Cognitive,emotional,and behavioral contributors to early childhood weight status

Abstract

Pediatric obesity is a serious public health concern affecting almost 16% of two- to five-year-olds. Prior research has not sufficiently addressed how various factors combine to contribute to the heterogeneous condition of obesity. The goal of this study was to assess multiple individual factors to determine how they collectively contribute to weight status in young children, as this information could lead to tailored interventions. This was a cross-sectional, population-based study of three- to five-year-olds. Child height and weight were measured. Parents completed a demographic survey and validated questionnaires regarding these child characteristics: internalizing and externalizing behaviors, sleep problems, executive functions, and food approach and food avoid behaviors. Data for 154 participants (mean age: 4.4 ± 0.8 years; mean body mass index-z: .28 ± 1.0; 50% male) were analyzed using linear and logistic regression and a stepwise regression procedure. In the stepwise selection procedure for the binary outcome of obese/overweight versus normal weight, food avoid (p = .151), food approach (p = .017), and the White demographic variable (p = .117) were identified as important predictors. In conclusion, when considering various cognitive, emotional, and behavioral factors, only food approach and food avoid eating behaviors predicted weight status in young children, suggesting prevention and intervention efforts should specifically address these aspects in young children.

Keywords

Early childhood eating behavior executive function externalizing behavior internalizing behavior pediatric obesity

Introduction

According to most recent estimates, nearly 16% of two- to five-year-olds in the United States have obesity (i.e. body mass index (BMI) ≥ 95th percentile for age and sex) (Skinner et al., 2018), and elevated weight status portends significant risk for short- and long-term health problems (Reilly et al., 2003; Reilly and Kelly, 2011). Multiple factors contribute to obesity in young children, for example, genetic predisposition, calorie dense foods, sedentary behavior, and socioeconomic status (Robinson et al., 2012). Although energy imbalance is the common proximal cause of this condition, it is unclear what combination of factors contributes to the energy imbalance and the extent to which groupings of factors can be identified and subsequently targeted for intervention. Most studies have examined a relatively limited number of individual factors and their isolated relation to obesity, but few have evaluated the manner in which multiple factors jointly contribute to different pediatric obesity subtypes, particularly in the very young (Boutelle et al., 2014; Kral, 2018).

Liang et al. (2014) proposed a neurocognitive (i.e. cognitive, emotional, and behavioral factors) model of pediatric obesity and obesity-related behaviors in which neurocognitive functioning, obesity-related behaviors, and obesity each influences the other domains. One aspect of neurocognitive functioning is executive functions, which are higher order processes of the brain used for self-regulation and control (e.g. inhibition, cognitive flexibility). In this systematic review of the literature, executive functions were consistently related to obesity and obesity-related behaviors. However, many questions remain on directionality of the relationship of executive functions with obesity and obesity-related behaviors (i.e. do poor executive functions lead to obesity or does obesity lead to weaker executive functions). Further, it is unclear if there are age- and BMI-related differences (Liang et al., 2014).

Children with obesity may struggle with emotional and behavioral problems, particularly in samples seeking weight management treatment (Puder and Munsch, 2010). In a recent large-scale population study of children, internalizing (e.g. anxiety, depression) and externalizing (e.g. attention problems, aggression) behaviors were associated with being overweight at later time points (Camfferman et al., 2016). Specifically, the presence of internalizing or externalizing concerns at 1.5 years of age was associated with overweight at 3 years of age, and the presence of these concerns at 3 years was associated with overweight at 6 years. Effect sizes were small but significant. Weight at initial time points did not predict internalizing or externalizing concerns at later time points (Camfferman et al., 2016). In another longitudinal study, externalizing behaviors, aggressive behaviors, and anger at age two years predicted higher BMI at age 10 (Holm-Denoma et al., 2013). Both research groups suggested physical inactivity as a possible mediator between behavior problems and BMI (Camfferman et al., 2016; Holm-Denoma et al., 2013). Children with behavior problems may be less likely to participate in activities or spend time with peers, leading to increased screen time or other sedentary behaviors. Additionally, parenting practices may be altered by the presence of internalizing or externalizing behaviors. For example, children with externalizing behavior struggle with self-control and it may be hard for parents to manage their behavior. Therefore, food may be used as a tool for behavior management. Similarly, children with internalizing problems may be provided food for coping (Camfferman et al., 2016). Notably, not all research supports a relationship between emotional and behavioral concerns and childhood obesity. A large-scale study of toddlers in Norway identified no cross-sectional or longitudinal association between internalizing or externalizing behaviors and increased BMI (Garthus-Niegel et al., 2010). In general, the directionality and development of the relationship between these concerns are not well understood, suggesting the need for further exploration (Puder and Munsch, 2010).

