The authors have declared that no competing interests exist.
Analyzed the data: AvEF DW MdZ. Wrote the paper: AvEF DW MdZ.
The aim of the study was to assess the association between attention deficit/hyperactivity disorder (ADHD) symptoms and potentially obesogenic behaviors.
Data of 11,676 German children and adolescents (6–17 years) were analyzed. Television/video exposure, physical activity, food frequency and portion size were assessed using questionnaires. A dietary quality index, energy density and volumes of consumed food, and total energy intake were calculated. The parent-rated hyperactivity/inattention subscale of the Strengths and Difficulties Questionnaire (SDQ-HI) was used as a continuous measure of ADHD symptoms. Associations were analyzed with general linear models adjusting for sex, age, socioeconomic status, migrant status, parental BMI, and parental smoking.
SDQ-HI scores correlated positively with physical activity, average energy density of food, volume of beverages, total energy intake, and television exposure and negatively with the nutritional quality score (HuSKY) even after adjustment for parental variables (BMI, smoking, socioeconomic status, migrant status), age, sex, as well as the other SDQ subscales. The adjusted association of the SDQ-HI scores with the nutritional quality score was stronger in girls and the associations with food volume, food energy, and total energy intake was significant only in girls.
Poor nutritional quality, high energy intake and television exposure appear to be independently associated with ADHD symptoms. The relationship between food energy intake and ADHD symptoms was especially pronounced in girls and this may help to explain the reported association of ADHD symptoms with overweight in adolescent girls.
Attention-deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity, and impulsivity
Motor hyperactivity, especially regarding size and variability of movements, seems to be a core feature of both the combined type and the hyperactive-impulsive type of ADHD
Television exposure may be associated with childhood obesity for several reasons. First it is a sedentary activity that reduces time spent in more active enterprises
It has been suggested, though not empirically proven, that impulsive children tend to overeat in an obesogenic environment
In one cross-sectional study in 375 school-aged children, ADHD was significantly associated with increased intake of sweets and fast-food measured by a semi-quantitative food-frequency questionnaire
To our knowledge no study has investigated the association between ADHD or ADHD symptoms and relevant behavioral parameters such as quality of nutrition, energy density of foods, or energy intake simultaneously while carefully controlling for putative confounding variables. It has been shown that parent-rated hyperactive behavior (SDQ-HI)
The cross-sectional German Health Interview and Examination Survey for Children and Adolescents (KiGGS) provides comprehensive and nationally representative data of children living in Germany. The KiGGS study allows the examination of the association between ADHD symptoms based on the parent-rated SDQ-hyperactivity-inattention subscale (SDQ-HI) and behaviors relevant for obesity such as television exposure, physical activity, energy density of food, and energy intake, while adjusting for a variety of possible confounding variables. A more detailed knowledge of the association and interaction between health behaviors, gender, individual risk factors (e.g. ADHD symptoms), and parental risk factors is required to design effective preventive and therapeutic interventions in childhood.
Based on previous results we expected that the SDQ-HI subscale score would be positively correlated with high television exposure, high energy density and energy intake, and negatively correlated with high dietary quality. According to our earlier findings
From May 2003 to May 2006, a total of 17,641 children and adolescents aged 0–17 years participated in the cross-sectional population survey KiGGS. The sample was derived from 167 representative German communities, as described elsewhere
Information about sociodemographic characteristics, living conditions, health, and health behaviors were obtained with self-administered questionnaires filled in by the parents
The present analysis is based on children and adolescents aged 6–17 years (n = 11,967). After excluding 291 subjects with missing data on the parent-rated SDQ-HI subscale and missing data regarding socioeconomic status, migrant status, parental smoking, and parental BMI, the number of participants was reduced to n = 9,428 (these subject numbers were determined before weighting).
