Exploring the Relationship of Dietary Intake With Inattention, Hyperactivity, and Impulsivity, Beyond ADHD

Abstract

Objectives:

This study investigates the association between dietary intake and ADHD diagnosis and its dimensions in adolescents.

Methods:

In the KOALA Birth Cohort Study, 810 adolescents aged 16 to 20 years provided information on ADHD diagnosis and completed a food frequency questionnaire. Dietary patterns were extracted using Principal Component Analysis. Parents reported on ADHD symptoms using the Conners’ Parent Rating Scale—Revised Short form, and the Impulsivity subscale from the Temperament in Middle Childhood Questionnaire.

Results:

The 80 adolescents with ADHD scored higher on the Snacking dietary pattern compared to those without ADHD, while they did not differ on Healthy, Animal-based, Sweet, or Beverage dietary patterns. All ADHD symptom scores (Hyperactivity, Inattention and Impulsivity, and ADHD-index) correlated with increased Snacking. Impulsivity was inversely related to Sweet dietary patterns and positively to Beverage dietary patterns.

Conclusion:

The results highlight the importance of considering ADHD dimensions beyond diagnosis in understanding adolescents’ dietary intake.

Introduction

ADHD is one of the most common neurodevelopmental disorders globally. It affects about 8% of children aged 3 to 12 years and 6% of adolescents aged 12 to 18 years (Salari et al., 2023; Sayal et al., 2018). ADHD, characterized by inattention, hyperactivity, and impulsivity, presents in various forms: predominantly inattentive, predominantly hyperactive/impulsive, and combined (American Psychiatric Association, 2013). The current study explores the relationship between dietary intake and ADHD in Dutch adolescents aged 16 to 20 years. While the current DSM-5 system offers a binary classification (ADHD yes/no), a dimensional perspective on ADHD examines symptoms (e.g., deviating levels of attention problems) across the disorder’s spectrum (Nigg et al., 2020). The current study adopts both approaches, also exploring ADHD symptoms independent of the diagnosis.

Dietary Behavior and ADHD

Despite numerous studies exploring this link, the exact nature of the relationship between dietary intake and both ADHD diagnosis and symptoms (in short: ADHD) remains unclear, as dietary behavior may act as both a cause and a consequence of ADHD. Recent research focused on identifying dietary patterns associated with ADHD, often comparing unhealthy diets rich in sugar and saturated fat, commonly referred to as the Western, or Westernized, diet, with high consumption of snacks, takeaways, and “junk” food, against healthier patterns characterized by high consumption of fruits, vegetables, fish, and whole grains (Shareghfarid et al., 2020). Systematic reviews and meta-analyses indicate that a healthy dietary pattern significantly decreases the risk of ADHD, while unhealthy patterns, such as the Western diet, are more commonly observed among children and adolescents with ADHD (Del-Ponte et al., 2019; Shareghfarid et al., 2020). Notably, the term “Western diet” is not exclusive to Western populations: studies linked ADHD to increased consumption of, for example, snack foods in children across various countries, including China (Yan et al., 2018), South Korean (Woo et al., 2014), Iran (Abbasi et al., 2019), Spain (Ríos-Hernández et al., 2017), and Norway (Oellingrath et al., 2014).

ADHD Symptoms as a Dimension

The cited studies predominantly treated ADHD as a binary condition, yet they also acknowledged its broad phenotypic variations (Lange et al., 2022). This suggest that considering ADHD symptoms as continuous variables could offer valuable insights because of their nuanced spectrum and varying severity levels. As this approach is relatively new, few have employed its relation to dietary intake. For instance, Maitre et al. (2021) analyzed data from six European countries and found that increased childhood consumption of ready-made foods and sweets correlated with higher ADHD index scores (combining inattention, hyperactivity, and impulsivity) in children. Investigating ADHD symptoms separately, studies showed that having more inattentive symptoms is linked to reduced vegetable and fruit intake (Park et al., 2012; Robinette et al., 2023), but increased consumption of sweets, salt (Park et al., 2012), and refined grains (Robinson et al., 2021) among school-aged children. The underlying mechanism of the link between inattention and reduced fruit and vegetable intake has not yet been explored. However, one explanation may be that maintaining a healthy diet requires constant planning, but those who are more inattentive are less likely to schedule their meals and thus, more often choose quick and convenient food options, which usually contain less fruits and vegetables.

