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European Journal of Psychotraumatology logoLink to European Journal of Psychotraumatology
. 2023 Jun 19;14(2):2218248. doi: 10.1080/20008066.2023.2218248

Adverse childhood experiences, problematic internet use, and health-related quality of life in Chinese adolescents

Experiencias adversas en la infancia, uso problemático de Internet y calidad de vida relacionada con la salud en adolescentes chinos

中国青少年的不良童年经历、问题性互联网使用和与健康相关生活质量

Dezhong Chen a, Li Lin a, Xiuqiong Feng b, Shengyu Luo a, Hongyu Xiang a, Kang Qin a, Xun Guo a, Weiqing Chen a, Vivian Yawei Guo a,CONTACT
PMCID: PMC10281346  PMID: 37335002

ABSTRACT

Background: The influence of adverse childhood experiences (ACEs) on an individual’s health is substantial. However, the associations between ACEs, problematic internet use (PIU), and health-related quality of life (HRQOL) in adolescents remain underexplored.

Objective: To assess the association between ACEs and HRQOL in Chinese adolescents and to evaluate the mediating role of PIU in this association.

Method: A sample of 6,639 adolescents (3,457 boys and 3,182 girls) aged between 11–20 years (mean [SD] age: 14.5 [1.6] years) were recruited from 6 junior and senior middle schools using a proportional sampling approach in a cross-sectional study. Data on ACE exposure was collected through the short form of Childhood Trauma Questionnaire, the ACE-International Questionnaire, and two additional questions. HRQOL was assessed by the Pediatric Quality of Life Inventory version 4.0. The associations between ACEs and HRQOL were estimated using linear regression models. Mediation analysis was further conducted to explore the possible mediating role of PIU in the association between ACEs and HRQOL.

Results: Our study collected 13 different ACEs. We found that adolescents exposed to any ACE had significantly lower scores in all HRQOL dimensions, psychosocial health summary scale, and total scale, than those without such exposure. Specifically, adolescents with ≥ 3 ACE exposure had a total scale score that was 14.70 (95%CI: 15.53 to 13.87) points lower than their non-exposed counterparts. Mediation analysis identified PIU as a significant mediator, with the proportion of the total effect attributable to PIU ranging from 14.38% for social functioning to 17.44% for physical functioning.

Conclusions: Exposure to ACEs was associated with poorer HRQOL in Chinese adolescents, underscoring the importance to prevent ACEs and their negative impacts on adolescent well-being. These findings also highlighted the need of promoting appropriate internet use among adolescents exposed to ACEs, in order to avert potential impairment in their HRQOL.

HIGHLIGHTS

  • Adolescents with adverse childhood experiences have poorer health-related quality of life.

  • The association between adverse childhood experiences and health-related quality of life shows a dose–response pattern.

  • Problematic internet use partially mediates the associations between adverse childhood experiences and health-related quality of life in adolescents.

KEYWORDS: Adverse childhood experience, health-related quality of life, problematic internet use, mediation, adolescent

1. Background

Adverse childhood experiences (ACEs) refer to various types of traumatic or stressful events that occurred during childhood, such as abuse, neglect, and household dysfunction (Felitti et al., 1998). According to the data from the 2016–17 United States National Survey of Children's Health, nearly half of the children aged between 3 and 17 years had experienced at least one form of ACE (Mansuri et al., 2020). The prevalence of ACEs was even higher in developing countries like China (Wang et al., 2021; Kidman et al., 2019; Rocha et al., 2021). A recent study conducted in four provinces across China has found that approximately 86.6% of the students aged between 10 and 20 years had experienced one or more ACEs (Wang et al., 2021).

Since the seminal ACE study conducted by Felitti and his colleagues (Felitti et al., 1998), decades of research has consistently demonstrated that ACEs were associated with a wide range of behavioural and health outcomes in children and adolescent, including internalizing and externalizing behavioural problems, chronic physical diseases, and mental disorders (Lowthian et al., 2021; Anda et al., 1999; Elsenburg et al., 2017; Liming & Grube, 2018). The detrimental impact of ACEs could even persist into adulthood, leading to morbidity and mortality later in life (Lin et al., 2022; Lin et al., 2022; Wang et al., 2021; Lin et al., 2022; Lin et al., 2022). Previous evidence has also shown a link between ACEs and reduced health-related quality of life (HRQOL) (Vink et al., 2019; Balistreri, 2015; Meinck et al., 2017; Jud et al., 2013; Flaherty et al., 2013; Luo et al., 2022), a multidimensional concept that could reflect an individual’s overall well-being across physical, psychological, and social dimensions (Guyatt et al., 1993). For example, a cross-sectional study of children aged between 9–13 years has found that a higher number of ACEs was significantly associated with poorer HRQOL as measured by Kidscreen-10 (Vink et al., 2019). Another study with 59,360 children and adolescents in the United States has also suggested that those with greater ACE exposures were more likely to experience worse overall well-being (Balistreri, 2015).

