Diet,Physical Activity,and Screen Time to Sleep Better: Multiple Mediation Analysis of Lifestyle Factors in School-Aged Children with and without Attention Deficit Hyperactivity Disorder

Abstract

Objective:

This study examined the mediation roles of multiple lifestyle factors in school-aged children. Structural equation modeling (SEM) tested how lifestyle factors play mechanism roles one another in the impact of ADHD to seek theoretical and intervention insights.

Method:

An online survey assessed children’s lifestyle factors including diet, physical activity, screen time, sleep difficulties, and having ADHD diagnosis. A multi-country sample from English speaking nations included 309 caregivers. Multiple regression and SEM were planned to identify significant correlates and mediators of ADHD in explaining lifestyle differences.

Results:

Preliminary multiple regression showed only sleep quality was significantly different between children with and without ADHD. Significant triple mediation effects suggested diet, physical activity, and screen time mediated the ADHD impact on sleep quality.

Conclusion:

Researchers and practitioners may incorporate the findings to develop intervention models for children with ADHD attending to the mediational roles of lifestyle factors to improve sleep quality.

Keywords

attention deficit hyperactivity disorder lifestyle factors mediation effect school-aged children structural equation modeling

When support and interventions are concerned for childhood attention deficit hyperactivity disorder (ADHD), would there be any mediating roles among lifestyle factors such as diet, physical activity, screen time, and sleep in understanding the impact of ADHD on each of the life style variables? More simplistically instead, are they merely parallel correlates with ADHD? While approximately 7% of children have ADHD worldwide (Thomas et al., 2015) they experience difficulties in these lifestyle factors significantly more than their typically developing (TD) peers (Bowling et al., 2017; San Mauro Martin et al., 2019; Weiss et al., 2011), in addition to other cognitive and behavioral impairments (Becker, 2020). While a myriad of studies have shown the associations between ADHD diagnosis and lifestyle concerns (Holton & Nigg, 2016; Nikkelen et al., 2014; Ra et al., 2018; Tong et al., 2016) a practically and theoretically novel query arises if the various lifestyle factors are interwoven one another and embed any mediation roles in the impact of having ADHD. Identifying potential mediation mechanisms can be insightful to entangle the complications of managing ADHD symptoms and coping with everyday life routines in efforts to enhance their wellbeing and quality of life.

Specifically, recent research has shown that healthy dietary patterns (Heilskov Rytter et al., 2015; Mian et al., 2019) and behavioral treatment to improve the quality of sleep (Lucas et al., 2019; Sciberras et al., 2020) were beneficial to children with ADHD. Regarding dietary patterns, fat, fiber, and vegetable consumption as well as a Mediterranean diet, fish intake, and commercially baked food consumption have been shown associated with ADHD diagnosis (Mian et al., 2019; San Mauro Martin et al., 2019). A longitudinal Dutch sample of 3680 pre-school children aged 6 to 8 years showed having ADHD increased the risk to have unhealthy dietary patterns but not vice versa (Mian et al., 2019) suggesting lifestyle concerns are more likely to be resultant to ADHD rather than precedents. These findings are in parallel with the comorbidity issues with sleep problems in children with ADHD (Lucas et al., 2019; Moreau et al., 2014). Other studies with more comprehensive models of lifestyle factors showed that American children with ADHD were almost twice less likely to have fewer healthy lifestyle behaviors such as water intake, beverage consumption, screen time, physical activity, and quality sleep compared with children without ADHD even when demographic covariates and IQ were all controlled (Holton & Nigg, 2016; Reale et al., 2017; Tong et al., 2016). Other samples from Germany, Canada, Spain, and China also confirmed the associations of childhood ADHD with a poor diet, less physical activity and sedentary behaviours, and more screen time (Ko et al., 2012; San Mauro Martin et al., 2019; van Egmond-Fröhlich et al., 2012; Wu et al., 2016). According to these results, promoting children with healthier lifestyles may assist to deal with ADHD symptoms and daily functioning difficulties. Particularly with the recent exponential growth in the use of internet and mobile devices, screen time and over access to internet is also associated with ADHD and symptom severity (Weiss et al., 2011).

In terms of covariate variables to be controlled for in any modeling of these lifestyle factors, previous studies suggested demographic variables are relevant. Socio-economic status (SES) of caregivers is associated with the challenges and difficulties in children with ADHD (Rowland et al., 2018) reflecting a lower level in SES as indexed parental education and income related to poorer access to health services and support. Age and gender of children are correlated with sleep problems in children with ADHD like with other comorbidities (Reale et al., 2017).

Based on these theoretical and practical concerns, the current research attempted to integrate major lifestyle factors in a single study design to better investigate their mediational associations in children with and without ADHD in English speaking countries. Careers and parents were surveyed anonymously about their children’s lifestyle variables and potential covariates of SES and other demographics. The specific aims were to: 1) examine if childhood ADHD significantly explains the variance in lifestyle patterns of diet, physical activity, sleep quality, screen time and 2) explore mediation effects of specific lifestyle factors in understanding the impact of ADHD on any significant lifestyle challenges related to ADHD. Based on the literature, we hypothesized that the lifestyle factor scores would be significantly different requiring more attention in children with ADHD compared to their TD peers. Multiple mediations of the lifestyle factors in the impact of ADHD were also hypothesized. Because of the pioneering nature of this study and its reliance on empirical data, the mediations were explored without prespecifying which factors would play the mechanism roles on which factors. Instead all potential mediation models were tested and the model(s) were selected with the best fit to the data.

Method

Participants and Procedures

With the research ethics approved by the university human research ethics (No. 21068) a crowdsourcing platform called Microworkers was used to widely recruit eligible participants in English speaking countries, that is, New Zealand, United States of America, Canada, United Kingdom, and Australia. Previous studies have shown the reliability and validity of study findings from the samples through the crowdsourcing platform (Brown & Allison, 2014; Crone & Williams, 2017; Liu & Sundar, 2018; Miller et al., 2017; Pittman & Sheehan, 2016). The participants from Microworkers received a remuneration credit of 2USD to their account.

