Effect of Autism on Overweight in Children from a Socio-ecological Perspective

doi:10.21203/rs.3.rs-4916687/v1

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Research Article

Effect of Autism on Overweight in Children from a Socio-ecological Perspective

https://doi.org/10.21203/rs.3.rs-4916687/v1

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Background

Although autism spectrum disorder (ASD) is known to be correlated with chronic physical conditions such as gastrointestinal disorders, musculoskeletal problems, and sensory processing disorders, a nationwide nonclinical sample of overweight children with ASD is limited. We aimed to use a large nationally representative non-clinical sample to explore the relationship between ASD and childhood adjusted for socio-ecological factors.

Methods

This cross-sectional data analysis from the 2021 National Survey of Children’s Health was conducted with the modified ecological systems theory model as the guiding framework. A propensity-score matching analysis helped isolate the factors affecting the weight status (i.e., being overweight) in children with ASD, controlling for demographic characteristics, physical activity habits, and familial and environmental circumstances.

Results

Among the 20,091 individuals (mean age, 13.77 years; girls, 48.59%; equivalent to 32,211,963 individuals in the general population) identified for this study, 1,348 individuals (mean age, 13.94 years; girls, 22.55%; representing 1,904,381 individuals from the general population) were examined in our final matched model to determine the link between ASD and being overweight in children. ASD had a statistically significant effect on being overweight. Notably, children with ASD and those with similar socio-ecological factors showed a higher risk of being overweight if they were Hispanic, had less healthy parents, or were involved in more structured activities (all p-values < .05).

Conclusions

Our findings underscore a pronounced association between autistic children and being overweight. This underscores the necessity for careful attention towards preventing excessive weight gain and for tailored management in children who experience the neuropsychological difficulties associated with ASD.

autism spectrum disorder

children

quality of life

socio-ecological perspective

overweight.

Over the past several years, the coronavirus disease 2019 (COVID-19) pandemic has imposed unprecedented limitations on physical and social activities, leading to a worrying increase in overweight children (1). Several contributing factors, including a shift to online learning, reduced children’s daily physical activity and increased their screen time (1). Additionally, restrictions on public spaces and organized sports activities limited children's opportunities to exercise (2–4). Furthermore, social isolation tilted children’s dietary patterns toward unhealthy choices, with increased consumption of processed and calorie-dense foods (5). As societies aim for post-pandemic normalization, prioritizing interventions to combat the adverse effects of these lifestyle changes on children’s health has become increasingly crucial (1).

The prevalence of being overweight in children has been extensively investigated and reported. More than one in three children and adolescents (hereafter referred to as “children”) are overweight or obese (hereafter referred to as “overweight”), and the body mass index (BMI) of some children doubled during the COVID-19 pandemic (1, 6). Childhood overweight and obesity are defined by a sex- and age-adjusted BMI between the 85th and 95th percentile and greater than the 95th percentile, respectively (7).

In children, being overweight can have negative effects on an individual’s general health and well-being. For example, overweight children are more likely to be at risk for cardiovascular diseases, including high blood pressure and/or high cholesterol levels (8), and show higher rates of pre-diabetes resulting in diabetes later in life (9), increased risk for bone and joint problems and sleep difficulties (10), and psychosocial problems such as low self-esteem and stigmatization (11). Moreover, overweight children have an increased likelihood of obesity later in life (12), as well as higher risks for heart disease, type 2 diabetes, stroke, several types of cancer, and osteoarthritis compared to normal weight children (10). It also shows a strong association with many types of cancers, such as breast, colon, kidney, pancreas, thyroid, ovary, cervix, and prostate cancers, multiple myeloma, and Hodgkin’s lymphoma (13).

In general, being overweight in childhood is highly associated with lifestyle habits, such as high-calorie intake, less physical activity, and more sedentary lifestyles (10). These lifestyle patterns are attributable to families (14), schools (15), communities (16), and the environment (17). Families, schools, and communities play important roles in preventing and minimizing children’s obesity, especially by establishing healthy and positive environments with policies and initiatives that provide opportunities for children to learn and practice healthy eating and become more physically active (18).

A child's entire daily life is shaped and influenced by socio-ecological factors, such as parents, family dynamics, community environment, and educational settings (19–24). The causes of being overweight in childhood are multifactorial and involve genetic, environmental, and behavioral factors (7, 10, 14, 15, 17). Autism spectrum disorder (ASD) has been identified as a potential risk factor for chronic physical conditions such as gastrointestinal disorders, musculoskeletal problems, and sensory processing disorders (25–30). However, research on the association between children with ASD and being overweight in childhood remains limited, even when socio-ecological factors are controlled for in nationwide nonclinical data (19, 31, 32). The incidence of ASD in children is increasing worldwide. Concurrently, the prevalence of being overweight or obese among children has also increased significantly. Previous studies have suggested a link between having ASD and having a higher risk of being overweight (19, 32). This study also aimed to delve deeper into this relationship while considering the socio-ecological influences, which play a significant role in children’s health outcomes (33). Factors such as socioeconomic status, accessibility to parks and recreational spaces, and parental knowledge of nutrition can directly influence children’s weight (19–22, 24). Previous studies have suggested a possible link between ASD and being overweight or obese in children (34). However, many of these studies did not thoroughly consider socio-ecological influences, which could act as confounders.