The mechanisms underlying the association between ACEs and poorer HRQOL remain uncertain. One possible explanation is related to the chronic stress originating from ACEs (Shonkoff & Garner, 2012), which could lead to changes in allostatic systems and ultimately cause the development of physical and psychological diseases, as well as poor HRQOL (Dempster et al., 2021; Nusslock & Miller, 2016; Fox et al., 2010). In addition, individuals who had experienced ACEs might develop maladaptive coping strategies, making them more susceptible to adopting addictive behaviours (e.g. smoking or drinking) as a means to alleviate the ACE-related stress and negative emotions (Anda et al., 1999; Miller et al., 2006). These health risky behaviours could further undermine HRQOL (Goldenberg et al., 2014; Levola et al., 2014).

With the development of technology, screen-based media have become a central part of daily life for the current young generations. The dramatic increase in digital media usage has raised concerns about problematic internet use (PIU), which refers to excessive or addictive engagement towards internet use that can have detrimental consequences on an individual's life, resulting in psychological, social, academic, and professional difficulties (Beard & Wolf, 2001). A study involving over 355 million adolescents has found a negative association between PIU and well-being (Orben & Przybylski, 2019). Another cross-sectional study of 12,285 adolescents has also demonstrated a significant correlation between PIU and lower scores of HRQOL (Machimbarrena et al., 2019). Individuals exposed to ACEs may be more susceptible to developing PIU, as they might turn to the internet as a coping mechanism to escape from or alleviate the stress associated with ACEs, particularly among children and adolescents (Jackson et al., 2021; Jhone et al., 2021; Wang et al., 2020; Seo et al., 2020; Liu et al., 2020). For instance, a longitudinal study of 21,954 children and adolescents has shown that those with experience of ≥ 4 ACEs had a significantly higher likelihood of heavy digital use compared to their counterparts without any ACE exposure (Jackson et al., 2021).

Based on these studies, it is plausible that exposure to ACEs could cause addictive behaviour of PIU and subsequently impair HRQOL. Nevertheless, the possible mediating role of PIU in the association between ACEs and HRQOL requires further investigations. In addition, the association between ACEs and HRQOL in adolescents has been underexplored in developing countries like China, where ACEs are prevalent (Wang et al., 2021). Therefore, in this study, we aimed to (1) explore the association between ACEs and HRQOL in Chinese adolescents, and (2) examine whether PIU mediates this association.

2. Methods

2.1. Study design and participants

This cross-sectional study was conducted from November 2021 to December 2021 in the Huangpu district of Guangzhou, a mega-city located in southern China. To obtain a representative sample of adolescents attending middle schools, we randomly selected 6 junior middle schools and 2 senior middle schools, proportionate to the total number of junior and senior middle schools in the district, respectively. A total of 6 schools (two of which included both junior and senior middle schools simultaneously) were selected, and all students and their parents were invited to join the study. Ultimately, 6,982 adolescents with parental consent completed a questionnaire, resulting a response rate of 90.1%. Of the included adolescents, 4,330 were from junior middle schools and 2,652 were from senior middle schools. We first excluded 217 adolescents due to missing data on HRQOL. Then, an additional 126 adolescents who lacked ACE data required for categorization were further excluded, resulting in a final sample of 6,639 adolescents for the current analysis.

Ethical approval for this study was granted by the Ethics Committee of School of Public Health, Sun Yat-sen University with an approval number of 2021[116]. Prior to the survey, parents of each student have signed an informed consent for their children to participate in this study.

2.2. Measures

2.2.1. Adverse childhood experiences

Adolescents self-reported their experiences of ACEs through paper-based surveys using the short form of Childhood Trauma Questionnaire (CTQ-SF) (Bernstein et al., 2003), the Adverse Childhood Experiences International Questionnaire (ACE-IQ) (World Health Organization, 2021), and two additional questions regarding parental disability.

