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. Although 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 8 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 8 weeks, whereas 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 behavioral regulation also improved in one research site. These early findings suggest that a rhythm and movement intervention has the potential to support the development of self-regulation skills in preschool; however, further research is required.

Keywords

Self-regulation rhythm intervention early childhood

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 behavior 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 socioeconomic backgrounds (Dilworth-Bart, 2012). Intervention efforts to improve early self-regulation provide promising directions to address these socioeconomic disparities (Diamond, 2016). The current study examines a novel intervention designed to improve self-regulation for children living in low socioeconomic 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 socioeconomic 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 behavior (Diamond, 2016). In the preschool period attentional regulation refers to children’s behavioral 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 behavioral 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 that direct flexible, goal-directed behaviors 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 5 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 them). 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 behavior (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 behavior 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 behavioral 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 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 behaviors. 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 socioeconomic circumstances that involve various dimensions of social position, including prestige, power, and economic wellbeing (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 socioeconomically disadvantaged homes experience higher levels of stress, which impact on developing brain architecture (Farah, 2017) and thus self-regulatory development (Blair et al., 2011). These neurological effects are considered the underlying mechanisms through which socioeconomic 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 has been designed to address self-regulation prior to school with a number focused 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, United States researchers delivered games to preschool children over 8 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 behavioral self-regulation (measured by the Head-Toes-Knees-Shoulder [HTKS] 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 socioeconomic 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 synchronization) is an important neurodevelopmental marker (Thompson, White-Schwoch, Tierney, & Kraus, 2015). Like self-regulation, beat synchronization 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 synchronization 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 synchronization 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 synchronization 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 socioeconomic 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

Although the research reviewed above and previously (Williams, 2018) suggests that self-regulatory deficits might be addressed through a focus on beat synchronization 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 practicing 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.

Methods

A purposefully designed rhythm and movement intervention was implemented in a quasi-experimental design. Three early childhood centers, one in each of three communities, which enrolled preschool-aged children aged 4–5 years participated. Each center 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 socioeconomic 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, 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 second or third 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 (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, whereas 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).

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 Census; ATSI = Aboriginal and Torres Strait Islander; NESB = Non-English speaking background; SEIFA = Socio-economic Indexes for Areas.

Participants

At each of the three kindergartens, children attend on a sessional basis for a full-day program for 5 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 1 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 centers, of the potential 132 child participants, parental written consent was gained for 117 children (89%). At each center, classes were assigned to either the intervention or control condition. Class assignment to condition in each preschool center 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 centers, had a different classroom teacher, so risk of intervention contamination was minimized. 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.

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

Notes. 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 (six-point scale) and household income (four-point scale), descriptive statistics for the lowest and highest bracket only are provided.

Procedures

Baseline data (Time 1) were collected at each center across 1 week during July to September 2016, with follow-up data (Time 2) collected 10 weeks later, following the 8-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 randomized 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 (a 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 8 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, 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 practice key skills of attentional, emotional, and behavioral 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 synchronization to changing tempos. Original backing tracks provided rhythmic auditory cueing and leveraged rhythmic entrainment principles to stimulate more coordinated movement. Low-cost instrument and visual resource packs were also created.

Within each session plan there were a series of seven short activities with the above key elements represented in each: (a) warm-up involving body percussion; (b) becoming familiar involving an adaptation of a familiar early childhood song; (c) moving to the beat involving large gross motor movements; (d) playing to the beat involving simple rhythm sticks or castanets; (e) dancing to the beat involving slightly more complex gross motor movement patterns to activity three of the session and often involving visual motor skills and coordination such as mirroring the shape of rhythm sticks on the floor with bodies; (f) moving to a story in which a narrative involving four characters (e.g., man, bird, cat, fish) was 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 appear in a different order to the story, requiring working memory if children are to match their movement correctly; and 7) calming, which includes a yoga-based series of movements accompanied by relaxation music to support physiological entrainment to a calmer state that targeted emotional regulation. Materials are publicly available through the intervention website (https://ramsrblog.wordpress.com/).

Measures of intervention fidelity and acceptability of intervention to children

These 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 8-week period (Howard et al., 2016). Full psychometric details on these tasks are provided by Howard and Melhuish (2016).

Working memory

This 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

This 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

This 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., color 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

This 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 behaviors, on a scale from 1 (not true) to 5 (certainly true). Three subscales were used in this study: Cognitive Self-Regulation (five items, e.g., “persists with difficult tasks”), Behavioral Self-Regulation (five items, e.g., “waits their turn in activities”), and Emotional Self-Regulation (six 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.

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.

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*
Behavioral SR T2	.30*	.19	.36*	.03	−.24*
Emotional SR T2	.28*	.18	.23*	−.04	−.05
Cognitive SR T2	.20	−.05	.21*	.03	−.25*

Notes. SR = self-regulation; T1 = Time 1; T2 = Time 2; ATSI = Aboriginal and Torres Strait Islander; NESB = non-English speaking background. *p < .05.

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 = (M_change-T/SD_{T Time 1}) – (M_change-C=SD_{C Time 1}); where M_change-T is the mean change for the intervention group; M_change-C is the mean change for the control group; SD_{T Time 1} is the pretest standard deviation for the intervention group; and SD_{C 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 behavioral 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.

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. Behavioral 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. Behavioral 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
M		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

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

Table 5.

Means and Standard Deviations for Each Outcome Measure at Each Time Point for The Control and Intervention Groups.

Outcome	Control group				Intervention group
	T1		T2		T1		T2
	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

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

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 centers, intra-class correlations (ICCs) representing center level variance in Time 1 outcomes measures were examined. Although 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 centers, and then separately for each of the three centers. Sub-group analyses were also performed for girls and boys given the systematic differences in Time 1 measures favoring 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 ( $χ^{2}$ p-values > .05). All outcome measures showed moderate to high stability across the 8-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), behavioral 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).

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.

In modelling each community site separately (Table 6), there were large intervention effects for Community A for teacher-reported behavioral 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.

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
Behavioral 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.

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 behavioral 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 8 weeks in preschool (Schmitt et al., 2015). The latter program was effective in improving shifting (with an effect size of .16) and a behavioral 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 3 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 hypothesized 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). Although 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. Although 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 behavior (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. Although 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 organization of all three executive functions and self-regulatory capacity (HTKS task) have shown intervention effects over an 8-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 behaviorally focused measure, such as the HTKS task, may have illuminated these in action. Intervention effects for teacher-reported cognitive and behavioral 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 the positive impact documented here reflects processes implicated in the musician advantage, related to reinforcement of shared neural networks for motor-synchronization 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. Early childhood music and movement group 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. Although 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 (8 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 behavioral 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, 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 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.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Kate E. Williams

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