The Effect of an 8-Week Aerobic Dance Program on Executive Function in Children

Abstract

In the current study, we examined the effect of an aerobic dance program as part of physical education (PE) classes on aspects of primary school children’s executive functions (EFs) (inhibition, working memory, and cognitive flexibility). Participants were 41 children (21 boys and 20 girls; M age =10.30, SD = 0.50 years, M height = 134.09, SD= 3.9 cm; M weight = 35.61, SD = 7.85 kg) who were divided into an experimental group (EG) and a no-PE control group (CG). The EG followed an aerobic dance intervention as part of their PE program (45 minute sessions two days per week over eight weeks). Participants in both groups performed EF tests before and after the intervention period to evaluate their mental flexibility, inhibition, and working memory. A two-way mixed model repeated measures ANOVA revealed a significant effect of the aerobic dance program on participants’ cognitive flexibility (i.e., on Trails Making Tests _B-A times and committed errors) (p <0.001), and on Stroop measures of inhibition (corrected number of words and corrected errors) (p <0.001 and p <0.01, respectively), with post-hoc analyses showing an improved performance by the EG in working memory (digit recall score) from pre-test to post-test and in comparsion to the CG (p < 0.001). Thus, this 8-week aerobic dance program promoted EF development among primary school children.

Keywords

executive function children aerobic dance physical education exercise

Introduction

In childhood, especially in late childhood, motor skills undergo dynamic development (Myer et al., 2015), and cognitive functions, especially executive functions (EFs), are identifiable and mature at different rates (Anderson, 2002). There is considerable debate regarding which cognitive skills represent EFs, but Diamond (2013) has asserted that they include three types of brain functions: working memory, inhibitory control, and cognitive flexibility. EF skills develop during childhood in parallel with the development of neural synapses, myelination, and the recruitment and consolidation of neural networks for specific cognitive tasks (Stevens et al., 2009). These functions are multidimensional and include a broad variety of skills such as attentional control, cognitive flexibility, inhibition, and strategic planning (Reader et al., 1994). EFs play an important role in determining the cognitive and academic functioning that support learning and scholarly achievement (McClelland & Cameron, 2012; Raver et al., 2011; Ursache et al., 2012). On the other hand, poor EF skills may put children at risk for ineffective environmental interactions leading to significant and lasting cognitive, academic, and social difficulties (Biederman et al., 2004; Clark et al., 2002; Ellis et al., 2004; Tapert et al., 2002). Indeed, students with poor working memory are more likely to experience difficulties following instructions for an activity, performing mental calculations, and retaining relevant information for other academic work (Cosnefroy, 2010). As EFs involve cognitive processes responsible for organizing and controlling goal-directed behavior, they are directly relevant to success in school and life in general (Kulinna et al., 2018).

Previous researchers have noted that cognitive skills develop in parallel with motor skills (Diamond, 2000; Hillman et al., 2005; Metcalf et al., 2011). Physical activity (PA) has been specifically associated with children’s EF (Sibley & Etnier, 2003). In this context, a previous study showed that PA improved EF in children aged 6–7 years old (Abdelkarim et al., 2017). Indeed, PA may exert beneficial effects on working memory (Kamijo et al., 2011), inhibition and cognitive flexibility (Hillman et al., 2014). Drygas et al. (2001) showed that school-aged children who devoted at least an hour a day to intensive PA showed much better cognitive functioning than those who were sedentary (Drygas et al., 2001). Specifically, both acute and chronic aerobic exercises have effectively improved children’s EF (Best, 2010), and insufficient PA in childhood has been associated with limited perception and developmental disorders (Schmidt et al., 2015).

Despite these crucial benefits to PA, there has been a shift in children’s lifestyles in recent years, particularly in their late childhood, suggesting that children today spend most of their time engaged in sedentary activities, perhaps influenced by the availability of electronic forms of entertainment (e.g., television, internet, mobile telephones, and video games) (Bidzan-Bluma & Lipowska, 2018). A reduction in physically demanding activities begins during the school years (Papaioannou et al., 2004) and may be explained in part by students’ diminished interest in PE from primary grades to senior high school, linked, in turn, to lack of motivation for PA (Owen et al., 2014; Yli-Piipari, 2011). Likewise, Jaakkola et al. (2008) confirmed that intrinsic motivation was a definitive influence on moderate or vigorous PA in the context of PE (Jaakkola et al., 2008). Recently, some have recommended that school PE programs be made more attractive and interesting by training new challenging skills in order to enhance motivation for PE lessons (Rokka et al., 2019).