CTQ-SF was used to assess maltreatment-related ACEs in adolescents (Bernstein et al., 2003), which has been demonstrated to be valid and reliable for Chinese adolescents (Zhang, 2011). The questionnaire consists of 28 items (including three validity items) that cover five types of maltreatment, including physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect. Each type of maltreatment contains five items rated on a 5-point Likert scale (1 = never true, 2 = rarely true, 3 = sometimes true, 4 = often true, and 5 = very often true). We first reverse-scored two items in the physical neglect subscale and all items in the emotional neglect subscale, and then calculated subscale scores by summing all items within each type of maltreatment. Each subscale score ranged from 5 to 25, with higher scores representing greater severity of maltreatment. We further dichotomized each type of maltreatment into the exposed (coded as 1) or nonexposed (coded as 0) groups based on cut-off values of 8 for physical abuse, 9 for emotional abuse, 6 for sexual abuse, 8 for physical neglect, and 10 for emotional neglect (Spinhoven et al., 2014; Li et al., 2015).

ACE-IQ was applied to evaluate seven types of ACEs, including witnessing domestic violence (3 items), household substance abuse (1 item), household mental illness (1 item), incarcerated household member (1 item), parental separation or divorce (1 item), parental death (1item), and peer bullying (1 item) (World Health Organization, 2021). It has been demonstrated to be valid in adolescents (Kidman et al., 2019). Items in the witnessing domestic violence and peer bullying subscales were rated based on a 4-point Likert scale (1 = never, 2 = once, 3 = a few times, and 4 = many times). Adolescents were defined as the exposed group if they answered ‘once’, ‘a few times’, or ‘many times’ to any item in that ACE indicator. For other ACE subscales, items only contained a two-point scale response (‘yes’ and ‘no’), and those with an affirmative response were classified into the exposed group for the corresponding ACE.

Following previous research (Lin et al., 2022; Lin et al., 2022; Wang et al., 2021), parental disability was assessed by two questions: (1) ‘Did your female/male guardian have a serious deformity when you were young?’ and (2) ‘Did your female/male guardian have a long time being sick on bed when you were young?’. Adolescents were considered as ‘exposed to the parental disability’ if they answered ‘yes’ to either question.

A cumulative ACE score (range: 0-13) was calculated by summing the 13 dichotomized ACE indicators. According to previous studies (Flaherty et al., 2013; Von Cheong et al., 2017; Haatainen et al., 2003), all adolescents were further classified into four groups based on their cumulative ACE score, i.e. 0, 1, 2, and ≥ 3 ACEs.

2.2.2. Health-related quality of life

HRQOL was assessed using the Pediatric Quality of Life Inventory version 4.0 (PedsQL 4.0) through self-reported paper questionnaires (Varni et al., 2001). The PedsQL 4.0 has shown good validity and reliability for Chinese children and adolescents (Hao et al., 2010; Fadhullah et al., 2022; Chen et al., 2023). It includes 23 items, which can be categorized into four dimensions, i.e. physical functioning (8 items), emotional functioning (5 items), social functioning (5 items), and school functioning (5 items). Each item was rated based on a 5-point Likert scale (0 = never a problem, 1 = almost never a problem, 2 = sometimes a problem, 3 = often a problem, and 4 = almost always a problem), and was reversely scored and linearly transformed into a 0–100 range. Then, the score of each dimension was calculated by averaging all items within the corresponding dimension. A psychosocial health summary score was further calculated by averaging the scores of emotional, social, and school functioning dimensions. The total scale score was also computed as the mean value of all items in the questionnaire. Scores of each dimension, psychosocial health summary scale, and total scale ranged from 0-100, with higher scores reflecting better HRQOL.

2.2.3. Problematic internet use

Adolescents were asked to fill out the Young Diagnostic Questionnaire (YDQ), a valid and reliable self-reported questionnaire that was used to evaluate the level of internet addiction (Young, 1999). The YDQ consists of eight two-point scale questions (0 = ‘no’ or 1 = ‘yes’) regarding the internet use, including (1) preoccupation with internet use, (2) common fantasies about the internet, (3) unsuccessful attempts to reduce internet use, (4) emotional problems caused by reducing internet use, (5) longer internet use than originally intended, (6) loss of significant relationship or opportunity due to internet use, (7) lying about time spent on the internet, and (8) using the internet to achieve a better emotional state. The cumulative score of the YDQ was calculated by summing the eight items, with higher scores indicating a greater level of internet addiction. Adolescents were then categorized into two groups: the PIU group (score ≥ 5) and the normal internet use group (score < 5) (Young, 1999).

