Despite the rapid increase in problematic media device use, relatively little is known about specific characteristics and extent of problematic media device and how they relate to different psychological features.
Data extracted from the Panel Korea Study for the Child Cohort Study were used. At the age of 9 years, media device addiction severity was assessed using the K-scale, and children’s behavioral outcomes were assessed using the Child Behavior Checklist. Among children with problematic media device use (n=339), we performed latent profile analysis using the K-scale to identify subtypes of problematic media device use, and then compared the child behavioral problems and executive function according to the different subtypes of problematic media device use.
Children with problematic media device use were divided into class 1 (n=51), class 2 (n=138), and class 3 (n=150). Compared with classes 2 and 3, class 1 had more severe problematic media device use, including daily activity disturbance, withdrawal, and tolerance. Class 1 had the most serious behavioral problems and executive function difficulties among classes. Class 2 had greater daily activity disturbance and tolerance than those of class 3, but executive function showed no significant difference between the two classes. In logistic regression analysis, behavioral problems except for somatization were more common in class 1 than in the control group.
Results suggest that problematic media device use is associated with significant behavioral problem and executive function difficulties and underscore the need for further clinical and research attention for these specific subgroup members.
Due to the rapid growth of information technology and mobile communication, personal computers and smartphones have become very popular among children and adolescents [
Researchers have demonstrated that excessive media device use has a negative influence on children’s physical and psychological health and academic achievement [
Internet gaming disorder (IGD) is registered as a research category in DSM 5 and has received considerable clinical attention [
Latent profile analysis (LPA) is a statistical approach used to identify subtypes of problematic media device use [
Therefore, this study aimed to identify the subtypes of children with problematic media device use and compare behavioral problems and executive function according to the identified subtypes.
This study used data from the 10th year of the Panel Study on Korean Children (PSKC), an annual follow-up survey of 2,150 newborn babies born in 2008. The detailed methodology of the PSKC study has been described elsewhere [
We had complete data on media device addiction severity, behavioral problems, and executive function as evaluated in 9-year-old children using the Korean internet addiction scale, Child Behavior Checklist (CBCL 6-18), and executive function difficulty questionnaire scale (EFDSC). The 1,484 households that participated in the 10th year survey conducted in 2017 showed a panel retention rate of 69% among the original 2,150 households. In this study, the Internet addiction risk group consisted of 339 elementary school 3rd grade children with a media device addiction score of ≥28, comprising 211 boys (62.24%) and 128 girls (37.76%).
Children’s addiction severity to media devices was measured using the K-scale (Korea Internet Addiction Scale) provided by the Korea Information Society Agency as a modified tool in the Korean Children’s Panel Study [
To assess children’s problem behaviors, the CBCL 6–18 was used [
Children’s executive function was evaluated using the executive function difficulty screening questionnaire developed by Song [
In this study, a latent profile analysis (LPA) was conducted to identify potential groups according to the level of the child’s media device addiction. To determine the optimal model selection, the Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, Vuong-Lo-Mendell-Rubin likelihood ratio test, and bootstrapped likelihood ratio test (BLRT) verification and composition ratio distribution using potential profiles were comprehensively considered. Second, to examine if there was a difference between problem behavior and executive function according to the class of media device addiction identified in the potential profile analysis, a one-way analysis of variance was performed. To explore the association between different classes of problematic media device user and behavioral problems, logistic regression was performed with 65 or more cutoff points for each CBCL subscale as dependent variables and sex and maternal education as covariates. In further analysis, suicidal thoughts were assessed using CBCL item 91, “Talks about killing self,” and suicidal behavior was assessed using CBCL item 18, “Deliberately harms self or attempts suicide.” Analyses were performed using SPSS (version 21.0; IBM Corp., Armonk, NY, USA) and M plus version 7.0 (Muthén & Muthén, Los Angeles, CA). Statistical significance was defined as a p-value of <0.05.
Latent profile analysis was conducted to determine the number of classes of problematic media device user group, and the results are shown in
Out of a total of 1,389, 339 children had an Internet addiction scale score of 28 or higher, comprising 211 boys (62.24%) and 128 girls (37.76%).
