Introduction
Attention deficit hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity, and impulsivity, affecting neurodevelopment: motor behaviors, cognition, socialization, emotions, and others1.
ADHD is the most common neuropsychiatric disorder in childhood, with a worldwide prevalence of around 5.2%2, although a more recent review reported a prevalence of 3.4%3.
Overweight and obesity (OW/OB) are defined as a condition in which the body mass index (BMI) is > 25 and are among the most important nutritional disorders in children worldwide. Their causes require more research because prevention in infants and children can help improve the quality of life of children, adolescents, and adults4. Reducing the high percentage of children with OW/OB requires identifying to what extent some risk factors favor the development of childhood obesity. In some countries, the higher prevalence of OW/OB demands more aggressive research to understand the causes of OB/OW and actions to reduce its prevalence, such as in the case of Mexico with a prevalence of OB/OW of 33.5% in children < 5 years, and 32% and 36.9% in girls and boys aged 5-11 years, which has been increasing in recent years5. The association between ADHD and OB/OW has produced results that go in many directions. However, a meta-analysis showed mixed findings of a significant relationship between ADHD and OB/OW with many likely confounding factors, such as abnormal eating patterns, sedentary lifestyle, and genetic alterations. Some authors found that inattention, opposition, and the duration of breastfeeding were associated with OB/OW in children and adolescents with ADHD. From the above, it is clear that the relationships between ADHD and OB/OW need more research6.
Our objectives in this research were
- Describe the frequency of obesity and overweight in school children with ADHD
- Report the differential distribution of OW/OB in the different clinical subtypes in school children with ADHD
- Correlate BMI with the level of physical activity in school children with ADHD by clinical subtype.
Methods
We studied a sample of children residing in Mexico City, Mexico based on a prospective study of children with ADHD. The children were from middle and low socioeconomic strata and attended public elementary schools.
The inclusion criteria for children in this study were as follows: having ADHD, age between 7 and 12 years, and a full intelligence quotient (IQ) ≥ 90.
The identification of children with ADHD was conducted in three steps:
- Step #1 was the suspicion of ADHD by teachers and school principals.
- Step #2 was the study of the children using the Revised Conners Rating Scales (CRS) questionnaires administered to parents and teachers, where we explained the correct way to respond to each item, emphasizing that the alterations in the children must have been present for at least 6 months.
- In step #3, children were studied through a multidisciplinary evaluation with the help of a pediatric psychiatry department (conducted with a pediatric clinical history, physical examination, and semi-structured interview; the pediatric neurology department conducted a clinical history and a pediatric neurological examination; the neuropsychology department conducted intelligence and neuropsychological exams). The children were also studied using the Wechsler Intelligence Scale for children (WISC), the fourth edition to measure their IQ7.
To assess the level of physical activity, the physical activity questionnaire (PAQ)-C Questionnaire, a widely used self-administered questionnaire in research, was used. The child was diagnosed with ADHD only when presenting alterations in two environments: school and home. Children with ADHD were divided according to their symptoms into one of the following three subtypes: combined (-C); inattentive (I); and hyperactive (-H)8. All children were medication-free at the time of the study. The exclusion criteria were epilepsy, deafness, and other severe chronic neurological or psychiatric disorders that cause school absenteeism or are studied with the same exams used in this study. Other exclusion criteria included hypothyroidism, Cushing’s syndrome, Prader–Willi syndrome, or other diseases concurrent with obesity and overweight.
A group of school children diagnosed with ADHD without obesity and overweight was studied for comparison purposes. These children were selected from the same elementary schools and similar socioeconomic backgrounds.
Parents and children were informed about the study, and the importance of their participation, and gave their consent for the children’s participation and the publication of the results. The research was approved by the Ethics and Research Committee of the institution, and informed consent was signed by the parents of the participating children according to the Declaration of Helsinki.
Instruments
Revised CRS
To assess behavioral changes in both the school and home environments, the CRS questionnaires (Conners, 1999) were studied in parents and teachers9, as recommended by the American Psychiatric Association criteria for identifying ADHD. Each question on the scale can be answered in one of the following four ways: “never,” “sometimes,” “often,” or “very often.” The first nine items of the questionnaire refer to inattention, the following nine to hyperactivity, and the last items to impulsivity. A child is considered to meet the criteria for ADHD when parents and teachers respond “often” or “very often” to, at least, six items in each part of the questionnaire. ADHD was classified according to the three subtypes recognized by the DSM-510 as follows: combined (-C), presentation (-I), and hyperactive-impulsive presentation (-H).