The parent-rated
The items were rated by the parents as “not correct”, “partly correct”, or “completely correct”. Each subscale has possible values from 0 to 10. Cronbach’s alphas in our sample were .663 (emotional symptoms), .548 (conduct problems), .775 (hyperactivity/inattention), .603 (peer relationship problems), and .652 (pro-social behavior). We adopted the cut-offs for borderline and abnormal scores of the SDQ-HI subscale from the English normative data
We used this instrument as a continuous measure of ADHD symptoms rather than a categorical measure for ADHD diagnosis since a large-scale twin study
The socioeconomic status score was calculated from parental occupational status and education as well as family income
In 6 to 13 year olds parents reported their children’s approximate average daily hours of television and video viewing separate for weekdays and weekends. Response options were “none”, “30 minutes”, “1–2 hours”, “3–4 hours”, and “more than 4 hours” which were recoded (using 0, 0.5, 1.5, 3.5, and 5, respectively) into average hours of use per day, then weighted with 5/7 for weekday exposure and with 2/7 for weekend exposure, and summed up. Children and adolescents aged 11 years and older were asked directly about their daily media time with identical response options (but not differentiating weekday and weekend exposure). The responses were recoded into average hours of use per day as above. In 11–13 year old participants both parent-ratings and self-ratings were available. Hence, in this age group both ratings were combined and means were calculated.
Data on physical activity are only available in children and adolescents aged 11 years and older by self-report. They were asked to report the hours per week of leisure time physical activity (e.g. organized sports, bicycling) where they really start to sweat or get out of breath. This was used as a measure of weekly hours of medium to high intensity physical activity.
Parents of children under the age of 11 and children and adolescents aged 11 and older filled in the semi-quantitative food frequency questionnaire (FFQ)
The responses regarding frequencies and portion sizes were calculated into average portions per day and food weights or beverage volumes per portion of each item. Frequencies and portion sizes were then multiplied to estimate the daily amount consumed of each item.
The average energy density values for each item of the FFQ was derived from a subgroup of 12–17 year old participants in the KiGGS survey, who also contributed detailed dietary assessments as participants in the EsKiMo (Eating Study as a KiGGS Module)
The daily energy intake of food and of beverages as well as the total daily energy intake (in kJ/d) was estimated as the sum of the daily energy intakes from the items. This daily energy intake was calculated for each item as the product of energy density and the average daily amount consumed.
As an alternative analysis we also calculated the Healthy Nutrition Score for Kids and Youth (HuSKY) based on the FFQ items as described in detail elsewhere
The health behavior variables included television exposure, days with moderate to vigorous physical activity, as well as energy intake and the HUSKY nutritional quality score. In order to identify putative differential associations between ADHD symptoms and different aspects of food and beverage intake, we examined volume and energy density of food and beverages separately in addition to total energy intake.
We selected potential parental confounders based on their association with both the SDQ-HI scores and overweight status as demonstrated in a previous study using the same database
We used separate GLMs to examine the association of SDQ-HI scores with each of the health behavior variables. SDQ-HI scores were entered as the independent variable in each GLM. In a first model we adjusted only for age and sex and in a second model also for the parental confounding variables (shown to be associated with both the SDQ-HI scores and the behavioral variables) and the other SDQ subscales.
We also examined interactions of the SDQ-HI scores with sex and age regarding the health behavior variables.
As a sensitivity analysis we eliminated subjects for whom the parents reported medication for neurological conditions including medication for ADHD.
We used a weighing procedure
A p-value <0.05 (in interaction analyses p<0.10) was considered to be statistically significant. All analyses were performed using SPSS (version 17.0 for Windows; SPSS, Inc., Chicago, IL).
The descriptions of the health behavior variables are listed in
Variable | N |
Mean | SD | P25 | Median | P75 |
Television/video exposure (h/d) | 12060 | 1.6 | 1.1 | 0.8 | 1.5 | 1.8 |
Medium-high intensity physical activity (h/wk) |
6300 | 6.9 | 7.1 | 3 | 5 | 9 |
HuSKY nutritional quality score (0–100) | 9462 | 55 | 11 | 48 | 55 | 62 |
Food energy density (kJ/100 g) | 11553 | 955 | 136 | 868 | 953 | 1042 |
Beverage energy density (kJ/100 ml) | 11553 | 100 | 58 | 51 | 100 | 148 |
Food volume (g/d) | 11553 | 1094 | 593 | 727 | 966 | 1292 |
Beverage volume (ml/d) | 11553 | 1922 | 1494 | 919 | 1439 | 2531 |
Food energy intake (MJ/d) | 11553 | 10.52 | 6.26 | 6.72 | 9.04 | 12.38 |
Beverage energy intake (MJ/d) | 11553 | 1.74 | 1.71 | 0.63 | 1.20 | 2.16 |
Total energy intake (MJ/d) | 11520 | 12.2 | 6.8 | 8.0 | 10.6 | 14.4 |
N is calculated after weighting and therefore exceeds the number of participants.
