Response inhibition in neurodiverse children and the association with excessive screen time use

doi:10.21203/rs.3.rs-4946366/v1

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Response inhibition in neurodiverse children and the association with excessive screen time use

https://doi.org/10.21203/rs.3.rs-4946366/v1

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The aim of this study was to examine response inhibition using a gamified version of the Stroop task in a heterogeneous cohort of neurodiverse and neurotypical children, and to identify any key risk factors of screen time associated with performance-based measures of cognition. A total of 229 participants ages 3-16 (89 neurotypical children [54% boys] 90 children with ADHD [51% boys], and 50 children with ASD [72% boys]) were recruited to the study. Using a validated online cognitive battery, participants completed the Stroop task. Parents completed questionnaires regarding children’s screen time use (passive TV watching, social media, video games), sleep, daily/weekly physical activities, socializing, reading, and extracurricular activities. Very few children in the study met national guidelines for screentime, regardless of their neurodiagnostic group (X²=3.71, p=0.16). Based on a multivariate model, performance on congruent and incongruent trials on the Stroop task were comparable between the groups, however autistic children were more likely to make more attempts on the tasks (F(2)=4.35, p=0.014), indicative of reduced impulse control. In a subsequent model examining screen time and other lifestyle factors in relation to performance on the Stroop task, increased video game use was a significant predictor of more attempts. An interaction analysis revealed that only autistic children who spent more time playing video games used more attempts on the Stroop task (B=0.095, p<0.001).

Conclusion: Autistic children demonstrated reduced impulse control compared to neurotypical children and children with ADHD. Further, time spent playing video games was associated with decreased response inhibition only in autistic children. Findings indicate that screen time use, particularly video games, may be a modifiable risk factor for response inhibition processes in autistic children. Findings could inform school- or community-based programs focused on screen time awareness and monitoring screen time use in neurodiverse children.

-Neurodiverse children experience challenges with response inhibition.

-Response inhibition is crucial for academic achievement and social functioning.

-Several lifestyle factors have emerged that may influence response inhibition.

-Excessive screen time use is an emergent risk factor for delayed cognition functions, particularly in neurodiverse children.

What is new?

Screen time, particularly video game use was associated with decreased response inhibition in autistic children.

Current estimates indicate that a growing percentage of children in Canada are identified as being neurodiverse, with recent studies indicating approximately 2% of Canadian children are autistic, and 5% of Canadian children are diagnosed with ADHD (Statistics Canada, 2019; Public Health Agency of Canada, 2018; Espinet et al., 2022). While autism and ADHD have distinct diagnostic criteria in the DSM-V, challenges with executive functioning (EF) skills have been seen among both groups (Benallie et al., 2021; Otterman et al., 2019; Carter et al., 2017). EF skills include a broad range of higher order cognitive skills such as response inhibition, self-regulation and mental flexibility (Fuhs, Farran & Nesbitt, 2015; Blijd-Hoogewys, Bezemer & van Geert, 2014). EF abilities play a significant role in the development of academic skills across core subject areas such mathematics, reading, and writing in school-aged children (Cheung & Chan, 2022; Fuhs, Hornburg & McNeil, 2016; Liu, Chung & Fung, 2019; Khng & Ng, 2021; Valcan et al., 2020; Fuhs et al., 2014). This means that neurodiverse children may be more vulnerable to lower academic achievement with increased EF deficits.

Inhibition is an EF skill that involves actively suppressing or delaying action or ignoring unimportant stimuli (Bjorklund & Harnishfeger, 1995; Clark, 1996). This ability becomes increasingly important as children develop, as it is a key skill in goal directed behaviour used within everyday life (Koglin & Petermann, 2004). Challenges with response inhibition have been associated with behaviour and emotional difficulties across various groups of neurodiverse children, including autistic children, preterm-born children, and children with ADHD (Schmitt et al., 2019; Réveillon et al., 2016; López-Martín et al., 2015; Hwang et al., 2016; Fernades et al., 2016). Studies have shown that both autistic children and those with ADHD have significantly lower response inhibition when compared to neurotypical controls, likely due to neurological alterations primarily in the prefrontal cortex (PFC) (Puiu et al., 2018; Saraçaydın et al., 2024; Gerhardt et al., 2021; Geurts, van den Bergh & Ruzzano, 2014; Kelly et al., 2021). As neurodivergent children are at higher risk for response inhibition difficulties, understanding the environmental risk and resilience factors that can impact the development of these skills is crucial.

