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Williams, Kate & Berthelsen, Donna(2019)Implementation of a rhythm and movement intervention to support self-regulation skills of preschool-aged children in disadvantaged communities.Psychology of Music, 47 (6), pp. 800-820.

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https://doi.org/10.1177/0305735619861433

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https://doi.org/10.1177/0305735619861433

RHYTHM AND MOVEMENT FOR SELF-REGULATION 1

Implementation of a Rhythm and Movement Intervention to Support Self-

Regulation Skills of Preschool-Aged Children in Disadvantaged Communities

Kate E. Williams and Donna Berthelsen

Faculty of Education, Queensland University of Technology, Brisbane, Australia

Kate E. Williamsa (corresponding author)

Donna Berthelsena

aSchool of Early Childhood and Inclusive Education

Faculty of Education

Queensland University of Technology

Victoria Park Road

Kelvin Grove QLD 4059

Australia

Tel: +61 7 3138 3080

Email: [email protected]

Implementation of a Rhythm and Movement Intervention to Support Self-Regulation

Skills of Preschool-Aged Children in Disadvantaged Communities

Abstract

Self-regulation skills are an important predictor of school readiness and early school

achievement. Research identifies that experiences of early stress in disadvantaged households

can affect young children’s brain architecture, often manifested in poor self-regulatory

functioning. While there are documented benefits of coordinated movement activities to

improve self-regulation, few interventions have focused exclusively on music and rhythmic

activities. This study explores the effectiveness of a preschool intervention, delivered across

eight weeks, which focused on coordinated rhythmic movement with music to improve self-

regulation and executive function. The study involved 113 children across three preschools in

disadvantaged communities. The intervention group received 16 sessions of a rhythm and

movement program over eight weeks, while the control group undertook the usual preschool

program. Executive functions were directly assessed, and teachers reported on children’s self-

regulation, before and after the intervention. Path analyses found positive intervention effects

for emotional regulation reported by teachers; and, for boys, on the measure of shifting in the

executive function assessment. Teacher-reported cognitive and behavioural regulation also

improved in one research site. These early findings suggest that a rhythm and movement

intervention has potential to support the development of self-regulation skills in preschool,

however further research is required.

Keywords:


self-regulation, rhythm, intervention, early childhood


Introduction

Early childhood is a critical period for learning and development during which brain

neural pathways are building rapidly. An important task during this period is for children to

acquire effective self-regulation skills. These capacities to manage emotions, cognition, and

behaviour have important implications for future learning and wellbeing (Diamond, 2016)

and strong self-regulation skills act as a buffer against poorer developmental outcomes for

children from lower socio-economic backgrounds (Dilworth-Bart, 2012). Intervention efforts

to improve early self-regulation provide promising directions to address these socio-

economic disparities (Diamond, 2016). The current study examines a novel intervention

designed to improve self-regulation for children living in low socio-economic communities.

The intervention incorporates music and rhythmic movement activities that are known to

support neurocognitive development (Hyde et al., 2009; Putkinen, Tervaniemi, Saarikivi, &

Huotilainen, 2015).

Self-regulation, executive function, and socio-economic disparities

Self-regulation is an umbrella term for a set of processes that enable control and

regulation of emotions and attention, supporting individuals to maintain optimal cognitive

arousal and manage behaviour (Diamond, 2016). In the preschool period attentional

regulation refers to children’s behavioural persistence in completing tasks and maintaining

attention when faced with distractions. Emotional regulation comprises the interplay between

a child’s natural reactivity to emotion-inducing events and the behavioural capacities to

manage these reactions (Ponitz, McClelland, Matthews, & Morrison, 2009). These self-

regulatory processes contribute to the development of (and are in turn strengthened by)

higher-order brain processes of executive function which direct flexible, goal-directed

behaviours associated with the prefrontal cortex (Best & Miller, 2010). The executive

functions include inhibition (control of impulsive reactions), shifting (flexible shifting of

attention to complete a task), and working memory (holding information in mind required for

task completion).

Self-regulation develops most rapidly in the first five years of life through integration

of various neural mechanisms (Calkins & Williford, 2009). Early environmental supports

underpinning early development of self-regulation include co-regulation with responsive

caregivers to satisfy immediate needs (e.g., when an infant cries, the caregiver soothes her).

However, a major task for children across the early years is to learn to self-manage this

fulfilment of needs through emotional and cognitive control over behaviour (McClelland et

al., 2010). The extent to which children successfully learn to manage and employ these skills

in early childhood has been linked with a number of important life outcomes including: fewer

behaviour problems in later childhood (Wang, Deater-Deckard, Petrill, & Thompson, 2012);

lower levels of adolescent risk-taking (Honomichl & Donnellan, 2012); higher academic

achievement (Fitzpatrick et al., 2014); and, increased likelihood of college completion as an

adult (McClelland, Acock, Piccinin, Rhea, & Stallings, 2013).

An early behavioural mechanism through which infants learn to self-regulate is by

orienting their attention to important features of their experiences and specific objects in their

environment (Rothbart, Sheese, Rueda, & Posner, 2011). Such orientation involves the

inferior and superior parietal areas of the brain, as well as the frontal eye fields (Rothbart et

al., 2011). Over the course of the first year of life through attentional control and developing

abilities to actively self-soothe, infants become more engaged in the pursuit of self-

regulation, for example, by thumb-sucking and other motor behaviours. Beginning in the

second year of life, connections to the limbic system, associated with emotions, are built in


the anterior cingulate cortex, as well as in the prefrontal cortex which is associated with

executive functions (Best & Miller, 2010). This integration of neural circuitry for emotional

and cognitive regulation in the frontal areas of the brain builds capacities for self-regulation

(Rothbart et al., 2011). In the toddler and preschool years, ongoing maturation of executive

functions continue to support self-regulation through the prefrontal cortex (McClelland et al.,

2010).

