Explaining the Prospective Association of Positive and Negative Parenting Behaviors and Child ADHD Symptoms: Pathways Through Child Executive Function and Reward Responsivity

Abstract

Objective: Parenting behavior is a well-established correlate of offspring ADHD. However, little is known about how parenting exerts its effects on offspring ADHD symptomatology. We examined whether prospective associations between positive and negative parenting behaviors and child ADHD symptoms are mediated by deficits in child executive function (EF) and reward responsivity (RR). Method: One hundred and thirty-five children with and without ADHD were assessed across two Waves, when children were mean ages 6 and 8 respectively. Children completed tasks on EF, and parents completed questionnaires about their parenting behaviors and their children’s RR and ADHD symptoms. Results: Negative parenting behavior at Wave 1 was indirectly associated with offspring ADHD symptoms at Wave 2 via offspring EF. Conclusion: Individual differences in EF, but not RR, during early childhood may constitute a potential pathway by which negative parenting behaviors exerts its effects on subsequent offspring ADHD symptomatology. Treatment implications are discussed.

Keywords

parenting ADHD executive function reward responsivity longitudinal

Negative parenting behaviors are strong correlates and robust predictors of attention-deficit/hyperactivity disorder (ADHD) in children (Deault, 2010; Modesto-Lowe et al., 2008). Parents of children with ADHD are often more harsh, critical, and less involved with their children compared to parents of typically-developing children (Sonuga-Barke & Halperin, 2010). Furthermore, inconsistent parental discipline is associated with child ADHD symptoms, even after accounting for parent ADHD and other childhood disruptive behavior disorders (i.e., oppositional defiant disorder and conduct disorder) (Ellis & Nigg, 2009). Not surprisingly, negative parent behaviors are the target of most behavioral interventions for children with ADHD (Coates et al., 2015; Rimestad et al., 2019; Van der Oord et al., 2008). Yet despite the efficacy of behavioral parenting interventions for ADHD, the underlying mechanisms that explain this association are unclear, specifically as it pertains to how parenting behaviors exert their impact on offspring ADHD symptoms in the first place (Deault, 2010; Johnston & Chronis-Tuscano, 2015; Nigg, 2012). Gaining insight into this mechanism has clinical importance because it may help shed light on explaining why not all children and their families benefit from parent-based interventions for ADHD (Fabiano et al., 2015).

Importantly, the well-established association between negative parenting behavior and childhood ADHD goes beyond correlational evidence; parenting behavior has also been shown to play a direct role in the development of offspring ADHD and associated cognitive deficits (Deault, 2010). In animal models, maternal-offspring rat studies have shown how variations in maternal licking and grooming behavior affect brain and cognitive development in the offspring pups (Barha et al., 2007; Champagne et al., 2003). Human adoption designs have also provided strong evidence of the contributions that early parenting behaviors play in the development of offspring ADHD. Unlike traditional family designs that feature genetically related parent-offspring dyads, adoption studies have a distinct advantage of being able to disentangle shared genetic from non-genetic familial (i.e., parenting) influences on offspring ADHD (Harold et al., 2013; Sellers et al., 2021). Harold et al. (2013) used data from two independent adoption studies to show that parental hostility among adoptive mothers was associated with greater child ADHD symptoms. A more recent study using a longitudinal parent-offspring adoption design found that both genetic (birth mother ADHD symptoms) and environmental influences (adoptive mother and father hostile parenting) prospectively predicted child ADHD symptoms (Sellers et al., 2021). Collectively, this evidence strongly suggest that negative parenting behaviors play a critical role in the development of offspring ADHD. However, and as mentioned previously, relatively little is known about how parents exert their influence in the development of ADHD.

One possible mechanism that may explain how parenting behaviors influence the development of childhood ADHD symptoms is through deficits in child executive functions (EF). EF have been referred to as the cognitive processes used to respond to novel or ambiguous situations and to achieve goals (Hughes et al., 2004). Children with ADHD typically exhibit some form of an EF deficit (Barkley, 2002; Martel et al., 2007), but it is plausible that EF deficits may be exacerbated by poor parental behaviors in early life. From attachment theory, children are expected to expend cognitive resources in self-directed exploration of the environment, resulting in more opportunities for EF development (Lam et al., 2018). However, high levels of negative parenting behaviors may limit the development of children’s EF skills (Blair et al., 2014; Pechtel & Pizzagalli, 2011), which may precede (or exacerbate) their ADHD symptoms. Several studies have shown the early associations between negative parenting and offspring EF. For instance, maternal insensitivity and hostility were associated with offspring EF deficits as early as age 3 (Blair et al., 2011), and parental hostility was negatively associated with offspring EF at child age 4 (Hopkins et al., 2013). It is important to note that associations between parenting behavior more generally and offspring EF may vary when multiple dimensions of parenting behavior are considered. Holochwost (2013) found that positive parenting, operationalized as a composite of sensitivity, engagement, positive regard, animation, and stimulation, did not associate with general EF among preschoolers. Similarly, parental stimulation was unassociated with children’s general EF in early childhood (Nayena Blankson et al., 2011; Weber, 2011). Studies should examine variations in both dimensions of parenting behavior, including both negative and positive, and their associations with offspring EF and ADHD.