Eating behaviors have also been identified as contributors to pediatric obesity. In a study of treatment-seeking 8- to 11-year-old children, researchers identified three distinct overeating phenotypes through direct observation of eating as well as parent- and child-reported eating behaviors (Boutelle et al., 2014). These types were classified as children with (1) moderate satiety and food response, (2) high satiety response, and (3) high food response. Interestingly, similar factors— satiety responsiveness and food cue responsiveness—have been shown to be related to adiposity in children as young as three years of age, which suggests that over time such factors could contribute to a lifetime risk of obesity (Carnell and Wardle, 2008).

Treatment guidelines for childhood obesity recommend a staged approach that moves from prevention for children with normal weight through four stages of intervention for those with overweight or obesity (Barlow, 2007). Stages 1 and 2 are completed in the primary care setting. Stages 3 and 4 are in a structured weight management program or tertiary care setting, respectively, where more intensive approaches are available. In each stage, dietary and physical activity recommendations are provided (Barlow, 2007). However, the guidelines do not specifically address how the constellation of cognitive, emotional, and behavioral contributors to obesity may represent obesity phenotypes. Various phenotypes may respond differently to different treatments.

In sum, prior research shows associations between childhood obesity and several individual neurocognitive and eating behavior factors. There has been less research on the way in which combinations of these factors converge to influence weight, particularly in young children. The purpose of the current study was to measure multiple cognitive, emotional, and behavioral obesity determinants in young children that may collectively identify obesity subtypes. Classification of early childhood obesity subtypes may provide insight into contributing mechanisms as well as potential avenues for more personalized intervention.

Method

Study design and participants

For this cross-sectional, population-based study, 154 three- to five-year-olds and their parents/guardians were recruited from the 2016 Minnesota State Fair (mean age: 4.4 ± 0.8 years; 50% male). The Minnesota State Fair is an annual public gathering which operates for a 10-day period at the end of every summer in the greater Minneapolis-St Paul metropolitan area. Participants were randomly recruited during four, six-hour blocks of time in the Driven to Discover Building, a dedicated research space at the Minnesota State Fair. Consent was given by a parent/guardian prior to participating in the study. Assent was not obtained given the young age of the children. The Internal Review Board at the University of Minnesota approved this study. Over the 10 days in 2016, there were 1,943,710 Fair attendees and approximately 50,000 passed through the Driven to Discover Building.

Measures

Children’s height and weight were measured (to compute BMI) using a stadiometer and scale. Height was measured twice, and if the heights differed by more than 0.5 cm, height was measured a third time. A mean of the height measurements was used as the final height. Weight was also measured twice, without heavy outerwear or shoes. If weight differed by more than 0.3 kg, a third measurement was taken. The mean of weights was used as the final weight.

Parents completed the Child Behavior Checklist (CBCL) for ages 1.5–5, Behavior Rating Inventory of Executive Functions–Preschool Version (BRIEF-P), and Child Eating Behavior Questionnaire (CEBQ) on an electronic tablet. In addition to these validated instruments, parents completed a general demographic survey (i.e. child’s and parent’s age, sex, race/ethnicity, parent education, and household income).