The exclusion criteria were that children have not been diagnosed with intellectual disability with less than IQ 70 (American Psychiatric Association & American Psychiatric Association, 2013), and no epilepsy experienced in the past year. These two conditions have been shown to substantially impact sleep quality and engagement in physical activity, and thus excluded in ADHD studies (Sciberras et al., 2016, 2019; Ziereis & Jansen, 2015). Eligible participants provided online consent before starting the survey in Qualtrics (http://www.qualtrics.com). Responses with less than 4 min in total were regarded as ingenuine and excluded from further analysis. After this cleaning procedure, 309 parents or caregivers were included in the final analysis dataset (statistical power = .82). Table 1 summarises the demographic information of the children as reported by their parent participants. The mean age of the parents was 34.61 (SD = 7.36) years and 52 % of them were male while both children groups were approximately 9 years old on average (SD = 1.11, 1.27 respectively) with more boys in the sample. United States of America (63%) with 18.5% of the children with ADHD in the US subgroup was the most frequent residency country followed by the United Kingdom (14%) with 7.7% of children reported having ADHD.

Table 1.

Demographics and Descriptive Statistics of the Children.

Variable	Children with ADHD(n = 54)	Typically developing children(n = 255)
Mean age year (SD)	9.35 (1.11)	9.43 (1.27)
Gender (%)
Male	30 (69.8%)	146 (64.0%)
Female	13 (30.2%)	81 (35.5%)
Other^a	0	1 (0.4%)
ADHD subtype (%)
Inattentive	14 (25.92%)	–
Hyperactivity-Impulsive	18 (31.03%)
Combined	11 (20.37%)
Not specified	11 (20.37%)
Residency (% of the ADHD and Typically developing groups in each country group)
Australia	0 (0%)	13 (100%)
New Zealand	0 (0%)	5 (100%)
United Kingdom	3 (7.7%)	36 (92.3%)
United States of America	32 (18.5%)	141 (81.5%)
Canada/North America	2 (9.5%)	19 (90.5%)
Other	2 (8.7%)	21 (91.3%)
Medication (%)
Adderall	4 (7.4%)	–
Methylphenidate	4 (7.4%)
Lisdexamfetamine	2 (3.7%)
Airway Medication	1 (1.9%)
Other-Psycho	1 (1.9%)
Other	4 (7.4%)
Not specified	38 (70.4%)

Note. ^aDue to the small number of the other gender, this single case was treated as missing in the later analysis to prevent statistical instability.

Measures

Demographic variables

Demographic questions included children’s age, gender, body weight, ADHD diagnosis, comorbidity status, medication, and other therapy treatments. Parent demographic items were about their current residency country, ethnicity, past or current psychiatric diagnosis, relationship status, annual household income, highest education level, and the parent’s having been diagnosed with ADHD.

Dietary patterns

The brief food frequency questionnaire (Buzzard et al., 2001) was used to ask parents about the dietary patterns of their children in the past week regarding the consumption of fat, fiber, and fruit or vegetable. This measure comprises 25 frequency items and 10 supplemental items to adjust the scores from the frequency items. Example items were “How often does your child eat pizza?” and “How often does your child eat fruit? (include all kinds-fresh, canned, cooked or dried fruits)” and responded on a Likert scale from 0 “Never” to 6 “3 or more times a day.” The fiber, fat, and vegetable nutrition components of each food item were retrieved from the AUSNUT 2011-13 food nutrient database (“AUSNUT 2011-13 food nutrient database”) and calibrated with the serving size from the Australian Dietary Guideline (National Health and Medical Research Council, 2013). The Cronbach’s alpha (α) was .91 in the present sample. The supplemental items were used to weight the total fat scores according to the method by Buzzard et al. (2001). For instance, the responses from 1 “Never” to 4 “Always” to the item “How often does your child trim fat from meat?” was used to multiply by as a weight for each fat score of the individual frequency item responses.

Physical activity

Weekly hours of physical activity were assessed with the Children’s Leisure Activities Study Survey-Parent Questionnaire, CLASS (Telford et al., 2004). This measure included 19 items to assess moderate intensity physical activity and 12 items for vigorous intensity physical activity. The reference timeframes of physical activities were during typical weekdays and over a typical weekend. To examine the reliability of internal consistency, the intraclass correlation coefficient (ICC) of all the binary, yes or no, variables was used as the indicator. The value of ICC in total activities was .8 in the current sample and the same as in the scale development sample (Telford et al., 2004), indicating an acceptable level of agreement among question items.

Sleep quality

Subjective sleep quality was assessed with the Children’s Sleep Habits Questionnaire, CSHQ (Owens et al., 2000), a 45-item parent-proxy measure regarding sleep behaviors of their child in the previous week. The final scoring used 33 items according to previous studies (Fletcher et al., 2018; Moreau et al., 2014). This measure has been used as a valid screening tool for sleep problems in children with ADHD (Lycett et al., 2015) both in community and clinical samples. The reliability of the 33 items, the Cronbach’s α coefficient, was .92 in this sample.

Screen time

Due to the lack of standard screen time measures for children, the research team developed five questions based on previous studies (Lucas et al., 2019; Parent et al., 2016; Tong et al., 2018). The questions were “How many types of electronic devices does your child use in her/his leisure time?”; “How many days would your child spend on these devices as entertainment, leisure, or relaxation tool over the past week?”; and “How long would your child spend time on these devices as entertainment, leisure, or relaxation tool in 1 day?”. The response options were less than half an hour, 30 to 59 min, 1 to 2 hr, more than 2 hr. The same questions were asked about 1 hr before bedtime both in terms of the type of devices and time spent on these devices. The method to calculate the total weekly screen time was referenced to a previous study (van Egmond-Fröhlich et al., 2012) and computed the average minutes per day of the responses on electronic devices used for 15, 45, 90, or 120 min. Next, the daily average time was computed by multiplying by the number of days using the electronic devices by the average daily time recorded. For instance, if the respondent answered 3 days for the use of the electronic devices and selected “1 to 2 hr” per day, the estimated weekly screen time was calculated as 90 min (the average of 1 to 2 hr) multiplied by 3 days. Hence, the total weekly screen time was 270 min.

Statistical analysis: Overview

First, stepwise hierarchical multiple regression was conducted with SPSS v.26 to examine if ADHD explained variance in dietary patterns, physical activity time, sleep quality, and screen time. Due to the unequal group sizes of the residency countries, five dummy variables were used to control for the country effects in the model (Hair, 2019). The reference country was set to United States of America because of the largest number of American residents in the sample. Other control variables including parents’ having an ADHD diagnosis were all taken into account. Since the normality assumption was violated in all the lifestyle outcome variables, the bootstrapping technique (Briere et al., 2017; DiCiccio et al., 1996; Hair, 2019; Johnson, 2001) was used to estimate coefficients of the explanatory variables in the hierarchical multiple regression.