Another possible mechanism that may explain how parenting behavior influences childhood ADHD symptoms is through disruptions to the child’s reward responsivity (RR) (Becker et al., 2013). Children with ADHD symptoms have been shown to demonstrate aberrant sensitivities to rewards and reinforcement (Luman et al., 2005). For instance, children with ADHD symptoms tend to prefer smaller and immediate rewards over larger and delayed rewards (Sonuga-Barke et al., 2008). A recent meta-analysis found that there was an increase in inhibitory control task performance in children with ADHD (relative to children without ADHD) when rewards are presented (Ma et al., 2016), suggesting that youths with ADHD symptoms may have a heightened sensitivity to reward. Li (2018) found that RR was associated with more symptoms of ADHD among young children, but only under conditions of low negative parenting and high positive parenting. Variations in parenting behavior have also been shown to independently associate with children’s RR via neuroimaging studies (King et al., 2013). For example, Kopala-Sibley et al. (2020) found that parental hostility among 3 years-old children predicted greater ventral striatal sensitivity to reward of children 7 years later, suggesting that hostile parents may negatively impact their children’s intrinsic motivation for reward and desired goals. These studies suggest the plausibility that individual differences in children’s RR may be a constituent in mechanistic models of ADHD.

Importantly, disruptions to RR may be independent from EF deficits, suggesting that mechanistic models of ADHD symptoms involving EF and RR may not necessarily be “one in the same.” Sonuga-Barke (2002) proposed a dual pathway model in which there are two independent systems affecting ADHD symptoms: executive dysfunction grounded in dorsal frontostriatal dysregulation, and aberrant reward processing underpinned by disruptions in the ventral frontostriatum. Follow up research on this model suggest that deficits in these pathways may be ADHD-presentation specific. For example, Thorell (2007) found that EF deficits were more associated to symptoms of predominantly inattentive (ADHD-I) symptoms whereas delay aversion (i.e., a closely linked construct to RR) was more associated with predominantly hyperactive/impulsive (ADHD-H) symptoms among kindergarten children. Similarly, two studies showed children with ADHD-H presentations exhibited fewer EF deficits by late childhood and adolescence (Chhabildas et al., 2001; Schmitz et al., 2002) relative to children with ADHD-I symptoms (Chhabildas et al., 2001). Overall, these lines of research suggest EF and RR deficits may be differentiated, where deficits in either (or both) of these pathways may lead to differences in the development of ADHD in children (Sonuga-Barke et al., 2010). Our study extends this work by examining whether deficits in either of these pathways (EF and RR) may explain the prospective association between parenting behavior and child ADHD.

The present study utilized a well-characterized sample of 4 to 6-year-old children, with and without ADHD diagnoses, who have been followed at two time points (roughly 2 years apart) to test the hypothesis that the association between positive and negative parenting behaviors (measured at Wave 1) and ADHD symptom presentations (measured at Wave 2) are mediated by child EF and RR (measured at Waves 1 and 2). We examined ADHD symptoms dimensionally rather than as discrete categorical entities in order to account for heterogeneity and individual differences in ADHD (Li et al., 2016; Nigg et al., 2020). Additionally, because there may be differential associations between EF/RR deficits and ADHD presentations (Nigg et al., 2005), we separately tested sequential multiple mediation models for ADHD-I and ADHD-H symptom presentations. Based on prior evidence from the dual pathway model, EF deficits are hypothesized to mediate associations between both negative and positive parenting behaviors and ADHD-I symptoms specifically, and RR deficits are expected to mediate associations between both dimensions of parenting behaviors and ADHD-H symptoms.

Method

Participants and Procedures

The current study is part of an ongoing longitudinal study on biological and environmental predictors of externalizing behaviors in 4 to 6-year-old children, with and without attention and behavioral problems, who are followed over time. The baseline (Wave 1) sample included 210 children (mean age = 6.02, SD = 0.43) and their parents (93% parents were mothers). Participants were recruited through multiple resources, including via “backpack mail” delivered via children in local pre-kindergarten and kindergarten classrooms, child development research registries, social media, and via brochures and flyers distributed throughout local communities. Participants were ineligible to participate if they were previously diagnosed with an intellectual disability or autism spectrum disorder, did not live with a biological parent at least half of the time, or were not fluent in English. Crucially, the study was not designed to be case-control, but rather, enriched with young child participants with elevated levels of ADHD symptoms and other externalizing problems. These children are being followed prospectively to study the environmental and biological factors that predict the remission, development, and persistence of ADHD symptoms over time. Thus, at Wave 1, 20.5% of child participants met full diagnostic criteria for any presentation of ADHD (4.8% ADHD-I, 9.0% ADHD-H, and 6.7% ADHD-C), and an additional 26.2% were “subthreshold” for ADHD (defined as having met three or more symptoms within either the ADHD-I or ADHD-H presentations). One hundred thirty-five children returned for a follow-up approximately 2 years later (mean age = 8.35, S.D = 0.64), among which 20.7% met diagnostic criteria of any presentation of ADHD (7.4% ADHD I, 5.9% ADHD-H, and 7.4% ADHD-C) and 23.0% were subthreshold for ADHD. The present study used data from participants who completed the first and the second Waves of data collection. Among the final analytic sample, 56.3% of the children were boys, and 85.2% were White, 1.5% Black/African American, 2.2% Asian, 2.2% Latino/Latina, and 8.1% Multiracial American. Moreover, 79.2% of the parents in the study reported having a bachelor’s degree or higher. Parent-reported gross household income ranged from $70,000 to $400,000, with a median income of $115,000. The study sample is demographically representative of the community (i.e., small urban city in the United States with a very large public university).

