https://orcid.org/0000-0002-1175-0021
Figures
Abstract
The association between executive function and excess weight is becoming increasingly evident. However, the results of previous studies are still inconclusive, and there is a lack of evidence in early childhood. This study aims to examine the association between executive function, in terms of overall and subscales of executive function (e.g., inhibition, working memory, and shifting), and weight excess in preschoolers. A population-based cross-sectional study was conducted on children aged 2–5 years of age from public and private schools in Chiang Mai, Thailand. Participants’ weights and heights were measured and classified into three weight status groups (i.e., children with normal weight, overweight, and obesity groups). Executive function was assessed using the parent-report Behavior Rating Inventory of Executive Function-Preschool (BRIEF-P). Multivariable polynomial regression was performed to analyze the association between executive function and weight status. A total of 1,181 children were included in the study. After adjusting for confounders, impaired overall executive function significantly increased the probability of being overweight (odds ratio [OR] = 2.47; 95% confidence interval [CI] 1.33 to 4.56). A similar trend of association was also found between impaired inhibition and overweight status (OR = 2.33; 95%CI 1.11 to 4.90). Furthermore, poor working memory was associated with both overweight and obesity (OR = 1.87; 95%CI 1.09 to 3.20 and OR = 1.74; 95%CI 1.09 to 2.78, respectively). Our data suggest that deficits in executive function, particularly inhibition and working memory, are associated with weight excess in preschoolers. Early promotion of executive function may be needed at this developmental age to prevent unhealthy weight status.
Citation: Likhitweerawong N, Khorana J, Boonchooduang N, Phinyo P, Patumanond J, Louthrenoo O (2022) Association between executive function and excess weight in pre-school children. PLoS ONE 17(10): e0275711. https://doi.org/10.1371/journal.pone.0275711
Editor: Hae Sang Lee, Ajou University School of Medicine, REPUBLIC OF KOREA
Received: May 10, 2022; Accepted: September 20, 2022; Published: October 10, 2022
Copyright: © 2022 Likhitweerawong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data and code used to generate plots and perform statistical analyses have been uploaded to the Open Science Framework archive: https://osf.io/jdq6z/?view_only=b2081b6607804ab8b95ada2ae27ba6d4.
Funding: This work was supported by the Faculty of Medicine, Chiang Mai University (Grant number 087/2564 to Narueporn Likhitweerawong) (https://www.med.cmu.ac.th/web/research/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Children and adolescents are increasingly facing the health problems of weight excess. The worldwide prevalence of overweight and obesity in young children has continuously increased over the past few decades, regardless of geography and culture [1]. In the conceptual understanding of obesity, it is a chronic metabolic disease that primarily involves increased calorie intake and decreased physical exercise. To date, the evidence is apparent that obesity is a multifactorial disease, originating not only from obesity-related behaviors but also influenced by genetics, environments, and gene-environment interactions [2]. Robust evidence has demonstrated several risk factors associated with weight excess in childhood, such as preterm, low birth weight, lack of breastfeeding, excessive screen time, and deficient amount of sleep [3–7]. Neurocognition, particularly executive function (EF), has been assumed to contribute to excess weight through the theoretical hypothesis of lacking control related to eating behaviors [8, 9].
EF involves the high-level mental processes required to control thoughts, emotions, and behaviors. Three fundamental EFs are inhibitory control/inhibition, working memory, and cognitive flexibility/shifting [10]. These three core EFs contribute to higher-order EF, such as problem-solving, reasoning, and planning [10]. EF develops dramatically in the preschool period [11], corresponding to the time of decline in body fat mass, before reaching the inflection point of adiposity rebound (adiposity rebound usually occurs about 6 years of age) [12]. The energy expenditure to develop the brain, especially the area of the brain related to EF, during this early childhood might influence the composition of body fat and the changes in body mass index (BMI) later [13, 14].
