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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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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:
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RHYTHM AND MOVEMENT FOR SELF-REGULATION 2
self-regulation, rhythm, intervention, early childhood
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RHYTHM AND MOVEMENT FOR SELF-REGULATION 3
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
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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-
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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
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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
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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
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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).
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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) –
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(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
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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.
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RHYTHM AND MOVEMENT FOR SELF-REGULATION 12
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
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RHYTHM AND MOVEMENT FOR SELF-REGULATION 13
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,
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RHYTHM AND MOVEMENT FOR SELF-REGULATION 14
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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RHYTHM AND MOVEMENT FOR SELF-REGULATION 15
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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
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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.
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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.
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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.
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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.
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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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