartphone's light prevents the brain from releasing melatonin, the natural sleep chemical substance; this can result in confusion for the body's sleep‐wake cycle and lead to disrupted sleep patterns (Randler et al., 2016; Ting & Chen, 2020). Smartphone users tend to use it at bedtime and place it next to them due to a compulsion to check notifications at all times (Randler et al., 2016). These devices can also make the adolescents maintain a state of alertness to compulsive notifications and checking of personal status, which can also interfere with sleep and cause daytime fatigue (Cha & Seo, 2018; Xie et al., 2018). A Brazilian study has already identified the association between SRSA and fewer hours of sleep among adolescents (Nunes et al., 2021).

In addition, most of the adolescents use their devices even during meals. Such behavior was also associated with SRSA among the adolescents in the quantitative study. The qualitative research allowed understanding that they find it difficult to let go of the smartphone during meals, presenting withdrawal symptoms when they try to eat away from it. They understand that this behavior exerts a negative impact on food quality, causing them to be less attentive during the meal. When engaging with their smartphones, the adolescents tend to get distracted and forget to eat. They also eat faster, to be free soon and develop activities that satisfy them on their smartphones (Y. Kim et al., 2017).

Most of the adolescents in this research reported using their smartphones during school classes. Although this variable was not maintained in the final model of association with SRSA, the qualitative data analysis from the meetings carried out with the adolescents emphasizes their recognition of the implications of SRSA in their studies. For them, closure of schools and adoption of ERT exerted a negative impact on smartphone use, defocusing attention on their studies. The scientific literature highlights the relationship between SRSA and low level of attention, cognitive impairment, academic performance, and socioemotional malfunction (Abi‐Jaoude et al., 2020; S.‐Y. Lee et al., 2018).

Several activities of interest developed on smartphones were discussed by the participants in this research. Subject matter experts announce that contemporary adolescents tend to spend hours on their smartphones performing a range of activities, among which social networks and games stand out (Rich et al., 2019). Social networks presented a higher use estimate in the quantitative phase of this study, as well as they were highlighted in the meetings from the qualitative phase. Previous studies have indicated an association between use of the social networks and SRSA (Chun, 2018; Körmendi et al., 2016); however, in the current study, this association was not maintained in the final regression model, although it was identified in the simple regression.

The adolescents felt stuck with their smartphones in the face of so many resources and possibilities for distraction, especially through the social networks. However, the tendency of adolescents, especially girls, is to use social networks for social interactions (H. Lee et al., 2017). As most of the participants in this study were female, there may be such limitation in the findings. However, during the FGs in the qualitative phase, the boys also mentioned using social networks to keep themselves abreast of events and entertained. Added to all of the above is the endless search for a parallel virtual life as an avoidance strategy, escape from reality, filling the void resulting from free time and alleviating the boredom originated in the COVID‐19 context. The smartphone keeps up with people all the time and has created an opportunity for leisure and to fill time, anytime and anywhere (Leung, 2020).

This increase in smartphone use related to the feeling of boredom and frustration in the pandemic has already been pointed out in other research studies (Ting & Chen, 2020; J. Yang, Fu et al., 2020). Boredom propensity is one of the predictors for SRSA. Bored people tend to have a low arousal state and can look for new stimuli to reach the ideal level of arousal and more pleasurable sensations through the smartphones (J. Yang, Fu et al., 2020), as observed in the second phase of this study. However, a bidirectional relationship was seen as the adolescents also revealed the feeling of boredom when spending a lot of time connected to their smartphones, suggesting that its use may not be a very effective solution to alleviate such feelings.

4.1. Implications for nursing and healthcare providers

The impacts of excessive use of smartphones on health, nutrition, and sleep quality/duration are extremely worrying. Poor nutrition and sleep deprivation can cause physical and mental disorders, which suggests conscious and monitored use is important (Nunes et al., 2021). Therefore, it is recommended that daily use time be limited to 2−3 h/day, avoiding “overnighting” on the device, and disconnecting from it approximately 1−2 h before bedtime and to suspend screen use during meals (SBP & de, 2019).