Several risk and resilience factors related to response inhibition outcomes have been identified in both neurotypical and neurodiverse children. An increasing prevalent risk factor for the development of response inhibition in both neurodiverse and neurotypical children is excessive screen time. Screen time in children with and without neurodevelopmental disorders increased significantly during the pandemic (Hmidan et al., 2023; Choi et al., 2021, Poitras et al., 2017). Current national guidelines suggest children under 2 years old should not have any screen time, children between 2–4 years old should be limited to 1 hour/day, and children who are 5 years and older should be limited to 2 hours/day of screen time (Ponti, 2023). Concerns regarding excessive screen time use in both neurotypical and neurodivergent children have arisen, as this may be associated with delayed language acquisition (Al Hosani et al., 2023; Dong et al., 2021) and difficulties with executive functioning, including response inhibition (Lin et al., 2020; Soltani Kouhbanani et al., 2023; Law et al., 2023; Efraim et al., 2021).

In order to promote healthy cognitive outcomes in neurotypical and neurodiverse children, two main exploration strategies emerge: 1) preventing risk factors, and 2) promoting resilience factors. Within the literature, it is widely recognized that resilience must be viewed through an ecological framework, considering how internal and external factors interact simultaneously to promote positive outcomes (Rutter, 2006; Avdagic et al., 2020; Ungar, Ghazinour & Richter, 2013). Currently, there is a gap in the literature examining cognitive risk factor.

In our study, we explored how screen time is predictive of response inhibition in distinct groups of neurodivergent children, specifically those with ADHD or ASD. Response inhibition was measured using a Stroop task from a well-established and widely validated online cognitive test battery (Burns et al., 2010). The Stroop task is a valid and reliable method for measuring both response inhibition and working memory (Duell et al., 2018; Sugg & McDonald, 1994). Although online cognitive testing limits some standardization of testing procedures and control over the testing environment, it can increase accessibility to the research study and lower costs, while providing a similar level of accuracy when compared to traditional cognitive testing (Health and Medicine Week, 2023; Tuerk et al., 2023). Using an ecological model of resilience, we aimed to identify common lifestyle factors, which are predictive of performance on a response inhibition task in neurodivergent children.

In a heterogenous cohort of neurodivergent and neurotypical children, we addressed two main aims. The first was to examine differences in lifestyle factors including screen time use, in relation to current national guidelines in different age and neurodiagnostic groups. The second aim was to examine the association between hours of screen time (passive TV watching, social media, video games) and other lifestyle factors (sleep, outdoor time, extracurricular activities, free play, socializing) with performance on the Stroop response inhibition task in relation to diagnosis, adjusting for age and sex. We hypothesized that all children would exceed the amount of recommended screen time based on current national guidelines, and that screen time use would be higher in children with ADHD or ASD. We further predicted that screen time is a risk factor for response inhibition, and that performance on inhibition tasks will have a negative association with screen time for autistic children and children with ADHD.

Participants

Canadian, English-speaking children were recruited through the online platform Prolific to participate in a study to examine young children’s performance on cognitive tasks (http://prolific.com). Parents/guardians of potential participants were pre-screened to determine study eligibility through this platform. Parents were asked to provide the details on the neurodevelopmental disorder diagnosis their child had received. Exclusion criteria included diagnosed global developmental delay (GDD), fine or gross motor difficulties, other neurological or behavioral disorders (e.g., Obsessive Compulsive Disorder, Oppositional Defiant Disorder, sensory disorder). The study protocol was approved by the [Blinded for Peer Review] Research Ethics Board. Parents provided informed written consent and children provided written assent. The research was conducted in accordance with the Declaration of Helsinki.

