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Changmin Yoo Department of Social Welfare, Inha University, 100, Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea

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Abstract

Background and aims

The current research aimed to discover classification concerning problematic smartphone use in children. Furthermore, to investigate their longitudinal trajectories, as well as to discover the connection concerning problematic smartphone usage by individual, parental, and school factors.

Methods

A total of 2,399 South Korean children who were in the 4th grade (female 1,206 (50.3%), age 10–13 years) at baseline. Latent class growth analysis was utilized to discover typologies in problematic smartphone use and their longitudinal trajectories. Multinomial logistic regression analysis was used to find various associations among problematic smartphone use and individual, parental, as well as school factors.

Results

The results identified three distinct trajectories of problematic smartphone use: (1) a high-level group (7.7%), (2) a mid-increasing group (62.5%), and (3) a low-increasing group (29.8%). The increasing group showed the highest level of problematic smartphone use. Gender, self-esteem, social withdrawal, exercise, parental inconsistency, monthly income, and teacher support were significant predictors.

Discussion and Conclusions

The findings suggest that there are distinct developmental trajectories concerning problematic smartphone usage of childhood. The results show that the early discovery of children in danger of problematic smartphone use and targeted interventions aimed at reducing parental inconsistency and social withdrawal, improving self-esteem, exercise, and teacher support may be effective strategies for preventing problematic smartphone usage during childhood.

Abstract

Background and aims

The current research aimed to discover classification concerning problematic smartphone use in children. Furthermore, to investigate their longitudinal trajectories, as well as to discover the connection concerning problematic smartphone usage by individual, parental, and school factors.

Methods

A total of 2,399 South Korean children who were in the 4th grade (female 1,206 (50.3%), age 10–13 years) at baseline. Latent class growth analysis was utilized to discover typologies in problematic smartphone use and their longitudinal trajectories. Multinomial logistic regression analysis was used to find various associations among problematic smartphone use and individual, parental, as well as school factors.

Results

The results identified three distinct trajectories of problematic smartphone use: (1) a high-level group (7.7%), (2) a mid-increasing group (62.5%), and (3) a low-increasing group (29.8%). The increasing group showed the highest level of problematic smartphone use. Gender, self-esteem, social withdrawal, exercise, parental inconsistency, monthly income, and teacher support were significant predictors.

Discussion and Conclusions

The findings suggest that there are distinct developmental trajectories concerning problematic smartphone usage of childhood. The results show that the early discovery of children in danger of problematic smartphone use and targeted interventions aimed at reducing parental inconsistency and social withdrawal, improving self-esteem, exercise, and teacher support may be effective strategies for preventing problematic smartphone usage during childhood.

Introduction

Children's problematic smartphone use (PSU) is becoming a worldwide issue. The spread of smartphone is increasing and mobile application technology's development in education, entertainment is used for various purposes. Children are spending more time on their smartphone. Bae (2017)'s research stated that within Asian countries, over 80% of individuals above 12 years have been found to use smartphones. According to the Pew Research Center (2019), Korea is famous for the highest usage of smartphones reaching approximately 95% of the population. Furthermore, a media panel survey stated that, the age at which smartphones are used is gradually getting younger. In South Korea, the rate for the possession of smartphone among elementary school students steadily increased from approximately 50% in 2015 to 81.2% in 2019 (Choi, Jeon, Oh, & Hong, 2020). Additionally, a recent study showed that more than 95% of students in South Korea own smartphones (Hong, Yeom, & Lim, 2021). As smartphone penetration increases, PSU rates are also increasing. In 2020, compared to 2018, PSU increased by approximately 19% among adolescents, which was greater than the average rise of 13% within high school students (Ministry of Gender Equality and Family, 2020). Furthermore, a national survey conducted in 2018 stated that Korean adolescents' prevalence of PSU was 29.3% which is the highest in all the age groups (Ministry of Science and ICT, 2019). Increase of smartphone usage and PSU must be considered as they give a potentially negative influence on children's growth in various areas. According to various studies, PSU effects children to have negative consequences such as sleep difficulty, academic issues, decreased physical activity, and psychological issues such as depression as well as anxiety (Elhai, Dvorak, Levine, & Hall, 2017; Samaha & Hawi, 2016). Furthermore, behavioral problems such as aggression and impulsivity may be caused by excessive smartphone use (Lee et al., 2018).

