Mediation of Parental Aggravation in the Association Between ADHD Severity and Electronic Media Use: A National Survey of Children’s Health Study

Abstract

Objective:

To determine whether the association between ADHD severity and electronic media use was mediated by parental aggravation.

Methods:

This was a retrospective analysis from the 2016 to 2017 National Survey of Children’s Health (NSCH) involving children ages of 3 to 17 years with parent-reported ADHD (n = 5,930). Path analyses were used to model the relationships between ADHD severity with parental aggravation (PA) as a mediator, and electronic device (ED) and television (TV) use as outcomes, controlling for covariates.

Results:

Parental aggravation mediated the relationship between ADHD severity and ED use and TV use (indirect effects: β = .02, p < .001; β = .01, p = .004). When stratified by age, the mediation effect between ADHD and ED use remained significant for adolescents and school-age children, and mediation between ADHD and TV use remained significant only for adolescents.

Conclusion:

These findings suggest a need to develop targeted interventions to address PA and manage excessive electronic media use in children with moderate/severe ADHD.

Keywords

ADD/ADHD stress media adolescents children

In the National Survey of Children’s Health (NSCH) between 2016 and 2019, 8.7% of children in the United States had an ADHD diagnosis (Danielson et al., 2022). Children and adolescents with ADHD experience challenges in academic, home, behavioral, and social functioning due to symptoms of inattention and/or hyperactivity/impulsivity (Booster et al., 2012; Strine et al., 2006). Children with ADHD exhibit higher rates of emotional dysregulation and social difficulties which contribute to more peer rejection and parental conflict (Graziano & Garcia, 2016; McQuade et al., 2021).

Children and adolescents’ media use has increased in the past decade (Rideout et al., 2022). In 2019, U.S. children ages 8 to 12 years spent on average 4 hours daily of screen time while adolescents spent on average 7 hours daily (Rideout et al., 2022). Research has shown that children with ADHD use media for longer durations compared to non-ADHD children (Ceranoglu, 2018). Individuals with ADHD struggle with impulse control, have more problematic play skills, and are attracted to fast-paced activities with immediate rewards which may make them more susceptible to problematic media and video game use (Bioulac et al., 2008; Ferguson & Ceranoglu, 2014). Children with ADHD may also use internet video games as a way of regulating their symptoms (Han et al., 2009). Due to problematic offline peer relationships, media use can also provide an escape and a way to foster online social interactions for individuals with ADHD (Blais et al., 2008; Ceranoglu, 2018).

Based on a child’s developmental phase, the relationship between ADHD severity and media use may change over time. Prevalence of current ADHD increases with age with higher rates in adolescents compared to younger children. The severity of hyperactivity or inattention symptoms and impacts on functional impairment change over time with adults reporting less hyperactivity (Wilcutt et al., 2012). A longitudinal study also showed that parental stress and negative parenting practices tended to increase over time in parents of children with ADHD symptoms (Glatz et al., 2011). Though overall media use is increasing in children, adolescents tend to have higher average daily media use compared to younger children (Rideout et al., 2022).

Types of electronic media use also varies across developmental phases. A Pew Research Study found that the most common device used among young children tended to be television whereas older children and adolescents are more likely to use gaming devices, social media sites, tablets, or have a personal smartphones. TV tends to allow for more parent co-viewing and active parent mediation of content whereas other personal electronic devices may provide more challenges to monitoring a child’s exposure to specific content on video sharing sites, social media, or gaming platforms. Despite both being types of electronic media use, there may be distinct effects of ED and TV use (Coyne et al., 2017; Nadeem, 2020). Another study looking at parental stress and media use in children looked at different types of media use individually (McDaniel & Radesky, 2020).

Though media use can provide opportunities for self-expression, social connection, entertainment, information sharing, and educational programming for children with ADHD, there are also negative consequences of excessive media use (Blais et al., 2008; Hill, Ameenuddin, Chassiakos, Cross, Radesky, et al., 2016; Olson, 2010; Valkenburg & Peter, 2011). Excessive media use is associated with negative social, physical, and mental health outcomes such as learning difficulties, sleep problems, externalizing problems, obesity, and increased exposure to cyberbullying (Borghese et al., 2015; Kross et al., 2013; Mundy et al., 2017; Waasdorp & Bradshaw, 2015). Individuals with ADHD are more susceptible to the negative effects of excessive media exposure such as poor academic performance and impaired behavioral functioning (Ceranoglu, 2018; Shuai et al., 2021). Parents of ADHD children also experience greater difficulties monitoring or limiting their children’s media use (Bioulac et al., 2008; Ceranoglu, 2018). Parents of children with disruptive behaviors may also use media as a reward or as a distraction for the child to provide respite and alleviate parent frustration (Nikken, 2018).

Parents of children with ADHD also experience high levels of stress (Pimentel et al., 2011; Samiei et al., 2015). More severe ADHD symptoms are associated with higher levels of parental stress (Pimentel et al., 2011). Previous studies demonstrated that parents of children with developmental and behavioral problems had higher levels of parental aggravation (PA), which is defined as a measurement of stress experienced by parents associated with caring for children (Ehrle & Moore, 1999; Kwon et al., 2022). Higher parental aggravation is associated with risk factors such as poverty, single parenthood, maternal depression, and lack of parenting emotional support (Ehrle & Moore, 1999; Schieve et al., 2011; Theule et al., 2013). Higher parental aggravation in parents of ADHD children has also been associated with child externalizing and internalizing behaviors and lower cognitive development in the child (Ehrle & Moore, 1999; Pimentel et al., 2011).