Regarding hyperactivity and/or impulsivity, Lumley et al. (2016) found, for example, that higher impulsivity is linked to greater consumption of Western-style foods, particularly sugar-sweetened beverages, and take-away items, but this consumption was not related to other domains of ADHD. Furthermore, Park et al. (2012) observed that greater hyperactivity/impulsivity is associated with increased intake of sweetened desserts and salt. Similarly, Wiles et al. (2009) found an association between more hyperactive behavior and increased junk food consumption in children. However, Peacock et al. (2011), for example, found no association between hyperactivity and high junk food intake in children.

In line with the DSM-5, most of these studies grouped hyperactivity and impulsivity into one single dimension following a bi-factor model. Yet, more recent research suggested that the symptom network of ADHD is more complex than initially anticipated. Using network analysis, Martel et al. (2016) identified a single ADHD cluster during preschool years (ages 3–6 years), which then evolve into two separate clusters: inattention and hyperactivity/impulsivity during childhood (ages 6–12 years). Subsequently, these clusters further differentiate into the three main symptoms of ADHD described in the DSM-5—inattention, hyperactivity, and impulsivity—during adolescence (ages 13–17 years), a pattern that persists into adulthood (ages 18–36 years). Considering adolescents’ increased autonomy, impulsivity may have a more substantial impact on their dietary decisions, possibly elucidating the attenuated associations between diet and hyperactivity observed earlier. Hence, the present study examines hyperactivity and impulsivity as distinct dimensions.

Current Study

The objective of the current study is to examine the association between ADHD and dietary behavior in Dutch adolescents aged 16 to 20 years. ADHD is examined dichotomously based on diagnosis, while ADHD-like symptoms are also explored regardless of an ADHD diagnosis. To evaluate dietary behavior, dietary patterns are identified through principal component analysis. Given the ambiguity surrounding specific patterns and our inability to predict the precise dietary patterns that might emerge in our adolescent sample, the relationship between ADHD and dietary patterns is examined in an explorative manner.

Methods

Participants and Procedure

Data was derived from the prospective KOALA Birth Cohort Study in the Netherlands (Kummeling et al., 2005). From 2000 to 2002, healthy pregnant women with a conventional lifestyle (N = 2,343) were recruited from an ongoing study on pregnancy-related pelvic girdle pain. They were not selected on having pelvic girdle pain at the moment of recruitment, and we therefore consider the sample representative of the general population. Additionally, pregnant women with alternative lifestyles (N = 491) in terms of dietary habits, vaccination uptake, and/or antibiotic use, were recruited through alternative lifestyle channels. Parents filled out questionnaires during pregnancy and periodically after birth. For the current study, questionnaire data were collected in 2021 from the children (at that time between 16 and 20 years old, hereafter referred to as “child” or “adolescent” depending on context) and one of their parents (preferably the mother, if not possible the father or a guardian). Informed consent was obtained from both the parents and the adolescents.

Measures

Dietary Behavior

Dietary behavior was assessed through a 28-item list of selected foods and beverages in the categories of snacks, beverages, dairy products, plant-based products, fruits, raw vegetables, cooked vegetables, meat, fish, and eggs. Adolescents were asked to indicate how frequently they consumed each item per week (never; less than 1 day/week; 1 day/week; 2–3 days/week; 4–5 days/week; and 6–7 days/week). The scale was based on a dietary questionnaire used at earlier timepoints of the KOALA Birth Cohort study (Gubbels et al., 2012), but it was adapted to the children’s age and current market products (e.g., energy drinks).

ADHD Diagnosis

Adolescents were asked whether they had ever received a diagnosis of ADHD or predominantly inattentive subtype (formerly known as attention deficit disorder (ADD)), and if so, by whom (general practitioner, psychologist, psychiatrist, pediatrician, or other), at what age, whether they were still being treated, and how (medication, psychotherapy, or combined). As the term ADD is now distinguished as the ADHD predominantly inattentive presentation in the DSM-5 (Epstein & Loren, 2013), both diagnoses were combined into one “ADHD diagnosis” category (no; yes).