In this study, the authors propose using the term ‘PSU' to describe behaviors that may not reach the same level of impairment as addiction, but involve excessive use. Excessive usage is frequently assessed by measuring the duration and frequency of smartphone usage, as explored by Bae (2017) and similar studies. Problematic use is distinguished by uncontrolled behaviors leading to adverse outcomes in daily life, as indicated by Billieux et al. (2015). The terms ‘PSU' and ‘smartphone addiction' have alternating usage at times, contingent upon how researchers perceive the fundamental concepts (Ellis, 2019; Flayelle, Schimmenti, Starcevic, & Billieux, 2022; Panova & Carbonell, 2018). Delving into behavior patterns considered indicative of addiction, Yen et al. (2009) ardently advocates for the term ‘smartphone addiction.' On the other hand, Elhai et al. (2017) and Panova and Carbonell (2018) comment on less clarity concerning addiction criteria and argue for the use of ‘PSU’ instead. In this study, ‘PSU’ is used. This is because ‘PSU' provides a more flexible framework for discussing related issues.

Accordingly, many studies have been conducted on PSU. Specifically, Herrero, Torres, Vivas, and Urueña (2019), Lai et al. (2022) confirmed how PSU changes over time. In particular, Lai et al. (2022) confirmed the change trajectory targeting adolescents between the ages of 10 and 18. As a result, it was confirmed that the PSU increased over time. These studies have important significance in that they identify characteristics over time that cannot be confirmed in cross-sectional analysis. However, these studies have the limitation of assuming that the population is ‘one' and missing the fact that there may be various sub-potential groups. Parent, Bond, Wu, and Shapka (2022) overcame the limitation of assuming only one population and performed a latent class analysis to identify various potential groups. However, although this study is meaningful in that it identified various potential groups, it has limitations in that it failed to consider changes over time by analyzing only one point in time. In other words, existing studies have limitations in that they either conducted a longitudinal analysis without identifying various potential groups, or conducted a cross-sectional analysis although they identified various potential groups.

Building upon the significance and limitations of these prior studies, this research aims to identify developmental patterns in children's PSU and examine the predictive factors associated with these patterns. In contrast to prior research, our study employs a latent class growth model to simultaneously explore how developmental trajectories of PSU vary over time and to uncover a range of latent classes associated with these trajectories. This approach aims to address the gaps left by previous research by examining both unexplored developmental trajectories and latent subpopulations in PSU simultaneously.

Based on Ecological Systems Theory (Bronfenbrenner, 1977), this study seeks to examine individual factors and environmental factors (family, school) related to children's PSU. Ecological Systems Theory (Bronfenbrenner, 1977) is a theory that explains that an individual's psychosocial development must consider not only the individual's internal factors but also various surrounding environmental factors. Therefore, it is a good grounding theory for comprehensively considering various factors that can affect children's PSU level. Based on Ecological Systems Theory, this study divided predictive factors into individual factors, parental factors, and school factors.

Individual factors include gender, self-esteem, social withdrawal, and exercise. Regarding gender, various studies have confirmed the relationship between gender and PSU. In some studies, women were found to have a higher risk of PSU than men (Demirci, Akgönül, & Akpinar, 2015; Gutiérrez, de Fonseca, & Rubio, 2016), but in other studies, on the contrary, men were found to have a higher risk of PSU than women (Gentina & Rowe, 2020). Another study found no relationship between the two (Chen et al., 2017). As a result, it is essential to determine the connection of gender and PSU. Self-esteem is also an important predictive factor. According to a study by Bae and Nam (2023), it was confirmed that adolescents' self-esteem and PSU have a negative relationship with each other. Similarly, a study by Li, Liu, and Dong (2019) found that low self-esteem increased the danger of PSU in adolescents. Social withdrawal is also associated with PSU. According to a study by Lim (2022), PSU was found to moderate the connection of social withdrawal and peer relationships. Physical activity level has also been shown to be related to PSU (Oh & Park, 2022), and excessive smartphone use has also shown connection to physical health issues like vision impairment as well as neck pain (Hanphitakphong, Thawinchai, & Poomsalood, 2021).

Relationships with parents have an important relationship with PSU. Qiu, Li, Luo, Li, and Nie (2022) analyzed the longitudinal relations between the parent-child relationship and PSU. As a result, the parent-child relationship was positively related to the child's life satisfaction and showed a negative relationship with the level of PSU. Another study confirmed that the household income was positively related to the PSU (Long et al., 2016). Relationships with teachers and peers at school are also significantly related to PSU. According to various studies, it has been confirmed that positive connections with peers and teachers exhibit a negative association with PSU (Ouyang et al., 2020; Zhen, Liu, Hong, & Zhou, 2019; Zhou et al., 2022).