One key mechanism through which the environment is known to impact the

development of early childhood self-regulation is family socio-economic circumstances that

involve various dimensions of social position, including prestige, power, and economic well-

being (Conger, Conger, & Martin, 2010). Socio-demographic risks include: low parental

education levels, parental unemployment, young parents, parents with health problems, or

being from minority cultures. Social causation theories propose that children living in socio-

economically disadvantaged homes experience higher levels of stress which impact on

developing brain architecture (Farah, 2017) and thus self-regulatory development over time

(Blair et al., 2011). These neurological effects are considered to be the underlying

mechanisms through which socio-economic disadvantage leads to poorer educational

outcomes, mediated through self-regulation (Blair & Raver, 2015; Dilworth-Bart, 2012).

Across the last decade, a range of early childhood interventions have been designed to

address self-regulation prior to school with a number of these focussed on boosting life

chances for children from disadvantaged backgrounds (Pandey et al., 2018). However, none

of these have taken a specific rhythm and movement approach underpinned by neurological

understandings. In an intervention with some similarities to the current intervention study, US

researchers delivered games to preschool children over eight weeks (Schmitt, McClelland,

Tominey, & Acock, 2015). Although rhythm and music were not described as key elements,

many activities involved children: dancing to music of various tempos and shifting attention

in response to cues (e.g., dancing slow to fast music or dancing fast to slow music); playing

instruments with conductor cues (e.g., stop/start and shifting attention in relation to tempo);

and responding to drum beats with movement (Tominey & McClelland, 2011). In a series of

studies this intervention has shown positive effects for behavioural self-regulation (measured

by the Head Toes Knees Shoulder task; Ponitz et al., 2009; Schmitt et al., 2015; Duncan,

Schmitt, Burke, & McClelland, 2017), the directly assessed executive function of shifting

(Schmitt et al., 2015), and later growth in literacy and numeracy (Duncan et al., 2017). No

studies of this intervention have included a measure of emotional regulation as a distinct

construct, a gap addressed by the current study.

Potential for a Rhythm and Movement Intervention to Improve Self-Regulation

Despite evidence that neurobiological deficits underpin socio-economic gradients in

self-regulation development (Blair & Raver, 2015; Diamond, 2016), very few interventions

have taken a neurobiological approach. No interventions have been identified that

purposefully leverage the neurological benefits of music and rhythm (Pandey et al., 2018). It

is proposed that rhythmic coordinated movement activities have the potential to build

neurological pathways and brain connectivity related to self-regulation, with the potential to

remediate neurological impacts of early socioeconomic disadvantage. The full rationale for

this approach is detailed in a previously published paper (Williams, 2018). Briefly, four areas

of research support this proposition.

First, there is evidence that the ability to keep time by moving or tapping to a given

beat (beat synchronisation) is an important neurodevelopmental marker (Thompson, White-


Schwoch, Tierney, & Kraus, 2015). Like self-regulation, beat synchronisation improves with

age and is positively associated with markers of school readiness including language and

auditory perception skills (Woodruff Carr, White-Schwoch, Tierney, Strait, & Kraus, 2014).

Children with deficits in executive function also show deficits in rhythm perception (Lesiuk,

2015), suggesting there may be shared underlying neural mechanisms for self-regulation and

rhythm perception. There is strong potential that improving beat synchronisation skills in

children may address self-regulatory functioning. This proposal echoes other recent

interdisciplinary calls for a focus on music-based intervention studies for individuals with

developmental disorders characterized by self-regulatory problems (Slater & Tate, 2018;

Srinivasan & Bhat, 2013)

Second, formal music training has been associated with enhanced neural plasticity and

executive functioning in child and adult musicians, termed “the musician advantage” (George

& Coch, 2011; Luo et al., 2012; Putkinen et al., 2015). This advantage is thought to result

from enhancement of shared neural networks involved in rhythm perception and parallel non-

musical cognitive functions (George & Coch, 2011) including sound discrimination and

auditory attention (Putkinen et al., 2015). These effects extend to early childhood. Children

who have had formal music instruction from the age of 5 years, or younger, are found to have

better inhibition skills (an executive function) than matched controls without musical training

(Joret, Gerneys, & Gidron, 2017). The musician advantage has been leveraged by programs

such as The Harmony Project in Los Angeles, in which children from disadvantaged areas

who were provided with instrumental music instruction, have shown gains in neural encoding

of speech and reading scores (Kraus, Hornickel, Strait, Slater, & Thompson, 2014). One of

the key mechanisms through which the musician advantage is conferred is likely to be

through enhanced beat synchronization skills gained through rhythmic movement practice

(Williams, 2018). This notion is supported by a group of studies that have found enhanced

attentional and inhibitory skills in professional percussionists and drummers, who arguably

move rhythmically and in more complex ways, over and above those gains found in other

musicians (Slater et al., 2017). It is possible that some of the “musician advantage” can be

conferred through an early childhood rhythmic movement program, with a focus on

coordinated rhythmic movement and beat synchronisation skills.

Third, music therapy offers evidence for the role of rhythm engagement in stimulating

non-musical, domain-general benefits, including self-regulation skills (Thaut et al., 2009).

Music therapists use beat synchronisation and rhythmic auditory cueing to improve cognitive

and motor functions in brain-injured patients (Thaut et al. 2009), with strong evidence for

rhythmic auditory stimulation and motor rehabilitation in particular (Thaut & Abiru, 2010).

