Does Parental Control Work With Smartphone Addiction?

A Cross-Sectional Study of Children in South Korea

Abstract

The purposes of this study were to (a) examine the relationship between personal characteristics (age, gender), psychological factors (depression), and physical factors (sleep time) on smartphone addiction in children and (b) determine whether parental control is associated with a lower incidence of smartphone addiction. Data were collected from children aged 10–12 years (N = 208) by a self-report questionnaire in two elementary schools and were analyzed using t test, one-way analysis of variance, correlation, and multiple linear regression. Most of the participants (73.3%) owned a smartphone, and the percentage of risky smartphone users was 12%. The multiple linear regression model explained 25.4% (adjusted R² = .239) of the variance in the smartphone addiction score (SAS). Three variables were significantly associated with the SAS (age, depression, and parental control), and three variables were excluded (gender, geographic region, and parental control software). Teens, aged 10–12 years, with higher depression scores had higher SASs. The more parental control perceived by the student, the higher the SAS. There was no significant relationship between parental control software and smartphone addiction. This is one of the first studies to examine smartphone addiction in teens. Control-oriented managing by parents of children’s smartphone use is not very effective and may exacerbate smartphone addiction. Future research should identify additional strategies, beyond parental control software, that have the potential to prevent, reduce, and eliminate smartphone addiction.

INTRODUCTION

Globally, the use of smartphones has brought tremendous convenience to modern society. At the same time, there is a rising concern that smartphone addiction may be as risky as heroin use for the next generation. Humans have become increasingly dependent on the smartphone because of its advantages of convenience (^{Kwon et al., 2013}). It is estimated that 2.6 billion people will own a smartphone in 2017 (^{Statista, 2017}). The smartphone penetration rate in Asia was 27.1% in 2016 and impressively high at 88% in South Korea, a country known for high technology use (^{Statista, 2017}). Because of this high penetration rate, South Korea is an optimal place to study smartphone addiction. Nearly every person in South Korea has a mobile phone, with many Koreans having more than one phone. In a population of over 50 million in South Korea, the number of cellular mobile phones in use in 2014 was nearly 60 million (^{Central Intelligence Agency, 2016}). Children’s smartphone use in South Korea is rapidly growing; the penetration rate is 40.8% in ages 7–9 years and even higher in teens, aged 10–12 years, at 72.3%. Smartphone penetration rate in teens surged from 4.4% in 2011 to 59.3% in 2015 (^{Kim, 2015}). With the increased use of smartphones, smartphone addiction has emerged as a key concern, especially for the pediatric population.

Smartphone addiction has been defined as “a psychological state, in which mental, and emotional states are altered and scholastic, occupational, and social interactions are impaired by the overuse of the smartphone” (^{National Information Society Agency [NIA], 2011)}. The risk of addiction is particularly concerning for children because the human brain is not fully developed until 25 years old, and behavioral addiction to a mobile phone could potentially have negative implications for brain development (^{Hong et al., 2013}). High school students in South Korea who overused smartphones also damaged interpersonal skills; had difficulty reading nonverbal language cues, such as facial expressions; experienced bullying (^{Nam, 2013}); and exhibited lower concentration in class (^{Kwon et al., 2013}). In addition, 20% of adolescents were in contact with pornography by cell phone (^{The National Campaign to Prevent Teen and Unplanned Pregnancy, 2008}). Furthermore, mobile phone use beyond their bedtime was related to increased tiredness and depression in children and adolescents (^{Lemola, Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015}). Teenagers have also been known to be viewing the screens of their smartphones when crossing the street, putting themselves and others in danger (^{Kwon et al., 2013}). College students in the United States use the cell phone an average of 8.8 hours per day (^{Roberts, Yaya, & Manolis, 2014}). A recent U.S. survey sampled 1,000 adults, 18 years old and older, and found that 71% were sleeping with or next to their mobile phones; 34% of younger millennials aged 18–24 years were most likely to sleep with their smartphone on the bed; and 35% of respondents reported that they reached for their mobile device first thing in the morning (^{Bank of America, 2015}). Most of these studies focused on teens and adults, and there is a paucity of research on teens (children aged 10–12 years). These findings in college-aged students indicate significant concerns related to smartphone addiction; however, these findings may or may not be similar in younger children.

