ABC Grow Healthy Practices and obesity-related health behaviors in family child care homes in South Carolina

Abstract

Background:

Many states throughout the United States have introduced Quality Rating and Improvement Systems (QRIS) to address childhood obesity in preschool-age children but few have examined the impact of these standards in Family Child Care Homes (FCCHs). In South Carolina, the ABC Grow Healthy Practices are specific QRIS items that include diet, physical activity, and sleep practices.

Methods:

The purposes of this cross-sectional study are to 1) describe physical activity levels, sleep duration, and diet quality of children attending FCCHs in South Carolina and 2) compare physical activity levels, sleep duration, and diet quality between children attending FCCHs that were enrolled vs. not enrolled in the ABC program. Means and percentages were used to summarize child-level and parent-level descriptive characteristics, physical activity levels, diet quality, and sleep behaviors overall and by sex, race/ethnicity, and ABC participation. T-tests and chi-square analyses were used to compare child-level and parent-level demographic characteristics as well as child-level behaviors by ABC status.

Results:

Results revealed low physical activity levels (11.2 ± 4.1 minutes/hour of total physical activity and 5.5 ± 2.6 minutes/hour of moderate and vigorous physical activity) and poor diet quality (Healthy Eating Index 56.0 ± 10.3) in all children. There were statistically significant differences in fat and protein consumption between ABC and non-ABC FCCHs, as well as differences in demographic characteristics.

Conclusion:

Future research is recommended to assess whether strengthening guidelines and improving implementation of obesity prevention standards will improve physical activity levels and diets of children attending FCCHs.

Introduction

Nearly 23% of pre-school-age children in the United States are currently affected by overweight or obesity, and this prevalence is even higher among children from marginalized communities and low-income communities (Ogden et al., 2018, 2020). Children with overweight and obesity suffer from multiple comorbidities, including metabolic and cardiovascular disease, inflammation, and psychological disturbances (Daniels et al., 2005). Although genetic factors and infant feeding practices both have a significant effect on obesity, this effect is small (Robinson et al., 2012). Consistent evidence shows that enhancing physical activity and improving nutrition appear to be the most effective ways to treat and prevent childhood obesity (Psaltopoulou et al., 2019). Physical activity and diet are both influenced by factors at multiple levels, including by parents who may be highly sedentary, home and school environments that may have a large supply of energy-dense foods, social factors such as poverty, and environmental factors such as neighborhood safety or rurality (Robinson et al., 2012). Whether or not a child has a regular child care arrangement and the type of child care they receive is also associated with their risk of having overweight and obesity (Black et al., 2017).

Around two-thirds of children currently attend some form of child care in the United States, with over half attending center-based care (Corcoran & Steinley, 2019). In the state of South Carolina, parents have the option of enrolling their children in a variety of different types of child care, including child care centers and family child care homes (FCCHs). Child care centers in South Carolina are licensed to serve 13 or more children out of non-residential, commercial buildings, and usually operate more than two days a week with care provided more than 4 hours per day. Group child care homes care for 7–12 children, while family child care homes (FCCHs) typically care for <6 children, both of which are located in a residential building (South Carolina Department of Social Services, 2021b; Types of Child Care, 2021). Although limited, a previous comparisons of these two types of facilities in Florida showed that children attending FCCHs had lower physical activity levels, although FCCHs are more likely to serve fresh fruit and limit servings of bread/rolls to children compared to child care centers (Natale et al., 2014). Studies focusing specifically on the FCCH setting have shown that children participate in 10 to 33 minutes of moderate to vigorous physical activity (MVPA) per hour (Chai et al., 2020; Delaney et al., 2014; Gunter et al., 2012; Mazzucca et al., 2018; Rice & Trost, 2014) although most of these studies were limited by small, homogeneous samples and all varied in their accelerometer placement (Delaney et al., 2014; Mazzucca et al., 2018) Similarly, studies that have focused on the FCCH setting noted that diet quality is low (Benjamin-Neelon et al., 2018; Tovar et al., 2018; Welker et al., 2018), with much of the research based on staff reports (Copeland et al., 2013; Welker et al., 2018) and small sample sizes (Kenney et al., 2020; Woodward-Lopez et al., 2018). No studies have reported on sleep in the FCCH setting. Furthermore, to ensure that current national-level estimates of obesogenic health behaviors in children attending FCCHs are truly representative, it is essential to gather data from a diverse range of states and regions across the US.

