Adverse Childhood Experiences and Digital Media Overuse Among Korean Middle-School Students: Findings From the Panel Study on Korean Children

Abstract

This study examined the association between adverse childhood experiences (ACEs) and digital media overuse among Korean adolescents using Bronfenbrenner's ecological systems theory. A secondary analysis of the Panel Study on Korean Children (n = 1,316) was conducted. Overall, 63.1% reported at least one ACE, and 56.6% met criteria for digital media overuse. In adjusted logistic regression models, having three or more ACEs was significantly associated with higher odds of digital media overuse (3 ACEs: adjusted odds ratio [aOR] = 1.97, 95% confidence interval [CI] = 1.14–3.41, p = .015; 4 ACEs: aOR = 3.81, 95% CI = 1.69–8.58, p < .001). Older age, female sex, and better school adjustment were associated with lower odds, whereas higher maternal parenting stress and academic stress were associated with greater odds. These findings support ACE-informed, family-centered prevention strategies within school health systems.

Keywords

adverse childhood experiences adolescent students internet addiction disorder Korea

Introduction

Adverse childhood experiences (ACEs) refer to a range of negative exposures that may occur during childhood, including physical and emotional abuse, neglect, household violence, parental separation or divorce, parental mental health problems, alcohol or substance misuse in the household, and parental incarceration (Felitti et al., 1998). More recently, the scope of ACEs has expanded to include violence and threats experienced in schools and communities (Centers for Disease Control and Prevention [CDC], 2024; Mlouki et al., 2024). ACEs have been consistently linked to adverse health outcomes across the life course, affecting physical and mental health from adolescence into adulthood. Recent systematic reviews and meta-analyses indicate that ACEs are associated with a wide range of conditions, including hypertension, diabetes, and cancer in adulthood (Senaratne et al., 2024), as well as childhood health problems such as delayed cognitive development, infections, and sleep disturbances (Oh et al., 2018). These studies also report a robust dose–response relationship, such that greater cumulative exposure to ACEs corresponds to higher health risks.

Mechanistically, chronic stress resulting from ACEs may increase inflammatory biomarkers such as high-sensitivity C-reactive protein (hs-CRP) and interleukin-6 (IL-6), thereby elevating the risk of chronic diseases including cardiovascular disease, metabolic dysfunction, respiratory illness, obesity, and diabetes (Soares et al., 2022). Recurrent adversity may also disrupt hypothalamic–pituitary–adrenal (HPA) axis functioning (Niu et al., 2025), alter neural circuitry involved in threat processing and memory (e.g., reduced volume and atypical functional connectivity; Tomoda et al., 2025), and contribute to insecure attachment, emotion dysregulation, and limited coping strategies (Al-Mamun et al., 2025). Through these pathways, ACEs increase vulnerability to mental health problems such as depression, anxiety, and suicide attempts (Petruccelli et al., 2019), as well as addictive behaviors including smoking, substance use, and alcohol misuse (Lam et al., 2024). However, because many of these sequelae become more prominent in adulthood, early detection and timely professional intervention remain challenging. In this context, digital media overuse among children and adolescents may serve as a salient and observable manifestation of the more proximal impacts of ACEs.

Digital media overuse is commonly defined as excessive engagement with digital devices—such as smartphones, tablets, and computers—and online content to a degree that disrupts daily functioning and psychological well-being (Montag et al., 2021). In adolescence, problematic digital media use has been conceptualized as behaviorally similar to substance-related addictions (e.g., alcohol, drugs) and behavioral addictions (e.g., gambling) through at least two mechanisms. First, excessive digital media use may share neurobiological features with other addictive disorders; repeated experiences of immediate reward may dysregulate dopaminergic pathways and impair executive control, reducing the ability to inhibit or avoid addictive behaviors (Darnai et al., 2019; Weinstein & Lejoyeux, 2020). Second, heavy exposure to digital environments may increase contact with content related to risk behaviors, alcohol and drug use, smoking, and gambling, thereby heightening the likelihood of experimentation and associated harms (Romer & Moreno, 2017).

A growing body of evidence suggests that ACEs are associated with problematic digital media use (Domoff et al., 2021; Hao et al., 2024; Jackson et al., 2021). In particular, Hao et al. (2024) conducted a systematic review and meta-analysis across 12 countries (37 studies) and found a significant association between ACEs and internet addiction. Notably, the association appeared stronger among children in Asia than among those in North America or Europe, underscoring the need for more context-sensitive research. Similarly, Jackson et al. (2021), using data from the 2018 National Survey of Children's Health in the United States, reported a strong relationship between ACEs and digital media overuse, and further suggested that low family resilience and high parental stress strengthened this association. Domoff et al. (2021) focused on socioeconomically vulnerable families and found that ACEs remained a strong predictor of problematic digital media use even after adjusting for sociodemographic characteristics and screen time. Nevertheless, most studies to date have been observational, and many have adjusted for a limited set of confounders, leaving uncertainty regarding the extent to which broader ecological influences shape the ACEs–digital media overuse association.