Moreover, research on obesity in children with ASD is limited owing to the relatively few cases in comparison with the general population. Children with ASD often face substantial social and behavioral challenges from both the family and society (26), making them more vulnerable to obesity and its associated complex behavioral, psychosocial, and physical difficulties and hardships (35). Thus, the association of being overweight in children with ASD may be attributable to a variety of reasons. Some of the most significant symptoms in children with ASD are a requirement for guidance in self-reliance, thriving with external motivation and reinforcement, and need for assistance in social interaction skills (25, 27, 36). Children with ASD have limited opportunities for safe and appropriate physical activity (37, 38). Additionally, these children are more likely to participate less in structured physical activities (38) or family meals, causing unhealthy weight gain (39) and poor sleep quality and potentially resulting in obesity (40). Owing to the lack of autonomy and social motivation, healthy and sound societal environments, including parents’ health, neighborhood and school safety, and accessibility of facilities, play important roles in maintaining healthy weight in children with ASD (19, 25, 31, 34, 41).

This study aimed to examine a large, non-clinical sample of children with ASD to evaluate the factors associated with being overweight in children with ASD. This cross-sectional study utilized the 2021 National Survey of Children’s Health data (42, 43), and the assessment of children in need and their families of child safeguarding and promoting welfare was applied as a conceptual framework to guide the study in capturing any significant factors and their associations with being overweight in children with ASD. We hypothesized that ASD is (a) associated with being overweight in children after controlling for socio-ecological factors; (b) social interactions (e.g., social involvement, voluntary activities, and family meals) have a positive influence on the weight status of overweight children; (c) parents’ physical and mental health statuses positively influence the weight status of overweight children; (d) insufficient sleep has a negative influence on the weight status of overweight children; and (e) neighborhood conditions (e.g., accessibility to and number of parks and recreational facilities) are associated with being overweight in children with ASD and those with similar socio-ecological backgrounds.

2.1. Data Source and Study participants

The data used in this study were obtained from the 2021 National Survey of Children’s Health (NSCH) (43), which provides a wealth of data on multiple intersecting aspects: physical and mental health; access to quality healthcare; and the child’s family, neighbors, schools, and social context. The NSCH is funded and administered by the Health Resources and Services Administration (HRSA) Maternal and Child Health Bureau (MCHB) (42). NSCH is conducted annually in the United States, with online and mail surveys sent to randomly selected households nationwide (42) Although the original data were obtained from 50,892 children, it did not include the BMI data of children aged 0–9 years, so the data for 20,091 children aged 10–17 years were analyzed to determine the effects on being overweight in children with and without ASD. This study was verified as a non-human subject study by the relevant institutional review boards.

2.2. Conceptual Framework

To elucidate the complexity of the causes of being overweight in children with ASD, a modified socio-ecological approach was employed as the main conceptual framework in this study (33). Urie Bronfenbrenner's socio-ecological model (33) is a comprehensive framework for understanding human development, emphasizing the interplay between an individual and their environment. It includes the microsystem (e.g., child’s interaction with parents and teachers), mesosystem (e.g., relationship between home and school), exosystem (e.g., parents' workplaces, community services), macrosystem (e.g., cultural values, norms), and chronosystem (e.g., aging, significant life events). Bronfenbrenner’s model highlights the importance of multiple environmental layers influencing development. We adapted this model to examine how factors such as the individual, family and home, parents, workplace and peers, community and neighborhood, and broader environments influence being overweight in childhood.

The five main aspects that were assessed in this framework were as follows: (a) children’s age, sex, race/ethnicity, physical activity level, sedentary lifestyle habits, and non-adherence to preventive checkups due to the COVID-19 pandemic; (b) parental competence, which included parental education level, income level, and household structure; (c) family health and activities, which included parental health, drinking and smoking status, familial eating patterns, and stability of food provision; (d) community and school activities, which included children’s participation in activities after school and community service, and parental participation in children’s activities related to the community and school; and (e) neighborhood safety and support, which included the degree of safety and support in the neighborhood, the stability of the school and neighborhood, the cooperation from the neighborhood, and the accessibility of facilities for children’s activities in the neighborhood (Fig. 1).

[Insert Fig. 1 here]

2.3. Dependent Variable

The main outcome variable of the study was overweight status: overweight (age- and sex-adjusted BMI ≥ 85th percentile) versus non-overweight (0th percentile < age- and sex-adjusted BMI < 85th percentile) (7). Children’s BMI was obtained by dividing their weight in kilograms by the square of their height in meters (44). The age- and sex-adjusted BMI percentile was calculated using a growth chart to determine the severity of being overweight in children (45).