A literature review by Francis and colleagues concluded that FCCH environments exhibit multiple obesogenic attributes, including inadequate nutrition-related communication with families and feeding practices, and insufficient equipment and space for both indoor and outdoor playtime activities. What’s more, many FCCH providers held inaccurate nutrition-related beliefs and perceptions, and lacked an overall training in nutrition and physical activity best practices. What’s more, they found that FCCHs lacked comprehensive written policies on nutrition and physical activity. (Francis et al., 2018). Some states have introduced Quality Rating and Improvement Systems (QRIS) that address these issues, including Maryland, Nevada, Texas, North Carolina and South Carolina to name a few. These systems can include specific obesity prevention standards that award providers with a high-quality care designation when these standards are met. These standards vary by state, but are found in a majority of the United States (Geary et al., 2017). In South Carolina, the ABC Grow Healthy Practices are specific QRIS items that include diet and physical activity practices for child care centers. ABC Quality staff inspect, monitor, and rate, licensed FCCHs that participate in this program in order to evaluate FCCHs based on their performance on five elements: Program Administration and Structure; Staff Education and Professional Development, Child Well-being; Family Communication, Engagement, and Cultural Competence; and Intentional Teaching Practices. The South Carolina Department of Social Services (DSS) informs parents about the child care programs enrolled in South Carolina’s QRIS, and delivers training and resources to providers enrolled in the program so that they can successfully implement obesity prevention standards (Understanding ABC Quality, 2020).

South Carolina’s DSS and the Department of Health and Environmental Control introduced the ABC Grow Healthy Child Care (ABC) program in 2017. All FCCHs enrolled in the ABC program were required to begin implementing these new standards by October 2019, which allowed for a natural experiment comparing demographic and behavioral characteristics of children attending FCCHs enrolled in ABC versus FCCHs not enrolled in ABC. Therefore, the purpose of the Childcare Home Eating and Exercise (CHEER) study was to 1) describe physical activity levels and diet quality in the FCCH and nighttime sleep duration of children attending FCCHs in South Carolina and 2) compare physical activity levels, sleep duration, and diet quality between children attending FCCHs that were enrolled vs. not enrolled in a QRIS with specific obesity prevention standards. A secondary aim was to describe the diet quality and sleep duration and quality of parents with children enrolled in FCCHs.

Methods

Study Design

The CHEER Study measured the physical activity, sleep, dietary behaviors, and anthropometry of children attending FCCHs located in South Carolina. FCCHs without any open cases of neglect or abuse with the state licensing agency, and that received state subsidies for providing care to low-income children through a statewide program, were eligible to participate in the study. The sample was drawn from the population of all FCCHs in South Carolina (n=1088). A cross-sectional study design was employed, and data were collected between 2017 and 2019. Data collection preceded the date on which FCCHs in the ABC group were expected to meet the Grow Healthy standards. Accordingly, the comparison between ABC and non-ABC FCCHs was intended to determine if there were differences between the groups of children attending the two types of FCCHs in advance of implementation of the Grow Healthy standards by the ABC homes.

The South Carolina Child Care Resource & Referral Network helped connect the research team with FCCH providers by mailing a packet with study information to all members throughout the state. The research team called 1088 FCCHs in South Carolina who had publicly available contact information, and successfully contact was made with 834 FCCHs. In all other instances, numbers were either disconnected, or FCCH providers did not pick up or respond to voicemails. Follow-up calls to FCCHs where providers were responsive after the first call were made up 5 times. Additionally, members of the research team attended events four regional conferences held by the South Carolina Child Care Resource & Referral Network in larger metropolitan areas of the state to connect directly with providers and regional leaders and to network with Child and Adult Care Food Program (CACFP) Child Care Home Sponsors. A total of 133 FCCHs were recruited, of which 56 withdrew, leading to a final sample of 77 FCCHs. Child-level data were collected in 68 homes which included 23 ABC and 45 non-ABC FCCHs. This study focuses on the 260 children receiving child care in these homes from whom data were collected.

A FCCH is a home-based child care center with 6 or fewer children. South Carolina does not require licensure for FCCHs, but FCCHs that wish to obtain a license must meet the state’s health and safety requirements, which include conducting staff background checks, maintaining staff to child ratios, and complying with investigations and random assessments by the Department of Social Services. All licensed and non-licensed FCCHs must be registered with the state (South Carolina Department of Social Services, 2021c). FCCH providers provided written, informed consent to participation in the study. Once provider consent was obtained, parent information packets including parent consent forms were distributed by the provider. Children who’s parent’s signed consent forms prior to data collection were enrolled in the study. If more than one child attended the FCCH, data were collected on both children. Only data from one child was selected at random and chosen for analyses. Two consecutive days were scheduled for data collection, based on provider preferences. A follow-up day was scheduled 8 days following the first visit to retrieve parent and provider surveys and monitors. Accelerometers and surveys were fitted to children and distributed to parents, respectively, on the first day of data collection. Data collectors completed all observations during the two, scheduled visits. Height and weight of participating children was collected at a time that was convenient for the provider during the two observation days or on the follow-up day when surveys and monitors were being retrieved.

Measures

Anthropometry

Trained data collectors measured children’s height to the nearest cm using a Shorr board (Shorr/Weigh and Measure, LLC, Olney, MD) and their weight to the nearest 0.1 pound using a Seca Scale (model 334; Chino, CA). Both measurements were taken twice and then averaged. These height and weight values were used to calculate body mass index (BMI) z-score based on World Health Organization age- and sex-specific growth charts (World Health Organization, 2006).