LITERATURE REVIEW

Earlier research has provided evidence that smartphone addiction is associated with both psychological (depression) and physiological (sleep) factors. Studies have also found variations in addiction by age and gender. Perceived parental control and parental control software have been offered as strategies to mitigate negative outcomes associated with mobile phone addiction; however, there is little research showing that parental control software or behavior impacts the psychological, behavioral, or physiological factors associated with phone addiction.

Smartphone addiction varies by gender and age, with females and younger children more likely to be affected. In ultrawired South Korea, by the age of 11–12 years, 72.2% of children own a smartphone and spend 5.4 hours per day on smartphones (vs. 3.8 hours per day in adults), and 25.5% are addicted to smartphones (vs. 8.9% of adults; ^{Jeong, Kim, Yum, & Hwang, 2016}). Thus, the use of smartphones by children is proliferating, and a concern about smartphone addiction is growing. However, the number of homes with smartphone usage guidelines is relatively fewer compared with those with TV or Internet game usage guidelines (^{Kim, 2015}).

In terms of gender, females tended to favor the smartphone for chatting, sending messages, blogging, and updating personal homepages (^{Choi et al., 2015}), resulting in greater mobile phone addiction compared with males (^{Kim, 2015}; ^{Roberts et al., 2014}). Elementary school girls in higher grades generally used instant messenger and social networking sites (SNSs), such as Facebook, whereas boys overwhelmingly used the smartphone for gaming applications (^{Kim, 2015}).

Smartphone addiction is also significantly related to psychological factors, including depression. Depression was significantly higher in addicted smartphone users (^{Demirci, Akgönül, & Akpinar, 2015}; ^{Lemola et al., 2015}), and smartphone addiction was also associated with depression and stress (^{Davey & Davey, 2014}). However, ^{Choi et al. (2015)} found that increased depression scores were associated with lower levels of smartphone addiction.

Sleep is a major physiological outcome impacted by smartphone addiction. During the teen period, this may be particularly important as puberty starts and sleep patterns change. These changes occur only around the time that children begin to possess their own electronic and smartphone devices. As part of their normal developmental process, their sleep time may be delayed because of physiological and environmental factors. Intrinsic changes may either compel or control the adolescent sleep phase (^{Carskadon, Acebo, & Jenni, 2004}). For many teens, this sleep-delaying pattern may cascade into a chronic pattern of insufficient school-day sleep and forced waking at a biologically inappropriate time, resulting in negative impacts on mood (^{Carskadon et al., 2004}). Furthermore, age-related change in bedtime has several environmental factors, including reduced parental involvement with bedtimes, increased schoolwork, and other activities (e.g., clubs and part-time work). Other environmental factors may be stimulating activities, including watching TV and playing computer games, which influence bedtime (^{Crowley, Acebo, & Carskadon, 2007}).

Many researchers have reported associations between smartphone addiction and sleep time. Increased smartphone use may be associated with decreased sleep quality, and it may predict other mental and physical health outcomes (^{Davey & Davey, 2014}; ^{Demirci et al., 2015}; ^{Lemola et al., 2015}). Light from computer monitors may inhibit the secretion of melatonin and delay the onset of sleep (^{Higuchi, Motohashi, Liu, Ahara, & Kaneko, 2003}). However, other studies showed no significant difference (^{Gaina et al., 2005}; ^{Yen, Ko, Yen, & Cheng, 2008}). Therefore, we need more investigation.

To address the negative psychological and physiological outcomes of smartphone addiction, some have recommended perceived parental control and parental control software. However, there is limited research available on the impact of parental control on smartphone addiction. ^{Valkenburg, Krcmar, Peeters, and Marseille (1999)} described “the three styles of parental mediation: restrictive mediation, instructive mediation, and co-viewing. In restrictive mediation, parents set specific hours or forbid a particular program for their child.” Parents could influence children’s smartphone use through monitoring (^{Hwang & Jeong, 2015}). Adolescents living apart from their parents seem to be vulnerable to Internet addiction (^{Ko et al., 2015}), indicating that parents play an important role in controlling Internet use. Adequate parented monitoring can prevent adolescents from becoming addicted to the Internet (^{Lin, Lin, & Wu, 2009}). The ^{American Academy of Pediatrics (2013)} suggested that “parents should establish a family home use plan for all media.” In fact, many Korean parents are limiting their children’s use of smartphones to prevent smartphone addiction. Conceptually, parental control could affect children’s smartphone addiction; however, research in this area is sparse.