2.4. Independent Variables

This study examined how ASD affects being overweight in childhood in terms of socio-ecological factors. ASD in children was measured as a dichotomous variable by evaluating whether they currently had Asperger’s disorder, autism, including pervasive developmental disorders, or ASD. Obesogenic variables were extracted from the following five main factors: individual factors, parental competence, family activities, community and school activities, neighborhood support, and environmental factors.

First, among the socio-ecological factors related to being overweight in childhood, the individual factors evaluated in this study included age, sex, race/ethnicity, physical activity, sedentary lifestyle, and quality of sleep. Since BMI data of younger children were not available, only participants aged 10 to 17 years were included in this study. The participants’ race/ethnicity was categorized as Hispanic, non-Hispanic white, non-Hispanic Black, non-Hispanic Asian, and others. Physical activity was measured on a 4-point Likert scale (1 = 0 days to 4 = every day) based on the number of days the children participated in at least 60 min of physical activity, exercise, or sports during the past week. Sedentary activities over the past week, such as watching television, computers, cell phones, or other electronic devices, playing games, surfing the Internet, or spending time on non-academic social networks, were evaluated using a five-point Likert scale (1 = less than one hour to 5 = more than four hours). Adequate sleep quality was measured as the number of hours a child slept on an average day on most weekdays during the past week and converted into a binary variable based on the recommendation for appropriate sleep duration by the American Academy of Pediatrics (46) (i.e., children aged 6 to 12 years should sleep 9 to 12 hours every 24 h; adolescents aged 13 to 18 years old should sleep 8 to 10 hours every 24 h). The binary variable of missed, delayed, or skipped preventive checkups owing to the coronavirus pandemic (PrevCOVID_21) in the previous 12 months was also assessed.

Childhood obesogenic factors related to parental competence included parental education and income levels and family structure. Parents’ education levels were measured using a 4-point Likert scale (1 = less than high school to 4 = college degree or higher) for the highest education level among adults in the children’s homes. Parents' income levels were measured on a 4-point Likert scale (1 = 0–99% of the Federal Poverty Level [FPL] to ≥ 400% of the FPL) for the household income level. Family structure was initially measured on a five-point Likert scale and then restructured to a binary variable (i.e., only parents versus other family structures).

Childhood obesogenic factors related to family health and activities included overall parental health, drinking and drug use status, stress-coping ability, meals together, and food security. Overall parental health included the physical and mental health of the primary caregiver, which was measured on a 4-point Likert scale and summated to minimum and maximum total scores of 1 and 12, respectively, with higher scores indicating better overall parental health. Drinking and drug use status was evaluated as a binary variable that evaluated whether the child lived with a person who had alcohol or drug use problems. Stress-coping ability was evaluated using a 3-point Likert scale that evaluated how frequently the child’s family showed qualities of resilience (0 = All or most of the time to 0–1 items to 2 = All or most of the time to all four items). Meals together were measured using a 4-point Likert scale that evaluated how frequently all members of the family ate together over the past week (1 = 0 days to 4 = everyday). Food security was measured on a four-category scale that evaluated the ability of the child’s household to afford food over the past 12 months (1 = always get enough nutritious food to 4 = often insufficient).

Three overweight children-related community and school activity variables were evaluated in this study. First, organized activities after school were assessed on a binary scale by evaluating whether the child had participated in an organized activity or class after school or on weekends over the past 12 months. The child’s participation in community service or volunteering was assessed on a binary scale by evaluating whether the child had participated in community service or volunteering at school, church, or in the community over the past 12 months. The parental participation variable was assessed using a 4-point Likert scale evaluating how often the child’s parents participated in these events and activities (1 = rarely or never to 4 = always).

Four variables were used to evaluate neighborhood safety and support. School and neighborhood safety were measured on a 3-point Likert scale using the question “How much do you agree that this child is safe in the school or neighborhood” (1 = Somewhat/definitely disagree to 3 = Definitely agree). The supportive neighborhood variable was assessed on a binary scale by evaluating whether the child lived in a supportive neighborhood. The neighborhood amenities variable was assessed using a 5-point Likert scale that evaluated how many amenities the neighborhood contained, such as parks, recreation centers, sidewalks, or libraries.

2.5. Statistical Analyses

Pearson’s χ2 tests and t-tests with weighted counts and column percentages were utilized for descriptive statistics. In this study, to reduce selection bias when comparing children with ASD to those without ASD, a propensity-score matching (PSM) analysis was implemented (47). Propensity scores were calculated using stepwise logistic regression, incorporating significant covariates as identified in previous studies (47–49). Optimal matching methods, including variable, pair, and full matching, were employed to minimize bias, with the final model selected based on the smallest total sample distance (47–49). An imbalance check was performed to ensure that key covariates were balanced between the ASD and non-ASD groups (47–49). Weighted logistic regression analysis on the matched sample was then conducted to examine the association between having an overweight status and having ASD, considering socio-ecological obesity-related factors. This analysis produced predicted ordinal dependent variables with 95% confidence intervals for adjusted odds ratios (AORs). All statistical analyses, including weighted logistic regression for variables such as sex, race/ethnicity, socioeconomic status, family, school, community, and other relevant characteristics, were conducted using STATA version 15.1 and SAS version 9.4.

