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Comparative Effectiveness of Early Childhood Interventions (public)

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September 12, 2025

What are the comparative effect sizes of early childhood educational interventions across different age groups and learning domains?

Early childhood educational interventions demonstrate varying effectiveness across domains, producing effect sizes of 0.10-1.45 standard deviations for cognitive outcomes, 0.18-1.22 for mathematics, 0.10-0.44 for language (up to 1.10 for literacy), and 0.19-0.56 for socioemotional and motor development, with comparable benefits observed across preschool and infant age groups.

Abstract

Early childhood educational interventions yield effect sizes that vary by learning domain, intervention modality, and age group. Studies targeting cognitive outcomes report gains ranging from 0.10 to 1.45 standard deviations—with intensive, multi-year programs (e.g., Ramey and Ramey, 2023) achieving the largest benefits—while language-focused programs generally produce improvements of 0.10 to 0.44 standard deviations, with some literacy interventions reaching 1.10. Mathematics interventions typically exhibit effects from 0.18 to 1.22 standard deviations, especially when curriculum-based and structured (as in Yıldız et al., 2025 and Clements et al., 2011). Socioemotional and motor development interventions tend to yield modest gains (approximately 0.19–0.56), a range further supported by evaluations of responsive caregiving and physical activity programs. Most studies concentrate on preschool-aged children, though interventions for infants show comparable benefits. Greater intervention intensity, duration, and multi-component design are associated with larger effects, according to the reported findings.

Methods

We analyzed 40 sources from an initial pool of 997, using 8 screening criteria. Each paper was reviewed for 6 key aspects that mattered most to the research question. More on methods

Papers identified with Elicit search

n = 997

Papers screened using: Population Age Range, Quantitative Data Availability, Study Design Rigor, Educational Setting, Learning Domain Focus, Typical Development Population, Intervention Component, Educational vs. Medical Focus

n = 997

Papers screened out

n = 957

Papers included for extraction

n = 40

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Paper search

Using your research question “What are the comparative effect sizes of early childhood educational interventions across different age groups and learning domains?”, we searched across over 126 million academic papers from the Semantic Scholar corpus. We retrieved the 997 papers most relevant to the query.

Screening

We screened in sources based on their abstracts that met these criteria:

• Population Age Range: Does the study focus on children aged 0-8 years (early childhood period)?
• Quantitative Data Availability: Does the study report quantitative effect sizes or provide sufficient quantitative data to calculate effect sizes?
• Study Design Rigor: Is the study a randomized controlled trial, quasi-experimental study, or controlled study with comparison groups?
• Educational Setting: Is the intervention implemented in early childhood educational settings (e.g., preschools, kindergartens, childcare centers, or home-based educational programs)?
• Learning Domain Focus: Does the intervention target specific learning domains (cognitive, language, literacy, numeracy, social-emotional, or motor skill development)?
• Typical Development Population: Does the study include typically developing children (rather than focusing solely on children with diagnosed developmental disabilities or special needs)?
• Intervention Component: Does the study include an intervention component (rather than being purely observational)?
• Educational vs. Medical Focus: Is the intervention primarily educational in nature (rather than primarily focused on medical or therapeutic treatments)?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

• Study Design Type:

Identify the specific type of study design used. Look in the methods section for precise description. Categorize as:

• Randomized Controlled Trial (RCT)
• Quasi-experimental study
• Comparative study
• Longitudinal study

If multiple design elements are present, list all that apply. If uncertain, note “unclear” and provide a brief explanation of why.

• Intervention Type and Components:

Comprehensively list all components of the educational intervention:

• Primary pedagogical approach (e.g., direct instruction, inquiry-based)
• Specific learning domains targeted
• Additional support services (if any)
• Delivery method (group size, individual/group)

Be as detailed as possible. If multiple intervention components exist, list each separately. Include frequency, duration, and specific content of interventions where mentioned.

• Participant Demographics:

Extract the following participant details:

• Age range of children
• Socioeconomic status (if reported)
• Risk factors or special circumstances
• Total sample size
• Gender distribution (if reported)

Use exact numbers or percentages from the study. If a range is given, record the full range. If any demographic information is not reported, clearly mark as “NR” (not reported).

• Outcome Domains and Measurement:

List all primary outcome domains measured:

• Cognitive development
• Language development
• Motor development
• Socioemotional development
• Academic achievement
• Other specific domains

For each domain:

• Note specific measurement tools/instruments used
• Record effect sizes if provided
• Indicate statistical significance
• Note follow-up time points for measurements

If multiple measurements exist for a domain, list all with their respective details.

• Long-term Outcomes:

Identify and extract any long-term outcomes measured beyond immediate child development:

• Educational attainment
• Employment
• Social relationships
• Health indicators
• Economic outcomes

Record:

• Specific outcome measured

• Age/time point of measurement

• Effect size or key findings

• Statistical significance (if reported)

• Geographical and Contextual Factors:

Extract:

• Country or region of study
• Income level classification (low, middle, high-income)
• Urban/rural setting
• Specific contextual challenges or environmental factors

If multiple contexts are represented, list each separately. Use the study’s own classification or standard international definitions for income levels.

Results

Characteristics of Included Studies

Study

Intervention Type

Age Group

Learning Domain

Study Design

Full text retrieved

Love et al., 2013

Direct coaching, video modeling, self-reflection, resource/referral services

Prenatal to 5 years

Cognitive, language, attention, behavior, health, parenting, mental health, employment

Randomized controlled trial (RCT), Longitudinal, Comparative

Yes

Meghir et al., 2023

Early stimulation (home-based), enhanced preschool (center-based)

7-16 months to ~4 years

Cognition, language, executive function, school readiness

Randomized controlled trial (RCT), Longitudinal

No

Olive et al., 2023

Physical activity intervention (AEL)

3-5 years

Executive function, expressive vocabulary, motor skills

Randomized controlled trial (RCT)

Yes

Camilli et al., 2010

Direct instruction, inquiry-based, teacher/small-group

Pre-Kindergarten

Cognitive, social, school progress

Comparative, Quasi-experimental, Randomized controlled trial (RCT)

No

Campbell et al., 2001

Full-time, high-quality educational child care

Infancy to 21 years

Cognitive, academic (reading, math)

Randomized controlled trial (RCT), Longitudinal

No

Campbell and Ramey, 1994

Infant/preschool/primary school educational treatment

Infancy to 12 years

Cognitive, academic achievement

Randomized controlled trial (RCT), Longitudinal

Yes

Reynolds et al., 2001

Comprehensive education, family, health services

3-9 years, followed to 20

Academic, social, educational attainment, crime

Longitudinal, Quasi-experimental, Comparative

Yes

Blewitt et al., 2018

Universal curriculum-based social-emotional learning (SEL)

2-6 years

Social, emotional, behavioral, early learning

Randomized controlled trial (RCT), Quasi-experimental

Yes

Jeong et al., 2021

Parenting interventions (responsive caregiving, stimulation, etc.)