Some research has examined the relationship between childhood behavioral problems, parental stress, and electronic media use. One study looked at early childhood externalizing behavior as measured by the Child Behavior Checklist (CBCL) and media use over 6 months in children <5 years of age. The CBCL measured parent-reported internalizing and externalizing symptoms but does not specifically measure ADHD. They found that child externalizing behaviors predicted parenting stress at 1 month, based on the Parenting Stress Index (PSI), which then predicted child media use at 6 months follow-up. This study found that the relationship between child behavioral problems and increased media use was mediated by parental stress (McDaniel & Radesky, 2020). Similar to other stress-related indicators, parental aggravation is associated with overall parental well-being and greater parental aggravation has been associated with negative impacts on the child (McGroder, 2000).

Currently, there have not been any studies exploring the relationships between ADHD severity, parental aggravation, and electronic media use between preschool-age, school-age, and adolescents with ADHD. Therefore, the purpose of this study was to determine whether higher severity levels of ADHD are associated with increased PA and childhood electronic media use and whether PA mediates greater levels of media use in children with ADHD overall and for specific age groups.

Methods

Study Design

This was a retrospective study using data from the combined 2016 to 2017 National Survey of Children’s Health (NSCH), which is the largest national-level electronic and paper-based survey on the health of children under 18 years and their families. The NSCH is conducted annually by the U.S. Census Bureau and is sponsored by the Maternal and Child Health Bureau (MCHB). The NSCH randomly sampled households from all 50 states and randomly selected one parent or guardian respondent for a child living in the household to complete the survey. Details of the survey methodology were published previously (Child and Adolescent Health Measurement Initiative (CAHMI), 2017; U.S. Census Bureau, 2018).

Participants

Children between the ages of 3 and 17 years with an ADHD diagnosis, whose parent or primary caregiver completed the NSCH, and who had complete data on ADHD and corresponding severity levels, were included in this study (n = 5,930).

Variables

ADHD Severity

The predictor variable was ADHD severity. Parents reported whether they were ever told by a doctor or other health care provider that their child has ADHD. Parents were then asked if their child currently has ADHD and to rate their symptoms as mild, moderate, or severe. This variable was categorized as dichotomous, where mild ADHD was coded as 0 and moderate/severe was coded as 1.

Parental Aggravation (PA)

PA was the mediator and utilized questions from the Aggravation in Parenting Scale (APS): Over the past month, how often have you felt: (1) That this child is much harder to care for than other children his or her age? (2) That this child does things that really bother you a lot? (3) Felt angry with this child? Response options included: never (1), rarely (2), sometimes (3), usually (4), or always (5). The three questions were measured on a 5-point Likert scale to generate a total sum score with a range between 3 to 15. Cronbach’s alpha for the three items is 0.93 (La Charite et al., 2023). This variable was modeled as continuous. The APS was derived from the Parenting Stress Index (Abidin, 1997). It measures the level of frustration and stress that parents experience in relation to caring for their children. The APS was found to be a reliable measure of parental stress (Ehrle & Moore, 1999).

Media Use

The outcome variable was media use, which was measured as average daily use of both electronic device (ED) use and television (TV) use through a single question for each:

TV Use

Over the last 2 weeks, about how much time does this child usually spend in front of the TV watching TV, videos, or playing video games? (0, <1, 1, 2, 3, or ≥4 hours). TV use was categorized as a dichotomous variable: ≤2 hours (0), and >2 hours (1).

Electronic Device (ED) Use

Over the last 2 weeks, about how much time does this child usually spend with computers; cell phones; hand-held video games; and other electronic devices, doing things other than schoolwork? Parents selected one of the following: (0, <1, 1, 2, 3, or ≥4 hours). ED use was categorized as a dichotomous variable: ≤2 hours (0), and >2 hours (1).

Covariates

Child Covariates

Child age was initially examined as a continuous variable (range: 3–17 years). In subsequent models, we stratified by age to examine developmental differences in ED and TV use: preschool-age (3–5 years), school-age (6–12 years), and adolescence (13–17 years). Child sex was defined as male/female. Child race/ethnicity included categories: Hispanic; White non-Hispanic; Black non-Hispanic; and Other. This variable was categorized as dichotomous due to small cell sizes in some groups, with non-Hispanic White (1) and all other races (0). A child receiving ADHD medication (yes/no), and a child who received treatment or counseling by a mental health professional during the last 12 months (yes/no) were also included as covariates.

Parent/Family Covariates

Parental education level was dichotomized as having a college degree or higher (1) or having less than a college degree (0). Poverty was measured by percent of the Federal Poverty Level (FPL) defined as 0% to 99%, 100% to 199%, 200% to 399%, or ≥400%. FPL is a measure of income issued annually and by the number of people per household by the Department of Health and Human Services (DHHS). FPL was used as a proxy for socioeconomic status.

Availability of parental emotional support for parenting or raising a child in the past 12 months was measured as yes/no. Parental coping with daily demands of raising a child was measured on a 3-point Likert scale: not very well/not very well at all (1) to very well (3). Mother and father’s self-reported general mental health status ranged between fair/poor (1) to excellent/very good (3).

Sampling and Study Size

In the NSCH, households in the U.S. were randomly sampled to determine those with at least one child under the age of 18 years. The survey sample size included 71,811 children. Of these, 6,115 children between the ages of 3 to 17 years were reported to have ADHD. ADHD severities were reported on 6,042 children. There were 5,930 with no missing data on variables of interest. The Overall Weighted Response Rate was 40.7% for 2016 and 37.4% for 2017 (U.S. Census Bureau, 2018). A series of sensitivity analyses were run to determine whether any differences among key variables between eligible participants without full information (n = 6,042) and our final sample were found. There were no significant differences between the two samples (all comparisons: p > .05).