only available n 11–16 years old participants.
P25 = 25th percentile; P75 = 75th percentile.
The mean (SD) SDQ-HI subscale score was 3.1±2.3; it was normal in 86.8%, borderline in 5.7%, and abnormal in 7.8% of the participants.
Social class was low in 27%, medium in 46%, and high in 27% of the children and adolescents. Daily smoking was reported by 25.2% of the mothers and 31.8% of the fathers where data were available. Of the mothers and fathers 24.2% and 47.4% were overweight but not obese and 12.5% and 13.5% were obese, respectively. Employing International Obesity Task Force (IOTF) methods and using German BMI percentiles
Maternal (p<0.0005) and paternal (p<0.005) BMI, maternal (p<0.0005) and paternal (p<0.0005) smoking, as well as migrant status (p<0.05) were significantly and positively associated with the SDQ-HI score whereas socioeconomic status (p<0.0005) was negatively associated with the SDQ-HI score after adjusting for the other potential confounders, age, and sex.
The associations between the parental variables (BMI, smoking, socioeconomic status, migrant status) and the health behaviors are summarized in
Health behavior variables asdependent variables | N |
MaternalBMI | PaternalBMI | Maternalsmoking | Paternalsmoking | Socioeconomicstatus | Migrant |
Television/video exposure (h/d) | 9.199 | .004*** | .002*** | .005*** | .002*** | . |
.006*** |
Medium-high intensity physical activity (h/wk) |
4.863 | n.s. | n.s. | n.s. | n.s. | . |
n.s. |
HuSKY nutritional quality score | 7.325 | n.s. | . |
. |
n.s. | .013*** | n.s. |
Food energy density | 8.857 | n.s. | n.s. | n.s. | n.s. | . |
.021*** |
Beverage energy density | 8.856 | . |
n.s. | .001** | n.s. | . |
. |
Food volume | 8.857 | n.s. | n.s. | n.s. | n.s. | . |
.011*** |
Beverage volume | 8.856 | n.s. | .001** | .003** | .001** | . |
n.s. |
Food energy intake | 8.857 | n.s. | n.s. | n.s. | n.s. | . |
.019*** |
Beverage energy intake | 8.895 | n.s. | n.s. | .004*** | .002*** | . |
. |
Total energy intake | 8.839 | n.s. | n.s. | n.s. | .001** | . |
.013*** |
All parental variables were entered together. Partial eta2 values for significant independent associations are listed (with negative associations in italic style).
N is calculated after weighting and therefore exceeds the number of participants.
P<0.05; **p<0.01; ***p<0.001.
only available n 11–16 years old participants.
In the models adjusted for sex and age only (
Health behavior variables asdependent variables | SDQ-HI as independent variable | ||||
N |
B | 95% CI | P | partial eta2 | |
Television/video exposure (h/d) | 11 349 | 0.064 | 0.055–0.073 | 0.000 | 0.017 |
Medium-high intensity physical activity (h/wk) |
6 098 | 0.227 | 0.143–0.310 | 0.000 | 0.005 |
HuSKY nutritional quality score (0–100) | 8 905 | −0.565 | −0.661 – −0.468 | 0,000 | 0,015 |
Food energy density (kJ/100 g) | 10 861 | 5.60 | 4.42–6.77 | 0.000 | 0.008 |
Beverage energy density (kJ/100 ml) | 10 864 | 1.06 | 0.56–1.54 | 0.000 | 0.002 |
Food volume (g/d) | 10 861 | 6.1 | 1.0–11.2 | 0.020 | 0.001 |
Beverage volume (ml/d) | 10 864 | 48.1 | 35.6–61.2 | 0.000 | 0.005 |
Food energy intake (kJ/d) | 10 861 | 120 | 66–174 | 0.000 | 0.002 |
Beverage energy intake (kJ/d) | 10 915 | 61 | 47–76 | 0.000 | 0.006 |
Total energy intake (kJ/d) | 10 830 | 181 | 123–240 | 0.000 | 0.003 |
N is calculated after weighting and therefore exceeds the number of participants.
