Executive Function in Infants and Toddlers Born Low Birth Weight and Preterm

Abstract

Infants born low birth weight (LBW) and preterm were evaluated in a high-risk follow-up clinic and compared with infants born full term. A multivariate linear model was used to examine the overall differences on Bayley Scales of Infant Toddler Development (BSID-III) among three groups: full term, heavy LBW (<2,500 g ≥2,000 g), light LBW (<2,000 g). Results indicated no significant differences in BSID-III scores between the groups. The BSID-III was used to extrapolate indicators of executive function (EF) components. Dimensions of Mastery Motivation (DMQ 18) was correlated with the EF components. Findings showed that both LBW groups scored significantly lower than the full-term group on the EF components which include attention, plan/organize, working memory, and inhibit. Implications for research is discussed.

Keywords

low birth weight preterm executive function assessment

Executive function (EF) is regulated by complex systems of interrelated neural networks that connect and organize information in the brain, resulting in cognitive and behavioral responses (Anderson, 2002). EF is repeatedly cited in the literature as an “umbrella term” because it encompasses multiple functions in the brain related to other cognitive constructs including self-regulation and mastery motivation (Burnett et al., 2018; Isquith et al., 2005; Keilty et al., 2015; Ten Eycke & Dewey, 2016). EF is typically measured behaviorally as working memory, inhibitory control, and cognitive flexibility (Miyake et al., 2000). It is clear that these components of EF are manifested in numerous behaviors which include sustained focus/attention, self-regulation, goal-directed persistence, planning/organizing, and shifting.

The early childhood years are periods of rapid development (Diamond, 2006). According to Zelazo et al. (2016), “the preschool period may be a window of opportunity for the cultivation of these skills via well-timed, targeted scaffolding and support” (p. 16). Indeed, longitudinal studies also provided evidence that pre-academic learning opportunities improve EF in young children (Fuhs et al., 2014; Welsh et al., 2010). Therefore, it is important that the field of early intervention (EI) conceptualize EF as an signficant outcome that affects the child’s development and learning.

Sasser et al. (2015) examined predictive links between children’s EF skills and trajectories of academic skill development and social-emotional adjustment at school. They followed 4-year-old children in 22 Head Start classrooms through the third grade and found that preschool EF abilities as measured by the peg-tapping task (Diamond & Taylor, 1996) and the Dimensional Change Card Sort (DCCS; Frye et al., 1995) significantly predicted later math skills, academic functioning, and social competence (Sasser et al., 2015). Other studies of children in Head Start found EF skills to be positively associated with social competence in kindergarten (Razza & Blair, 2009). Still others found the EF skills of inhibitory control, attention-shifting, and cognitive shift using similar tasks in preschool to be related to math (Blair & Razza, 2007) and literacy abilities (Noble et al., 2005) in kindergarten. EF skills in preschoolers as measured using the Head-Toes-Knees-Shoulder Task (HTKS; McClelland et al., 2014), which includes inhibitory control, attention, and working memory, predicted math and reading achievement. According to Cragg and Gilmore (2014), attention and working memory performance at age 5 were significant predictors of performance on measures of mathematics at age 8 years. These longitudinal studies made important contributions to the field by increasing our understanding of EF as an important predictor of later school success.

Researchers who have measured EF in early childhood have found many essential outcomes that last a lifetime. These include early kindergarten achievement (Blair & Razza, 2007; McClelland et al., 2007), social competence in adolescence (Devine et al., 2016), improved physical health, higher socioeconomic status (SES), and fewer drug and criminal convictions in adulthood (Moffitt et al., 2011).

As demonstrated in the above cited early childhood literature, an extensive body of research has highlighted the importance of gaining EF skills early before school age. Since EF skills are an essential component of early development that critically affect later school outcomes, children at risk of developmental delays are in need of services to ameliorate or buffer the impact of EF deficits (Blasco et al., 2014). Children born LBW and preterm are a population vulnerable to EF deficits that often go unrecognized until school-age when it may be too late for effective intervention (Blasco et al., 2017). There is growing research evidence that the development of EF skills protects against risks associated with LBW, poverty, and adversity, factors that are often evident in the backgrounds of children born LBW and preterm (Masten et al., 2012).

EF and the Impact on LBW and Preterm

EF has been associated with important neurodevelopmental outcomes, related to later social competence and academic achievement in children who were born early (Loe et al., 2014, 2015; Zelazo et al., 2016). According to the Centers for Disease Control and Prevention (CDC, 2018), approximately 1 in 10 babies was born early in the United States. Preterm birth occurs when a baby is born, before 37 weeks of a 40-week pregnancy have been completed. Most but not all preterm babies have birth weights less than 5.5 lbs. or 2,500 g (CDC, 2018). A birth weight of less than 5.5 lbs. or 2,500 g or less is considered LBW (CDC, 2018). According to the CDC National Center for Health Statistics, the preterm birth rate increased in 2015 to 9.2%; this is the first increase since 2007 (Martin et al., 2015). The prevalence of LBW also increased in 2015 to 8.07% (Hamilton et al., 2016). These increases may lead to increased diagnoses of disabilities and/or lifetime cognitive impairments. Thus, this is a large subgroup of children who are vulnerable to developmental delays and subsequent risk for learning difficulties (Blasco et al., 2017).

The prevalence of diagnosed learning disability (LD), both with and without attention-deficit/hyperactivity disorder (ADHD), was greater among children born LBW than among children born full term (Lampi et al., 2012). Squarza et al. (2016) followed up 102 infants from Neonatal Intensive Care Unit (NICU) discharge to age 6 and found that 23% of extremely low birth weight (ELBW) and extremely low gestational-aged children met criteria for an LD, and 13% had learning disabilities in multiple domains. In addition, attention and/or emotional difficulties were found in 70% of those children identified with LD.

A literature review of retrospective studies of school-age children who were born LBW and preterm has shown that even for those without major disabilities, lasting cognitive and behavioral impairments may occur, including EF skill deficits (Barre et al., 2011; Chan & Quigley, 2014; Grunau et al., 2002; McCormick et al., 2006; Mulder et al., 2010; Vohr et al., 2005). A meta-analysis of neurodevelopmental and behavioral outcomes of children who were very low birth weight (VLBW < 1,500 g) and very preterm (<33 weeks’ gestation) showed they were more likely to have moderate to severe performance difficulties on measures of academic achievement and exhibit both challenging behaviors and EF deficits (Aarnoudse-Moens et al., 2009). EF deficits included difficulties with self-regulation, working memory, cognitive flexibility, and/or verbal fluency. Camerota et al. (2015) examined six EF tasks with children born LBW at 36, 48, and 60 months. Interestingly, they found that children who experienced high levels of sensitive parenting based on parent/child dyadic play activities (recorded at 6, 15, 24, and 36 months) to had faster rates of EF improvement over the first two test periods, and by 60 months, the LBW sample’s EF scores were the same as the full-term group. Higher level of parent sensitivity in infancy and toddlerhood predicted better EF skills at 48 months in both LBW and full terms groups. Conversely, harsh intrusive parenting in toddlerhood predicted poorer EF at 48 months. Thus, it is important to consider parental influences on the development of EF during the early years. Duvall et al. (2017) studied EF in preschoolers (M = 46.16 months) born VLBW (<1,500 g) and preterm (<37 weeks) and found that the full-term group had higher scores on all EF performance measures (see Duvall et al., 2017, for a full list of assessment tasks). They also examined parent report (Behavior Rating Inventory of Executive Function–Preschool Version [BRIEF-P]; Gioia et al., 2003) and a measure of naturalistic compliance during a clean-up task. Children born VLBW had poorer EF outcomes regardless of the measurement method. This study demonstrated the importance of using multiple measurements (e.g., performance-based, parent report, naturalistic observation) when assessing EF in young children.