Bronfenbrenner's ecological systems theory (1979) conceptualizes child development as the product of dynamic interactions between individual characteristics and multiple environmental contexts, including family, school, and community. This framework provides a robust conceptual basis for examining ACEs and digital media overuse while accounting for a wider set of confounding factors relevant to child development. Prior ecological research has linked adversity—including childhood maltreatment—to outcomes such as substance misuse, adolescent suicidality (Aytur et al., 2022), depression (Chen et al., 2022), and problematic alcohol use (Forster et al., 2023). Importantly, the school represents a key microsystem in which the behavioral and health-related manifestations of adversity may become observable in daily functioning, positioning school health professionals—particularly school nurses—as frontline observers of early risk signals (Sypniewski, 2016). Although Lin et al. (2023) suggested that adolescent digital media addiction is related to microsystem-level social environments such as parent and peer relationships, studies that explicitly integrate ACEs and digital media outcomes within an ecological model remain limited.

Therefore, the present study examined the association between ACEs and digital media overuse among Korean adolescents using nationally representative data from the Panel Study on Korean Children (PSKC; Korea Institute of Child Care and Education [KICCE], 2021a). PSKC is a longitudinal cohort study that began in 2008, following children born that year to assess growth and development, caregiving environments, and the impacts of child and family policies. PSKC collects annual data from primary caregivers, fathers, and mothers, and has included child self-reports since 2013 (age 5). Beginning in 2015, PSKC also collected teacher reports as children entered school (KICCE, 2021a). Although Kang and Ki (2023) analyzed the association between ACEs and media addiction using PSKC and focused on sex differences, they did not include extensive adjustment for confounding factors. Building on this work, the current study constructed an ecological analytic model incorporating individual, family, school, and community factors to evaluate the association between ACEs and digital media overuse among adolescents.

Methods

Study Design

This study employed a cross-sectional, descriptive design using secondary data analysis.

Data Source and Study Sample

We used the 14th-wave PSKC dataset collected in 2021. The PSKC cohort initially enrolled 2,150 newborns in 2008. For the current analysis, we excluded 822 participants who did not respond to the 14th-wave survey and an additional 12 participants with missing values on key variables (child sex and age, parental age, and region of residence). The final analytic sample comprised 1,316 adolescents.

The 14th-wave survey corresponds to the time when cohort children entered the first year of middle school, and thus includes a broader range of child self-reported measures. Details of the PSKC sampling procedures have been described elsewhere (Kum & Bang, 2023). Data were collected via structured questionnaires administered to primary caregivers, fathers, mothers, adolescents, and teachers. De-identified datasets are publicly available through the PSKC website (KICCE, 2021b). However, teacher-report data were not included in this study because the teacher survey had substantial nonresponse (n = 622; >50% missing). The study was granted exemption by the Institutional Review Board of the authors’ institution (No. 250317-1A).

Measures

Independent Variable: Adverse Childhood Experiences

Based on the CDC (2024) definition, ACEs were operationalized using seven indicators available in PSKC: physical abuse, emotional abuse, neglect, parental separation, parental depression, parental high-risk drinking, and peer violence victimization (school violence). Physical abuse, emotional abuse, neglect, and school violence victimization were assessed via adolescent self-report, while the remaining indicators were assessed through parent report. Each indicator was coded as 1 (experienced) or 0 (not experienced), and a cumulative ACE score was calculated by summing the seven indicators (range: 0–7).

Physical abuse was assessed with the item: “My parent/guardian treated me severely enough to leave bruises or injuries.” Responses of “never” were coded 0; all other responses were coded 1. Emotional abuse was assessed with two items: “My parent/guardian used harsh words or swore at me,” and “My parent/guardian scolded me to the extent that I felt shame or humiliation.” Participants who responded “never” to both items were coded 0; all other response patterns were coded 1. Neglect was assessed with the item: “Even when I was sick, my parent/guardian was bothered and did not take me to a hospital.” “Never” was coded 0; all other responses were coded 1. Parental separation was coded 1 if the parent-reported current marital status as widowed, divorced, or separated; otherwise coded 0. Parental depression was defined as 1 if either parent met criteria for at least mild/moderate depressive symptoms on the Kessler psychological distress scale (Kessler et al., 2002), which includes six items referring to emotional states in the past 30 days rated on a 5-point Likert scale (total score 6–30). Scores ≤13 indicate normal range, 14–18 indicate mild/moderate depression, and ≥19 indicate severe depression. Internal consistency in this study was high (mother α = .922; father α = .921). Parental high-risk drinking was coded 1 if either parent met the Korea Disease Control and Prevention Agency definition of high-risk drinking: average drinking quantity per occasion of ≥7 drinks for men or ≥5 drinks for women, at least twice per week (Korea Disease Control and Prevention Agency, 2023). School violence victimization was measured using 13 items adapted from the Revised Olweus Bully/Victim Questionnaire (Olweus, 1996). Adolescents who reported no victimization across all items were coded 0; those reporting at least one victimization experience were coded 1.

Dependent Variable: Digital Media Overuse

Digital media overuse was measured using a modified version of the observer-rated Internet Addiction Proneness Scale for Youth (K-scale), originally developed by the National Information Society Agency and adapted in PSKC (KICCE, 2022). PSKC removed eight items from the original 20-item scale and added three new items; item wording was modified by replacing “internet” with “PC/smartphone.” Parents rated adolescents’ behaviors using a 4-point Likert scale. Total scores range from 15 to 60, with higher scores indicating greater overuse. According to PSKC classification criteria (KICCE, 2022), scores are categorized into general users (≤27), potential-risk users (28–29), and high-risk users (≥30) for digital media addiction as part of national screening guidelines. In the present study, consistent with this classification, adolescents scoring ≥30 were operationally defined as exhibiting digital media overuse. Internal consistency was good (α = .875).