Methods

Participants

This study was primarily based on the 2018 Korean Children and Youth Panel Survey (2018 KCYPS), graciously provided by the National Youth Policy Institute (NYPI). The information obtained from this survey furnishes an extensive view of the development and experiences of children and adolescents in the various settings of their families, schools, peer groups, and communities. A stratified multi-stage cluster sampling method was utilized to pick a representative sample of fourth-grade elementary and first-grade middle school students in 2018, who were then followed through multiple consecutive cycles. Schools were selected proportionally to the number of students in 17 cities and provinces nationwide, and each school surveyed students from one class.

The initial step involved allocating a minimum sample of two schools from each of the 17 regions. Then, the sample size for each school was calculated proportionally based on the number of students, following a probability proportional to size sampling method. IRB approval was confirmed by the NYPI, then the selected schools were contacted to verify the children and adolescents' consent for the survey participation. The study employed tablet PCs for data collection. Interviewers carried out in-person interviews with both adolescents as well as the guardians during home visits. Each student, along with their parent or primary guardian, provided a written agreement concerning participation of the research. The KCYPS survey was carried out annually from August to November.

For the evaluation of elementary school students (4th–7th grade) and their parents, data from the 1st time point (2018) to the 4th time point (2021) was utilized. A total of 2,607 individuals were surveyed in the baseline panel. For this study, we excluded those who did not respond to the PSU questions in the first year, resulting in a final study population of 2,399 individuals (female 1,206 (50.3%), age 10–13 years). The attrition rate (2019∼2021) was 6.5% (surveyed 2,437), 7.5% (surveyed 2,411), 12.7% (surveyed 2,275).

Measures

Dependent variable: problematic smartphone use

The reference tool utilized in the current research was the Smartphone Addiction Proneness Scale (SAPS) by Kim, Lee, Lee, Nam, and Chung (2014), according to the Korean Children and Adolescents Panel Survey. The scale which consists 15 items measured by the 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). A higher score means a higher PSU. This scale revealed good reliability and construct validity in the Korean adolescent samples (Jeong, Kim, Ryu, & Lee, 2022), and the Cronbach's α for PSU was 0.881 in 2018, 0.875 in 2019, 0.888 in 2020, and 0.850 in 2021 in this study.

Individual factors: gender, self-esteem, social withdrawal, exercise time

Regarding individual factors, male was coded as ‘1’ and female as ‘0’ for the gender variable. Self-esteem was assessed using a scale by Rosenberg's (1965) self-esteem scale consisting of 10 items measured on a 4-point Likert scale ranging from 1 (not at all true) to 4 (very true). In some items, reverse coding was applied, also, a higher score indicated greater self-esteem. The Cronbach's α for self-esteem was 0.836. A revised and expanded version of Kim and Kim (1998)'s social withdrawal scale was utilized. The scale composes of a sum of five items. Responses are measured by the 4-point Likert scale ranging from 1 (not at all) to 4 (strongly agree). A greater score shows a greater level of social withdrawal. This scale indicates good reliability in the Korean adolescent samples (Kim, Han, Park, & Kang, 2020), and the Cronbach's α was 0.860. The questionnaire asked about the amount of exercise in the past week based on the time spent sweating during exercise. Reactions were verified by a 5-point scale ranging from 0 (no exercise) to 4 (4 or more hours).

Parental factors: household income, parenting style (warmth, rejection, inconsistency)

The monthly household income was measured from 1 (No income) to 12 (More than 8000 USD), and the greater the score, the greater the average monthly household income. Parenting style was found using Kim and Lee's (2017) parenting style scale, with the “warmth” factor representing positive parenting style (four items), and the “rejection” (four items) and “inconsistency” (four items) factor representing negative parenting style. Each question was measured on a 4-point Likert scale ranging from 1 (not at all) to 4 (very much). The greater the score indicating the greater the warmth, rejection, and inconsistent parenting style. This scale revealed good reliability in the Korean adolescent samples (Kim, Kang, & Lee, 2020), and the Cronbach's α of this study was 0.910 for warmth, 0.633 for rejection, and 0.753 for inconsistency. Although this value is slightly lower compared to the other subscales, it falls within an acceptable range and is compatible with findings from preceding research (Kim, Kang, & Lee, 2020) employing similar measures. Among the parental factors, children responded to parental parenting styles (warmth, rejection, inconsistency), while parents responded to income.

School factors: peer relationship, student-teacher relationship

A 13-item scale by Bae, Hong, and Hyun (2015) was utilized to discover peer relationship quality among adolescents using a 4-point Likert scale ranging from 1 (not at all) to 4 (very much), with negative items reverse-coded. A higher score indicates a better peer relationship. This scale has shown good reliability in the Korean adolescent samples (Lim, 2023), and the Cronbach's α was 0.808. Additionally, a 14-item tool by Kim and Kim (2009) was utilized to find the student-teacher relationship using the same Likert scale. The greater the score indicating a better teacher relationship. This scale reveals good reliability in the Korean adolescent samples (Kim, Lee, & Park, 2022), and the Cronbach's α was 0.905.