Several studies investigating the correlation between lower EF and higher BMI have focused mainly on school-age children, adolescents, and adults [15–18]. Most of the key results from these studies are found to be a small to medium effect size of the association. A paucity of studies regarding EF and weight excess has been conducted on preschool children due to the relatively low prevalence of overweight and obesity in preschoolers (whose body fat typically declines during this period) and the scarcity of valid instruments to measure EF in this age group [19]. Furthermore, the results of some studies conducted on young children are inconsistent. For example, Pieper and Laugero reported that BMI percentile was not correlated with most measures of EF in children 3–6 years old [20]. Tandon and colleagues showed that effortful control and delay ability were not associated with BMI changes [21]. Similarly, a recent study by Gross and colleagues reported that the EF score was not a predictor of weight status in preschoolers [22]. On the other hand, Hughes and colleagues indicated that eating self-regulation was related to the child’s BMI-for-age z-scores [23]. Schmitt and colleagues also suggested a negative correlation between the composite EF score and BMI percentile in preschoolers [24].
Due to a few studies focusing on young children and the inconsistent results mentioned above, this study aims to investigate the association between EF and weight excess in preschool-aged children by controlling for potential confounders reviewed from previous literature. The findings of this study may contribute to an understanding of the relationship between these variables in early childhood and obesity intervention by promoting EF.
Materials and methods
Study design and participants
This cross-sectional study was conducted on preschoolers from seven public and private schools in Chiang Mai, Thailand, between June to December 2021. Seven participating schools in Chiang Mai represented middle-class socioeconomic status (SES) based on the characteristics and facilities of the individual schools, such as the size of the school, number of students, teaching facilities, and school fees. The inclusion criteria were children aged 2–5 years and studying at the pre-kindergarten or kindergarten level. The exclusion criteria were 1) underweight children (BMI less than the 5th percentile); 2) children diagnosed with neurodevelopmental disorders or genetic diseases reported by parents (e.g., autism spectrum disorder, attention-deficit/hyperactivity disorder, intellectual disability, and Down syndrome); 3) not being ethnically Thai; and 4) parents refusing to participate in the study. This study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University (051/2564). Before participating in the research, all parents/guardians of the child were informed of the relevant study detail. Their written informed consent was obtained.
Measures
Study determinants–executive function.
The parent-report version of the Behavior Rating Inventory of Executive Function–Preschool version (BRIEF-P) in Thai was used to measure EF of the child [25]. There were 63 items with a three-point Likert scale asking whether each behavior issue is problematic relative to other children of the same age, as never, sometimes, or often in the preceding six months. BRIEF-P measured overall EF (global executive composite) and EF subscales (i.e., inhibit, shift, emotional control, working memory, and plan/organize). The T-score for each scale was calculated based on standardized age and sex. Participants were categorized into impaired or normal EF groups, according to the cut-off point (T-score at or above 65), which was considered clinically significant regarding the difficulty of executive functioning [25]. This tool demonstrated good reliability with a Cronbach’s alpha of 0.80–0.95 for the EF subscales and global executive composite scale [25].
Study endpoints–weight status.
The weight status was divided into three groups: 1) normal weight group was defined as preschoolers with BMI equal to or greater than the 5th percentile but not reaching the 85th percentile for children of the same age and sex; 2) overweight group was defined as preschoolers with BMI equal to or greater than the 85th percentile but not reaching the 95th percentile; 3) obesity group was defined as preschoolers with BMI equal to or greater than the 95th percentile [26]. The body weight and height of the participants were measured at school using a digital weighing scale and stadiometer by teachers or school nurses. Then, the outcome assessors computed the percentile of BMI.
Pre-specified confounders.
Potential confounders of overweight/obesity, including child’s age, sex, birth weight, gestational age, breastfeeding, maternal age, maternal BMI, maternal education, socioeconomic status, screen time, physical activity, sleep, and parenting styles, were reviewed from previous literature [3–7, 27–30]. Due to this substantial number of variables contributing to obesity and their potential complex interactions, we created a directed acyclic graph (DAG) using DAGitty [31] to determine confounding factors that require conditioning and to evaluate minimally sufficient adjustment sets of confounders when handling causal assumptions [32], as summarized in S1 Fig. A consensus was reached with all authors (i.e., developmental-behavioral pediatricians and clinical epidemiologists) to determine the conditioning and adjustments of confounders in DAG.