2.2.4. Covariates

Adolescents self-reported their age, sex, and single child status through written questionnaires. Single child status was divided into yes (only one child in the family) or no (more than one child in the family).

We also collected information about parental educational background and employment status through web-based questionnaires. Parental educational background was categorized into three groups as (1) junior middle school or below, (2) senior middle school, and (3) bachelor’s degree or above. Employment status was grouped into two categories as employed and unemployed.

2.3. Statistical analysis

Descriptive statistics were reported as mean ± standard deviation (SD) for continuous data and frequency (percentage) for categorical data. Characteristics across the four ACE groups were compared by one-way ANOVA for continuous variables, and χ2 test for categorical variables. To assess the dose–response patterns for each characteristic based on the cumulative number of ACEs, we used polynomial contrasts of ANOVA trend tests for continuous data and the Mantel-Haenszel statistic for categorical data.

The association between ACEs and HRQOL was evaluated by linear regression models. Model 1 was a crude model without adjustment for any covariate. Model 2 was further constructed with adjustment for adolescents’ age, sex, and single child status, as well as parental educational background and employment status. The results were presented as β coefficients and corresponding 95% confidence intervals (CIs). Linearity, normality, homoscedasticity, and absence of multicollinearity were examined for all linear regressions. Dose–response associations between the cumulative number of ACEs and HRQOL scores were further assessed using linear trend tests.

The potential mediating role of PIU in the associations between ACEs and HRQOL scores was examined using Baron and Kenny's causal steps method (Figure 1) (Baron and Kenny, 1986). Four subsequent regression models were established to test the mediation hypothesis. First, ACEs were regressed on HRQOL (path c), which showed the total effect of the independent variable on the dependent variable. Second, ACEs were regressed on the mediator variable PIU (path a). Third, PIU was regressed on HRQOL with adjustment for ACEs (path b). Fourth, ACEs and PIU were regressed on HRQOL simultaneously (path c’). All regression models adjusted for the same covariates listed in aforementioned multivariable linear regression analyses. The total effect of ACEs on HRQOL was further decomposed into direct effect that was unmediated by PIU and indirect effect that was mediated by PIU. Bias-corrected 95% CIs were obtained based on 1,000 bootstrap samples. The proportion of total effect that explained by the mediator was further calculated.

Figure 1.

Figure 1.

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Framework of mediation analysis in this study. ACEs, adverse childhood experiences; PIU, problematic internet use; HRQOL, health-related quality of life. Path c: ACEs were regressed on HRQOL; path a: ACEs were regressed on PIU; path b: PIU was regressed on HRQOL with adjustment for ACEs; and path c’: ACEs and PIU were regressed on HRQOL simultaneously.

All data analyses were performed with Stata/SE 15.1 (Stata-Corp, College Station, TX, USA). Statistical significance was two-sided with a p-value < .05.

3. Results

The study population consisted of 3,457 (52.1%) boys and 3,182 girls (47.9%). The mean age of the adolescents was 14.5 (SD = 1.6) years, ranging from 11 to 20 years. As presented in Table 1, approximately 77.3% (n = 5,130) of the adolescents had experienced one or more ACEs and 34.2% (n = 2,268) were exposed to at least three ACEs. Compared to adolescents without any ACE exposure, those who had experienced three or more ACEs were younger and more likely to have siblings and fathers with a lower educational background and unemployed status. A total of 1,103 (16.7%) adolescents had PIU. The prevalence of PIU increased from 7.3% in the non-exposed group to 26.1% in adolescents with ≥ 3 ACEs (p value for linear trend < .001). In addition, as the cumulative number of ACEs increased, we observed a significantly decreasing linear trend of HRQOL scores in all dimensions, psychosocial health summary scale, and total scale (Figure 2).

Table 1.

Comparison of characteristics by the cumulative number of ACEs in adolescents.