In summary, the current evidence indicates that smartphone addiction is becoming a serious social problem with psychological and physiological consequences. The number of children addicted to smartphones is growing. Being a female and a teen increases the risk of smartphone addiction. The evidence related to smartphone use, depression, parental control, and sleep time requires further investigation. Some have offered parental control software as a strategy to offset the negative consequences of smartphone addiction, yet there is limited research examining this relationship.

The purposes of this study were to (a) examine the relationship between personal characteristics (age, gender), psychological factors (depression), and physical factors (sleep time) on smartphone addiction in children aged 10–12 years and (b) determine whether parental control is associated with less smartphone addiction.

Hypotheses

1. Smartphone addiction score (SAS) of girls will be higher than that of boys.
2. SAS of the nonparental control group will be higher than that of the parental control group.
3. There will be a correlation between depression scores and SASs.
4. There will be a negative correlation between smartphone addiction and sleep time.

Conceptual Framework

Problem Behavior Theory (PBT; ^{Jessor & Jessor, 1977}) serves as the framework for this study. PBT explains the human behavior structure. Researchers have used the model most commonly to explain and predict the behavior problems of youth (^{Magoon & Ingersoll, 2006}; ^{Mobley & Chun, 2013}). The concepts included in the model are congruent with the literature and evidence related to smartphone addiction in earlier studies (^{Hwang & Jeong, 2015}; ^{Lin, Tung, Hsieh, & Lin, 2011}; ^{Park & Park, 2014}). As depicted in the model (see Figure 1), demography (gender, age), personality system (depression), and perceived environment system (parental control) are associated with behavior system (smartphone addiction). The relationship between smartphone addiction and depression is bidirectional, meaning smartphone addiction can lead to depression and depression can lead to smartphone addiction. Individuals with smartphone addition also experience decreased sleep time.

Figure 1.: Conceptual framework of this study (modified by the Problem Behavior Theory [^{Jessor & Jessor, 1977}]).

Study Variables

Conceptually, smartphone addiction has been defined as “a psychological state, in which mental, and emotional states are altered and scholastic, occupational, and social interactions are impaired by the overuse of the smartphone” (^{NIA, 2011}). For this study, the Self-Rated Smartphone Addiction Scale for Youth, developed by NIA, was administered; it consists of 15 items in four domains—disturbance of adaptive functions (five items), virtual life orientation (two items), withdrawal (four items), and tolerance (four items)—and uses a 4-point Likert scale from 1 (strongly disagree) to 4 (strongly agree). The risk of smartphone addiction increases as the SAS increases. The instrument provides three categories, depending on the level of smartphone addiction risk: 41 or less for normal users, 42–44 for potential risk users, and 45 or above for high-risk users (see Figure 2; ^{Kim et al., 2014}; ^{NIA, 2011}). Only one child among our participants was a high-risk user; therefore, we combined potential risk and high-risk users into one risk group and recoded them into two categories: normal users and risk users. The tool has good internal validity with a Cronbach’s alpha of .88 in earlier studies (^{NIA, 2011}) and .85 in this study. In addition, we added one question to ascertain if the participants owned a smartphone: “Do you have your own smartphone?”

Figure 2.: Self-Rated Smartphone Addiction Scale for Youth (^{Kim, Lee, Lee, Nam, & Chung, 2014}).