In addition, given the repercussions from excessive use of the smartphone, with the resumption of in‐person classes schools should be included in health actions and policies aimed at raising awareness among the children, adolescents, and families regarding smart and safe use of the devices, both at home and during classes and study times.

In this sense, nurses have relevant roles as professionals who work in Primary Health Care providing consultations for the growth and development of children and adolescents. Nurses should also become involved in training activities developed in schools about socioemotional skills and self‐regulation, supported by the notion of harm reduction for this population. It is understood that total smartphone restriction tend not to be an adequate way to manage SRSA, a more appropriate avenue is opting for harm reduction strategies. This is because better coping with addiction problems is achieved when prevention and health promotion actions are articulated with the encouragement of the affected adolescent to take a leading role in the search for self‐care and management of harmful health behavior.

4.2. Limitations of the research

It is to be noted that this study is limited to a local survey, in a capital city of the Brazilian Midwest region, and developed through the collection of virtual data. However, as the invitation to participate in the research was mediated by the schools and, later, telephone contacts were established with most of the adolescents, their identities were confirmed and, thus, there was lower risk of bias in relation to reliability of the answers to the quantitative data collection instruments. It is worth emphasizing that the cross‐sectional data provided do not imply causality and it is suggested that future studies use longitudinal designs. Also, the self‐report questionnaires used are susceptible to social desirability bias, so future studies, including other sources of information, can help to minimize it.

5. CONCLUSION

The prevalence of SRSA among young adolescents in a capital city from the Brazilian Midwest region was 56.37%. The event was associated with longer use of the device during the COVID‐19 pandemic, longer periods of time connected to it, preference for sleeping during the day, use of the device immediately after waking up, smartphone use after 9 p.m., amount of sleep less than 8 h a day, and use during meals. Faced with the pandemic, the adolescents spent more time connected to their smartphones developing various activities, especially on the social networks. They claimed to be distracted by the various apps available on their devices but remained connected to alleviate the feeling of boredom, fill their free time and escape from the reality imposed by COVID‐19.

This addictive relationship established with the smartphone resulted in changes in participants' sleep quality, diet, and academics exerting a negative impact on their quality of life. To ensure adolescents' full development and to mitigate future health problems, it falls upon health and education professionals to act in an interdisciplinary, interprofessional, and intersectoral way to promote adolescents' health in the smartphone era, as well as in preventing harmful behaviors resulting from its use. Future longitudinal studies that include other sources of information may expand current knowledge of this subject, as well as intervention research, especially with the aim of investigating ways to prevent SRSA and reduce the harm caused by it in the adolescent population.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Freitas, B. H. B. M. D. , Gaíva, M. A. M. , Diogo, P. M. J. , & Bortolini, J. (2022). Factors related to self‐reported smartphone addiction among Brazilian adolescents in the face of the COVID‐19 pandemic: A mixed‐method study. Journal of Child and Adolescent Psychiatric Nursing, 1–10. 10.1111/jcap.12401

This article was taken from doctoral thesis of Bruna Hinnah Borges Martins de Freitas.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