Prenatal to 3 years

Cognitive, language, motor, socioemotional, behavior, attachment

Randomized controlled trial (RCT)

Yes

Cahoon et al., 2023

Home-based literacy/math interventions

3.07–5.32 years

Literacy, mathematics

Randomized controlled trial (RCT), Quasi-experimental

Yes

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Intervention Types:

• Curriculum-based interventions:13 studies (including literacy, math, social-emotional learning, spatial skills, etc.).
• Center-based early childhood education/preschool programs:10 studies.
• Home-based or parenting interventions:7 studies.
• Multi-component interventions (education plus health, nutrition, or social services):6 studies.
• Physical or motor skill interventions:3 studies.
• Technology-based interventions (educational apps, computer-supported learning):3 studies.
• Policy or large-scale program evaluations:3 studies.

Age Groups:

• 25 studies targeted preschool-aged children (3-5/6 years).
• 15 studies included prenatal or infancy (0-2 years).
• 5 studies included early primary ages (6-8 years).
• 7 studies followed participants into adolescence or adulthood.
• We didn’t find mention of the age group in 7 studies.

Learning Domains:

• Language and literacy outcomes: 22 studies.
• Cognitive or academic outcomes: 20 studies.
• Math outcomes: 14 studies.
• Social-emotional or behavioral outcomes: 15 studies.
• Motor or physical development outcomes: 5 studies.
• Health outcomes: 4 studies.
• Economic or employment outcomes: 5 studies.

Study Designs:

• 26 studies used randomized controlled trial (RCT) designs.
• 14 studies used quasi-experimental designs.
• 10 studies used comparative designs.
• 17 studies included longitudinal follow-up.
• 6 studies were systematic reviews or meta-analyses.

Effects

Effect Sizes by Age Group

Study

Age Group

Learning Domain

Effect Size Range

Number of Studies

Love et al., 2013

1-5 years

Cognitive, language, socioemotional

0.10–0.20 (cognitive/language), 0.31–0.38 (engagement)

1

Meghir et al., 2023

7-48 months

IQ, school readiness

0.13–0.24 standard deviations

1

Olive et al., 2023

3-5 years

Executive function, vocabulary

Cohen’s d=0.24–0.29

1

Camilli et al., 2010

Pre-Kindergarten

Cognitive, social

Largest for cognitive, no mention found

1

Campbell et al., 2001

3-21 years

Cognitive, academic

Moderate-large, no mention found

1

Campbell and Ramey, 1994

Infancy-12 years

Cognitive, academic

Maintained advantage, no mention found

1

Reynolds et al., 2001

3-20 years

Educational, social

11.2% increase in high school completion, 8.2% decrease in arrest

1

Blewitt et al., 2018

2-6 years

Socioemotional, cognitive

Cohen’s d=0.18–0.54

1

Jeong et al., 2021

0-3 years

Cognitive, language, motor, socioemotional

Standardized mean difference (SMD)=0.19–0.56

1

Cahoon et al., 2023

3-5 years

Literacy, math

Cohen’s d=0.10 (literacy), 0.18 (math)

1

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Learning Domains Targeted:

• Mathematics: 13 studies.
• Cognitive outcomes: 12 studies.
• Language outcomes: 11 studies.
• Socioemotional or emotional outcomes: 6 studies.
• Academic or educational outcomes: 5 studies.
• Literacy and reading outcomes: 5 studies (3 literacy, 5 reading; some overlap).
• Other domains: executive function (2 studies), motor/physical/fine motor skills (4 studies), health (2 studies), social (3 studies), behavior (2 studies), school readiness (1 study), problem-solving/STEM (1 study), spatial skills (1 study), phonological/alphabet (4 studies), pre-literacy (1 study).

Effect Size Reporting:

• Numeric effect size estimates (e.g., standard deviation, Cohen’s d, Hedges’ g, standardized mean difference, correlation coefficient, or percentage) were found in 26 studies.
• 13 studies reported only qualitative descriptors of effect (e.g., “significant”, “modest”, “moderate-large”).
• Of the 26 studies with numeric effect sizes, 2 reported only percentage or non-standard deviation outcomes (e.g., percentage increase in school completion).
• All included studies reported at least a qualitative or numeric effect size.

Age Groups:

• 16 studies targeted preschool or pre-Kindergarten children (3-5/6 years).
• 6 studies targeted infants (0-2 years).
• 3 studies targeted early childhood (0-6/8 years).
• 8 studies included school-age children (6+ years), with some spanning into adolescence or adulthood.
• We didn’t find mention of the age group in 7 studies.

Effect Sizes by Learning Domain

Cognitive Development:

• Effect sizes reported in the included studies range from 0.10–1.45 standard deviations, with larger effects observed in intensive, multi-year interventions (e.g., as reported by Ramey and Ramey, 2023).
• Meta-analyses included in this review (Jeong et al., 2021; Blewitt et al., 2018) reported pooled standardized mean differences of 0.18–0.32 for cognitive and early learning outcomes.

Language Development:

• Effect sizes typically ranged from 0.10–0.44 standard deviations, with some studies reporting larger effects for emergent literacy (Barnett et al., 2018: 1.10).
• Computer-supported and storybook-based interventions yielded small to moderate effects (Verhoeven et al., 2020: 0.28; Carolin and Fardzadeh, 2018: 0.21–0.27).

Mathematics:

• Effect sizes ranged from 0.18–1.22 standard deviations, with the largest effects in structured, curriculum-based interventions (Yıldız et al., 2025; Clements et al., 2011).
• Meta-analyses (Wang et al., 2016; Malofeeva, 2005) reported moderate effects (0.43–0.68; 0.47).

Socioemotional Development:

• Effect sizes were generally small to moderate (Blewitt et al., 2018: Cohen’s d=0.19–0.54; Jeong et al., 2021: standardized mean difference=0.19).
• Classroom-wide and universal social-emotional learning interventions showed consistent, though modest, benefits.

Motor Development:

• Physical activity and fine motor interventions yielded moderate effects (Grady et al., 2024: standardized mean difference=0.19–0.54; Strooband et al., 2020: moderate).

Academic Achievement:

• Reading and math achievement effect sizes ranged from 0.10–0.60 standard deviations, with larger effects in high-intensity or multi-year programs.
• Long-term follow-up studies (Reynolds et al., 2001; Campbell and Ramey, 1995) reported sustained academic benefits.

Other Domains:

• Executive function, school readiness, spatial skills, and problem-solving showed moderate to large effects in targeted interventions (Meghir et al., 2023; Yang et al., 2020; Burns et al., 2025).

Methodological Factors Influencing Effect Sizes

• Study Design:In the included studies, randomized controlled trials and large-scale longitudinal studies tended to report more robust and reliable effect sizes, while quasi-experimental and comparative studies appeared more susceptible to bias.
• Sample Size:Larger studies and meta-analyses provided more precise estimates, though heterogeneity remained a concern.
• Intervention Intensity and Duration:More intensive, longer-duration, and multi-component interventions were associated with larger and more sustained effects.
• Measurement Tools:Use of standardized, validated instruments enhanced comparability; studies using researcher-developed measures often reported larger effects.
• Fidelity and Implementation:Higher fidelity and professional development for educators were associated with greater effects (as reported by Carolin and Fardzadeh, 2018).
• Contextual Moderators:Socioeconomic status, country income level, and risk status of participants moderated effect sizes, with disadvantaged and low- and middle-income country populations often showing larger gains.