Dancing, as a type of aerobic activity, has been described as a patterned rhythmic movement in space and time (Murrock & Graor, 2014). In this context, dance-based interventions have shown promise for improving cognitive performances (Norouzi et al., 2019; Predovan et al., 2019), including improved memory and attention (Kattenstroth et al., 2013) among elderly persons. When practiced by children, dance may be an effective strategy for engaging children’s cognitive development (Giguere, 2011). Existing school opportunities for focusing on cognitively engaging PA have improved children’s selective attention (Kulinna et al., 2018), and dance interventions during primary school PE classes have improved children’s working memory capacity (Oppici et al., 2020). One recent study reported that an 8-week dance training improved inhibitory control and working memory capacity in primary school children (Rudd et al., 2021), but van den Berg et al. (2019) found no evidence of any cognitive benefit to children’s practicing dance 10 minutes a day for nine weeks. Oppici et al. (2020) called for further studies to understand how dance may influence cognition in children.

Aerobic dance is a motivational form of PA that has become popular in recent decades (Iermakov et al., 2016). Interestingly, an aerobic dance program is easy for PE teachers to design and implement, and it is generally enjoyable for students (aged 12–13); it holds promise for promoting intrinsic motivation and greater enjoyment of PE classes (Rokka et al., 2019). It is an easy and fun form of exercise that everyone can practice, and it has attracted much attention for its positive effects on functional abilities (Pantelić et al., 2007) and social integration in our modern society (Rokka et al., 2019). Recent studies showed positive effects of aerobic dance programs on health-related physical abilities in children aged 8- to 12-years-old (Kaya et al., 2011; Mavridis et al., 2004). Furthermore, children have adapted to such programs easily and with pleasure (Kremenitzer, 1990), as dance is not a competitive group exercise (Ciomag & Dinciu, 2013), and it does not require highly technical skills.

Based on all the foregoing considerations, we suspected that aerobic dance would have beneficial effects on EF for older children. To our best knowledge, most studies carried out for children in this age period focused on the motor and psychosocial benefits of aerobic dance. Therefore, in this study, we aimed to explore the effects of an 8-week aerobic dance program, on aspects of EF (inhibition, working memory, and cognitive flexibility) in primary school children aged 9–11 years. We hypothesized that children who underwent the 8-week aerobic dance program would exhibit EF improvement compared to those who participated in no PE or physical training program.

Method

Estimating a Required Sample Size

We calculated an a priori power analysis to determine an estimated required sample for this study, using G * power software (version 3.1.9.4; Faul et al., 2007). We set the statistical probability value or α at 0.05, statistical power at 0.80 and the non-sphericity correction = 1. Based on previous literature (Greeff et al., 2018) and discussions between the authors, we estimated the effect size for this research at 0.22. The power analysis then indicated that, to reach this desired statistical power with these assumptions, data from 40 participants would be sufficient.

Participants

Our recruitment strategy was a three-stage screening process. In the first stage, we recruited 47 primary school children between 9–11 years old from a single primary school in which no PE classes were scheduled. In the second stage, 43 of these screened children met our inclusion and exclusion criteria (see details below) and were selected. According to Tanner’s (1962) criteria, a pediatrician classified these children as pre-pubertal (stage 1). Our inclusion criteria were for children (a) from a middle socio-economic status (based on parents' income, education level, and occupation), (b) without any physical or mental illness that could interfere with our pre- and post-testing (see below for details), and (c) who experienced no locomotor system surgeries, respiratory dysfunctions, visual or auditory problems, and/or cardiovascular or metabolic disorders. All these details were collected from the school’s database for enrolled students. In the third stage, we excluded two children because they did not pass all the pre-training measures or because they were absent during familiarization or pre-training test sessions. Consequently, 41 children remained in the study. After a clear explanation of the procedures, including the risks and benefits of participation, children gave their assent, and their parents or legal guardians signed an informed consent form prior to the children’s participation in this study. The present study was conducted in accordance with the Declaration of Helsinki and its protocol was approved by the local ethics committee.