Families who were eligible for the study were mailed a packet with questionnaires to complete before the laboratory visit. If the child was prescribed a stimulant medication for their ADHD, parents were asked to bring their children in on a medication wash of 24 hours prior to their visit. On the day of the laboratory visit, child participants were asked to complete a battery of age-appropriate EF tasks during both data collection waves. While the children were being assessed, a fully-structured clinical interview Diagnostic Interview Schedule for Children, Version IV (DISC-IV) (Shaffer et al., 2000) was administered by a second research assistant to a parent in a different room. Of the children who participated in our study, 92% of them were brought into our lab by their biological mothers. Fifty-four child participants had only mother-completed questionnaires and nine participants had only father-completed the questionnaires at Wave 2. The remaining child participants had both parents complete the same set of questionnaires (i.e., mother and father; n = 72 out of 135 at Wave 2). Child participants for whom both the mother and father provided data were combined (i.e., variables mean scored) as to maximize our available data for the current analysis. This analytic decision was also made as to not arbitrarily prioritize one informant over another. Nine of our child participants were from single parent households.

Measures

Parenting behaviors

Alabama parenting questionnaire (APQ)

Parenting behaviors were measured using the APQ (Shelton et al., 1996), which was assessed at Waves 1 and 2. It is comprised of 42 items by which parents self-reported on the frequency of parenting behaviors on a 5-point Likert scale (i.e., 1 = never–5 = always). There are five subscales, including parents’ level of involvement (e.g., “you ask your child about his/her homework”; 10 items), frequency of using positive reinforcement strategies (e.g., “you let you child know when he/she is doing a good job with something”; 6 items), parents’ self-reported frequency of using corporal punishment (e.g., “you slap your child when he/she has done something wrong”; 10 items), having poor monitoring (e.g., “your child is out with friends you don’t know”; 10 items), and being inconsistent in discipline (e.g., “the punishment you give your child depends on your mood”; 6 items) with their child. Based on previous factor analytic studies (Kaiser et al., 2011; Li & Lee, 2012), the positive parenting dimension of the APQ includes parents’ level of involvement and frequency of using positive reinforcement strategies with their child. The negative parenting dimension of the APQ includes the parents’ self-reported frequency of the use of corporal punishment, poor monitoring, and inconsistent disciplining of their child. Furthermore, higher scores on the positive parenting dimension represent more positive parental practices whereas higher scores on the negative parenting dimension represent less suitable parental practices. The APQ scales demonstrate a moderate internal consistency (Cronbach’s alpha ranged from .58 to .70) (Shelton et al., 1996) and a convergent validity with observed parent-child interaction measures (Chronis-Tuscano et al., 2011). The Wave 1 APQ scores were used in this study. Overall, 76% of the measure were completed by mother only, 4.8% of the data were completed by father only, and 18.6% were completed by both parents. When both parents completed the APQ, the mean score of two informants was used. Cronbach’s alpha values were .82 and .65 for the positive parenting (16 items) and negative parenting (26 items) dimensions, respectively.

EF measures

EF Touch

EF Touch (Willoughby et al., 2010) was assessed to children at Wave 1 and is a computerized-cognition battery of tasks that were specifically designed for preschool-aged children. A trained research assistant read the script displayed in one monitor while the child responded to stimuli using the other monitor. The full battery has seven EF tasks and two non-EF tasks (one warm-up/orientation and one simple reaction time). We used data from seven EF tasks: Spatial Conflict Arrows (36-item spatial conflict task measuring inhibitory control and cognitive flexibility), Silly Sounds Stroop (17-item Stroop-like task measuring inhibitory control), Animal Go/No-Go (40-item go/no-go task measuring inhibitory control), Working Memory Span (18-item span task measuring working memory), Pick the Picture (32-item task measuring working memory), Farmer (36-item task measuring visual spatial working memory), and Something’s the Same (30-item task measuring attention shifting and flexible thinking. Additional details for each task can be found in Supplemental Materials. The EF Touch has modest test-retest reliability (r = .60) and strong criterion validity, as scores on the EF-touch were robustly associated with ADHD symptoms and intelligence in 1,292 children (Willoughby et al., 2017). As recommended previously, scores for the seven EF tasks were combined into an EF composite score (Willoughby et al., 2018). For the current study, all tasks were scored as the percentage of correct responding. The average score was standardized to yield a composite z-score to represent global levels of EF, with higher scores indicating higher levels of EF.