Second, possible mediations among lifestyle factors were examined using Structural Equation Modeling (SEM). While correlations between ADHD and lifestyle factors have been well documented as reviewed above, further mediation paths have also been suggested in lifestyle literature. Specifically, diet, exercise, and screen time have been shown to impact on sleep problems (Hale & Guan, 2015; Tan et al., 2013). Hence exploring potential mechanisms among these lifestyle factors in the impact of ADHD, a series of mediation models (Hayes, 2018; MacKinnon, 2008) were compared in terms of their model fit and the finalist model was further analysed.¹ The Mplus default imputation option was used to deal with the missing data while the MLR (The maximum likelihood parameter estimation with robust standard errors) estimator was used to deal with the non-normality in the variables (Muthén & Muthén, 1998–2017).

Results

Descriptive statistics of all the lifestyle variables with their correlations in the present study are presented in Supplemental Table 1 . Since zero-order correlations are less indicative of complex relationships, further examination of the role of ADHD diagnosis in explaining the variance in the lifestyle variables was carried with multiple regression analysis. This analysis was to cross-validate previous studies that investigated the associations between the lifestyle factors and diagnosis of ADHD in school-aged children (Holton & Nigg, 2016). Furthermore, SEM was used to explore significant paths and mediation relationships among the lifestyle variables.

The multiple regression model approach was adopted to analyze the associations between the main explanatory variable of ADHD diagnosis and each of the lifestyle outcomes as dependent variables. Due to the large group size difference between the ADHD (n = 54) and non-ADHD (n = 255) groups, regression analysis was chosen over ANCOVA to deal with the concerning unequal variances between the two groups. Using stepwise hierarchical regression modeling, in each regression model all the covariates were accumulatively added for controlling. The explanatory variables were in (1) model 1: their residency countries in five dummy variables with the US as the reference country (2) model 2: children’s age and gender added (3) model 3: the education and income of the parent added (4) model 4: whether the parent had been diagnosed with ADHD added. In Model 5 finally the child’s ADHD diagnosis as either 0 “No” or 1 “Yes” was entered in addition to all the covariates included in the previous models. As lifestyle variables of sleep, screen time, diet, and physical activity time were not normally distributed, the bootstrapping technique was used to estimate the confidence intervals.

Multiple Regression Models

As shown in Table 2, only sleep problems were significantly explained by ADHD diagnosis (p = .001) so that 7.06 points higher CSHQ total scores (when a 0.22 standard deviation increase) were estimated for children with ADHD than children without ADHD. Approximately 12% of the total variance in sleep problem scores was explained by the regression model when all the covariates were also taken into account. While no covariate variable was significant in explaining sleep difficulties, significant covariates in the screen time model included children’s age (p = .001) and New Zealand (p = .039) with 6% of explained variance. One-year older age predicted 43.83 more minutes (0.21 more standard deviation) of screen time per week while living in New Zealand predicted 4.57 less hours (0.13 less standard deviation) as their weekly screen time compared with the US.² To progress from the limited modelling of each individual lifestyle variable, more comprehensive relationships between all the lifestyle variables were explored in explaining sleep problems using SEM as described in the following.

Table 2.

Standardized Regression coefficients (β) of Regression Analysis on Lifestyle Dependent Variables: Effects of ADHD Diagnosis with Demographic and Parent ADHD Diagnosis Covariates Controlled.

Variable	Dependent variables: β [b, LCI, UCI]
	Physical activity	Sleep problems	Screen time^c	DIET
	Physical activity	Sleep problems	Screen time^c	Fat score	Fiber score	Fruits/Vegetables
Childhood ADHD	−0.04	0.22** [7.06, 1.91, 12.07]	0.04	0.10	0.58	0.03
Age of children	0.09	−0.10	0.21** [150.09, –71.02, 514.06]	−0.06	−1.16	−0.06
Gender of children	0.04	−0.07	−.02	−0.11	−0.07	0.003
Parental ADHD	−0.04	−0.07	0.02	−0.14	−0.002	0.03
Parental SES^a	0.006	−0.12	−0.03	0.04	0.07	0.04
Country 1: Australia^b	−0.09	0.05	−0.03	−0.03	−0.02	0.02
Country 2: New Zealand^b	−0.14** [–1.66, –1.87, –1.43]	.04	−0.13**[–4.57, –5.75, –3.45]	0.21** [150.09, –71.02, 514.06]	0.12	.08
Country 3: United Kingdom^b	0.05	−0.03	−.05	−0.11	−0.08	−0.01
Country 4: Canada/North America^b	0.11	−0.08	−.03	−0.13** [–45.38, –63.30, –17.03]	−11	−0.05
Country 5: Other Countries^b	0.04	0.03	−.02	−0.07	−0.07	0.02
Model Fit	∆F (5, 251) = 2.51**	∆F (1, 238) = 11.84**	∆F (2, 244) = 5.64**	∆F (1, 243) = 5.47**	NS	NS
R ²	0.05	0.11	0.06	0.12	NS	NS

Note: β = Standardized coefficient; b = unstandardized coefficient; LCI = Bootstrapping estimated 95% Lower Confidence interval; UCI = Bootstrapping estimated 95% Upper Confidence interval, ADHD diagnosis: 0 “No,” 1 “Yes,” Gender: male = 1, female = 2, Physical Activity was coded as from 0 “Sedentary” to 2 “High level physical activity”(refer to the Method section); a = Parental income was computed from 0 (Low) to 2 (High) based on the median income in each country; b = The reference country was United States of America, c :Screen time was measured in minutes, but the b coefficients and LCI and UCI are presented in hours per week

p < .05. **p < .01. NS = Not Significant.

Structural Equation Modeling: The Mediation Model Explaining Sleep Difficulties

In exploring mediation paths among the lifestyle variables to explain sleep problems with ADHD diagnosis, four mediation models were tested as presented in Figure 1. Notably, diet was examined as a latent variable with three subfactors of the fat, fiber, and vegetable scores.

Figure 1.

Main paths of the tested models with structural equation modeling: Mediation models 1–4.