Measurement of EF in Young Children

Young children have been observed performing components of EF through individualized structured tasks typically in a laboratory setting (Keilty et al., 2015). As an example, Diamond (1985) used the “A not B” task, also known as a measure of object permanence, to study early working memory. Marcovitch et al. (2016) stated this task is a measure of EF (working memory) as it requires various levels of representational strength as the task becomes more difficult. The “A not B” error occurs when the infant does not perceive the replacement of an object in the first year of life. For example, 9-month-old infants will look in the same place (under a cloth) even after the toy has been moved under a different cloth. After 1 year of age, infants realize the object continues to exist and did not disappear, they are able to mentally store the image in their mind and look for it in the correct place. In other words, these infants are using working memory to recall the object placement.

Lawson and Ruff (2004) used measures of focused attention with infants to predict later cognitive ability. These researchers believed that an infant’s focused attention could be observed during active exploration of play objects by mouthing and banging. A toddler also demonstrates this attention when engaged with a cause and effect toy such as a pop-up toy, whereas a 3-year-old may show this attention by placing pieces in the correct position in a puzzle board.

Multidimensional EF Assessment

The majority of tools to measure EF were developed for school-aged children. Only recently have measures of EF that include more than one dimension such as inhibit or working memory become available for young children who may be assessed in early childhood settings (Camerota et al., 2018). Furthermore, no assessments included LBW and preterm infants and toddlers in their sample populations. The Division for Early Childhood (DEC, 2018) of the Council for Exceptional Children developed a position statement to address the need to identify and intervene with young children who are born LBW and preterm and to provide national standards for EI services that can be used at states’ discretion. The statement also supports the critical need for assessment and development of early learning skills, including EF in young children at risk of developmental delays and/or disabilities.

Despite the lack of multidimensional EF measures from birth to 3, EF remains an important construct for child development and learning, especially for those who are at risk of developmental delay and/or disability. Not only do we need reliable, sensitive, and specific measures but also strategies to improve EF skills before children become school-aged. As stated by Diamond (2016):

One of the most critical societal needs is to develop effective, scalable, sustainable, and affordable strategies for supporting children from the youngest age possible, their parents, and their early child care providers to get children started with good EF skills when they first enter school, thereby launching them on a promising, positive trajectory, improving their life prospects and preventing problems, rather than trying to treat problems after they have been allowed to develop. To be able to determine whether a strategy is successful or not, sensitive and valid measures of EF that can be administered longitudinally are absolutely essential. (p. 26)

Components of EF in Established Early Childhood Assessments

Multidimensional measures of EF components for children under the age of 3 years are needed in the field with developmentally, and culturally, and linguistically appropriate items. Also, it is important to consider ease of use for practitioners and families. We hypothesized that established infant and toddler developmental assessments (e.g., Bayley Scales of Infant and Toddler Development [BSID-III]; Bayley, 2005) contain items with EF components that could be extrapolated.

As guidance to our work, Lowe et al. (2009a) used the BSID-II (Bayley, 1993) group of related items as a single measure of early working memory. Using these items, they found babies born full term were 4.6 times more likely to achieve object permanence on these BSID-II tasks than children who were born LBW. In another study of LBW infants, girls outperformed boys on object permanence tasks on the BSID-II (Lowe et al., 2009b).

Another measure used to address EF as related to self-regulation and the young child’s persistence at goal-directed tasks (EF components of attention and planning) is the Dimensions of Mastery Questionnaire (DMQ 18; Morgan et al., 2019). This parent completed questionnaire assesses mastery motivation, or a child’s ability to engage in solving challenging tasks and exhibit emotional pleasure and/or negative behavior through frustration (EF skill of emotional control), starting as young as 6 months. Emotional regulation is a component of EF exhibited through inhibitory control. Early experiences can affect neurobiological emotion regulatory systems through the influence on higher and lower brain regions (Barrett et al., 2013). Researchers have found a relationship between emotional control and sensitive parenting that can affect a child’s mastery drive; mastery motivation is a key developmental construct related to the umbrella of EF (Keilty et al., 2015).

In the current study, in addition to examining differences in global performance on the BSID-III for infants born LBW and those born full term, we explored whether the DMQ 18 categories and specific clusters of items from the BSID-III could be used to identify early indicators of EF in young children born LBW (e.g., attention, plan/organize, working memory).

The research questions included the following:

Research Question 1: Are there differences on the BSID-III scores between children born LBW (light, LBW < 2,000 g; heavy, LBW <2,500 g ≥2,000 g) and those born full term?

Research Question 2: Are there differences on the EF components derived from the BSID III between children born LBW (light, heavy) and those born full term?

Research Question 3: Are there differences on the DMQ 18 between children born LBW (light, heavy) and those born full term?

Research Question 4: Are EF components and DMQ 18 related within the groups?

Research Question 5: For children born LBW (light, heavy), are there differences in EI enrollment?

Method

Participants

Children born LBW (<2,500 g) and preterm (<37 weeks), excluding those with a known syndrome, genetic disorder, or diagnosed disability (e.g., cerebral palsy) and small or large for gestational age, were recruited and evaluated in a NICU follow-up clinic for children ages 3 months to 3 years. The setting features family friendly furniture and child size testing areas (e.g., washable tablechairs, and mats). A neurodevelopmental team including a developmental pediatrician, clinician (e.g., represented by a special educator, psychologist, speech therapist, or occupational therapist), and audiologist evaluate the children while parents complete the paper and pencil forms. The purpose of this clinic is to evaluate children who were born LBW and preterm without major medical diagnoses such as cerebral palsy or genetic disorders.

One hundred and two children born LBW and preterm were recruited for this IRB approved study at age 6 months to 8 months corrected age; however, on further review of medical records, one child was removed from the study due to new genetic diagnosis, one was small for gestational age (SGA), and a third one was over 2,500 g but <37 weeks’ gestation. The number of LBW children remaining in the study for analysis purposes was 99. The children were categorized as heavy LBW (<2,500 g >2,000 g) (N = 35), and light LBW (<2,000 g) (N = 64). These weight categories were previously determined in a longitudinal study of children born LBW and preterm through the Infant Health Development Program (Litt et al., 2015; McCormick et al., 2006). Children who were full term were recruited through a Northwestern university and pediatric offices close to the university using flyers and through social media including parent Facebook pages (N = 41). Children were seen in either the same children’s hospital or in a university setting with similar furnishings in each setting. The total sample size used for analysis was 140.