There was no significant difference between the four groups in terms of parental age. Maternal education level showed a significant difference between the groups (p<0.001). In the control group, high school graduate or lower accounted for only approximately 23.90% of mothers, whereas in classes 1, 2, and 3, it accounted for 47.06%, 36.96%, and 32.67% of mothers, respectively. There was no significant difference between groups in terms of paternal education level or household income.
Statistically significant differences were found in all media device addiction severity according to the class of problematic media device user, and the results of addiction severity comparison are shown in
According to the class of problematic media device user, statistically significant differences were found in all four subscales of difficulty in execution function, and the analysis results are shown in
Behavioral problems were among 339 problematic media device user compared based on the class of problematic media device user (
In logistic regression analysis, total, internalizing, anxious/depressed, and immature problems were more frequent in class 1 than in the control group (
In this study, three classes were identified in children with problematic media device use. Class 1 had significantly more severe daily activity disturbance, tolerance, and withdrawal than other classes. Behavioral problems and executive function difficulties were most severe in class 1. Classes 2 and 3 had less severe media device addiction scores than class 1, and executive function difficulties and behavioral problems were also significantly less severe than those in class 1.
Class 1 accounted for 51 out of 1,484 (3.44%), which is similar to the Internet addiction prevalence rate of 5% reported in previous studies [
Previous studies have reported high levels of anxiety, depression, and social withdrawal in adolescents with Internet addiction [
In our study, a high level of executive function difficulty was demonstrated in the highest problematic media device use class, which corroborates previous studies [
Regarding the addiction scale, class 2 had a higher score for daily life disturbance and tolerance than those of class 3, but there was no significant difference between classes 2 and 3 with respect to the withdrawal subscale. Classes 2 and 3 had no significant differences in executive function difficulties despite the difference in addiction scale. In class 2, suicidal behavior, withdrawn/depressed, and attention problems were significantly more common than in the control group, and in class 3, rule-breaking, aggressive behavior, thought, and externalizing problems were more common than in the control group. In sum, class 2 is characterized by depressive symptoms and attention problems, and class 3 can be characterized by externalizing problems as the main manifestation. Although media device addiction severity was lower in class 3 than in class 2, externalizing problems were higher than in class 2. A possible explanation for this is that although there was no statistically significant difference between class 2 and class 3, it may reflect the greater executive function difficulties of class 3. Therefore, children with problematic media device use may have behavioral problems according to the severity of addiction, and even in children with mild media device addiction levels, careful screening of aggressive behaviors may be clinically useful.
Among children with media device addiction, children in classes 1 or 2 (n=189, 55.75%) were more likely to be included in the clinical risk group for CBCL attention problems (cutoff ≥65 T score) than those in the control group. In a previous study, ADHD was reported to be a common comorbidity in Internet addiction [
This study had several limitations. First, as this study was a cross-sectional design study, the causal relationship between variables could not be confirmed. Second, no information was collected on the quantitative use of the media device, purpose of use, type of use, or environment of use.
In conclusion, we confirmed three subtypes of children with problematic media device use. Behavioral problems and executive function difficulties were most severe in the subtype with the highest media device addiction severity. Also, even in children with mild media device addition, significant levels of externalizing behavior were observed. Problematic media device use is highly correlated with mental illness in children and adolescents, and careful screening and clinical attention are required in the children with problematic media device use. Future research should determine whether the contents of media device use and parental supervision can reduce the burden of mental health conditions.
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
The authors have no potential conflicts of interest to disclose.
Conceptualization: Yunhye Oh, Yoo-Sook Joung. Data curation: Yunhye Oh. Formal analysis: Yunhye Oh, Youngmi Kim. Investigation: all authors. Supervision: Yoo-Sook Joung. Writing—original draft: Yunhye Oh, Yoo-Sook Joung. Writing—review & editing: all authors.
None.
On behalf of all authors, the corresponding author states that there are no conflicts of interest. The findings and conclusions of this study are those of the authors and do not necessarily represent the views of the funding agency. The authors declare no competing financial interests. The results of the present study do not constitute endorsement by “Psychiatry Investigation.