Measures

Parent-report Survey

Prior to children completing the online cognitive tasks, parents were asked to complete an online survey regarding their child’s demographic information and relevant lifestyle factors. Parents were asked to report their child’s age, biological sex, and neurodevelopmental diagnosis (if any). Information surrounding their child’s lifestyle was also collected from parents to identify relevant risk and resilience factors associated with cognitive outcomes. The survey asked parents to rate lifestyle factors such as the amount of time the child spends completing extracurricular activities, physical activities, outdoor activities, socializing with peers in person, watching content on a screen, playing video games, socializing with peers online, reading, engaging in free play, and their average amount of sleep per night. The surveys were available through Qualtrics and took approximately 15 minutes to complete.

Online Test Battery

After their parents completed the online survey, children were asked to participate in the online cognitive testing. The test battery was provided through the web platform Creyos (http://www.creyos.com) and took approximately 40 minutes to complete. Testing included 13 unique tasks which were presented in the format of online games. Parents were encouraged to allow their child to complete the tasks independently, as the tasks were completed within the home. In the current study, we focused on the Stroop task.

The online Stroop Task (Stroop, 1935), Double Trouble, is a gamified test that measures cognitive inhibition. During the task, a target word (e.g., ‘RED’ or ‘BLUE’) appears at the top of the screen in red-coloured or blue-coloured font (Fig. 1). Participants must select one of two probe words from the bottom of the screen, which accurately describes the font colour of the target word. As in the original version of the task, the description and font colour can match, either the target word or the probe words are written in the opposite colour of what they described as, or both words are written in the opposite colour of what they describe. Scores represent the number of correct responses produced in 90 seconds. Incorrect answers deduct one point. Test-retest reliability calculated from a large sample of adults (N = 1151) indicated a strong association between two test periods and learning effects of 4.90% improvement.

The online cognitive battery has been developed by Creyos over the course of 25 years and has been validated in several large-scale studies totaling hundreds of thousands of participants in both clinical and non-clinical populations, from children to geriatric adults (Hampshire et al., 2012; Brenkel et al., 2017; Lopez et al., 2023; Tuerk et al., 2023). Performance on the tasks within the battery have well-established associations with measures of general intelligence (g) and has had comparable results to in-person cognitive assessments such as the Montreal Cognitive Assessment (MoCA) and the Wechsler Adult Intelligence Scale-Revised (WAIS-R) (Burns et al., 2010; Brenkel et el., 2017; Levine et al., 2013; Sandison et al., 2023).

In a previous study, enhanced audiovisual instructions were validated in focus groups of young children with neurodevelopmental disorders between the ages of 4–11 years old (Hennessy et al., 2023). During the validation study, parents rated 80–90% of task instructions as developmentally appropriate for their child, and 100% of children were able to complete the tasks correctly after receiving advanced instructions. These enhanced audiovisual instructions were used in the present study.

Statistical Analyses

Statistical analyses were conducted using SPSS (v27, IBM, Armonk, NY).

To address our first aim to examine the amount of screen time use in children compared to the national guidelines, we classified screen time data into two categories: those that met the current Canadian screen time recommendations for their age group (i.e., children under 2 years old = no screen time, children between 2–4 years old = 1 hour/day, and children who are > 5 years = 2 hours/day) and those who exceeded the amount of recommended screen time for their age group. To examine differences between children with ADHD, autism, and neurotypical children, we performed two Pearson Chi-Square tests: first between diagnostic groups, then between age groups (preschool, school-aged, adolescents). To better understand the types of media used during screen time, we use post-hoc testing to examine the types of screen time use in minutes. Screen time use was broken down into three categories: social media, video games, and passive screen time (i.e., watching a show or movie). To examine differences in the types of media used between diagnostic groups, we conducted a General Linear Model (GLM) to determine if the type of screen time use differed between diagnostic groups, while controlling for age.