Procedures

The current research utilized the latent class growth analysis (LCGA) to discover longitudinal trajectory patterns of PSU from 4th to 7th grade. The number of latent classes are confirmed by information indices, classification quality, and model comparison tests (Nylund, Asparouhov, & Muthén, 2007). Akaike information criterion (AIC), Bayesian information criterion (BIC), and sample size-adjusted BIC (SSA-BIC) are commonly used indices, and a smaller value means a better model (Muthén & Shedden, 1999). The Entropy index is a measure of classification quality, ranging from 0 to 1, and a higher value means a better group classification (Clark, 2010). The Lo-Mendell-Rubin adjusted likelihood ratio test (LMR-LRT) and parametric bootstrapped likelihood ratio test (BLRT) are utilized for model comparison. If the p value is not significant, the k-1 latent group number model is judged to be appropriate. The minimum proportion of each latent class varies from scholar to scholar; some argue that it should be at least 5% (Jung & Wickrama, 2008), while others consider it acceptable if it is only 1% or more (Nooner et al., 2010).

A logistic regression analysis was performed applying the r3step method suggested by Asparouhov and Muthén (2014). The r3step method conducts analysis by considering errors due to the influence of predictive factors when classifying latent groups. To compare the mean differences of key variables across latent class, the BCH method was used. The Mplus 8.7 program was used to perform the analysis. When assessing the relationship between latent class and predictors, predictors measured at the 1st time point were employed.

Ethics

The research process was conducted referring to the Declaration of Helsinki. Ethics consent was given by the Author's Institutional Review Board. All members provided informed consent.

Results

Classification of latent groups according to changes in PSU

Before identifying the number of unobservable groups based on the changes in PSU, we conducted a chi-square difference test on a linear change model and a quadratic curve model for the entire group. Latent growth curve models have been employed for examination of both linear change as well as quadratic curve model. The null hypothesis was rejected, showing that the quadratic curve model was more suitable compared to the linear model. Consequently, we decided to use the quadratic curve model to classify the latent groups, as presented in Table 1.

Table 1.

The results of the chi-square difference test

Modelx2dfCFIIFIRMSEA
Linear model211.12850.8640.8640.131
Quadratic curve model16.00110.9900.9900.079
Testing for differences in models195.1274 (p < 0.001)0.1260.1260.052

A LCGA was conducted to identify the number of latent classes that exists within change trajectory of PSU. The results of comparing the models can be seen in Table 2.

Table 2.

Comparison of fit indices for latent class growth models with 1–4 classes for problematic smartphone use

VariableClass 1Class 2Class 3Class 4
AIC12,730.41311,562.40811,342.62611,295.267
BIC12,770.89311,626.01911,429.36811,405.140
SSA-BIC12,748.65211,591.06911,381.71011,344.773
LMRT p value0.00000.03930.3491
BLRT p value0.00000.00000.0000
Entropy0.6190.6670.637
class12,399 (100.0%)1,007 (42.0%)184 (7.7%)678 (28.3%)
class21,392 (58.0%)1,499 (62.5%)171 (7.1%)
class3716 (29.8%)118 (4.9%)
class41,432 (59.7%)

As the number of latent classes heightened by one, both the AIC and BIC values decreased, and the LMR-LRT test was significant as well. However, in the case of class 4, the proportion of the sample in terms of the minimum case numbers was small at 4.9% (Jung & Wickrama, 2008) and the LMRT p-value was not significant. After a comprehensive consideration of various indices and interpretability, the optimal model was determined to be the three-class model, which had an appropriate distribution of case numbers. The average latent class probabilities of the three classes were 0.781, 0.849 and 0.850, respectively.

Next, the results of estimating the average of the primary value, linear rate of change, and second rate of change for every latent class of the three-class model are shown in Table 3, and the changes in PSU for the derived groups are depicted in Fig. 1.

Table 3.

Classification of individuals based on their most likely latent class pattern

MeanWhole groupClass 1 (High-level)

N = 184
Class 2 (Mid-increasing)

N = 1,499
Class 3 (Low-increasing)

N = 716
estimateS.E.estimateS.E.estimateS.E.estimateS.E.
Initial value1.799***0.0102.485***0.2391.835***0.0431.521***0.029
Linear slope0.251***0.0130.2300.1760.375***0.0400.030***0.027
Quadratic slope−0.048***0.004−0.0690.036−0.079***0.0130.016*0.007

Note. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 1.
Fig. 1.