Generalizability and Study Context

• Geographical Diversity:The included studies spanned high-, middle-, and low-income countries, with interventions adapted to local contexts.
• Population Diversity:Most studies focused on low-income or high-risk populations, which may limit generalizability to more advantaged groups.
• Setting:Interventions were delivered in a variety of settings, including center-based, home-based, community, and digital environments. The context of delivery influenced both feasibility and effectiveness, as reported in several studies.
• Long-term Outcomes:Sustained benefits were more likely in intensive, multi-year interventions, but evidence was limited by the small number of long-term follow-up studies.
• Adverse Effects:Few studies reported adverse effects. One study (Love et al., 2013) noted increased parent-reported aggression with formal program participation. We didn’t find mention of other adverse effects in the available full texts or abstracts.

Limitations:

• Heterogeneity in intervention types, outcome measures, and reporting limited direct comparison across studies.
• Some studies lacked detailed reporting of effect sizes, statistical significance, or confidence intervals.
• Risk of bias was present in studies with small samples, non-randomized designs, or low fidelity.
• Generalizability was limited by the focus on high-risk and low-income populations in many studies.

References

Gregory Camilli, Sadako Vargas, S. Ryan, W. Barnett\ (2010).Meta-Analysis of the Effects of Early Education Interventions on Cognitive and Social Development. Teachers College Record

F. Campbell, E. Pungello, S. Miller-Johnson, M. Burchinal, C. Ramey\ (2001).The development of cognitive and academic abilities: growth curves from an early childhood educational experiment. Developmental Psychology

F. Campbell, C. Ramey\ (1994).Effects of early intervention on intellectual and academic achievement: a follow-up study of children from low-income families. Child Development

A. Reynolds, Judy A Temple, Dylan L. Robertson, Emily A. Mann\ (2001).Long-term effects of an early childhood intervention on educational achievement and juvenile arrest: A 15-year follow-up of low-income children in public schools. Journal of the American Medical Association (JAMA)

C. Blewitt, M. Fuller‐Tyszkiewicz, A. Nolan, Heidi J Bergmeier, D. Vicary, and 4 more\ (2018).Social and Emotional Learning Associated With Universal Curriculum-Based Interventions in Early Childhood Education and Care Centers. JAMA Network Open

Joshua Jeong, E. Franchett, C. V. Ramos de Oliveira, K. Rehmani, A. Yousafzai\ (2021).Parenting interventions to promote early child development in the first three years of life: A global systematic review and meta-analysis. PLoS Medicine

Abigail Cahoon, Carolina Jiménez Lira, Nancy Estévez Pérez, Elia Verónica Benavides Pando, Yanet Campver García, and 2 more\ (2023).Meta-Analyses and Narrative Review of Home-Based Interventions to Improve Literacy and Mathematics Outcomes for Children Between the Ages of 3 and 5 Years Old. Review of Educational Research

S. Ramey, Craig T. Ramey\ (2023).Early Childhood Education that Promotes Lifelong Learning, Health, and Social Well-being: The Abecedarian Project and its Replications. Medical Research Archives

F. Campbell, C. Ramey\ (1995).Cognitive and School Outcomes for High-Risk African-American Students at Middle Adolescence: Positive Effects of Early Intervention

A. Reynolds, Judy A Temple\ (1998).Extended early childhood intervention and school achievement: age thirteen findings from the Chicago Longitudinal Study. Child Development

D. Clements, J. Sarama, M. E. Spitler, Alissa A. Lange, Christopher B. Wolfe\ (2011).Mathematics Learned by Young Children in an Intervention Based on Learning Trajectories: A Large-Scale Cluster Randomized Trial

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A. Grady, Rebecca Lorch, Luke Giles, Hannah Lamont, Amy Anderson, and 6 more\ (2024).The impact of early childhood education and care‐based interventions on child physical activity, anthropometrics, fundamental movement skills, cognitive functioning, and social–emotional wellbeing: A systematic review and meta‐analysis. Obesity Reviews

C. Ramey, F. Campbell, M. Burchinal, M. Skinner, D. Gardner, and 1 more\ (2000).Persistent Effects of Early Childhood Education on High-Risk Children and Their Mothers

Li Luo, B. Reichow, Patricia A. Snyder, J. Harrington, Joy C. Polignano\ (2020).Systematic Review and Meta-Analysis of Classroom-Wide Social–Emotional Interventions for Preschool Children. Topics in Early Childhood Special Education

Aubrey Wang, Janine M. Firmender, Joshua R. Power, James P. Byrnes\ (2016).Understanding the Program Effectiveness of Early Mathematics Interventions for Prekindergarten and Kindergarten Environments: A Meta-Analytic Review

Marit Carolin, Ebad Fardzadeh\ (2018).University of Southern Denmark The Effectiveness of a Large-Scale Language and Preliteracy Intervention The SPELL Randomized Controlled Trial in Denmark

Weipeng Yang, Haidan Liu, N. Chen, Peng Xu, Xunyi Lin\ (2020).Is Early Spatial Skills Training Effective? A Meta-Analysis. Frontiers in Psychology

A. Reynolds, Judy A Temple, Suh-Ruu Ou, Dylan L. Robertson, J. Mersky, and 2 more\ (2007).Effects of a school-based, early childhood intervention on adult health and well-being: a 19-year follow-up of low-income families. Archives of Pediatrics & Adolescent Medicine

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Howard Goldstein, Lindsey A Peters-Sanders, Keri M. Madsen, Jeffrey M. Williams, J. Drobisz, and 4 more\ (2023).Efficacy of a Supplemental Small-Group Early Literacy Intervention Implemented by Early Childhood Educators. American Journal of Speech-Language Pathology

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Physical activity intervention improves executive function and language development during early childhood: The active early learning cluster randomized controlled trial.

L. Olive, R. Telford, E. Westrupp, R. Telford

Child Development·

2023·

9 citations

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Study Design Type

Randomized Controlled Trial (RCT)

Intervention Type and Components

- Primary pedagogical approach: Pragmatic approach integrating physical activity into the daily curriculum, informed by Vygotsky's Social Development Theory and the concept of physical literacy. - Specific learning domains targeted: Physical activity, gross and fine motor skills, executive function (inhibition, working memory, attention shifting), and expressive vocabulary. - Additional support services: Professional development for educators by AEL coaches, resources for educators. - Delivery method: Group size not specified; delivered by AEL coaches and early learning educators; activities include group/mat time, movement education, cross-curricular movement education, and encouraging challenging free play. - Frequency and duration: 22-week intervention with components introduced over 4 weeks and integrated into the daily schedule for 6 weeks. - Specific content: Motor skills development (running, jumping, tumbling), object control (catching, throwing, passing), problem-solving exercises, risky play (climbing, balancing), linking physical activity with storybooks for verbal communication.