The principal of rhythmic entrainment is important, referring to the proclivity of the human

body to match physical functions to an externally provided beat. Movement activities

supported by providing a beat stimulate the auditory-motor system through entrainment to the

beat, and support more timely and coordinated movement than is possible without rhythmic

support. Coordinated movement activities have been linked with improved self-regulation, as

they both require employment of the self-regulatory systems of the brain and build the neural

circuitry relevant to self-regulatory functions (Chang, Tsai, Chen, & Hung, 2013). These

areas of clinical research suggest that auditory-cued and rhythmically supported movement

hold potential for stimulating coordinated movement improvements in young children which,

in turn, may lead to improved self-regulatory functioning.

Finally, active music participation is developmentally appropriate for preschool

children, given the prevalent role of music in their lives (Lamont, 2008). Higher levels of

informal parent-child music activity in the home at 2–3 years has been linked with both lower

levels of tested auditory distractibility at 2–3 years (Putkinen et al., 2015), and enhanced


parent-reported attentional regulation skills at 4–5 years (Williams, Barrett, Welch, Abad, &

Broughton, 2015). Arts-enriched preschool environments with strong music components

(Brown & Sax, 2013) and formal music and dance classes (Putkinen et al., 2015; Winsler,

Ducenne, & Koury, 2011) have also been linked with self-regulatory benefits for young

children. Importantly, children from lower socio-economic homes are likely to have lower

levels of parent-child music engagement at home (Williams et al., 2015) and are less likely to

access enrichment activities such as extra-curricular, early learning music programs

(Kaushal, Magnuson, & Waldfogel, 2011).

The Current Study

While the research reviewed above and previously (Williams, 2018) suggests that

self-regulatory deficits might be addressed through a focus on beat synchronisation combined

with coordinated movement skills, there has not yet been a specifically designed intervention

for early childhood self-regulation that embeds these elements. This study explores whether

the core experience of practising rhythmic movement can simulate some of the effects of the

musician advantage through a low cost intervention that can be embedded in regular

preschool programs. The current study assesses the feasibility of such an approach, through

exploring the extent to which children show engagement with rhythmic activities, and

provides initial data on the effectiveness of the intervention to improve self-regulation skills

for preschool-aged children living in disadvantaged areas.

Methodology

A purposefully designed rhythm and movement intervention was implemented in a

quasi-experimental design. Three early childhood centres, one in each of three communities,

which enrolled preschool-aged children, aged 4-5 years, participated. Each centre had two

classrooms (22 children per classroom), which were assigned to either the intervention or

control condition. Assessments were conducted pre- and post-intervention to evaluate

children’s self-regulation skills through teacher ratings and through direct assessment of

children’s executive function skills. Ethical clearance was gained through a University

Human Research Ethics Committee.

Selection of Communities

Three low socio-economic communities in the outer suburbs of a large city in one

Australian state were identified for participation. Disadvantage of communities was assessed

using the Index of Relative Socio-economic Advantage and Disadvantage (SEIFA), a

composite score derived from census variables related to income, education level,

employment, occupational status, and housing (Australian Bureau of Statistics, 2013a).

Participating communities were in the 2nd or 3rd decile nationally, indicating relatively high

levels of disadvantage (Australian Bureau of Statistics, 2013b).

Additional information on the child population in each community was available

through the Australian Early Development Census (AEDC; Australian Government

Department of Education and Training, 2016), a national population measure of young

children’s developmental status in their first year of full-time school. Compared to the

national average, Community A and Community B had higher levels of child developmental

vulnerability (Table 1) and more children identified as Aboriginal or Torres Strait Islander,

while Community C had lower levels of child developmental vulnerability but a higher

percentage of children from non-English speaking backgrounds. Each of the communities had

shown a significant increase in the number of children developmentally vulnerable in


emotional and/or social domains from the Australian Early Development 2012 census to the

2015 census (Australian Government Department of Education and Training, 2016).

Participants

At each of the three kindergartens, children attend on a sessional basis for a full-day

program for five days per fortnight — one class at the beginning of the week (alternating

attendance from 2 to 3 days in successive weeks) and the other class at the end of the week

(alternating attendance from 3 to 2 days in successive weeks), with different children enrolled

in each class. Children attend for one year, in the year prior to beginning full-time formal

schooling. Usual program activities in the play-based curriculum include indoor and outdoor

play, table activities, and group time. Across centres, of the potential 132 child participants,

parental written consent was gained for 117 children (89%). At each centre, classes were

assigned to either the intervention or control condition. Class assignment to condition in each

preschool centre was based on availability of a visiting music specialist to conduct the

intervention sessions on the same day in each week. Each preschool class, within and across

centres, had a different classroom teacher, so risk of intervention contamination was

minimised. Children in the control classes continued with their usual program.

The final analytic sample comprised 113 children who completed at least one of

baseline or follow-up data collection (54% female; mean age = 55.9 months ranging from 48

to 67 months; SD = 4.5 months). Demographic data were available for 84% (n = 95) of the

participants for whom parents returned the demographic survey (Table 2). This data included:

child gender (1 = boy; 0 = girl); Aboriginal or Torres Strait Islander status (1 = yes; 0 = no);

non-English home language (1 = yes; 0 = no); household income (four brackets ranging from

1 = less than $500 per week to 4 = $2,000 or more per week); highest level of parent

education (six brackets ranging from 1 = elementary school to 6 = university degree); and

concerns about the child for any developmental delay (1 = yes; 0 = no). Comparisons with

community-level data provided in Table 1 suggested that the study sample was approximately

representative of the community population with regard to number of children from

Aboriginal and Torres Strait Islander backgrounds and non-English speaking homes.