Covariates: Ecological Systems Factors

Covariates were selected based on Bronfenbrenner's ecological systems theory (1979) and grouped into four domains: individual, family, school, and community.

Individual factors. Individual-level variables included adolescent sex, age in months, weight (kg), height (cm), body mass index (kg/m²), emergency department visit and hospitalization in the past year, subjective health status, self-esteem, ego-resilience, life satisfaction, and depressive symptoms. Sex, age in months, emergency department visit, hospitalization, and subjective health were reported by primary caregivers; other variables were adolescent self-reports. Subjective health status was assessed with the caregiver item “How would you rate your child's health?” on a 5-point scale (1 = very poor to 5 = very good). Self-esteem was assessed using five items adapted from the Millennium Cohort Study based on Rosenberg's self-esteem scale (Bannink et al., 2016; Rosenberg, 1965), rated on a 4-point Likert scale (range 5–20; higher scores indicate higher self-esteem). Reliability in this study was α = .862. Ego-resilience was measured using 14 items translated and adapted by Yoo and Shim (2002) from Block and Kremen's scale (1996), rated on a 4-point Likert scale (range 14–56; higher scores indicate higher resilience). Reliability was α = .833. Life satisfaction was measured using three items revised by Kim et al. (2006) based on a 2005 pilot study by the Korea Youth Development Institute, rated on a 4-point Likert scale (range 3–12). Reliability was α = .648. Depressive symptoms were assessed using the Korean short form of the CES-DC (Heo et al., 2017; Weissman et al., 1980), consisting of 11 items rated on a 4-point Likert scale with reverse scoring for two items (range 0–33; higher scores indicate greater depression). Reliability was α = .825. Additional details are available in prior PSKC publications (e.g., Kum & Bang, 2023; Yun et al., 2022).

Family factors. Family-level variables included paternal and maternal age and education, parenting stress, parent–adolescent attachment, and monthly household income. All were parent-reported. Parenting stress was measured using 17 items developed by Kang (2003), rated on a 5-point Likert scale (range 17–85; higher scores indicate higher stress). Reliability was α = .938 (father) and α = .931 (mother). Parent attachment was assessed using the Korean version of the Inventory of Parent and Peer Attachment (Armsden & Greenberg, 1987; Korean adaptation by Heo & Kim, 2022), with 12 items rated on a 5-point Likert scale (range 12–60; higher scores indicate stronger attachment). Reliability was α = .856 (father attachment) and α = .903 (mother attachment). Monthly household income was defined as total take-home income from all household members, including wages, financial income, business income, rental income, transfers, and other sources, net of taxes and major deductions.

School factors. School-level variables included peer attachment, school adjustment, and academic stress, all measured via adolescent self-report. Peer attachment was assessed using nine items from the peer attachment subscale of the Inventory of Parent and Peer Attachment (Armsden & Greenberg, 1987), rated on a 4-point Likert scale (range 9–36). Reliability was α = .795. School adjustment was measured using Lee's (2005) 38-item scale rated on a 5-point Likert scale (range 38–190; higher scores indicate better adjustment). Reliability was α = .941. Academic stress was measured using three items adapted from Kim and Lee (1996), rated on a 5-point Likert scale (range 3–15; higher scores indicate greater stress). Reliability was α = .803. Further instrument information is available in prior studies (e.g., Yang & Kim, 2024).

Community factors. Community-level variables included residential area and subjective socioeconomic status (SES) at national and community levels, assessed by primary caregivers. Residential area was categorized as metropolitan, small/mid-sized city, or rural township. Subjective SES was measured using the MacArthur Scale of Subjective Social Status (Adler et al., 2000). Caregivers rated their perceived position on a 10-rung ladder within (a) Korean society (national level) and (b) their meaningful community (e.g., workplace, religious group, relatives, friends, clubs), with higher scores indicating higher perceived status.

Data Analysis

Analyses were conducted using SPSS version 27.0. Descriptive statistics (means, standard deviations, frequencies, and percentages) summarized participant characteristics and variable distributions. Sex differences and differences between adolescents who met the criteria for digital media overuse and those who did not were examined using independent t tests and Chi-squared tests. To evaluate the association between ACEs and digital media overuse, logistic regression models were estimated, and results were reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs) and p values. All analyses used two-tailed tests with α = .05.

Results

Descriptive statistics by sex are presented in Table 1. Boys comprised 51.0% of the sample and girls 49.0%. Mean age was 159.8 ± 1.7 months. Compared with girls, boys reported higher self-esteem (16.3 ± 2.6 vs. 15.6 ± 2.6; t = 4.31, p < .001), higher ego-resilience (41.1 ± 6.0 vs. 40.3 ± 5.6; t = 2.65, p = .008), and higher life satisfaction (9.2 ± 1.7 vs. 8.8 ± 1.5; t = 4.50, p < .001). At the family level, maternal parenting stress was higher among mothers of boys than mothers of girls (44.6 ± 11.5 vs. 42.8 ± 11.5; t = 2.76, p = .006). At the school level, boys reported lower peer attachment (28.0 ± 3.8 vs. 28.7 ± 4.3; t = −3.07, p = .002) and lower academic stress (8.9 ± 3.2 vs. 10.0 ± 3.3; t = −5.05, p < .001). At the community level, boys had slightly lower national subjective SES than girls (5.3 ± 1.4 vs. 5.4 ± 1.4; t = −2.07, p = .039). Regarding digital media use, smartphone ownership was 98.2%, and mean daily media use time was 6.5 ± 3.5 hours, with no sex difference. Based on parent-reported ratings, 49.0% were classified as high-risk users, and the high-risk proportion was higher among boys than girls (56.2% vs. 41.6%; χ² = 28.26, p < .001).