We randomly divided participants into two groups: (a) an experimental group (EG) (n = 19; 10 boys, 9 girls) and (b) a control group (CG) (n = 22; 12 boys, 10 girls). No participants were engaged in any other structured PA or exercise program and all reported a low PA level (Kowalski et al., 2004) based on their responses to nine questionnaire items from the Physical Activity Questionnaire for Older children (PAQ-C) (Crocker et al., 1997). The PAQ-C has been considered a valid measure within which older children can recall their participation in activities over the last seven days to compute their physical activity level (scored between 1 and 5) (Benítez-Porres et al., 2016; Kowalski et al., 1997, 2004; Moore et al., 2007). The intra-tester reliability of this questionnaire has been found to be good to excellent with an intraclass correlation coefficient of .70–0.96 (Benítez-Porres et al., 2016). Table 1 shows the participants' descriptive characteristics.

Table 1.

Participants’ Anthropometric Characteristics, Expressed as Means (and SDs).

	EG (n=19)	CG (n=22)	Degrees of Freedom	Independent t-Test
Age (years)	10.3 (0.57)	10.03 (0.43)	39	0.07
Height (cm)	138.85 (7.07)	139.33 (7.73)	39	0.09
Weight (kg)	33.67 (7.89)	37.55 (7.81)	39	0.83
BMI (kg/m²)	17.37 (3.09)	19.20 (4.54)	39	0.15
PAQ-C scores	1.97 (0.49)	1.95 (0.62)	39	0.74

Note: NS: not significant p>.05; BMI: Body mass index; EG: experimental group; CG: control group.

Study Design

As noted, our study design was a randomized controlled training intervention in which each participant was randomly assigned to either the EG or CG. The EG followed an 8-week-aerobic dance program within PE classes at school, and the CG participated in no PE or physical training program, simply continuing their normal daily activities during the same 8 week-period. The aerobic dance sessions were designed by a professional aerobic dance instructor with 20 years experience in aerobic dance. A single dance instructor with 5 years experience in aerobic dance supervised all training sessions for the EG group. The aerobic dance exercises were delivered in a sequence of progressive difficulty levels to minimize the risk of injuries. We requested that all participants practice no outdoor PA during the period of this experiment.

At pre- and post-intervention sessions, we administered three EF tests to all participants. To assess cognitive flexibility, inhibition and working memory, we administered the Trail-Making Test (TMT; Howie et al., 2015; Lezak et al., 2004), the Stroop test (Stroop, 1935; van der Niet et al., 2016), and the Digit Recall Test (Gathercole et al., 2003), respectively. Before baseline measurements, we conducted a familiarization session in order to ensure that the test instructions were correctly understood. All baseline and post-testing was performed in the morning, 2 days before and then 2 days after the intervention period.

Intervention Program

Participants in the EG followed the aerobic dance program in an equipped aerobic room. This program lasted 8 weeks at a rate of two 45 minute sessions per week, with sessions consisting of moderate to vigorous dance exercise intensity, and dance activities conducted according to specific objectives (see Table 2). All sessions were performed in the mornings between 9:00–9:45 a.m. Each aerobic session was composed of 10 minutes of warm-up followed by 30 minutes of continuous aerobic dance exercises, with five minutes of recovery time. The same warm-up (simple upper and lower limb movements and stretching exercises) was kept throughout the training period. The aerobic dance exercises consisted of three aerobic dances for 10 minutes each. Finally, the five minute recovery included relaxation exercises with slow rhythmic music. In each session, children learned four new aerobic dance steps to build at the end of the session a small routine that they repeated two or three times without stopping to reinforce and evaluate the choreography learning. The music intensity was 100 beats/minute for warm-up. The intensity of the main program was 60–75% of the maximum heart rate (music 125–130 beats per minute) in the first 6 weeks. From the 7th to 14th weeks, the intensity was increased to 75–85% of the maximum heart rate (music 135–145 beats per minute). In the next two weeks, the intensity of the program was reduced to 60–70% of the maximum heart rate (music 130 beats per minute). The music tempo was set using a CD player with a speed controller.