Whole group and class specific trajectories of problematic smartphone use

Citation: Journal of Behavioral Addictions 2024; 10.1556/2006.2024.00002

The initial value of PSU for the whole sample (n = 2,399) was 1.799 (p < 0.001), the linear rate of change was 0.251 (p < 0.001), and the second rate of change −0.048 (p > 0.001). Whole sample exhibited moderate level of PSU initially, followed by a linear increase in PSU. However, this increase gradually slowed over time.

Class 1 comprised 7.7% of the sample (n = 184). The initial value of PSU for class 1 was 2.485 (p < 0.001), the linear rate of change was 0.230 (p > 0.05), and the second rate of change −0.069 (p > 0.05). Class 1 exhibited the highest level of PSU among the three groups in the fourth grade and maintained it thereafter. Therefore, it was named the “high level group.” Class 2 comprised 62.5% of the sample (n = 1,499). The initial value of PSU for class 2 was 1.835 (p < 0.001), the linear rate of change was 0.375 (p < 0.001), and the second rate of change −0.079 (p < 0.001). Class 2 exhibited a moderate level of PSU initially, followed by a linear increase in PSU. However, this increase gradually slowed over time. Therefore, it was named the “mid-increasing group.” Class 3 comprised 29.8% of the sample (n = 716). The initial value of PSU for class 3 was 1.521 (p < 0.001), the linear rate of change was 0.030 (p < 0.001), and the second rate of change 0.016 (p < 0.05). Class 3 displayed the lowest initial PSU level, followed by a linear increase in PSU. Interestingly, this increase accelerated over time. Therefore, it was named the “low-increasing group.”

Descriptive statistical results of key variables

Table 4 displays the correlation coefficients for the key variables. The sociodemographic characteristics of the study participants and descriptive statistics for each latent class group's key variables are presented in Table 5 (first year point data) and Table 6 (fourth year point data). Furthermore, to test for differences in the mean values of key variables among each latent class, BCH methods analysis was conducted. As a result, most variables showed significant differences between the classes.

Table 4.

Correlation coefficient analysis among variables at first wave

GSESWEXWARJINSINCPETEPSU1PSU2PSU3PSU4
G10.052*−0.076**0.295**0.006−0.021−0.015−0.051**−0.138**−0.058**0.084**0.042*0.0120.010
SE1−0.408**0.156**0.496**−0.494**−0.409**0.090**0.447**0.381**−0.347**−0.222**−0.212**−0.159**
SW1−0.154**−0.220**0.252**0.239**−0.100**−0.311**−0.226**0.267**0.144**0.146**0.126**
EX10.072**−0.068**−0.078**0.070**0.125**0.081**−0.096**−0.088**−0.097**−0.074**
WA1−0.512**−0.387**0.097**0.364**0.381**−0.249**−0.146**−0.124**−0.074**
RJ10.462**−0.102**−0.310**−0.226**0.241**0.144**0.134**0.110**
INS1−0.062**−0.286**−0.265**0.298**0.178**0.151**0.144**
INC10.100**0.120**−0.157**−0.075**−0.047*−0.070**
PE10.422**−0.245**−0.156**−0.145**−0.120**
TE1−0.245**−0.185**−0.174**−0.126**
PSU110.409**0.312**0.231**
PSU210.449**0.342**
PSU310.488**
PSU41
Mean3.212.033.503.581.631.896.553.072.991.811.982.132.11
SD0.480.741.410.530.530.622.220.420.490.510.490.530.45
Range1–41–40–41–41–41–41–121.38–41–41–41–3.671–3.871–4
Skewness−0.6070.315−0.314−1.2240.8430.3840.610−0.108−0.3140.5580.2040.0940.077
Kurtosis0.211−0.635−1.3391.2930.952−0.1240.2150.0470.6810.250−0.338−0.533−0.019

Note. G = gender, SE = self-esteem, SW = social withdrawal, EX = exercise time, WA = parent warmth, RJ = parent rejection, INS = parent inconsistency, INC = income, PE = peer relationship, TE = teacher relationship, PSU = problematic smartphone use, SD = standard deviation. For continuous variables, Pearson's correlation coefficient was employed, while for ordinal variables, Spearman's rank order correlation coefficient was utilized. Regarding gender, 'male' was coded as '1,' and 'female' as '0.' Exercise time was coded as follows: 0 (no exercise), 1 (1 h), 2 (2 h), 3 (3 h), and 4 (4 or more hours).

*p < 0.05, **p < 0.01, ***p < 0.001.

Table 5.