Physical Activity

Physical activity was measured using ActiGraph accelerometers (GT3X+ model, Pensacola, FL), which children wore for one week on the right hip. Monitors were only removed for water based activities and at nighttime. Accelerometers were placed on children by researchers on the first day of data collection at the FCCH, and were initialized to begin collecting data at midnight on the first day of distribution. Parents and the providers were provided with information about proper wear of the monitor. Parents were asked to remove the monitors for nighttime sleep and to place the monitor back on in the morning. Children wore monitors during all waking hours both in and out of the FCCH setting for 7 whole days, which included a weekend. Data were stored in 15-second epochs. Compliance standards were set as >10 hours of wear time per day on >3 days, and periods of non-wear as determined by 60 minutes of continuous zeroes were excluded from analyses. Data from all children who met these compliance standards were included regardless of the number of hours they spent at the FCCH. The total daily minutes of total physical activity and MVPA were determined using age-specific cut-points for children (Pate et al., 2006), and were expressed for physical activity performed at the FCCH and total daily physical activity, which included physical activity performed at home in the evenings and over the weekend.

Sleep

Children’s sleep behavior was assessed using questions from the Baby NAP SACC parent questionnaire, which asked parents to report how many hours their children slept at night. Parents were asked, “In the past 4 weeks, on average, how many hours total did your child sleep during the day between 7am and 7pm? Remember to include the time for all of the naps. Please round to the nearest hour.” And, “In the past 4 weeks, on average, how many hours total did your child sleep during a typical night between 7pm and 7am? Please round to the nearest hour.”

Dietary Intake

Children’s dietary intake while in the FCCH was assessed by trained and certified data collectors using the Diet Observation in Child Care methodology (Ammerman et al., 2007; Ball et al., 2007; Benjamin et al., 2007; Ward, 2008). The DOCC method was developed, tested, and validated in 2007 to objectively measure young children’s dietary intake in child care(Ammerman et al., 2007; Ball et al., 2007; Benjamin et al., 2007). Three children from each participating FCCH who were enrolled in all other portions of the CHEER study, were randomly selected by data collectors, and all foods and beverages served to, consumed by, and spilled or discarded by these children was discreetly observed and documented throughout one regular day of care. The DOCC requires data collectors to record as much detail as possible about the foods and beverages consumed, including brand names, food preparation methods, and specific amounts served and consumed. This information was entered into the Nutrition Database for Scientific Research (NDS-R) version 2012 (Nutrition Coordination Center, University of Minnesota) (University of Minnesota Nutrition Coordinating Council, 2012). and used to calculate a Healthy Eating Index (HEI) score (Krebs-Smith et al., 2018) for 174 children to assess diet quality. In order to assess dietary intake in the children’s own homes in the evenings and on the weekends, and to obtain a full day of dietary data, parents completed a 24-hour dietary recall the day following direct observation of dietary intake in the FCCH. Additional 24-hour recalls using NDS-R were conducted on randomly selected weekday and weekend days to improve the understanding of children’s dietary intake. Parents provided up to three 24-hour dietary recalls for their children. Parents were called twice between Tuesday and Saturday regarding information about weekday meals and once between Sunday and Monday for weekend meals. These dietary recalls were conducted during the same timeframe when children were observed in the FCCH. More detail on these methods has been described previously (Zaltz et al., 2023).

Parent-reported demographic and health data

Parents completed surveys reporting their children’s age, sex (male, female) and race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic/Latino, Other). Parents reported their highest level of education (high school or below, some or above), their income (<40k, >=40k), whether the family received government assistance (Supplemental Nutrition Assistance Program (SNAP), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), other), and the total number of household members. Parents were also asked to report their own physical activity levels, dietary intake, and sleep.

To report dietary intake, parents were asked to report the number of servings or times they ate or drank the following items on a typical day: soda, sugar sweetened beverages, fast food, French fries or chips, glasses of milk, vegetables, fruit, yoghurt, and sweets.

To report sleep quality and quantity, parents were asked, “On the average, how many hours did you sleep each night during the past week?”. The Sleep Problems Index II questionnaire from the Baby NAPSACC asked parents to report their subjective sleep quality by reporting, “How often during the past week did you: feel that your sleep was not quiet (moving restlessly, feeling tense, speaking, etc. while sleeping)?; get enough sleep to feel rested upon waking in the morning?; awaken short of breath or with a headache?; feel drowsy or sleepy during the day?; have trouble falling asleep?; awaken during your sleep time and have trouble falling asleep again?; have trouble staying awake during the day?; snore during your sleep?; take naps (5 minutes or longer) during the day?; get the amount of sleep you need?” Responses could include; None of the time; A little of the time; Some of the time; A good bit of the time; Most of the time; All of the time.