Determination of model selection
No. of classes | AIC | BIC | aBIC | Entropy | VLMR-LRT | BLRT | Group ratio (%) |
---|---|---|---|---|---|---|---|
2 | 8,990.64 | 9,338.81 | 9,050.14 | 0.863 | 0.00 | 0.00 | 54.57/45.43 |
3 | 8,779.95 | 9,304.11 | 8,869.53 | 0.877 | 0.09 | 0.00 | 15.04/40.71/44.25 |
4 | 8,614.25 | 9,314.41 | 8,733.90 | 0.900 | 0.08 | 0.00 | 11.50/37.76/36.87/13.86 |
5 | 8,515.44 | 9,391.59 | 8,665.17 | 0.929 | 0.81 | 0.00 | 5.90/38.05/4.72/12.09/39.23 |
6 | 8,467.84 | 9,519.99 | 8,647.64 | 0.926 | 0.76 | 0.00 | 5.90/15.04/28.02/41.00/3.84/6.20 |
AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, sample-size adjusted BIC; VLMR-LRT, Vuong-Lo-Mendell-Rubin likelihood ratio test; BLRT, bootstrap likelihood ratio test
Demographic characteristics of problematic media device user
Class 1 (N=51) | Class 2 (N=138) | Class 3 (N=150) | Control (N=1,050) | p-value | |
---|---|---|---|---|---|
Male, N (%) | 35 (68.63) | 84 (60.87) | 92 (61.33) | 491 (46.76) | <0.001 |
Maternal age, mean (SD) | 39.64 (4.64) | 40.28 (3.56) | 39.50 (3.65) | 39.81 (3.59) | 0.330 |
Paternal age, mean (SD) | 42.37 (4.51) | 42.79 (3.98) | 41.85 (3.93) | 42.21 (3.91) | 0.240 |
Maternal education, N (%) | <0.001 | ||||
High school graduate or lower | 24 (47.06) | 51 (36.96) | 49 (32.67) | 251 (23.90) | |
University graduate | 26 (50.98) | 78 (56.52) | 94 (62.67) | 723 (68.86) | |
Postgraduate | 1 (1.96) | 7 (5.07) | 7 (4.67) | 72 (6.86) | |
No response | 0 (0) | 2 (1.45) | 0 (0) | 4 (0.38) | |
Paternal education, N (%) | 0.720 | ||||
High school graduate or lower | 18 (35.29) | 39 (28.26) | 46 (30.67) | 264 (25.14) | |
University graduate | 29 (56.86) | 85 (61.59) | 89 (59.33) | 655 (62.38) | |
Postgraduate | 4 (7.84) | 13 (9.42) | 14 (9.33) | 126 (12.00) | |
No response | 0 (0) | 1 (0.72) | 1 (0.67) | 5 (0.48) | |
Household income, N (%) | 0.564 | ||||
Low | 5 (9.80) | 15 (10.87) | 14 (9.33) | 71(6.76) | |
Intermediate | 39 (76.47) | 95 (68.84) | 109 (72.67) | 772 (73.52) | |
High | 4 (7.84) | 11 (7.97) | 16 (10.67) | 101 (9.62) | |
No response | 3 (5.88) | 17 (12.32) | 11 (7.33) | 106 (10.10) |
Media device addiction severity among four groups (N=1,389)
Variables | Class 1 (N=51) | Class 2 (N=138) | Class 3 (N=150) | Control (N=1,050) | F | p-value | Post hoc level |
---|---|---|---|---|---|---|---|
Daily activity disturbance (scores), mean (SD) | 12.12 (1.99) | 10.39 (0.97) | 9.36 (1.45) | 6.28 (1.18) | 929.88 | <0.001 |
1>2>3>C |
Withdrawal (scores), mean (SD) | 10.63 (1.87) | 8.76 (0.89) | 8.97 (1.40) | 6.37 (1.45) | 348.92 | <0.001 |
1>2, 3>C |
Tolerance (scores), mean (SD) | 9.08 (2.00) | 8.43 (0.97) | 7.11 (1.12) | 5.26 (1.27) | 430.87 | <0.001 |
1>2>3>C |
Total media device addiction severity (scores), mean (SD) | 38.06 (4.92) | 32.13 (2.15) | 30.20 (2.12) | 20.88 (3.32) | 1,150.56 | <0.001 |
1>2>3>C |
p<0.001.