To address our second aim concerning screen time use, diagnosis, and the association with performance on the Stroop task, we examined performance measures on the tasks in relation to the hours spent on screens. This was done using multivariate general linear model, with performance measures including reaction times on CC/CI/IC/II trials and number of attempts across all trial types. We subsequently examined the association of the performance measures in relation to screen time and lifestyle factors in a GLM. Models were adjusted for age and sex.

Participants

After pre-screening, 229 participants between the ages of 3–16 years old participated in the study. Of these participants, 89 children identified as neurotypical (n = 48 [54%] boys). Of the neurodivergent children, 90 reported a diagnosis of ADHD (n = 46 [51%] boys) and 50 children reported a diagnosis of ASD (n = 36 [72%] boys), and these sex ratios reflect what is typically observed in these groups.

Screen time and lifestyle factors

Lifestyle factors between the autistic children, children with ADHD, and neurotypical children, were examined in a multivariate GLM. No significant group differences were found (F(2) = 0.77, p = 0.78, Table 1), adjusting for age and sex.

Table 1

Minutes spent on lifestyle factors in neurodivergent and neurotypical children
	ADHD	ASD	TD	Total
N	90	50	89	229
Weekly extracurricular physical activities	186.5	164.94	161.35	172.02
SD	196.48	191.48	209.02	199.87
Weekly non-sport extracurricular activities	175.33	169.64	131.01	156.86
SD	370.72	258.31	199.92	289.56
Daily physical activities	177.72	116	139.16	149.26
SD	240.41	113.70	143.49	184.09
Weekly outdoor time	446.5	455.6	457.42	452.73
SD	405.50	392.22	521.19	449.57
Nightly sleep	519	521.1	528.93	523.32
SD	102.70	75.87	89.75	92.11
Daily Passive Screen Time	131.56	138.4	123.54	129.93
SD	113.05	93.78	103.00	104.91
Daily Social media	69.94	34.9	34.49	48.52
SD	201.76	57.18	66.33	136.35
Daily Video games	85.72	92.7	75.79	83.38
SD	98.76	77.55	81.65	87.84
Daily Reading	48.61	46.6	51.12	49.15
SD	50.36	42.35	49	48.01
Daily Free play	89.72	87.2	77.13	84.28
SD	97.06	88.509	57.48	81.67
Daily Socializing	198.02	108.2	181.92	172.15
SD	288.10	155.04	299.70	270.98
Number of minutes per week/night/day spent on lifestyle activities. Values reflect means and standard deviations (SD).

Overall screen time by neurodiagnostic groups

Very few children in the study met national guidelines for screentime, regardless of their neurodiagnostic group (X² = 3.71, df = 2, p = 0.16). Of the children within the ADHD group, 78% (n = 71) exceeded the recommended screen time use. Children within the ASD group had the highest number of children exceeding the recommended guidelines, with 88% (n = 44) of children not meeting screen time recommendations. In the neurotypical group, 74% (n = 66) of children were exceeding the recommended screen time based on parent-report.

Table 2

Meeting screen time guidelines by diagnosis
	Meeting screen time guidelines
	Yes, n (%)	No, n (%)	Total (n)
TD	23 (26)	66 (74)	89
ASD	6 (12)	44 (88)	50
ADHD	18 (22)	71 (78)	89

Screen time by age and neurodiagnostic groups

When examining differences in meeting guidelines (i.e., 2–4 years < 1 hour of screen time/day, >5years 2 hours of screen time/day) based on age group (preschool, school age, adolescents), significant group differences were evident. In preschool children, significant differences in terms of meeting screen time guidelines were evident based on a Chi-square test ((X² = 8.37, df = 2, p = 0.015, n = 43).

In preschool children, we found that the ASD group (100%, N = 11, X² = 4.9, p = 0.027) and the neurotypical group (N = 17, 94.4%, X² = 4.7, p = 0.03) had higher screentime compared to the ADHD group (N = 9, 64.3%), based on posthoc Chi-square tests.