Statistical analysis

Means, standard deviations (SDs), and percentages, were used to summarize child-level and parent-level descriptive characteristics, and child-level and parent-level physical activity, diet, and sleep behaviors overall and by sex, race/ethnicity, and ABC participation. For continuous variables, multi-level linear regression models were fit to compare child-level and parent-level demographic characteristics as well as child-level behaviors by ABC status; for categorical variables, multi-level logistic regression (two categories) or ordinal or multinomial regression (more than two categories) were used. For all tests of comparison, the models were fit using generalized estimating equations (GEE) with FCCH as the clustering variable. An exchangeable working correlation covariance structure was used with the linear and logistic models to account for clustering of children within FCCH. For the ordinal and multinomial models, an independent working correlation matrix was assumed. For all comparisons, robust standard errors were computed and used to derive test statistics that account for clustering. Hypothesis tests were two-sided and performed at the alpha=0.05 significance level. Analyses were performed using Stata/SE version 16.0 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.) and SAS version 9.4 (SAS Institute, Cary, NC).

Results

A majority of children attending FCCHs in this sample were Non-Hispanic Black (46%), followed by Non-Hispanic White (40%), other race/ethnicity (10%), and Hispanic/Latino(a) (5%). A majority of parents had “Any college education” or above (83%) and an income of ≥$40k per year (54%). Children had an average BMI z-score of 1.8 (SD=3.0). Additional information and a breakdown of these demographic characteristics by children’s sex and race/ethnicity as well as by FCCH ABC program status can be found in Table 1.

Table 1.

Child, parent, and household characteristics in the CHEER study (n=260)