Statistical Analysis

Univariate analyses were conducted using IBM SPSS Statistics v27 to report both descriptive statistics and frequencies. Bivariate linear and logistic regression analyses between the predictor variable, mediator variable, and outcome variables were examined to establish whether there were significant relationships between the hypothesized pathways. Mplus version 8.4 was used to build the mediated models; a simple path analysis was used to establish whether PA mediated the relationship between ADHD and media use (ED use and TV use separately) with the specification of indirect effects in the models. Covariates of interest were next added into the models and pathways that were nonsignificant were systematically removed, one at a time, to provide the most parsimonious models. All models were informed by the modification indices to provide the best model fit (CFI > 0.95; RMSEA < 0.05; SRMR < 0.05; Hu & Bentler, 1999).

Stratification by age was then used to examine whether the relationships between ADHD, PA, and electronic media use differed at different developmental periods. Path analyses for each of the three age groups were modeled without covariates to determine whether the relationships remained significant. For the models that held, covariates were added, and any nonsignificant pathways were removed, similar to the original procedure.

Results

There were 5,930 children included in the study. The majority of children were male (68.2%) and non-Hispanic White (80%), with a mean (SD) age of 12.2 years (3.4). Children were assigned as either having mild (43.9%) or moderate/severe (56.1%) ADHD symptoms. Parents had a mean (SD) score of 7.2 (2.6) on the PA scale used as the mediating variable. About 64.5% of children had ≤2 hours of ED use and 71.4% had ≤2 hours of TV use (Table 1).

Table 1.

Univariate Statistics of 5,930 Participants.

Predictors	Frequency (%)
ADHD severity
Mild	2,605 (43.9)
Moderate/severe	3,325 (56.1)
Child age
Preschool	176 (3.0)
School age	2,763 (46.6)
Adolescent	2,991 (50.4)
Gender
Female	1,887 (31.8)
Male	4,043 (68.2)
Race
White	4,744 (80.0)
All others	1,186 (20.0)
Child medication	3,811 (64.3)
Child individual counseling	2,676 (45.1)
Parent education
Less than college degree	1,012 (17.1)
College degree or higher	4,828 (81.4)
Poverty level/Family income
0%–99% FPL	848 (14.3)
100%–199% FPL	1,065 (18.0)
200%–399% FPL	1,711 (28.9)
400% FPL or greater	2,306 (38.9)
Parental emotional support	4,722 (79.6)
Predictors	M (SD); range
Parental coping	2.45 (0.57); 1–3
Mother mental health	2.53 (0.68); 1–3
Father mental health	2.65 (0.60); 1–3
Mediator	M (SD); range
Parental aggravation	7.15 (2.62); 3–15
Outcomes	Frequency (%)
Time spent watching TV (hours)
≤2	4,232 (71.4)
>2	1,698 (28.6)
Time spent with ED (hours)
≤2	3,823 (64.5)
>2	2,107 (35.5)

Bivariate Analyses

Bivariate linear and logistic regression analyses were first assessed. Parents that reported moderate/severe ADHD symptoms were more likely to report higher levels of PA (β = .29, p < .001; 95% CI [0.27, 0.31]). Higher levels of PA were related to increased odds of more than 2 hours of ED use (OR = 1.06, p < .001; 95% CI [1.04, 1.08]) and TV use (OR = 1.04, p = .001; 95% CI [1.02, 1.06]). Finally, more severe ADHD was associated with increased odds of TV use (OR = 1.14, p = .021; 95% CI [1.02, 1.28]), but not ED use (OR = 1.03, p = .563; 95% CI [0.93, 1.15]).

ED Use

Path Analysis

Similar to the bivariate regression findings, more severe ADHD was significantly associated with higher levels of PA (β = .29, p < .001; 95% CI [0.27, 0.32]), and higher levels of PA were associated with an increased odds of ED use (OR = 1.06, p < .001; 95% CI [1.04, 1.08]). The relationship between ADHD and ED use was nonsignificant (OR = .94, p = .287; 95% CI [0.84, 1.06]). The indirect effect between ADHD and ED use through PA (β = .03, p < .001; 95% CI [0.02, 0.03]) was significant, demonstrating mediation (Figure 1).

Figure 1.

Schematic mediated path analysis of base models for ADHD, parental aggravation and ED use and TV use.

Path Analysis With Covariates

Receiving individual counseling (β = .19, p < .001; 95% CI [0.16, 0.21]) and having a higher % FPL (β = .06, p < .001; 95% CI [0.03, 0.08]) were associated with higher levels of PA. Older age (β = −.04, p = .011; 95% CI [−0.06, −0.01]), using ADHD medication (β = −.03, p = .023; 95% CI [−0.06, −0.004]), and higher levels of maternal (β = −.04, p = .010; 95% CI [−0.08, −0.01]) and paternal (β = −.04, p = .020; 95% CI [−0.07, −0.01]) mental health were associated with lower PA. Having a higher % FPL (OR = 0.84, p < .001; 95% CI [0.80, 0.93]) was associated with less ED use, while older age (OR = 1.33, p < .001; 95% CI [1.17, 1.45]) was associated with increased odds of ED use. The indirect effect remained significant (β = .02, p < .001; 95% CI [0.01, 0.03]); the relationship between ADHD and ED use was mediated through PA.

Age Stratification

Path models were next stratified by three age groups: adolescent, school-age, and preschool-age, to determine whether relationships were driven by a particular group. Both the adolescent and school-age groups held the structure of the overall model, demonstrated mediation, with few differences in covariates (Figure 2a and b). However, within the preschool-age group, only ADHD was associated with PA (β = .26, p < .001; 95% CI [0.13, 0.38]); the relationships of ADHD and PA with ED use were nonsignificant (Figure 2c).

Figure 2.

Schematic mediated path analysis of ADHD, parental aggravation and ED use by age group. (a) Adolescence (13–17 year olds). (b) School age (6–12 year olds). (c) Preschool age (3–5 year olds).