SD, standard deviation
Comparison of executive function difficulties among four groups (N=1,389)
Variables | Class 1 (N=51) | Class 2 (N=138) | Class 3 (N=150) | Control (N=1,050) | F | Post hoc level |
---|---|---|---|---|---|---|
Planning-organization difficulties (scores), mean (SD) | 23.16 (5.40) | 19.63 (4.62) | 20.11 (4.78) | 17.21 (4.48) | 46.06 |
1>2, 3>C |
Behavior control difficulties (scores), mean (SD) | 18.75 (4.54) | 14.75 (3.37) | 15.46 (3.96) | 13.39 (2.91) | 64.80 |
1>2, 3>C |
Emotional control difficulties (scores), mean (SD) | 15.24 (4.57) | 12.17 (4.08) | 12.99 (3.80) | 11.06 (3.29) | 35.75 |
1>2, 3>C |
Attention-concentration difficulties (scores), mean (SD) | 19.86 (5.43) | 16.96 (5.08) | 17.55 (4.70) | 14.72 (4.12) | 44.60 |
1>2, 3>C |
p<0.001.
SD, standard deviation
Comparison of behavioral problems between four groups (N=1,389)
Variables | Class 1 (N=51) | Class 2 (N=138) | Class 3 (N=150) | Control (N=1,050) | F | Post hoc level |
---|---|---|---|---|---|---|
Total | 56.33 (11.30) | 49.79 (10.92) | 51.65 (8.92) | 47.05 (9.88) | 23.17 |
1>2, 3>C |
Internalizing | 54.86 (9.75) | 50.20 (10.08) | 50.83 (8.55) | 47.91 (8.94) | 14.53 |
1, 3>C |
1>2 | ||||||
Externalizing | 56.55 (10.38) | 50.50 (9.40) | 52.18 (8.88) | 48.20 (8.86) | 22.26 |
1>2, 3>C |
Anxious/depressed | 56.00 (6.39) | 54.18 (6.12) | 54.27 (5.49) | 53.00 (5.36) | 7.82 |
1>C |
Withdrawn/depressed | 57.98 (8.29) | 54.18 (5.36) | 54.27 (5.30) | 52.78 (4.98) | 20.22 |
1>2, 3>C |
Somatization | 55.45 (6.71) | 53.29 (5.66) | 53.52 (5.13) | 52.73 (4.90) | 5.61 |
1>C |
Immature | 58.39 (7.09) | 53.81 (5.41) | 54.29 (5.27) | 53.01 (5.18) | 18.67 |
1>2, 3, C |
Thought problem | 56.41 (7.05) | 54.28 (6.19) | 54.77 (5.78) | 53.36 (5.43) | 7.60 |
1, 3>C |
Attention problem | 57.16 (6.84) | 53.28 (4.96) | 53.89 (5.06) | 52.17 (4.79) | 21.43 |
1>3>C |
1>2 | ||||||
Rule-breaking | 58.84 (6.87) | 55.07 (5.20) | 55.27 (5.44) | 53.39 (4.93) | 24.86 |
1>2, 3>C |
Aggressive behavior | 57.24 (7.74) | 53.69 (5.45) | 54.51 (5.45) | 52.63 (5.00) | 17.69 |
1>3>C |
1>2 | ||||||
Miscellaneous | 57.16 (7.10) | 54.87 (6.40) | 55.27 (5.63) | 53.44 (5.65) | 11.69 |
1, 3>C |
p<0.001
Results of logistic regression for associations between classes of problematic media device user and CBCL subscales
Variable | β (SE) | aOR | 95% CI | p-value |
---|---|---|---|---|
CBCL 6–18 subscales at 9 years | ||||
Total problem | ||||
Class 1 | 1.59 (0.46) | 4.89 | 1.39–11.98 | <0.001 |
Class 2 | 0.20 (0.50) | 1.22 | 0.46–3.21 | 0.694 |