As shown in Table 3, the most parsimonious model was Mediation Model 2 achieving the best model fit out of the four models. In other words, the least difference of the model variance-covariance matrix from the data was achieved in Mediation Model 2. With 19% explained of the total variance by the model, sleeping problems were significantly explained by ADHD diagnosis (p < .01), diet (p < .01), and physical activity (b = −1.53, p < .01) directly while all control variables did not show any significant effects (See Table 4 and Figure 2). As reflected in the standardized coefficients (β), children’s diet was the strongest predictor of sleep problems followed by physical activity and ADHD diagnosis. In terms of mediation paths, the impact of childhood ADHD through physical activity and diet was marginally significant upon sleep problems (β = .007, p = .09). The inner mediations of screen time through physical activity on sleep problems was significant (β = .001, p < .05), although with a small effect size, suggesting a complete mediation of physical activity due to lack of the direct impact that was lost the model competition by Mediation Model 1. The direct path from diet through physical activity upon sleep problems was also significant (β = .04, p < .05) suggesting partial mediation of physical activity in the impact of diet on sleep problems due to the significant direct effect of diet.

Table 3.

Mediation Model Comparisons.

Models	Model fit ²	df	RMSEA	CFI	TLI	SRMR	R ²	∆²(∆df)
Model 1
Children ADHD → Diet → Physical activity & Screen Time → Sleep	37.248	34	0.02C.I.: .000–0.052	0.99	0.99	0.05	0.183	Reference
Model 2
Children ADHD → Diet & screen time → Physical activity & → Sleep	33.834	34	0.00C.I. 0.000–0.047	1.00	1.00	0.045	0.191	3.414 (0)
Model 3
Children ADHD → Physical activity & [Screen time] → Diet → Sleep	40.338	34	0.028C.I. 0.000–0.057	0.988	0.983	0.054	0.176	−3.09 (0)
Model 4
Children ADHD → Physical activity & [Screen time] → Diet → Sleep	61.081	39	0.048C.I. .022–0.071	0.962	0.945	0.066	0.147	−23.383 (5)
N		235	137	271	158	170	110	110

Note. RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; C.I. = 90% Confidence Interval.

Table 4.

Selected SEM Coefficients of the Finalist Model, Mediation Model 2: Direct and Indirect Effects of ADHD Diagnosis on Sleep Problems via Diet, Physical Activity, and Screen Time with Demographic Variables Controlled.

Predictors & paths	Coefficients
Predictors & paths	b	SE	β	p
Child ADHD diagnosis	5.61**	2.08	.170	.007
Child age	−0.79	0.55	−.90	.154
Child gender	−0.20	1.38	−.009	.154
Parental education	−0.17	0.55	−.019	.76
Parental income	−1.49	0.80	−.116	.06
Diet	0.33**	0.12	.24	.006
Physical activity	−1.53**	0.50	−.200	.002
Parental ADHD→ Child ADHD	0.27**	0.13	−.267	.005
Children ADHD → Diet	4.14*	2.09	.17	.048
Screen time → Diet	−0.003	0.002	−.101	.133
Screen time → Physical activity	0.001**	0.000	.193	.001
Diet → Physical Activity	−0.04**	0.011	−.198	.001
Children ADHD → Physical activity → Diet → Sleep	0.22**	0.13	.007	.009
Screen time → Physical activity → Sleep	−0.002*	0.001	−.04	.033
Diet → Physical activity → Sleep	0.054*	0.026	.04	.036

Note. β = Standardised coefficient; b = unstandardized coefficient; SE = Standard Error, Being diagnosed of ADHD: 0 “No,” 1 “Yes,” Gender: male = 1, female = 2, Parental income: computed from 0 (Low) to 2 (High) based on the median income in each country, Parental education: coded from 1 “Primary School” to 5 ‘Tertiary education’. Physical Activity: coded from 0 “Sedentary” to 2 “High level physical activity” (refer to Method section), Parental ADHD: 1 “No,” 2 “Yes,” Screen time: measured in minutes per week.

p < .05. ^**p < .01.

Figure 2.

Final mediation SEM of sleep problem with children be diagnosed with ADHD, dietary patterns, physical activity, total weekly screen time with standardized coefficients.

Discussion

While previous studies have shown that numerous lifestyle factors are associated with symptoms in children with ADHD (Holton & Nigg, 2016; Tong et al., 2016), little research has yielded mediation mechanisms in these associations (Becker, 2020). Integrative investigation of lifestyle factors in understanding childhood ADHD is of crucial interest to theorists, practitioners, policy makers, and caregivers to be informed about the focus factors and understanding the mechanisms to efficiently work on various lifestyle concerns. The present study surveyed parents of a school-aged child with and without ADHD in English speaking countries using comprehensive measurement tools in investigating physical activity, diet, screen time, and sleep problems. The present study considered all of these major lifestyle factors in a single study design and tested direct effects of ADHD upon the lifestyle concerns when multiple demographic variables and the parent’s ADHD status were considered. In addition, further mediation mechanisms among these lifestyle variables were examined. Multiple regression analyses showed that sleep problems were significantly associated in children with ADHD when all covariates were controlled. Subsequent structural equation modeling showed that diet and physical activity significantly mediated the effect of ADHD on sleep difficulties while screen time also influenced physical activity. These findings provide empirical insights with which for researchers and practitioners to design how intervention resources can be allocated covering various lifestyle factors in assisting management of ADHD symptoms. A precautionary point in this study is that the findings were correlational and require careful interpretation calling for future causal research designs.