Significance testing for group differences between intervention and control groups did not

identify demographic differences.

Procedures

Baseline data (Time 1) were collected at each centre across one week during July to

September 2016, with follow-up data (Time 2) collected 10 weeks later, following the eight-

week implementation of the intervention. Time 1 and Time 2 data collection for all

participants included a teacher questionnaire reporting on children’s self-regulation (return

rates of 100% at Time 1 and 80% at Time 2) and three direct measures of children’s

executive function at both time points. Direct assessments were conducted by trained

assessors using tasks on iPads. Children were withdrawn from classroom activities for up to

20 minutes. The order of delivery of the tasks was randomised for each child. The assessors

also provided ratings at Time 1 and Time 2 on the level of children’s task engagement and

understanding (low, medium, high).

Intervention Design


The intervention was designed by the lead author (Registered Music Therapist and

child development researcher) with input from a leading neurologic music therapist from the

author’s professional network. There were 16 class group sessions of 30 minutes duration

conducted twice per week across eight weeks. The intervention was delivered by two visiting

early childhood music specialists (session leaders) trained to deliver the program. One

session leader conducted sessions at Communities A and B and the other leader at

Community C.

The program was designed as a series of four stages, with each stage consisting of

four repeated sessions to make up the total of 16 sessions. Each of the stages had more

challenging activities over time which is considered to be an important element of stimulating

change in development of self-regulation skills (Diamond & Lee, 2011). All intervention

activities were designed to practise key skills of attentional, emotional and behavioural

regulation, inhibition, shifting, and working memory through embedding these skills in

coordinated movement activities enhanced by rhythmic auditory cueing. Common activity

elements across the sessions included start / stop (inhibition), reversal of instruction (shifting,

e.g., move in the silence and freeze in the music), working memory games, and beat

synchronisation to changing tempos. Original backing tracks provided rhythmic auditory

cueing and leverage rhythmic entrainment principles to stimulate more coordinated

movement. Low-cost instrument and visual resource packs were also created.

Within each session plan there are a series of seven short activities with the above key

elements represented in each: 1) Warm-up involving body percussion; 2) Becoming familiar

involving an adaptation of a familiar early childhood song; 3) Moving to the beat involving

large gross motor movements; 4) Playing to the beat involving simple rhythm sticks or

castanets; 5) Dancing to the beat involving slightly more complex gross motor movement

patterns to activity 3 of the session and often involving visual motor skills and coordination

such as mirroring the shape of rhythm sticks on the floor with bodies; 6) Moving to a story in

which a narrative involving four characters (e.g. man, bird, cat, fish) is created with

percussion sounds matched to each character. Children match their movement to the story

characters and the percussion sound. Once the narrative is learned the percussion sounds may

appear in a different order to the story, requiring working memory if children are to match

their movement correctly; 7) Calming which includes a yoga-based series of movements

accompanied by relaxation music to support physiological entrainment to a calmer state

which targeted emotional regulation. All intervention materials are publicly available through

the intervention website (https://ramsrblog.wordpress.com/).

Measures of intervention fidelity and acceptability of intervention to children

were collected through ratings made by session leaders on a number of items at completion of

each session. Levels of overall child attention, enjoyment, participation and success in the

activities were each rated on a three-point scale (1 = low; 2 = moderate; 3 = high). The degree

to which activities in each section of the plan for each session were conducted according to

the plan was rated again using a three-point-scale (1 = not conducted; 2 = conducted with

some variation from the plan; 3 = conducted as per the plan).

Child Assessment Measures

Three executive function measures from the Early Years Toolbox (EYT) iPad tasks

were used. These tasks have shown good convergent validity, correlating with other

established measures tapping the same constructs and have also been used to detect

intervention effects over an eight-week period (Howard et al., 2016). Full psychometric

details on these tasks are provided by Howard and Melhuish (2016).

https://ramsrblog.wordpress.com/


Working memory was measured through the EYT Mr. Ant task, which measures

visual-spatial working memory. Children were asked to remember the spatial locations of

“stickers” placed on a cartoon ant and identify these locations after a brief retention interval.

The possible score range is 0 to 8.

Inhibition was measured using the EYT Go/No-Go task, which required participants

to tap the screen on “go” trials (“catch the fish”) and not tap the screen on “no-go” trials

(“avoid catching sharks”). As the majority of stimuli were “go” trials (80% fish), this

generated a prepotent tendency to respond, requiring participants to inhibit this response on

no-go trials (20% sharks). Inhibition was indexed by an impulse control score with a possible

range of 0 to 1.

Shifting was measured using the EYT Card Sorting task based on the protocols of the

commonly used Dimensional Change Card Sort task (Zelazo, 2006). Children were required

to sort cards (i.e., red rabbits, blue boats) by a sorting dimension (i.e., colour or shape) into

one of two locations (identified by a blue rabbit or a red boat), and then switch to the

alternate sorting rule. Scores represented the number of correct sorts after the switch phase

with a possible range of 0 to 12.

Self-regulation was measured through teacher report on three subscales of the EYT

Child Self-Regulation and Behaviour Questionnaire (CSBQ). The CSBQ is a 33-item

educator-report (or parent-report) questionnaire that yields seven subscales. Each item

requires the respondent to evaluate the general frequency of target behaviours, on a scale

from 1 (not true) to 5 (certainly true). Three subscales were used in this study: Cognitive

Self-Regulation (5 items, e.g. “persists with difficult tasks”), Behavioural Self-Regulation (5

items, e.g. “waits their turn in activities”), and Emotional Self-Regulation (6 items, e.g. “gets

over being upset quickly”). Internal reliability for each of the subscales was adequate (Table

4).