Table 1.

Descriptive Characteristics of Participants (N = 1,316).

		All	Boys (n = 671)	Girls (n = 645)	χ²/t (p)
Variables	Categories	Frequencies (%) or Mean ± SD
Individual
Age (months)		159.8 ± 1.7	159.7 ± 1.7	159.9 ± 1.7	−1.85 (.064)
Weight (kg)		55.1 ± 11.9	59.0 ± 12.9	51.2 ± 9.2	12.61 (<.001)
Height (cm)		161.6 ± 6.8	164.2 ± 7.2	159.0 ± 5.3	14.70 (<.001)
BMI (kg/m²)		21.0 ± 3.7	21.8 ± 4.0	20.2 ± 3.0	8.15 (<.001)
ER visits	Yes	37 (2.8)	22 (3.3)	15 (2.3)	1.09 (.296)
	No	1,279 (97.2)	649 (96.7)	630 (97.7)
Hospital admission	Yes	28(2.1)	18 (2.7)	10 (1.6)	2.02 (.155)
	No	1,288 (97.9)	653 (97.3)	635 (98.4)
Subjective health status		4.1 ± 0.7	4.1 ± 0.7	4.1 ± 0.6	0.53 (.596)
Self-esteem		16.0 ± 2.6	16.3 ± 2.6	15.6 ± 2.6	4.31 (<.001)
Resilience		40.7 ± 5.8	41.1 ± 6.0	40.3 ± 5.6	2.65 (.008)
Life satisfaction		9.0 ± 1.6	9.2 ± 1.7	8.8 ± 1.5	4.50 (<.001)
Depression		16.4 ± 4.5	16.2 ± 4.3	16.6 ± 4.6	−1.61 (.107)
Family
Father's age		46.6 ± 4.0	46.5 ± 3.9	46.7 ± 4.0	−0.66 (.510)
Mother's age		44.1 ± 3.7	44.0 ± 3.6	44.2 ± 3.8	−0.91 (.365)
Father: college graduate ≤	Yes	959 (72.9)	493 (73.5)	466 (72.2)	0.25 (.618)
	No	357 (27.1)	178 (26.5)	179 (27.8)
Mother: college graduate ≤	Yes	959 (72.9)	502 (74.8)	457 (70.9)	2.61 (.106)
	No	357 (27.1)	169 (25.2)	188 (29.1)
Father's parenting stress (n = 1,150)		42.3 ± 12.0	42.6 ± 12.1	41.9 ± 12.0	1.06 (.289)
Mother's parenting stress (n = 1,285)		43.7 ± 11.5	44.6 ± 11.5	42.8 ± 11.5	2.76 (.006)
Attachment with father (n = 1,316)		42.0 ± 7.6	42.1 ± 7.6	41.8 ± 7.6	0.70 (.482)
Attachment with mother (n = 1,316)		45.0 ± 7.1	44.9 ± 7.0	−5.2 ± 7.2	−0.88 (.380)
Household income (10,000 KRW/month)		644.0 ± 868.1	596.8 ± 375.7	−92.2 ± 1173.1	−1.89 (.059)
School
Peer attachment		28.3 ± 4.1	28.0 ± 3.8	−8.7 ± 4.3	−3.07 (.002)
School adaptation		146.8 ± 19.3	145.9 ± 20.0	−47.7 ± 18.5	−1.66 (.097)
Academic stress		9.3 ± 3.3	8.9 ± 3.2	−0.0 ± 3.3	−5.05 (<.001)
Community
Residential area	Metro city	466 (35.4)	233 (34.7)	233 (36.1)	0.29 (.867)
	Medium city	597 (45.4)	308 (45.9)	289 (44.8)
	Rural	253 (19.2)	130 (19.4)	123 (19.1)
Subjective socioeconomic status	National level	5.4 ± 1.4	5.3 ± 1.4	5.4 ± 1.4	−2.07 (.039)
Subjective socioeconomic status	Community Level	5.5 ± 1.4	5.5 ± 1.4	5.6 ± 1.5	−0.59 (.555)
Digital media use
Smartphone ownership	Yes	1292 (98.2)	656 (97.8)	636 (98.6)	1.30 (.255)
Smartphone ownership	No	24 (1.8)	15 (2.2)	9 (1.4)
Digital media usage (hour/day)		6.5 ± 3.5	6.6 ± 3.6	6.4 ± 3.4	1.19 (.234)
Digital media overuse reported by parents	High risk	645 (49.0)	377 (56.2)	268 (41.6)	28.26 (<.001)
	At risk	100 (7.6)	43 (6.4)	57 (8.8)
	No risk	571 (43.4)	251 (37.4)	320 (49.6)

SD = standard deviation.

The sex-stratified distribution of ACEs is shown in Table 2. Overall, 63.1% of adolescents experienced at least one ACE. The distribution by ACE count was 33.6% (1 ACE), 19.1% (2 ACEs), 6.7% (3 ACEs), and 3.7% (≥4 ACEs). Boys were more likely than girls to report ≥2 ACEs (2 ACEs: χ² = 9.66, p = .002; 3 ACEs: χ² = 10.41, p = .001; ≥4 ACEs: χ² = 5.26, p = .022). Among the seven ACE types, parental depression was most common (43.9%), followed by school violence victimization (22.2%) and parental alcohol problems (22.0%). Boys had higher prevalence of physical abuse (10.7% vs. 3.7%; χ² = 23.89, p < .001), emotional abuse (25.2% vs. 17.2%; χ² = 12.49, p < .001), and school violence victimization (27.7% vs. 16.4%; χ² = 24.26, p < .001) than girls.