Age, sex, birth weight, gestational age, breastfeeding, maternal age, maternal BMI, maternal education, and screen time were evaluated using a general questionnaire. SES was assessed using the parent-report family income and classified into low (a monthly family income of not more than 18,000 Thai baht aTHB), and high SES (a monthly family income of over 85,000 THB) based on the Thai SES classification [33]. Physical activity and sleep questions were developed to assess the child’s duration of moderate to vigorous physical activity and sleep. These questions were validated and found to be acceptable in reliability and validity, as presented in S1 Table. Parenting styles were evaluated and classified into authoritative, authoritarian, and permissive styles based on Baumrind [34] using the Short Form of Parenting Styles and Dimensions Questionnaires (PSDQ)–Thai version [35]. PSDQ demonstrated good reliability and validity [35].
Sample size
The sample size was calculated using the combination of ‘comparing two independent means’ and ‘rule of thumb of logistic regression’ methods, and then the largest sample size would be selected. Using the comparing two independent means method with alpha 0.05 (two-sided test), power 0.80, and effect size (mean and SD) from the previous study [24] required at least 500 samples, while using the rule of thumb of logistic regression needed at least 600 samples (overweight 60/obesity 60/normal weight 480) to evaluate six factors (5 EF subscales and 1 global executive composite scale), considering the prevalence of overweight and obesity approximately 10–20% [1, 36–38]. Finally, the total sample sizes of the overweight, obesity, and normal weight groups were at least 72, 72, and 576 individuals, respectively, considering an expected incomplete data/no response rate of 20%.
Statistical analysis
Statistical analyses in this study were performed using Stata version 16 (StataCorp, College Station, Texas, USA) [39]. We used frequency (%), mean (SD), and median (IQR) to report descriptive statistics. To evaluate statistically significant differences between groups, the exact probability test and the one-way ANOVA test were performed for categorical data and normally distributed continuous data, respectively. The study endpoint “weight status” were considered as three discrete categories: overweight, obesity, and normal weight (a reference category) in the polynomial logistic regression model, rather than a range in the ordinal one. Although it was likely to have severity grading of weight excess (i.e., overweight and obesity), it could not be guaranteed that there is an association between the levels of the variables in this context of EF impacting weight excess. However, the goodness of fit of the two models was compared using the log-likelihood ratio test with no significant differences between the two models. Therefore, we selected the polynomial logistic regression as our representative model to assess the association between executive functioning and overweight/obesity in preschoolers. We also performed a multivariable polynomial logistic regression analysis separately for each EF subscale/global executive composite scale on weight status and reported the odds ratio estimates with 95% CI. We used a pairwise deletion technique to handle missing data. Statistical significance was defined as a p-value of less than 0.05.
Results
Baseline characteristics and comparing variables among individuals with overweight, obesity, and normal weight
A total of 1,181 healthy children were included in this study, as presented in the study flow diagram (Fig 1). The baseline characteristics are shown in Table 1. The potential variables contributing to weight excess categorized by weight status are presented in Table 2. The results show that there are significant differences in the variables of birth weight (p = 0.002), breastfeeding (p = 0.042), and maternal weight status (p < 0.001) across the three groups.
Comparing executive function among individuals with overweight, obesity, and normal weight
For EF T-scores, overweight and obesity groups have higher T-scores on all EF subscales and global executive composite scale compared to the normal weight group, as presented in Fig 2A. The result also shows that the working memory subscale has the highest T-score compared to the other EF subscales. No significant differences are found in T-scores between the three groups in most of the EF subscales and global executive composite scale, except for the marginally significant contribution of the working memory subscale (p = 0.045). After using a Bonferroni method for the multiple pairwise comparisons of the working memory subscale among the three groups, there are no significant differences in the T-scores between groups. When categorizing EF T-scores into EF status based on the cut-off point (impaired EF and normal EF), overweight and obesity groups show a higher percentage of participants with impaired EF than normal weight group on the global executive composite scale and most EF subscales, except for emotional control and plan/organize, as shown in Fig 2B. There is a significant difference only in the global executive composite scale (p = 0.005) between the three groups. After using the exact probability test for pairwise comparison for the global executive composite scale, the significant difference in the percentage of participants is found only in the normal weight and overweight groups (p = 0.001), while the other two comparisons (normal weight vs. obesity groups and overweight vs. obesity groups) do not show significant differences.