Characteristics Number of ACEs (n = 6,639) p value for group difference p value for linear trend *
0 (n = 1,509) 1 (n = 1,584) 2 (n = 1,278) ≥ 3 (n = 2,268)
Sex, n (%)         .002 .634
 Girls 747 (49.5%) 738 (46.6%) 561 (43.9%) 1,136 (50.1%)    
 Boys 762 (50.5%) 846 (53.4%) 717 (56.1%) 1,132 (49.9%)    
Age (years), mean ± SD 14.6 ± 1.6 14.5 ± 1.6 14.5 ± 1.6 14.3 ± 1.6 < .001 < .001
Status of single child, n (%)         < .001 .003
 Yes 448 (30.1%) 417 (26.5%) 384 (30.5%) 551 (24.6%)    
 No 1,042 (69.9%) 1,155 (73.5%) 877 (69.5%) 1,687 (75.4%)    
Maternal educational background, n (%)         .196 .012
 Junior middle school or below 354 (24.3%) 411 (27.0%) 329 (26.7%) 622 (28.5%)    
 Senior middle school 436 (29.9%) 431 (28.3%) 363 (29.5%) 624 (28.6%)    
 Bachelor degree or above 669 (45.9%) 682 (44.8%) 538 (43.7%) 937 (42.9%)    
Paternal educational background, n (%)         .023 < .001
 Junior middle school or below 305 (21.8%) 315 (21.8%) 282 (24.3%) 512 (25.1%)    
 Senior middle school 399 (28.6%) 427 (29.6%) 337 (29.0%) 631 (30.9%)    
 Bachelor degree or above 692 (49.6%) 703 (48.7%) 543 (46.7%) 896 (43.9%)    
Paternal employment status, n (%)         .001 < .001
 Employed 1,405 (93.9%) 1,463 (93.4%) 1,173 (93.5%) 2,018 (90.8%)    
 Unemployed 91 (6.1%) 104 (6.6%) 81 (6.5%) 204 (9.2%)    
Maternal employment status, n (%)         .640 .499
 Employed 1,221 (81.0%) 1,309 (82.7%) 1,042 (81.9%) 1,852 (82.3%)    
 Unemployed 286 (19.0%) 273 (17.3%) 230 (18.1%) 399 (17.7%)    
PIU, n (%)         < .001 < .001
 Yes 110 (7.3%) 187 (11.8%) 217 (17.0%) 589 (26.1%)    
 No 1,399 (92.7%) 1,397 (88.2%) 1,061 (83.0%) 1,679 (73.9%)    
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ACEs, adverse childhood experiences; PIU, problematic internet use; SD standard deviation.

* p value for linear trend test was obtained using polynomial contrasts of ANOVA trend tests for continuous data and the Mantel-Haenszel statistic for categorical data.

Figure 2.

Figure 2.

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Comparison of HRQOL by the cumulative number of ACEs in adolescents. HRQOL, health-related quality of life; ACEs, adverse childhood experiences.

The associations between ACEs and HRQOL are shown in Table 2. In Model 1 without any adjustment, adolescents with experience of one or more ACEs had significantly lower scores in every HRQOL dimension, as well as the psychosocial health summary scale and total scale than the adolescents without any ACE exposure. A dose–response pattern was evident in these associations. After adjustment for covariates, similar findings were observed in Model 2 (≥ 3 ACEs versus no ACE, β = −9.86 [95%CI: −10.73 to −8.99] for physical functioning; β = −20.97 [95%CI: −22.29 to −19.65] for emotional functioning; β = −13.62 [95%CI: −14.57 to −12.68] for social functioning; β = −14.35 [95%CI: −15.45 to −13.25] for school functioning; β = −16.32 [95%CI: −17.23 to −15.40] for psychosocial health summary scale; and β = −14.70 [95%CI: −15.53 to −13.87] for total scale score). Since exposure to one ACE was already significantly associated with poorer HRQOL, ACEs were further dichotomized into the exposed (i.e. experience of at least one ACE) and non-exposed groups (i.e. without experience of ACEs) in the subsequent mediation analysis.

Table 2.

Association between the cumulative number of ACEs and HRQOL in adolescents.