NIH toolbox cognition battery (NIH-TCB)

Given that children in the study “aged out” of the EF Touch by their 1 year follow up, we used the NIH-TCB (Bauer & Zelazo, 2013) to measure comparable EF domains in children at Wave 2. The NIH-TCB is a developmentally sensitive battery of computerized assessments used to measure cognitive, emotional, sensory, and motor health from ages 3 to 89. Our study used the age 3 to 11 cognition battery. The NIH-TCB was normed using a U.S. sample of 1,020 typically developing children and adolescents aged 3 to 20 years (Akshoomoff et al., 2014). The battery consisted of seven tasks, including Dimensional Change Card Sort Test (a measure of cognitive flexibility), Flanker Inhibitory Control and Attention Test (a measure of inhibition and visual attention), Picture Sequence Memory Test (a measure of episodic memory), List Sorting Working Memory Test (a measure of working memory), Pattern Comparison Processing Speed Test (a measure of the speed of processing), Oral Reading Recognition Test (a measure of reading decoding skills), and Picture Vocabulary Test (a measure of receptive vocabulary). Additional details of the battery are described in Supplemental Materials. Age-corrected cognitive function composite scores were used in the current study, for which mean and standard deviation are 100 and 15, respectively. NIH-TCB have demonstrated adequate convergent and discriminant validity (Weintraub et al., 2013). The composite score also has shown acceptable internal consistency (Cronbach’s alpha = .77) and excellent test-retest reliability (r = .90) (Heaton et al., 2014).

RR measure

Sensitivity to punishment/reward responsivity questionnaire for children (SPSRQ-C)

SPSRQ-C (Colder & O'Connor, 2004) was assessed at Waves 1 and 2 to parents and is a 33-item measure of a child’s sensitivity to punishment and rewards, where items were rated on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree). This measure includes two composite scales: Sensitivity to Punishment (15 items) and Sensitivity to Reward (18 items). Sensitivity to Reward consisted of three subscales: RR, drive, and impulsivity/fun-seeking. Due to the overlap between the drive (i.e., “your child likes displaying their physical abilities even though it may involve danger”) and impulsivity/fun-seeking (i.e., “your child often has trouble resisting the temptation of doing forbidden things”) subscales and ADHD symptoms, the present study used only the RR subscale. The SPSRQ-C has demonstrated good external and convergent validity (Luman et al., 2012). For the current study, RR demonstrated high internal consistency at both time points (Cronbach’s alpha = .85 and .88, respectively).

ADHD symptoms

Vanderbilt Assessment Scale

The Vanderbilt Assessment Scale (NICHQ, 2002) was assessed to parents at Waves 1 and 2 and consists of DSM-5 screening criteria for ADHD, oppositional defiant disorder, conduct disorder, and a brief assessment for anxiety and depressive disorders. Parents were asked to rate on the frequency of these symptoms in their children over the past 6 months on a 4-point Likert scale, where 0 = rarely and 3 = very often. A score of 2 or 3 is coded as a positive endorsement of the symptom and a score of 0 or 1 indicates no presence of the symptom. Present inattention symptoms (9 items) were summed to derive an ADHD-I symptom score, and present hyperactive-impulsive symptom (9 items) were summed to create an ADHD-H symptom score. Forty seven percent of the scores were completed by mother only (n = 63), and 53% were completed by both parents (n = 72). In cases where both parents completed the measure, the average scores of both parent reports were used. The Vanderbilt Assessment Scale has demonstrated a high level of internal consistency and concurrent validity (Wolraich et al., 2003). ADHD-I and ADHD-H symptom scores were used in the current study, all of which showed excellent internal consistency (Cronbach’s alpha = .92 and .91, respectively).

Diagnostic Interview Schedule for Children, Version IV (DISC-IV)

ADHD symptoms were also assessed via DISC-IV (Shaffer et al., 2000) to parents at Waves 1 and 2. The DISC-IV is a structured, computer-assisted clinical interview conducted with parents based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for a range of disorders (including ADHD).¹ The DISC-IV requires the interviewer to ask questions and enter answers directly on the computer, and based on previous answers, a computer algorithm determines the questions, and ultimately, the diagnosis for the child. The ADHD module has demonstrated a good test-retest reliability (r = .79) and internal consistency (Cronbach’s alpha = .84) in a large community sample (Shaffer et al., 2000). The current study used a dimensional conceptualization of ADHD by summing the symptom count for child ADHD symptoms in all analytic models where higher scores represent higher levels of ADHD symptoms. ADHD-I and ADHD-H symptom scores were used in the current study.

Data Analysis

Analyses were performed in R version 4.0.3. Serial multiple mediator models using structural equation modeling (SEM) were tested to investigate longitudinal indirect and direct effects of predictors (i.e., positive and negative parenting) on the outcome (i.e., ADHD symptoms), via the effects of EF and RR measured between the predictor (at Wave 1) and the outcome (at Wave 2). Because it is plausible that there might be transactional contributions between EF and RR, we also estimated the pathways in our models (Castellanos et al., 2006; Sonuga-Barke, 2003). The effect of the predictor on the outcome is considered a “stage sequential” effect as predictor, outcome, and mediator are not required to be correlated as previously believed (Preacher & Hayes, 2008). ADHD symptoms were a latent construct as measured via two observed variables (i.e., DISC-IV and Vanderbilt Assessment Scale).