The boxplot depicting T-scores of executive function subscales/global executive composite scale (a) and bar chart depicting the percentage of the number of children with impaired executive function among three weight status groups (b).
Association between executive function and excess weight
The association between EF and excess weight using multivariable polynomial logistic regression (overweight vs. normal weight groups and obesity vs. normal weight groups) is presented in Table 3. After adjusting for confounders, the significant odds ratio observed in the polynomial logistic regression model (overweight vs. normal weight groups) indicates that individuals with impaired inhibition, working memory, and global EF are more likely to be overweight compared to those with normal EFs. The polynomial logistic regression model (obesity vs. normal weight groups) suggests that participants with impaired working memory are significantly associated with higher odds of being obese compared to those with normal working memory. The increased probability of being both overweight and obese is associated with impaired working memory. However, the effect of impaired working memory on overweight vs. normal weight does not significantly differ from the effect of impaired working memory on obesity vs. normal weight (X2 = 0.05, p = 0.832).
Discussion
After adjusting for confounders using multivariable polynomial regression analysis, we found the associations between overall EF/EF subscales and excess weight. These associations were: i) overall EF was significantly related to the overweight status, but not obesity; ii) EF subscale–inhibition was significantly associated with overweight, but not obesity; and iii) EF subscale–working memory was significantly related to both overweight and obesity.
For the overall EF, this study was in line with previous studies that showed lower EF was significantly associated with weight excess [24, 40, 41]. Evidence shows that executive dysfunction can cause excess weight through unhealthy lifestyle behaviors such as increased high-calorie intake, decreased fruit/vegetable consumption, less physical activity, and more sedentary behaviors [17]. However, when we categorized excess weight into overweight and obesity separately, a significant association remained only in overweight. In our opinion, deficits in overall executive functioning in young children may not be a strong factor contributing to obesity, as seen in the previous study [41]. As we all know, additional factors contribute to obesity, such as genetics, eating behaviors, physical activity, and parenting and feeding styles. Most of the time, access to food and eating behaviors in young children depends on parental management of the food environment. Therefore, the EF of children may not have a direct and robust impact on their weight [22].
When considering the aspect of EF subscales, we found only inhibition and working memory related to excess weight. This finding was consistent with the previous study that showed a significant association between these EF subscales and weight excess [42, 43]. For EF subscale–inhibition, the possible link between inhibition and the risk of having excess weight is unhealthy eating behaviors. For example, children with lower inhibition usually have larger self-served portions and more food servings, display more compulsive overeating due to difficulties inhibiting response to food stimuli, and have less control to eat despite the absence of hunger [9, 23]. Subsequently, all these obesity-related eating behaviors lead to excess weight in later life. For EF subscale–working memory, working memory is the fundamental EF supporting self-regulation and inhibitory control by holding mental representation, cutting off distractions, and maintaining behaviors to achieve long-term goals [44, 45]. The explanation of the association between working memory and weight excess is that, in terms of maintaining a healthy weight, working memory helps engage with healthy habits and consequently leads to favorable physical health outcomes [46].