In this study, children born preterm were assessed at corrected ages between 6.0 and 8.9 months. The average corrected age was 6.5 months. Table 1 shows the chronological and gestational age distributions for each of the two LBW groups. All parents of children in both the LBW and full-term group completed a demographic form identifyingbasic demographic factors: race, ethnicity, parent education, enrollment in EI, enrollment in supportive therapies (e.g., speech/language therapy, physical therapy, including the length of the services received), home visits from public health nurses, and whether their child was in a child care program (e.g., preschool, family child care, Early Head Start). For the purpose of analyses, parental education status was coded as follows: 1 = high school graduate or less, 2 = some college, or 3 = college graduate or higher.

Table 1.

Weight and Age Based on Birthweight Categories (N = 99).

NICU variables	Heavy LBW (N = 35)		Light LBW (N = 64)
NICU variables	Range	M	Range	M
Birth weight (g)	2,010–2,466	2,215.63	620–1,989	1,597.28
Chronological age (months)	6.08–10.38	7.92	7.16–10.87	8.52
Age corrected for prematurity (months)	6.00–8.90	6.54	5.70–8.90	6.66
Gestational age (weeks and days)	31.6–36.9	34.26	25.00–36.60	32.02

Note. LBW = low birth weight; NICU = Neonatal Intensive Care Unit

Qualified language interpreters were used in all evaluations when the primary language was not English. Time to complete parent forms was estimated between 10 and 15 minutes and information was collected as part of their child’s routine clinical visit. In addition, developmental testing was completed as a part of routine clinical care. All data were then entered into an IRB-approved database.

Table 2 shows demographic information by the three groups (full term, heavy LBW, and light LBW). Analyses showed no significant differences among the three groups for gender or race/ethnicity. Significant differences were found for both mother’s and father’s educational level. Mothers of full-term children had significantly higher educational levels than mothers of children in the heavy LBW group, χ²(2) = 9.037, p = .002, and mothers of children in the light LBW group, χ²(2) = 14.15, p = .001. Similarly, fathers of full-term children had significantly higher educational levels than fathers of children in the heavy LBW group, χ²(2) = 4.00, p = .025, and fathers of children in the light LBW group, χ²(2) = 17.40, p = .000. No significant differences for either maternal or paternal educational level were found between the two LBW groups. Out of the 99 children born LBW, 28 had received EI services at the time of their clinic visit, as indicated on the demographic form. Of these 28 children, seven (28.3%) were in the heavy LBW group and 21 (21.5%) were in the light LBW group. For the heavy LBW group, this means that 20.0% received EI services, while this increases to 32.8% among the light LBW group.

Table 2.

Demographic Information Based on Birthweight Categories (N = 140).

Demographic variables	Full term (N = 41)	Heavy LBW (N = 35)	Light LBW (N = 64)
Demographic variables	n (%)	n (%)	n (%)
Gender
Male	22 (53.7)	20 (57.1)	32 (50)
Female	19 (46.3)	15 (42.9)	32 (50)
Race/ethnicity^a
Caucasian	28 (70.0)	17 (53.1)	35 (56.5)
African American	–	–	4 (6.5)
American Indian/Alaska Native	–	1 (3.1)	1 (1.6)
Asian American	3 (7.5)	2 (6.3)	4 (6.5)
Hispanic	6 (15.0)	8 (25.0)	8 (12.9)
Multiple ethnicities	3 (7.5)	4 (12.5)	10 (16.1)
Mother education^a
High school or less	3 (7.3)	9 (25.7)	22 (37.9)
Some college	7 (17.1)	11 (31.4)	12 (20.7)
College graduate or higher	31 (75.6)	15 (42.9)	24 (41.4)
Father education^a
High school or less	4 (10.0)	12 (35.3)	28 (50.9)
Some college	13 (32.5)	6 (17.6)	9 (16.4)
College graduate or higher	23 (57.5)	16 (47.1)	18 (32.7)

Note. LBW = low birth weight.

Percentages are based on valid responses. Information on demographic variables missing for some respondents.

Measures

BSID-III

Children at ages 6 to 8 months were assessed on the BSID-III (Bayley, 2005), a standardized assessment that aims to measure physical, motor, sensory, and cognitive development in infants and toddlers from birth to age 42 months. The BSID-III provides composite and standard scores on all areas of development. According to the manual, the BSID-III measures sensorimotor development, exploration and manipulation, object relatedness, concept formation, and memory. The language scale measures preverbal behaviors and vocabulary development. For this study, the following BSID-III composite scores were obtained: cognitive, language, and motor. These standard scores have a mean of 100 and an SD of 15. Scaled scores of receptive and expressive language and fine and gross motor had a mean of 10 and an SD of 3. In the LBW group, children under the age of 24 months were corrected for age according to the BSID-III protocol. According to the BSID-III technical manual, researchers should report both scaled scores and compositive scores to provide the most accurate description of a child’s strengths and needs.

The BSID-III was standardized on over 1,700 young children from 16 days to 42 month, 15 days. The technical manual provides evidence of technical adequacy. In addition, 85 children in the standardization sample were preterm with corrected ages from 2 to 24 months, and noncorrected to 42 months. These children obtained the following means and standard deviations on scaled scores: Cognitive 9.7 (3.1), Receptive Communication 9.6 (3.1), Expressive Communication 9.3 (3.3), Fine Motor 9.4 (2.8), and Gross Motor 9.4 (2.7).

EF components from BSID-III

Early indicators of EF skills were extracted from the BSID-III using a similar clustering of items as described previously by other researchers (Lowe et al., 2009a, 2009b). Researchers independently conducted an examination of BSID-III items related to EF skill components of emotional control, attention, working memory, inhibit, plan/organize, and shift (i.e., cognitive flexibility).

Examples of items for attention included the following: “Shifts attention between a bell and rattle” and “Searches with head turn for the sound of a rattle and bell.” Attention is related to inhibition as the child restrains urges to focus his or her attention (Diamond, 2013). Examples for plan/organize included the following: “Intentionally pulls a cloth to obtain a block” and “Imitates a play interaction by holding a cloth over their own head when the adult says, “Peek-a-boo.” Examples of working memory include the following: “Reacts to disappearance of face” and “Recognize familiar words.” Examples of inhibit included the following: “Looks up and pauses in play when name is called” and “Child stops reaching for an object when they hear ‘no no’.”

Two experts in child development and the research team independently reviewed the EF components developed from the BSID-III items and agreed or disagreed on the item’s categorization. Interrater agreement was within one item of total agreement. In addition, the project consultant who is an expert in the field also reviewed the extrapolated EF components. Based on the collective feedback, several items were deleted relating to gross motor development, but no new items were added. Minor disagreements were resolved by consensus after comparing evidence.