Participant Demographics

- Age range of children: 3-5 years - Socioeconomic status: Slightly higher than the Australian average; average SES index: 1032 ± SD 78 (range 767-1167) - Risk factors or special circumstances: 10% of children reported having a disability - Total sample size: 314 children - Gender distribution: 134 girls; implied number of boys: 180

Outcome Domains and Measurement

- Cognitive development: Executive function - Measurement tools: Early Years Toolbox - Effect sizes: Inhibition (d = 0.29), Attention shifting (d = 0.26), Working memory (d < 0.01) - Statistical significance: Inhibition (p = .033), Attention shifting (p = .084), Working memory (p = .827) - Follow-up time points: Baseline, 6 months post-randomization - Language development: Expressive vocabulary - Measurement tools: Pictorial stimuli naming - Effect sizes: d = 0.24 - Statistical significance: p = .001 - Follow-up time points: Baseline, 6 months post-randomization

Long-term Outcomes

Not mentioned (the paper does not discuss long-term outcomes beyond immediate child development effects)

Geographical and Contextual Factors

- Country or region of study: Australia (New South Wales and southern Queensland) - Income level classification: Middle to high-income - Urban/rural setting: Not explicitly mentioned, likely a mix - Specific contextual challenges or environmental factors: Not detailed

This study aimed to determine the effects of the Active Early Learning (AEL) childcare center-based physical activity intervention on early childhood executive function and expressive vocabulary via a randomized controlled trial. Three-hundred-and-fourteen preschool children (134 girls) aged 3-5 years from 15 childcare centers were randomly assigned to the intervention (8 centers; n = 170 children) or control group (7 centers, n = 144 children) in May 2019. Participants were mostly Australian (85%) and from slightly higher areas of socio-economic status than the Australian average. There was an AEL intervention effect on inhibition (β = 0.5, p = .033, d = 0.29) and expressive vocabulary (β = 1.97, p = .001, d = 0.24). Integration of the AEL physical activity intervention into the daily childcare routine was effective in enhancing children's executive function and expressive language development.

Executive function describes the cognitive processes important for planning, focusing attention, remembering information, and the ability to switch between tasks. These processes develop from infancy into early adulthood (Best & Miller, 2010), are integral to the selfregulation of emotions and behavior, and represent an important indicator for future health and emotional, behavioral, and social functioning (Clark et al., 2010;Espy et al., 2011;Mattera et al., 2021;Romer & Pizzagalli, 2021;Trossman et al., 2021). Executive function develops rapidly during the preschool years (age 3-5 years; Klenberg et al., 2001). At this age, executive function has been shown to be more predictive of school readiness than intelligence (Blair, 2002;Morrison et al., 2010) and is associated with academic success in later school years (Duncan et al., 2007). Given these associations, understanding the conditions for optimal executive function development has become an important and rapidly growing focus of contemporary research, including investigations of planned interventions (Diamond & Ling, 2016) in which physical activity-based programs have been prominent (Li et al., 2020).

The active early learning (AEL) child-care centerbased program, employing a pragmatic approach to imbed physical activity into the daily curriculum, has been shown to increase physical activity and gross and fine motor skills (Telford et al., 2022). Findings from recent systematic reviews and meta-analyses have reported that physical activity has a positive effect on executive function in children and adolescents (Alvarez-Bueno et al., 2017;Carson et al., 2016;de Greeff et al., 2018;Ludyga et al., 2016;Verburgh et al., 2014;Xue et al., 2019). However, less is known of these associations during early childhood or whether introducing a physical activity-based program into a child-care center setting can influence executive function (Li et al., 2020). The current study investigated whether a program such as AEL, which links physical activity with the daily proceedings of the child-care center, can enhance the development of child executive function and language development, namely expressive vocabulary.

Several reviews have summarized the effect of physical activity on executive function during early childhood (Carson et al., 2016;Li et al., 2020;Tandon et al., 2016;Timmons et al., 2012). Overall, these reviews provide preliminary evidence that physical activity has beneficial effects on cognitive development during this time. However, the authors of one review note that the current body of evidence has been small and weak in quality, prompting the call for more high-quality research (Carson et al., 2016). Another recent review (Li et al., 2020) performed a meta-analysis investigating the effect of physical activity interventions greater than 4 weeks duration on executive function in children aged 3-7 years and concluded that there was a small yet positive effect on overall executive function. This effect was found to be independent of most characteristics of physical activity interventions (e.g., duration of intervention, session length, and frequency of sessions). Interestingly, subgroup analysis indicated that physical activity programs with a cognitive component (6 of the 10 included studies, e.g., paired with a language task, activities to develop specific motor skills that required participants to follow cue words) had a positive effect on executive function, whereas physical activity-only programs did not. This then poses the question regarding mechanistic pathways and whether it is the level of physical activity itself that influences executive function, or whether this effect might be better explained by the associated cognitive tasks and/or social interaction. There has also been evidence of an association between physical activity and improved child language outcomes (Alvarez-Bueno et al., 2017;Becker et al., 2014;Billings & Moos, 1981;Kirk, Vizcarra, et al., 2014;Mavilidi et al., 2015;Telford et al., 2012;Tominey & McClelland, 2011). Most of these studies investigated the effect of combining physical activity with other learning areas, namely literacy activities, and for the most part found that those participating in the more active condition also had better language development outcomes (e.g., picture naming, expressive vocabulary, word-coding skills, alliteration, and reading). However, as suggested in the case of executive function, factors that accompany increased physical activity (such as increased cognitive and communicative demands involved with the associated literacy activities), rather than physical activity itself, have been proposed as mechanisms by which physical activity-based interventions might improve language development (Connor-Kuntz & Dummer, 1996;Crova et al., 2014;Kaczmarek, 1985;Kirk, Price, et al., 2014;Morrison, 1988;Schmidt et al., 2015;Verna Hildebrand, 1991).

While mechanistic pathways are yet to be confirmed with high-quality evidence in humans, a number of plausible pathways linking physical activity-based interventions to improvements in cognitive function have been proposed in the literature. These have largely centered on neurobiological and psychosocial pathways. Neurobiological mechanisms include the capacity for physical activity to increase cerebral blood flow (Suzuki et al., 2004;Timinkul et al., 2008), thereby facilitating oxygenation of the brain; physical activity-induced release of neurotrophin (Gold et al., 2003;Winter et al., 2007), thus promoting the efficiency of neuronal processes; and physical activity induced secretion or upregulation of neurotransmitters, with noradrenaline and dopamine in the prefrontal cortex thought to play crucial roles (Floresco & Magyar, 2006;Robbins & Arnsten, 2009). However, psychosocial mechanisms may include improvements in mood and symptoms of mental ill-health (Stillman et al., 2016) and functional outcomes, including improvements in depressive-like behavior (animal studies; Dong et al., 2020) and sleep (Master et al., 2019;Mendelson et al., 2016).