As predicted for the lifestyle factors, childhood ADHD were positively correlated with scores of the total sleep problems as assessed by the CSHQ measure. This result from the step-wise hierarchical multiple regression is consistent with previous studies (Becker et al., 2018; Bundgaard et al., 2018; Lucas et al., 2019; Moreau et al., 2014) suggesting that the present multi-country sample cross-validated the prominent sleep difficulties in children with ADHD. While Holton et al. (2016) showed children with ADHD were less likely to have a healthy lifestyle compared with their TD peers, their outcome variable was a composite lifestyle index, a sum score of the binary “yes” or “no” healthy status in the water intake, sweetened beverage consumption, multivitamin/supplement use, reading, screen time, physical activity, and sleep which showed a significant difference between the two groups. However, the present study resulted in non-significant differences in other lifestyle variables of diet, physical activity, and screen time except sleep problems when each of the factors was separately examined. Although the substantial range of covariates including children’s IQ and comorbid conditions were controlled in Holton’s research, the present study had a distinct strength as it measured and controlled for the parents’ ADHD diagnosis. The inconsistency may be from the heterogeneity of the current sample despite the controlling for the residency country or the less comprehensive control of other covariates such as comorbidity. More controlled international study design may further advance our understanding on ADHD over this inconsistency in the future. The measurement of each lifestyle factors also has been far from consistency or consensus across studies in this field whereas other studies have also shown differences in screen time (Parent et al., 2016), dietary patterns (San Mauro Martín et al., 2018), and physical activity (Tandon et al., 2019) between these two groups. Hence, further future research should address and cross-validate the contradicting findings. Notably, the generalization of the present findings is thus limited so that future research is warranted to cross-validate all the significant and non-significant findings.

The next step analysis of the main SEM explored whether the lifestyle variables would have any mediation effects in the impact of children having ADHD upon their sleep problems. This modeling aimed to identify mediators that can be given more weight in terms of allocating resources and foci in designing ADHD treatment interventions. Accordingly, four exploratory mediation models were compared to identify the most suitable model in explaining the variance in sleep difficulties among children. In explaining sleep problems with ADHD, other lifestyle variables such as dietary patterns, physical activity, and total weekly screen time were variously modeled as possible mechanism mediators. Along with the parents’ ADHD diagnosis status, all other demographic variables such as age, gender, parental education and income were controlled in the SEMs. With the mediation model comparison, Model 2 was suggested as having the best model fit to explain the total variance in sleep problems. Except the impact of screen time on diet, all the hypothesized paths were significant or marginally significant in the mediation model. Specifically, having ADHD impacted diet which in turn significantly explained physical activity and sleep. The indirect effect from ADHD, diet, physical activity, sleep was only marginally significant, yet the relatively smaller sample size to the number of estimated coefficients (n = 29) suggests larger samples in further studies would yield significant results. The impact of screen time and diet on physical activity was significant while physical activity also explained sleep problems in turn. Other mediation models were not selected due to their worse model fit in the model comparisons, so the mediation path from ADHD to physical activity as the first mediator or the other path from ADHD to screen time as the first mediator was not supported. These findings imply that mediation effects may be significant particularly for the impact paths (1) from ADHD to dietary patterns, (2) from dietary patterns to engagement in physical activity, and (3) from screen time to physical activity that may in turn explain the variance in sleep problems as the final outcome lifestyle variable. These findings are consistent with previous literature on the significant impact of diet, exercise, and screen time have been on sleep problems (Hale & Guan, 2015; Tan et al., 2013). This set of evidence has been further extended with the present child sample with significant mediation paths especially in the impact of ADHD. Further future research can explore the exact effect of ADHD on diet in exploring ADHD intervention strategies to improve sleep problems. To illustrate, recent research findings on adulthood ADHD can also be applied to children. Hershko et al. (2019, 2020) alarmed the attractiveness and convenience of food items can exacerbate obesity issues in the clinical population with more responsiveness to advertisement and resultant unhealthy food choices than non-ADHD groups. With the present findings at this stage, the four lifestyle factors can be monitored in children with ADHD to arrange maximized intervention effects with the supported mediation model in mind while the measurement issues that are discussed above posit caution in interpreting and applying the current findings.

While the findings suggest complex mechanisms among lifestyle factors in understanding the role of ADHD in explaining sleep difficulties in children with ADHD, the present study addresses a notable finding on the impact of the parent’s ADHD diagnosis on their child’s ADHD. This result suggests both genetic and environmental factors as shown in the previous research (San Mauro Martín et al., 2018). When a parent has been diagnosed with ADHD, their child is more likely to have ADHD so this consideration should be modelled in all future studies.

A few limitations in the present study guides future directions. In particular, self-reported ADHD diagnosis used in this study is limited in terms of external validity while more rigorous clinical diagnosis process embedded at the recruitment stage is recommended (Holton & Nigg, 2016) for prospective studies. Furthermore, the sampling was limited due to the combination of two distinct recruitment sources. Although, the data validity for the studies with the Microworkers sampling has been evidenced (Crone & Williams, 2017; Liu & Sundar, 2018) more rigorous random sampling will bring more confidence in the results of future studies. Despite the proactive sampling strategy, the ADHD group had still a relatively small number of children in the sample. A balanced number of group sizes would yield more rigorous group comparison results, yet, the current sample may reflect closely to a naturalistic prevalence. More considerations on sampling methods and equal group sizes should be discussed further in the field.

Another limitation to this exploratory survey study represents the general measurement concerns in this field. It was challenging to find suitable measures assessing physical activity, dietary patterns, and screen time for school-aged children. Hence, it is urgent for researchers to develop validated scales to assess important lifestyle factors in children with ADHD adequately. Moreover, cross-validation between the results obtained from subjective self-reported from children or proxy-reports with objective physical measurements such as actigraphy monitoring (Fletcher et al., 2018; Moreau et al., 2014), is still needed to affirm the validity of the findings in the field (Burrows et al., 2020; Doma et al., 2019; Garriguet et al., 2015; Park et al., 2013). Although the survey measures show acceptable psychometric properties of reliability and validity, humans and parents are known sometimes biased and less consciously correct when compared with objective physical measurements (Burrows et al., 2019; Garriguet et al., 2015). Hence, further use of digital and portable tools to assess lifestyle factors will strengthen and advance our understanding of ADHD and treatment strategies (Lee & Suen, 2017).

Remarkably, the present findings call for further research with a more comprehensive and sophisticated research design to investigate the correlation and mediation among lifestyle variables, with a particular focus on childhood ADHD symptom severity. The issue of the smaller ADHD group size in the current dataset did not allow statistical power to explore any mediation effects in explaining the symptom severity with SEM. In a previous study lifestyle variables included physical activity, dietary patterns, media exposure, and the subscale of hyperactivity/inattention (HI) from the parent-rated Strengths and Difficulties Questionnaire (van Egmond-Fröhlich et al., 2012). The results from van Egmond-Fröhlich’s study indicated that the severity of ADHD symptoms were significantly associated with poor quality of diet and high media use in German children with age range from 0 to 17-year-old, however, it is worth noting they invested 3 years to reach the large sample (N = 17,641) in considering the statistical power. In the future, we would like to see more research exploring the mediation effects within integrated lifestyle factors and measuring the severity of symptoms in children with ADHD when achieving a large group of participants to perform SEM.