This study examined the effects of ASD on children’s BMI status in relation to socio-ecological obesogenic factors. The unweighted (number of participants and mean) and weighted (standard deviation [SD] and percentage) descriptive statistics of the study population are presented in Table 1. A total of 20,091 children (mean age, 13.51 years; girls, 48.59%; representing 32,211,963 children at the population level), including 738 (2.98%) children with ASD, were recruited for this study. Table 1 shows that the participants were of diverse races and ethnicities, including non-Hispanic white (49.78%), Hispanic (26.69%), non-Hispanic black (13.29%), non-Hispanic Asian (4.40%), and others (5.85%).

Table 1

Weighted descriptive statistics of the study sample with overweight status
			% (n), Mean (SD)
Variables			Overweight	Non-overweight	Overall	p-Value
Dependent variables	Overweight	Binary variable (1 = Overweight, 0 = Non-overweight)	33.87% (6,084)	66.13% (14,007)	100% (20,091)
Treatment	Autism	Binary variable (1 = yes, 0 = no)	4.09% (299)	2.42% (439)	2.98% (738)	< 0.01
covariates	1. Individual
		Age	13.51 (2.17)	13.88 (2.33)	13.77 (2.28)	< 0.01
		Males	56.88% (3,493)	48.61% (6,944)	51.41% (10,437)	< 0.01
		Race/Ethnicity				< 0.01
		Non-Hispanic white	40.71% (3,682)	54.42% (9,537)	49.78% (13,219)
		Non-Hispanic black	15.93% (577)	11.93% (818)	13.29% (1,395)
		Hispanic	34.83% (1,055)	22.52% (1,713)	26.69% (2,768)
		Non-Hispanic Asian	2.75% (240)	5.25% (891)	4.40% (1,131)
		Others	5.79% (530)	5.88% (1,048)	5.85% (1,578)
		Physical activity score	2.33 (0.88)	2.56 (0.94)	2.49 (0.92)	< 0.01
		Sedentary activity score	3.93 (1.06)	3.79 (1.21)	3.83 (1.16)	< 0.01
		Adequate sleep quality	60.84% (3,766)	67.17% (9,707)	65.02% (13,473)	< 0.01
		Missed or delayed preventive checkups due to the COVID-19 pandemic	28.30% (1,757)	28.91% (3,979)	28.70% (5,736)	0.65
	2. Parenting capacity
		Parents’ education				< 0.01
		Less than high school	14.51% (299)	8.26% (320)	10.38% (619)
		High school degree or GED	25.21% (1,156)	16.64% (1,652)	19.54% (2,808)
		Some college or technical school	24.17% (1,721)	19.27% (2,909)	20.93% (4,630)
		College degree or higher	36.11% (2,908)	55.82% (9,126)	49.15% (12,034)
		Income level of the child’s household				< 0.01
		0%-99% FPL	22.79% (1,058)	16.07% (1,500)	18.34% (2,558)
		100%-199% FPL	24.89% (1,164)	18.32% (2,096)	20.54% (3,260)
		200%-399% FPL	30.34% (1,968)	29.40% (4,125)	29.71% (6,093)
		400% FPL or greater	21.98% (1,894)	36.22% (6,286)	31.40% (8,180)
		Familial structure (Two parents)	61.49% (3,873)	68.64% (10,169)	65.22% (14,042)	< 0.01
	3. Familial health and activities
		Parents’ health score	7.76 (3.56)	8.79 (3.97)	8.48 (3.85)	< 0.01
		Alcohol & drugs	12.47% (932)	9.97% (1,630)	10.82% (2,562)	0.01
		Smoking	18.88% (1,088)	12.16% (1,653)	14.47% (2,741)	< 0.01
		Familial stress-coping score	1.74 (0.57)	1.79 (0.54)	1.77 (0.56)	< 0.01
		Meals together score	2.95 (0.89)	2.94 (0.93)	2.94 (0.92)	0.88
		Food security				< 0.01
		Often not enough	0.59% (40)	0.48% (40)	0.52% (80)
		Sometimes not enough	4.83% (248)	3.03% (294)	3.64% (542)
		Always enough not nutritious	28.61% (1,740)	21.24% (2,554)	23.74% (4,294)
		Always enough nutritious	64.00% (3,918)	71.80% (10,767)	69.12% (14,685)
	4. Community and school activities
		Participation in organized activities after school	62.25% (4,111)	72.92% (10,783)	69.31% (14,894)	< 0.01
		Participation in community service or volunteer work	29.41% (2,072)	38.70% (5,780)	35.56% (7,852)	< 0.01
		Parent score for participation in child’s events/activities	3.36 (1.00)	3.42 (0.99)	3.40 (1.00)	0.07
	5. Neighborhood safety and support
		Safe school score	2.67 (0.49)	2.72 (0.51)	2.71 (0.51)	0.01
		Supportive neighborhood	50.60% (3,249)	58.68% (8,568)	55.94% (11,817)	< 0.01
		Safe neighborhood score	2.64 (0.55)	2.71 (0.57)	2.69 (0.56)	0.02
		Neighborhood amenities score	2.51 (1.29)	2.67 (1.38)	2.62 (1.35)	< 0.01
Data are presented as % (number) or mean (standard deviation).
COVID-19, Coronavirus disease 2019; FPL, Federal poverty line; GED, general educational development.
n = 20,091; weighted n = 32,211,963. Source: 2021 National Survey of Children's Health (NSCH).