  β (95% CI) p value for linear trend
0 ACE 1 ACE 2 ACEs ≥ 3 ACEs
Model 1
 Physical functioning Ref −1.57 (−2.36, −0.77) * −3.42 (−4.30, −2.54) * −9.89 (−10.72, −9.06) * < .001
 Emotional functioning Ref −3.82 (−5.03, −2.60) * −8.03 (−9.40, −6.66) * −20.34 (−21.61, −19.07) * < .001
 Social functioning Ref −2.14 (−2.83, −1.46) * −5.08 (−5.94, −4.22) * −13.55 (−14.44, −12.67) * < .001
 School functioning Ref −3.34 (−4.41, −2.26) * −6.61 (−7.78, −5.44) * −14.07 (−15.12, −13.02) * < .001
 Psychosocial health summary scale Ref −3.10 (−3.91, −2.29) * −6.57 (−7.49, −5.65) * −15.99 (−16.86, −15.11) * < .001
 Total scale score Ref −2.71 (−3.45, −1.98) * −5.79 (−6.62, −4.95) * −14.46 (−15.26, −13.67) * < .001
Model 2
 Physical functioning Ref −1.85 (−2.67, −1.02) * −3.63 (−4.55, −2.71) * −9.86 (−10.73, −8.99) * < .001
 Emotional functioning Ref −4.34 (−5.58, −3.09) * −8.83 (−10.23, −7.42) * −20.97 (−22.29, −19.65) * < .001
 Social functioning Ref −2.19 (−2.92, −1.45) * −4.87 (−5.78, −3.96) * −13.62 (−14.57, −12.68) * < .001
 School functioning Ref −3.58 (−4.70, −2.47) * −6.86 (−8.08, −5.63) * −14.35 (−15.45, −13.25) * < .001
 Psychosocial health summary scale Ref −3.37 (−4.21, −2.53) * −6.85 (−7.80, −5.90) * −16.32 (−17.23, −15.40) * < .001
 Total scale score Ref −2.99 (−3.75, −2.23) * −6.05 (−6.90, −5.19) * −14.70 (−15.53, −13.87) * < .001
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* p value < .05.

ACEs, adverse childhood experiences; HRQOL, health-related quality of life; CI, confidence interval.

Model 1 was a crude model without any adjustment; Model 2 adjusted for adolescents’ age, sex, and status of single child, as well as parental educational background and employment status.

Table 3 presents the results of the mediation analysis. After incorporating PIU into the models, the direct effect of ACEs on HRQOL attenuated slightly, but remained statistically significant. This indicates that PIU was a partial mediator in the association between ACEs and HRQOL. The indirect effects of ACEs on HRQOL through PIU were significant across all HRQOL dimensions, psychosocial health summary scale, and total scale. Specifically, the β for the indirect effect of ACE exposure on HRQOL total scale score was −1.39 (95%CI: −1.63 to −1.16), with approximately 15.65% of the total effect being mediated.

Table 3.

Mediation effect of PIU in the association between ACEs and HRQOL.

  β (95% CI) †
Physical functioning Emotional functioning Social functioning School functioning Psychosocial health summary scale Total scale score
ACEs → PIU 0.13 (0.11, 0.15) * 0.13 (0.11, 0.15) * 0.13 (0.11, 0.15) * 0.13 (0.11, 0.15) * 0.13 (0.11, 0.15) * 0.13 (0.11, 0.15) *
PIU → HRQOL −7.80 (−8.82, −6.69) * −14.71 (−16.43, −13.19) * −8.76 (−9.99, −7.45) * −11.73 (−12.86, −10.47) * −11.73 (−12.81, −10.73) * −10.75 (−11.73, −9.79) *
ACEs → HRQOL −4.78 (−5.43, −4.08) * −10.83 (−11.72, −9.80) * −6.73 (−7.38, −6.08) * −7.61 (−8.42, −6.69) * −8.39 (−9.06, −7.68) * −7.49 (−8.19, −6.86) *
Standard effect
Indirect effect ‡ −1.01 (−1.21, −0.82) * −1.90 (−2.26, −1.56) * −1.13 (−1.40, −0.91) * −1.52 (−1.80, −1.28) * −1.52 (−1.78, −1.26) * −1.39 (−1.63, −1.16) *
Total effect −5.79 (−6.52, −5.12) * −12.73 (−13.66, −11.68) * −7.86 (−8.53, −7.26) * −9.13 (−10.05, −8.26) * −9.91 (−10.62, −9.23) * −8.88 (−9.60, −8.22) *
Mediation (%) § 17.44% 14.93% 14.38% 16.65% 15.34% 15.65%
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* p value < .05.

† Bias-corrected CIs were obtained using 1,000 bootstrap samples.

‡ The effects of ACEs on HRQOL through PIU (i.e. ACEs → PIU → HRQOL).