Table 3

Meeting screen time guideline by age group and diagnosis
		Meeting screen time guidelines
		Yes n (%)	No n (%)	Total (n)
Preschool	TD	1 (6)	17 (94)	18
	ASD	0 (0)	11 (100)	11
	ADHD	5 (36)	9 (64)	14
School age	TD	21 (36)	39 (65)	60
	ASD	6 (17)	30 (83)	36
	ADHD	10 (15)	56 (85)	66
Adolescence	TD	1 (9)	10 (91)	11
	ASD	0 (0)	3 (100)	3
	ADHD	3 (33)	6 (67)	9

In school aged children, the proportions of children meeting guidelines also differed significantly by neurodiagnostic groups (X² = 8.03, df = 2, p = 0.018). Fewer school-aged children with ASD met guidelines (n = 30, 83.3%) compared to TD children (n = 30, 65%, p = 0.016). A comparable number of children with ASD and ADHD (n = 56, 83.6%) did not meet guidelines (p > 0.05).

In adolescence, no difference in meeting screen time differences were evident for adolescents (X² = 2.8, df = 2, p = 0.25). Very few adolescents in any of the diagnostic groups met guidelines.

As part of a post-hoc analysis, we used a multivariate model to examine differences in time spent (in minutes) in media types between the diagnostic groups, adjusting for age and sex. Preschool children with ASD spent more time using video games overall (F = 3.7, df = 2, p = 0.03). Preschool children with ASD spent significantly more time (mean difference = 52.66, p = 0.049) playing video games relative to preschool children with ADHD, based on pairwise comparisons.

Response Inhibition

When examining performance in the Stroop task, a main effect of NDD group was evident (F = 1.91, df = 434, p = 0.034). No significant differences in reaction time on the CC/IC/CI/II trials were observed between children with ADHD or ASD compared to TD children (Fig. 2A to 2D). However, the number of attempts differed significantly between group (Fig. 2E, F = 4.35, p = 0.014). Post-hoc t-tests revealed that autistic children made significantly more attempts compared to neurotypical children (p = 0.014, Bonferroni corrected).

We then performed a GLM to examine the association of number of attempts related to screen time (passive TV watching, social media, video games) and lifestyle factors. A significant main effect of diagnostic group was evident (X² = 8.3, p = 0.016). Children with ASD demonstrated a greater number of attempts (B = 8.79, p = 0.005) compared to TD children, an effect not seen in children with ADHD (B = 4.1, p = 0.1). Furthermore, time spent on video games was significantly associated with the number of attempts (B = 0.032, p = 0.03). An interaction analysis revealed a significant main effect for group x video games, whereby children with ASD who spent more time playing video games were more likely to demonstrate greater number of attempts on the Stroop (B = 0.095, p < 0.001). A significant main effect of biological sex was evident, where boys made more attempts than girls (B=-4.6, p = 0.048). No effect of age was evident in the model. No other lifestyle factors, which were included in the model were significant.

We examined the role of lifestyle factors including media screen time use on response inhibition in a large sample of both neurodiverse (autistic children and children with ADHD) and neurotypical children. No significant group differences in the amount of time spent in various lifestyle activities (sleep, exercise, extracurriculars, social activities, screen time) were evident; however, we found that a large majority of the sample was exceeding the recommended screen time use for their age groups (i.e., < 1 hours/day for preschool children, < 2 hours/day for children over 5 years of age). When looking at our three age groups, we found that significantly fewer preschool-aged children with ADHD reported excessive screen time compared to the ASD and neurotypical groups. Among school aged children; however, neurodiverse children in both the ADHD and ASD groups were significantly more likely to exceed screen time recommendations compared to the neurotypical group. Very few adolescents met screen time guidelines regardless of diagnostic status. When examining specific types of screen time (video games, social media, passive screen time) we found that preschool-aged autistic children spent the most amount of time playing video games, compared to neurotypical children and those with ADHD of the same age. When considering performance on the Stroop task, we found that autistic children used a significantly higher number of attempts than children in the neurotypical group, indicative of lower response inhibition. Autistic children who spend more time playing video games made more attempts when completing the response inhibition task.