DMQ 18

The DMQ 18 (Morgan et al., 2019) is a caregiver-completed, 38-item questionnaire designed to measure persistence in mastery tasks across the dimensions of both cognitive (e.g., complex tasks such as puzzles, cause, and effect) and social tasks (e.g., expression of persistence during social and symbolic play; Busch-Rossnagel & Morgan, 2013). The DMQ 18 has separate versions for infant (6–17 months), preschool (18 months to 5 years), school age (6–12 years), and adolescent. The DMQ 18 is also available in Spanish and Chinese. The DMQ 18 infant version used in the present study measures seven dimensions of mastery motivation: (a) cognitive-object persistence (six items), (b) social persistence/mastery motivation with adults (six items), (c) social persistence/mastery motivation with children/peers (six items), (d) gross motor persistence (five items), (e) mastery pleasure (five items), (f) negative reactions to challenge-frustration/anger (five items), and (g) general competence (five items). Sample items include the following: “Gets excited when he or she figures something out (Mastery pleasure)” and “Gets frustrated when not able to complete a challenging task (Negative reactions).” For full details of the psychometric properties of the DMQ 18, see the technical manual (Morgan et al., 2019).

Data Analysis

To address the first three research questions—are there any differences between children born LBW (light, heavy) and those born full term on the BSID-III, the EF components, and the DMQ 18, multivariate linear models were analyzed with parent educational status used as a covariate. Correlations were examined between the DMQ 18 scales and the EF components to examine any significant relationships between the three groups. For the final research question, percentages were used to examine whether children born LBW were in EI.

Results

Table 3 shows the average BSID-III scaled and composite scores, broken out for each of the three birthweight groups. A multivariate linear model was used to examine the overall differences on the eight composite and subtests of the BSID-III scores among the three groups. Effect size $(η_{p}^{2})$ was calculated to determine the amount of variance explained by each measure.

Table 3.

Means and Standard Deviations for the Effects of Birthweight Groups on BSID-III Scores.

BSID-III scales	Full term (N = 41)	Heavy LBW (N = 35)	Light LBW (N = 64)
BSID-III scales	M (SD)	M (SD)	M (SD)
Cognitive Scaled	10.10 (1.99)	10.26 (2.11)	10.69 (2.07)
Cognitive Composite	100.49 (9.93)	101.29 (10.53)	103.20 (10.25)
Receptive Scaled	9.02 (1.64)	8.71 (1.74)	9.02 (2.10)
Expressive Scaled	9.90 (1.74)	9.37 (2.12)	8.87 (1.75)
Language Composite	96.95 (8.08)	94.49 (7.52)	93.97 (9.32)
Fine Motor Scaled	11.22 (2.46)	10.49 (2.57)	10.73 (2.73)
Gross Motor Scaled	10.76 (2.82)	10.46 (2.57)	9.52 (2.93)
Motor Composite	105.95 (10.75)	103.09 (13.48)	100.64 (14.88)

Note. BSID-III = Bayley Scales of Infant and Toddler Development; LBW = low birth weight.

As both maternal and paternal educational levels differed significantly between the full-term and the LBW groups, educational status was used as a covariate in the analyses. Results showed that controlling for either maternal educational level, F(8, 117) = .055, p = .557; $η_{p}^{2} = . 055$ , or paternal educational level, F(8, 117) = 1.098, p = .370; $η_{p}^{2} = . 070$ , did not significantly impact the overall model. Results indicated no significant difference on any of the BSID-III scores for the three groups, F(16, 236) = .124, p = .242; $η_{p}^{2} = . 077$ .

Table 4 shows the average EF component scores in each of the three birthweight groups as derived from the BSID-III. The number of items for each EF component is listed on the table. A multivariate linear model was completed to examine the overall differences on the five scores among the three groups. Results showed that controlling for either maternal educational level, F(5, 119) = .666, p = .650; $η_{p}^{2} = . 027$ , or paternal educational level, F(5, 119) = 447, p = .815; $η_{p}^{2} = . 018$ , did not significantly impact the overall model. There was a significant difference for the three groups (full, light, heavy) overall, F(10, 240) = 2.418, p =.009; $η_{p}^{2} = . 092$ . Table 5 shows that the components of emotional control, working memory, and inhibit are not statistically significantly different. Attention and plan/organize are statistically different with regard to birthweight group. For the measure of attention, F(2, 123) = 8.856, p = 000; $η_{p}^{2} = . 126$ , a medium effect. For the measure of plan/organize, F(2, 123) = 5.294, p = .006; $η_{p}^{2} = . 079$ , also considered a medium effect. Subsequent simple contrasts were performed to examine these differences in detail. Both LBW groups received significantly lower scores than the full-term group on the measures of attention and plan/organize. No significant differences on any of the EF component measures were found between the heavy and light LBW groups.

Table 4.

Means and Standard Deviations for the Effects of Birthweight Groups on EF Components Extrapolated from BSID-III.

EF components	Full-term (N = 41)	Heavy LBW (N = 35)	Light LBW (N = 64)
EF components	M (SD)	M (SD)	M (SD)
Emotional control (five items)	1.00 (0.00)	0.99 (0.03)	0.99 (0.06)
Attention (17 items)	0.90 (0.08)	0.83 (0.08)	0.81 (0.09)
Working memory (22 items)	0.31 (0.09)	0.32 (0.11)	0.30 (0.10)
Inhibit (four items)	0.38 (0.16)	0.44 (0.16)	0.42 (0.20)
Plan/organize (26 items)	0.60 (0.97)	0.54 (0.12)	0.54 (0.11)

Note. EF = executive function; LBW = low birth weight; BSID-III = Bayley Scales of Infant and Toddler Development. Items were extrapolated from the BSID-III for each component.

Table 5.

Test of Between-Subject Effects for EF Components.

Dependent variable	SS	df	MS	F	$η_{p}^{2}$
Emotional control	0.001	2	0.001	0.902	.014
Attention	0.121	2	0.060	8.856*	.126
Working memory	0.009	2	0.004	0.406	.007
Inhibit	0.092	2	0.046	1.604	.025
Plan/organize	0.121	2	0.60	5.294*	.079

Note. SS = sum of squares; MS = mean sum of squares; EF = executive function.

p < .05.

Table 6 shows the means and standard deviations for the DMQ 18 seven dimensions for each of the three birthweight groups. A multivariate linear model was completed to examine the overall differences on the five scores among the three groups. As both maternal and paternal educational levels differed significantly between the full-term and the LBW groups, educational status was used as a covariate in the analyses. Results showed that controlling for paternal educational level, F(7, 105) = 2.094, p = .050; $η_{p}^{2} = . 123$ , did not significantly affect the overall model. Controlling for maternal educational level, F(7, 105) = 2.337, p = .029; $η_{p}^{2} = . 135$ , was significant; however, indicating that educational status on the mother’s side was a significant covariate. Specifically, maternal education status was statistically significant for Mastery Pleasure, F(1, 111) = 5.134, p = .25; $η_{p}^{2} = . 044$ , and general competence, F(1, 111) = 5.879, p = .008, $η_{p}^{2} = . 061$ . In both cases, the effect was small. In general, mothers with more education reported higher average scores on these two dimensions. Results further indicated a significant difference on the DMQ 18 scales for the three groups, F(14, 212) = 2.221, p = .008; $η_{p}^{2} = . 128$ . The DMQ 18 dimension of general competence was statistically different with regard to birthweight group, F(2, 111) = 7.017, p = .001; $η_{p}^{2} = . 112$ . Subsequent simple contrasts were performed to examine this difference. Both LBW groups received significantly lower scores than the full-term group but did not differ significantly from each other.