Class 3 | 0.09 (0.49) | 1.10 | 0.42–2.88 | 0.853 |
Control | ||||
Internalizing | ||||
Class 1 | 1.53 (0.43) | 4.63 | 2.00–10.70 | <0.001 |
Class 2 | 0.58 (0.39) | 1.78 | 0.84–3.78 | 0.134 |
Class 3 | -0.15 (0.49) | 0.86 | 0.33–2.22 | 0.753 |
Control | ||||
Externalizing | ||||
Class 1 | 1.68 (0.44) | 5.38 | 2.29–12.61 | <0.001 |
Class 2 | 0.44 (0.43) | 1.64 | 0.70–3.81 | 0.254 |
Class 3 | 0.87 (0.37) | 2.39 | 1.17–4.88 | 0.017 |
Control | ||||
Anxious/depressed | ||||
Class 1 | 1.09 (0.42) | 2.97 | 1.30–6.83 | 0.010 |
Class 2 | 0.51 (0.34) | 1.66 | 0.85–3.24 | 0.139 |
Class 3 | 0.18 (0.36) | 1.20 | 0.59–2.43 | 0.623 |
Control | ||||
Withdrawn/depressed | ||||
Class 1 | 1.86 (0.39) | 6.43 | 3.01–13.75 | <0.001 |
Class 2 | 0.76 (0.36) | 2.14 | 1.06–4.32 | 0.034 |
Class 3 | 0.17 (0.43) | 1.19 | 0.52–2.72 | 0.684 |
Control | ||||
Somatization | ||||
Class 1 | 0.97 (0.51) | 2.64 | 0.97–7.17 | 0.057 |
Class 2 | -0.05 (0.49) | 0.96 | 0.37–2.49 | 0.927 |
Class 3 | 0.20 (0.43) | 1.22 | 0.53–2.81 | 0.634 |
Control | ||||
Immature behavior | ||||
Class 1 | 1.34 (0.43) | 3.80 | 1.65–8.76 | 0.002 |
Class 2 | -0.21 (0.49) | 0.81 | 0.31–2.10 | 0.665 |
Class 3 | 0.17 (0.40) | 1.18 | 0.54–2.58 | 0.672 |
Control | ||||
Rule-breaking behavior | ||||
Class 1 | 1.59 (0.40) | 12.95 | 6.61–25.38 | <0.001 |
Class 2 | -0.46 (0.44) | 1.74 | 0.82–3.70 | 0.148 |
Class 3 | 1.00 (0.32) | 2.72 | 1.46–5.09 | 0.002 |
Control | ||||
Aggressive behavior | ||||
Class 1 | 1.04 (0.51) | 2.83 | 1.04–7.73 | 0.042 |
Class 2 | 0.56 (0.41) | 1.75 | 0.78–3.90 | 0.174 |
Class 3 | 0.80 (0.36) | 2.23 | 1.09–4.54 | 0.028 |
Control | ||||
Thought problem | ||||
Class 1 | 1.59 (0.39) | 4.90 | 2.28–10.54 | <0.001 |
Class 2 | 0.45 (0.36) | 1.57 | 0.77–3.19 | 0.212 |
Class 3 | 0.76 (0.31) | 2.14 | 1.17–3.94 | 0.014 |
Control | ||||
Attention problem | ||||
Class 1 | 2.39 (0.43) | 10.89 | 4.74–25.06 | <0.001 |
Class 2 | 0.88 (0.45) | 2.42 | 1.01–5.80 | 0.048 |
Class 3 | 0.42 (0.50) | 1.52 | 0.56–4.07 | 0.409 |
Control | ||||
Suicidal ideation | ||||
Class 1 | 1.19 (0.44) | 3.29 | 1.38–7.81 | 0.007 |
Class 2 | 0.37 (0.38) | 1.45 | 0.69–3.06 | 0.325 |
Class 3 | 0.50 (0.35) | 1.64 | 0.83–3.26 | 0.155 |
Control | ||||
Suicidal behavior | ||||
Class 1 | 1.16 (0.32) | 3.20 | 1.72–5.94 | <0.001 |
Class 2 | 0.47 (0.19) | 1.61 | 1.11–2.32 | 0.011 |
Class 3 | 0.54 (0.18) | 1.71 | 1.20–2.44 | 0.003 |
Control |
Adjusted for sex and maternal education level.
p<0.05.
CBCL, Child behavior Checklist