Descriptive statistics of problematic smartphone use by latent classes at first wave (N = 2,399)

VariableClass 1 (High-level)

N = 184
Class 2 (Mid-increasing)

N = 1,499
Class 3 (Low-increasing)

N = 716
Overall

Chi square test
Post-hoc pairwise comparisonsEffect size (partial eta squared)
MSEMSEMSEGroupsChi-Square
Individual factorGender (n, %)male97 (52.7%)758 (50.6%)338 (47.2%)2.907
female87 (47.3%)741 (49.4%)378 (52.8%)
Self-esteem2.8260.0433.1700.0163.4150.020201.794***C1 vs. C249.414***0.073
C1 vs. C3162.869***
C2 vs. C375.700***
Social withdrawal2.5650.0692.0460.0241.8280.034101.641***C1 vs. C243.777***0.041
C1 vs. C395.704***
C2 vs. C322.088***
Exercise2.9470.1453.4630.0463.7500.06133.556***C1 vs. C29.844**0.014
C1 vs. C327.033***
C2 vs. C311.172**
Parental factorWarmth3.3110.0523.5480.0183.7300.02278.991***C1 vs. C215.714***0.030
C1 vs. C356.684***
C2 vs. C333.193***
Rejection1.9760.0561.6500.0171.4810.02288.390***C1 vs. C227.079***0.038
C1 vs. C370.914***
C2 vs. C328.562***
Inconsistency2.3350.0561.9350.0201.6680.027142.696***C1 vs. C239.149***0.054
C1 vs. C3120.054***
C2 vs. C350.185***
Income5.6740.1946.4940.0736.9330.10137.549***C1 vs. C213.239***0.014
C1 vs. C334.455***
C2 vs. C39.902**
School factorPeer support2.7850.0363.0470.0133.1840.020105.286***C1 vs. C240.477***0.039
C1 vs. C399.600***
C2 vs. C326.243***
Teacher support2.7440.0472.9390.0163.1670.022102.313***C1 vs. C213.370***0.042
C1 vs. C368.845***
C2 vs. C355.296***

Note. M = mean, SE = standard error. In this table, for between-group mean comparisons, we used the BCH procedure.

*p < 0.05, **p < 0.01, ***p < 0.001.

Table 6.

Descriptive statistics of problematic smartphone use by latent classes at fourth wave (N = 2,090)

VariableClass 1 (High-level)

N = 163
Class 2 (Mid-increasing)

N = 1,282
Class 3 (Low-increasing)

N = 645
Chi square test or F/WelchPost-hoc pairwise comparisons (Bonferroni)Effect size (partial eta squared)
MSDMSDMSDGroupsMD
Individual factorGender (n, %)Male82 (50.3%)650 (50.7%)307 (47.6%)1.680
Female81 (49.7%)632 (49.3%)338 (52.4%)
Self-esteem2.770.442.880.423.100.4962.992***C1 vs. C2−0.1182*0.057
C1 vs. C3−0.3313*
C2 vs. C3−0.2130*
Social withdrawal2.400.742.100.661.980.7724.039***C1 vs. C20.2995*0.023
C1 vs. C30.4198*
C2 vs. C30.1203*
Exercise2.341.312.531.332.851.3816.142***C1 vs. C2−0.19140.015
C1 vs. C3−0.5153*
C2 vs. C3−0.3239*
Parental factorWarmth3.160.533.180.573.430.5843.725***C1 vs. C2−0.02670.040
C1 vs. C3−0.2746*
C2 vs. C3−0.2479*
Rejection2.060.671.970.641.690.6347.143***C1 vs. C20.09590.043
C1 vs. C30.3753*
C2 vs. C30.2794*
Inconsistency2.280.612.170.571.970.6730.261***C1 vs. C20.10350.028
C1 vs. C30.3067*
C2 vs. C30.2032*
Income6.392.066.901.997.332.1417.507***C1 vs. C2−0.5041*0.017
C1 vs. C3−0.9422*
C2 vs. C3−0.4382*
School factorPeer support2.900.373.030.393.180.4348.537***C1 vs. C2−0.1287*0.044
C1 vs. C3−0.2858*
C2 vs. C3−0.1571*
Teacher support2.680.462.750.442.930.5038.564***C1 vs. C2−0.07780.036
C1 vs. C3−0.2564*
C2 vs. C3−0.1786*

Note. M = mean, SD = standard deviation. In this table, for between-group mean comparisons, we used ANOVA procedure based on the latent groups classified at the first wave as the reference point.

*p < 0.05, **p < 0.01, ***p < 0.001.