The aim of this study is to investigate whether a physical activity-based intervention, previously reported as effective in increasing physical activity levels in a childcare center setting (Telford et al., 2021), has any effect on early childhood cognitive development, and specifically, executive function and language development. Our primary hypothesis is that children aged 3-5 years receiving the AEL program will have: (1) improved executive function across the domains of (a) working memory, (b) inhibition, and (c) attention shifting; and (2) improved language development (expressive vocabulary). Our secondary hypothesis seeks to investigate the role of physical activity as a causal agent and whether any effect of the AEL program on executive function and language development can be explained by levels of physical activity, or put another way, whether physical activity mediates any identified effect of the AEL intervention. This study takes a confirmatory approach given prior evidence that physical activity has beneficial effects on executive function (see Carson et al., 2016;Li et al., 2020;Tandon et al., 2016;Timmons et al., 2012) and that feasible mechanisms have been identified to explain the potential pathways linking physical activity with cognitive development (see Lubans et al., 2021 for a summary). Further in line with a confirmatory approach, the hypotheses of this study were made a priori, as demonstrated by the trial approach and outcomes being pre-registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619000638134).

Study design

A cluster randomized controlled trial was used to assess the effectiveness of a 22-week physical activity intervention (the AEL program) on executive function and language development in preschool children. While it is not possible to undertake a formal power analysis with sufficient precision for the current study, statistical knowledge gained from our previous work in the LOOK study (ACTRN12612000027819; Olive et al., 2019;Telford et al., 2013Telford et al., , 2016) ) indicates that 16 centers will ensure internal validity of the design (i.e., there is sufficient information to estimate the "experimental" error and intervention and control effects with adequate precision). In addition, n = 15 children per center are needed to estimate center effects with high precision. The window of opportunity for testing at the start and the end of the year precludes greater numbers of schools and students. Three separate child-care providers, with 16 child-care centers located across two Australian states (New South Wales and Queensland) agreed to participate. Baseline data collection occurred over 3.5 weeks in May 2019, with the post-intervention measures occurring for the same duration in November/December of the same year. The study was approved by the University of Canberra and Deakin University Human Research Ethics Committees and was prospectively registered with the Australian New Zealand Clinical Trials Register (ACTRN12619000638134).

Study setting

The child-care centers in the current study are located in New South Wales and southern Queensland in Australia. All centers are privately owned and provide allday or part-time care for children aged 6 months to 5 years. In addition, they provide a preschool education program for children (typically 3-5 years of age) prior to commencing primary school. The curriculum is guided by the Early Years Learning Framework developed by The Council of Australian Governments and is assessed against the National Quality Framework (NQF), which provides a national approach to regulation, assessment, and quality improvement for early childhood education and care settings (Australian Children's Education and Care Quality Authority, 2019). Children in this age group have their own room, and their supervision must include an early childhood teacher with university degree qualification; and all other educators must be working toward an approved certificate course in early child care with a minimum educator to preschool child ratio of 1:11. In the current study, the average number of preschool children was similar across the centers (mean = 29, range = 20-35), and the mean national quality rating (NQR) score of the centers (14.1) was lower than the mean of all child-care services in Australia (16.4 ± SD 5.7). Child-care centers allocated to the intervention group received the AEL program and control centers continued with their usual practice.

Participants

Sixteen Australian child-care centers from Southeast Queensland and Northern New South Wales regions were invited to participate in the study. Letters of invitation and consent forms including study details were provided to parents/guardians of preschool children aged 3-5 years at participating centers. Families were asked to return the written consent form to a research representative at their child's child-care center, including consent to publish data in peer-reviewed journals. Children were eligible to participate if they were at least 3 years of age at baseline assessment and enrolled in the preschool class at a participating center.

Randomization and masking

Each of the 16 child-care centers was randomly assigned to either the intervention or control group, stratified by center-level covariates (first, socioeconomic status [SES], followed by NQR, and geographic location) to ensure these were balanced across arms. The Socio-Economic Indexes for Areas (SEIFA) index (Australian Bureau of Statistics, 2013) was used as a measure of SES. SEIFA utilizes Australian Census information, providing details on the level of advantages and disadvantages by geographical area. The NQR is a government rating system for child-care centers and is based on information relating to the physical environment, health and safety, and educational programs and practice. This resulted in four strata: (1) higher SES/higher NQR, (2) higher SES /lower NQR, (3) lower SES/higher SES, and (4) lower SES/lower NQR. Child-care centers from each stratum were randomized to the intervention or control arm after baseline data collection, using a computer-generated randomization procedure by an independent researcher not involved in the current study.

The intervention

A detailed description of the intervention has been published elsewhere (Telford et al., 2021). Core aspects are presented here.

Theoretical framework

The AEL approach was informed by Vygotsky's Social Development Theory (Shabani, 2016;Vygotsky, 1978), which informed the role of the AEL coach and key stakeholder relationships, including relationships between the AEL coach and child-care educator, and between the child-care educator and the children under their care. The design of the intervention activities and resources was informed by the concept of physical literacy, which acknowledges the interplay between an individual's level of physical activity and their level of motivation, confidence, physical competence, and knowledge toward physical activity pursuits. Each element of the AEL program can be categorized within the four domains of physical literacy set out by the Australian government (Sport Australia, 2019): physical domain (e.g., movement skills); psychological domain (e.g., confidence and self-regulation); social domain (e.g., relationships); and cognitive domain (e.g., decision making).

Description

The AEL program targets four opportunities to increase physical activity experiences, as identified by an intervention mapping process. These four areas are described in Figure 1 , along with the sequence and period of their introduction, and were classified as: (1) group/ mat time and transitions, (2) movement education, (3) cross-curricular movement education, and (4) encouraging challenging free play. Of the 22-week intervention, each component was introduced by the AEL coach and the early learning educator, who worked together to develop each opportunity over 4 weeks, with all four components incorporated into the daily schedule during the following 6 weeks. The AEL coach provided professional development to the educators by providing in-class assistance with activities delivered to children and further personal guidance on content and pedagogy. Resources provided to educators included a printed summary of each activity, which set out the preparation and equipment requirements, and, in simple terms, the specific physical, social, and psychological objectives aligned with each activity. In our preparatory work prior to the commencement of this RCT, focus groups held with early learning educators uncovered that educator's indicated a preference for F I G U R E 1 The Active Early Learning program components, frequency, and description of opportunistic physical activity promotion. *Intervention components 1. to 4. are re-introduced concurrently by childcare educators during weeks 17-22, with daily inclusion of components 1. to 2. (or 3.).

1.Group/mat time and Transitions

Weeks 1 -4 Group/Mat time: daily periods when children are gathered together, movement experiences with a focus on fundamental movement skill; 5 to 20 minutes. Transitions: periods between different centre events e.g. arrival at the centre, moving from inside to outside, or from group time to meal tables with a focus on movement creativity and exploration; 1 to 5 minutes.

2.Movement education

Weeks 5 -8 Educator enhancement of free-time active play to promote exploration and opportunities to develop children's confidence and risk assessment skill; 10-30 minutes

3.Cross-curricular movement education

Weeks 9 -12

Activities complementing the day's curriculum learning theme; usually integrating a story and book reading; 20-30 minutes.

4.Encouraging challenging free play

Weeks 13 -16

Educator enhancement of free-time active play to promote exploration and opportunities to develop children's confidence and risk assessment skill; 10-30 minutes.

physical printouts rather than online resources, so a library of laminated cards was developed in place of an app.