Table 2.

Objectives of Each Session of the Aerobic Dance-Training Program.

Session No	Training Program
Session 1+2	Be able to listen to music and integrate the notion of rhythm on different BPM and style-Detect the structure of a block (4 * 8) and identify the master down bit in intergroup game situations
Session 3+4	Identify the beginning and the end of the sentences by keeping the down beats (1. 3. 5. 7) and keep the leader foot right-Learn the walking steps on a BPM of 125–130 beats/min
Session 5	Reproduce a sequence A of 4 blocks of steps: Marching. Mambo. V step. Basic step (each of these dance elements was 32 counts in length ‘‘1 block’’) in synchronization with the rhythm- Name the steps and memorize the cueing’s on a BPM of 125–130 beats/min and keep the leading foot right
Session 6	Repeat a B sequence of 4 basic step blocks: Forward slap, lunge, knee step, kick
Session 7	Reproduce a C sequence of 4 basic step blocks: Step touch, heel step, side to side, A step (1 step for each block) in synchronization with the rhythm
Session 8	Creation of a sequence of 4 basic step blocks already learned (1 step for each block) in synchronization with the rhythm. in group work situation to be seen and judged on a BPM of 135–145 beats/min and right leader foot
Session 9	Repeat a sequence of 2 basic step blocks (marching, V step, knee step, double step touch) 2 * 8 for each step on a BPM of 135–145 beats/min while respecting the rhythm- Name the steps and reproduce the cueing’s
Session 10	Repeat a sequence of 2 basic step blocks (mambo, heel step, lunge, double step touch) 2 * 8 for each step on a BPM of 135–145 beats/min while respecting the rhythm
Session 11	Create a sequence of 2 blocks of basic steps already seen (2 * 8 for each step) in an intergroup situation to be seen and to judge (with observation grid) with the music on a BPM of 135–145 beats/min
Session 12	Take the sequences of 2 blocks created in session n ° 11 and cut them into 1 block (1 * 8 for each step)-Add arm movements and be in synchronization with the music on a BPM of 135–145 beats/min
Session 13+14	Create a new one-block sequence using another basic step (1 * 8 for each step) in synchronization with the rhythm to be seen and judged in an intergroup situation on a BPM of 135–145 beats/min
Session 15	Preparation of the show. To place oneself in different shapes (square; triangle; star; diagonal) (BPM 130 beats/min)
Session 16	Perform a short dance routine consisting of two blocks on a BPM of 130 beats/min with the right lead foot to be seen and judged

Note: 1 block = 32 counts in length; BPM = beats per minute; 2*8 = two aerobic dance sentence (16 counts).

Measurements

Cognitive flexibility: We measured the children’s cognitive flexibility with the 25 circles version of the Trail-Making Test (TMT; Lezak et al., 2004). Though Retian (1979) described this as an adult version of the TMT, it has been used in many child studies (Alesi et al., 2020; Elghoul et al., 2014; Howie et al., 2015; Ledochowski et al., 2019) and it has been shown to be a valid and appropriate test of cognitive flexibility for children (Howie et al., 2015; Lezak et al., 2004) with moderate test-retest reliability in children (Howie et al., 2015) and an intraclass correlation coefficient of .50–.60. The TMT is comprised of two parts, A and B, each consisting of 25 circles distributed on a sheet of paper (Reitan, 1979). In Part A, the circles are numbered 1–25, and participants were asked to draw lines to connect the numbers in ascending order (1-2-3 … 25). In Part B, the circles include both numbers (1–13) and letters (A–L), and participants were asked to connect the circles in an alternating ascending and alphabetical pattern (i.e., 1-A-2-B-3-C, etc.). Children were timed for when they completed the “trail.” Before the two-part test (TMT A and B), we administered a pre-test containing six example items to ensure that the instructions were correctly understood. The forms used for the paper and pencil task were those proposed in Reitan (1979). In both Parts A and B of the test, the participant was required to connect items as rapidly as possible without lifting the pencil from the paper (Lezak et al., 2004). Both the participants’ execution time and corrected errors were calculated, and we also calculated difference scores between TMT Part B-A (TMT_B-A) for both completed time and corrected errors as a measure of cognitive flexibility (Sánchez-Cubillo et al., 2009).