Unlike working memory, inhibition was significantly associated with overweight but not obesity. The undetectable association between inhibition and obesity in this study may reflect, to some extent, the clues to the direction of the association. Impaired inhibitory control could precede weight excess through overeating and obesity-related behavior mechanisms. Nonetheless, inhibition is not a sufficiently potent factor by itself to affect obesity, and hence it merely produces overweight. Conversely, working memory deficit could be a result from excessive weight via inflammation activity. Inflammatory responses from adipose tissue chronically induce brain alterations via neurohormonal transport perturbation, endothelial-neurovascular disruption, and ultimately changes in cortical gray matter and microstructure of white matter, which connect areas of the brain that facilitate working memory [47–49]. Therefore, once obesity presents, both overweight and obesity can impair working memory. However, there may be a bidirectional effect between these EFs and weight excess [15, 50]. Executive dysfunction may lead to a cycle of obesogenic behaviors and excess weight, which in turn induces inflammatory activity in the EF-related brain areas, resulting in impaired executive control over time [46].
We did not find a significant association between the other EF subscales (e.g., shifting and planning) and excess weight. Our finding contrasts with a previous study that reported shifting was significantly negatively correlated with the BMI percentile in preschool children [24]. This could be from the prior study that used performance tasks to assess EF, while our study used the parent-report BRIEF-P questionnaire. Another explanation is that a relatively low number of participants identified impaired shifting, which decreased the power to detect the significant association. The possible reason for the absence of significant relationships between planning and weight status is that planning is a higher-level EF that needs time to develop; thus, this ability at the younger age of our study is comparable among the normal weight and overweight/obesity groups.
The strength of our study is that we conducted a large sample size of typically developing preschool-aged children. We used DAG to manage the potential source of confounding for causal assumptions, which is an efficient and systematic method [51]. The findings found in this study support the body of knowledge in the prevention and early intervention of childhood obesity by promoting EF since early childhood. Emerging EF-targeted interventions such as computerized EF training and episodic future thinking, adjunct to the mainstay treatment of obesity, are mostly in preliminary studies but promising therapeutic paradigms to help promote EF and weight loss-related behaviors in children [52].
There are some limitations to this study. Firstly, we were unable to conclude the association as a causal relationship due to the cross-sectional nature of the design. Secondly, an indirect measure, such as BMI, may not be the best representative measure of obesity as body fat. However, BMI could be a good substitute for measuring obesity, as it is closely related to body fat, particularly in prepubescent children [53]. Thirdly, we were unable to examine all potential associated factors of weight excess, including unmeasured genetic effects that might confound the weight excess outcomes. Nonetheless, we collected maternal BMI and weight status as surrogate variables of genetic influence. Fourthly, there were a large number of sample exclusions; however, this study yielded sufficient power to detect the differences in executive functioning between weight status groups. Lastly, there could be benefits and weaknesses due to the homogeneity of the middle-class SES samples included in this study. This sample homogeneity could facilitate controlling covariates between individuals as they shared common ethnic and cultural behaviors, but it might limit the generalizability of the study to the low-/high-class SES populations.
In conclusion, this study found an association between deficits in EF and weight excess in preschoolers. The result suggests that inhibition and working memory might be key components of such a relationship. Strategies directly targeting the promotion of EF, specifically inhibition and working memory, in early childhood might help young children maintain a healthy weight throughout their developmental period. However, longitudinal studies are needed to investigate further the causal effect between EF and weight excess, and the treatment effect of EF intervention on weight outcomes.
Supporting information
S1 Fig. Directed acyclic graph (DAG).
DAG presents the causal model of the association between executive function and overweight/obesity. Thirteen confounders (ancestor of exposure and outcome) were determined as the minimal sufficient adjustment sets for estimating the effect of executive function on overweight/obesity.
https://doi.org/10.1371/journal.pone.0275711.s001
(TIF)
S1 Table. Reliability and validity of the study questions related to physical activity and sleep.
https://doi.org/10.1371/journal.pone.0275711.s002
(PDF)
Acknowledgments
The authors thank these people comprising: Prof. Weerasak Chonchaiya for translating the PSDQ and BRIEF-P into the Thai version; Mahippathorn Chinnapha, Eric B Tedstrom, and the Language Learning Center team for editing and proofreading the manuscript; and all children and their parents for participating in this study.