Executive function and expressive vocabulary

Executive function was assessed using the Early Years Toolbox (Howard & Melhuish, 2017), an iPad-based assessment tool that measures three aspects of executive function, namely inhibition, working memory and attention shifting, and a measure of expressive vocabulary. The Early Years Toolbox was selected as it is a rigorous, valid, and reliable measure, drawn from the contemporary literature, and has been normed on an Australian population. Measures were administered face to face by a trained assessor who was also a clinical psychologist.

Each measure takes ≤5 min to administer and instructions are provided both visually and verbally. Baseline assessments of executive function and expressive vocabulary were conducted by members of the research team prior to randomization and were repeated 6 months post-randomization, within a 3.5-week widow on the completion of the physical literacy intervention.

Inhibition

Inhibition was assessed via the "Go/No Go" task, following previously established protocols (Howard & Okely, 2015;Wiebe et al., 2012). Participants are required to tap the screen on "go" trials ("catch the fish") and not tap the screen on "no-go" trials ("avoid catching sharks"). A pre-potent tendency to respond is generated by having the majority (80%) of trials as "go" trials, with a total of 75 "go" or "no go" Stimuli presented across three evenly divided blocks. The final inhibition score is derived from the product of proportional "go" and "no-go" accuracy.

Working memory

This task is adapted from Case's (1985) Mr. Cucumber task (Case, 1985) and follows previously published protocols (Morra, 1994). Participants are required to remember the spatial locations of "stickers" placed on a cartoon ant and to recall these locations after a brief retention interval, by tapping the spatial location on Mr. Ant. Three trials are presented at each level of difficulty, which increases as the task progresses. The trials progress until the earlier of either completion or failure on all three trials at the same level of difficulty occurs. A final score for working memory is calculated as a point score (Morra, 1994) derived from the following scoring method: beginning from Level 1, one point for each consecutive level in which at least two of the three trials were performed accurately, plus 1/3 of a point for all correct trials thereafter.

Attention shifting

Attention shifting was assessed using the "card sorting" task, which is based on the protocols of Zelazo (2006). This task asks children to sort cards by one of two dimensions (e.g., color or shape) a place them into one of two locations. Children are then instructed to switch to a different sorting rule. Participants are required to sort by one dimension (e.g., color) for six trials and then in the following post-switch phase, they are required to sort cards by the alternate sorting dimension (e.g., shape). If the participant correctly sorts at least five of the six pre-and post-switch stimuli, they proceed to a more difficult task, which adds another dimension of a border. In this phase, participants are instructed to sort by color if the card has a black border or sort by shape if the card has no black border. A score of attention shifting is indexed by the sum of correct sorts after the pre-switch phase.

Expressive vocabulary

Expressive vocabulary development was assessed by presenting 45 pictorial stimuli, of which participants were instructed to verbally produce the correct name for each stimuli presented. In the case of an incorrect label being provided by the participant, the researcher provides a prompt by asking "what else might this be called" until either a correct label is produced or there is evidence that participant is unable to produce the correct label. This measure was developed by Howard and Melhuish (2017) for the Early Years Toolbox and has demonstrated validity and reliability (Howard & Melhuish, 2017). A sixitem stop rule was implemented and a score of expressive vocabulary was provided as an overall accuracy score across all 45 items.

AEL coach logbook

To assess implementation fidelity, a daily online logbook was maintained by the AEL coach, which summarized day-to-day activities. This included records of the site visits, the type of activity, coaching strategy, and details of educator progress. Lead educators at each center were also asked to maintain a record of intervention activities on a wall chart provided by the AEL coach.

Rubric assessment of educators' practice

To assess educator fidelity to conducting the AEL program as intended, a custom rubric was created to assess the quality and frequency of the implemented activities. These rubric assessments were completed by a researcher using direct observation of educator practice followed by an interview.

Height and weight

Height and weight were used to calculate the child's body mass index, which was calculated using the equation: Weight (kg)/Height (m) 2 . Height was measured to the nearest 0.001 m using a portable stadiometer (model), while body weight was measured to the nearest 0.05 kg using portable electronic scales (model). Children were asked to remove their shoes and jackets and measurements were conducted in a quiet and private space at the child-care center.

Physical activity

Physical activity during child-care hours was assessed using ActiGraph GT3X+ accelerometers. Children wore the accelerometers on an elastic belt placed at the right side of the hip for three consecutive days during child-care hours. A research team member provided written and verbal instruction and a demonstration to center staff on how to fit children with the accelerometers. Over 3 days as each child arrived at a center, a staff member fitted the waist belt and removed the belt at time of departure. The physical activity outcome variables were number of steps per hour, minutes of total physical activity per hour, and minutes per hour of moderate-to-vigorous physical activity (MVPA). Activity cut points were set for light activity (800-1679) and MVPA (above 1680) based on a previous calibration study for preschool children (Pate et al., 2006). Total physical activity was the combination of light and MVPA. Using an epoch length of 15 s, data were included for analyses if there were ≥3 or more hours of valid wear time after screening for non-wear periods of ≥20 mins. The selection of physical activity outcome variables, cut points, and validation criteria were based on a previous study in preschool children (Okely et al., 2020).

Statistical analysis

General linear mixed-effects modeling was used to determine the intervention effect on executive function and language development. This model accounts for the complexity of factors likely to influence cognitive outcomes at both the child and center (cluster) levels. Therefore, center, subject, and time were included in the model as random effects to account for the sample design and hence possible dependence structure in the data. Explanatory variables (fixed effects) explored for inclusion in the model included sex, age, and SES.

The interaction between time and group represents the intervention effect. The statistical significance of an effect was assessed using Type II F tests with Kenward-Rogers degrees of freedom approximation, and we report results as estimated mean effects with 95% confidence intervals. To evaluate physical activity as a potential mediator, a series of linear mixed models were fitted according to the steps described by Baron and Kenny (1986) and demonstrated by Krull and MacKinnon (1999). Our analysis was conducted using an intent-to-treat approach and therefore included all randomized participants and all available data at each time point. Independent sample t-tests were used to assess differences in baseline measures. The approach taken in the current study was based on a confirmatory analysis effort (e.g., hypothesis testing based on prior evidence and theory). Analyses were conducted using R version 4.1.1.

Number of participants recruited and included in analyses

Participant recruitment is summarized in Figure 2 . Fourhundred-and-fifty participants from 16 child-care centers were assessed for eligibility. One child-care center was deemed ineligible due to the center having low enrolment numbers (n = 8). Written informed consent was received for 322 children (73% consent rate) and baseline assessments were collected from 314 children from 15 childcare centers between April 2019 and May 2019. Eight child-care centers (n = 170 children) were randomized to the intervention group with the remaining seven centers (n = 144 children) being randomized to the control group. Thirty-three participants from the intervention group and 26 from the control group, respectively, were lost to follow-up due to being absent on the day of testing (N = 10 intervention, N = 5 control) or were no longer attending the child-care center (N = 23 intervention, N = 21 control). One-hundred-and-twenty-seven intervention children (75% response rate) and 109 controls (76% response rate) completed 6-month follow-up outcome assessments between November 2019 and December 2019. Demographic characteristics of the intervention and control children are shown in Table 1 . Overall, children who provided follow-up assessments were similar to the initial study population (see Table 1 ).