TV Use

Path Analysis

Moderate/severe ADHD was associated with higher levels of PA (β = .29, p < .001; 95% CI [0.27, 0.32]), and higher levels of PA were associated with an increased odds of TV use (OR = 1.03, p = .004; 95% CI [1.01, 1.06]). There was no significant relationship between ADHD and TV use (OR = 1.09, p = .193; 95% CI [0.96, 1.22]). The indirect effect was small, but significant (β = .01, p = .004; 95% CI [0.01, 0.02]), suggesting that PA mediates the relationship between ADHD and TV use (Figure 1).

Path Analysis With Covariates

Receiving individual counseling (β = .21, p < .001; 95% CI [0.18, 0.24]), higher levels of education (β = .06, p = .001; 95% CI [0.02, 0.09]), %FPL (β = .08, p < .001; 95% CI [0.05, 0.11]), and worse maternal (β = .12, p < .001; 95% CI [0.08, 0.15]) and paternal (β = .09, p < .001; 95% CI [0.05, 0.12]) mental health were associated with higher levels of PA. Older age (β = −.03, p = .049; 95% CI [−0.06, 0.00]) and receiving medication (β = −.05, p < .001; 95% CI [−0.08, −0.02]) were associated with lower levels of PA. Older age (OR = 1.13, p < .001; 95% CI [1.10, 1.15]) and being male (OR = 1.59, p < .001; 95% CI [1.35, 1.87]) were associated with increased likelihood of TV use, while %FPL (OR = 0.81, p < .001; 95% CI [0.75, 0.87]) was associated with a decreased likelihood of TV use. This model demonstrated mediation (β = .06, p = .005; 95% CI [0.02, 0.10]).

Age Stratification

Stratification by age was used for TV use, and the adolescent group demonstrated a similar model to that of the overall one (Figure 3a) as a mediated model (β = .02, p = .006; 95% CI [0.02, 1.22]). Higher levels of both maternal (β = −.10, p < .001; 95% CI [−0.14, −0.05]) and paternal (β = −.09, p < .001; 95% CI [−0.13, −0.04]) mental health led to lower levels of PA. The school-age group (Figure 3b) showed a significant relationship between ADHD and PA (β = .29, p < .001; 95% CI [0.25, 0.32]) and TV use (OR = 1.26, p = .039; 95% CI [1.04, 1.52]), where higher levels of ADHD were associated with increased PA and increased odds of more TV use. Similarly, the preschool-age group (Figure 3c) showed a significant relationship between ADHD and PA (β = .26, p < .001; 95% CI [0.13, 0.38]), where higher levels of ADHD are associated with higher levels of PA. However, there was a negative association between ADHD and TV use (OR = .58, p = .044; 95% CI [0.28, 1.18]), where increased ADHD in this age group resulted in less likelihood of TV use.

Figure 3.

Schematic mediated path analysis of ADHD, parental aggravation and use by age group. (a) Adolescence (13–17 year olds), (b) school age (6–12 year olds), and (c) preschool age (3–5 year olds).

Discussion

This is the first study to indicate that parental aggravation mediates the relationship between ADHD severity and media use. PA mediated the relationship between mild versus moderate/severe ADHD and ED and TV use. For ED use, this relationship was evident in the school-age and adolescent groups. For TV use, this relationship only remained in the adolescent group. Increased ED use in these age groups may be due to school-aged children and adolescents being more likely to have their own devices (Rideout et al., 2022). Older children may have more proficiency in ED use than their parents, which may make it less likely for parents to set limits around media use (Warren & Aloia, 2019). Children and adolescents with ADHD due to their poor impulse control are also at higher risk of developing problematic internet use (Andreassen et al., 2016; Gao et al., 2017). Individuals with ADHD who exhibit problematic internet use are more likely to have more severe core ADHD symptoms, have impaired family interactions, increased psychological distress such as depression, and lower learning motivation (Chou et al., 2017; Shuai et al., 2021). As adolescents with ADHD are more susceptible to psychological distress that may be further exacerbated by excessive media use, including social media or smartphone use, it is important to identify targets for intervention. Targeting parenting stress in older adolescents with moderate/severe ADHD may be important in addressing excessive media use and its downstream consequences.

In the present study, youth receiving individual counseling was associated with increased PA in all ages of children with ADHD, while medication use was associated with decreased PA. Participating in individual counseling was likely a reflection of more severe ADHD symptoms and particularly more challenging behaviors that could exacerbate parental aggravation. Additionally, better parental mental health was associated with lower levels of PA. One study found that mothers with lower well-being had children that had higher media use (McDaniel & Radesky, 2020). Stimulant medication and behavioral therapy, particularly behavioral parent training, are recommended evidence-based treatments used to address the core symptoms of ADHD in school-age children. A meta-analysis on the efficacy of methylphenidate and psychosocial treatments in children with ADHD showed that combined treatments were more effective than psychosocial treatments alone in treating ADHD and co-morbid oppositional or conduct problems (Van Der Oord et al., 2008). A follow-up study also showed that parenting stress on the Parenting Stress Index (PSI) significantly decreased from pre-test to post-test and remained stable at 7.5 year follow up for children with combined treatment or stimulant medication (Van Der Oord, Prins, et al., 2012). The current study also suggests that adequate treatment of core ADHD symptoms can result in decreased parenting aggravation.

Due to concerns about excessive electronic media use and its consequences in ADHD teens such as poor sleep, interpersonal conflict, and poorer school performance, identifying interventions to address excessive electronic media use may be needed. Offering parents information on how to manage stress and media use may be helpful. Due to increased parenting stress, parents of ADHD children and adolescents can become more rejecting and reactive to their child’s behavior (Bögels et al., 2010). A combination of the child’s ADHD behavior, parents’ history of ADHD, and parenting stress can contribute to parenting problems and thus interventions targeting both the child and the parent are beneficial (Deault, 2010; Miller-Lewis et al., 2006). An 8 week mindfulness training program for parents and school-age children with ADHD led to a significant reduction in parent-rated ADHD behavior, parental stress, and parental over-reactivity (Van Der Oord, Peijnenburg, et al., 2012). Further studies may be needed to evaluate if parent stress reduction programs may be beneficial for older adolescents with ADHD and their parents to reduce parental aggravation and in turn decrease media use.