Screen time and development

Overall screen time was high in all children and adolescents sampled, with no significant differences in terms of the time spent on passive TV watching, social media, and playing video games. However, when examining screen time use according to national guidelines, significant differences emerged between age groups. Of particular concern was that very few preschool aged children met guidelines, with no autistic children meeting guidelines from our sample. Excessive screentime (> 2 hours/day) is associated with negative outcomes such as increased behavioural, emotional, and psychological problems, particularly in preschool children (Domingues-Montanari, 2017; Cost et al., 2023; Byrne & Trost, 2021). Negative associations have been observed between increase overall screen time and cognitive development in children, including lower executive functioning skills, communication ability, and global cognitive functioning in neurotypical children (Rai et al., 2023; John et al., 2021; Gastaud et al. 2023). In autistic children, preliminary research has shown that excessive TV-watching is associated with delayed language development, particularly in language comprehension (Fridberg, Khokhlovich, & Vyshedskiy, 2021), and similar patterns were seen preschoolers with ADHD (Hill et al., 2020). Conversely, limiting screen time and replacing it with healthy lifestyle factors can benefit children within this area; higher levels of physical activity and low levels of screen time are independently associated with positive physical, psychosocial and cognitive outcomes in childhood (Poitras et al., 2017) Sleep is one such factor; individuals with disrupted sleep or sleep deprivation perform significantly lower on response inhibition tasks than those with consistent, uninterrupted sleep (Song et al., 2019; Nota, Schubert & Coles; 2016; Zhao et al., 2019). Regular, consistent sleep is particularly important for children, as it has been shown to moderate the relationship between response inhibition and increased self-regulation skills (Schumacher et al., 2017). Despite these associations, screen time has continued to increase in children of all ages, particularly since the start of the COVID-19 pandemic (Kerai et al., 2022; Byrne, Terranova & Trost, 2021; Choi, King & Duerden, 2023). The current sample was tested during the COVID-19 pandemic and may reflect global increases in screen time in children due to lack of access to resources and continued health and safety concerns with attending activities in person rather than virtually.

Response inhibition and screen time in autistic children

The results from our study showed that autistic children used the highest number of attempts when completing an online Stroop task. Functional neuroimaging of autistic children completing a similar Stroop task indicated that this may be due to a reduced hemodynamic response in the prefrontal cortex, which is the area of the brain primarily responsible for executive functioning (Uratani et al., 2019). This mechanism is of particular concern for autistic children, as lower response inhibition has been associated with behavioural outcomes in autistic children, such as increased restrictive and repetitive behaviours and interests (RRBIs) and mental flexibility (Kelly et al., 2021; Mostert-Kerckhoffs et al., 2015). The results of our study showed that screen time use, specifically time spent playing video games, was associated with lower response inhibition and higher number of attempts on the Stroop task. These results indicate there may be a higher level of impulsivity in autistic children with increased video game use, a pattern similar to what has been observed in neurotypical children and youth who spend excessive amounts of time playing video games (van Holst et al., 2012; Ko et al., 2014; Argyriou, Davison & Lee, 2017). The results of our study could indicate that response inhibition is more of a challenge for autistic children than those with ADHD; one possible explanation for this result is the impact of common ADHD medications on response inhibition (Kowalczyk, 2021) When considering interventions to prevent excessive video game use in autistic children, there may be a “sensitive period”, as one longitudinal study found that autistic children’s video game use increased significantly at the age of nine (Dahlgren at al., 2021). Future research should examine the role of video game use in relation to the development of executive functioning skills such as response inhibition and impulsivity throughout early childhood and adolescence in autistic children.