References
- 1. World Health Organization (WHO). Obesity and overweight. 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
- 2. Lin X, Li H. Obesity: epidemiology, pathophysiology, and therapeutics. Frontiers in Endocrinology. 2021;12. https://doi.org/10.3389/fendo.2021.706978
- 3. Ou-Yang MC, Sun Y, Liebowitz M, Chen CC, Fang ML, Dai W, et al. Accelerated weight gain, prematurity, and the risk of childhood obesity: a meta-analysis and systematic review. PLoS One. 2020;15(5):e0232238. pmid:32369502
- 4. Andriani H. Birth weight and childhood obesity: effect modification by residence and household wealth. Emerging Themes in Epidemiology. 2021;18(1):6. pmid:33975611
- 5. Yan J, Liu L, Zhu Y, Huang G, Wang PP. The association between breastfeeding and childhood obesity: a meta-analysis. BMC Public Health. 2014;14(1):1267. pmid:25495402
- 6. Fang K, Mu M, Liu K, He Y. Screen time and childhood overweight/obesity: a systematic review and meta-analysis. Child: Care, Health and Development. 2019;45(5):744–53. https://doi.org/10.1111/cch.12701
- 7. Miller MA, Kruisbrink M, Wallace J, Ji C, Cappuccio FP. Sleep duration and incidence of obesity in infants, children, and adolescents: a systematic review and meta-analysis of prospective studies. Sleep. 2018;41(4). https://doi.org/10.1093/sleep/zsy018
- 8. Byrne M, LeMay-Russell S, Tanofsky-Kraff M. Loss-of-control eating and obesity among children and adolescents. Current Obesity Reports. 2019;8. https://doi.org/10.1007/s13679-019-0327-1
- 9. Fogel A, McCrickerd K, Goh AT, Fries LR, Chong Y-S, Tan KH, et al. Associations between inhibitory control, eating behaviours and adiposity in 6-year-old children. International Journal of Obesity. 2019;43(7):1344–53. pmid:30923368
- 10. Diamond A. Want to optimize executive functions and academic outcomes? simple, just nourish the human spirit. In: Zelazo PD, Sera MD, editors. Minnesota Symposia on Child Psychology. 2014;37:205–32. https://doi.org/10.1002/9781118732373.ch7 pmid:25360055
- 11. Garon N, Bryson SE, Smith IM. Executive function in preschoolers: a review using an integrative framework. Psychological Bulletin. 2008;134(1):31–60. pmid:18193994
- 12. Rolland-Cachera MF, Deheeger M, Bellisle F, Sempé M, Guilloud-Bataille M, Patois E. Adiposity rebound in children: a simple indicator for predicting obesity. The American Journal of Clinical Nutrition. 1984;39(1):129–35. pmid:6691287
- 13. Blair C, Kuzawa CW, Willoughby MT. The development of executive function in early childhood is inversely related to change in body mass index: evidence for an energetic tradeoff? Developmental Science. 2020;23(1):e12860. pmid:31102547
- 14. Kuzawa CW, Blair C. A hypothesis linking the energy demand of the brain to obesity risk. Proceedings of the National Academy of Sciences. 2019;116(27):13266–75. pmid:31209026
- 15. Favieri F, Forte G, Casagrande M. The executive functions in overweight and obesity: a systematic review of neuropsychological cross-sectional and longitudinal studies. Frontiers in Psychology. 2019;10:2126. pmid:31616340
- 16. Mamrot P, Hanć T. The association of the executive functions with overweight and obesity indicators in children and adolescents: a literature review. Neuroscience & Biobehavioral Reviews. 2019;107:59–68. pmid:31470031
- 17. Liang J, Matheson BE, Kaye WH, Boutelle KN. Neurocognitive correlates of obesity and obesity-related behaviors in children and adolescents. International Journal of Obesity. 2014;38(4):494–506. pmid:23913029
- 18. Pearce AL, Leonhardt CA, Vaidya CJ. Executive and reward-related function in pediatric obesity: a meta-analysis. Childhood Obesity. 2018;14(5):265–79. pmid:29874102