Approach to Analyses

Data screening followed protocol for the Go/No-Go (inhibition) task (Howard &

Melhuish, 2016), removing data where accuracy and response times suggested children were

not engaged with the task or indiscriminately responding. We also removed data for children

where assessors had rated their understanding of specific tasks as low. In only one case this

procedure resulted in all three executive function scores removed for a single child (at Time

1).

Path modelling within MPlus Version 7.3 (Muthén & Muthén, 2012) was used to

estimate intervention effects separately for each outcome measure. Each model controlled for

the corresponding Time 1 measure (baseline; Figure 1). Adjusted models included child

gender and level of parent education as covariates in relation to the Time 1 outcome measure,

given the relatively consistent correlations among these covariates and outcome measures

(Table 3). This approach equates to multiple regression modelling and calculations in

G*Power show that, with the sample size of 113, the model had a power of 0.91 to detect

effect sizes of 0.10. Because this approach of modelling each outcome separately increases

the chance of Type 1 errors unless the alpha level for tests is adjusted downward (Schochet,

2008), we treat only p values ≤ .01 as significant.

A robust intention-to-treat model was assumed for the analyses. Full information

likelihood estimation was used to account for missing data. When the intervention effect was

found to be significant, the size of the effect was computed using the formula for an

independent-groups pretest–posttest design (Feingold, 2009): d = (Mchange-T/SDT Time 1) –


(Mchange-C=SDC Time 1); where Mchange-T is the mean change for the intervention group; Mchange-C

is the mean change for the control group; SDT Time 1 is the pretest standard deviation for the

intervention group; and SDC Time 1 is the pretest standard deviation for the control group.

Results

Feasibility: Attendance, Engagement, and Fidelity of Intervention

Children in the intervention group attended from 10 to 16 of the 16 sessions available,

with 78% (n = 42) attending at least 14 of the 16 sessions. Session leaders who delivered the

intervention rated child enjoyment as high for 77% of the total 48 sessions conducted

(enjoyment was moderate for the remaining 23%); child participation was rated high (46%)

or moderate (52%) for most sessions; child attention was rated high (40%) or moderate (54%)

for most sessions; and child success in accomplishing activities was rated moderate (90% of

sessions).

Fidelity ratings indicate that activities were implemented in accordance with the plan

from 77% to 98% of the time depending on the specific activity. There were no reported

instances of session leaders failing to implement any part of each session plan. Adjustments

reported typically related to slight modifications of activities to provide higher levels of

scaffolding on some activities.

Outcome Measures: Descriptive and Correlational Data

Bivariate correlations among socio-demographic and outcome variables are provided

in Table 3. In Table 4, bivariate correlations among outcomes measures and group

membership (intervention and control), as well as descriptive statistics for outcome measures,

are reported. Largest correlations were among teacher-reported self-regulation scales at each

time point and across time points. Inhibition and shifting scores showed moderate

correlations over time. Teacher-rated behavioural and cognitive self-regulation were also

moderately positively correlated with most measures of executive function at both time

points. There were no significant differences in outcome measures between intervention and

control groups (Table 5). Differences among communities in Time 1 and Time 2 measures

were also examined (contact author for details), with very few differences found.

Intervention Effects

The demographic data indicated no socio-demographic differences between

intervention and control groups (Table 2), suggesting the groups were initially equivalent.

However, because of the nested structure of the data within three preschool centres, intra-

class correlations (ICCs) representing centre level variance in Time 1 outcomes measures

were examined. While ICCs were small to moderate (.01 to .06), corresponding variance

inflation factors ranged from relatively low (1.04) to moderate (3.47). Because the number of

clusters was too small to use multilevel modelling or other approaches that take account of

clustering within the data, models were run first for the whole sample across all three centres,

and then separately for each of the three centres. Sub-group analyses were also performed for

girls and boys given the systematic differences in Time 1 measures favouring girls in this

study (Table 3), and the documented gender differences in self-regulatory development in the

preschool years (Gagne & Goldsmith, 2011; Matthews, Ponitz, & Morrison, 2009).

Both the unadjusted and adjusted path models for each outcome across the full sample

were a good fit to the data (𝑥2 p values > .05). All outcome measures showed moderate to


high stability across the eight-week period (β = .42, p < .01 for inhibition to β = .80, p < .01

for teacher-reported cognitive self-regulation), with the exception of working memory (β =

.20, p = .11). Results for intervention effects for the fully adjusted model for the whole

sample show an intervention effect for emotional regulation with a moderate effect size (β =

.35, p = .01, d = .35; Figure 2a), no statistically significant intervention effects were found for

working memory (β = .07, p = .72), inhibition (β = -.01, p = .97), shifting (β = .27, p = .09),

behavioural self-regulation (β = .21, p = .11), or cognitive self-regulation (β = -.08, p = .58).

In modelling each gender group separately, there was an additional treatment effect for boys

only for the directly assessed executive function of shifting, with a large effect size (β = .55, p

= .01, d = .60; Figure 2b).

In modelling of each community site separately (Table 6), there were large

intervention effects for Community A for teacher-reported behavioural regulation (β = .82, p

< .01, d = .99) and emotional regulation (β = .78, p < .01, d = .98), and a small significant

effect for teacher-reported cognitive regulation (β = .68, p = .01, d = .21). In Community C,

there was a moderate intervention effect for emotional regulation (β = .44, p = .01, d = .39).

Teacher-reported outcomes for Community B could not be modelled independently of the full

dataset due to low covariance coverage for this community related to the large amount of

missing data for Time 2 teacher reports of self-regulation data.