Table 6.

Means and Standard Deviations for the Effects of Birthweight Groups on DMQ 18 Dimensions.

DMQ 18 categories	Full-term (N = 41)	Heavy LBW (N = 35)	Light LBW (N = 64)
DMQ 18 categories	M (SD)	M (SD)	M (SD)
Object-oriented persistence	3.29 (0.75)	3.31 (0.85)	3.23 (0.87)
Social persistence with adults	2.93 (1.05)	2.79 (1.00)	2.80 (0.97)
Social persistence with children	2.37 (1.02)	2.56 (1.06)	2.58 (1.07)
Gross motor persistence	3.79 (0.71)	3.35 (1.03)	3.45 (0.74)
Mastery pleasure	4.05 (0.81)	3.94 (0.84)	3.83 (0.78)
Negative reaction to failure	2.53 (0.73)	2.65 (0.89)	2.63 (0.95)
General competence	3.35 (0.95)	2.61 (0.98)	2.61 (0.94)

Note. DMQ = dimensions of mastery motivation; LBW = low birth weight.

Table 7 shows the correlations between the five dimensions of the EF components and the eight DMQ 18 scales, broken out by the three groups. Among the full-term children, the DMQ 18 scale of social persistence with children and the EF component of working memory showed a significant positive correlation (r = .359, p = .021). Likewise, the DMQ 18 scale of general competence was positively correlated with the working memory among these children (r = .335, p = .035). Among the light LBW group, two correlations were significant: the DMQ 18 scale of social persistence with children was positively correlated with inhibit (r = .265, r = .041) and negatively correlated with plan/organize (r = -.261, p = .042).

Table 7.

Correlations Between DMQ 18 Scales and EF Components.

DMQ18 categories	EF components
DMQ18 categories	Emotional control	Attention	Working memory	Inhibit	Plan/organize
Full term (N = 41)
Object-oriented persistence	a	0.108	0.161	−0.28	0.161
Social persistence with adults	a	−0.141	−0.061	−0.219	−0.032
Social persistence with children	a	0.041	0.359*	−0.208	0.119
Gross motor persistence	a	0.268	0.155	−0.162	0.197
Mastery pleasure	a	0.288	−0.067	−0.199	0.007
Negative reaction to failure	a	0.077	0.166	−0.091	0.069
General competence	a	0.064	0.335*	−0.093	0.243
Heavy LBW (N = 35)
Object-oriented persistence	−0.108	−0.03	0.115	−0.151	0.067
Social persistence with adults	−0.125	0.089	0.129	−0.142	0.12
Social persistence with children	−0.212	0.105	0.05	−0.018	0.007
Gross motor persistence	−0.113	0.189	0.123	0.035	0.159
Mastery pleasure	−0.054	0.202	0.068	−0.031	0.261
Negative reaction to failure	−0.272	−0.14	0.244	0.041	0.141
General competence	−0.218	0.17	0.155	0.073	0.031
Light LBW (N = 64)
Object-oriented persistence	−0.065	−0.037	−0.03	0.111	−0.107
Social persistence with adults	−0.104	−0.044	−0.06	0.227	−0.187
Social persistence with children	−0.235	−0.157	−0.093	.265*	−.261*
Gross motor persistence	0.104	0.15	0.018	0.057	0.003
Mastery pleasure	−0.084	0.021	0.014	0.056	−0.032
Negative reaction to failure	−0.128	−0.005	0.175	0.101	−0.19
General competence	0.026	0.13	0.1	0.081	−0.011

Note. a: Cannot be computed because the values for emotional control is a constant of 1.0. DMQ = dimensions of mastery motivation; EF = effective function; LBW = low birth weight.

Correlation is significant at the .05 level (two-tailed).

To address the final research question, percentages were used to evaluate how many children were enrolled in EI depending on their birthweight group. For children in the light LBW group, 32.8% were enrolled in Part C of EI and 20.0% of the heavy LBW were enrolled in EI.

Discussion

This study was designed to examine early development of children born LBW and preterm with no major medical conditions. The objectives were to examine the relationship between currently used measures of infant and toddler development and EF and to extrapolate early indicators of EF. EI practitioners would benefit from including the measurement of EF when working with children who are at risk.

Results show no significant differences between the light LBW, heavy LBW, and full-term groups on the BSID-III. This is not surprising to us as researchers and clinicians since the BSID-III item density is low for this age group. Many clinicians have recommended increasing the items on the BSID-III under 1 year of age and this change was implemented in the recent new version.

In addition, the BSID-III has been reported to provide higher than anticipated scores in preterm populations when using corrected age calculation (Anderson et al., 2010; Duncan et al., 2015; Hack, 2012), which may have contributed to the lack of significant differences.

Other researchers have noted an overestimation of cognitive ability on the BSID-III. Vohr et al. (2012) compared BSID-II and BSID-III cognitive scores in a large cohort of 18- to 22-month-olds born extremely early (<27 weeks and a birth weight of 401–1,000 g) over two time periods and found that BSID-III scores were 11% higher for matched infants on the BSID-II. The researchers suggested the BSID-III may not be effective in identifying mild developmental delay and has limited sensitivity at early ages for this population.

Parental ratings of general competence differed across weight groups using the DMQ 18. General competence was significantly higher for children who were full term. Examples of general competence questions included the following: “Is developing faster than other children his or her age” and “Does most things better than other children his or her age.” Consistent with this evidence, it is possible that parents may overestimate the skills of their full-term child and/or parents of preterm children are perhaps being overprotective and/or perceiving their preterm infant as more vulnerable. However, these results are similar to early research in mastery motivation which found preterm infants to have reduced motivation related to challenging tasks including less task-directed behavior and exploration than their full-term counterparts (Morgan et al., 2016). Another factor may be that parents are not accounting for corrected age when answering questions about their children. Indeed, during their clinic pediatric interview, parents often compared their child with siblings or peers at that same chronological age.

Some researchers have also suggested parents will rate children with developmental delays as lower on mastery motivation tasks although there were no noted motivational differences found on moderately challenging behavioral tasks (Gilmore et al., 2015; Wang et al., 2014). Of course, it is also possible that true neurological differences exist, as some more recent research suggests, related to impaired cognitive development and differences in white matter maturation (Dodson et al., 2017; Young et al., 2018).