A total of four models were tested using SEM. Model fit indices were evaluated to assess goodness of fit. We used the Comparative Fit Index (CFI, good fit > 0.95), Tucker-Lewis Index (TLI, good fit > 0.95), Root Mean Square Error Approximation (RMSEA, good fit < 0.05), and the Standardized Root Mean Squared Residual (SRMR, good fit < 0.08). General model 1 examined the direct and indirect effects of positive parenting behaviors on child ADHD symptoms through the effects of EF and RR at Wave 1 and Wave 2, controlling for child sex, child ADHD symptoms at Wave 1, gross household income, and children’s age at second visit. Because we analyzed the presentations of ADHD separately, there were two sub-models under the general model, which examined the direct and indirect effects of positive parenting on latent dimensions ADHD-I and ADHD-H, respectively.² General model 2 examined the direct and indirect effects of negative parenting behaviors on children’s ADHD through the effects of EF and RR at Wave 1 and Wave 2, controlling for child sex, child ADHD symptoms at Wave 1, gross household income, and children’s age at second visit. Once again, we tested two sub-models reflecting the two latent ADHD presentations as separate outcomes.

Results

Descriptives

Table 1 presents means, standard deviations, and bivariate correlations of all study variables. Child ADHD-I and ADHD-H symptoms at Wave 2 measured by the DISC-IV were positively correlated with negative parenting (r = .20 and .18 for ADHD-I and ADHD-H, respectively; p < .05). Positive parenting was not correlated with Wave 2 ADHD measured from both the DISC-IV and the Vanderbilt Assessment Scale. Wave 2 Child ADHD-I symptoms measured by both DISC-IV and the Vanderbilt Assessment Scale were negatively correlated with child Wave 1 EF (r = −.47 and −.46, respectively; p’s < .001) and Wave 2 EF (r = −.27 and −.25, respectively, p’s < .05). Similarly, Wave 2 ADHD-H symptoms measured by the same two measures were significantly correlated with Wave 1 EF (r’s = −.41 and −.30, respectively, p < .001). Wave 2 ADHD-I and ADHD-H symptoms measured by the two measures were also positively correlated with Wave 1 and Wave 2 RR (r’s = .18 and –.43, p < .05).

Table 1.

Descriptive Statistics and Correlations for Study Variables.

Variable	n	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13
1. APQ positive parenting (W1)	131	66.19	5.98	—
2. APQ negative parenting (W1)	131	28.92	3.75	−.17	—
3. EF Touch EF (W1)	128	.08	.56	.13	−.26^**	—
4. NIH-TCB EF (W2)	118	108.74	13.92	.06	−.14	.45^***	—
5. SPSRQ RR (W1)	131	24.64	3.59	.12	.12	−.06	.11	—
6. SPSRQ RR (W2)	128	24.20	3.23	.13	.00	−.13	−.08	.63^***	—
7. DISC ADHD-I symptoms (W1)	135	1.70	2.47	−.06	.08	−.38^***	−.31^***	.41^***	.35^***	—
8. DISC ADHD-H symptoms (W1)	135	2.18	2.18	.03	.19^*	−.42^***	−.16	.52^***	.41^***	.60^***	—
9. Vanderbilt ADHD-I symptoms (W1)	129	1.31	2.21	−.03	.05	−.32^***	−.25^**	.44^***	.45^***	.77^***	.46^***	—
10. Vanderbilt ADHD-H symptoms (W1)	129	1.61	2.31	−.05	.11	−.36^***	−.20^*	.51^***	.45^***	.70^***	.73^***	.68^***	—
11. DISC ADHD-I symptoms (W2)	135	1.94	2.73	−.12	.20^*	−.47^***	−.27^**	.23^**	.37^***	.62^***	.62^***	.66^***	.60^***	—
12. DISC ADHD-H symptoms (W2)	135	1.97	2.58	−.02	.18^*	−.41^***	−.16	.30^***	.43^***	.49^***	.71^***	.42^***	.51^***	.62^***	—
13. Vanderbilt ADHD-I symptoms (W2)	129	1.33	1.97	−.06	.08	−.46^***	−.25^**	.18^*	.31^***	.61^***	.47^***	.66^***	.52^***	.76^***	.52^***	—
14. Vanderbilt ADHD-H symptoms (W2)	129	1.28	1.89	.05	.03	−.30^***	−.11	.23^**	.42^***	.50^***	.56^***	.58^***	.62^***	.55^***	.75^***	.61^***

Note. W1 = Wave 1; W2 = Wave 2; APQ = Alabama Parenting Questionnaire; EF Touch EF = EF Touch, executive function score; NIH-TCB EF = NIH Toolbox Cognition Battery, executive function score; SPSRQ RR = Sensitivity to Punishment/Reward Responsivity Questionnaire, reward responsivity score; DISC = Diagnostic Interview Schedule for Children; Vanderbilt = Vanderbilt Assessment Scale.

p < .05. **p < .01. ***p < .001.

Model 1: Positive Parenting on Child ADHD Symptoms via EF and RR

First, we examined the direct and indirect effects of positive parenting on the latent dimension of ADHD symptoms measured at Wave 2 through the effects of EF and RR at Waves 1 and 2. Overall, the models examining latent ADHD-I symptoms (CFI = 0.94, TLI = 0.86, RMSEA = 0.08, SRMR = 0.06) and latent ADHD-H symptoms (CFI = 0.92, TLI = 0.80, RMSEA = 0.11, SRMR = 0.07) showed acceptable fit.