Conners’ Parent Rating Scale—Revised Short form

The parental questionnaire included the Conners’ Parent Rating Scale—Revised Short form (CPRS—R:S; Conners, 1997; Conners et al., 1998). The scale consists of 27 items on behavioral problems in children on a 4-point Likert scale (never; seldom; occasionally; often, quite a bit; very often, very frequently). Problems are summarized in three subscales: Cognitive Problems/Inattention (six items), Hyperactivity (six items), Oppositional (six items), each with a score range of 0 to 18; and one overall scale called “ADHD index” (ADHDi; 12 items, score range of 0–36), which includes the remaining items and three items from the Cognitive Problems/Inattention scale. The ADHDi has been found to accurately differentiate people with and without an ADHD diagnosis (Mueller et al., 1999). All scales were reported to have good reliability in children (coefficients for internal consistency ranging from .88 to .95; Kuny et al., 2013; Modesto et al., 2015; Troost et al., 2006). We translated CPRS—R:S to the Dutch language, and obtained written permission for its use in the KOALA study from the copyright holder Multi-Health Systems Inc. The parents were asked to retrospectively rate their child’s behavior during secondary school age (12–16 years), using this standardized questionnaire. The current study used the Cognitive Problems/Inattention, Hyperactivity, and ADHDi scales. We did not use the ADHDi to discriminate between children with and without ADHD as earlier research has done (Mueller et al., 1999) but as a comprehensive continuous scale. More exhibited symptoms are indicated by higher scores.

Temperament in Middle Childhood Questionnaire

The impulsivity scale from the parent-reported version of the Temperament in Middle Childhood Questionnaire (TMCQ) was used to measure impulsivity operationalized as speed of response initiation (Simonds & Rothbart, 2004). The applicability of the TMCQ in the Dutch population was supported by a previous validation study on a sample of 8- to 9-year-old children (Cronbach’s alpha of .78 for the Impulsivity Scale; Sleddens et al., 2013). Parents rated 13 statements on a 5-point Likert scale based on their child’s behavior between age 12 to 16 years (almost never; mostly not; sometimes yes, sometimes not; most of the time; almost always). Impulsivity scores were calculated as the mean of all items included in the scale (possible range 1–5).

Background Variables

The following background data were collected by mother-reported questionnaires: sex assigned at birth (hereafter: sex; male/female), maternal prepregnancy BMI (kg/m²), maternal smoking during pregnancy (no/yes), maternal alcohol consumption during pregnancy (no/yes), maternal age at birth (years), maternal education level (low/middle/high/other or unknown), birth weight (g), and gestational age at birth (weeks).

Data Analysis

Statistical analyses were conducted in SPSS v28. For comparison between groups, independent t-tests were used for normally distributed continuous variables, or Mann-Whitney U-tests in case of deviation from normality. Statistical significance was determined if p < .05. Internal consistency of the ADHD symptom scales was evaluated with Reliability analysis in SPSS v28, and confirmatory factor analysis with Jamovi 2.3.21.

To identify dietary patterns, principal component analysis (PCA) was conducted with orthogonal (varimax) rotation to simplify the factor structure and improve interpretability (Abdi, 2003). First, components with eigenvalues >1 were considered, then the break in the scree plot was examined, and finally, the interpretability of the detected patterns was evaluated. Items with absolute component loadings of >0.40 were regarded as part of the pattern (Field, 2009). Cross-loading items were not omitted.

Univariable and multivariable linear regression analysis was used to estimate the associations between each ADHD symptom scale (independent variables) and the dietary patterns (dependent variables). Standardized regression coefficients (with 95% confidence intervals) are reported because the standardization makes the effect sized scale-independent and thereby enables comparison of effect sizes between the ADHD symptom scales. All analyses were controlled for sex, recruitment group, maternal pre-pregnancy BMI, maternal smoking, and alcohol consumption during pregnancy, maternal age at birth, maternal education level, birth weight, and gestational age at birth. The data-analytic plan was not preregistered.

Results

Response and Baseline Characteristics

The total number of returned questionnaires was 880 from adolescents and 910 from parents, with an overlap of 824, resulting in complete information on all ADHD variables for 810 adolescent-parent pairs (overall follow-up rate 28% of the original birth cohort of N = 2,834). Response analysis (Table 1) indicates that slightly more female than male adolescents were involved in the current follow-up round. Additionally, the response rate was higher among families with high maternal education. The follow-up rate was also higher in the alternative recruitment group (191/491 = 39%) compared to the conventional recruitment group (619/2,343 = 26%). Nonetheless, sensitivity analysis showed no differences in results between these two groups.