Depression is conceptually defined as “a common mental disorder characterized by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, tiredness, and poor concentration. Depression can be long lasting or recurrent, substantially impairing an individual’s ability to function at work or school or cope with daily life” (^{World Health Organization, 2017}). Kovacs’ Children’s Depression Inventory (CDI-K) was used after being translated and modified into the Korean form (^{Cho & Lee, 1990}). The CDI-K consists of 27 items rated 0–2 each, with higher scores indicating more depressive symptoms. This instrument provides four cutoff scores: 21 or less for no depression, 22–25 for mild depression, 26–28 for moderate depression, and 29 or above for severe depression. Two-week test–retest reliability (r = .82) and split-half reliability (Pearson correlation = .71) were assessed (^{Cho & Lee, 1990}). Cronbach’s alpha was .88 in ^{Cho and Lee’s (1990)} study and .85 in this study. Construct validity was ensured through factor analysis (^{Cho & Lee, 1990}). In addition, predictive validity was ensured using Pearson’s correlation between CDI-K and suicide probability (^{Go, Kim, & Lee, 2000}).

Parental control, restrictive style, was measured using two items gleaned from the literature review (^{American Academy of Pediatrics, 2013}; ^{Park & Park, 2014}). One is concerned with parental control software: “Is there any parental control software in your smartphone?” Another question is about perceived parental control: “My parents control the use of my smartphone.” It is scored using a 4-point Likert scale from 1 (hardly ever) to 4 (almost always); the higher the score, the more parental control is exerted over the use of the child’s smartphone.

Sleep time was measured using four items based on the literature review (^{Könen, Dirk, Leonhardt, & Schmiedek, 2016}; ^{Oka, Suzuki, & Inoue, 2008}; ^{Yamaguchi, Kawamura, & Maeda, 2000}): “What time do you usually go to bed at night during weekdays?”, “What time do you usually get up in the morning during weekdays?”, “What time do you usually go to bed at night during the weekend?”, and “What time do you usually get up in the morning during the weekend?”

We collected demographic variables, including gender, age, and school name and location.

METHODS

Design

The study used a cross-sectional survey design, and data were collected from June 1 to 14, 2016. We selected two elementary schools for a convenience sample. We purposely selected one school in an urban area and another in a rural area in Korea.

The institutional review board (Registration no. 2016-07-008-002) provided ethical approval of this study.

Participants

The 2013 survey by the Ministry of Gender Equality and Family found that smartphone addiction generally presented at the age of 10 years and reached its peak at the age of 13 years (^{Yu, Kim, & Na, 2013}). Therefore, to understand better the teen population, we surveyed smartphone addiction in children aged 10–12 years. Inclusion criteria were children aged 10–12 years who had no problem in reading, understanding, and writing. According to the power analysis, the minimum sample size was 98 subjects for 80% statistical power (α = .05, medium effect size = .15) in a linear multiple regression with four predictors (G-Power Version 3.1.9.2). The final sample size exceeded the minimal requirement (final N = 208).

Before this study started, the researcher contacted school health teachers, principals, and homeroom teachers to explain the background, purpose, and process of the study and gained approval. School health teachers sent out notice letters with parental informed consent to explain the purpose of the study. The parents who agreed to let their children participate in this research returned the signed form. Only students with parental informed consent participated in the study. Before the data collection, the researcher explained the purpose of the study and right of refusal to the children.

Statistical Analysis

Data were analyzed using SPSS 21. Frequencies, percentage, means, and standard deviations were used to describe the participants. The Kolmogorov–Smirnov test showed no significance (p = .200) in the smartphone addiction variable, so we conducted t test, analysis of variance, and chi-square test to compare the differences between independent variables and smartphone addiction. We performed correlation coefficient analysis to investigate the association between variables as well as multiple linear regression to examine the relationship between smartphone addiction and independent variables.

RESULTS

We distributed and received 390 questionnaires, and 286 (73.3%) of the participants owned a smartphone. The depression variable had significant missing data. Among the 286 participants who owned a smartphone, there were additional missing data: 63 had missing data in depression (22.0%); 11, in smartphone addiction (3.8%); five, in perceived parental control (1.7%); three, in bedtime and sleep time in weekend (1.0%); and two, in bedtime and sleep time in weekdays, age, and parental control software (0.7%). According to Little’s Missing Completely at Random test in SPSS, the missing data in this study were missing completely at random (χ² = 25.22, p = .667). Consequently, 78 cases with at least one item of missing data were deleted, resulting in 208 cases entered in the final database.