Table 2.

Adverse Childhood Experiences of Participants (N = 1,316).

		All	Boys (n = 671)	Girls (n = 645)	χ²/t (p)
Types of ACEs	Categories	Frequencies (%) or M ± SD
Total # of ACEs	0	486 (36.9)	224 (33.4)	262 (40.6)	Reference
	1	442 (33.6)	213 (31.7)	229 (35.5)	0.41 (.522)
	2	251 (19.1)	146 (21.8)	105 (16.3)	9.66 (.002)
	3	88 (6.7)	57 (8.5)	31 (4.8)	10.41 (.001)
	4 ≤	49 (3.7)	31 (4.6)	18 (2.8)	5.26 (.022)
Physical abuse	Yes	96 (7.3)	72 (10.7)	24 (3.7)	23.89 (<.001)
Physical abuse	No	1220 (92.7)	599 (89.3)	621 (96.3)	23.89 (<.001)
Emotional abuse	Yes	280 (21.3)	169 (25.2)	111 (17.2)	12.49 (<.001)
Emotional abuse	No	1036 (78.7)	502 (74.8)	534 (82.8)	12.49 (<.001)
Neglect	Yes	43 (3.3)	25 (3.7)	18 (2.8)	0.91 (.340)
Neglect	No	1273 (96.7)	646 (96.3)	627 (97.2)	0.91 (.340)
Separation from parents	Yes*	60 (4.6)	25 (3.7)	35 (5.4)	2.19 (.139)
Separation from parents	No	1256 (95.4)	646 (96.3)	610 (94.6)	2.19 (.139)
Parents’ depression	Yes	578 (43.9)	293 (43.7)	285 (44.2)	0.16 (.690)
	No	656 (49.8)	340 (50.7)	316 (49)
	Missing	82 (6.2)	38 (5.6)	44 (6.8)
Parents’ problematic drinking	Yes	289 (22.0)	146 (21.8)	143 (22.2)	0.06 (.807)
	No	851 (64.7)	437 (65.1)	414 (64.2)
	Missing	176 (13.4)	88 (13.1)	88 (13.6)
School violence victimization	Yes	292 (22.2)	186 (27.7)	106 (16.4)	24.26 (<.001)
School violence victimization	No	1024 (77.8)	485 (72.3)	539 (83.6)	24.26 (<.001)

ACE = adverse childhood experience; *Yes = divorced, widowed, separated.

Table 3 presents comparisons between adolescents who met the criteria for digital media overuse and those who did not. The overuse group had a higher prevalence of ≥2 ACEs than the non-overuse group (2 ACEs: 20.9% vs. 17.3%; χ² = 7.73, p = .005; 3 ACEs: 9.3% vs. 4.2%; χ² = 18.97, p < .001; ≥4, ACEs: 5.9% vs. 1.6%; χ² = 21.38, p < .001). Compared with the non-overuse group, the overuse group had lower mean age, a lower proportion of girls, poorer subjective health, lower self-esteem and life satisfaction, and higher depressive symptoms. At the family level, parenting stress was higher and attachment to both parents was lower in the overuse group. At the school level, peer attachment and school adjustment were lower, while academic stress was higher. At the community level, subjective SES was lower at both the national and community levels.

Table 3.

Characteristics of Participants by Level of Media Overuse Reported by Parents (N = 1,316).