[Insert Table 1 here]

3.1. Imbalance Test of PSM for the Pair-Matching Procedure

Table 2 shows that the pair-matching procedure matched 674 children with ASD with 674 counterparts without ASD. The matches in this study sample were not significantly different from the observed covariates.

Table 2

Propensity-score comparison between matched and unmatched covariates
Variables
Dependent Variables	Overweight	Binary Variable (1 = Overweight, 0 = Non-overweight)	Unmatched (n = 17,322) Matched (n = 674)	Mean		t-Test
Dependent Variables	Overweight	Binary Variable (1 = Overweight, 0 = Non-overweight)	Unmatched (n = 17,322) Matched (n = 674)	Treated	Control	t	p > \|t\|
Covariates	1. Individual
		Age (years)	U	13.90	13.75	1.73	0.08
			M	13.90	13.98	-0.65	0.52
		Male sex	U	0.78	0.51	14.20	< 0.01
			M	0.78	0.76	0.91	0.36
		Race/Ethnicity
		Non-Hispanic white	U	-	-	-	-
			M	-	-	-	-
		Non-Hispanic black	U	0.05	0.06	-1.01	0.31
			M	0.05	0.07	-0.80	0.42
		Hispanic	U	0.12	0.14	-1.15	0.25
			M	0.12	0.12	-0.00	1.00
		Non-Hispanic Asian	U	0.03	0.06	-2.83	0.01
			M	0.03	0.05	-1.64	0.12
		Other	U	0.08	0.08	0.27	0.79
			M	0.08	0.08	-0.10	0.92
		Physical activity	U	2.13	2.51	-10.68	< 0.01
			M	2.13	2.13	0.03	0.98
		Sedentary activity	U	4.14	3.83	7.18	< 0.01
			M	4.14	4.14	0.00	1.00
		Adequate sleep quality	U	0.68	0.68	050	0.96
			M	0.68	0.69	-0.23	0.82
		Missed or delayed preventive checkups due to the COVID-19 pandemic	U	0.39	0.29	5.68	< 0.01
			M	0.39	0.44	-1.71	0.09
	2. Parenting capacity
		Parents’ education
		Less than high school	U	-	-	-	-
			M	-	-	-	-
		High school degree or GED	U	0.14	0.13	0.24	0.81
			M	0.14	0.13	0.56	0.58
		Some college or technical school	U	0.24	0.23	1.13	0.26
			M	0.24	0.24	0.06	0.95
		College degree or higher	U	0.60	0.61	-0.68	0.50
			M	0.60	0.61	-0.28	0.78
		Income level of the child’s household
		0%-99% FPL	U	-	-	-	-
			M	-	-	-	-
		100%-199% FPL	U	0.20	0.16	2.82	0.01
			M	0.20	0.21	-0.67	0.50
		200%-399% FPL	U	0.34	0.31	1.98	0.05
			M	0.34	0.35	-0.51	0.61
		400% FPL or greater	U	0.33	0.42	-4.46	< 0.01
			M	0.33	0.32	0.52	0.60
		Familial structure (Two parents)	U	0.69	0.73	-2.17	0.03
			M	0.69	0.68	0.53	0.60
	3. Family health and activities
		Parents’ health	U	7.86	8.84	-7.33	< 0.01
			M	7.86	7.76	0.51	0.61
		Alcohol & drugs	U	0.18	0.13	3.41	< 0.01
			M	0.18	0.20	-0.98	0.33
		Smoking	U	0.18	0.14	3.46	<0.01
			M	0.18	0.19	-0.49	0.63
		Stress-coping	U	1.68	1.78	-4.51	< 0.01
			M	1.68	1.69	-0.04	0.97
		Meals together	U	2.90	2.95	-1.50	0.13
			M	2.90	2.88	0.29	0.77
		Food security
		Often not enough	U	-	-	-	-
			M	-	-	-	-
		Sometimes not enough	U	0.04	0.03	3.00	< 0.01
			M	0.04	0.05	-0.26	0.80
		Always enough not nutritious	U	0.31	0.21	5.65	< 0.01
			M	0.31	0.30	0.36	0.72
		Always enough nutritious	U	0.64	0.76	-7.02	< 0.01
			M	0.64	0.64	-0.17	0.87
	4. Community and school activities
		Participation in organized activities after school	U	0.53	0.76	-13.56	< 0.01
			M	0.53	0.51	0.65	0.51
		Participation in community service or volunteer work	U	0.23	0.41	-9.16	< 0.01
			M	0.23	0.24	-0.26	0.80
		Parent participation in child’s events/activities	U	3.22	3.42	-5.56	< 0.01
			M	3.22	3.17	0.85	0.40
	5. Neighborhood safety and support
		Safe school	U	2.57	2.71	-7.43	< 0.01
			M	2.57	2.53	1.25	0.21
		Supportive neighborhood	U	0.47	0.62	-7.70	< 0.01
			M	0.47	0.47	-0.05	0.96
		Safe neighborhood	U	2.53	2.70	-8.03	< 0.01
			M	2.53	2.53	0.13	0.89
		Neighborhood amenities	U	2.63	2.62	0.08	0.94
			M	2.63	2.67	-0.56	0.58
COVID-19, Coronavirus disease 2019; FPL, Federal poverty line; GED, general educational development.
Source: Source: 2021 National Survey of Children's Health (NSCH).