Smartphone Addiction, Measured With SAS According to General and Smartphone-Related Variables

The observed range of SAS was from 15 to 52 (mean = 29.13, SD = 6.83). The total number of normal smartphone users (SAS ≤ 41) was 183 (88.0%), that of potential risk users (SAS = 42–44) was 24 (11.5%), and that of high-risk users (SAS ≥ 45) was 1 (0.5%). We recoded these groups and analyzed two categories in this article: normal users and risk users (combining potential and high risks).

Table 1 shows descriptive statistics of the study variables and the comparison of the SAS between the groups in each variable. Of the participants, 52.4% were female. The mean age was 11.1 (SD = 0.81) years, and the SAS was significantly higher in those 12 years old than in those 10–11 years old (F = 4.27, p = .015). The percentage of the students who lived in the urban area was 57.2%. The percentage of the students who had parental control software on the smartphone was 43.3%; there was no significant difference in the SAS between those who had parental control software on their phones and those who did not. The percentage of students who perceived that their parents controlled their phone use “hardly ever” was 13.0%; the SAS was significantly lower in this group than in the “occasionally” (53.4%) and “frequently or almost always” (33.7%) groups. The percentage of the students who had depression symptoms was 3.4%, and the SAS was significantly higher in this group than in those who had no depression symptoms (t = −3.41, p = .001).

TABLE 1: Smartphone Addiction Score According to General and Smartphone-Related Characteristics (N = 208)

Compare General and Smartphone-Related Variables Between Normal Users and Risk Users of Smartphone

Table 2 shows the mean difference between normal users (88.0%) and risk users (12.0%) in SAS. The normal users were children who have good control of the use of the smartphone and use it appropriately. The risk users were children with less control of the use of smartphone, impaired activities of daily living, and demonstrated withdrawal and tolerance. In normal users, perceived parental control (F = 13.56, p < .001) was the only significant variable, and the other variables showed no significant difference in SAS, including gender, age, geographic region, parental control software, and depression. However, in risk users, there were no significant differences in the SAS by all variables.

TABLE 2: Comparison Between Normal and Risk Users in Smartphone Addiction According to General and Smartphone-Related Characteristics (N = 208)

A chi-square test and/or Fisher’s exact test was conducted to examine the differences in terms of independent variable categories when comparing normal and risk users. The chi-square test revealed significance by depression (χ² =13.95, p = .005). Although there were no significant differences by age (χ² = 5.62, p = .060), in the age group of 12 years, 35.5% of boys were normal users but 60% of boys were risk users. Students who perceived that their parents frequently or almost always controlled their smartphone use tended to be higher-risk users (56.0%) compared with normal users (30.6%); however, these differences were not statistically significant (χ² = 5.75, p = .050).

Correlation Among Age, Depression, and Sleep Time

Table 3 shows the coefficients estimated for the bivariate correlation between participants’ personal characteristics (age), psychological factors (depression), and physiological factors (sleep time) and their SASs. The results show a statistically positive correlation between age and smartphone addiction (r = .193, p < .001), indicating that older age was related to a higher SAS. Depression had a significant correlation with smartphone addiction (r = .369, p < .001), indicating that a higher depression score was related to a higher SAS. We also found no significant correlation between smartphone addiction and sleep time.

TABLE 3: Correlations Between Age, Depression, Sleep Time, and Smartphone Addiction (N = 208)

Multiple Linear Regression on Age, Depression, and Parental Control Factors on Smartphone Addiction

Table 4 shows the results from stepwise multiple linear regression. Three variables were significantly associated with the SAS (age, depression, and parental control), and three variables were excluded (gender, geographic region, and parental control software). The multiple linear regression model explained 25.4% (adjusted R² = .239) of the variance in SAS. The assumption of regression related to independent variables, variance inflation factor value (less than 10 [1.019–2.391]), and no multicollinearity was met. Perceived parental control was the strongest predictor; the change in SAS was higher in the group that indicated their parents “frequently or almost always” controlled smartphone use than in the “hardly ever” control group (β = 0.43). Smartphone addiction is a dependent variable in this regression analysis, and sleep time is considered a consequence of the smartphone addiction; therefore, we did not put sleep time variable in this regression analysis. In addition, the change in the SAS was higher in the group in which parents “occasionally” controlled smartphone use than in the “hardly ever” controlled group (β = 0.46). Depression (β = 0.34) and age (β = 0.15) predicted smartphone addiction significantly.