Limitations

Strengths of the current study include a large heterogeneous sample from an online platform where families were not limited to specific geographic regions, as is commonly required when conducting in-person cognitive testing. In terms of screen time use, the sample was also largely unbiased as participants were recruited for a study of children’s cognitive abilities. This is opposed to a focused study on screen time, which could have biased recruitment or parents’ responses.

Our study also had several limitations that should be noted. First, our sample relied on self-report for the diagnosis of ASD and ADHD. Parents were screened in advance of the study and were asked if their child had received a formal diagnosis of ASD or ADHD. Additionally, no measures of symptom severity were taken, which may impact both lifestyle and cognitive factors. Secondly, our while our overall sample size was well-powered, individual diagnostic groups were relatively small when considering different age groups. This may have made it difficult to determine whether the effects of lifestyle factors differed between groups. The cognitive performance measures we used were completed virtually, and parents were asked to be present while their child completed the online testing, but were instructed to have their children complete the testing as independently as possible. While the research team communicated this instruction to parents before participating, there was no way for us to monitor the level of parent involvement during the online cognitive battery. The online format of the testing also meant that children and parents were responsible for understanding each task's demands without asking for clarification from the research team. To combat this, we developed enhanced audio-visual instructions which were validated among a pilot group of children with neurodevelopmental disorders (Hennessy, 2023). While these instructions were highly rated for clarity and understanding among parents and children within the pilot study, there is still the chance that the child did not understand the task instructions. Finally, it should be noted that while the Creyos online battery has been widely used in large-scale studies and validated against in-person cognitive tests such as the MoCA and the WAIS-R, much of this data is from adults in clinical and non-clinical populations. While this battery has been used in research with children, the battery was not designed specifically for children, which limits our ability to interpret scores.

Furthermore, reading comprehension is an important component of the modified Stroop task used here, and was not measured within our study. While parents were asked to be present with their child and monitor the tasks which they were able to complete independently, there is the possibility that the ability to complete the Stroop task was affected by the participant’s reading level. Additionally, the preschool sample was relatively small. Future research in this area could focus on larger as well as longitudinal samples to further examine whether young children with neurodevelopmental disorders, particularly children with ASD may be more likely to spend time on screens as well as the long term sequalae.

Summary and conclusions

We found that screen time, specifically video game use, was associated with lower response inhibition in autistic children. This finding is particularly concerning as we found that regardless of neurodiagnostic group, most children in our study were exceeding the recommended amount of screen time for their age. Furthermore, autistic children are already at higher risk for experiencing reduced response inhibition, which is associated with poor behavioural outcomes. Future research should examine the longitudinal impacts of screen time and other lifestyle factors on executive functioning skills to prevent cognitive impairments on these children.

Ethics approval and consent to participate:

The study was approved by the Health Sciences Research Ethics Board. For participants younger than 18 years of age, participants’ parents provided consent by completing an electronic form on their child’s behalf.

Conflict of interest:

The authors declare that they have no conflict of interest.

Permission to reproduce material from other sources

Not applicable.

Consent for Publication:

The manuscript does not contain any images or material that would require approval for reproduction.

Funding:

Funding for the study was provided by the New Frontiers in Research Fund, MITACS, the Social Sciences and Humanities Research Council of Canada and the Canada First Research Excellence Fund.

Author Contribution

All authors were involved in the conceptualization of the stuy, data curation and analysis of the data. E.K. and E.G.D. wrote the main manuscript text. E.K. and E.G.D. prepared figures 1-2. All authors reviewed the manuscript.

Acknowledgement

The authors would like to thank Ella Christaans for providing research administrative support.

Data Availability

The data are available upon reasonable request.

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Response inhibition in neurodiverse children and the association with excessive screen time use

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Introduction

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Parent-report Survey

Online Test Battery

Statistical Analyses

Results

Participants

Screen time and lifestyle factors

Overall screen time by neurodiagnostic groups

Screen time by age and neurodiagnostic groups

Response Inhibition

Discussion

Screen time and development

Response inhibition and screen time in autistic children

Limitations

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