Table 2 provides the standardized effects of positive parenting on child ADHD symptoms through EF and RR. Figure 1a provides the results of each path estimate in the model with ADHD-I symptoms as the outcome. Although positive parenting was not associated with latent ADHD-I symptoms with or without accounting for the effects of EF and RR, Wave 2 latent ADHD-I symptoms were associated with Wave 1 EF (β = −.41, 95% CI [−0.58, −0.23], p < .001) and Wave 2 RR (β = .23, 95% CI [0.04, 0.43], p < .01). There was no evidence of indirect effects of positive parenting on Wave 2 latent ADHD-I symptoms through the effects of EF and RR, but Wave 2 EF was significantly associated with Wave 1 RR (β = .18, 95% CI [0.02, 0.34], p < .01).

Table 2.

Standardized Results and 95% CIs of the Total and Specific Indirect Effects for Model 1 and Model 2.

	Model 1		Model 2
	Predictor: positive parenting		Predictor: negative parenting
	Std. Est	95% CI	Std. Est	95% CI
Outcome: Latent ADHD-I
Total indirect effects	−0.06	[−0.24, 0.11]	0.17	[0.04, 0.29]
W1 Parenting → W1 EF → W2 ADHD-I	−0.06	[−0.20, 0.08]	0.12	[0.02, 0.22]
W1 Parenting → W2 EF → W2 ADHD-I	0.01	[−0.03, 0.05]	<0.001	[−0.01, 0.02]
W1 Parenting → W1 EF → W2 EF → W2 ADHD-I	−0.01	[−0.03, 0.02]	0.01	[−0.03, 0.04]
W1 Parenting → W1 RR → W2 ADHD-I	<0.001	[−0.02, 0.03]	0.02	[−0.02, 0.06]
W1 Parenting → W2 RR → W2 ADHD-I	−0.01	[−0.05, 0.03]	−0.01	[−0.04, 0.03]
W1 Parenting → W1 RR → W2 RR → W2 ADHD-I	<0.001	[−0.02, 0.03]	0.02	[−0.01, 0.05]
W1 Parenting → W1 EF → W2 RR → W2 ADHD-I	<0.001	[−0.02, 0.01]	0.01	[−0.01, 0.02]
W1 Parenting → W1 RR → W2 EF → W2 ADHD-I	<0.001	[0.00, 0.00]	<0.001	[−0.01, 0.01]
Outcome: Latent ADHD-H
Total indirect effects	−0.07	[−0.25, 0.10]	0.15	[0.01, 0.30]
W1 Parenting → W1 EF → W2 ADHD-H	−0.05	[−0.18, 0.08]	0.10	[0.01, 0.18]
W1 Parenting → W2 EF → W2 ADHD-H	−0.01	[−0.05, 0.03]	<0.001	[−0.01, 0.01]
W1 Parenting → W1 EF → W2 EF → W2 ADHD-H	0.01	[−0.02, 0.03]	−0.01	[−0.05, 0.04]
W1 Parenting → W1 RR → W2 ADHD-H	<0.001	[−0.04, 0.04]	0.03	[−0.02, 0.08]
W1 Parenting → W2 RR → W2 ADHD-H	−0.02	[−0.08, 0.05]	−0.02	[−0.08, 0.05]
W1 Parenting → W1 RR → W2 RR → W2 ADHD-H	<0.001	[−0.04, 0.05]	0.03	[−0.01, 0.08]
W1 Parenting → W1 EF → W2 RR → W2 ADHD-H	−0.01	[−0.03, 0.01]	0.02	[−0.01, 0.04]
W1 Parenting → W1 RR → W2 EF → W2 ADHD-H	<0.001	[0.00, 0.00]	<0.001	[−0.01, 0.01]

Note. Bolded font represents statistically significant indirect pathways. W1 = Wave 1; W2 = Wave 2; EF = executive function; RR = reward responsivity; Std. Est = standardized estimate; CI = confidence interval.

Figure 1.

Structural equation model of positive parenting predicting Child ADHD symptoms: (a) outcome: ADHD-I symptoms and (b) outcome: ADHD-H symptoms.

Next, we examined the direct and indirect effects of the positive parenting on Wave 2 latent ADHD-H symptoms through the effects of EF and RR at Wave 1 and Wave 2 (Table 2). Results from this model were in line with the model with latent ADHD-I symptoms (Figure 1b). Positive parenting was not significantly associated with Wave 2 latent ADHD-H symptoms, whereas Wave 1 EF and Wave 2 RR was associated with Wave 2 latent ADHD-H symptoms (β = −.35, 95% CI [−0.54, −0.16], p < .001 and β = .43,95% CI [0.23, 0.63], p < .001, respectively). There was no evidence of indirect effects of positive parenting on latent ADHD-H symptoms through the effects of EF and RR.

Model 2: Negative Parenting on Child ADHD Symptoms via EF and RR

The direct and indirect effects of negative parenting on child Wave 2 ADHD symptoms through the effects of EF and RR at Wave 1 and Wave 2 were examined. The model with latent ADHD-I symptoms (CFI = 0.93, TLI = 0.84, RMSEA = 0.09, SRMR = 0.06) and latent ADHD-H symptoms (CFI = 0.89, TLI = 0.74, RMSEA = 0.12, SRMR = 0.07) showed acceptable fit to the data.