only available n 11–16 years old participants.
Weakened but mostly still significant relationships between SDQ-HI scores and health behaviors were found in the models adjusting for parental variables (BMI, SES, smoking, and migrant status) as well as age and sex (not shown).
When we additionally adjusted for the other 4 SDQ subscales (conduct problems, emotional problems, peer problems, pro-social behavior) the associations between the SDQ-HI subscale score and the behavioral variables remained statistically significant with the exception of energy intake from food. However, regression coefficients were moderately reduced (
Health behavior variables asdependent variables | SDQ-HI as independent variable | ||||
N |
B | 95% CI | P | partial eta2 | |
Television/video exposure (h/d) | 9 199 | 0.021 | 0.010–0.032 | 0.000 | 0.002 |
Medium-high intensity physical activity (h/wk) |
4 863 | 0.056 | 0.014–0.232 | 0.026 | 0.001 |
HuSKY nutritional quality score (0–100) | 7 325 | −0.365 | −0.478 – −0.252 | 0.000 | 0,004 |
Food energy density (kJ/100 g) | 8 857 | 4.18 | 2.68–5.68 | 0.000 | 0.003 |
Beverage energy density (kJ/100 ml) | 8 856 | 0.98 | 0.35–1.62 | 0.002 | 0.001 |
Food volume (g/d) | 8 857 | 0.5 | −6.0–7.0 | 0.881 | 0.000 |
Beverage volume (ml/d) | 8 856 | 16.6 | 0.7–32.6 | 0.041 | 0.000 |
Food energy intake (kJ/d) | 8 857 | 44 | −25–112 | 0.209 | 0.000 |
Beverage energy intake (kJ/d) | 8 895 | 36 | 18–54 | 0.000 | 0.002 |
Total energy intake (kJ/d) | 8 839 | 80 | 5–154 | 0.035 | 0.000 |
N is calculated after weighting and therefore exceeds the number of participants.
only available in 11–16 years old participants only.
Significant interactions of SDQ-HI scores with sex were found for the dietary quality index, food volume and energy intake from food, and as a trend also for total energy intake (
Health behavior variables asdependent variables | SDQ-HI x sex interaction as independent variable per unit SDQ-HIfor girls (with boys as reference) | ||||
N |
B | 95% CI | P | partial eta2 | |
Television/video exposure (h/d) | 9 199 | −0.003 | −0.021 – 0.016 | 0.791 | 0.000 |
Medium-high intensity physical activity (h/wk) |
4 863 | −0.089 | −0.277 – 0.099 | 0.352 | 0.000 |
HuSKY nutritional quality score (0–100) | 7 325 | −0.110 | −0.485 – −0.054 | 0,014 | 0,001 |
Food energy density (kJ/100 g) | ´8 857 | 0.334 | −2.27–2.94 | 0.802 | 0.000 |
Beverage energy density (kJ/100 ml) | 8 856 | 0.60 | −1.05–1.17 | 0.916 | 0.000 |
Food volume (g/d) | 8 857 | 14.2 | 3.0–25.5 | 0.013 | 0.001 |
Beverage volume (ml/d) | 8 856 | −13.2 | −40.8–14.4 | 0.348 | 0.000 |
Food energy intake (kJ/d) | 8 857 | 143 | 25–262 | 0.018 | 0.001 |
Beverage energy intake (kJ/d) | 8 895 | −20 | −51–21 | 0.226 | 0.000 |
Total energy intake (kJ/d) | 8 839 | 124 | −5–53 | 0.060 | 0.000 |
GLM adjusted for SDQ-HI, migrant status, parental BMI, SES, and smoking as well as age and sex.