Child Behavior Checklist for ages 1.5–5

The CBCL is a measure of emotional and behavioral concerns, including internalizing, externalizing, attention, and sleep problems. The CBCL has been shown to have strong reliability and validity (Achenbach and Rescorla, 2000). Parents provide ratings on a number of mood and behavior characteristics observed in the prior 2 months, using the categories not true, somewhat true, and very often true. T-scores are calculated for the scales, with an average range of 40–60. The internalizing, externalizing, and sleep problems scales were analyzed. The sleep problems scale was included due to the relationship of sleep with weight (Hart et al., 2011).

Behavior Rating Inventory of Executive Function–Preschool Version

The BRIEF-P is an assessment of executive functions measuring inhibition, shifting, emotional control, working memory, planning, and organization. The BRIEF-P has strong validity as well as high internal consistency and test–retest stability (Gioia et al., 2003). Parents report on various behaviors as occurring never, sometimes, or often in the prior six months. T-scores are calculated for the scales, with an average range of 40–60. For the current study, the inhibitory self-control, flexibility, and emergent metacognition indices were included in the analyses.

Child Eating Behavior Questionnaire

The CEBQ is a measure of eating behaviors for children 2- to 12-years old, including food approach and food avoidant scales. The food approach scales include food responsiveness, emotional overeating, enjoyment of food, and desire to drink; the food avoidant scales include satiety responsiveness, slowness in eating, emotional undereating, and food fussiness (Carnell and Wardle, 2007; Wardle et al., 2001). Parents use a five-point Likert-type scale (i.e. never to always) to provide ratings of how frequent the eating behaviors occur. The overarching food avoid and food approach scales were used for the current analysis.

Data analysis

We summarized continuous covariates as mean (standard deviation) and categorical covariates as frequency (percentage) both overall and by obesity group (normal weight, overweight, and obese). Univariate associations between obesity outcomes of interest and the various measures from CBCL, BRIEF-P, and CEBQ used linear regression (BMI z-score) or logistic regression (underweight/normal weight vs overweight/obese). To identify which of the various measures may be important to describe the outcomes of BMI z-score and overweight status when accounting for the other measures, the automatic stepwise regression procedure was used. Stepwise regression starts with the full regression model including all potential variables: all CBCL, BRIEF-P, and CEBQ measures in addition to the demographic variables of male, race (White vs. other), and ethnicity (Hispanic vs. non-Hispanic). Then, using the Akaike information criterion (AIC), the stepwise regression procedure identifies the ‘best’ model that minimizes the AIC value. The ‘best’ model from the stepwise process may include variables with insignificant p-values (p > .05); however, their removal does not improve the AIC significantly enough to warrant their exclusion from the model. All analyses were completed using R v3.4.2 (Team, 2017).

Results

The final sample included 154 participants (50% male; mean BMI-z: .28 ± 1.0). Mean age was 4.4 ± 0.8 years; there were 56 (36.4%) 3-year-olds, 53 (34.4%) 4-year-olds, and 45 (29.2%) 5-year-olds. Of the total sample, 5 (3.2%) had a BMI in the underweight range, 116 (75.3%) had a BMI in the normal range, 26 (16.9%) were overweight (BMI ≥ 85th to <95th percentile), and 7 (4.5%) were obese (≥95th percentile). On average, parents did not report elevated concerns regarding behavioral regulation, sleep, or executive functioning. Responses on the CEBQ resulted in mean scores of 2.67 for food approach domain and 3.04 on food avoid domain based on the one to five Likert-type scale (Table 1).

Table 1.