Table 1.

Response Analysis Comparing Demographic Characteristics of the Original Cohort and the Current Study Population.

	KOALA birth cohort (2001–2003; N (%))	Complete data from parent-adolescent pairs for current study; N (%))
Participants, N (%)	2,834 (100)	810 (100)
Sex, Female	1,376 (49)	421 (52)
Maternal education
Low	289 (10)	42 (5)
Middle	1,060 (38)	253 (31)
High	1,341 (47)	477 (59)
Other or unknown	144 (5)	38 (5)

An ADHD diagnosis was reported by 80 adolescents with complete data, half of which was of the predominantly inattentive subtype. The baseline characteristics of the current study population by ADHD diagnosis are shown in Table 2. One-third of adolescents with diagnosed ADHD were female. Adolescents diagnosed with ADHD had a higher percentage of mothers who smoked during pregnancy, as well as a higher percentage of mothers and fathers who reported a history of mental health disorders or ADHD, compared to adolescents without an ADHD diagnosis.

Table 2.

Characteristics of the Current Study Population by Adolescent Self-reported ADHD Diagnosis.

			ADHD diagnosis
		No	Yes
Participants	Time point a	730	80
Age, mean (SD)	3	18.4 (0.7)	18.4 (0.7)
Sex, Male	1	394 (54%)	27 (34%)
Maternal education	1
Low		36 (5%)	6 (7%)
Middle		222 (30%)	31 (39%)
High		439 (60%)	38 (48%)
Other or unknown		33 (5%)	5 (6%)
Maternal smoking in pregnancy b	1	23 (3%)	7 (9%)
Maternal alcohol use in pregnancy c	1	135 (18%)	14 (18%)
Gestational age <37 weeks	1	18 (2%)	3 (4%)
Birth weight <2,500 g	1	25 (3%)	3 (4%)
Maternal age at birth, mean (SD)	1	32.7 (3.8)	33.0 (3.3)
Prepregnancy BMI (kg/m2)	1
<25		564 (77%)	61 (76%)
25–30 (overweight)		127 (17%)	15 (19%)
30+ (obese)		39 (6%)	4 (5%)
Maternal mental health problem d	3	93 (13%)	22 (28%)
Paternal mental health problem d	3	66 (9%)	13 (16%)
Maternal ADHD	3	9 (1%)	9 (11%)
Paternal ADHD	3	15 (2%)	8 (10%)

Note. SD = standard deviation.

^aTimepoints: 1: at birth; 2: child age 8 to 10; 3: adolescent age 16 to 20 years.

^bMother smoked in either the first, third, or both trimesters.

^cMother used alcohol in either the first, third, or both trimesters.

^dMother and father had at least one mental health disorder (burnout, anxiety, and/or depression).

ADHD Diagnosis, Clinical Characteristics and ADHD Symptoms

In most cases, adolescents with ADHD reported that the diagnosis was established by a psychologist, psychiatrist, or paediatrician, while in 15% the diagnosis was made by school professionals like educationalists (Table 3). About a quarter of them were still being treated with medication and/or psychotherapy at the time of the follow-up questionnaire (Table 3).

Table 3.

Clinical Characteristics of Adolescents With Self-reported ADHD Diagnosis.

Participants	80 (100%)
Who diagnosed you with ADHD? N (%)
Psychologist	40 (50)
Psychiatrist	19 (24)
Paediatrician	9 (11)
School	12 (15)
Are you still being treated for ADHD? Yes	18 (23)
If yes, what treatment do you receive?
Medication	8 (10)
Psychotherapy	4 (5)
Combined	6 (7)

Reliability analysis of the ADHD symptom scales from the CPRS—R:S and TMCQ showed that internal consistency was acceptable for the Hyperactivity subscale (Cronbach’s alpha .71); high for the Cognitive problems/Inattention subscale (.92) and for the ADHDi scale (.93); and good for Impulsivity (.88; Supplemental Information S1: Reliability analysis and Confirmatory factor analysis). The adolescents with an ADHD diagnosis had higher mean scores for all ADHD symptom scales (Cognitive problems/Inattention, Hyperactivity, Impulsivity and for ADHDi) than those without ADHD diagnosis (details in Supplemental Information S2, Figure S2). In multivariable regression analysis, the presence of an ADHD diagnosis was independently associated with Cognitive problems/Inattention and Hyperactivity, but not with Impulsivity (Supplemental Information S2, Table S2).