Inhibition: We used the Stroop test to measure cognitive inhibition. The Stroop test was found to be a valid measure of inhibition in children (Stroop, 1935), with good intra-tester reliability in children (van der Niet et al., 2016) and an intraclass correlation coefficient higher than .80. The children completed three reading stages (45 seconds each) in the following order: (a) children were asked to name a series of words written in black ink (Word card), (b) then children were asked to name the colors of colored rectangles (Color card), and (c) finally, children were asked to read the ink color in which words were written (e.g., the word “green” written in blue ink; Color-Word card). In all three stages, we scored the participants’ correctly mentioned words (corrected words) and corrected error numbers (Diamond, 2013).

Working Memory: We assessed working memory with the Digit Recall Test, a valid measure of working memory in children (Gathercole et al., 2003). The intra-tester reliability of this test when used with children was found to be moderate to good (Howie et al., 2015) with an intraclass correlation coefficient of .63–.80. Participants were given a list of three to seven numbers (e.g., 5, 7, 3, 9), and were then given five seconds to write them in chronological order. The Digit Recall score was the sum of the sequences processed that the participant answered correctly, adjusted according to the length of the sequence (Gathercole et al., 2003).

Statistical Analysis

We processed and analyzed data using STATISTICA 10 software (Fournet et al., 2019), and descriptive data were expressed as means (M), SDs (SD) and 95% confidence intervals (95% CI). We confirmed the normality and homogeneity of data distributions with by the Shapiro–Wilk test and Levene’s test, respectively, and we used an independent t-test to compare the participants’ age, height, and body weight across the two groups.

We analyzed group score differences on the TMT, Stroop and Digit Recall tests using a two-way mixed model repeated measures ANOVA [2 groups (EG/CG) × 2 testing times (pre-/post-intervention)] (group as a between factor, time as a within factor). We conducted a Bonferroni adjustment for multiple comparisons and calculated the effect size as partial ETA-squared (η²_p) to estimate the meaningfulness of significant findings, interpreting effect size according to Cohen (1988), with partial eta-squared values of 0.01 defined as small, 0.06 (medium) and 0.14 (large). We set the alpha level for statistical significance at p < 0.05.

Results

Participant characteristics on anthropometric variables for the two groups are presented in Table 1. Independent t-tests revealed no group significant differences in participants’ age, height, or body weight.

Cognitive Flexibiltiy

For TMT execution time, the two-way repeated measures ANOVA on TMT_B-A scores revealed a significant main effect for time of testing (i.e., pre-test to post-test) (F_(1,43) = 28.35, p < 0.001, η²_p = 0.39). The main effect for group was not significant, but there was a significant interaction between group and time (F_(1,43)= 12.15, p = 0.001, η²_p = 0.22) (Table 3). Post-hoc testing revealed that the TMT execution time was faster only for participants in the EG at post-test as compared with pre-test (p < 0.001) (Table 4). Moreover, post-hoc testing showed a significant time execution improvement from pre-test to post-test for the EG compared with the CG participants(p < 0.001) (Figure 1).

Table 3.

Summary of ANOVA Results of Participant Performances on EF Tests (TMT _B-A, Stroop Test and the Digit Recall Test).

	ANOVA
	Test Time (Pre vs. post)			Group (Pre vs. post)			Interaction (Group vs. Test time)
	F	p	η2	F	p	η2	F	p	η2
Stroop corrected word number	17.17	p< 0.001	0.28	1.58	p<0.21	0.03	15.80	p< 0.001	0.26
Stroop CE	8.80	p<0.01	0.17	1.13	p< 0.31	0.02	4.73	p< 0.05	0.10
TMT _B-A time	28.35	p< 0.001	0.39	2.08	p< 0.15	0.04	12.15	p< 0.001	0.22
TMT _B-A CE	43.32	p< 0.001	0.50	1.85	p< 0.18	0.04	17.44	p< 0.001	0.28
WM digits score	33.22	p< 0.001	0.43	4.22	p<.01	0.08	12.29	p<0.001	0.22

Note: CE: corrected the error; WM: working memory; EFs: executive functions; TMT _B-A: Trail-Making Test: difference between Trail-Making Test Part A and Part B

Table 4.