	All	Overuse Group (n = 645)	Non-Overuse Group (n = 671)	χ²/t (p)
Variables	Frequencies (%) or Mean ± SD
Cumulative number of ACEs
0	486 (36.9)	209 (32.4)	277 (41.3)	Reference
1	442 (33.6)	203 (31.5)	239 (35.6)	0.80 (.371)
2	251 (19.1)	135 (20.9)	116 (17.3)	7.73 (.005)
3	88 (6.7)	60 (9.3)	28 (4.2)	18.97 (<.001)
4 ≤	49 (3.7)	38 (5.9)	9 (1.6)	21.38 (<.001)
Individual
Age (months)	159.8 ± 1.7	159.7 ± 1.8	160.0 ± 1.7	2.52 (.012)
Gender, girl	645 (49.0)	268 (41.6)	377 (58.4)	28.18 (<.001)
Weight (kg)	55.1 ± 11.9	55.7 ± 12.1	54.6 ± 11.6	−1.71 (.088)
Height (cm)	161.6 ± 6.8	161.9 ± 7.1	161.4 ± 6.5	−1.47 (.141)
BMI (kg/m²)	21.0 ± 3.7	21.1 ± 3.7	20.9 ± 3.7	−1.27 (.205)
ER visits, yes	37 (2.8)	22 (3.4)	15 (2.2)	1.66 (.197)
Hospital admission, yes	28 (2.1)	17 (2.6)	11 (1.6)	1.57 (.211)
Subjective health status	4.1 ± 0.7	4.1 ± 0.7	4.2 ± 0.6	2.19 (.028)
Self-esteem	16.0 ± 2.6	15.8 ± 2.6	16.1 ± 2.6	2.32 (.021)
Resilience	40.7 ± 5.8	40.6 ± 6.0	40.9 ± 5.7	1.01 (.315)
Life satisfaction	9.0 ± 1.6	8.9 ± 1.6	9.1 ± 1.7	1.87 (.062)
Depression	16.4 ± 4.5	16.8 ± 4.5	16.0 ± 4.4	−3.39 (<.001)
Family
Father's age	46.6 ± 4.0	46.6 ± 4.0	46.6 ± 3.9	−0.17 (.864)
Mother's age	44.1 ± 3.7	44.1 ± 3.8	44.1 ± 3.6	−0.05 (.960)
Father: college graduate ≤	959 (72.9)	463 (71.8)	496 (73.9)	0.76 (.384)
Mother: college graduate ≤	959 (72.9)	461 (71.5)	498 (74.2)	1.25 (.263)
Father's parenting stress (n = 1,150)	42.3 ± 12.0	44.7 ± 11.5	40.0 ± 12.1	−6.71 (<.001)
Mother's parenting stress (n = 1,285)	43.7 ± 11.5	47.2 ± 10.3	40.4 ± 11.7	- 11.11 (<.001)
Attachment with father (n = 1,316)	42.0 ± 7.6	41.5 ± 7.6	42.5 ± 7.5	2.40 (.017)
Attachment with mother (n = 1,316)	45.0 ± 7.1	44.2 ± 7.0	45.9 ± 7.1	4.34 (<.001)
Household income (10,000 KRW/month)	644.0 ± 868.1	675.7 ± 1183.4	614.9 ± 398.6	−1.12 (.225)
School
Peer attachment	28.3 ± 4.1	28.0 ± 4.0	28.6 ± 4.1	2.35 (.019)
School adaptation	146.8 ± 19.3	144.0 ± 19.1	149.5 ± 19.1	5.30 (<.001)
Academic stress	9.3 ± 3.3	9.7 ± 3.2	8.9 ± 3.3	−4.45 (<.001)
Community
Residential area, metro city	466 (35.4)	228 (35.3)	238 (35.5)	1.49 (.476)
Subjective socioeconomic status
National level	5.4 ± 1.4	5.2 ± 1.4	5.5 ± 1.4	4.76 (<.001)
Community level	5.5 ± 1.4	5.4 ± 1.4	5.7 ± 1.4	3.94 (<.001)

SD = standard deviation.

Logistic regression results are shown in Table 4. Model 1 included individual-level variables only (n = 1,289), and Model 2 additionally included family, school, and community variables (n = 1,186 due to missingness). In Model 1, compared with those with no ACEs, adolescents with ≥2 ACEs had higher odds of being in the digital media overuse group, with the odds increasing as ACE count increased (approximately 1.3- to 3.7-fold). Female sex (aOR = 0.60, 95% CI [0.47, 0.75], p < .001) and older age (aOR = 0.79, 95% CI [0.63, 0.99], p = .041) were associated with lower odds of digital media overuse, whereas higher depressive symptoms were associated with higher odds (aOR = 1.45, 95% CI [1.12, 1.87], p = .004).

Table 4.

Multiple Logistic Regression to Predict Digital Media Overuse (N = 1,316).

	AOR	95% CI	p	AOR	95% CI	p
Variables	Model 1 (n = 1,289)			Model 2 (n = 1,186)
Cumulative number of ACEs
0	Reference				Reference
1	1.09	[0.84, 1.43]	.519	1.05	[0.77, 1.42]	.763
2	1.32	[0.96, 1.82]	.088	1.12	[0.78, 1.61]	.554
3	2.30	[1.40, 3.80]	.001	1.97	[1.14, 3.41]	.015
4 ≤	3.65	[1.79, 7.44]	<.001	3.81	[1.69, 8.58]	<.001
Individual
Age ≥ 160 months	0.79	[0.63, 0.99]	.041	0.67	[0.52, 0.87]	.002
Gender, girls	0.60	[0.47, 0.75]	<.001	0.56	[0.43, 0.73]	<.001
BMI ≥ 21 kg/m²	1.02	[0.81, 1.29]	.874	1.09	[0.84, 1.40]	.530
Subjective health status ≥ 4	0.72	[0.51, 1.02]	.063	0.73	[0.50, 1.07]	.105
Self-esteem ≥ 16	0.80	[0.62, 1.03]	.086	0.92	[0.69, 1.22]	.547
Resilience ≥ 40	1.07	[0.84, 1.37]	.584	1.13	[0.86, 1.49]	.384
Life satisfaction ≥ 9	1.26	[0.97, 1.65]	.088	1.40	[1.04, 1.89]	.027
Depression ≥ 16	1.45	[1.12, 1.87]	.004	1.32	[0.99, 1.76]	.061
Family
Father's age ≥ 46				1.03	[0.77, 1.38]	.836
Mother's age ≥ 44				1.14	[0.85, 1.52]	.378
Father's parenting stress ≥ 42				0.88	[0.67, 1.16]	.367
Mother's parenting stress ≥ 42				2.48	[1.91, 3.23]	<.001
Attachment with father ≥ 42				1.15	[0.86, 1.53]	.340
Attachment with mother ≥ 45				1.06	[0.79, 1.43]	.680
Household income (10,000 KRW/month) ≥ 644				0.78	[0.59, 1.02]	.073
School
Peer attachment ≥ 28				1.22	[0.93, 1.61]	.159
School adaptation ≥ 147				0.74	[0.55, 0.99]	.045
Academic stress ≥ 9				1.79	[1.36, 2.36]	<.001
Community
Residential area, city				0.99	[0.76, 1.28]	.937
Subjective socioeconomic status
National level ≥ 6				0.77	[0.53, 1.13]	.175
Community level ≥ 6				1.10	[0.76, 1.61]	.608
Model fitness	−2 Log likelihood = 1706.568, Nagelkerke R² = 0.080, Hosmer–Lemeshow χ² (p) = 3.59 (.892), Model χ² (df), p = 79.71 (12), p < .001			−2 Log likelihood = 1474.647, Nagelkerke R² = 0.175, Hosmer–Lemeshow χ² (p) = 6.90 (.548), Model χ² (df), p = 167.04 (25), p < .001

Note. AOR = adjusted odds ratio; CI = confidence interval.