Participant characteristics

As summarized in Table 1 , a higher proportion of boys (57%) than girls participated in the study and there was a higher proportion of girls in the intervention compared to the control arm (45% vs. 40%). In the study jurisdiction, child-care staff are required by law to meet minimum standards of qualification and education, and those characteristics of the control and intervention groups were essentially the same, with no significant group differences observed in educator age, SES, or NQR. The average SES index of the suburbs in our study (1032 ± SD 78 and range 767-1167) was slightly higher than the average index of all towns and cities throughout Australia (980 ± 84, 598-1251) . The majority of children were Australian (65%), while 15% reported being born outside of Australia and missing data was present for the remaining 20% of children. Parent/ guardian's reported that 10% of child participants had a disability that may limit their ability to participate in activities (e.g., physical impairment, developmental delay, and neurodevelopmental disorder). The majority of parent/guardians of child participants were university educated (47% undergraduate/bachelor degree; 14% postgraduate degree).

Intervention activities

The coach logbook was used to determine the frequency of coach visits to each child-care center. One-hundredand-sixty-four of a possible 176 site visits (93% completion of intended weekly visits) were completed by the coach, who visited each center on average 21 times ). The frequency of intervention activities delivered by child-care center staff was determined via a rubric, which kept a tally of activities. On average, there were four group time sessions (range 3-5), three movement education or movement education extension activities (range 1-4), and five transition activities (all educators reported 5).

Effect of the AEL intervention on executive function

The AEL intervention had a small but statistically significant positive effect on measures of inhibition (β = 0.05, p = .033, 95% CI = 0.01-0.11, d = 0.29) and expressive vocabulary (β = 1.97, p = .001, 95% CI = 0.78-3.16, d = 0.24). A non-significant effect of the AEL program was observed for attention shifting (β = 1.04, p = .084, CI = -0.14-2.22, d = 0.26) and visual-spatial working memory (β = 0.03, p = .827, CI = -0.22-0.27, d < 0.01). A summary of these effects is displayed in Table 2 .

Relationships between executive function and quantity of physical activity

There were no significant relationships between any of the executive function domains (e.g., working memory, attention shifting, and inhibition) or expressive vocabulary and the quantity of physical activity for either total physical activity or MVPA (all p > .1). Furthermore, the introduction of physical activity into the model explaining the AEL intervention effect on each executive function in turn left the relationship essentially unchanged; indicating that the quantity of physical activity (as distinct from the quality) had little to do with the AEL intervention effect. In other words, no evidence emerged to suggest that physical activity operated as a mediator in any of the relationships.

DI SC US SION

This cluster randomized controlled trial in child-care centers tested the effect of a physical activity intervention on executive function and language development. Despite its absence of any specific training in executive function, our data showed that the AEL intervention enhanced inhibition, accompanied by a positive impact on expressive vocabulary development. However, despite the positive AEL intervention effect on physical activity (Telford et al., 2021), we were unable to infer that physical activity per se was the influential factor on the current AEL effects. While the AEL intervention increased both total physical activity and MVPA (Telford et al., 2021), no evidence emerged of any relationship between physical activity (i.e., total or MVPA) and executive function or expressive vocabulary development; nor was there any influence of total physical activity and MVPA on the size of the intervention effect. Together, these findings suggest that factors other than the physical activity detected by accelerometers may have operated as the causative components of the current intervention.

The effect sizes of the AEL intervention are comparable to those reported in the recent meta-analysis by Li et al. (2020). For inhibition, the effect size for the AEL intervention was 0.29, compared with -0.01 to 1.32 in seven other studies; for attention shifting, the effect size was 0.26 compared with 0.25-0.9 in four other studies, noting that the AEL program was based on seeking ways to introduce physical activity into the usual curriculum. This AEL benefit to cognitive development extended to the children's ability to communicate, where an effect size of 0.24 was found for expressive vocabulary. This outcome is consistent with and extends previous work demonstrating the link between increases in physical activity during free play and improvements in early literacy, such as that by Becker et al. (2014) and Kirk, Price, et al. (2014), where interpretation of the findings was limited by a quasiexperiment approach involving only two child-care centers; and further corroborate findings from Mavilidi et al. (2015), where improvements in preschool children's learning of a foreign language vocabulary were positively affected by the cognitive effects of enacting vocabulary through physical exercises or gestures.

In addressing the component(s) of the AEL intervention likely to have been influential in producing the effects on executive function, one might anticipate this to be the level of the physical activity (i.e., the muscular work), especially with the previously demonstrated positive intervention effect on physical activity (Telford et al., 2021). Various mechanisms by which physical activity might influence psychological adaptation have been proposed. These include the capacity for physical activity to increase cerebral blood flow (Suzuki et al., 2004;Timinkul et al., 2008), thereby facilitating oxygenation of the brain; physical activity-induced release of neurotrophins (Gold et al., 2003;Winter et al., 2007), thus promoting the efficiency of neuronal processes; and physical activity induced secretion or upregulation of neurotransmitters, with noradrenaline and dopamine in the prefrontal cortex thought to play crucial roles (Floresco & Magyar, 2006;Robbins & Arnsten, 2009). However, these potential mechanisms remain speculative, especially in the context of explaining improved executive function among 4-year-olds in a child-care center setting. In addition, given that executive function can be improved by interventions devoid of physical activity (Au et al., 2015;Diamond, 2012;Diamond & Lee, 2011;

T A B L E 2 Linear mixed-model analyses of the intervention effect on vocabulary, inhibition, working memory, and task shifting. Spencer-Smith & Klingberg, 2015), it is feasible that coincidental characteristics of a physical activity-based intervention, other than the physical activity itself, may be partly or wholly causative. Indeed, whether physical activity per se has any role at all in cognitive development has been questioned. In a review of interventions, irrespective of participant age, it was concluded that physical activity of various types devoid of cognitive challenge and social interaction appears to be ineffective in improving executive function (Diamond & Ling, 2016). More specifically to the age group of children in the current study, in a meta-analysis investigating the effect of physical activity interventions in young children (Li et al., 2020), it was concluded that while there was a small yet positive effect on executive function overall, only those with a cognitive component had a positive effect. Programs focusing solely on physical activity were ineffective. Our findings of an AEL intervention effect on executive function and vocabulary development do not shed light on this issue. While the AEL effect on physical activity was evident, the lack of any significant relationships between executive function and physical activity or mediating effect of physical activity on executive function provides no support for the physical activity component of the AEL program being a causative agent. The same situation prevailed in the case of expressive vocabulary with no evidence of any direct effect of physical activity.