Foremost of the future directions would be aiming to conduct an interventional experimental design or longitudinal projects over time to test the advanced correlation effect in lifestyle patterns with identifying the mediation effect between the severity of symptoms in children with ADHD and lifestyle variables. The correlations and associations found in the present study should evolve for researchers to examine causational directions if interventions with lifestyle factors such as physical activity can ameliorate the severity of symptoms in children with ADHD.

Conclusion

The present study explored the associations of childhood ADHD with four lifestyle factors of diet, screen time, physical activity, and sleep problems. While supplementary interventions to pharmaceutical medications have been suggested through empirical research and treatment strategies, the present research particularly examined the relationships of lifestyle factors and ADHD with comprehensive modeling using a multi-country parent sample. The variance in sleep problems was significantly explained by childhood ADHD when covariates of child age, gender, parental ADHD, education, income, and residency countries were taken into account. Other lifestyle factors appeared as significant mediators in the impact of ADHD in explaining sleep difficulties. Specifically, diet, screen time, and physical activity may mediate the ADHD impact on sleep difficulties. The findings suggest possible weighted considerations for the mediation paths when designing ADHD interventions to treat symptoms and daily life difficulties. Notably, more rigorous longitudinal designs and experimental designs can claim the causal nature of the relationships in the future. To conclude the present study have shown not only significant mediation effects among lifestyle factors upon sleep problems in children with ADHD but also urges to incorporate the complexity and distinct weights in lifestyle factors into research and intervention designs. With more empirical ground, researchers may tailor more efficient management strategies and programs that aim to assist with ameliorating the severity of symptoms in children with ADHD.

Supplemental Material

Supplemental_material – Supplemental material for Diet, Physical Activity, and Screen Time to Sleep Better: Multiple Mediation Analysis of Lifestyle Factors in School-Aged Children with and without Attention Deficit Hyperactivity Disorder

Supplemental material, Supplemental_material for Diet, Physical Activity, and Screen Time to Sleep Better: Multiple Mediation Analysis of Lifestyle Factors in School-Aged Children with and without Attention Deficit Hyperactivity Disorder by George C.C. Hong, Russell Conduit, Jason Wong, Mirella Di Benedetto and Eunro Lee 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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

George C.C. Hong

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

George C.C. Hong, MSc, is a PhD candidate in Psychology at School of Health and Biomedical Sciences, RMIT University. He has conducted research projects in lifestyle factors among school-aged children with attention deficit hyperactivity disorder (ADHD) both in the quantitative and qualitative approaches during the candidature. His research interests including the association between mental health and lifestyle factors.

Eunro Lee, PhD, is a lecturer in Psychology at School of Health and Biomedical Sciences, RMIT University, Australia. Her research areas include social psychology, mental health and wellbeing, and applied statistics.

Russell Conduit, PhD, is a sleep technologist and senior lecturer in Psychology at the School of Health and Biomedical Sciences, RMIT University. He has worked in sleep research for the past 15 years. His research interests are in pediatric sleep health and the development of novel treatments for sleep disorders.

Jason Wong, PhD, is a lecturer at the School of Education, RMIT. His qualifications include a PhD in Physical Activity and Health. He was a PE teacher in Singapore and Australia, and has conducted physical activity research.

Mirella Di Benedetto, PhD, is a lecturer in Psychology at School of Health and Biomedical Sciences, RMIT University, Australia and a practicing health psychologist. Her research areas include health psychology, chronic illness, mental and physical health and self-care.

References

American Psychiatric Association, & American Psychiatric Association (Eds.). (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5th ed.). American Psychiatric Association.

AUSNUT 2011-13 food nutrient database. (n.d.). Retrieved August 29, 2019, from, http://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx

Becker

S. P.

(2020). ADHD and sleep: Recent advances and future directions. Current Opinion in Psychology, 34, 50–56. https://doi.org/10.1016/j.copsyc.2019.09.006

Becker

S. P.

Cusick

C. N.

Sidol

C. A.

Epstein

J. N.

Tamm

(2018). The impact of comorbid mental health symptoms and sex on sleep functioning in children with ADHD. European Child & Adolescent Psychiatry, 27(3), 353–365. https://doi.org/10.1007/s00787-017-1055-2

Bowling

Davison

Haneuse

Beardslee

Miller

D. P.

(2017). ADHD medication, dietary patterns, physical activity, and BMI in children: A longitudinal analysis of the ECLS-K study: Childhood ADH medication, lifestyle, and BMI. Obesity, 25(10), 1802–1808. https://doi.org/10.1002/oby.21949

Briere

Runtz

Eadie

Bigras

Godbout

(2017). Disengaged parenting: Structural equation modeling with child abuse, insecure attachment, and adult symptomatology. Child Abuse & Neglect, 67, 260–270. https://doi.org/10.1016/j.chiabu.2017.02.036

Brown

A. W.

Allison

D. B.

(2014). Using crowdsourcing to evaluate published scientific literature: Methods and example. PloS One, 9(7), e100647. https://doi.org/10.1371/journal.pone.0100647

Bundgaard

A.-K. F.

Asmussen

Pedersen

N. S.

Bilenberg

(2018). Disturbed sleep and activity in toddlers with early signs of attention deficit hyperactivity disorder (ADHD). Journal of Sleep Research, 27(5), e12686. https://doi.org/10.1111/jsr.12686

Burrows

Goldman

Rollo

(2020). A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labelled water. European Journal of Clinical Nutrition, 74(5), 669–681. https://doi.org/10.1038/s41430-019-0480-3

10.

Buzzard

I. M.

Stanton

C. A.

Figueiredo

Fries

E. A.

Nicholson

Hogan

C. J.

Danish

S. J.

(2001). Development and reproducibility of a brief food frequency questionnaire for assessing the fat, fiber, and fruit and vegetable intakes of rural adolescents. Journal of the American Dietetic Association, 101(12), 1438–1446. https://doi.org/10.1016/S0002-8223(01)00347-9

11.