  1. Abi‐Jaoude, E. , Naylor, K. T. , & Pignatiello, A. (2020). Smartphones, social media use and youth mental health. Canadian Medical Association Journal, 192(6), E136–E141. 10.1503/cmaj.190434 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andrade, A. L. M. , Scatena, A. , de Oliveira Pinheiro, B. , de Oliveira, W. A. , Lopes, F. M. , & De Micheli, D. (2021). Psychometric properties of the smartphone addiction inventory (SPAI‐BR) in Brazilian adolescents. International Journal of Mental Health and Addiction, 20, 2690–2705. 10.1007/s11469-021-00542-x [DOI] [Google Scholar]
  3. Bardin, L. (2016). Análise de conteúdo (E. 70 (ed.)).
  4. Cha, S. S. , & Seo, B. K. (2018). Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychology Open, 5(1). 1–15. 10.1177/2055102918755046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chen, J. , Liang, Y. , Mai, C. , Zhong, X. , & Qu, C. (2016). General deficit in inhibitory control of excessive smartphone users: Evidence from an event‐related potential study. Frontiers in Psychology, 7(April), 1–9. 10.3389/fpsyg.2016.00511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chun, J. (2018). Conceptualizing effective interventions for smartphone addiction among Korean female adolescents. Children and Youth Services Review, 84(November), 35–39. 10.1016/j.childyouth.2017.11.013 [DOI] [Google Scholar]
  7. Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches.
  8. Duan, L. , Shao, X. , Wang, Y. , Huang, Y. , Miao, J. , Yang, X. , & Zhu, G. (2020). An investigation of mental health status of children and adolescents in China during the outbreak of COVID‐19. Journal of Affective Disorders, 275(June), 112–118. 10.1016/j.jad.2020.06.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Griffiths, M. (1996). Gambling on the Internet: A brief note. Journal of Gambling Studies, 12(4), 471–473. [DOI] [PubMed] [Google Scholar]
  10. Hong, Q. , Pluye, P. , Fàbregues, S. , Bartlett, G. , Boardman, F. , Cargo, M. , Dagenais, P. , Gagnon, M.‐P. , Griffiths, F. , Nicolau, B. , Rousseau, M.‐C. , & Vedel, I. (2018). Mixed methods appraisal tool (MMAT), Version 2018. User guide. In McGill. Department of Family Medicine. http://mixedmethodsappraisaltoolpublic.pbworks.com/w/file/fetch/127916259/MMAT_2018_criteria-manual_2018-08-01_ENG.pdf%0Ahttp://mixedmethodsappraisaltoolpublic.pbworks.com/
  11. Khoury, J. M. , De Freitas, A. A. C. , Roque, M. A. V. , Albuquerque, M. R. , Das Neves, M. C. L. , & Garcia, F. D. (2017). Assessment of the accuracy of a new tool for the screening of smartphone addiction. PLoS One, 12(5), e0176924. 10.1371/journal.pone.0176924 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kim, S. G. , Park, J. , Kim, H. T. , Pan, Z. , Lee, Y. , & McIntyre, R. S. (2019). The relationship between smartphone addiction and symptoms of depression, anxiety, and attention‐deficit/hyperactivity in South Korean adolescents. Annals of General Psychiatry, 18(1), 1–8. 10.1186/s12991-019-0224-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Kim, Y. , Lee, N. , & Lim, Y. (2017). Gender differences in the association of smartphone addiction with food group consumption among Korean adolescents. Public Health, 145, 132–135. 10.1016/j.puhe.2016.12.026 [DOI] [PubMed] [Google Scholar]
  14. Kinalski, D. D. F. , Paula, C. C. , Padoin, S. M. M. , Neves, E. T. , Kleinubing, R. E. , & Cortes, L. F. (2017). Focus group on qualitative research: Experience report. Revista Brasileira de Enfermagem, 70(2), 424–429. 10.1590/0034-7167-2016-0091 [DOI] [PubMed] [Google Scholar]
  15. Király, O. , Potenza, M. N. , Stein, D. J. , King, D. L. , Hodgins, D. C. , Saunders, J. B. , Griffiths, M. D. , Gjoneska, B. , Billieux, J. , Brand, M. , Abbott, M. W. , Chamberlain, S. R. , Corazza, O. , Burkauskas, J. , Sales, C. M. D. , Montag, C. , Lochner, C. , Grünblatt, E. , Wegmann, E. , … Demetrovics, Z. (2020). Preventing problematic internet use during the COVID‐19 pandemic: Consensus guidance. Comprehensive Psychiatry, 100, 152180. 10.1016/j.comppsych.2020.152180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Körmendi, A. , Brutóczki, Z. , Végh, B. P. , & Székely, R. (2016). Smartphone use can be addictive? A case report. Journal of Behavioral Addictions, 5(3), 548–552. 