- 19. O’Meagher S, Norris K, Kemp N, Anderson P. Examining the relationship between performance-based and questionnaire assessments of executive function in young preterm children: implications for clinical practice. Child Neuropsychology. 2019;25(7):899–913. pmid:30301415
- 20. Pieper JR, Laugero KD. Preschool children with lower executive function may be more vulnerable to emotional-based eating in the absence of hunger. Appetite. 2013;62:103–9. pmid:23211377
- 21. Tandon P, Thompson S, Moran L, Lengua L. Body mass index mediates the effects of low income on preschool children’s executive control, with implications for behavior and academics. Childhood Obesity. 2015;11(5):569–76. pmid:26440385
- 22. Gross AC, Kaizer AM, Vock DM, Siddiqui S, Fox CK. Cognitive, emotional, and behavioral contributors to early childhood weight status. The Journal of Child Health Care. 2019;23(3):382–91. pmid:31159558
- 23. Hughes SO, Power TG, O’Connor TM, Orlet Fisher J. Executive functioning, emotion regulation, eating self-regulation, and weight status in low-income preschool children: how do they relate? Appetite. 2015;89:1–9. pmid:25596501
- 24. Schmitt SA, Korucu I, Jones BL, Snyder FJ, Evich CD, Purpura DJ. Self-regulation as a correlate of weight status in preschool children. Early Child Development and Care. 2019;189(1):68–78. https://doi.org/10.1080/03004430.2017.1299715
- 25.
Gioia GA, Espy KA, Isquith PK. BRIEF-P: Behavior Rating Inventory of Executive Function-Preschool version: professional manual. Florida: Psychological Assessment Resources; 2003.
- 26. Barlow SE, and the Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120(Supplement_4):S164–S92. https://doi.org/10.1542/peds.2007-2329C
- 27. Heslehurst N, Vieira R, Akhter Z, Bailey H, Slack E, Ngongalah L, et al. The association between maternal body mass index and child obesity: a systematic review and meta-analysis. PLoS Med. 2019;16(6):e1002817. pmid:31185012
- 28. Ruiz M, Goldblatt P, Morrison J, Porta D, Forastiere F, Hryhorczuk D, et al. Impact of low maternal education on early childhood overweight and obesity in europe. Paediatric and Perinatal Epidemiology. 2016;30(3):274–84. pmid:26945670
- 29. Bridger Staatz C, Kelly Y, Lacey RE, Blodgett JM, George A, Arnot M, et al. Socioeconomic position and body composition in childhood in high- and middle-income countries: a systematic review and narrative synthesis. International Journal of Obesity. 2021;45(11):2316–34. pmid:34315999
- 30. Sokol R, Qin B, Poti J. Parenting styles and body mass index: a systematic review of prospective studies among children. Obesity Reviews. 2017;18. https://doi.org/10.1111/obr.12497
- 31. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package ’dagitty’. The International Journal of Epidemiology. 2016;45(6):1887–94. pmid:28089956
- 32. Tennant PWG, Murray EJ, Arnold KF, Berrie L, Fox MP, Gadd SC, et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International Journal of Epidemiology. 2020;50(2):620–32. https://doi.org/10.1093/ije/dyaa213
- 33.
Poomontre J, Setthawong P. A prediction system based on the TMRS standardized socio-economic status (SES) classification of Bangkok and Metropolian subjects XIV International Business and Economy Conference (IBEC) Bangkok, Thailand. 2015. http://dx.doi.org/10.2139/ssrn.2550078
- 34. Baumrind D. The discipline controversy revisited. Family Relations. 1996;45(4):405–14. https://doi.org/10.2307/585170
- 35.
Robinson C, Mandleco B, Olsen S, Hart C. The parenting styles and dimensions questionnaire. California: Sage; 2001.
- 36.