TABLE 4: Multiple Linear Regression on Personal, Psychological, and Parental Control Factors on Smartphone Addiction (N = 208)

DISCUSSION

Consistent with the PBT model that framed this study, personal characteristics of the teens in this study were associated with smartphone addiction.

Smartphone Ownership and Risky Users

The prevalence of smartphone ownership was also consistent with the survey by the ^{Ministry of Science, ICT, and Future Planning (2015)}, with 73.3% of teens in this study owning a smartphone compared with 72.2% of children owning a smartphone by the age of 11–12 years (^{Jeong et al., 2016}). The percentage of risky smartphone users was 12% in this study; we cannot compare this percentage with those of other studies because there was no previous study in this age group. According to the survey, the highest rate of smartphone addiction is in adolescents (29.9%), and the rate has been rising every year (^{Ministry of Science, ICT, and Future Planning, 2015}). Most children start to have their own smartphone at this age (^{Kim, 2015}) and start to form the smartphone use habit. Therefore, teens are the most important age group to study to prevent smartphone addiction.

Personal Characteristics

Age and gender were significant predictors in earlier studies. However, in this study of teens (aged 10–12 years), age was significant, but gender was not. In this study, younger children were less likely to be addicted than were older children. This is congruent with other studies that showed that older children have greater smartphone addiction. There was no difference in gender on smartphone addiction in our study. Therefore, Hypothesis 1, “SAS of girls will be higher than that of boys,” was not supported. Earlier studies resulted in ambivalent findings related to gender. In two studies of college students, female students preferred the socially related activities of a smartphone and had a higher score on the SAS compared with male students (^{Choi et al., 2015}; ^{Roberts et al., 2014}). However, in another study of adults, there was no gender difference (^{Kwon et al., 2013}). Currently, there is no research with teens. To resolve these contradictory findings, more research regarding the relationship between various age groups and gender, and smartphone addiction are needed.

In previous studies, female smartphone addiction rate was higher compared with that of males. Time spent on SNSs was a strong indicator of smartphone addiction (^{Roberts et al., 2014}). The biggest reason was that females spend more time on SNSs and instant messaging sites (^{Kim, 2015}; ^{Roberts et al., 2014}) because they seek social interaction. Social networking service use rate was low in those aged 10 years or younger (6.7%) but increased rapidly in teenagers (51.3%; ^{Kim, 2016}). It may be low in our participants (10–12 years old) compared with middle and high school students. For this reason, there may not be a difference in the SAS according to gender.

This study found no statistically significant correlations between smartphone addiction and sleep time. In this study, we asked children about their sleep time directly, so the data provided are based on self-report and were not measured objectively. Future studies should solicit information from parents and children and use objective measures of sleep time.

Parental Control

The second hypothesis of this study is “SAS of the nonparental control group will be higher than that of the parental control group.” In this study, parental control was measured in two ways, with parental control software and student perceptions of parental control. Surprisingly, Hypothesis 2 was not supported, suggesting that greater perceived parental control may not prevent smartphone addiction, and parental control software is not associated with lower smartphone addiction. We found more than 40 parental control software products in South Korea in 2016. This software is offered either free or for a fee. The functions of each software package are similar: web-filtering from harmful contents, management of install–uninstall of software, management of a child’s screen time, and tracking of a child’s location. These functions restrict the child’s use of the smartphone. However, children often know more about technology than do their parents because of a lifetime of access to the equipment. Children have been known to use proxy sites, which allow users to bypass filters like parental control software in a smartphone (^{ABC News, 2016}). Parents often want to let their child embrace technology in healthy ways within limits, and some have relied on apps and other software to help monitor smartphone behavior. However, this study’s findings suggest that smartphone parental-control software programs may not be the best resource to prevent smartphone addiction.