WISC
The WISC test was used to measure the IQ. It consists of a test with four subtests: (1) Verbal Comprehension (VC), divided into the following subscales: similarities, comprehension, information management, and words in context; (2) perceptual reasoning composed of block design, picture concepts, incomplete matrix, and pictures; (3) working memory, constructed by: digit span, number-letter sequencing, and arithmetic; and (4) processing speed, which includes: coding, symbol search, and records. The scale was administered by a clinical psychologist to the child without the presence of any other subjects whenever possible, following the instructions from the manual, and the items were taken into account based on the subject’s age to determine the beginning of the test and the final score (Wechsler, 2015). The T-IQ scores were categorized into the following seven levels: ≥ 130, very superior; 129-120, superior; 119-110, high average; 109-90, average; 89-80, low average; 79-70, borderline, and ≤ 69, intellectual disability.
PAQ-C questionnaire
The level of physical activity was evaluated with the PAQ-C Questionnaire, whose original version was developed and validated in English by its authors Crocker, Bailey, Faulkner, Kowalski, and McGrath11, and is used to measure the level of physical activity, from moderate to vigorous, performed in the past 7 days, in children between 7 and 14 years of age. This instrument was used because it is a reliable, low-cost, and easy-to-administer tool.
Anthropometric measurements
WEIGHT
It was measured in all cases by a certified nutritionist, using standardized techniques, always in the mornings, with the child in underwear only, on a Seca scale (Hamburg, Germany) model 703, with a precision of 0.5 g steps and a capacity of up to 150 kg, with an integrated telescopic ruler, model 220 (measurement range: 60–200 cm, minimum precision of 0.5 cm)12.
HEIGHT
It was measured with the child standing upright with the back against the stadiometer, head looking forward, with the Frankfurt plane parallel to the ground, heels, spine, and occiput supported on a hard plane with arms extended along the trunk.
BMI: It was calculated based on the internationally known equation = kg/height m2: data from each child were looked up in the U.S. Centers for Disease Control and Prevention tables by sex and age and classified as adequate when it was between the 10th and 85th percentiles; BMI < 10th percentile was classified as underweight; BMI between the 86th and 95th percentiles was identified as overweight; and BMI > 95th percentile was classified as obesity13.
Statistics
The mean and standard deviation of quantitative variables were calculated. Proportions of qualitative variables were calculated. The normal distribution of the variables was determined using the Shapiro–Wilk test. Since none of the variables had a normal distribution, non-parametric tests were used for comparisons. To find differences between quantitative variables, the Mann–Whitney U-test was used. Pearson’s X2 test was used to explore differences between dichotomous variables. Correlation analysis was performed using Spearman’s rank correlation coefficient. All calculations were performed using GraphPad Prism software (Dotmatics, USA). p < 0.05 were selected to determine significant differences among variables.
Results
The school population included 280 schoolchildren, distributed as follows: 152 boys and 128 girls, with a mean age of 9.03 ± 1.74 years. By clinical subtype, they were distributed as follows: combined ADHD, 133 children (mean age = 9.2 ± 1.5); inattentive ADHD, 93 children (mean age = 9.03 ± 1.74); and hyperactive-impulsive ADHD, 54 children (mean age = 9.3 ± 1.7) (Table 1).
Table 1. Distribution of the population by gender and clinical subtype
| Clinical subtype | Gender | Total | |
|---|---|---|---|
| Male | Female | ||
| ADHD-C | 78 | 55 | 133 |
| ADHD-I | 32 | 61 | 93 |
| ADHD-HI | 42 | 12 | 54 |
| TOTAL | 152 | 128 | 280 |
|
ADHD-C: attention deficit hyperactivity disorder, combined subtype; ADHD-I: attention deficit hyperactivity disorder, inattentive subtype; ADHD-HI: attention deficit hyperactivity disorder, hyperactive-impulsive subtype. |
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Table 1 shows that the prevalence of combined ADHD is higher, followed by inattentive ADHD versus hyperactive-impulsive ADHD. Regarding sex, only in the inattentive ADHD clinical subtype is the female sex more prevalent.
Of the 280 schoolchildren diagnosed with ADHD, the prevalence of overweight in the sample was 36.43% (102 schoolchildren), with the remaining 63.57% (178 schoolchildren) being used as the control group. The prevalence of obesity, assessed with the previously mentioned guide, was 20.00% (56 children). Table 2 contains the demographic data of the study groups.