Characteristic	Overall (n=260)	Number Missing	Sex		Race/Ethnicity				ABC
			Female (n=128)	Male (n=132)	Non-Hispanic Black (n=119)	Non-Hispanic White (n=103)	Hispanic (n=13)	Other (n=25)	ABC (n=89)	Non-ABC (n=171)	p-value1
Age of child at anthropometry assessments, years	3.2 (1.0)	66	3.2 (1.0)	3.1 (1.0)	3.1 (1.1)	3.2 (1.0)	3.4 (1.3)	3.2 (1.1)	3.1 (1.0)	3.2 (1.1)	0.44
Height, cm (child)	95.9 (9.4)	62	95.7 (9.7)	96.0 (9.1)	96.0 (10.1)	95.8 (8.4)	95.5 (8.9)	96.1 (10.9)	96.1 (9.4)	95.7 (9.4)	0.79
Weight, kg (child)	15.5 (4.1)	63	15.4 (4.4)	15.6 (3.8)	15.7 (5.0)	15.2 (2.7)	14.1 (2.0)	16.1 (4.6)	15.8 (4.8)	15.3 (3.7)	0.49
BMI, kg/m^2 (child)	16.7 (2.8)	63	16.7 (3.2)	16.8 (2.4)	16.9 (3.7)	16.5 (1.5)	15.4 (1.3)	17.2 (2.5)	17.0 (4.1)	16.5 (1.7)	0.46
BMI-for-age z-score, WHO growth reference (child)	1.8 (3.0)	67	1.8 (3.1)	1.9 (2.9)	2.0 (3.4)	1.7 (2.4)	1.1 (2.9)	2.1 (3.1)	1.9 (3.6)	1.8 (2.6)	0.83
Race/ethnicity (child)		0									<0.0012
Non-Hispanic Black	46% (119)		50% (64)	42% (55)					73% (65)	32% (54)
Non-Hispanic White	40% (103)		35% (45)	44% (58)					12% (11)	54% (92)
Hispanic	5% (13)		5% (7)	5% (6)					3% (3)	6% (10)
Other	10% (25)		9% (12)	10% (13)					11% (10)	9% (15)
Parent education		54									0.33
Some/all high school	17% (34)		16% (16)	17% (18)	20% (19)	12% (10)	11% (1)	27% (4)	22% (16)	14% (18)
Any college or more	83% (172)		84% (81)	83% (91)	80% (78)	88% (75)	89% (8)	73% (11)	78% (57)	86% (115)
Parent income		60									0.04
<40k/year	46% (93)		46% (44)	47% (49)	68% (63)	20% (17)	50% (4)	60% (9)	62% (43)	38% (50)
40k+/year	54% (107)		54% (51)	53% (56)	32% (29)	80% (68)	50% (4)	40% (6)	38% (26)	62% (81)
Total household members	3.2 (1.6)	63	3.4 (1.5)	3.1 (1.6)	3.4 (1.9)	3.1 (1.2)	4.2 (1.6)	3.0 (1.2)	3.4 (1.8)	3.1 (1.4)	0.46
SNAP	24% (48)	64	22% (20)	27% (28)	37% (34)	6% (5)	38% (3)	40% (6)	35% (24)	19% (24)	0.06
WIC	20% (38)	67	16% (15)	23% (23)	29% (27)	8% (6)	38% (3)	13% (2)	28% (19)	15% (19)	0.07
Other (e.g., housing voucher)	12% (22)	82	10% (9)	14% (13)	19% (16)	3% (2)	0% (0)	29% (4)	23% (14)	7% (8)	0.006
Parent Lifestyle Factors
Sleep Problems Index II	41.3 (15.0)	61	41.4 (14.8)	41.1 (15.2)	38.5 (14.1)	42.6 (12.7)	47.1 (29.0)	48.5 (20.6)	38.7 (12.8)	42.6 (16.0)	0.04
Sleep, hours/night	6.6 (1.7)	72	6.6 (2.0)	6.6 (1.2)	6.6 (2.0)	6.8 (1.1)	6.1 (1.1)	6.1 (1.9)	6.8 (2.2)	6.6 (1.3)	0.39
How many times do you drink soda and/or sweetened beverages in a typical day?		71									0.17
None	25% (48)		23% (21)	27% (27)	16% (15)	35% (27)	29% (2)	31% (4)	15% (10)	31% (38)
Not every day, but at least a few times per week	31% (59)		32% (29)	30% (30)	40% (36)	22% (17)	43% (3)	23% (3)	38% (25)	28% (34)
1/day	18% (34)		14% (13)	21% (21)	13% (12)	26% (20)	14% (1)	8% (1)	20% (13)	17% (21)
2+/day	25% (48)		30% (27)	21% (21)	31% (28)	18% (14)	14% (1)	38% (5)	27% (18)	24% (30)
How many times per week do you usually eat from a fast-food restaurant?		69									0.53
None	9% (18)		9% (8)	10% (10)	9% (8)	5% (4)	57% (4)	14% (2)	3% (2)	13% (16)
More often than never, but less than once per week	18% (35)		20% (18)	17% (17)	13% (12)	27% (21)	0% (0)	14% (2)	21% (14)	17% (21)
1/week	25% (47)		21% (19)	28% (28)	22% (20)	28% (22)	14% (1)	29% (4)	28% (19)	23% (28)
2/week	26% (49)		28% (25)	24% (24)	32% (29)	19% (15)	14% (1)	29% (4)	25% (17)	26% (32)
3+/week	22% (42)		22% (20)	22% (22)	24% (22)	22% (17)	14% (1)	14% (2)	22% (15)	22% (27)
How many times do you eat French fries or chips in a typical day?		69									0.005
None	14% (27)		18% (16)	11% (11)	9% (8)	19% (15)	29% (2)	14% (2)	9% (6)	17% (21)
Not every day, but at least a few times per week	70% (134)		64% (58)	75% (76)	66% (61)	76% (59)	71% (5)	64% (9)	66% (45)	72% (89)
1/day	8% (16)		8% (7)	9% (9)	13% (12)	3% (2)	0% (0)	14% (2)	10% (7)	7% (9)
2+/day	7% (14)		10% (9)	5% (5)	12% (11)	3% (2)	0% (0)	7% (1)	15% (10)	3% (4)
How many glasses of milk do you drink in typical day?		68									0.77
None or less than one glass per day	69% (133)		69% (63)	69% (70)	74% (68)	67% (53)	57% (4)	57% (8)	74% (50)	67% (83)
1/day	18% (35)		20% (18)	17% (17)	15% (14)	22% (17)	14% (1)	21% (3)	7% (5)	24% (30)
2+/day	12% (24)		11% (10)	14% (14)	11% (10)	11% (9)	29% (2)	21% (3)	19% (13)	9% (11)
How many servings of fruits or vegetables do you eat in a typical day?		70									0.66
None or less than one serving per day	9% (16)		7% (6)	10% (10)	9% (8)	9% (7)	14% (1)	0% (0)	8% (5)	9% (11)
1/day	27% (50)		30% (25)	25% (25)	20% (17)	34% (26)	14% (1)	46% (6)	27% (17)	28% (33)
2/day	21% (39)		17% (14)	25% (25)	21% (18)	25% (19)	0% (0)	15% (2)	22% (14)	21% (25)
3+/day	42% (77)		46% (38)	39% (39)	49% (42)	32% (25)	71% (5)	38% (5)	44% (28)	42% (49)
How many servings of sweets do you eat in a typical day?		73									0.40
None or less than one serving per day	60% (113)		55% (48)	66% (65)	55% (49)	64% (50)	57% (4)	77% (10)	56% (37)	63% (76)
1/day	21% (40)		25% (22)	18% (18)	20% (18)	24% (19)	29% (2)	8% (1)	26% (17)	19% (23)
2/day	14% (27)		16% (14)	13% (13)	20% (18)	8% (6)	14% (1)	15% (2)	14% (9)	15% (18)
3+/day	4% (7)		5% (4)	3% (3)	4% (4)	4% (3)	0% (0)	0% (0)	5% (3)	3% (4)

¹p-values for categorical variables from Pearson’s Chi-squared test; p-values for continuous variables from Wilcoxon rank sum test

²% Non-Hispanic White

Children participated in total physically active for an average of 11.2 (SD=4.1) minutes per hour and MVPA for 5.5 (SD=2.6) minutes per hour. Parents reported that children slept an average of 11.9 (SD=2.4) hours per day. Based on observed dietary behavior of 174 children while in child care, children consumed an average of 531.9 (SD=206.7) calories with an average HEI score of 56 (SD=10.3). Additional information and a breakdown of physical activity, sleep and diet by children’s sex and race/ethnicity as well as by FCCH ABC status can be found in Table 2.