Now that media usage is highly individualized, for ages 5 to 18 years, the American Academy of Pediatrics (AAP) recommends that pediatricians guide families in developing their own Family Media Use Plan, which includes setting rules and promoting family communication about the risks and benefits of media use (Hill, Ameenuddin, Chassiakos, Cross, Radesky, et al., 2016). Parents of children in the 2 to 5 years age range should continue to limit screen time to 1 hour per day of high quality co-viewed programming (Hill, Ameenuddin, Chassiakos, Cross, Hutchinson, et al., 2016). In the present study, while media use was dichotomized to ≤2 and > 2 hours based off previous guidance from the AAP, the most recent guidelines for child and adolescent media use are now more flexible toward each family’s individual needs (Hill, Ameenuddin, Chassiakos, Cross, Radesky, et al., 2016). The majority of children and adolescents in the study reported <2 hours daily of ED and TV use (Table 1) which is lower than average screen time values reported by other studies. This may be due to the fact that many studies tend to combine all screen media use together resulting in higher values. Some studies have also shown that how certain media is used may be more important than the number of hours spent (Rideout et al., 2022).

Earlier age of media use, higher media use hours, and viewing of low quality content are predictors of poor impulse control and self-regulation (Reid Chassiakos et al., 2016). Children and adolescents with ADHD are at risk of poor executive functioning which poses additional challenges in managing their media use (Ferguson & Ceranoglu, 2014). Heavy parental media use is associated with increased parent-child conflict and poorer family functioning. Since parental media use is a strong predictor of the child’s media use, reducing excessive parental media use may enhance parent-child interactions and decrease PA (Reid Chassiakos et al., 2016).

This study had several strengths. NSCH is a nationally representative sample of households with U.S. children under the age of 18 years (CAHMI, 2017). The large sample allowed for more detailed analyses than previous studies, as stratification by age allowed for examination of children at different developmental levels (Schieve et al., 2011). This was one of few studies that examined parent and child level variables, including the relationship between child ADHD severity and media use. Mediation analyses allowed for examination of a possible underlying mechanism between ADHD severity and increased use of different forms of electronic media.

This study had several limitations. Due to the retrospective nature of the study, the data was obtained from predetermined questionnaires including parent-report measures for ADHD diagnosis, rather than a validated tool, which may have been subject to recall bias. ADHD may have been underreported, particularly in children with mild severity who may not have received a diagnosis confirmed by a healthcare provider. However, a previous study showed that parent reported ADHD diagnosis resulted in similar prevalence estimates compared to administrative claims data suggesting convergent validity of ADHD prevalence from both these sources (Visser et al., 2014). Parent-reported ADHD diagnosis can be reliable over time and often discrepancies between parent-reported diagnosis and DSM-based criteria is a reflection of milder symptoms or treated ADHD (Cree et al., 2022). Parent-reported ADHD and severity from the NSCH is used for national ADHD prevalence estimates for the Centers for Disease Control and Prevention (Danielson et al., 2022). It was also not possible to measure different parenting styles which may impact parent-child relationships and parenting aggravation.

Electronic media use was obtained from parent report which can have discrepancies with the child or adolescent’s report of their media use and may be influenced by recall and social desirability bias. However, previous studies have found these discrepancies in duration to be fairly small with some studies reporting within 10 to 30 minutes difference between parent and adolescent responses (Nagata et al., 2021; Wood et al., 2019). This study also focused solely on ADHD and did not examine other possible co-occurring mental or emotional health conditions. There are potential confounding factors such as ADHD medication compliance, parental ADHD diagnosis, and parental media use that were not collected in the study but may influence both parental aggravation and media use in children with ADHD (Reid Chassiakos et al., 2016; Van Der Oord, Prins, et al., 2012). This may be an area of interest to explore in future studies.

Healthcare practitioners may want to prioritize asking parents of children with ADHD within the 6- to 17-year age range about their children’s media use, as it may be an indicator of how well a family system is functioning or the level of parental aggravation present. Ideally, starting discussions about electronic media use during the preschool period would help to better prevent problematic media use behaviors and its consequences in adolescence. Furthermore, greater levels of media use may be an indication of untreated or undertreated ADHD, which would suggest the need for further ADHD management by clinicians. Based on this study’s findings, behavioral interventions in the 6- to 12-year age group appear to be important in reducing excessive media use (Wolraich et al., 2019). Among 13- to 17-year-olds, treatment with medication, in addition to behavioral interventions, may be particularly important in reducing PA that may be contributing to increased electronic media use.

Conclusion

The relationship between higher ADHD severity levels and both ED and TV use was mediated by PA in the overall sample. Additional investigation into differences among age groups revealed that these relationships were mostly driven by the older age groups, rather than the preschool-age group. In addition to early discussion of appropriate electronic media use at younger ages, identifying targeted interventions for parents of older adolescents with ADHD may be helpful in addressing parenting stress and subsequent excessive electronic media use in this population.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was also supported by the Maternal and Child Health Bureau, Children’s Hospital Los Angeles Developmental-Behavioral Pediatrics Training Program grant T77MC25732.

ORCID iDs

Jennifer Han

Alexis Deavenport-Saman

Author Biographies

Jennifer Han completed a developmental-behavioral pediatrics fellowship at Children’s Hospital Los Angeles. She is currently a developmental-behavioral pediatrician treating children and adolescents with ADHD at Kaiser Permanente Riverside Medical Center.

Karen Koser is a developmental-behavioral pediatrician at Children’s Hospital Los Angeles. She treats children and adolescents with various neurodevelopmental conditions.