10.1556/2006.5.2016.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Lee, H. , Kim, J. W. , & Choi, T. Y. (2017). Risk factors for smartphone addiction in Korean adolescents: Smartphone use patterns. Journal of Korean Medical Science, 32(10), 1674–1679. 10.3346/jkms.2017.32.10.1674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lee, S.‐Y. , Lee, D. , Nam, C. R. , Kim, D. Y. , Park, S. , Kwon, J.‐G. , Kweon, Y.‐S. , Lee, Y. , Kim, D. J. , & Choi, J.‐S. (2018). Distinct patterns of Internet and smartphone‐related problems among adolescents by gender: Latent class analysis. Journal of Behavioral Addictions, 7(2), 454–465. 10.1556/2006.7.2018.28 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Leung, L. (2020). Exploring the relationship between smartphone activities, flow experience, and boredom in free time. Computers in Human Behavior, 103(June), 130–139. 10.1016/j.chb.2019.09.030 [DOI] [Google Scholar]
  20. Malterud, K. , Siersma, V. D. , & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760. 10.1177/1049732315617444 [DOI] [PubMed] [Google Scholar]
  21. Nunes, P. , Abdon, A. , Brito, C. B. , Silva, F. , Santos, I. , Martins, D. Q. , Meira, P. , & Frota, M. A. (2021). Factors related to smartphone addiction in adolescents from a region in Northeastern Brazil. Ciencia & saude coletiva, 26(7), 2749–2758. 10.1590/1413-81232021267.08872021 [DOI] [PubMed] [Google Scholar]
  22. Panova, T. , & Carbonell, X. (2018). Is smartphone addiction really an addiction. Journal of Behavioral Addictions, 7(2), 252–259. 10.1556/2006.7.2018.49 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Randler, C. , Wolfgang, L. , Matt, K. , Demirhan, E. , Horzum, M. B. , & Beşoluk, Ş. (2016). Smartphone addiction proneness in relation to sleep and morningness–Eveningness in German adolescents. Journal of Behavioral Addictions, 5(3), 465–473. 10.1556/2006.5.2016.056 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Rich, M. , Tsappis, M. , & Kavanaugh, J. R. (2019). Uso problemático de mídias interativas entre crianças e adolescentes: Dependência, compulsão ou síndrome? In Young K. S. & Abreu C. N. (Eds.), Dependência de internet em crianças e adolescentes (Vol. 30, pp. 7–31). [Google Scholar]
  25. Salzano, G. , Passanisi, S. , Pira, F. , Sorrenti, L. , La Monica, G. , Pajno, G. B. , Pecoraro, M. , & Lombardo, F. (2021). Quarantine due to the COVID‐19 pandemic from the perspective of adolescents: The crucial role of technology. Italian Journal of Pediatrics, 47(1), 40. 10.1186/s13052-021-00997-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Santana‐Vega, L.‐E. , Gómez‐Muñoz, A.‐M. , & Feliciano‐garcía, L. (2019). Adolescents problematic mobile phone use, fear of missing out and family communication. Comunicar, 27(59), 39–47. 10.3916/C59-2019-04 [DOI] [Google Scholar]
  27. SBP, S. B. , & de, P. (2019). Manual de Orientação: Menos telas Mais Saúde. In Sociedade Brasileira de Pediatria, 829(Issue), 1–11. https://www.sbp.com.br/fileadmin/user_upload/_22246c-ManOrient_-__MenosTelas__MaisSaude.pdf [Google Scholar]
  28. Ting, C. H. , & Chen, Y. Y. (2020). Smartphone addiction, Adolescent addiction (2nd ed., pp. 215–240). Elsevier Inc. 10.1016/b978-0-12-818626-8.00008-6 [DOI] [Google Scholar]
  29. Xie, X. , Dong, Y. , & Wang, J. (2018). Sleep quality as a mediator of problematic smartphone use and clinical health symptoms. Journal of Behavioral Addictions, 7(2), 466–472. 10.1556/2006.7.2018.40 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Yang, J. , Fu, X. , Liao, X. , & Li, Y. (2020). Association of problematic smartphone use with poor sleep quality, depression, and anxiety: A systematic review and meta‐analysis. Psychiatry Research, 284(August), 112686. 10.1016/j.psychres.2019.112686 [DOI] [PubMed] [Google Scholar]
  31. Yu, S. , & Sussman, S. (2020). Does smartphone addiction fall on a continuum of addictive behaviors. International Journal of Environmental Research and Public Health, 17(2), 422. 10.3390/ijerph17020422 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Journal of Child and Adolescent Psychiatric Nursing are provided here courtesy of Wiley

RESOURCES