Table 2. Demographic values of the study groups
| Demographic Values | Overweight | Control |
|---|---|---|
| Age, years (mean ± SD) | 8.9 (1.4) | 9.3 (1.6) |
| Boys, male (%) | 58 (56.86) | 94 (52.81) |
| Girls, female (%) | 44 (43.14) | 84 (47.19) |
| Weight, g (mean ± SD) | 37.4 (6.04) | 24.9 (5.5) |
| Height, cm (mean ± SD) | 1.32 (0.096) | 1.24 (0.107) |
| BMI (mean ± SD) | 21.34 (2.80) | 15.97 (1.68) |
The BMI between the two study groups showed a significant difference (p < 0.0001). No correlations were found between the ADHD subclinical type in either study group. When separating the population by ADHD subclinical type (combined, hyperactive-impulsive, and inattentive), no significant differences were found regarding BMI (p = 0.12). In the overweight pediatric population, two subgroups were created: children with obesity and children without it. A significant difference in BMI was found between these two subgroups (p < 0.001); however, no correlation was found between subclinical type and BMI (correlation coefficients p = 0.086 and p = −0.129, respectively).
Subsequently, the population was analyzed by separating overweight and non-overweight children regarding their gender and subclinical type. Using the X2 test, an association was found between the gender variable and the clinical subtype (p < 0.05 in both genders) (Table 3).
Table 3. Population distribution by gender in relation to overweight and clinical subtype
| Clinical subtype | Overweight, n (%) | Without overweight, n (%) | ||
|---|---|---|---|---|
| Male | Female | Male | Male | |
| Combined | 36 (63.79)* | 13 (29.55)* | 42 (44.68)* | 42 (50.00)* |
| Inattentive | 12 (18.97)* | 24 (54.55)* | 20 (21.28)* | 37 (44.05)* |
| Hyperactive-impulsive | 10 (17.24)* | 7 (15.91)* | 32 (34.04)* | 5 (5.95)* |
|
* p < 0.05, using the X2 test. |
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Furthermore, in these subgroups, a correlation was sought between BMI and clinical subtype, but none was found. Comparing the BMI of each clinical subtype, a significant difference was found between the BMI of boys and girls with the combined clinical subtype (p = 0.004).
Correlations were observed between BMI and the level of physical activity in the categories BMI with overweight and a low level of physical activity, as well as between BMI with obesity and a regular level of physical activity, specifically in children with combined ADHD (Table 4). Correlations were also observed between BMI and the level of physical activity in the categories BMI with overweight and a very low level of physical activity, as well as between BMI with obesity and a very low level of physical activity, specifically in children with inattentive ADHD (Table 5).
Table 4. Correlations between BMI and level of physical activity in schoolchildren with combined ADHD
| BMI | Level of physical activity | Intense | ||
|---|---|---|---|---|
| Underweight | Very low | Low | Regular | |
| Normal | ||||
| Overweight | 0.75* | |||
| Obesity | 0.81* | |||
|
Positive correlations obtained through Pearson’s statistical analysis (r). ADHD: attention deficit hyperactivity disorder, Inattentive subtype; BMI: body mass index. |
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Table 5. Correlations between BMI and level of physical activity in schoolchildren with inattentive ADHD
| BMI | Level of physical activity | Intense | ||
|---|---|---|---|---|
| Underweight | Very low | Low | Regular | |
| Normal | ||||
| Overweight | 0.83** | |||
| Obesity | 0.79** | |||
|
Positive correlations obtained through Pearson’s statistical analysis (r). ADHD: attention deficit hyperactivity disorder, inattentive subtype; BMI: body mass index. |
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Discussion
In recent years, ADHD and its comorbidities have gained relevance; such is the case with the relationship between ADHD and OW/OB rates. Among studies measuring the prevalence of ADHD in obese subjects, there is an analysis14 of five studies conducted on clinical samples and two conducted on the general population. The clinical sample studies included: 215 obese adults treated at a specialized obesity clinic; 90 adolescents (12–16 years), of which 30 were in obesity treatment, 30 were obese without treatment, and 30 had adequate weight; 26 children and adolescents (8–17 years) with obesity hospitalized in an eating disorder unit; 75 women with obesity referred to a specialized obesity treatment clinic; 56 obese children; and 56 children with adequate weight (10–18 years)15. Of these five studies, four16 showed a significantly higher prevalence of ADHD in obese patients versus the control group or the reference body weight for each age. In the study that did not find a higher prevalence of ADHD in obese patients, more symptoms of impulsivity, hyperactivity, and attention deficit were described versus controls in several tests. This lack of association could be due to the study focus on the association between obesity and impulsivity, not using instruments specifically oriented toward detecting ADHD, nor investigating this diagnosis as a primary or categorical variable.