Factors affecting latent classes of PSU

Table 7 presents the results of logistic regression analysis using r3step, which shows the probabilities of belonging to each class based on the predictive factors. First, when comparing the high-level group (class 1), which had the highest level of PSU, with the mid-increasing group (class 2), individuals with higher income, lower social withdrawal, and lower parental inconsistency were inclined to be part of the mid-increasing PSU group than the high-level PSU group. Second, when comparing the high-level group (class 1) and low-increasing group (class 3), female, individuals with high self-esteem, exercise for ‘one hour, two hours, three hours, and four hours or more’ compared to not exercising at all, higher income, higher teacher support, lower social withdrawal, and lower parental inconsistency were inclined to be part of the low-increasing group than the high-level group. Lastly, when comparing the mid-increasing group (class 2) and low-increasing group (class 3), female, individuals with high self-esteem, exercise for ‘1 hour’ and ‘4 hours or more’ compared to not exercising at all, higher teacher support, and lower parental inconsistency were inclined to be part of the low-increasing group than the mid-increasing group.

Table 7.

Results of the r3step analysis on the relationship between PSU trajectory patterns and predictors

VariableDependent variable: Problematic smartphone use
C2: Mid-increasing group vs.

C1: High-level group (Ref)
C3: Low-increasing group vs.

C1: High-level group (Ref)
C3: Low-increasing group vs.

C2: Mid-increasing group (Ref)
BS.E.ORCI (95%)BS.E.ORCI (95%)BS.E.ORCI (95%)
Individual factorMale (ref: female)–0.2280.2470.7960.4901.292–0.615*0.2550.5410.3280.891–0.387*0.1490.6790.5070.910
Self-esteem0.5090.3201.6630.8883.1151.429***0.3374.1762.1588.0800.921***0.2162.5111.6433.836
Social withdrawal–0.607***0.1700.5450.3910.760–0.667***0.1770.5130.3630.726–0.0600.1100.9410.7591.167
Exercise (Ref: 0 h)1 h0.3880.3891.4740.6883.1570.982*0.4382.6711.1336.2980.594*0.3231.8120.9613.415
2 h0.5860.4441.7960.7524.2921.314**0.4853.7221.4399.6300.7280.3322.0721.0803.974
3 h0.8590.4992.3610.8896.2761.403**0.5344.0681.42911.5820.544**0.3411.7230.8833.362
≥4 h0.7360.4032.0870.9484.5971.684***0.4465.3852.24512.9160.9480.3142.5801.3944.775
Parental factorWarmth–0.1620.2260.8510.5461.326–0.2100.2550.8110.4911.337–0.0480.1950.9530.6511.396
Rejection–0.1380.2700.8710.5131.479–0.2520.2770.7770.4511.338–0.1140.1700.8920.6391.246
Inconsistency–0.682**0.2310.5060.3210.796–1.117***0.2390.3270.2050.522–0.435**0.1350.6470.4960.844
Income0.167**0.0601.1821.0511.3290.220***0.0611.2461.1061.4040.0530.0311.0550.9921.121
School factorPeer support0.4320.2871.5410.8782.7050.3380.3101.4030.7642.575–0.0940.2100.9100.6031.374
Teacher support0.1180.2441.1250.6981.8140.770**0.2662.1601.2833.6380.652***0.1791.9201.3512.730

Note. *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

Patterns of PSU trajectories

As a result of the study, three PSU change trajectory types were identified: a high-level group (7.7%), a mid-increasing group (62.5%), and a low-increasing group (29.8%). The finding is compatible with those of Parent et al. (2022), whom identified three latent groups of PSU, this is a similar result, but at the same time, it is a new result that was not confirmed in previous studies. In the study by Parent et al. (2022), three latent classes were identified: the ‘connected class,' the ‘problematic class,' and the ‘distracted class.' Previous studies have identified various subtypes of PSU. However, the current study is different from existing studies in that it not only identified various subtypes of PSU, but also confirmed longitudinal changes in these subtypes. For example, in previous studies, individuals belonging to the ‘connected class' are a class classified at a certain point in time, but if these characteristics change over time, they may be classified into a different latent class in a longitudinal study. Additionally, according to Ecological Systems Theory, an individual's psychosocial and behavioral aspects are influenced by interactions with the surrounding environment. Since the environment, such as technological changes and social norms, can change over time, this longitudinal research is very important. It highlights the importance of considering both the temporal aspects and the broader context when studying behaviors like PSU.

In summary, the findings of this research may serve as important evidence to develop mid- to long-term intervention strategies related to children's PSU. First, the high-level group shows consistently high PSU characteristics over time. It can be seen that mid- to long-term interventions such as counseling and cognitive behavioral therapy may be necessary for these groups. Second, the mid-increasing group is the group with the highest proportion, and early intervention related to PSU is important. In particular, it is thought that the risk of PSU will be lowered if education on healthy smartphone use is provided to both children and parents and if they are involved in alternative events. Lastly, the fact that the low-increasing group also has a low PSU level at the initial point, but shows a continuous increasing trend must be considered. Therefore, preventive intervention is needed to prevent PSU in the future by intervening on social withdrawal or low self-esteem related to this group.