Summary of Means (SDs) and 95% Confidence Interval (95% CI) Values for EF Performance Measures (TMT _B-A, Stroop test and Digit Recall Test) at Pre- versus Post-Testing.

	EG (n=19)		CG (n=22)		Difference Means (95% CI)
	Pre-test	Post-test	Pre-test	Post-test	Post-test - Pre-test		EG – CG
	M (SD)(95% CI)	M (SD)(95% CI)	M (SD)(95% CI)	M (SD)(95% CI)	EG	CG	Pre-test	Post-test
Stroop corrected word number	25.84 (6.5)(22.70 to 28.97)	31.42 (5.87)(28.59 to 34.25)	26.48 (6.13)(24.10 to 29.12)	26.66 (3.8)(25.14 to 28.31)	5.57 *** (3.47 to 7.68)	0.11 (−1.68 to 1.91)	−0.77 (−4.63 to 3.08)	4.69 *** (1.75 to 7.63)
Stroop CE	1.68 (1.37)(1.02 to −2.34)	0.68 (0.74)(0.32 to 1.04)	1.57 (1.21)(1.07 to 2.07)	1.42 (1.18)(0.93 to 1.91)	−1 ** (−1.59 to −0.40)	−0.15 (−0.66 to 0.35)	0.10 (−0.68 to 0.897	−0.73 ** (−1.37 to −0.10)
TMT _B-A time	194.36 (73.99)(158.70 to 230.03)	116.73(57.06)(89.23 to 144.24)	186.84 (62.44)(161.62 to 212.06)	170.65(49.84)(150.52 to 190.78)	−77.63 *** (−104.64 to −50.62)	−16.19 (−39.28 to 6.86)	7.52 (−33.57 to 48.62)	−53.91 *** (−86.16 to −21.66)
TMT _B-A CE	12 (5.52)(9.33 to 14.66)	4.94 (4.70)(2.67 to 7.21)	11.15 (5.03)(9.12 to 13.18)	9.57 (5.07)(7.52 to 11.62)	−7.05 *** (−9.06 to −5.04)	−1.57 (−3.29 to 0.14)	0.846 (−2.34 to 4.03)	−4.63 *** (−7.62 to −1.63)
WM Digit Recall	4 (0.88)(3.57 to 4.42)	5.57 (1.12)(5.03 to 6.11)	4.23 (0.57)(3.99 to 4.46)	4.61 (0.69)(4.33 to 4.89)	1.57 *** (1.05 to 2.10)	0.38 (−0.06 to 0.83)	−0.23 (−0.67 to 0.21)	0.96 *** (0.41 to 1.51)

Note. CE: Corrected error; WM: Working memory; EG: experimental group; CG: control group; EFs: executive functions; TMT _B-A: Trail-Making Test: difference between the Trail-Making Test Part A and Part B. ***: significant difference p< 0.001, **: significant difference p< 0.01.

Figure 1.

TMT B-A Time Scores of EG and CG at Pre- and Post-Test. Note: *** = p< 0.001.

Concerning corrected errors for TMT_B-A, there was again a significant main effect for time (F_(1,43) = 43.32, p < 0.001, η²_p = 0.50) but no significant main effect for group. Again, there was a significant interaction between group and time (F_(1,43) = 17.44, p < 0.001, η²_p = 0.28) (Table 3). Post-hoc testing revealed a significant decrease in error scores for the EG (but not for CG) participants from pre-test to post-test (Table 4), and a there was a significant difference in improvement for the EG when compared to CG participants (p < .0001) (Figure 2).

Figure 2.

TMT B-A Corrected Error (CE) Scores of EG and CG at Pre- and Post-Test. Note: *** = p< 0.001.