§ Proportion of total effect that was mediated by PIU.

ACEs, adverse childhood experiences; HRQOL, health-related quality of life; PIU, problematic Internet use; CI, confidence interval.

Models adjusted for adolescents’ age, sex, and status of single child, as well as parental educational background and employment status.

4. Discussion

In this cross-sectional study, approximately 77.3% of the adolescents were exposed to at least one ACE. We found a dose–response association between the cumulative number of ACEs and lower HRQOL scores in all dimensions, psychosocial health summary scale, and total scale in Chinese adolescents aged between 11–20 years. Furthermore, our results indicated that PIU partially mediated the association between ACEs and adolescents’ HRQOL.

Our study’s findings, which revealed a significant association between ACEs and poorer HRQOL in adolescents, aligned with previous research (Vink et al., 2019; Balistreri, 2015; Meinck et al., 2017; Jud et al., 2013; Flaherty et al., 2013; Luo et al., 2022). However, the comparability of results from related studies has been limited, primarily due to varying methods of measuring ACEs and HRQOL. In our study, we utilized both CTQ and ACE-IQ, along with supplementary questions, to more comprehensively assess ACE exposure in adolescents. Furthermore, most previous studies have focused on the impact of ACEs on the overall well-being, without distinguishing between specific dimensions of HRQOL (Vink et al., 2019; Balistreri, 2015; Meinck et al., 2017). In contrast, our study used the PedsQL 4.0, a multidimensional scale, to investigate the associations between ACEs and various aspects of HRQOL. Consequently, we discovered negative associations across all HRQOL dimensions, psychosocial health summary scale, and total scale, underscoring the detrimental sequelae of ACEs on various aspects of adolescent development. Moreover, there has been limited evidence concerning the dose–response pattern of ACEs in relation to HRQOL in adolescents (Balistreri, 2015; Meinck et al., 2017; Jud et al., 2013). Our study contributed to the literature by examining the impact of cumulative ACE exposure on HRQOL scores.

The mechanisms underlying the association between ACEs and HRQOL have not been fully elucidated. One possible explanation is related to the chronic stress resulting from ACEs, which can cause biological alterations in the nervous, endocrine, and immune systems (Shonkoff & Garner, 2012; Dempster et al., 2021; Danese & McEwen, 2012), and eventually lead to impaired HRQOL. For example, chronic stress can disrupt the hypothalamic–pituitary–adrenal (HPA) axis (Dempster et al., 2021), contributing to cognitive deficits, emotional dysregulation, and impaired decision-making (Raymond et al., 2018). These mental health problems during childhood or adolescence can have long-term negative impact on an individual’s development, with increased risk of poor academic performance, strained relationships with family and friends, and limited community engagement (Kessler et al., 2009). In addition, altered stress hormone secretion, such as cortisol, may also lead to alterations of brain structure (Raymond et al., 2018), which can further affect cognitive function and emotion regulation, predisposing adolescents to aggression and violence (Raymond et al., 2018). As a consequence, adolescents exposed to ACEs may be more vulnerable to worse psychosocial health aspects of HRQOL, including emotional functioning, social functioning, and school functioning. Moreover, the chronic low-grade inflammation originating from ACEs can lead to adiposity, insulin resistance, and other pre-disease states, ultimately causing various physical issues (Nusslock & Miller, 2016; Fox et al., 2010).

Another possible explanation for the association between ACEs and HRQOL may be related to the unhealthy behaviours caused by ACEs, such as lack of physical activity, sleep disturbances, and addictive behaviours of smoking, alcoholism, and substance abuse (Anda et al., 1999; Miller et al., 2006; Troy et al., 2021; Park et al., 2021). These health risky behaviours can lead to increased inflammation levels, which have been linked to higher risks of impaired physical, psychological, and social development (Metsios et al., 2020; Atrooz & Salim, 2020; Costello et al., 2013; Chrousos, 2000). Consequently, adolescents’ HRQOL and well-being may be compromised. In addition, poor family environment and socioeconomic status have been identified as important determinants of HRQOL in children and adolescents (Von Rueden et al., 2006; Otto et al., 2017). While our results showed that adolescents with less educated and unemployed fathers were more likely to experience ACEs, the negative association between exposure to ACEs and HRQOL observed in adolescents was plausible. Nevertheless, further research is necessary to fully understand the underlying mechanisms and establish causal pathways involved.