Subject characteristics.^a

Covariate	Overall	Normal weight	Overweight	Obese
Covariate	(N = 154)	(N = 121)	(N = 26)	(N = 7)
Age (months), mean (SD)	52.6 (10.0)	52.5 (10.2)	53.5 (9.53)	52.7 (10.7)
Male, n (%)	77 (50.0)	60 (49.6)	12 (46.2)	5 (71.4)
Race, n (%)
American Indian/Alaskan Native	3 (1.9)	2 (1.7)	0 (0.0)	1 (14.3)
Asian	5 (3.2)	5 (4.1)	0 (0.0)	0 (0.0)
Black/African American	1 (0.6)	1 (0.8)	0 (0.0)	0 (0.0)
Hawaiian/Other Pacific Islander	2 (1.3)	2 (1.7)	0 (0.0)	0 (0.0)
White	129 (83.8)	98 (81.0)	25 (96.2)	6 (85.7)
Multiracial	13 (8.4)	12 (9.9)	1 (3.8)	0 (0.0)
Other	1 (0.6)	1 (0.8)	0 (0.0)	0 (0.0)
Hispanic	4 (2.6)	2 (1.7)	2 (7.7)	0 (0.0)
Weight (kg), mean (SD)	17.7 (2.89)	17.0 (2.38)	19.5 (2.37)	23.5 (3.46)
Height (cm), mean (SD)	105 (7.14)	105 (7.13)	106 (6.66)	108 (9.17)
BMI, mean (SD)	16.0 (1.52)	15.5 (0.91)	17.4 (0.37)	20.1 (2.72)
BMI z-score, mean (SD)	0.28 (1.03)	−0.07 (0.79)	1.32 (0.2)	2.55 (1.22)
BMI percentile, mean (SD)	58.5 (27.9)	49.5 (24.5)	90.2 (3.22)	97.7 (1.78)
% of the 95th BMI percentile, mean (SD)	88.5 (8.63)	85.4 (5.28)	96.3 (2.43)	112 (15.6)
Weight category, n (%)
Underweight	5 (3.2)	5 (4.1)	0 (0.0)	0 (0.0)
Normal weight	116 (75.3)	116 (95.9)	0 (0.0)	0 (0.0)
Overweight	26 (16.9)	0 (0.0)	26 (100.0)	0 (0.0)
Obese	7 (4.5)	0 (0.0)	0 (0.0)	7 (100.0)
Parent education, n (%)
Some HS	1 (0.6)	1 (0.8)	0 (0.0)	0 (0.0)
HS diploma/GED	9 (5.8)	4 (3.3)	3 (11.5)	2 (28.6)
Postsecondary nondegree award	7 (4.5)	6 (5.0)	0 (0.0)	1 (14.3)
Some college, no degree	24 (15.6)	20 (16.5)	3 (11.5)	1 (14.3)
Bachelor’s degree	73 (47.4)	60 (49.6)	10 (38.5)	3 (42.9)
Master’s degree	22 (14.3)	16 (13.2)	6 (23.1)	0 (0.0)
Doctoral or professional degree	13 (8.4)	10 (8.3)	3 (11.5)	0 (0.0)
Other	5 (3.2)	4 (3.3)	1 (3.8)	0 (0.0)
Income, n (%)
$49,000 or less	22 (14.3)	18 (14.9)	2 (7.7)	2 (28.6)
$50,000–$99,000	55 (35.7)	40 (33.1)	12 (46.2)	3 (42.9)
$100,000–$149,000	45 (29.2)	37 (30.6)	7 (26.9)	1 (14.3)
$150,000 or more	32 (20.8)	26 (21.5)	5 (19.2)	1 (14.3)
Test results, mean (SD)
Internal T-score	46.2 (10.2)³	46.4 (10.4)³	45.7 (10.3)	44.1 (7.29)
External T-score	44.2 (9.36)³	44.5 (9.34)³	42.8 (8.43)	45.0 (13.4)
Sleep problems T-score	54.2 (6.59)²	54.5 (6.96)²	53.4 (5.43)	53.4 (3.64)
ISCI T-score	44.1 (8.54)	44.4 (8.57)	41.4 (6.33)	49.9 (12.5)
FI T-score	43.4 (8.33)	43.6 (8.57)	40.9 (5.38)	48.3 (11.0)
EMI T-score	44.5 (9.28)	44.8 (9.46)	42.2 (7.85)	48.9 (10.1)
Food approach	2.67 (0.49)¹	2.61 (0.48)¹	2.91 (0.52)	2.84 (0.21)
Food avoid	3.04 (0.48)¹	3.08 (0.49)¹	2.88 (0.46)	2.89 (0.23)

SD: standard deviation; BMI: body mass index; HS: High school.