Table 2.

Child physical activity, sleep, and diet

Variables	Overall (n=260)	Number Missing	Sex		Race/Ethnicity				ABC
			Female (n=128)	Male (n=132)	Non-Hispanic Black (n=119)	Non-Hispanic White (n=103)	Hispanic (n=131)	Other (n=251)	ABC (n=89)	Non-ABC (n=171)	1p-value
Physical Activity *
Total Physical Activity* mins/hr	11.2 (4.1)	76	10.6 (4.1)	11.7 (4.0)	11.2 (4.4)	11.1 (3.5)	11.0 (6.4)	11.1 (4.7)	10.9 (4.0)	11.3 (4.1)	0.41
MVPA* mins/hr	5.5 (2.6)	77	5.0 (2.4)	6.0 (2.7)	5.6 (2.9)	5.4 (2.2)	6.0 (3.7)	5.5 (2.8)	5.2 (2.5)	5.6 (2.7)	0.31
Sleep
Parent Reported Total Sleep Time hrs/day	11.9 (2.4)	102	11.8 (2.9)	12.1 (2.0)	11.5 (2.6)	12.3 (2.2)	10.9 (0.9)	12.8 (2.2)	11.8 (2.4)	12.0 (2.5)	0.66
Nutrition *
Total Calories, kcal/day	531.9 (206.7)	92	513.8 (193.0)	548.1 (218.2)	484.5 (168.0)	616.5 (219.0)	422.1 (114.4)	472.6 (259.7)	483.9 (184.6)	558.6 (214.3)	0.86
Calories from Fat* %	30.1 (8.3)	92	30.1 (7.7)	30.0 (8.9)	28.4 (7.7)	32.6 (8.0)	25.4 (8.5)	30.6 (9.9)	27.2 (6.8)	31.6 (8.7)	0.03
Calories from Carbohydrates* *	54.3 (10.4)	92	53.3 (10.6)	55.1 (10.3)	54.7 (9.7)	52.8 (10.3)	60.8 (10.5)	53.5 (13.7)	56.3 (8.8)	53.1 (11.1)	0.40
Calories from Protein* %	15.4 (5.0)	92	15.9 (5.2)	14.9 (4.8)	16.2 (4.6)	14.5 (5.1)	13.8 (4.6)	15.9 (6.1)	16.5 (4.6)	14.7 (5.1)	0.02
Total HEI-2015 score*	56.0 (10.3)	92	57.3 (9.8)	54.8 (10.7)	57.4 (10.2)	53.6 (10.1)	62.2 (10.0)	54.2 (10.3)	56.2 (11.1)	(9.9)	0.60

^*During school day hours, from 9AM-4PM

¹p-values for categorical variables from Pearson’s Chi-squared test; p-values for continuous variables from Wilcoxon rank sum test

Over one fifth of parents (22%, SD=42) reported eating fast food more than three times per week. Parents reported sleeping an average of 6.6 (SD=1.7) hours per night. Additional information and a breakdown of these health behaviors by FCCH children’s sex and race/ethnicity as well as by FCCH ABC status can be found in Table 2.

Children in ABC FCCH had a lower percentage of their calories from fat (p=0.03) and had a higher percentage of their calories from protein (p=0.02) compared to children attending non-ABC FCCHs. There were no other significant differences in physical activity, sedentary behavior, or sleep in children receiving care in FCCHs with ABC and Non-ABC status. There was a significant difference between ABC and Non-ABC program status FCCHs in the distribution of race/ethnicity for children (p<0.0001). Non-ABC FCCHs had significantly more parents with incomes ≥$40,000 per year (p=0.04). Parents from ABC FCCH were less likely to have sleep issues based on the Sleep Problems Index II (p=0.04), but reported eating more French fries (p=0.005). Parents from Non-ABC FCCH were less likely to receive other government benefits (e.g., housing voucher) (p=0.006). Additional information can be found in Tables 1 and 2.

Discussion

This is the first study to compare FCCHs that participate in a quality assurance program to FCCHs that do not, based on device-based measures of children’s physical activity, comprehensive measures of children’s diet, and multiple questionnaires completed by parents to assess parental health behaviors and children’s demographic characteristics. The results of the present study showed that there were no differences in children’s behaviors based on the FCCHs enrollment in ABC quality standards. Results also suggest that policies and practices that target diet and physical activity in FCCHs are necessary, as these behaviors appear to be low amongst this population.

There were significant differences between ABC and non-ABC FCCHs in demographic characteristics, specifically in parent income and race/ethnicity. These differences may be the result of the nature of the ABC program. In South Carolina, SC Vouchers provide financial assistance to families to help pay for child care. Child care providers who receive these vouchers must be enrolled in the ABC program (South Carolina Department of Social Services, 2021a). Therefore, it is not surprising that FCCHs enrolled in ABC program had a lower percentage of children whose parents had incomes of ≥$40k per year. These results also suggest that FCCHs enrolled in the ABC program will be able to reach children from lower income households, who have been shown to be at higher risk of having obesity (Williams et al., 2018).