One of the studies in the general population was conducted on a sample of 991 children (9-16 years) and found no association between ADHD and obesity17. The other study used specific screening instruments and structured psychiatric interviews on an initial sample of 34,653 adults from the NESARC18. The objective was to measure the association between obesity and ADHD diagnosis (in adulthood and retrospectively), controlling for variables such as socioeconomic status, mood, anxiety, and substance use disorder. It concluded that ADHD detected in adults was not associated with obesity when confounding factors were controlled, but having had ADHD symptoms in childhood was associated with obesity in adult women. This provides a basis for future longitudinal studies to assess the effect of treating ADHD in childhood on the weight of adult women. There is evidence that obesity in a subset of cases is directly promoted and aggravated by untreated ADHD.
The nature of the condition is generated by the interaction between various factors: genetic, epigenetic, physiological, behavioral, sociocultural, and environmental. This leads to an imbalance, in which there is higher energy intake and lower energy expenditure over a prolonged period of time19. In Mexico, surveys show that obesity has currently displaced malnutrition as the main challenge to be solved. The data from the National Health and Nutrition Survey 2012 reported that the prevalence of OW/OB in Mexican adults aged 20 years or older was 71.3% (obesity 32.4% and overweight 38.8%), and they also reported that obesity is higher in females (37.5% vs. 26.8%)20.
A different study21 describes obesity and overweight in Mexico in a child population in which the objective was to update the prevalence of OW/OB and study some associated determinants in a population < 20 years, from the National Health and Nutrition Survey of Halfway 201622, they studied sociodemographic variables associated with overweight through logistic regression; the < 5-year national prevalence of OW/OB was girls 5.8%, boys 6.5%; school-age girls 32.8%, boys 33.7%; adolescent girls 39.2% and boys 33.5%. Adolescent girls from rural areas showed an increase from 2012 to 2016 of 9.5% points.
The prevalence of OW/OB in girls and women in rural areas shows a significant increase over a short period, which calls for immediate action.
Another study evaluated BMI and physical activity in schoolchildren with ADHD, using the PAQ-A questionnaire to assess physical activity in schoolchildren aged 7-12 years. The results showed an acceptable test-retest reliability ICC = 0.71 for the Spanish adaptation of the PAQ-A23.
Regarding physical activity, it is mentioned that there is a positive effect on ADHD symptoms, indicating that physical activity directly impacts behavior and cognitive performance, being an alternative treatment for schoolchildren with ADHD24.
Another investigation mentions that physical activity impacts the physiology present in ADHD and could be an important alternative and/or complementary treatment to medication25.
Furthermore, it is mentioned that physical activity influences emotion management, with greater tranquility and calm observed in schoolchildren with ADHD in the classroom26.
Conclusion, limitations, and suggestions
According to the results, it is concluded that more boys were diagnosed with ADHD than girls. By clinical subtype, it was observed that the combined subtype is generally more prevalent, and by sex, boys were more prevalent again; only in the inattentive subtype were more girls observed.
Regarding overweight, there is a higher percentage of schoolchildren with ADHD without overweight, and the same occurs with obesity. According to the results, there are more boys with ADHD and overweight than girls. The same phenomenon is observed by the clinical subtype. Data were analyzed by separating overweight and non-overweight children concerning their gender and ADHD subclinical type, and an association was found between the gender variable and clinical subtype.
Limitations
Regarding the limitations of this research, it should be noted that a larger sample would have given greater consistency to the obtained data. It is suggested that studies under this line of research consider larger samples to provide greater consistency to the results when establishing comparison parameters between children with ADHD and obesity, overweight, and physical activity.
Funding
The authors declare that they have not received funding.
Conflicts of interest
The authors declare no conflicts of interest.
Ethical disclosures
Protection of human and animal subjects. The authors declare that the procedures followed were in accordance with the regulations of the relevant clinical Research Ethics Committee and with those of the Code of Ethics of the World Medical Association (Declaration of Helsinki).
Confidentiality of data. The authors declare that no patient data appear in this article.
Right to privacy and informed consent. The authors declare that no patient data appear in this article.
Use of artificial intelligence for generating text. The authors declare that they have not used any type of generative artificial intelligence for the writing of this manuscript nor for the creation of images, graphics, tables, or their corresponding captions.