In terms of the EF components extrapolated from the BSID-III, we found significant differences on attention, plan/organize, working memory, and inhibit. EF components of inhibit and plan/organize were found to be significantly correlated with social persistence with children in the light LBW group. In the light LBW group, inhibit was positively correlated with social persistence with children but negatively correlated with plan/organize. It is not surprising that the light LBW group who had difficulty with inhibit also had difficulty with plan/organize. The items on the DMQ 18 that address social persistence with children include the following: “Tries to make other children feel better if they cry or seem sad” and “Tries to do things that keep other children interested.” Although these are not considered behaviors typically exhibited by infants, parents will often report these behaviors as demonstrated in this study. For example, a 6-month-old might verbalize to keep a sibling’s interest. An 8-month-old might cry when another child is crying. These findings may help identify the need for early assessment procedures that examine a child’s cognitive and social processes, motivational aspects, and regulatory approaches to learning. These results are similar to those of Edgin and colleagues (2008) who found higher effect sizes for EF difference in children born extremely LBW (≤750 g) or extremely preterm (≤28 weeks).

The extrapolated EF components from the BSID-III also showed relationships with the DMQ 18 parent report. For children in the full-term group, working memory was related to the general competence and social persistence with children. These findings are similar to Lowe et al. (2009b), as stronger emotional and attention regulation on a behavior rating scale was associated with more developed early working memory, although their samples were toddlers at 19 months old.

In conclusion, we were able to find correlations between standardized infant/toddler developmental assessments and EF components as early as 6 to 8 months in the areas of attention, plan/organize, working memory, and inhibit. These findings are among the first to examine EF components other than early working memory from infant and toddler development tests. These findings confirmed the Lowe et al. (2009b) study showing relationships between behavior rating scales and EF composite scores developed from individual BSID-III items and support the need for EI with attention to child’s EF skills.

Limitations

A limitation of the current study is the small sample recruited in the full-term group despite flyers widely circulated in pediatric and public health offices, through the university/hospitals’ IRB recruitment site, grocery stores, coffee shops, child care centers, and which included an offer of a small parent stipend. Another limitation is the homogeneity of the sample. The current study included children from one state, and the majority of the participants self-identified as Caucasian on the demographic form, and, hence, the results may not generalize to other populations. In addition, parents in the full-term control group were highly educated with many having at least a college education. This is due to the fact that the study was advertised through the IRB at a major children’s hospital, and many pediatricians and/or their spouses volunteered for our full-term cohort. Despite this limitation, there were no differences found when maternal and parental education were used as a covariate for the BSID-III results. There were, however, differences on the DMQ 18, the parent report. As mentioned previously, it is understandable that given an early birth, parents’ sense of vigilance might be heightened by the very need to have an examination in a high-risk follow-up clinic (McGrath & Vohr, 2017). This concern could result in lower scores for children in the LBW group.

Implications for Research

While acknowledging these limitations, our study findings have implications for the field and suggest several directions for future research. This study demonstrated that several EF components could be identified using well-known developmental measures as early as 6 months of age. An important implication is that clinicians and practitioners using the DMQ 18 should ensure that caregivers complete the DMQ 18 while considering their child’s corrected age (as appropriate) until the age of 2 years.

The field of EI/early childhood special education needs precision in the measurement of EF for all children. EF is a known multidimensional set of skills; however, many studies examine only one or two EF components. Our study found early differences in EF components extrapolated from established infant/toddler assessments. We hope to develop an assessment of EF for young children birth to three that would better enable EI practitioners and families to not only strengthen EF skills but also to buffer the effects of relative areas of weakness. Such a measure will contribute substantially to our understanding of child’s EF outcomes and how early development is affected by a child’s birth status. Similar to Duvall et al. (2017), we incorporated measures that included performance-based and parent report. Practitioners have the opportunity of including more naturalistic observations of EF at home and in the community. It would be important to consider this method when developing a useful tool for practitioners and families.

Conclusion

There is a need to continue the work of the measurement of EF with sensitivity and specificity to detect early EF areas of need in all children, including those born LBW and preterm. Currently, there are only parent report measures for children ages 2 and above related to EF (e.g., BRIEF-P), and performance-based measures of multidimensional EF begin at age 2. At a minimum, this study suggests EF can be assessed earlier than currently available measures indicate, which can provide an earlier window for intervention to support optimal outcome for these at-risk populations, including children born LBW and preterm.

As stated previously by Diamond (2016), sensitive and specific measures are needed not only to identify EF skills and deficits at an early age, the field also must move to identify strategies for intervention. This is particularly true for one of our most vulnerable populations of young children who are at high risk for developmental delay and/or disabilities. In recent years, researchers and stakeholders have addressed strategies to promote EF at an early age (Blasco et al., 2014; Center on the Developing Child at Harvard University, 2019). Although it is not the intent of this current study to address intervention strategies, we acknowledge the importance of the link between assessment, planning, and intervening to improve EF in all young children. The development of a multidimensional assessment tool that can be used with children as young as 6 months through the toddler years is needed in the field.

Footnotes

Acknowledgements

Special acknowledgments to Mithu Dasgupta, MISE, Kristi Atkins, EdD, Mandy Stanley, MS, and Parents and children who volunteered for this study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) Project EF # 901F0084-02-01.

ORCID iD

Patricia M. Blasco

References

Aarnoudse-Moens

C. S.

Weisglas-Kuperus

van Goudoever

J. B.

Oosterlaan

(2009). Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics, 124(2), 717–728. https://doi.org/10.1542/peds.2008-2816

Anderson

(2002). Assessment and development of executive function (EF) during childhood. Child Neuropsychology, 8(2), 71–82.

Anderson

P. J.

De Luca

C. R.

Hutchinson

Roberts

Doyle

L. W.

(2010). Underestimation of developmental delay by the new Bayley-III Scale. Archives of Pediatrics & Adolescent Medicine, 164(4), 352–356.

Barre

Morgan

Doyle

L. W.

Anderson

P. J.

(2011). Language abilities in children who were very preterm and/or very low birth weight: A meta-analysis. The Journal of Pediatrics, 158(5), 766–774.

Barrett

K. C.

Fox

N. A.

Morgan

G. A.

Fidler

D. A.

Daunhauer

L. A.

(2013). Handbook of self-regulatory processes in development: New directions and international perspectives. Taylor & Francis.

Bayley

(1993). Bayley Scales of Infant and Toddler Development (BSID-II). Psychological Corporation.

Bayley

(2005). Bayley Scales of Infant and Toddler Development (BSID-III). Psychological Corporation.

Blair

Razza

R. P.

(2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78(2), 647–663.

Blasco

P. M.

Guy

Saxton

S. N.

Duvall

S. W.

(2017). Are we missing a vulnerable population in early intervention? Infants & Young Children, 30(3), 190–203.

10.

Blasco

P. M.

Saxton

Gerrie

(2014). The little brain that could: Understanding executive function in early childhood. Young Exceptional Children, 17(3), 3–18.

11.