[Insert Table 2 here]

3.2. Imbalance Test of PSM with a Comparison between Matched and Unmatched Covariates

Six independent variables associated with having an overweight status in children showed significantly different odds ratios (ORs) in the overweight and non-overweight groups (Table 3). Our main variable of concern, autism, had a statistically significant effect on being overweight in children in the final matched model.

Table 3

Weighted logistic regression after propensity-score matching
Variables
Dependent Variables	Overweight	Binary variable (1 = Overweight, 0 = Non-overweight)	OR	CI
Treatment	Autism	Binary variable	1.59*	1.08–2.34
Covariates	1. Individual
		Age	0.98	0.89–1.07
		Male sex	1.42	0.87–2.32
		Race/Ethnicity
		Non-Hispanic white	-	-
		Non-Hispanic black	1.14	0.57–2.28
		Hispanic	2.27**	1.24–4.17
		Non-Hispanic Asian	2.39	0.97–5.89
		Other	0.78	0.37–1.63
		Physical activity	0.79	0.62–1.01
		Sedentary activity	0.95	0.78–1.15
		Adequate sleep quality	1.02	0.67–1.56
		Missed or delayed preventive checkups due to the COVID-19	1.25	0.82–1.90
	2. Parenting capacity
		Parents’ education
		Less than high school	-	-
		High school degree or GED	2.46	0.67–9.05
		Some college or technical school	3.14	0.87–11.28
		College degree or higher	2.16	0.59–7.92
		Income level of child's household
		0%-99% FPL	-	-
		100%-199% FPL	1.20	0.60–2.37
		200%-399% FPL	0.99	0.49–1.97
		400% FPL or greater	0.62	0.29–1.33
		Familial structure (Two parents)	1.71	0.92–3.18
	3. Family health and activities
		Parents’ health	0.83**	0.76–0.91
		Alcohol & drugs	1.10	0.66–1.85
		Smoking	0.73	0.43–1.24
		Stress-coping	0.91	0.67–1.23
		Meals together	0.87	0.70–1.09
		Food security
		Often not enough	-	-
		Sometimes not enough	0.70	0.11–4.55
		Always enough not nutritious	0.58	0.11–3.22
		Always enough nutritious	0.51	0.09–2.94
	4. Community and school activities
		Participation in organized activities after school	1.33	0.85–2.09
		Participation in community service or volunteer work	0.81	0.48–1.35
		Parent participation in child’s events/activities	1.29**	1.07–1.56
	5. Neighborhood safety and support
		Safe school	0.80	0.55–1.16
		Supportive neighborhood	0.90	0.57–1.43
		Safe neighborhood	1.46	0.97–2.19
		Neighborhood amenities	1.01	0.05–1.17
Total	n		1,348
	Weighted n		1,904,381
COVID-19, Coronavirus disease 2019; FPL, Federal poverty line; GED, general educational development.
* p < 0.05; ** p < 0.01; Source: Source: 2021 National Survey of Children's Health (NSCH).

[Insert Table 3 here]

In the final matched model, ASD had a statistically significant effect on the status of overweight children. Children with ASD were more likely to be overweight than those without ASD (OR = 1.59, 95% CI = 1.08–2.34). In terms of the individual obesogenic factors, Hispanic children showed significantly higher odds of being overweight (OR = 2.27, 95% CI = 1.24–4.17). In the assessments of family health and activities, a one-unit increase in the parents’ health score reduced the risk of being overweight in the child (OR = 0.83, 95% CI = 0.76–0.91). In assessments of community and school activities, children who participated in more organized activities after school or on weekends over the past 12 months were more likely to be overweight those who did not (OR = 1.29, 95% CI = 1.07–1.56).