The present spent a little over 340 minutes, or around 5.6 hours, per school day sedentary. In the child care setting, sedentary behavior in limited amounts may be necessary for educational purposes, such as when providers give instruction, read to children, or when children engage in coloring or crafts (Chai et al., 2020). Therefore, the fact that children in the present sample spent more time sedentary compared to children in other studies (Chai et al., 2020), is not necessarily a negative finding.

Overall, the sample in the present study was more sedentary (Chai et al., 2020) and less physically active (Delaney et al., 2014; Gunter et al., 2012) compared to other studies that also measured physical activity in the FCCH setting. Specifically, Chai et al. reported children spending 181 minutes sedentary while in child care (Chai et al., 2020). In the present study, children spent around 336 minutes sedentary, considering they were in the FCCH from 9AM-4PM and spent an average of 48 minutes per hour sedentary based on accelerometry data. Delaney and colleagues reported children spending 8.8 minutes per hour in MVPA (Delaney et al., 2014), compared to the 5,5 minutes per hour in MVPA spent by children in the present study. Children in a study by Gunter and colleagues spent 33.8 minutes per hour participating in total physical activity, compared to 11.2 minutes spent by children in the present study. Importantly, these previous studies were conducted primarily in the Northwestern region of the United States (Chai et al., 2020; Delaney et al., 2014; Gunter et al., 2012). The results of the present study suggest that differences in physical activity levels may exist by state or region, although more work is needed to understand the underlying reasons these regional differences may exist. For example, this may be due to differences in each state’s QRIS obesity prevention standards (Geary et al., 2017), or differences in the environmental factors related to children’s physical activity levels in each state (Villanueva et al., 2016).

Interestingly, the physical activity levels of the children attending South Carolina-based FCCHs in the present study were lower than levels of children attending preschools in South Carolina, who spent around 15 minutes per hour participating in physical activity (compared to 11 minutes per hour in the present sample) and around 8 minutes per hour in MVPA (compared to 5 in the present sample) (Byun et al., 2013). This is consistent with previous research that shows children attending FCCHs are less active to preschool-age children attending other types of child care (Natale et al., 2014). This may be due to the fact that policies and practices targeting physical activity in the child care environment can be more difficult to incorporate in FCCHs due to space and financial restrictions. Once a significant amount of time has passed for these new policies to be successfully implemented, future research can assess whether there are differences in physical activity levels based in ABC enrollment, and whether these homes are still less active than other child care facilities within the same state as well as other FCCHs in other states.

In terms of dietary intake of children in care, children in ABC FCCH had a lower percentage of their calories from fat (p=0.03) and had a higher percentage of their calories from protein (p=0.02) compared to children attending non-ABC FCCHs, although there were no significant differences in children’s overall HEI score between the two groups (56.2 versus 55.9, respectively). The mean HEI-2015 score was 56.0. These findings are similar to those of baseline data from a study of 492 children from 166 FCCHs in North Carolina. The mean HEI-2015 score was 58.9 at baseline for that study compared to 56.0 in the present study (Benjamin-Neelon et al., 2018). Conversely, a more recent study of 423 children from 120 FCCHs in Rhode Island revealed that children’s mean HEI-2015 scores were 63.4 at baseline (Gans et al., 2022). These differences may be attributed to the geographic locations of each study (Northeast versus Southeastern United States) or policies (e.g. state regulations) governing foods and beverages served to children in care (Ayers Looby et al., 2020; Benjamin et al., 2008; Lee et al., 2021). All three studies found relatively low HEI scores and were below the recommended minimum score of 80, which indicates good diet quality (Kennedy et al., 1995). Given the lack of more objectively measured dietary intake research in children attending FCCHs, which ideally would combine multiple data collection methods (Zaltz et al., 2023), there are few studies from which to compare.

This study was the first to assess sleep duration among children attending FCCHs. There has been an increase in the amount of research assessing relationships between sleep and childhood obesity risk the past decade, and this behavior is considered a key component of obesity prevention research (Yoong et al., 2016). Results of the present study were based on parent-reported sleep data and showed that children slept 11.9 hours per night, which is within the 10–13 hours per day range recommended by the American Academy of Sleep Medicine (Paruthi et al., 2016). This nighttime sleep duration is higher than the duration reported amongst other studies of preschool-aged children. For example, parents in a study by Parsons an colleagues reported that children slept an average of 9.7 hours per night, but child care providers also reported that children napped during the day, which increased the average sleep duration of participants over a 24-hour day to 11.2 hours per day (Parsons et al., 2018). It is important to note, though, that parent reported sleep data often overestimates children actual sleep durations (Nelson et al., 2014), therefore future research is needed assessing children’s sleep using device-based measures. Additionally, this study did not detail napping behaviors of children in FCCHs. Preschool-aged children’s napping behaviors change as children develop. The evidence on the benefits of napping for this age group remains inconclusive, and it is not yet clear if there is a specific age at which the value of daytime naps diminishes in favor of prioritizing longer nighttime sleep (Horváth & Plunkett, 2018). As the reported sleep durations only include nighttime sleep, it is possible that 24-hour sleep durations for children in the present study were longer.