Clustering of Food Items

Five patterns were discerned in Principal Component Analysis, explaining 39.6% of the variance in the dietary intake data (Supplemental Information S3 Table S3). The first pattern included high intakes of regular (non-diet) soft drinks, fruit juice/drinks, fried snacks, chips, nuts or snacks, and energy drinks, and was therefore named the “Snack” pattern. The second pattern was named “Healthy” as it included high intakes of lettuce, raw vegetables, fruit, eggs, and tea. The third pattern, named “Animal based,” included animal dairy products, meat and chicken, and fish, and had a negative factor loading for plant-based dairy products. The fourth pattern was named the “Sweet” pattern because it included pastry, chocolate bars, candy bars, cake or biscuits, and sweets. The fifth pattern was named “Beverage” and included diet soft drinks, light fruit juice/drink, sports drinks, and energy drinks.

ADHD Diagnosis, ADHD Symptom Scores and Dietary Patterns

Adolescents with an ADHD diagnosis had a significantly higher mean score on the Snack pattern than those without ADHD (difference in mean scores = 0.42, 95% CI [0.19, 0.65]). Differences for the other dietary patterns were not statistically significant (p > .05).

Various significant associations between ADHD symptom scales and dietary patterns emerged in univariate linear regression analyses (Table 4, upper part). Higher scores for Cognitive problems/Inattention, Hyperactivity, Impulsivity, and ADHDi were all associated with the Snack dietary pattern. In addition, a higher score for Impulsivity was inversely related to the Sweet dietary pattern and positively to the Beverage dietary pattern. The Healthy and Animal based dietary patterns were not related to any of the ADHD symptom scales. In multivariable linear regression analysis (Table 4, lower part), the strength of the associations of Cognitive problems/Inattention and Impulsivity with the Snack dietary pattern remained approximately the same (standardized regression coefficients .09 (p < .05) and .14 (p < .001), respectively). However, Hyperactivity was not significantly related to this pattern anymore. The positive relationship between Impulsivity scores and the Beverage dietary pattern also remained significant (standardized regression coefficients = .11, p < .05), however, the association between Impulsivity and the Sweet dietary pattern was attenuated (Table 4, lower part).

Table 4.

Association of ADHD Symptoms With Dietary Pattern Scores.

Univariate linear regression (N = 804)	Standardized regression coefficients β a
	Snack	Healthy	Animal based	Sweet	Beverage
Inattention	0.13*** (r2 = .111)	−0.03 (r2 = .109)	−0.01 (r2 = .116)	−0.06 (r2 = .030)	0.06 (r2 = .020)
Hyperactivity	0.08* (r2 = .102)	−0.00 (r2 = .108)	−0.05 (r2 = .118)	−0.06 (r2 = .030)	0.05 (r2 = .019)
Impulsivity	0.16*** (r2 = .119)	0.03 (r2 = .111)	−0.07 (r2 = .120)	−0.08* (r2 = .033)	0.11** (r2 = .028)
ADHDi	0.13*** (r2 = .110)	−0.03 (r2 = .110)	−0.02 (r2 = .116)	−0.07 (r2 = .016)	0.05 (r2 = .019)
Multivariable linear regression (N = 803) b	Standardized regression coefficients β a
	Snack (r2 = .124)	Healthy (r2 = .113)	Animal based (r2 = .105)	Sweet (r2 = .034)	Beverage (r2 = .031)
Inattention	0.09*	−0.05	0.04	−0.02	0.03
Hyperactivity	−0.05	−0.00	−0.03	−0.02	−0.03
Impulsivity	0.14***	0.05	−0.06	−0.06	0.11*

^aStandardized regression coefficients are presented here because they make the comparison of effect sizes between the ADHD symptom scales comparable (e.g., one standard deviation increase of the score for Inattention and Impulsivity is associated with an increase of .09 and .14 standard deviations, of the Snack factor, respectively).

^bAll three ADHD symptom scores in one model, with adjusted for recruitment group (conventional/alternative), maternal pre-pregnancy BMI, maternal smoking, and alcohol consumption during pregnancy, maternal age at birth, maternal education level, gestational age at birth, birthweight, and child’s sex. The ADHDi scale was not included in this multivariable linear regression due to its high overlap with the Cognitive problems/Inattention subscale.