Health Systems Research Institute, Mahidol University, Faculty of Medicine Ramathibodi Hospital. Management of obesity and complications [in Thai]. [Cited 2022 August 27]. Available from: http://164.115.27.97/digital/files/original/596c43663f7bfdb66c2d60d8dd57ccec.pdf
- 37. Nonboonyawat T, Pusanasuwannasri W, Chanrat N, Wongthanavimok N, Tubngern D, Panutrakul P, et al. Prevalence and associates of obesity and overweight among school-age children in a rural community of Thailand. Korean Journal of Pediatrics. 2019;62(5):179–86. pmid:30744316
- 38. Armoon B, Karimy M. Epidemiology of childhood overweight, obesity and their related factors in a sample of preschool children from central Iran. BMC Pediatrics. 2019;19(1):159. pmid:31109318
- 39.
StataCorp. Stata Statistical Software: Release 16. College Station. Texas: StataCorp LLC; 2019.
- 40. Ronan L, Alexander-Bloch A, Fletcher PC. Childhood obesity, cortical structure, and executive function in healthy children. Cerebral Cortex. 2019;30(4):2519–28. https://doi.org/10.1093/cercor/bhz257
- 41. Nelson TD, James TD, Hankey M, Nelson JM, Lundahl A, Espy KA. Early executive control and risk for overweight and obesity in elementary school. Child Neuropsychology. 2017;23(8):994–1002. pmid:27187746
- 42. Tomaso CC, James T, Nelson JM, Espy KA, Nelson TD. Longitudinal associations between executive control and body mass index across childhood. Pediatric Obesity. 2022;17(4):e12866. pmid:34725959
- 43. almusalam S, Asdaq SMB, Almazial N, Alsomali N, Alqahtani N, Mohammed R, et al. Examining the relationship between obesity and memory function in female school children of Riyadh, Saudi Arabia. Journal of King Saud University—Science. 2021;33(8):101663. https://doi.org/10.1016/j.jksus.2021.101663
- 44. Dohle S, Diel K, Hofmann W. Executive functions and the self-regulation of eating behavior: a review. Appetite. 2018;124:4–9. pmid:28551113
- 45. Hofmann W, Schmeichel BJ, Baddeley AD. Executive functions and self-regulation. Trends in Cognitive Sciences. 2012;16(3):174–80. pmid:22336729
- 46. Shields GS, Deer LK, Hastings PD, Hostinar CE. Adiposity, inflammation, and working memory: Evidence for a vicious cycle. Brain, Behavior, & Immunity—Health. 2021;13:100202. https://doi.org/10.1016/j.bbih.2021.100202
- 47. Alarcón G, Ray S, Nagel BJ. Lower working memory performance in overweight and obese adolescents is mediated by white matter microstructure. The Journal of the International Neuropsychological Society. 2016;22(3):281–92. pmid:26708324
- 48. García-García I, Michaud A, Dadar M, Zeighami Y, Neseliler S, Collins DL, et al. Neuroanatomical differences in obesity: meta-analytic findings and their validation in an independent dataset. International Journal of Obesity. 2019;43(5):943–51. pmid:30022057
- 49. Buie JJ, Watson LS, Smith CJ, Sims-Robinson C. Obesity-related cognitive impairment: the role of endothelial dysfunction. Neurobiology of Disease. 2019;132:104580. pmid:31454547
- 50. Lowe CJ, Reichelt AC, Hall PA. The prefrontal cortex and obesity: a health neuroscience perspective. Trends in Cognitive Sciences. 2019;23(4):349–61. pmid:30824229
- 51. Piccininni M, Konigorski S, Rohmann JL, Kurth T. Directed acyclic graphs and causal thinking in clinical risk prediction modeling. BMC Medical Research Methodology. 2020;20(1):179. pmid:32615926
- 52. Hayes JF, Eichen DM, Barch DM, Wilfley DE. Executive function in childhood obesity: promising intervention strategies to optimize treatment outcomes. Appetite. 2018; 124: 10–23. pmid:28554851
- 53. Dencker M, Thorsson O, Linden C, Wollmer P, Andersen L, Karlsson M. BMI and objectively measured body fat and body fat distribution in prepubertal children. Clinical Physiology and Functional Imaging. 2007;27:12–6. pmid:17204032