^aValues expressed are mean (SD) or n (%) where indicated.

Note: Superscript number denotes number of subjects missing given observation.

In the univariate analyses, only food approach and food avoid had significant results for both BMI z-score and overweight/obese status (p < .05). Higher food approach scores were associated with higher BMI z-scores and higher odds of being overweight/obese; higher food avoid scores were associated with the opposite relationship of lower BMI z-scores and lower odds of being overweight/obese (Online Supplemental Tables 1 and 2). In the stepwise selection procedure, food avoid (p = .004) and the White demographic variable (p = .154) were included in the ‘best’ model for BMI z-score based on the AIC (Table 2). Using the stepwise selection procedure for the binary outcome of obese/overweight versus normal weight, food avoid (p = .151), food approach (p = .017), and the White demographic variable (p = .117) were identified as important predictors from the ‘best’ model based on the AIC (Table 3). The coefficients from these stepwise models suggested similar relationships to the univariate associations.

Table 2.

Full and reduced models identified by stepwise selection for the outcome of BMI z-score and given covariates.

Covariate	Estimate	95% CI	p-Value
Full model
Intercept	.656	−1.608 to 2.921	.571
Internal T-score	−.009	0.013 to −0.697	.487
External T-score	.020	0.015 to 1.367	.174
Sleep problems T-score	.004	0.016 to 0.244	.807
ISCI T-score	−.008	0.024 to −0.346	.730
FI T-score	.011	0.020 to 0.573	.567
EMI T-score	−.008	0.016 to −0.517	.606
Food approach	.183	0.188 to 0.977	.330
Food avoid	−.514	0.207 to −2.482	.014
Male	−.070	0.185 to −0.379	.705
White	.325	0.234 to 1.389	.167
Hispanic	.556	0.545 to 1.020	.310
Reduced model
Intercept	1.557	0.408 to 2.706	.009
Food avoid	−.508	0.175 to −2.912	.004
White	.318	0.222 to 1.432	.154

CI: confidence interval; ISCI: inhibitory self-control index; FI: flexibility index; EMI: emergent metacognition index.

Table 3.

Full and reduced models identified by stepwise selection for the outcome of obese/overweight versus normal weight and given covariates.

Covariate	OR (overweight/obese = 1)	95% CI	p-Value
Full model
Intercept	.049	0.0002–15.368	.298
Internal T-score	1.006	0.943–1.073	.862
External T-score	.991	0.924–1.062	.797
Sleep problems T-score	1.005	0.917–1.089	.900
ISCI T-score	.955	0.842–1.080	.470
FI T-score	1.008	0.906–1.121	.882
EMI T-score	1.025	0.948–1.108	.529
Food approach	3.165	1.289–8.221	.014
Food avoid	.479	0.153–1.410	.191
Male	1.001	0.410–2.449	.998
White	3.712	0.933–25.247	.102
Reduced model
Intercept	.043	0.0004–3.237	.165
Food approach	2.920	1.229–7.282	.017
Food avoid	.480	0.170–1.281	.151
White	3.420	0.893–22.268	.117

OR: odds ratio; CI: confidence interval.

Discussion

When simultaneously evaluating multiple factors possibly related to early childhood BMI, including internalizing and externalizing behaviors, sleep problems, executive functions, and eating behaviors, only eating behaviors were significantly associated with BMI. In particular, higher BMI status was seen in children who scored high on food approach scales (e.g. food responsive, enjoyment of food) and low on food avoid scales (e.g. satiety, slow eating).