Crone

D. L.

Williams

L. A.

(2017). Crowdsourcing participants for psychological research in Australia: A test of microworkers: Crowdsourcing data collection in Australia. Australian Journal of Psychology, 69(1), 39–47. https://doi.org/10.1111/ajpy.12110

12.

DiCiccio

T. J.

Efron

Hall

Martin

M. A.

Canty

A. J.

Davison

A. C.

Hinkley

D. V.

Gleser

L. J.

Lee

S. M. S.

Young

G. A.

DiCiccio

T. J.

Efron

(1996). Bootstrap confidence intervals. Statistical Science, 11(3), 189–228. https://doi.org/10.1214/ss/1032280214

13.

Doma

Speyer

Parsons

L. A.

Cordier

(2019). Comparison of psychometric properties between recall methods of interview-based physical activity questionnaires: A systematic review. BMC Medical Research Methodology, 19(1), 43. https://doi.org/10.1186/s12874-019-0684-1

14.

Fletcher

F. E.

Conduit

Foster-Owens

M. D.

Rinehart

N. J.

Rajaratnam

S. M. W.

Cornish

K. M.

(2018). The association between anxiety symptoms and sleep in school-aged children: A combined insight from the children’s sleep habits questionnaire and actigraphy. Behavioral Sleep Medicine, 16(2), 169–184. https://doi.org/10.1080/15402002.2016.1180522

15.

Garriguet

Tremblay

Colley

R. C.

(2015). Comparison of physical activity adult questionnaire results with accelerometer data. Health Reports, 26(7), 11–17.

16.

Hair

J. F.

(2019). Multivariate data analysis (8th ed.). Cengage.

17.

Hale

Guan

(2015). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 50–58. https://doi.org/10.1016/j.smrv.2014.07.007

18.

Hayes

A. F.

(2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

19.

Heilskov Rytter

M. J.

Andersen

L. B. B.

Houmann

Bilenberg

Hvolby

Mølgaard

Michaelsen

K. F.

Lauritzen

. (2015). Diet in the treatment of ADHD in children—A systematic review of the literature. Nordic Journal of Psychiatry, 69(1), 1–18. https://doi.org/10.3109/08039488.2014.921933

20.

Hershko

Cortese

Ert

Aronis

Maeir

Pollak

(2019). Advertising influences food choices of university students with ADHD. Journal of Attention Disorders, 1087054719886353. Advance online publication. https://doi.org/10.1177/1087054719886353

21.

Hershko

Cortese

Ert

Aronis

Maeir

Pollak

(2020). The influence of attractiveness and convenience cues on food appeal in adults with and without ADHD. Appetite, 150, 104679. https://doi.org/10.1016/j.appet.2020.104679

22.

Holton

K. F.

Nigg

J. T.

(2016). The association of lifestyle factors and ADHD in children. Journal of Attention Disorders, 108705471664645. Advance online publication. https://doi.org/10.1177/1087054716646452

23.

Johnson

R. W.

(2001). An introduction to the bootstrap. Teaching Statistics, 23(2), 49–54. https://doi.org/10.1111/1467-9639.00050

24.

C. H.

Yen

J. Y.

Yen

C. F.

Chen

C. S.

Chen

C. C.

(2012). The association between Internet addiction and psychiatric disorder: a review of the literature. European Psychiatry, 27(1), 1-8.

25.

Lee

P. H.

Suen

L. K. P.

(2017). The convergent validity of Actiwatch 2 and ActiGraph Link accelerometers in measuring total sleeping period, wake after sleep onset, and sleep efficiency in free-living condition. Sleep & Breathing = Schlaf & Atmung, 21(1), 209–215. https://doi.org/10.1007/s11325-016-1406-0

26.

Liu

Sundar

S. S.

(2018). Microworkers as research participants: Does underpaying Turkers lead to cognitive dissonance? Computers in Human Behavior, 88, 61–69. https://doi.org/10.1016/j.chb.2018.06.017

27.

Lucas

Mulraney

Sciberras

(2019). Sleep problems and daytime sleepiness in children with ADHD: Associations with social, emotional, and behavioral functioning at school, a cross-sectional study. Behavioral Sleep Medicine, 17(4), 411–422. https://doi.org/10.1080/15402002.2017.1376207

28.

Lycett

Sciberras

Mensah

F. K.

Hiscock

(2015). Behavioral sleep problems and internalizing and externalizing comorbidities in children with attention-deficit/hyperactivity disorder. European child & adolescent psychiatry, 24(1), 31-40.

29.

MacKinnon

D. P.

(2008). Introduction to statistical mediation analysis. Lawrence Erlbaum Associates.

30.

Mian

Jansen

P. W.

Nguyen

A. N.

Bowling

Renders

C. M.

Voortman

(2019). Children’s attention-deficit/hyperactivity disorder symptoms predict lower diet quality but not vice versa: Results from bidirectional analyses in a population-based cohort. The Journal of Nutrition, 149(4), 642–648. https://doi.org/10.1093/jn/nxy273

31.

Miller

J. D.

Crowe

Weiss

Maples-Keller

J. L.

Lynam

D. R.

(2017). Using online, crowdsourcing platforms for data collection in personality disorder research: The example of Amazon’s Mechanical Turk. Personality Disorders, 8(1), 26–34. https://doi.org/10.1037/per0000191

32.

Moreau

Rouleau

Morin

C. M.

(2014). Sleep of children with attention deficit hyperactivity disorder: Actigraphic and parental reports. Behavioral Sleep Medicine, 12(1), 69–83. https://doi.org/10.1080/15402002.2013.764526

33.

Muthén

L. K.

Muthén

B. O.

(1998–2017). Mplus user’s guide (8th ed.). Muthén & Muthén.

34.

National Health and Medical Research Council (Australia), Australia, & Department of Health and Ageing. (2013). Eat for health: Australian dietary guidelines ; providing the scientific evidence for healthier Australian diets. National Health and Medical Research Council.

35.

Nikkelen

S. W. C.

Valkenburg

P. M.

Huizinga

Bushman

B. J.

(2014). Media use and ADHD-related behaviors in children and adolescents: A meta-analysis. Developmental Psychology, 50(9), 2228–2241. https://doi.org/10.1037/a0037318

36.

Owens

J. A.

Spirito

McGuinn

(2000). The Children’s Sleep Habits Questionnaire (CSHQ): psychometric properties of a survey instrument for school-aged children. Sleep-New York-, 23(8), 1043-1052.