N is calculated after weighting and therefore exceeds the number of participants.
only available in 11–16 years old participants.
There was no significant interaction of SDQ-HI scores with age regarding any of the behavioral variables for either or both sexes.
All results were virtually unchanged when participants taking medication for neurological conditions (e.g. stimulant medication for ADHD or antiepileptic medication known to affect food intake) were omitted from the analyses.
As expected from the previous research mentioned in the introduction we found significant associations between ADHD symptoms, low dietary quality, and high total energy intake. We could demonstrate that these associations were not entirely explained by the investigated parental variables or other psychopathology of the children. The association with the SDQ-HI scores was more pronounced for food quality than for food volume. In detail, the significant positive association between SDQ-HI scores and energy intake from beverages was due to both increased volume and increased energy density of the beverages. The association between SDQ-HI scores and energy intake from food was explained by the energy density of food but not by food volume. This might indicate that ADHD symptoms may be associated with poor food selection rather than overeating in terms of volume. This is in line with the results of a recent cross-sectional study showing that Australian children with ADHD symptoms were more likely to consume a “Western” diet high in saturated and total fat and refined sugar while low in fiber, than to consume a Mediterranean diet rich in fish, vegetables, fruit, legumes, and whole-grain foods
We found that girls showed a significantly stronger association between ADHD symptoms and dietary quality, food volume, food energy intake, and (marginally) total energy intake than boys. This corresponds with our previous results based on the same population
We could confirm the independent association between ADHD symptoms and television and video exposure
The weak but significant positive association between ADHD symptoms and medium to high intensity physical exercise in the 11–17 year old participants is in line with studies using actometry
Parental variables such as socioeconomic status, BMI, and smoking have been shown to be strongly associated with ADHD symptoms, television exposure, and dietary quality. However, these confounding variables did not entirely explain the positive association between ADHD symptoms and television exposure, dietary quality, and energy intake in our sample. In addition, the associations between the SDQ-HI subscale scores and the health behaviors remained significant for all but food energy intake when adjusting for the other SDQ-subscales and are, thus, largely independent of peer relations, emotional problems and other behavioral problems assessed with the SDQ.
The main strengths of our report lie in the large representative study on which it is based, and the multivariate analyses that allowed looking for independent effects of ADHD symptoms. ADHD symptoms were used as a dimensional and not a categorical variable. In our effort to control for parental confounding variables, we included a sophisticated socioeconomic status score, migrant status, and parental smoking in addition to parental BMI, age, and sex.
However, given the cross-sectional nature of the data, we cannot establish the direction of the detected relationships. In addition, the health behavior variables used in our study were based on self-reported data and are thus liable to social desirability and recall bias. Also, we have combined parent-rated and self-rated television exposure time because only the combined variable spans the whole age range of 6–17 years used in our study. Fortunately, both ratings were significantly associated with SDQ-HI scores, also after adjusting for parental confounders as well as sex and age.
Finally, while the associations were statistically significant, they were generally weak, explaining less than 1% (derived from the partial eta2 values given in
There is evidence that overweight/obese children have a significantly higher risk for ADHD symptoms than normal weight children. Our study adds to this finding in that poor nutrition and high television exposure time also seem to directly be associated with ADHD symptoms even after adjusting for potential confounding variables. Clinicians should be aware that children and adolescents with ADHD symptoms should be monitored with regard to food intake and television/video exposure. Environmental control measures and parental monitoring may be required to improve the dietary patterns and to reduce TV usage time of children and adolescents with ADHD symptoms, as previous studies have shown that these individuals profit less from behavioral weight loss interventions
Finally, further research based on large longitudinal cohorts is needed to address the direction of causality.
We thank the Robert Koch Institute for the provision of the KiGGS public use file. We are grateful to Christina Kleiser and Gert Mensink for their kind support regarding the calculation of the healthy eating index (HuSKY) and energy density of food data.