Predictors regarding patterns of PSU trajectories

The findings of this research propose that several individual factors are associated with a less likelihood of belonging to a group with increased levels of PSU during childhood. Specifically, being female, having higher self-esteem, spending more time exercising, and experiencing less social withdrawal had lower possibility in high-level group. These findings are compatible with or support past research discoveries (Chiang, Chang, Lee, & Hsu, 2019; Demirci et al., 2015; Gutiérrez et al., 2016; Lee et al., 2018; Lee & Kim, 2018; Lim, 2022). These results carry significant implications for mental health professionals, educators, and parents who care for children and teenagers. Strategies to promote self-esteem, encourage physical activity, and address social withdrawal could be useful in preventing or reducing PSU in childhood. Also, we found a higher likelihood of PSU among adolescent male compared to female. This result suggests that male may be more vulnerable to developing PSU. In this regard, several scholars have confirmed that women mainly use smartphones for social relationships, while men mainly use smartphones for fun, games, and gambling. In other words, the purpose of using a smartphone may differ depending on gender, and this difference may cause a difference in PSU (Frangos, Fragkos, & Kiohos, 2010; Gentina & Rowe, 2020; Van Deursen, Bolle, Hegner, & Kommers, 2015).

The parental factors play an important role. Specifically, previous research has shown that children whose parents have inconsistent parenting styles and lower monthly incomes are more likely to fall into the category of high-increasing PSU. These recent findings support the previous research (Brown, Campbell, & Ling, 2011; Kim, Kang, & Lee, 2020) and highlight the significance of parental environments in the development of children's PSU. Children's use of smartphones may increase as a way to relieve stress due to unfavorable environments due to low income and parents' inconsistent parenting methods, which can ultimately lead to PSU. Therefore, intervention related to parents' parenting attitude and economic situation is important. For example, if negative parenting by parents is confirmed, relevant education and counseling is provided, and in cases of economic poverty, children are provided with a variety of support.

Lastly, this study confirmed that positive relationships with teachers were an important protective factor in preventing PSU. This can be seen as being compatible to the findings of Shi et al. (2022), who found that teachers' positive support for children was negatively related to PSU. This means that positive interactions with teachers can strengthen children's sense of belonging and academic motivation, which can ultimately prevent PSU. In conclusion, teacher support can be a valuable tool in preventing PSU in children during their school years. By fostering a positive and supportive classroom environment, teachers can help children develop better social skills, academic performance, and a reduced need for escapism through smartphone use.

Conclusions

The current research goals were to verify various latent classes of children's PSU change trajectories and identify predictive factors associated with these latent classes. As a result, three change trajectories were identified: high-level, mid-increasing, and low-increasing. The high-level type showed a high PSU that persisted over time, the mid-increasing type showed a PSU gradually increasing over time, and the low-increasing type showed a gradual increase in PSU over time. However, it was still at a low level. Additionally, it was confirmed that gender, self-esteem, social withdrawal, exercise, parental parenting style, and teacher-student relationship were significantly related to this type of longitudinal change in PSU. These findings have the advantage of providing mid- to long-term evidence for children's PSU intervention by confirming the type of longitudinal change in PSU that was not confirmed in existing studies. Despite the contributions and implications of this study, several limitations should be considered. First, the applicability of the findings might be restricted to the specific demographic of South Korean children. This study acknowledges that not using the clustering option could potentially have an impact on the usual mistakes and significance levels of the statistical findings. Also, it is possible that individuals with more severe PSU may have had a higher dropout rate from the sample over time. Second, the study relied on self-report measures to assess PSU and associated factors. Information gathered through self-reporting can be influenced by certain biases like the desire to present oneself favorably or inaccuracies in recollection, that may influence the precision and trustworthiness of the data collected. Third, it is essential to note that, despite the utilization of a longitudinal design, we cannot establish causal relationships between variables. Also, it is worth noting that there may be other potentially relevant individual, parental, or school factors excluded in the current research which could influence the understanding of the dynamics of PSU as well as its developmental trajectories.

Future research might consider incorporating these additional factors such as media exposure, technological environment, academic stress, and mental health factors for a more comprehensive analysis. Nevertheless, the importance of this study lies in its contribution to the understanding of the developmental course of PSU in children. By identifying different trajectories of PSU, this research may notify the advancement of targeted involvement that may prevent and reduce PSU in children.

Funding sources

The author did not receive support from any organization for the submitted work.

Author's contribution

Changmin Yoo designed the study, performed statistical analyses and wrote the manuscript.

Conflict of interest

The author declares no conflict of interest.

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The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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