Discussion

The need to address individual differences in neurological processes that can produce

educational inequities for young children who experience disadvantage has become an

international educational policy priority (UNICEF, 2017; World Education Forum, 2016).

This study has documented the feasibility and effectiveness of a novel intervention to support

preschool self-regulation and executive function skills that can leverage the neurocognitive

benefits of rhythm and movement for improved self-regulation in educational contexts. The

intervention appears feasible given the high rates of child engagement in and enjoyment of

the intervention activities, suggesting the intervention format is acceptable to preschool

children living in disadvantaged communities. There were also indications of effectiveness

for some outcomes. This should be interpreted with caution given the small sample size, and

limitations of the study discussed below. Intervention effects were found for teacher-reported

emotional regulation across the three participating communities, and for teacher-reported

behavioural and cognitive self-regulation in one of the three communities. Improvements in

the directly assessed executive function of shifting for boys across the three communities was

also found to be a significant intervention outcome. There were no intervention effects found

for inhibition and working memory.

This study is the first, to the authors’ knowledge, to document the effects of a specific

rhythm and movement intervention designed to address self-regulation in preschool children.

The findings reflect outcomes in prior studies in related areas. These studies include

participation in weekly parent–infant active music classes for 12-month-old children (Gerry,

Unrau, & Trainor, 2012), an arts-enriched preschool program with a strong music component

for low-income children (Brown & Sax, 2013), and twice-weekly group game sessions with a

number of rhythmic and musical elements over eight weeks in preschool (Schmitt et al.,

2015). The latter program was effective in improving shifting (with an effect size of .16) and

a behavioural measure of self-regulation (with an effect size of .32), but not teacher-reported

self-regulation (Schmitt et al., 2015). Effect sizes in the current study are comparable to those

in previous studies and extend prior findings by including a specific measure of emotional

regulation along with cognitive self-regulation (executive functions) which has not been done

to date.


The Developmental Importance of Improvements in Emotional Regulation

Across the preschool years, self-regulation emerges through the coordination of

systems relating to emotional arousal and cognitive control (Blair & Diamond, 2008). From

three to 5 years, children begin to understand and distinguish between their own emotions

and those of others and can begin to deal with emotions in a more regulated way, gaining

greater cognitive control over their actions (Housman, 2017). The increased understanding of

neurological processes in early development has highlighted the coordinating role of the

anterior cingulate cortex as important to emotional regulation as well as impulse control, and

error-monitoring (Boes et al., 2009). Thus, the large effects for improved teacher-observed

emotional regulation found in this study are considered important.

It is hypothesised that emotional regulation improvements found for the intervention

group in this study might stimulate subsequent attentional regulation growth, given both the

known shared underlying neural processes for these (Boes et al., 2009), and observational

studies linking emotional regulation growth to subsequent attentional regulation growth

(Williams, Berthelsen, Walker, & Nicholson, 2017).While attentional regulation was not

specifically measured here, the cognitive self-regulation scale included a number of similar

items related to task persistence as used in these prior studies. Promisingly, cognitive

regulation improvements were found for the intervention group in one of the three

communities, but not across the whole sample. Given the neurologic self-regulation

development model in which emotional regulation is considered a bottom-up process with

implications for attentional regulation and higher-order executive functioning (Blair & Raver,

2016), it may be that, given a longer period of intervention, later benefits to attentional

regulation may have become apparent. Developmental pathways involving emotional

regulation and attentional regulation have been documented as important in supporting

academic achievement in the early years of school (Trentacosta & Izard, 2007; Williams,

White, MacDonald, 2016).

Other Intervention Effects

There were positive intervention effects for the executive function of shifting, but

only for boys. While baseline shifting scores did not differ by gender in the current study,

some prior research has suggested that young boys in some cultures may have poorer self-

regulation skills than girls (on some measures) and so may have more to gain from

intervention efforts (Gagne & Goldsmith, 2011; Matthews et al., 2009; Wanless et al., 2013).

Several activities within the intervention required shifting attention from one aspect to

another and all contained movement, which may have contributed to sustained engagement

for all children, but particularly boys. Boys from disadvantaged communities may be

particularly vulnerable to poor school transition due to lower levels of early academic

competency and classroom self-regulatory behaviour (Matthews et al., 2009; Walker &

Berthelsen, 2017). Improvements in shifting may be an important outcome that will support

transition to school for these boys where focusing and shifting attention will be required in a

busy classroom to support learning and adjustment.

There were no intervention effects found for working memory or inhibition. While a

prior study of book-reading with executive function activities did yield working memory

effects (Howard et al., 2016), intervention effects for inhibition when measured discretely

through an executive function task have generally not been found in prior studies (Barnett et

al., 2008; Biermann et al., 2008, Howard et al., 2016). However, global assessments of

children’s executive functions in action that include inhibition and which require organisation


of all three executive functions and self-regulatory capacity (HTKS task) have shown

intervention effects over an eight-week intervention period (Schmitt et al., 2015; Tominey &

McClelland, 2011). The intervention in the current study did include a number of activities

that required shifting and inhibition skills in children and it may be that a more global and

behaviourally focused measure, such as the HTKS task, may have illuminated these in action.

Intervention effects for teacher-reported cognitive and behavioural regulation in one of the

two communities may reflect improvements in underlying inhibition in ways that are

important for classroom functioning.