This study used a large non-clinical data sample to investigate the relationship between the presence of ASD in children and overweight, a primary indicator of physical health (6, 50), while controlling for socio-ecological factors related to overweight. The common comorbidities observed in individuals with ASD include neurological, psychiatric, and physical disorders (28). Understanding the intricate links between ASD and being overweight is crucial to develop tailored intervention strategies that address the neurodevelopmental and weight-related needs of this population.

The primary focus of this study was to examine the influence of ASD on the likelihood of being overweight in children. Our findings indicated that children with ASD had a significantly higher risk of being overweight than those without ASD, consistent with other studies that have examined the associations between being overweight and having autism in children (32, 34, 37, 41, 51–54). The factors related to being overweight in children with ASD are diverse (32, 34, 41, 51), and include individual factors (e.g., ethnicity, level of physical activity, food intake, sedentary lifestyle) (32, 34, 41, 52–54), parental factors (e.g., education level, income level, health status, alcohol consumption, and smoking), familial factors (e.g., family composition, food security), familial activities related to the child’s school and community activities (e.g., volunteering, level of parental participation), and the stability and support level in the community (e.g., safe schools, supportive surroundings, safe environment, nearby facilities) (19). Children with ASD often have physical (e.g, gastrointestinal disorders, musculoskeletal problems, and sensory processing) and psychological (e.g., sleep disorders, anxiety and depression) limitations in living independent and self-sufficient lives (25–30).Therefore, both personal and ecological factors must be considered simultaneously.

Previous studies have shown that children with ASD are at a higher risk of becoming overweight than typically developing children (34, 41, 53). Research across various disciplines to investigate the causes of being overweight in children has revealed that the major causes of obesity, such as activity levels, dietary habits, behaviors, and socioeconomic status, interact with each other to increase obesity rates in children (55–59). The same trend was observed in children with ASD. Several factors such as school activity levels, racial background, community support, and parental status influence the increase in obesity rates among children with ASD (19, 32, 34, 37, 41, 51–54). Within the context of the family environment, various obesity-inducing factors, such as dietary habits, physical activity, and sleep patterns, have been identified (19, 34, 51, 52). Therefore, parental and family dynamics are major criteria for investigating the risk factors for being overweight in children (19, 34, 52).

Children and adults with ASD often display unique physical and physiological patterns that can predispose them to obesity (19, 25, 34, 41). Some individuals with ASD have difficulty engaging in team sports or group physical activities due to social challenges, sensory sensitivities, or motor difficulties, which lead to decreased physical activity levels (37, 41, 51). In addition, certain medications prescribed to manage ASD symptoms can promote weight gain. Dietary preferences in individuals with ASD, which are often characterized by a limited range of food acceptance, can also lead to unbalanced nutrition (34, 41, 53). Neurologically, the brain of individuals with ASD may process rewards differently (28), and they may show an increased preference for high-calorie foods because of the resultant rewarding sensations (32, 34, 41, 51). This may increase the risk of overeating and obesity. Additionally, the challenges in executive functioning often seen in ASD, such as difficulties with impulse control and planning, can make it more difficult for these individuals to make healthier food choices or adhere to regular exercise routines (34, 37, 41). The psychological stress associated with ASD can also contribute to obesity (28). Difficulties in emotional regulation, coupled with experiences of anxiety, bullying, or social isolation, can lead to emotional eating as a coping mechanism (34, 41, 54). Furthermore, routines and repetitive behaviors, which are the hallmark features of ASD, can translate into fixed dietary habits, limiting the scope for introduction of healthier alternatives (34, 41, 52). Thus, the interplay of the social, emotional, and behavioral aspects of ASD can compound the risks associated with obesity. Therefore, an interdisciplinary approach is more suitable for determining the adiposity factors in patients with ASD.

Research has also indicated that the likelihood of being overweight in children is associated with various socio-ecological factors, including households with stepparents, less healthy parents and households, presence of smokers, lower quality of sleep, and greater participation in organized activities (19). The study highlights that the determinants of being overweight in children extend beyond the individual level, emphasizing the importance of family and community support (19). Therefore, to prevent or reduce obesity and enhance the quality of life for children, there is a need for increased attention to administrative policies at the family and community levels (19). While the research by Kim et al. aimed to identify factors contributing to obesity in children with symptoms of the ASD from a socio-ecological perspective (19), this study sought to determine whether there is a significant relationship between ASD and being overweight in children.