The present study was also the first to describe the sleep and diets of parents whose children attended FCCHs. Parents reported sleeping around 6.6 hours per night. This is below the 7 hours per night recommended by the AASM (Watson et al., 2015). Furthermore, 31% of parents reported drinking soda or sugar sweetened beverages on ≥1 times per day, 52% reported eating fast food ≥1 times per week, and less than 1/3 of parents reported eating ≥3 servings of vegetables each day. Not only do parents’ behaviors surrounding diet and physical activity have a significant effect on children’s health behaviors (Kininmonth et al., 2021), it is also important to consider that any interventions targeting these behaviors must take a multilevel approach (Ward et al., 2017). In other words, interventions that target diet, physical activity, and sleep among children attending FCCHs must also include strategies targeting these behaviors outside of the child care setting by communicating the importance of these behaviors to parents and other caregivers. This is supported by a recent review conducted by Tamayko and colleagues, who concluded that obesity intervention and prevention efforts both benefited from components that included parent or caregiver involvement, although it also indicated that more work in needed to define the ways in which parents and caregivers should be involved in these strategies (Tomayko et al., 2021). The present study described the sleep duration and diet behaviors of parents of children attending FCCHs. Future research should employ more comprehensive methods to assess these factors and determine whether they influence the effectiveness of large-scale policy changes aimed at preventing obesity in preschool-aged children. Given the nature of the study (i.e., a natural experiment), it was a challenge to find an appropriate comparison group. However, this paper underscores the importance of examining differences between ABC and non-ABC FCCHs following the introduction of new state policies focused on obesity prevention.

The strengths of this study include the use of device-based measures for physical activity and sedentary behavior, along with direct observation methods to assess diet in the FCCH setting. Some limitations to the present study must be addressed as well. Sleep was measured using parent-reported data. Although this method is both easy to administer and cost-efficient, parents are also known to overestimate their children’s sleep duration (Nelson et al., 2014) and would need to estimate nap times based on reports from FCCH staff. Furthermore, parents reported their own diet, which may be subject to response bias. Parent-reported data on sleep, and diet were collected using simplified methods to reduce participant burden and provide preliminary insights for future research. Consequently, these findings are described in this study but are not used to draw conclusions about children’s behaviors. Additionally, the use of BMI may not be the best indicator of body fatness in children, although it is a convenient tool to use in studies with a larger sample size (Nuttall, 2015). Importantly, the results of the present study may not be generalizable to children in other studies on FCCHs, as the BMI z-scores for participants were high (1.8 ± 3.0), indicating that many of the children were overweight or obese. Furthermore, because participants in this sample were all attending FCCHs in South Carolina, the data are not generalizable to other child care settings or to FCCHs in other parts of the United States. Future research based on these findings should explore the effect implementation of the new policies and practices have on the health behaviors of children attending FCCHs. Interestingly, 83% of the parents in the study reported having some college education or higher, yet only 54% reported an income of >$40,000, and ~25% were enrolled in SNAP or WIC. Importantly, having a college degree does not indicate employment status or income level. Additionally, SNAP and WIC enrollment is based on income, not education. Therefore, it is possible that underemployment and unemployment was high in this population. The cost of FCCH tuition is often lower than the cost of larger centers, making them more affordable for parents of children enrolled in this study.

Conclusions

Although policies and practices targeting health behaviors, specifically physical activity, diet, and sleep, have been introduced, their effects on health behaviors in children attending FCCHs in South Carolina were not yet evident. The results are consistent with previous research showing that children attending FCCHs had low physical activity levels and poor diet quality, although this study expanded on previous research by also discussing parental behaviors. Future research should focus on strengthening guidelines and improving implementation of obesity prevention standards to improve physical activity levels and diets of children attending FCCHs, while also developing multi-component interventions that align with these guidelines that target both children and parent or caregiver behaviors.

Key messages:

• Children attending Family Child Care Homes (FCCHs) in South Carolina were less physically active compared to children in other studies of physical activity in the FCCH setting.

• Dietary behavior in children attending South Carolina FCCHs was comparable to that reported in previous studies of children in FCCHs.

• Comparisons were made between FCCHs participating in ABC, the state’s quality assurance program, with non-ABC homes. Parents of children attending ABC homes were more likely to be Non-Hispanic Black and to be in a lower income stratum. There were no differences in children’s physical activity or sleep behaviors based on the FCCHs’ enrollment in the ABC program. Children attending FCCHs consumed a lower percentage of their calories from fat and a higher percentage of their calories from protein compared to children attending non-ABC FCCHs, although there were no differences in overall nutrition scores between the two groups.

Data availability statement:

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.