These findings are consistent with prior research showing satiety and food responsiveness are each related to BMI in children as young as three-years-old and into older childhood (Boutelle et al., 2014; Carnell and Wardle, 2008). Typically, childhood obesity interventions do not directly target aspects such as hunger or satiety responsiveness. Childhood obesity treatment guidelines primarily recommend food and physical activity changes (Barlow, 2007). However, past and current findings suggest a need to also consider factors such as hunger or satiety responsiveness in prevention and treatment efforts. For example, family-based obesity intervention could supplement traditional approaches with mindful eating strategies similar to those that have addressed eating behaviors of adults with obesity (Emley et al., 2017; Reilly et al., 2014), where mindfulness allows for self-recognition of level of hunger or fullness. In a recent study, parents of children two- to five-years-old were randomly assigned to a mindfulness plus nutrition and physical activity training or a control with nutrition and physical activity training. After the eight-week intervention, children of parents in the mindfulness group showed significantly improved BMI percentile trajectory compared to the control group (Jastreboff et al., 2018).

The current findings were inconsistent with previous research with regard to sleep. In the current study, sleep problems did not predict weight status. Previous studies have consistently found an association between short sleep duration and childhood obesity (Hart et al., 2011). One important distinction between prior and current studies is the definition of sleep concerns. In the current study, sleep concerns were an aggregate of general sleep problems (e.g. nightmares, sleeping too little), not a single measure of sleep duration. This difference in definition and resulting outcomes suggests that sleep duration may be more relevant to weight than general sleep disruption (Hart et al., 2011).

Similarly, executive functions and behavior or mood concerns did not predict weight status in this study. The difference in findings compared to prior studies may be related to the assessment methods or the study population (Liang et al., 2014). Parent report of general executive functions is not necessarily consistent with direct observation of a specific executive skill (e.g. impulse control). In prior research, direct observation of child behavior has been more closely associated with BMI than parent report of executive functions (Nederkoorn et al., 2006). Additionally, a child’s executive functions may not directly contribute to weight in this young cohort given parental control of a young child’s access to food. Relatedly, parental control of the food environment may prevent internalizing or externalizing problems from substantially affecting weight in the younger years. Finally, prior research in older youth shows the association of mental health and BMI is more consistent in samples of youth seeking weight management treatment than nontreatment-seeking samples (Puder and Munsch, 2010).

The strengths of recruiting from a venue such as a state fair include capturing those who may not otherwise be aware of, or able to access, university-based research studies. One limitation is the cross-sectional nature of the study. Another limitation is the high proportion of parents with advanced education and high incomes, as well as the majority of children identifying as White. With the small representation of participants who were non-White and participants from lower socioeconomic status, we were restricted to White versus ‘other’ for race differences in our statistical models and unable to provide more in-depth analyses of other cultural or socioeconomic factors. However, we were able to recruit an ample sample size of children who were diverse within the age group and weight status.

In conclusion, this study adds to the small but growing body of literature on how multiple factors contribute to weight status in young children. In a community sample of three- to five-year-olds, eating behavior, but not executive function, internalizing or externalizing behaviors, or sleep problems were predictive of weight status in young children. From a preventative standpoint, it may be important to monitor eating behavior characteristics in primary care. In regards to treatment, strategies that directly address eating behaviors such as food or satiety responsiveness or emotional under/overeating may improve outcomes of lifestyle modification therapy.

Supplemental material

Supplemental_Table_1_and_2 - Cognitive, emotional, and behavioral contributors to early childhood weight status

Supplemental_Table_1_and_2 for Cognitive, emotional, and behavioral contributors to early childhood weight status by Amy C Gross, Alexander M Kaizer, David M Vock, Sana Siddiqui and Claudia K Fox in Journal of Child Health Care

Footnotes

Acknowledgements

The authors would like to thank the Driven to Discover (D2D) facility and team at the University of Minnesota.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: CF receives research support from Novo Nordisk.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by an internal grant from the University of Minnesota’s Department of Pediatrics and UL1TR002494 from the National Institutes of Health's National Center for Advancing Translational Sciences.

Supplemental material

Supplemental material for this article is available online.

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