Mary Rose Mamey is a research scientist specializing in biostatistics/advanced statistical modeling. She specializes in the examination of the longitudinal trajectories of comorbid disorders and has conducted much of her research in behavioral and mental health outcomes in adolescents, including ADHD.

Douglas L. Vanderbilt is the Division Chief of Developmental-Behavioral Pediatrics (DBP) at Children’s Hospital Los Angeles and a Professor of Clinical Pediatrics at Keck School of Medicine and Occupational Science/Occupational Therapy at University of Southern California (USC). He is the Director of the ACGME-accredited DBP fellowship at Children’s Hospital Los Angeles.

David J. Schonfeld is a developmental-behavioral pediatrician at Children’s Hospital Los Angeles and Director of the National Center for School Crisis and Bereavement (NCSCB). He is a Professor of Clinical Pediatrics at Keck School of Medicine of USC. He has authored more than 100 scholarly articles, book chapters, and books.

Larry Yin is the Division Chief of General Pediatrics at Children’s Hospital Los Angeles and an Associate Professor of Clinical Pediatrics at Keck School of Medicine and Occupational Science/Occupational Therapy at USC. He is also Director of the University Center of Excellence in Developmental Disabilities at CHLA and serves as a California Governor appointee to the State Council on Developmental Disabilities.

Alexis Deavenport-Saman is an Associate Professor of Clinical Pediatrics at the Keck School of Medicine of USC. She is the Assistant Director of the University Center for Excellence in Developmental Disabilities.

References

Abidin

R. R.

(1997). Parenting stress index: A measure of the parent–child system. In Zalaquett

C. P.

Wood

R. J.

(Eds.), Evaluating stress: A book of resources (pp. 277–291). Scarecrow Education.

Andreassen

C. S.

Billieux

Griffiths

M. D.

Kuss

D. J.

Demetrovics

Mazzoni

Pallesen

(2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160

Bioulac

Arfi

Bouvard

(2008). Attention deficit/hyperactivity disorder and video games: A comparative study of hyperactive and control children. European Psychiatry, 23(2), 134–141. https://doi.org/10.1016/j.eurpsy.2007.11.002

Blais

Craig

W. M.

Pepler

Connolly

(2008). Adolescents online: The importance of internet activity choices to salient relationships. Journal of Youth and Adolescence, 37(5), 522–536. https://doi.org/10.1007/s10964-007-9262-7

Bögels

S. M.

Lehtonen

Restifo

(2010). Mindful parenting in mental health care. Mindfulness, 1(2), 107–120. https://doi.org/10.1007/s12671-010-0014-5

Booster

G. D.

DuPaul

G. J.

Eiraldi

Power

T. J.

(2012). Functional impairments in children with ADHD: Unique effects of age and comorbid status. Journal of Attention Disorders, 16(3), 179–189. https://doi.org/10.1177/1087054710383239

Borghese

M. M.

Tremblay

M. S.

Katzmarzyk

P. T.

Tudor-Locke

Schuna

J. M.

Leduc

Boyer

C. P.

LeBlanc

A. G.

Chaput

(2015). Mediating role of television time, diet patterns, physical activity and sleep duration in the association between television in the bedroom and adiposity in 10 year-old children. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 60. https://doi.org/10.1186/s12966-015-0221-5

Ceranoglu

T. A.

(2018). Inattention to problematic media use habits. Child and Adolescent Psychiatric Clinics of North America, 27(2), 183–191. https://doi.org/10.1016/j.chc.2017.11.009

Child and Adolescent Health Measurement Initiative. (2017). 2016 National Survey of Children’s Health (NSCH). [Data set] Data Resource Center for Child and Adolescent Health supported by the U.S. Department of Health and Human Services, Health Resources and Service Administration (HRSA), Maternal and Child Health Bureau (MCHB). Retrieved October 2, 2020, from www.childhealthdata.org.

10.

Chou

Huang

Chang

Chen

Yen

(2017). Social skills deficits and their association with Internet addiction and activities in adolescents with attention-deficit/hyperactivity disorder. Journal of Behavioral Addictions, 6(1), 42–50. https://doi.org/10.1556/2006.6.2017.005

11.

Coyne

S. M.

Radesky

Collier

K. M.

Gentile

D. A.

Linder

J. R.

Nathanson

A. I.

Rasmussen

E. E.

Reich

S. M.

Rogers

(2017). Parenting and digital media. Pediatrics, 140(Supplement_2), S112–S116. https://doi.org/10.1542/peds.2016-1758n

12.

Cree

R. A.

Bitsko

R. H.

Danielson

M. L.

Wanga

Holbrook

Flory

Kubicek

L. F.

Evans

S. W.

Owens

J. S.

Cuffe

S. P.

(2022). Surveillance of ADHD among children in the United States: Validity and reliability of parent report of provider diagnosis. Journal of Attention Disorders, 27(2), 111–123. https://doi.org/10.1177/10870547221131979

13.

Danielson

M. L.

Holbrook

J. D.

Bitsko

R. H.

Newsome

Charania

McCord

R. F.

Kogan

M. D.

Blumberg

S. J.

(2022). State-level estimates of the prevalence of parent-reported ADHD diagnosis and treatment among U.S. children and adolescents, 2016 to 2019. Journal of Attention Disorders, 26(13), 1685–1697. https://doi.org/10.1177/10870547221099961

14.

Deault

(2010). A systematic review of parenting in relation to the development of comorbidities and functional impairments in children with Attention-Deficit/Hyperactivity Disorder (ADHD). Child Psychiatry & Human Development, 41(2), 168–192. https://doi.org/10.1007/s10578-009-0159-4

15.

Ehrle

Moore

K. A.

(1999). 1997 NSAF benchmarking measures of child and family well-being (Report No. 6, Methodology Reports Series). Urban Institute. https://www.urban.org/sites/default/files/publication/69906/410137—NSAF-Benchmarking-Measures-of-Child-and-Family-Well-Being.PDF

16.