Our results have also shown a mediating role of PIU in the association between ACEs and HRQOL. The increased risk of PIU in ACE-exposed individuals has been supported by both empirical and theoretical evidence (Jackson et al., 2021; Jhone et al., 2021; Wang et al., 2020; Seo et al., 2020; Liu et al., 2020; Brand et al., 2016; Kardefelt-Winther, 2014). Empirically, existing literature have documented the association between ACEs and PIU in children and adolescents (Jackson et al., 2021; Jhone et al., 2021; Wang et al., 2020; Seo et al., 2020; Liu et al., 2020). Theoretically, the Interaction of Person-Affect-Cognition Executive (I-PACE) theory proposed a framework of the interactions between affective and cognitive factors that were involved in PIU, and suggested that adolescents might develop PIU due to interactions between ACEs and stress, and moderating variables such as coping styles (Brand et al., 2016). Another theoretical explanation is related to the compensatory internet use theory, which conceptualized ACEs as negative life situations (Kardefelt-Winther, 2014). Therefore, to alleviate negative feelings, ACEs can give rise to a strong motivation for going online that could lead to PIU. The addictive behaviour of PIU can subsequently lead to problematic outcomes in adolescents, including poor HRQOL (Orben & Przybylski, 2019; Machimbarrena et al., 2019). Our findings about the mediating role of PIU in the association between ACEs and poor HRQOL suggest that preventing excessive internet use may be an effective way to preserve HRQOL in adolescents with ACE exposures. Nevertheless, further randomized controlled trials are needed to confirm the conclusions.

Our study had several strengths that should be noted. First, we used HRQOL as the outcome to reflect the physical, psychosocial, and overall well-being of adolescents. Second, our sample size was large enough to provide adequate statistical power. Third, several established covariates were adjusted in the regression models to control for confounding bias. However, some limitations of this study should also be acknowledged. First, the cross-sectional nature of our study hindered the causal inference regarding the observed associations. Further longitudinal studies are needed to address the temporal associations between ACEs and HRQOL. Second, all adolescents were recruited from one mega-city in southern China. The generalization of our findings to other parts of China, especially rural areas, should be interpreted with caution. In addition, our study only included adolescents who attended schools on the survey day, while those who dropped out of school or were absent due to illness were not considered. Since students with ACEs might be more prone to chronic school absenteeism (Stempel et al., 2017), the associations between ACEs and HRQOL might be underestimated in our study. Third, although the frequency and intensity of ACEs have been found to be associated with poor health outcomes (Friedman et al., 2015), we did not collect such information in our study. Last, even though we have adjusted for several available confounders in the linear regression models, we cannot rule out the possibility of unmeasured residual confounders, such as parental quality of life (Guo et al., 2017; Wong et al., 2016) and health conditions (Guo et al., 2018; Bedford et al., 2020), which could further distort the observed associations.

5. Conclusions

In conclusion, the present study showed that ACEs were significantly associated with lower HRQOL scores in adolescents, across all dimensions, psychosocial health summary scale, and total scale. The associations were partially mediated by PIU. Our findings indicated that healthcare providers and policymakers should pay special attentions to adolescents with experience of ACEs, and focus on addressing the physical, psychological, and social consequences of ACEs, including the potential negative impact on internet use. In addition, strategies to promote proper internet use might be particularly important for preserving HRQOL in adolescents with ACE exposure. Nevertheless, further randomized controlled trials are needed to confirm these conclusions.

Authors’ contribution

D.C. carried out the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; L.L. designed the study, collected the data, and reviewed and revised the manuscript; X.F. conceptualized the study and reviewed and revised the manuscript; S.L. collected the data and reviewed and revised the manuscript; H.X. collected the data and reviewed and revised the manuscript; K.Q. collected the data and reviewed and revised the manuscript; X.G. collected the data and reviewed and revised the manuscript; W.C. conceptualized the study and reviewed and revised the manuscript; V.Y.G. conceptualized and designed the study, coordinated and supervised the study, drafted the initial manuscript, and reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.

Funding Statement

This work was supported by National Natural Science Foundation of China: [Grant Number 82204069]; Start-up fund from the Sun Yat-sen University: [Grant Number 51000-18841211].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, V.Y.G. The data are not publicly available due to ethical and privacy reasons.

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Associated Data

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

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, V.Y.G. The data are not publicly available due to ethical and privacy reasons.

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