Predictors

Should there be no direct cause of physical activity, then this would imply that cognitive components accompanying our physical activity-based intervention may have been instrumental in their effects on executive function, as has been previously suggested (Diamond & Ling, 2016;Moreau et al., 2015;Pesce et al., 2013). On the other hand, our data are not entirely supportive of this premise, as we might expect the cognitive components of the intervention to be proportionate to the volume of physical activity. Yet, we found no evidence of any relationship between executive function and physical activity. That cognitive and physical activity components are not correlated may be described by the fact that a child might choose to move less in a physical activity but still engage in the cognitive aspects of the task and alternatively, a physically active child who moves a lot may be off task. Another consideration is that our measure of physical activity (e.g., accelerometers) did not measure the various components of physical activity (e.g., motor skills) likely to exert an effect on executive function or expressive vocabulary. For example, the form of physical activity most conducive to cognitive challenge and social interaction may include free play on climbing equipment, stationary tasks such as balancing, and the relatively stationary activities of ball and other objects throwing and catching. Our physical activity measurements using accelerometers do not provide data on these types of activities. It is therefore possible that one or more components of these subsets of physical activities, undetected by accelerometers, operated as causal mediator(s) of the AEL intervention effects on executive function and expressive vocabulary. In order to explore this premise further, some considerations underlying the design of the AEL program may be helpful. The AEL program was informed by the concept of physical literacy (Sport Australia, 2019;Whitehead, 2013), which acknowledges the interplay between an individual's level of physical activity and broader social and cognitive skills, including their level of motivation, confidence, physical competence, and knowledge. AEL coaches regularly reminded educators to select activities that prioritized enjoyment, while developing movement competency and confidence, rather than solely focusing on increasing physical activity. Examples included group mat time (daily periods when children are gathered together, led by the educator), which focused on motor skills such as running, jumping, and tumbling; and object control, involving catching, throwing, and passing. Learning and practicing these new skills required concentration and problem-solving from children and the ability to inhibit a response to achieve a desired goal. Transitional activities often involved receiving instructions to work within a group or waiting for a turn, again requiring inhibition. Physical activity challenges set by the educators required problem-solving and switching focus from one challenge to another. Children were encouraged to challenge themselves in the playground through risky play, where climbing and balancing were frequent. This is noteworthy, given the assessment of risk has previously been linked with planning, concentration, and inhibition (Brussoni et al., 2012). In addition, these activities may have contributed to the AEL effect on expressive vocabulary, as opportunities arose for verbal communication between both educator-to-child and child-to-child interactions.

Furthermore, the AEL program's extension of activities, such as linking physical activity with a storybook, not only required concentration and cognitive flexibility but also facilitated verbal communication, perhaps also contributing to expressive vocabulary. Educator enhancement of free play set out challenges and associated educator-to-child communication. It may be that the quality and quantity of these interpersonal interactions with educators and between children, brought about by intervention activities, played an important role. As previously alluded to, these types of activities may have been missed or underestimated by our standard measure of physical activity (via accelerometry), explaining why physical activity, per se, did not emerge as a significant mediator of the AEL program. It is therefore possible that physical activity was indeed a mediator of the AEL effects on executive function and vocabulary development but confined to certain types of activity rather than just walking and running, which was not captured in our measurement.

There is one other aspect of potential relevance in explaining how the AEL program produced improved early cognitive function. In their review, Diamond and Ling (2016) alluded to the potential negative influence of psychological stress, suggesting that programs most likely to influence executive function are those that also address emotional and social needs and reduce psychological stress. Accordingly, we suggest that the effect of the AEL program may in part lie with its potential to reduce stress and improve emotional and behavioral symptoms. For example, AEL activities promoted communication and teamwork, and this may have reduced feelings of loneliness or sadness that may be present in some children in the child-care center setting away from home. These qualities of the intervention may also increase opportunities for, and quality of, interpersonal interactions and connections. A separate report found an AEL effect of increased heart rate variability in the early learning center (Speer et al., 2021), suggesting that children receiving the AEL program developed a more relaxed attitude to the child-care environment.

In the current study, there appeared to be a selective intervention effect on the components of executive function; a significant AEL effect emerging on inhibition but a non-significant effect for attention shifting and working memory. Working memory (i.e., utilizing information held within one's mind) might reasonably be expected to be trained by memory challenges. In speculating as to the lack of effect on working memory, it may be that the AEL intervention was not of sufficient duration, repetition, and/or intensity to have an effect on working memory itself. In addition, the variety and ongoing changes in the AEL program were such that repeated challenges were not a prominent feature, which may partly explain the lack of intervention effect on working memory; this being due to insufficient repeated memory challenges that may bring about enhanced development.

There were a number of strengths of the current study, including the pragmatic randomized controlled trial design and the objective assessment of executive function, language development, and physical activity. Another strength was the high setting level of recruitment (100% of centers approached agreed to participate), owing to the high levels of motivation for the intended program by center owners and staff. Limitations include the 6-month duration of the study, which may not have been sufficient to see further effects, and pre-and post-measurement outcomes may vary during interventions of shorter and longer duration, typical of children attending early child-care centers. As alluded to above, the measurement instrument used to detect physical activity, while objective and widely employed, was not able to adequately measure, or even failed to detect, physical activities absent of whole-body movements, such as balancing, climbing, catching, and throwing, which may have played an important role in explaining the AEL effect. For example, no relationships between motor skills and executive functions were tested in the current investigation. Our conclusions are also limited in that they apply to a specialized intervention, where physical activity was imbedded into the curriculum rather than physical activity being introduced with additional specific physical activity sessions. Furthermore, the children in our study were all, on average, 4 years of age and attending child-care centers compliant with Australian conditions and staff, which may not be typical internationally. In addition, the majority of participants were Australian and resided in areas of slightly higher than average SES compared to the Australian average, which may limit the generalizability of these findings to other areas of lower SES. However, this finding should be interpreted with caution given the large degree of missing data (20%).

CONC LUSION

The AEL 6-month peer-coach intervention, which imbedded physical activity into the daily curriculum of child-care centers, was instrumental in enhancing the development of executive function components inhibition, with weaker evidence for attention shifting, as well as improving expressive vocabulary. There was no evidence that accelerometer volume or intensity of physical activity exerted any causal effect, which suggests that cognitive processes and educator-child interaction occurring incidentally within the program may have been influential. However, given that AEL physical activities, such as balancing, climbing, and object control, were not represented appropriately by our accelerometer-based physical activity measure; this raises the possibility that these types of physical activities may also have mediated the positive AEL effects on executive function and expressive vocabulary.

AC K NOW L E DGM E N T S

The research team would like to thank the child-care center administrative staff, educators, and children who participated in this study. This study was funded by The Australian College of Physical Literacy, which was not involved in the design of the study, the analysis and interpretation of results, or the writing of this publication. Author LO is supported by the National Health and Medical Research Council (NHMRC) Early Career Fellowship (1158487) . Open access publishing facilitated by Deakin University, as part of the Wiley -Deakin University agreement via the Council of Australian University Librarians.

F U N DI NG I N FOR M AT ION

This study was funded by Litivity who were not involved in the design of the study or involved in analysis and interpretation of results or writing of this publication. L.Olive was supported by an National Health and Medical Research Council (NHMRC) Early Career Fellowship (1158487).

DATA AVA I L A BI L I T Y STAT E M E N T

Data supporting the results reported in this article are stored at the University of Canberra. These data are available upon request by contacting the first author.

SU PPORT I NG I N FOR M AT ION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

Funding

Funding informationAustralian College of Physical Literacy; National Health and Medical Research Council (NHMRC) Early Career Fellowship, Grant/Award Number: 1158487

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