In this study, ethnicity (non-Caucasian race) was a potential risk factor for being overweight in children (54). Moreover, among the factors related to family and family activities, the mental and physical health status of parents significantly influenced the likelihood of being overweight in children with ASD and similar socio-ecological characteristics. Children with ASD lack autonomy, self-motivation, and social inspiration (25). Healthy and sound societal environments (e.g., parents’ health) play an important role in maintaining healthy weight in children with ASD (19). Opportunities for safe facilities and proper physical activities among children with ASD are also often limited (19, 34, 37, 53). However, parental involvement in community and school activities appears to have a negative impact on weight in children with ASD and similar socio-ecological characteristics. One hypothesis to explain this finding is that mere parental involvement in school and community activities without diverse and well-organized physical and mental dynamics could adversely influence weight management in children with ASD and similar socio-ecological traits (60, 61). Thus, instead of multiple lower-quality activities, more organized and dynamic programs are crucial to effectively reduce being overweight in children. These studies also clarify that no single factor is responsible for being overweight in children with ASD and highlight the importance of a socio-ecological approach. As hypothesized, parents’ physical and mental health status and their active participation play a role in their overweight status.

The Centers for Disease Control and Prevention guidelines also recommend school- and community-based strategies to prevent children from being overweight through changing the environment and developing relevant policies (19, 62). Children require a multi-tiered approach because they are nested within families and classes in schools and within school districts or communities. By utilizing a clear conceptual framework, the complexity of being overweight in children with ASD can be understood systematically and deeply. The long-term goal of this study was to enhance our understanding of the personal and environmental associations with the mechanisms underlying being overweight or obese in children with ASD. This could lead to the development of public health prevention and intervention strategies to reduce being overweight or obese among these children. Therefore, a comprehensive and multifaceted approach is needed to address the many behavioral and environmental risk factors related to obesity in children with ASD. A multi-tiered approach toward analysis will provide more effective intervention programs and/or policies for youth obesity in multi-tiered settings (e.g., families, schools, communities, and states) (19, 63).

This study had certain limitations. First, the self-reporting methods employed to evaluate variables based on the participants' one-day recollection may have been susceptible to recall bias, respondent bias, or interview bias. This could have led to inaccurate results. Second, due to the nature of the data used, information for dietary factors that could influence being overweight in children, such as vegetable intake or high-calorie food consumption, could not be evaluated. Third, caution must be exercised when attempting to generalize the findings to more diverse areas, since the sample size was small and showed underrepresentation of certain groups (e.g., Hispanic and Asian). Larger and more diverse samples are likely to yield more robust results. Finally, the dataset excluded households without landlines or cell phones. This could have led to biased survey populations underrepresenting certain segments of the population. Therefore, studies on the prevention of being overweight in children with ASD should take these limitations into account. However, these limitations did not overshadow the contributions of this study.

This study examined the impact of ASD presence or absence on overweight children by controlling for socio-ecological factors, and also identified obesity-related factors in children with ASD and their socio-ecologically similar counterparts. Children with ASD have been shown to have a higher likelihood of becoming obese compared to those without ASD. Due to the neurodevelopmental challenges associated with ASD, it has been confirmed that special attention is required for the prevention and management of obesity, a physical health indicator, in these children. The results also indicated that being overweight in children with ASD is associated not only with individual factors but also with family and community support. Parental involvement in physical activity and familial and societal support crucially influence the overweight status of children with ASD. These findings highlight the need for more attention to providing familial and societal support to help children with ASD maintain healthy weight. Additionally, the 2021 National Survey of Children’s Health (NSCH), a nationwide non-clinical dataset, provides robust data offering more insights into the association between ASD and childhood overweight by examining how ASD affects being overweight in childhood when socio-ecological factors are controlled. The results indicate that administrative policies at the family and community levels are essential to prevent or reduce obesity in children with ASD and improve their quality of life.

AOR, adjusted odds ratio; ASD, autism spectrum disorder; BMI, body mass index; COVID-19, coronavirus disease 2019; FPL, Federal Poverty Level; HRSA, Health Resources and Services Administration; MCHB, Maternal and Child Health Bureau; NSCH, National Survey of Children’s Health; OR, Odds ratio; PSM, propensity-score matching; SD, standard deviation

Ethics approval and consent to participate

This study was verified as a non-human subject study by the Institutional Review Board at Seoul National University.

Consent for publication

Not applicable

Availability of data and materials

Data from our study are available at https://mchb.hrsa.gov/data-research/national-survey-childrens-health.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors received no funding for the submitted work.

Author Contributions

TaeEung Kim conceived the study and drafted the manuscript. TaeEung Kim and Eun Hye Kwon analyzed and interpreted the data. Eun Hye Kwon helped draft the manuscript and perform the analysis. TaeEung Kim and Eun Hye Kwon reviewed and edited the manuscript. All authors participated in the research concept of the paper and have read and agreed to the published version of the manuscript.

Acknowledgments

Not applicable

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Effect of Autism on Overweight in Children from a Socio-ecological Perspective

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Abstract

Figures

1 Background

2 Methods

2.1. Data Source and Study participants

2.2. Conceptual Framework

2.3. Dependent Variable

2.4. Independent Variables

2.5. Statistical Analyses

3 Results

3.1. Imbalance Test of PSM for the Pair-Matching Procedure

3.2. Imbalance Test of PSM with a Comparison between Matched and Unmatched Covariates

4 Discussion

5 Conclusions

Abbreviations

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