Implications for music interventions in preschool to support early self-regulation

Active rhythmic movement and music engagement is a unique activity with strong

potential to shore up brain architecture responsible for self-regulation, the same architecture

that is often compromised in young children from disadvantaged homes. Yet, these very

children are the least likely to gain access to early childhood music experiences and later

formal music tuition, meaning those that stand to gain the most from the neurological benefits

of the ‘musician advantage’ miss out. The findings presented here suggest that group music-

based interventions hold promise for supporting self-regulatory development in young

children, as has been theoretically proposed in recent publications (Slater & Tate, 2018;

Srinivasan & Bhat, 2013; Williams, 2018), but to-date remains largely untested. It is likely

that the positive impact documented here reflects processes implicated in the musician

advantage, related to reinforcement of shared neural networks for motor-synchronisation and

emotional and cognitive control, as well as social benefits of group music participation.

Active music making stimulates desired neural activation patterns implicated in emotional

regulation and may help support optimal levels of arousal, stimulating the reward systems of

the brain (Moore, 2013). Structured group musical play with peers has been shown to

motivate higher levels of emotional regulation in children who struggle with their emotional

control outside of music sessions (Zachariou & Whitebread, 2015). Importantly, gains in

emotional regulation are likely to translate to longer term development in cognitive control

and broader self-regulatory capacities.

More and more research aims to identify ways in which early childhood interventions can

enhance self-regulation through a specific focus on systematically teaching cognitive and

emotion regulation skills and supporting their integration. Group early childhood music and

movement activities offer a unique opportunity to support children in this integration through

stimulating auditory and motor processes that are also known to have strong implications for

self-regulatory brain architecture. Preschool teachers need to be supported to implement these

activities regularly and with purpose and in ways that enhance important teacher-student

relationships and align with existing early childhood curricula. Teachers can also share with

parents and the community the value of music engagement for children, especially in family

and community contexts in which parents may have fewer resources to afford formal music

activities but can provide such activities in the home. While the current exploratory study has

shown potential for this approach as delivered by visiting music specialists, future studies are

needed to understand what is needed to build sufficient skills and confidence in teachers to

implement these activities.

Limitations and Future Directions

There are several limitations in the current study. Teachers who provided ratings on

children’s self-regulatory skills pre- and post-intervention were not blind to intervention or

control group assignment. However, the time lag between data collection (eight weeks),

during which time teachers did not have access to the Time 1 data provided for each child,


limits the extent to which there may have been intentional upward bias in ratings for the

intervention group due to this non-blinding.

It is also unknown to what extent intervention effects were achieved, due to the extent

to which teachers of the intervention groups continued using the intervention ideas in other

areas of programming. For example, it may be that the additional intervention effects found

for behavioural and cognitive self-regulation in Community A were related to extra practice

of intervention activities implemented by the teacher between sessions. Future research

should collect data on existing music and movement practices implemented by kindergarten

teachers in both control and intervention groups, should introduce an active control condition,

and collect additional data throughout implementation on the ways that teachers embed

elements (or not) of the intervention in their practice outside of specific session times.

Conclusion

This study has documented the rationale, feasibility, and early effects of a rhythm and

movement intervention for self-regulation in preschool children from disadvantaged

communities. The innovative intervention design aimed to harness the well-documented

benefits of music and rhythm participation represented in the cognitive neuroscience and

music training literature (the musician advantage) and practiced extensively in the field of

music therapy. The findings suggest that the musician advantage, typically conferred only on

those children from advantaged backgrounds whose families pay for tuition, might be

extended to those children who are likely to need musical opportunities the most. It appears

that self-regulatory benefits might be accrued through participation in rhythmic movement

activities delivered in preschool settings. Any gains in self-regulatory ability in the preschool

period are likely to accrue benefits for positive school transition and future academic

achievement and so are important targets of intervention in disadvantaged communities.


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Table 1 Comparison of Three Study Communities and Australian National Data for Developmental Vulnerability and Sociodemographic Data

% of children

vulnerable on

one or more

AEDC

developmental

domains

% of children

vulnerable on

emotional

maturity

% of children

vulnerable on

social

competence

% ATSI % NESB % children with

special needs

SEIFA decile

(1 = most

disadvantaged;

10 = most

advantaged)

Community A 26 8 11 6.9 5 5 3

Community B 29 13 13 6.8 3.1 5.9 2

Community C 9.7 8 11 2.4 11.3 2.9 3

Australia 22 8.4 9.9 5.7 15 4.7 NA

Notes: AEDC = Australian Early Development Index; ATSI = Aboriginal and Torres Strait Islander; NESB = Non-English speaking

background; SEIFA = Socio-economic Indexes for Areas


21

Table 2 Demographic Data for the Full Sample, Intervention and Control Groups

Whole sample

(n = 113)

Intervention

(n = 59, 52%)

Control

(n = 54, 48%)

Significance of the

difference between

the intervention and

control groups# (p)

Mean child age (months) 55.9 55.6 56.2 .46

N (%)

Child gender (female) 54 (48%) 30 (51%) 24 (44%) .50

Those with completed demographic data Whole sample

(n = 95, 84%)

Intervention

(n = 52, 88%)

Control

(n = 43, 80%)

.16

Parent education: incomplete high school 17 (18%) 11 (21%) 6 (14%) .37

Parent education: university degree 36 (38%) 16 (31%) 20 (47%) .12

Family income: less than $500 per week 10 (11%) 7 (13%) 3 (7%) .27

Family income: $2000 or more per week 15 (16%) 9 (17%) 6 (14%) .47

Child Aboriginal or Torres Strait Islander 7 (7%) 4 (8%) 3 (7%) .89

Child non English speaking background 8 (8%) 2 (4%) 6 (14%) .10

Child developmental delay 19 (20%) 10 (19%) 9 (21%) .84

# significant (p) values yielded from regression estimates in Mplus. All demographic variables treated as categorical with the exception

of child age in months. For parent education level (6-point scale) and household income (4-point scale), descriptive statistics for the lowest and

highest bracket only are provided.