Burnett

A. C.

Anderson

P. J.

Lee

K. J.

Roberts

Doyle

L. W.

Cheong

J. L.

, & Victorian Infant Collaborative Study Group. (2018). Trends in executive functioning in extremely preterm children across 3 birth eras. Pediatrics, 141(1), Article e20171958.

12.

Busch-Rossnagel

N. A.

Morgan

G. A.

(2013). Introduction to the mastery motivation and self-regulation section ( Barrett

K. C.

Ed.). Psychology Press.

13.

Camerota

Willoughby

M. T.

Cox

Greenberg

M. T.

(2015). Executive function in low birth weight preschoolers: The moderating effect of parenting. Journal of Abnormal Child Psychology, 43(8), 1551–1562.

14.

Camerota

Willoughby

M. T.

Kuhn

L. J.

Blair

C. B.

(2018). The Childhood Executive Functioning Inventory (CHEXI): Factor structure, measurement invariance, and correlates in U.S. preschoolers. Child Neuropsychology, 24(3), 322–337.

15.

Center on the Developing Child at Harvard University. (2019). Guide to executive function. https://developingchild.harvard.edu/guide/a-guide-to-executive-function/

16.

Centers for Disease Control and Prevention. (2018). Premature birth. https://www.cdc.gov/features/prematurebirth/index.html

17.

Chan

Quigley

M. A.

(2014). School performance at age 7 years in late preterm and early term birth: A cohort study. Archives of Disease in Childhood-fetal and Neonatal Edition, 99(6), F451–F457.

18.

Cragg

Gilmore

(2014). Skills underlying mathematics: The role of executive function in the development of mathematics proficiency. Trends in Neuroscience and Education, 3(2), 63–68.

19.

Devine

R. T.

White

Ensor

Hughes

(2016). Theory of mind in middle childhood: Longitudinal associations with executive function and social competence. Developmental Psychology, 52(5), 758–771. https://doi.org/10.1037/dev0000105

20.

Diamond

(1985). Development of the ability to use recall to guide action, as indicated by infants’ performance on AB. Child Development, 56(4), 4868–4883.

21.

Diamond

(2006). The early development of executive functions. In Bialystok

Craik

F. I. M.

(Eds.), Lifespan cognition: Mechanisms of change (p. 70–95). Oxford University Press.

22.

Diamond

(2013). Executive functions. Annual Review of Psychology, 64, 135–168.

23.

Diamond

(2016). Why improving and assessing executive functions early in life is critical. In Griffin

J. A.

McCardle

Freund

L. S.

(Eds.), Executive function in preschool-age children: Integrating measurement, neurodevelopment, and translational research (pp. 11–43). American Psychological Association.

24.

Diamond

Taylor

(1996). Development of an aspect of executive control: Development of the abilities to remember what I said and to “do as I say, not as I do.” Developmental Psychobiology, 29(4), 315–334.

25.

Division for Early Childhood. (2018). Position statement on low birth weight, prematurity, and early intervention. https://www.decdocs.org/position-statement-low-birth-weight

26.

Dodson

C. K.

Travis

K. E.

Ben-Shachar

Feldman

H. M.

(2017). White matter microstructure of 6-year old children born preterm and full term. Neuroimage: Clinical, 16, 268–275. https://doi.org/10.1016/j.nicl.2017.08.005

27.

Duncan

A. F.

Bann

Boatman

Hintz

S. R.

Vaucher

Y. E.

Vohr

B. R.

, . . . Human Development Neonatal Research, N. (2015). Do currently recommended Bayley-III cutoffs overestimate motor impairment in infants born <27 weeks gestation? Journal of Perinatology, 35(7), 516–521. https://doi.org/10.1038/jp.2014.243

28.

Duvall

S. W.

Erickson

S. J.

MacLean

LaFavor

Lowe

J. R.

(2017). Multimodal executive function measurement in preschool children born very low birth weight and preterm: Relationships between formal lab-based measure performance, parent report, and naturalistic observation coding. Journal of Pediatric Neuropsychology, 3, 195–205. https://doi.org/10.1007/s40817-017-0047-y

29.

Edgin

J. O.

Inder

T. E.

Anderson

P. J.

Hood

K. M.

Clark

C. A.

Woodward

L. J.

(2008). Executive functioning in preschool children born very preterm: Relationship with early white matter pathology. Journal of the International Neuropsychological Society, 14(1), 90–101.

30.

Frye

Zelazo

P. D.

Palfai

(1995). Theory of mind and rule-based reasoning. Cognitive Development, 10(4), 483–527.

31.

Fuhs

M. W.

Nesbitt

K. T.

Farran

D. C.

Dong

(2014). Longitudinal associations between executive functioning and academic skills across content areas. Developmental Psychology, 50(6), 1698–1709. https://doi.org/10.1037/a0036633

32.

Gilmore

Cuskelly

Browning

(2015). Mastery motivation in children with intellectual disability: Is there evidence for a Down syndrome behavioural phenotype? International Journal of Disability, Development and Education, 62(3), 265–275. https://doi.org/10.1080/1034912X.2015.1020923

33.

Gioia

G. A.

Espy

K. A.

Isquith

P. K.

(2003). Behavior Rating Inventory of Executive Function: Preschool version (BRIEF-P). Psychological Assessment Resources.

34.

Grunau

R. E.

Whitfield

M. F.

Davis

(2002). Pattern of learning disabilities in children with extremely low birth weight and broadly average intelligence. Archives of Pediatrics & Adolescent Medicine, 156(6), 615–620.

35.

Hack

(2012). Dilemmas in the measurement of developmental outcomes of preterm children. The Journal of Pediatrics, 160(4), 537–538.

36.

Hamilton

B. E.

Martin

J. A.

Osterman

M. J.

(2016). Births: Preliminary data for 2015. National Vital Statistics Reports: From the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System, 65(3), 1–15.

37.

Isquith

P. K.

Crawford

J. S.

Espy

K. A.

Gioia

G. A.

(2005). Assessment of executive function in preschool-aged children. Mental Retardation and Developmental Disabilities Research Reviews, 11(3), 209–215.

38.

Keilty

Blasco

P. M.

Acar

(2015). Re-conceptualizing developmental areas of assessment for screening, eligibility determination and program planning in early intervention. Journal of Intellectual Disability: Diagnosis and Treatment, 3(4), 218–229.

39.

Lampi

K. M.

Lehtonen

Tran

P. L.

Suominen

Lehti

Banerjee

P. N.

Sourander

(2012). Risk of autism spectrum disorders in low birth weight and small for gestational age infants. The Journal of Pediatrics, 161(5), 830–836.

40.

Lawson

K. R.

Ruff

H. A.

(2004). Early focused attention predicts outcome for children born prematurely. Journal of Developmental & Behavioral Pediatrics, 25(6), 399–406.

41.

Litt

J. S.

Glymour

Hauser-Cram

Hehir

McCormick

M. C.

(2015). The effect of the Infant Health and Development Program on special education use at school age. Journal of Pediatrics, 166(2), 457–462.e451. https://doi.org/10.1016/j.jpeds.2014.09.066

42.