In addition, the responses indicated that, as perceived parental control increased, the SAS increased. No previous studies exist with which to compare associations between perceived parental control and smartphone addiction. South Koreans are the world’s largest tech users (^{Davey & Davey, 2014}) and have actively discussed and studied smartphone addiction. Some studies, written in Korean (^{Bae, Cho, Cho, & Kim, 2015}; ^{Park & Oh, 2016}), reported that restrictive perceived parental control leads to conflict between parents and children and has no effect on the prevention of smartphone addiction. Limiting usage time of a smartphone can exacerbate academic impairment (^{Park & Oh, 2016}), and active management is more effective, for example, parents explaining to and discussing with their children the pros and cons of smartphone use (^{Bae et al., 2015}). In a previous study that examined perceived parental control of bad behaviors, with increased perceived parental control, children tended to hide rather than improve their behavior (^{Stattin & Kerr, 2000}). Good relationships and communication between parents and children led to children having an open mind and voluntarily describing their behavior to their parents (^{Stattin & Kerr, 2000}). Parents and family members need to monitor adolescent addictions to use of all media (^{American Academy of Pediatrics, 2013}). This study was cross-sectional, and we could not distinguish between antecedents and consequences. Additional research is needed to examine the causal relation between smartphone addiction and perceived parental control.

Depression and Smartphone Addiction

The third hypothesis, “There will be a correlation between depression scores and SAS,” was supported. Several previous studies also reported that depression was significantly higher in addicted smartphone users (^{Davey & Davey, 2014}; ^{Demirci et al., 2015}; ^{Elhai, Dvorak, Levine, & Hall, 2017}; ^{Lemola et al., 2015}), and this is similar to the findings of this study. Depressed individuals may use their mobile phones as a coping method to deal with their depressive, negative emotions and not as a habitual way to pass time (^{Kim, Seo, & David, 2015}). Smartphones are also often repeatedly checked to make sure everything is okay with respect to a worry or an obsession (^{Billieux, Maurage, Lopez-Fernandez, Kuss, & Griffiths, 2015}). It is a maintenance factor in depression, and people use the mobile phone excessively to get consolation in affective relationships. The other possible explanation is related to the association between smartphone addiction and sleep time. Smartphone overuse at night could keep one awake late, thus impairing sleep and influencing stress and depression (^{Davey & Davey, 2014}; ^{Demirci et al., 2015}; ^{Lemola et al., 2015}). These findings are interesting because social withdrawal is a symptom of depression, yet people frequently use smartphones for the opposite process—social connection (^{Choi et al., 2015}). However, excess social connections could have negative consequences, such as cyberbullying, which could also lead to depression. Additional evidence is needed to clarify the relationship between smartphone addiction and depression.

LIMITATIONS

This study has several limitations. It used a cross-sectional design, and therefore we are unable to determine cause-and-effect relationships. The study also used a convenience sample in Korea, and findings may not be generalized to all teens. However, this is one of the first studies to examine smartphone addiction in teens and provides important early data for the rising concern regarding smartphone addiction in this population. Our measure of parental control only captured one style of parenting—restrictive. Additional research is needed to further evaluate the relationships between smartphone addiction and depression and varying measures of parental control, including software use.

CONCLUSIONS

Smartphone use in children will continue to rise and has the potential to lead to psychological and physiological challenges for the pediatric population. Some experts have offered parental control as a strategy to avoid smartphone addiction. However, few empirical studies have examined the relationship between parental control and smartphone addiction. This study adds to the state of the science by providing data related to the associations between parental control, depression, and personal characteristics for a new pediatric population—teens. The results of this study suggest that older teens who are more depressed are at a higher the risk for smartphone addiction. In addition, control-oriented management of children’s use of a smartphone by parents is not very effective and exacerbates smartphone addiction. There continues to be a significant gap in knowledge related to the negative impact of the smartphone on children. Further research is needed to improve understanding of how to avoid smartphone addiction in children.

Relevance for Clinical Practice

With the rising use of smartphones in the pediatric populations, health professionals will have an increasing responsibility to assist children and families to safely use smartphones. Nurses can take a leadership role in preventing smartphone addiction by teaching children how to balance smartphone use. When nurses and other health professionals encourage healthy use of smartphones for children, they should also keep in mind the potential devastating relationship between smartphone addiction and depression. Monitoring the risk for depression in the presence of smartphone addiction is an immediate intervention that nurses can begin to consider. In addition, they can provide family support by advocating for a collaborative child/parent approach rather than control-oriented management approach.