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Table 3 Correlations for Measures and Covariates

Female Family

income

bracket

Parent

education

ATSI NESB

Working memory T1 .04 -.06 -.19 -.07 -.04

Inhibition T1 .10 -.08 .13 -.06 .12

Shifting T1 .20 .11 .18 -.22* -.16

Behavioural SR T1 .33* .07 .24* -.09 -.18

Emotional SR T1 .24* .14 .24* -.19 -.08

Cognitive SR T1 .27* .06 .10 -.01 -.18

Working memory T2 -.03 .07 -.11 -.11 -.06

Inhibition T2 .11 -.03 .02 -.15 .03

Shifting T2 .29* -.01 .05 -.13 -.21*

Behavioural SR T2 .30* .19 .36* .03 -.24*

Emotional SR T2 .28* .18 .23* -.04 -.05

Cognitive SR T2 .20 -.05 .21* .03 -.25*

SR = self-regulation; T1 = Time 1; T2 = Time 2; ATSI = Aboriginal and Torres Strait Islander; NESB = non-English speaking background. * p

< .05


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Table 4 Bivariate Correlations and Descriptive Statistics for Group Membership, Child Age, and all Outcome Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Intervention group 1

2. Child age (months) -.07 1

3. Working memory T1 -.03 .12 1

4. Inhibition T1 -.05 .30* .27* 1

5. Shifting T1 .06 .08 -.09 .25* 1

6. Behavioural SR T1 .05 -.00 .15 .28* .16 1

7. Emotional SR T1 .01 -.09 .03 .18 -.05 .59* 1

8. Cognitive SR T1 .13 .20* .17 .33* .25* .68* .43* 1

9. Working memory T2 .02 .25* .23* .21* .09 .07 -.08 -.01 1

10. Inhibition T2 -.04 .19 .14 .43* .26* .35* .25* .39* .35* 1

11. Shifting T2 .16 .15 -.04 .03 .55* .21* .09 .26* .09 .30* 1

12. Behavioural SR T2 .18 -.04 .01 .22* .11 .79* .58* .61* .05 .32* .29* 1

13. Emotional SR T2 .18 -.10 .04 .12 -.01 .55* .78* .41* -.12 .18 .09 .65* 1

14. Cognitive SR T2 .07 .14 .16 .29* .24* .60* .33* .80* -.02 .30* .27* .66* .37* 1

Range NA 48 –

67

0 –

3.67

0 –

1

0 –

12

1.33

– 5

1.67

– 5

1 –

5

0 –

3.33

0 –

1

0 –

12

1.5

– 6

1.5

– 5

2 –

5

Mean 55.9 1.76 .54 4.98 3.97 4.07 3.70 1.74 .61 7.05 4.29 4.27 4.09

SD 4.45 .74 .23 4.22 .77 .67 .92 .71 .23 3.53 .75 .69 .79

Internal reliability (α) NA NA NA NA NA .87 .76 .92 NA NA NA .87 .77 .92

SR = self-regulation; T1 = Time 1; T2 = Time 2; * = significant at p < .01.


24

Table 5 Means and Standard Deviations for each Outcome Measure at each Time Point for the Control and Intervention Groups

Control group Intervention group

T1 T2 T1 T2

Outcome M SD M SD M SD M SD

Working memory 1.78 .59 1.72 .57 1.74 .87 1.76 .81

Inhibition .55 .22 .61 .22 .53 .234 .60 .23

Shifting 4.70 4.35 6.47 3.93 5.24 4.09 7.62 3.07

Behavioural SR 3.93 .85 4.29 .79 4.01 .68 4.41 .66

Emotional SR 4.06 .73 4.25 .62 4.07 .60 4.39 .68

Cognitive SR 3.58 .93 4.03 .87 3.81 .89 4.14 .73

SR = self-regulation; T1 = Time 1; T2 = Time 2. There were no significant differences.


25

Table 6 Means and Standard Deviations for each Outcome Measure at each Time Point for the Control and Intervention Groups for Two

Communities Tested Separately

Community A Community C

Control group Intervention group Control group Intervention group

T1 T2 T1 T2 T1 T2 T1 T2

Outcome M SD M SD M SD M SD M SD M SD M SD M SD

Working memory 1.80 .28 1.64 .70 1.83 .92 1.83 .79 1.78 .65 1.62 .66 1.72 .88 1.83 .6

Inhibition .57 .34 .65 .22 .52 .22 .65 .18 .50 .23 .64 .24 .39 .27 .49 .24

Shifting 5.50 4.74 7.33 3.92 6.44 3.84 8.89 2.42 5.29 4.65 7.86 2.89 4.89 3.80 8.00 2.16

Behavioural SR 4.24 .73 4.38 .51 4.39 .51 4.85 .20 4.28 .63 4.55 .57 3.58 .77 3.93 .88

Emotional SR 4.21 .74 4.24 .49 4.21 .37 4.60 .31 4.34 .45 4.54 .41 3.65 .74 3.93 .91

Cognitive SR 3.82 .77 3.88 .54 4.15 .64 4.63 .44 3.79 .76 4.44 .64 3.20 .95 3.82 .82

SR = self-regulation; T1 = Time 1; T2 = Time 2. There were no significant differences.


26

Figure 1. Path model approach to estimating intervention effects. The bold line represents the effects of the intervention on Time 2 (post-intervention) outcome measures controlling for Time 1 (baseline) measures.


27

Figure 2. Intervention effects for emotional regulation for the whole sample (a), and shifting for boys

(b). Intervention effect is shown in bold. All estimates are standardized and significant at p < .01.


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