Ferguson

C. J.

Ceranoglu

T. A.

(2014). Attention problems and pathological gaming: Resolving the ‘Chicken and Egg’ in a prospective analysis. Psychiatric Quarterly, 85(1), 103–110. https://doi.org/10.1007/s11126-013-9276-0

17.

Gao

Yao

Zhou

Liu

(2017). The association between attention deficit/hyperactivity disorder and internet addiction: A systematic review and meta-analysis. BMC Psychiatry, 17(1), 260. https://doi.org/10.1186/s12888-017-1408-x

18.

Glatz

Stattin

Kerr

(2011). Parents’ reaction to youths’ hyperactivity, impulsivity, and attention problems. Journal of Abnormal Child Psychology, 39(8), 1125–1135. https://doi.org/10.1007/s10802-011-9541-3

19.

Graziano

P. A.

Garcia

(2016). Attention-deficit hyperactivity disorder and children’s emotion dysregulation: A meta-analysis. Clinical Psychology Review, 46, 106–123. https://doi.org/10.1016/j.cpr.2016.04.011

20.

Han

D. H.

Lee

Y. S.

Ahn

J. Y.

Chung

U. S.

Daniels

M. A.

Haws

C. A.

Renshaw

P. F.

(2009). The effect of methylphenidate on Internet video game play in children with attention-deficit/hyperactivity disorder. Comprehensive Psychiatry, 50(3), 251–256. https://doi.org/10.1016/j.comppsych.2008.08.011

21.

Hill

D. J.

Ameenuddin

Chassiakos

Y. L. R.

Cross

Radesky

Hutchinson

J. W.

Levine

A. E.

Boyd

R. W.

Mendelson

R. A.

Moreno

M. A.

Swanson

W. S.

(2016). Media use in school-aged children and adolescents. Pediatrics, 138(5), e20162592. https://doi.org/10.1542/peds.2016-2592

22.

Hill

D. J.

Ameenuddin

Reid Chassiakos

Y. L.

Cross

Hutchinson

Levine

Boyd

Mendelson

Moreno

Swanson

W. S.

(2016). Media and young minds. Pediatrics, 138(5), e20162591. https://doi.org/10.1542/peds.2016-2591

23.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

24.

Kross

Verduyn

Demiralp

Park

Lee

D. J.

Lin

N. J.

Shablack

Jonides

Ybarra

(2013). Facebook use predicts declines in subjective well-being in young adults. PLoS One, 8(8), e69841. https://doi.org/10.1371/journal.pone.0069841

25.

Kwon

O’Neill

M. E.

Foster

C. C.

(2022). The associations of child’s clinical conditions and behavioral problems with parenting stress among families of preschool-aged children: 2018–2019 National Survey of Child Health. Children, 9(2), 241. https://doi.org/10.3390/children9020241

26.

La Charite

Macinko

Hedrick

Santoro

Dudovitz

. (2023). Extracurricular activities, child and caregiver mental health and parental aggravation – a national cross-sectional study. Academic Pediatrics. Advance online publication. https://doi.org/10.1016/j.acap.2023.01.001

27.

McDaniel

B. T.

Radesky

J. S.

(2020). Longitudinal associations between early childhood externalizing behavior, parenting stress, and child media use. Cyberpsychology, Behavior, and Social Networking, 23(6), 384–391. https://doi.org/10.1089/cyber.2019.0478

28.

McGroder

S. M.

(2000). Parenting among low-income, African American single mothers with preschool-age children: Patterns, predictors, and developmental correlates. Child Development, 71(3), 752–771. https://doi.org/10.1111/1467-8624.00183

29.

McQuade

J. D.

Breaux

R. P.

Mordy

A. E.

Taubin

(2021). Childhood ADHD symptoms, parent emotion socialization, and adolescent peer problems: Indirect effects through emotion dysregulation. Journal of Youth and Adolescence, 50(12), 2519–2532. https://doi.org/10.1007/s10964-021-01510-3

30.

Miller-Lewis

Baghurst

P. A.

Sawyer

M. G.

Prior

Clark

Arney

Carbone

J. A.

(2006). Early childhood externalising behaviour problems: Child, parenting, and family-related predictors over time. Journal of Abnormal Child Psychology, 34(6), 886–901. https://doi.org/10.1007/s10802-006-9071-6

31.

Mundy

L. K.

Canterford

Olds

Allen

N. B.

Patton

G. C.

(2017). The association between electronic media and emotional and behavioral problems in late childhood. Academic Pediatrics, 17(6), 620–624. https://doi.org/10.1016/j.acap.2016.12.014

32.

Nadeem

(2020, August 27). Children’s engagement with digital devices, screen time. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2020/07/28/childrens-engagement-with-digital-devices-screen-time/

33.

Nagata

J. M.

Cortez

C. A.

Iyer

Ganson

K. T.

Chu

Conroy

A. A.

(2021). Parent-adolescent discrepancies in adolescent recreational screen time reporting during the coronavirus disease 2019 pandemic. Academic Pediatrics, 22(3), 413–421. https://doi.org/10.1016/j.acap.2021.12.008

34.

Nikken

(2018). Parents’ instrumental use of media in childrearing: Relationships with confidence in parenting, and health and conduct problems in children. Journal of Child and Family Studies, 28(2), 531–546. https://doi.org/10.1007/s10826-018-1281-3

35.

Olson

C. L.

(2010). Children’s motivations for video game play in the context of normal development. Review of General Psychology, 14(2), 180–187. https://doi.org/10.1037/a0018984

36.

Pimentel

M. J.

Vieira-Santos

Santos

Vale

M. C.

(2011). Mothers of children with attention deficit/hyperactivity disorder: Relationship among parenting stress, parental practices and child behaviour. ADHD Attention Deficit and Hyperactivity Disorders, 3(1), 61–68. https://doi.org/10.1007/s12402-011-0053-3

37.