First, we examined the direct and indirect effects of negative parenting on child ADHD-I symptoms through the effects of EF and RR at Waves 1 and 2 (Table 2 and Figure 2a). With and without accounting for the effects of EF and RR, negative parenting was not significantly associated with latent ADHD-I symptoms. However, negative parenting indirectly influenced Wave 2 latent ADHD-I symptoms via the effects of Wave 1 EF (95% CI [0.02, 0.22], p < .05). Latent ADHD-I symptoms were significantly associated with Wave 1 EF and Wave 2 RR (β = −.44, 95% CI [−0.61, −0.26], p < .001 and β = .23, 95% CI [0.04, 0.40], p < .001, respectively). In addition, Wave 2 EF was significantly associated with Wave 1 RR (β = .17, 95% CI [0.01, 0.03], p < .05); no significant indirect effect of Wave 1 and Wave 2 RR was found.

Figure 2.

Structural equation model of negative parenting predicting child ADHD symptoms: (a) outcome: ADHD-I symptoms and (b) outcome: ADHD-H symptoms.

Next, we examined the direct and indirect effects of negative parenting on Wave 2 ADHD-H symptoms through the effects of EF and RR at Wave 1 and Wave 2 (Table 2 and Figure 2b). With and without accounting for the effects of EF and RR, the association between negative parenting and latent ADHD-H symptoms was not significant. Latent ADHD-H symptoms were significantly associated with Wave 1 EF and Wave 2 RR (β = −.35, 95% CI [−0.55, −0.16], p < .001 and β = .43, 95% CI [0.23, 0.62], p < .001, respectively). The indirect effect of negative parenting on Wave 2 latent ADHD-H symptoms through the effects of Wave 1 EF was significant (95% CI [0.01, 0.19], p < .05). There was no evidence of an indirect effect of negative parenting on Wave 2 latent ADHD-H through the effects of Wave 1 and Wave 2 RR.

Discussion

Negative parenting behavior is a well-established risk factor for offspring ADHD symptoms, but relatively little is known about how parenting behavior exerts its effects on offspring ADHD symptoms. This study tested the hypothesis that individual differences in child EF and RR may constitute as potential mechanisms underlying prospective associations between negative and positive parenting behaviors and offspring ADHD symptoms. We tested our hypotheses using sequential multiple mediation models in SEM from a well-characterized sample of children, with and without ADHD, followed at two time points approximately 2 years apart. No significant indirect effects of EF and RR between positive parenting behaviors and Wave 2 ADHD symptoms were observed. However, negative parenting and Wave 2 ADHD-I and ADHD-H symptoms were partially mediated via the effects of children’s EF at Wave 1. Our findings provide some evidence for the importance of considering individual differences in children’s EF as a possible mechanism by which negative parenting exerts its effects on offspring ADHD symptoms.

First, positive parenting was not significantly associated with either children’s EF, RR, or their Wave 2 ADHD symptoms. Although prior studies have also shown mixed results with respect to the association between positive parenting and EF (Bindman et al., 2013; Holochwost, 2013), we were surprised that positive parenting was not prospectively associated with ADHD symptoms (either directly or indirectly via EF/RR). One possible reason is that the APQ only assessed the frequency of parental involvement, whereas the quality of parental involvement may be just as crucial to consider as it pertains to developmental outcomes (Rogers et al., 2009). It is also possible that the APQ may not have been sensitive to other important forms of positive parenting, which may be more related to ADHD symptoms and EF/RR in children. For instance, autonomy support (i.e., scaffolding) is associated with positive development of child EF (Bernier et al., 2010), and parental emotional insensitivity is associated with child ADHD symptoms (Choenni et al., 2019). These constructs were not measured from the APQ. As our measure of positive parenting was limited to self-report, variation in this construct may be influenced by social desirability and other rater biases. Future research should consider other measures and dimensions of positive parenting behaviors and their contributions to child EF and RR development.

We did find that negative parenting was negatively associated with child EF at Wave 1, which in turn associated with both ADHD symptom presentations at Wave 2. These associations are broadly congruent with findings from previous EF studies (Blair et al., 2011; Hopkins et al., 2013) that have shown how negative parenting, such as harsh punishment and hostility, negatively associates with children’s EF development via altered brain development (Moriguchi, 2014). Importantly, EF not only partially mediated the association between negative parenting and ADHD-I symptoms, but also partially mediated the association between negative parenting and ADHD-H symptoms as well. EF deficits are predictive of a broad number of negative outcomes, and clearly beyond just a single presentation of ADHD (Chhabildas et al., 2001; Schmitz et al., 2002). This suggest that negative parental behaviors in early childhood robustly associate with the development of EF abilities at relatively early stages of development, which in turn may explain the development ADHD symptoms over time. Notably, child EF at Wave 2 was not associated with either presentation of ADHD. Although standardized lab-based cognitive tasks were used to assess children’s EF at both Waves, the task we used at Wave 1 (EF touch) differed from the task at Wave 2 (NIH toolbox). Even though both tasks have demonstrated good validity within younger samples, the EF Touch was specifically developed and tailored to assess EF abilities in pre-school and kindergarten-aged children (Willoughby et al., 2017, p. 18) whereas the NIH Toolbox was not (Bauer & Zelazo, 2013). Administration differences, along with the greater developmental sensitivity of the EF Touch, may help to explain why only Wave 1 EF associated with ADHD presentations in our study sample. Replication of the results using the same measure of EF over time would strengthen our findings by ruling out the possibility of differing measures effects.