Model 1 adjusted for individual-level variables.

Model 2 adjusted for individual, family, school, and community-level variables.

Hosmer–Lemeshow goodness-of-fit tests indicated adequate model fit.

In Model 2, having three or four ACEs was significantly associated with digital media overuse (3 ACEs: aOR = 1.97, 95% CI [1.14, 3.41], p = .015; 4 ACEs: aOR = 3.81, 95% CI [1.69, 8.58], p < .001). Female sex remained protective (aOR = 0.56, 95% CI [1.43, 0.73], p < .001), whereas depressive symptoms was no longer statistically significant. Among additional covariates, higher maternal parenting stress (aOR = 2.48, 95% CI [1.91, 3.23], p < .001), better school adjustment was associated with lower odds of digital media overuse (aOR = 0.74, 95% CI [0.55, 0.99], p = .045), and higher adolescent academic stress (aOR = 1.79, 95% CI [1.36, 2.36], p < .001) were associated with increased odds of digital media overuse. Model fit statistics indicated adequate fit for both models (Hosmer–Lemeshow test, p > .05), with improved explanatory power in the fully adjusted model (Nagelkerke R² increased from .080 to .175).

Discussion

Using Bronfenbrenner's ecological systems theory, this study examined the association between ACEs and digital media overuse among Korean adolescents while adjusting for individual, family, school, and community factors. Several key findings emerged. First, the distribution of ACEs was broadly consistent with prior studies, although the prevalence of ≥4 ACEs was lower than reported in many international studies. For example, Madigan et al. (2023) reported that 62% of adults had experienced at least one ACE and 28% had experienced four or more ACEs in a large systematic review and meta-analysis. Swedo et al. (2024), using the U.S. Youth Risk Behavior Survey, reported that 76.1% of high school students had experienced at least one ACE and 18.5% had experienced four or more. The comparatively lower prevalence of ≥4 ACEs in the present study may reflect differences in measurement and time frame. Whereas many prior studies assessed lifetime ACE exposure, the PSKC measures used here captured some parent-related adversities (e.g., parental depression and alcohol-related indicators) within a more recent period, and the time reference for other items was not always explicit, potentially leading to underestimation. In addition, the PSKC was not originally designed to comprehensively assess ACEs but as a nationally representative longitudinal cohort examining child development and family environments; therefore, ACEs in this study were operationalized using available proxy indicators rather than a standardized ACE instrument. As a result, several ACE domains commonly recommended in standardized frameworks (e.g., sexual abuse, parental mental illness beyond depression, substance use disorder, parental incarceration, and exposure to domestic violence), and neglect could not be differentiated into physical vs. emotional neglect. These differences in measurement scope may have contributed to the relatively lower ACE prevalence observed in this sample.

Second, the proportion classified as digital media overuse was higher than estimates in some prior studies, though direct comparisons are limited by variability in definitions, measures, and thresholds. A meta-analysis of international studies published between 2011 and 2017 reported a prevalence of problematic smartphone use of approximately 23% among children and youth (Sohn et al., 2019). In Korea, the National Information Society Agency (2024) reported that 42.6% of adolescents were classified as high-risk or potential-risk for smartphone overdependence. The higher proportion observed in the current study may reflect differences in measurement approaches as well as developmental and contextual factors. The 14th-wave PSKC data were collected in 2021, during the COVID-19 pandemic, a period characterized by substantial increases in digital media engagement among children and adolescents. Moreover, the PSKC outcome was based on parent observer ratings, which may yield higher prevalence estimates than adolescent self-report; prior work suggests that adolescents may underreport problematic behaviors compared with parent or clinician ratings (e.g., Jeong et al., 2018; Youn et al., 2018).

Finally, the likelihood of being classified as digital media overuse increased with cumulative ACE exposure, consistent with prior evidence (Hao et al., 2024; Jackson et al., 2021). In the fully adjusted ecological model, adolescents with three or more ACEs had significantly higher odds of digital media overuse, suggesting a dose–response relationship that persists even after accounting for individual, family, school, and community factors. These findings indicate that digital media overuse may function as an observable and actionable health marker for school nurses, comparable to routinely monitored indicators such as weight status, vision, or sleep patterns. Importantly, digital media overuse should not be interpreted merely as an expression of poor self-discipline, but rather as a potential manifestation of cumulative stress and maladaptation within the school context. In particular, students with a history of three or more ACEs may warrant priority for further assessment, counseling, and supportive intervention within school health services. In addition to ACEs, age, sex, academic stress, life satisfaction, maternal parenting stress, and school adaptation emerged as important correlates. Boys appeared to represent a “dual-burden” group with higher ACE exposure and higher digital media overuse, although sex differences have been mixed across studies. For example, Jackson et al. (2021) reported higher levels among boys, whereas Hao et al. (2024) found no significant sex moderation. Such inconsistencies may reflect differences in age groups, definitions and measures of digital media overuse, and the set of covariates included.