^*p < .05. **p < 01. ***p < .001.

Discussion

The objective of the present study was to explore the relationship between ADHD and dietary intake behavior in Dutch adolescents aged 16 to 20 years. Among the five dietary patterns identified (snack, healthy, animal-based protein, sweet, and beverage), increased levels of cognitive problems/inattention and impulsivity were associated with higher scores in the snack pattern, while impulsivity was also associated with the beverage pattern. Furthermore, adolescents who self-reported an ADHD diagnosis had significantly higher scores in the snack pattern compared to those without a diagnosis.

ADHD and the Snack Dietary Pattern

The observed association between higher impulsivity and more frequent consumption of items from the snack dietary pattern aligns with previous studies (Bénard et al., 2019; Coumans et al., 2018). Previous studies have also linked impulsivity to sugar-sweetened beverages (Lumley et al., 2016), which were also part of the snack pattern in the current study. However, unlike previous findings (Del-Ponte et al., 2019; Shareghfarid et al., 2020), none of the other dietary patterns were associated with measured ADHD symptom scales in the current study. The lack of a reverse association between ADHD symptoms, especially impulsivity, and the healthy pattern is unexpected and raises questions. As the majority of the current literature on this topic focuses on younger children, more research targeting adolescents is required to understand whether this association truly disappears with age, and to discover what is causing this discrepancy between age groups.

Although causality cannot be inferred from the present study design, it suggests that diet may not be as strongly influenced by ADHD symptom clusters during adolescence as it might in earlier stages of life. The increased impulsivity common in adolescents (Spear, 2000, 2013) may affect their food choices and preferences toward palatable foods, as indicated by previous research (Coumans et al., 2018). Considering the potential impact of youngsters’ diet on brain development (e.g., the mesolimbic dopamine pathway; Francis & Stevenson, 2013; Hartmann et al., 2020), early dietary patterns may contribute to the initial development and early symptom severity of ADHD, which is why elimination diets have been extensively studied as a potential treatment for ADHD (Pelsser et al., 2017). What is more, newer research has found that maintaining a healthy diet may be even more effective in lowering ADHD symptoms (Huberts-Bosch et al., 2024). Nevertheless, specific ADHD symptom clusters may, in turn, lead to poor or unhealthy dietary patterns, suggesting that the direction of the association between diet and ADHD symptom clusters may change with age. Also, during adolescence, this association seems to exist beyond ADHD as impulsivity is not a unique symptom of the disorder (Chamorro et al., 2012) and can also influence the food choices of those without an ADHD diagnosis. This finding may add to existing knowledge regarding the link between ADHD and food-related risk-taking behavior (Pollak et al., 2019) by further specifying that it may not be ADHD in general that is causing overeating and higher amounts of unhealthy food consumption, but rather impulsivity. Notably, the absence of a negative association between ADHD and healthy dietary patterns in adolescents in the present study, suggests that unhealthy eating habits may complement rather than replace a regular/healthy diet pattern by snacks, providing some reassurance.

ADHD Diagnosis and the Dimensional Approach

The findings underscore the potential benefits of adopting a dimensional perspective in conceptualizing ADHD, rather than relying on a dichotomous (yes/no) diagnosis. Despite employing the DSM-5 (American Psychiatric Association, 2013), our study’s separate investigation of hyperactivity and impulsivity aligns with previous research indicating that they form two distinct symptom clusters during adolescence (Martel et al., 2016, 2021). Importantly, impulsivity showed a stronger association than hyperactivity with both the snacking and beverage dietary patterns in the current study and showed no significant association with a reported ADHD diagnosis. Several factors may contribute to this. Firstly, the DSM-5’s criteria place greater emphasis on hyperactivity (i.e., it contains six items for hyperactivity and only three items for impulsivity), potentially influencing diagnosis outcomes. Thus, while it is possible to obtain a diagnosis by only exhibiting hyperactivity symptoms, only experiencing impulsivity symptoms without hyperactivity is not enough to reach the cut-off score (i.e., being clinically diagnosed with ADHD), which might explain some of the predominance of hyperactivity over impulsivity in adolescents with ADHD. Secondly, impulsivity may be more commonly observed in non-clinical populations. Further supporting this notion, while most participants without an ADHD diagnosis showed no to minimal hyperactive symptoms, they displayed some level of impulsivity (mean score above 1). This trend may be attributed to the typical increase in impulsivity during adolescence (Spear, 2013), coupled with a decline in hyperactivity symptoms (American Psychiatric Association, 2013) seen in both clinical and general populations. Martel et al. (2016) further support this, showing that in a sample including both individuals with and without ADHD, impulsivity symptoms were more central and interconnected during adolescence, while hyperactivity became less prominent. These trends may explain why impulsivity was more prevalent than hyperactivity in this study’s population, advocating for their separate consideration.