Reid Chassiakos

Y. L.

Radesky

Christakis

Moreno

M. A.

Cross

Hill

Ameenuddin

Hutchinson

Levine

Boyd

Mendelson

Swanson

W. S

. (2016). Children and adolescents and digital media. Pediatrics, 138(5), e20162593. https://doi.org/10.1542/peds.2016-2593

38.

Rideout

Peebles

Mann

Robb

M. B.

(2022). Common sense census: Media use by tweens and teens, 2021. Common Sense. https://www.commonsensemedia.org/sites/default/files/research/report/8-18-census-integrated-report-final-web_0.pdf.

39.

Samiei

Daneshmand

Keramatfar

Khooshabi

K. S.

Amiri

Farhadi

Farzadfard

S. Z.

Kachooi

Samadi

(2015). Attention Deficit Hyper Activity Disorder (ADHD) and Stress: A mutual relationship between children and mothers. Basic and Clinical Neuroscience, 6(2), 113–121.

40.

Schieve

L. A.

Boulet

S. L.

Kogan

M. D.

Yeargin-Allsopp

Boyle

C. A.

Visser

S. N.

Blumberg

S. J.

Rice

(2011). Parenting aggravation and autism spectrum disorders: 2007 National Survey of Children’s Health. Disability and Health Journal, 4(3), 143–152. https://doi.org/10.1016/j.dhjo.2010.09.002

41.

Shuai

Zheng

Wang

Qiu

Xia

Cao

X. T.

Zhang

(2021). Influences of digital media use on children and adolescents with ADHD during COVID-19 pandemic. Globalization and Health, 17(1), 48. https://doi.org/10.1186/s12992-021-00699-z

42.

Strine

T. W.

Lesesne

C. A.

Okoro

C. A.

McGuire

L. C.

Chapman

D. P.

Balluz

L. S.

Mokdad

A. H.

(2006). Emotional and behavioral difficulties and impairments in everyday functioning among children with a history of attention-deficit/hyperactivity disorder. Preventing Chronic Disease, 3(2), A52.

43.

Theule

Wiener

Tannock

Jenkins

J. M.

(2013). Parenting stress in families of children with ADHD: A meta-analysis. Journal of Emotional and Behavioral Disorders, 21(1): 3–17. https://doi-org.libproxy1.usc.edu/10.1177/1063426610387433

44.

U.S. Census Bureau. (2018). 2017 National Survey of Children’s Health Methodology Report. https://www.census.gov/content/dam/Census/programs-surveys/nsch/tech-documentation/methodology/2017-NSCH-Methodology-Report.pdf

45.

Valkenburg

P. M.

Peter

(2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121–127. https://doi.org/10.1016/j.jadohealth.2010.08.020

46.

Van Der Oord

Bögels

S. M.

Peijnenburg

. (2012). The effectiveness of mindfulness training for children with ADHD and mindful parenting for their parents. Journal of Child and Family Studies, 21(1), 139–147. https://doi.org/10.1007/s10826-011-9457-0

47.

Van Der Oord

Prins

P. J. M.

Oosterlaan

Emmelkamp

P. M. G

. (2008). Efficacy of methylphenidate, psychosocial treatments and their combination in school-aged children with ADHD: A meta-analysis. Clinical Psychology Review, 28(5), 783–800. https://doi.org/10.1016/j.cpr.2007.10.007

48.

Van Der Oord

Prins

P. J. M.

Oosterlaan

Emmelkamp

P. M. G

. (2012). The adolescent outcome of children with attention deficit hyperactivity disorder treated with methylphenidate or methylphenidate combined with multimodal behaviour therapy: results of a naturalistic follow-up study. Clinical Psychology & Psychotherapy, 19(3), 270–278. https://doi.org/10.1002/cpp.750

49.

Visser

S. N.

Danielson

M. L.

Bitsko

R. H.

Holbrook

Kogan

M. D.

Ghandour

R. M.

Perou

Blumberg

S. J.

(2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003–2011. Journal of the American Academy of Child and Adolescent Psychiatry, 53(1), 34–46.e2. https://doi.org/10.1016/j.jaac.2013.09.001

50.

Waasdorp

T. E.

Bradshaw

C. P.

(2015). The overlap between cyberbullying and traditional bullying. Journal of Adolescent Health, 56(5), 483–488. https://doi.org/10.1016/j.jadohealth.2014.12.002

51.

Warren

Aloia

(2019). Parenting style, parental stress, and mediation of children’s media use. Western Journal of Communication, 83(4), 483–500. https://doi.org/10.1080/10570314.2019.1582087

52.

Wilcutt

E. G.

Nigg

J. T.

Pennington

B. F.

Solanto

M. V.

Rohde

L. A.

Tannock

Loo

S. K.

Carlson

C. L.

McBurnett

Lahey

B. B.

(2012). DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121(4), 991–1010. https://doi.org/10.1037/a0027347

53.

Wolraich

M. L.

Hagan

J. F.

Allan

Chan

Davison

Earls

Evans

S. W.

Flinn

S. K.

Froehlich

Frost

Holbrook

J. R.

Lehmann

C. U.

Lessin

H. R.

Okechukwu

Pierce

K. L.

Winner

J. D.

Zurhellen

(2019). Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 144(4), e20192528. https://doi.org/10.1542/peds.2019-2528

54.

Wood

C. E.

Skinner

A. C.

Brown

J. D.

Brown

C. L.

Howard

J. B.

Steiner

M. C.

Perrin

A. J.

Levine

Ravanbakht

S. N.

Perrin

E. M.

(2019). Concordance of child and parent reports of children’s screen media use. Academic Pediatrics, 19(5), 529–533. https://doi.org/10.1016/j.acap.2019.04.002