Loe

I. M.

Chatav

Alduncin

(2015). Complementary assessments of executive function in preterm and full term preschoolers. Child Neuropsychology, 21(3), 331–353. https://doi.org/10.1080/09297049.2014.906568

43.

Loe

I. M.

Feldman

H. M.

Huffman

L. C.

(2014). Executive function mediates effects of gestational age on functional outcomes and behavior in preschoolers. Journal of Developmental & Behavioral Pediatrics, 35(5), 323–333.

44.

Lowe

Erickson

S. J.

Maclean

Duvall

S. W.

(2009a). Early working memory and maternal communication in toddlers born very low birth weight. Acta Paediatrica, 98(4), 660–663.

45.

Lowe

Watterberg

MacLean

P. C.

Shaffer

M. L.

(2009b). Early working memory in children born with extremely low birth weight: Assessed by object permanence. Journal of Child Neurology, 24(4), 410–415.

46.

Marcovitch

Clearfield

M. W.

Swingler

Calkins

S. D.

Bell

M. A.

(2016). Attentional predictors of 5-month-olds’ performance on a looking A-not-B task. Infant and Child Development, 25(4), 233–246.

47.

Martin

J. A.

Hamilton

B. E.

Osterman

M. J.

Curtin

S. C.

Matthews

T. J.

(2015). Births: Final data for 2013. National Vital Statistics Report, 64(1), 1–65.

48.

Masten

A. S.

Herbers

J. E.

Desjardins

C. D.

Cutuli

J. J.

McCormick

C. M.

Sapienza

J. K.

Long

J. D.

Zelazo

P. D.

(2012). Executive function skills and school success in young children experiencing homelessness. Educational Researcher, 41(9), 375–384. https://doi.org/10.3102/0013189x12459883

49.

McClelland

M. M.

Cameron

C. E.

Connor

C. M.

Farris

C. L.

Jewkes

A. M.

Morrison

F. J.

(2007). Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills. Developmental Psychology, 43(4), 947–959. https://doi.org/10.1037/0012-1649.43.4.947

50.

McClelland

M. M.

Cameron

C. E.

Duncan

Bowles

R. P.

Acock

A. C.

Miao

Pratt

M. E.

(2014). Predictors of early growth in academic achievement: The head-toes-knees-shoulders task. Frontiers in Psychology, 5, 599.

51.

McCormick

M. C.

Brooks-Gunn

Buka

S. L.

Goldman

Salganik

. . . Casey

P. H.

(2006). Early intervention in low birth weight premature infants: Results at 18 years of age for the Infant Health and Development Program. Pediatrics, 117(3), 771–780. https://doi.org/10.1542/peds.2005-1316

52.

McGrath

A. P.

Vohr

B. R.

(2017). Ear care for the most vulnerable infants: Hearing screening and intervention prove particularly challenging with NICU babies: Extra vigilance and parent support are required. The ASHA Leader, 22(8), 20–22.

53.

Miyake

Friedman

N. P.

Emerson

M. J.

Witzki

A. H.

Howerter

Wager

T. D.

(2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100.

54.

Moffitt

T. E.

Arseneault

Belsky

Dickson

Hancox

R. J.

Harrington

Sears

M. R.

(2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 2693–2698.

55.

Morgan

G. A.

Maslin-Cole

Harmon

R. J.

Busch-Rossnagel

N. A.

Jennings

K. D.

Hauser-Cram

Brockman

(2016). Parent and teacher perceptions of young children’s mastery motivation: Assessment and review of research. In Messer

D. J.

(Ed.), Mastery motivation in early childhood (pp. 109–131). Routledge.

56.

Morgan

G. A.

Wang

Barrett

K. C.

Liao

H.-F.

Wang

P.-J.

Huang

S.-Y.

Jόzsa

(2019, May). The revised Dimensions of Mastery Questionnaire: Manual and forms for its use and scoring. https://sites.google.com/a/rams.colostate.edu/georgemorgan/mastery-motivation

57.

Mulder

Pitchford

N. J.

Marlow

(2010). Processing speed and working memory underlie academic attainment in very preterm children. Archives of Disease in Childhood-Fetal and Neonatal Edition, 95(4), F267–F272. https://doi.org/10.1136/adc.2009.167965

58.

Noble

K. G.

Norman

M. F.

Farah

M. J.

(2005). Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8(1), 74–87. https://doi.org/10.1111/j.1467-7687.2005.00394.x

59.

Razza

R. A.

Blair

(2009). Associations among false-belief understanding, executive function, and social competence: A longitudinal analysis. Journal of Applied Developmental Psychology, 30(3), 332–343.

60.

Sasser

T. R.

Bierman

K. L.

Heinrichs

(2015). Executive functioning and school adjustment: The mediational role of pre-kindergarten learning-related behaviors. Early Childhood Research Quarterly, 30, 70–79.

61.

Squarza

Picciolini

Gardon

Giannì

M. L.

Murru

Gangi

Mosca

(2016). Learning disabilities in extremely low birth weight children and neurodevelopmental profiles at preschool age. Frontiers in Psychology, 7, 1–10.

62.

Ten Eycke

K. D.

Dewey

(2016). Parent-report and performance-based measures of executive function assess different constructs. Child Neuropsychology, 22(8), 889–906.

63.

Vohr

B. R.

Stephens

B. E.

Higgins

R. D.

Bann

C. M.

Hintz

S. R.

Das

Evans

P. W.

(2012). Are outcomes of extremely preterm infants improving? Impact of Bayley assessment on outcomes. The Journal of Pediatrics, 161(2), 222–228.

64.

Vohr

B. R.

Wright

L. L.

Poole

W. K.

McDonald

S. A.

(2005). Neurodevelopmental outcomes of extremely low birth weight infants <32 weeks’ gestation between 1993 and 1998. Pediatrics, 116(3), 635–643. https://doi.org/10.1542/peds.2004-2247

65.

Wang

Morgan

Hwang

L. C.

Chen

L. C.

Liao

H. F.

(2014). Do maternal interactive behaviors correlate with developmental outcomes and mastery motivation and toddlers with and without motor delay? Physical Therapy, 94(12), 1744–1754.

66.

Welsh

J. A.

Nix

R. L.

Blair

Bierman

K. L.

Nelson

K. E.

(2010). The development of cognitive skills and gains in academic school readiness for children from low-income families. Journal of Educational Psychology, 102(1), 43–53. https://doi.org/10.1037/a0016738

67.

Young

J. M.

Vandewouw

M. M.

Morgan

B. R.

Smith

M. L.

Sled

J. G.

Taylor

M. J.

(2018). Altered white matter development in children born very preterm. Brain Structure and Function, 223(5), 2129–2141.

68.

Zelazo

Blair

Willoughby

(2016). Executive function: Implications for education (NCER 2017-2000). National Center for Education Research. https://files.eric.ed.gov/fulltext/ED570880.pdf