37.

Parent

Sanders

Forehand

(2016). Youth screen time and behavioral health problems: The role of sleep duration and disturbances. Journal of Developmental & Behavioral Pediatrics, 37(4), 277–284. https://doi.org/10.1097/DBP.0000000000000272

38.

Park

J. Y.

Vollset

S. E.

Melse-Boonstra

Chajès

Ueland

P. M.

Slimani

(2013). Dietary intake and biological measurement of folate: A qualitative review of validation studies. Molecular Nutrition & Food Research, 57(4), 562–581. https://doi.org/10.1002/mnfr.201200105

39.

Pittman

Sheehan

(2016). Amazon’s mechanical turk a digital sweatshop? Transparency and accountability in crowdsourced online research. Journal of Media Ethics, 31(4), 260–262. https://doi.org/10.1080/23736992.2016.1228811

40.

C. K.

Cho

Stone

M. D.

De La Cerda

Goldenson

N. I.

Moroney

Tung

Lee

S. S.

Leventhal

A. M.

(2018). Association of digital media use with subsequent symptoms of attention-deficit/hyperactivity disorder among adolescents. JAMA, 320(3), 255–263. https://doi.org/10.1001/jama.2018.8931

41.

Reale

Bartoli

Cartabia

Zanetti

Costantino

M. A.

Canevini

M. P.

Termine

Bonati

, & Lombardy ADHD Group. (2017). Comorbidity prevalence and treatment outcome in children and adolescents with ADHD. European Child & Adolescent Psychiatry, 26(12), 1443–1457. https://doi.org/10.1007/s00787-017-1005-z

42.

Rowland

A. S.

Skipper

B. J.

Rabiner

D. L.

Qeadan

Campbell

R. A.

Naftel

A. J.

Umbach

D. M.

(2018). Attention-Deficit/Hyperactivity Disorder (ADHD): Interaction between socioeconomic status and parental history of ADHD determines prevalence. Journal of Child Psychology and Psychiatry, 59(3), 213–222. https://doi.org/10.1111/jcpp.12775

43.

San Mauro Martín

Blumenfeld Olivares

J. A.

Garicano Vilar

Echeverry López

García Bernat

Quevedo Santos

Blanco López

Elortegui Pascual

Borregon Rivilla

Rincón Barrado

. (2018). Nutritional and environmental factors in attention-deficit hyperactivity disorder (ADHD): A cross-sectional study. Nutritional Neuroscience, 21(9), 641–647. https://doi.org/10.1080/1028415X.2017.1331952

44.

San Mauro Martin

Sanz Rojo

Garicano Vilar

González Cosano

Conty de la Campa

Blumenfeld Olivares

J. A

. (2019). Lifestyle factors, diet and attention-deficit/hyperactivity disorder in Spanish children—An observational study. Nutritional Neuroscience, 1–10. Advance online publication. https://doi.org/10.1080/1028415X.2019.1660486

45.

Sciberras

Bisset

Hazell

Nicholson

J. M.

Anderson

Lycett

Jongeling

Efron

(2016). Health-related impairments in young children with ADHD: A community-based study: Health-related impairments in young children with ADHD. Child: Care, Health and Development, 42(5), 709–717. https://doi.org/10.1111/cch.12363

46.

Sciberras

Mulraney

Mensah

Oberklaid

Efron

Hiscock

(2020). Sustained impact of a sleep intervention and moderators of treatment outcome for children with ADHD: A randomised controlled trial. Psychological Medicine, 50(2), 210–219. https://doi.org/10.1017/S0033291718004063

47.

Tan

Saarinen

Mikkola

T. M.

Tenhunen

Martinmäki

Rahikainen

Cheng

Eklund

Pekkala

Wiklund

Munukka

Wen

Cong

Wang

Zhang

Tarkka

Sun

Partinen

Alen

Cheng

(2013). Effects of exercise and diet interventions on obesity-related sleep disorders in men: Study protocol for a randomized controlled trial. Trials, 14, 235. https://doi.org/10.1186/1745-6215-14-235

48.

Tandon

P. S.

Sasser

Gonzalez

E. S.

Whitlock

K. B.

Christakis

D. A.

Stein

M. A.

(2019). Physical activity, screen time, and sleep in children with ADHD. Journal of Physical Activity & Health, 16(6), 416–422. https://doi.org/10.1123/jpah.2018-0215

49.

Telford

Salmon

Jolley

Crawford

(2004). Reliability and validity of physical activity questionnaires for children: The Children’s Leisure Activities Study Survey (CLASS). Pediatric Exercise Science, 16(1), 64–78. https://doi.org/10.1123/pes.16.1.64

50.

Thomas

Sanders

Doust

Beller

Glasziou

(2015). Prevalence of attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. Pediatrics, 135(4), e994–e1001. https://doi.org/10.1542/peds.2014-3482

51.

Tong

Xiong

Tan

(2016). Attention-deficit/hyperactivity disorder and lifestyle-related behaviors in children. Plos One, 11(9), e0163434. https://doi.org/10.1371/journal.pone.0163434

52.

Tong

Yan

(2018). The moderating roles of bedtime activities and anxiety/depression in the relationship between attention-deficit/hyperactivity disorder symptoms and sleep problems in children. BMC Psychiatry, 18(1), 298. https://doi.org/10.1186/s12888-018-1879-4

53.

van Egmond-Fröhlich

A. W. A.

Weghuber

de Zwaan

. (2012). Association of symptoms of attention-deficit/hyperactivity disorder with physical activity, media time, and food intake in children and adolescents. PloS One, 7(11), e49781. https://doi.org/10.1371/journal.pone.0049781

54.

Weiss

M. D.

Baer

Allan

B. A.

Saran

Schibuk

(2011). The screens culture: Impact on ADHD. ADHD Attention Deficit and Hyperactivity Disorders, 3(4), 327–334. https://doi.org/10.1007/s12402-011-0065-z

55.

Ohinmaa

Veugelers

P. J.

(2016). The influence of health behaviours in childhood on attention deficit and hyperactivity disorder in adolescence. Nutrients, 8(12), 788.

56.

Ziereis

Jansen

(2015). Effects of physical activity on executive function and motor performance in children with ADHD. Research in Developmental Disabilities, 38, 181–191. https://doi.org/10.1016/j.ridd.2014.12.005

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