In sum, these findings emphasize the importance of considering both dimensional ADHD symptom measures and a categorical diagnosis in ADHD research, as each offer unique insights (Cabral et al., 2020).

Strengths and Limitations

One strength of the current study was applying a dimensional approach to assess ADHD symptoms independently of ADHD diagnosis. Specifically, considering impulsivity and hyperactivity as separate dimensions seems to be an important step toward acknowledging the disorder’s spectrum (Martel et al., 2016, 2021). Additionally, utilizing self-reported food consumption by adolescents instead of parent-reported consumption, acknowledges their autonomy in dietary choices, a notable aspect considering their age. Furthermore, focusing specifically on adolescents as a distinct group—which has not been studied extensively before—offers valuable insights tailored to this age group.

However, certain limitations warrant consideration. Firstly, reliance on self-reported food frequency questionnaires introduces the potential for socially desirable answers. Secondly, retrospective parental reporting of their child’s ADHD symptoms may be less reliable compared to real-time inquiries and may lead to recall bias. Third, selective non-response to the successive follow-up rounds of the KOALA study by parents and adolescents may compromise representativeness, for instance, by overrepresenting higher educated individuals who are more likely to follow a healthy diet (Mullie et al., 2010). However, it is difficult to predict whether this also distorts the associations between dietary patterns and ADHD symptom clusters. Fourth, another limitation of this study is the retrospective nature of parent reports on their children’s behavior during secondary school age (12–16 years). Retrospective reporting can be subject to recall bias, where parents may not accurately remember or may reinterpret past behaviors based on current circumstances. This potential bias should be considered when interpreting the findings. Fifth, we have operationalized impulsivity as speed of response initiation. However, since impulsivity is a multifaceted concept, future research should explore other types of impulsivity, such as response interference, to deepen our understanding of its relationship with dietary intake. And finally, because the data on diet and ADHD were collected cross-sectionally their relationship in time remains unclear, limiting causal interpretation.

Implications

The association between impulsivity and the snack dietary pattern implies that targeting adolescents’ impulsive behavior may decrease unhealthy food choices. Pavey and Churchill (2017) suggested that outlining the immediate health consequences over the long-term ones could benefit those with high impulsivity. Educating both adolescents and parents about this tendency among adolescents and offering strategies to promote healthier food choices could prove beneficial. However, as noted before, the absence of an association between ADHD and healthy dietary patterns in adolescents, as found in the present study, suggests that unhealthy eating habits do not necessarily replace a regular/healthy diet pattern with snacks in our study sample.

Conclusion

The current study found that impulsivity, rather than ADHD itself, exhibited the most robust link with dietary behavior among adolescents, notably through its association with increased snack consumption. Targeting adolescents’ impulsive behavior could notably influence their dietary choices, potentially offering substantial health benefits. Future longitudinal research is required to validate this relationship and to better understand how the associations between ADHD and dietary behavior evolve over time.

Author Biographies

Laura Dalnoki completed her MSc in Neuropsychology at Maastricht University with this study as a research thesis and works as a psychologist in a clinical setting.

Petra P. M. Hurks is a neuropsychologist examining inter- and intraindividual differences in cognitive and behavioral development, as well as the factors that influence them.

Simone J. P. M Eussen is a nutritional epidemiologist investigating the associations of dietary quality and timing in relation to health parameters.

Jessica S. Gubbels is a health scientist focusing on determinants of dietary intake and physical activity in children, as well as interventions intervening in these behaviors.

Monique Mommers is a biologist and epidemiologist studying the role of pregnancy and early life related factors in childhood health outcomes, with specific interest in underlying biological mechanisms.

Carel Thijs is a medical doctor and public health epidemiologist studying risk factors in early life for health outcomes at later age.