Contrary to our expectations, we did not observe any indirect pathways between negative parenting and Wave 2 ADHD symptoms via RR. According to the dual-pathway model, we expected that RR would be associated with ADHD-H symptoms given prior literature showing that delay aversion was strongly related to hyperactive/impulsive symptoms (Thorell, 2007). One possible reason for our findings is that parenting behavior may not directly influence children’s RR but may instead be better explained in models of ADHD as a moderator between children’s RR and ADHD. Two prior studies have shown that children with heightened levels of RR tend to have more symptoms of ADHD, but only when they are under stress or adversity as a function of negative parenting (Becker et al., 2013; Li, 2018). It is also important to consider that there may be many other factors that mediate the complex association between parental behavior and offspring ADHD symptoms, including parent-child relationships (Lifford et al., 2008) and socioeconomic disadvantage (Russell et al., 2016).

Our findings should be interpreted with some limitations in mind. First, our primary measures were based on parents’ self-report. Our scales for negative and positive parenting, for instance, showed modest reliability despite demonstrating strong convergent validity in other studies (Chronis-Tuscano et al., 2011; Li & Lansford, 2018). As such, we acknowledge that some of our findings may have been affected by social desirability bias, whereby (for example) parents might over report on their positive parenting and under report on their negative parenting practices. Integration of multiple measures, such as observational or ecological momentary assessments, may help to address informant biases in future studies of parenting behavior. Second, the sample was predominantly white, which is representative of the surrounding community but may lack generalizability to other racial-ethnic groups. Our sample was of relatively high socioeconomic status as well. Although household income was accounted for in our models as a covariate, high socioeconomic status may have strong influences on both parenting behavior and children’s EF and RR that could not be strictly accounted via statistical controls (Vrantsidis et al., 2020). Thus, the current results may need to be replicated in socioeconomically diverse populations as well. Third, although the current study used a prospective longitudinal design, our sample sizes were still relatively small (given the attrition by Wave 2) and limited to two time points. Even though we controlled for Wave 1 ADHD symptoms, we note that offspring ADHD symptoms at Time 1 may still exert unaccounted for effects on parenting at Time 1. For instance, child ADHD symptoms may increase parenting difficulties (Burke et al., 2008) such that the pathways from negative parenting and ADHD symptoms may be due to transactional child-parent effects. Given this possibility, we refrained from drawing strong causal inferences regarding parenting effects on child EF and ADHD symptoms in the current study.

Despite these limitations, the results may have important clinical implications. Comprehensive neuropsychological tests are already a standard part of ADHD assessments, but they tend to be of little clinical use outside the diagnosis and treatment decision (Pritchard et al., 2012, 2014). Clinicians can assess for EF performance beyond the baseline assessment, as our findings suggest measures of EF (and neuropsychological performance more generally) may be potential markers of treatment progress (or lack thereof). Additionally, findings from our study provide an empirical basis for future studies to examine the possibility that child EF may be an underlying mechanism of change regarding the effects of behavioral parenting interventions for ADHD. We note that the evidence is still unclear as to whether targeting offspring EF deficits on their own will attenuate downstream ADHD symptoms in children. There have been several randomized controlled trials (RCTs) of cognitive training interventions for youths with ADHD, with meta-analyses showing either limited or substantially less efficacy in the direct reduction of ADHD symptoms relative to pharmacological interventions (Coates et al., 2015; Sonuga-Barke et al., 2013). At least one RCT examined the simultaneous effects of behavioral parent training and cognitive training for adolescents with ADHD (Steeger et al., 2016), but we are not aware of any research that has examined the indirect effects of parenting interventions on ADHD via offspring cognitive performance. Again, the current study provides some evidence that improvements to child EF might be a proximal consequence of behavior parenting interventions that ultimately affect child ADHD symptomology. In sum, our findings provide insights into how negative parenting behaviors eventuate into ADHD symptoms as a function of childhood EF deficits. Such advances in our understanding may help to explain why some children with ADHD are more responsive to parenting-based interventions than others, and eventually lead to more tailored interventions for these children.

Supplemental Material

sj-docx-1-jad-10.1177_10870547221104079 – Supplemental material for Explaining the Prospective Association of Positive and Negative Parenting Behaviors and Child ADHD Symptoms: Pathways Through Child Executive Function and Reward Responsivity

Supplemental material, sj-docx-1-jad-10.1177_10870547221104079 for Explaining the Prospective Association of Positive and Negative Parenting Behaviors and Child ADHD Symptoms: Pathways Through Child Executive Function and Reward Responsivity by Qi Zhang and James J. Li in Journal of Attention Disorders

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: The authors express deep gratitude to all the families that participated in the study. The study was supported by grants from Wisconsin Alumni Research Foundation and the UW-Madison Center for Human Genomics and Precision Medicine. J.J.L. was supported in part by a center grant to the Waisman Center from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD105353).

ORCID iD

James J. Li

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Qi Zhang is a doctoral student in the Department of Educational Psychology at the University of Wisconsin-Madison. She received a bachelor’s degree in social work from Beijing Institute of Technology a master’s degree in applied psychology from Peking University Health Science Center.

James J. Li, PhD is an Associate Professor in the Department of Psychology, a Waisman Center Investigator, and a faculty affiliate at the Center for Demography for Health and Aging at the University of Wisconsin-Madison. His research examines the interplay between biological and environmental factors underlying the development of childhood externalizing disorders.

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