School adaptation, academic stress and parenting stress, conceptualized as ecological factors beyond the individual, may be particularly salient within the ACEs–digital media overuse nexus. Prior research indicates that higher levels of stress in these domains are associated with increased digital media use and risk of problematic engagement (Jackson et al., 2021; Shutzman & Gershy, 2023). Shutzman and Gershy (2023) found that greater interparental conflict and parental stress were associated with less involvement and more negative parenting behaviors, which in turn related to increased child screen exposure. Seema et al. (2024) reported that school burnout—characterized by loss of interest in school activities, excessive academic demands, perceived incompetence, achievement pressure, and homework overload—was significantly associated with digital addiction symptoms. Together, these findings suggest that stress within family and school environments may amplify vulnerability to problematic digital media engagement.

This study has limitations. First, as a secondary analysis, the study was constrained by the availability and quality of PSKC variables, and high-missingness variables could not be included. Second, the cross-sectional design precludes causal inference; longitudinal analyses are needed to clarify developmental pathways from ACE exposure to digital media overuse. Third, age variability within the 14th-wave cohort was limited (approximately within 12 months), and thus the observed age effects may reflect within-grade differences rather than broader developmental stage differences. Future studies should examine wider age ranges and include mechanisms that may explain age-related variability. Finally, PSKC did not capture all standardized ACE domains and used a shorter time frame for some items, which may have led to underestimation of ACE exposure. Despite these limitations, substantial ACE exposure was observed, and its association with digital media overuse was consistent with existing literature. The use of a large, nationally representative panel and an ecological analytic approach strengthens the contribution of this study and provides a foundation for future research.

Conclusion

This study used Bronfenbrenner's ecological systems theory to examine the association between ACEs and digital media overuse among Korean middle-school students while adjusting for individual, family, school, and community factors. The odds of digital media overuse increased with cumulative ACE exposure, and adolescents with ≥3 ACEs had significantly higher odds of digital media overuse in the fully adjusted model. Sex, academic stress, and maternal parenting stress were also significant correlates. These findings suggest that digital media overuse should be understood not merely as an individual habit, but as an outcome shaped by cumulative adversity and ecological stressors across developmental contexts.

Evidence-based interventions for digital media overuse have been examined at multiple ecological levels, although the overall evidence base remains limited and heterogeneous. At the individual level, most interventions for adults have focused on cognitive behavioral therapy, counseling, and structured physical activity programs (Lu et al., 2025). Among children and adolescents, multimodal approaches integrating individual, family, and school components appear more effective than single-level interventions (Theopilus et al., 2024). Digital media overuse in youth has been conceptualized as arising from complex interactions among individual attitudes and behaviors, family dysfunction, and peer and parent relationships (Theopilus et al., 2024). At the family level, parent involvement—particularly interventions targeting parenting stress, parent–child communication, and consistent media-use rules—has been associated with improved outcomes. At the school and community level, meta-analytic findings indicate that school-based programs demonstrate moderate effect sizes and are cost-effective and highly accessible to adolescents (Žmavc et al., 2025). Programs led by well-trained professionals, incorporating parent engagement, and targeting high-risk groups show stronger effects.

However, despite growing intervention efforts, robust longitudinal and implementation-focused intervention studies remain limited, particularly those integrating ACEs within an ecological framework. Many studies continue to focus on prevalence and correlational associations rather than testing scalable, trauma-informed prevention models. In this context, our findings suggest the importance of ACE-informed, multilevel prevention strategies within school health systems. School health professionals—including school nurses, teachers, and counselors—may benefit from systematic training to recognize ACE-related vulnerabilities and implement trauma-informed practices. Prevention programs should be tailored to sex differences, stress-related mechanisms, and contextual risk factors. Family-centered education for high-risk households may further support healthy digital media use. At the policy and research levels, national child and adolescent surveillance systems should incorporate standardized ACE measurement and emerging forms of digital victimization, such as cyberbullying and digital sexual violence. Future research should move beyond cross-sectional associations and prioritize rigorous, theory-driven intervention trials that test multilevel and trauma-informed approaches.

Implications for School Nursing Practice

School nurses are well positioned as early identifiers of students whose digital media overuse may reflect cumulative adversity rather than isolated “misuse.” As care coordinators, they can facilitate early detection and timely referral to appropriate professionals. Digital media overuse may also serve as a practical entry point for identifying more severe ACE-related concerns within the school setting. Findings support (a) incorporating ACE-informed, developmentally appropriate screening into routine health encounters and early referral pathways; (b) partnering with teachers and school counselors to address academic stress and school burnout through coordinated support plans; and (c) delivering family-centered interventions that target maternal parenting stress, strengthen caregiver–adolescent communication, and promote consistent digital routines at home. Rather than focusing solely on restriction, school nurses should emphasize trauma-informed, strengths-based strategies (e.g., coping skills, emotion regulation, and supportive adult connections) and facilitate linkage to community mental health resources for students with high ACE burden (≥4 ACEs). Programs may benefit from brief follow-up contacts or booster sessions to sustain behavior change and reduce risk escalation.

Footnotes

ORCID iDs

Hyoju Woo

Min Sohn

Author Contribution(s)

Hyoju Woo: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Validation; Writing – original draft; Writing – review & editing.

Min Sohn: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Supervision; Validation; Writing – original draft; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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