Inhibition

On the Stroop Test, for the corrected word numbers, a two-way repeated measures ANOVA showed a significant main effect for testing time (F _(1.44) = 18.22, p < 0.001, η²_p = 0.29) but no significant main effect for group. The interaction between group and time was significant (F_(1.44) = 15.95, p < 0.001, η²_p=0.26) (Table 3). Post-hoc testing showed a significant increase in the corrected words numbered at post-testing versus pre-testing for the EG (but not for the CG) participants (p < 0.001) (Table 4). In addition, post-hoc testing showed a significant pre-test to post-test improvement for the EG, compared with CG participants (p < 0.001) (Figure 3).

Figure 3.

Stroop test Corrected Word Number Scores of EG and CG at Pre- and Post-Testing. Note: *** = p< 0.001.

For the corrected error numbers, the ANOVA showed a significant main effect for testing time (F _(1.44) = 9.05, p = 0.004, η²_p = 0.17), but no significant main effect for group. There was a significant interaction (group × time) (F_(1.44) = 4.98, p = 0.03, η²_p = 0.10) (Table 3). Post-hoc testing showed a significant decrease in the corrected errors at post-testing versus pre-testing for the EG, but not for the CG, participants (p < 0.01) (Table 4). Moreover, post-hoc testing revealed fewer corrected errors for the EG compared with the CG participants at post-testing compared to pre-testing (p < 0.01); the CG performance remained at the same level before and after the intervention (Figure 4).

Figure 4.

Stroop Test Corrected Error (CE) scores of EG and CG at Pre- and Post-Testing. Note: ** = p<0.01.

Working Memory

Concerning working memory as measured by the Digit Recall score, the repeated measures ANOVA revealed significant main effects for testing time (F_(1,44) = 36.58, p < 0.001, η²_p = 0.45) and group (F_(1,44) = 9.54, p < 0.01, η²_p = 0.17). There was also a significant (group × time) interaction effect (F_(1,44) = 12.41, p < 0.001, η²_p = 0.22) (Table 3). Post-hoc testing revealed a better performance for the EG, but not CG, participants at post-testing when compared to pre-testing (p < 0.01; Table 4). In addition, post-hoc testing showed significantly higher digit recall scores for the EG compared with the CG participants at post-testing (p < 0.01) (Figure 5).

Figure 5.

The Digit Recall Test Number of Digits Score of EG and CG at Pre- and Post-Testing. Note: *** = p< 0.001.

Discussion

We aimed, in this study, to examine the effects of an aerobic dance program on inhibition, working memory, and cognitive flexibility among children aged 9–11 years. Our data supported our hypothesis that participating in an eight week aerobic dance program during school-based PE classes (vs. participating in no PE) would enable improvement on these cognitive measures of EF. Cognitive flexibility was improved in terms of gains in time execution and corrected errors recorded on the TMT for the EG (but not the CG) participants. Positive changes in inhibition were demonstrated through improvements in corrected word numbers and corrected errors on the Stroop Test for EG, but not CG participants. Working memory as measured by the Digit Recall score was also enhanced after the aerobic dance program but not for participants in the control group. These findings are in line with previous reports that creative dance training significantly influenced EF in children (Yetti et al., 2019) and with findings that a street dance program significantly improved cognitive flexibility, inhibition and working memory in preschool children (Shen et al., 2020). Oppici et al. (2020) gave support in their finding that dance practice coupled with a high cognitive challenge improved working memory in children. A very recent study has also reported that an 8-week dance training improved inhibitory control and working memory in primary school children (Rudd et al., 2021), although the current study is the only evidence of the effect of an aerobic dance program, specifically, on working memory, inhibition and cognitive flexibility in children of primary school ages.

One of the main findings in our study was a significant improvement in cognitive flexibility as measured by both TMT_B-A execution time (p <0.001, η²_p= 0.39) and TMT_B-A corrected error (p <0.001, η²_p= 0.5). It is likely that the combination of aerobic dance steps, requiring practice, repetition, and cognitive effort within the dance routine facilitated this cognitive flexibility improvement. In addition, in our aerobic dance program, participants were asked to follow the instructor’s lead when changing dance elements and to synchronize the dance steps with the rhythm of the music.

Previous studies also demonstrated that music training can stimulate cortical activity (Moreno et al., 2009). Moreno opined that short-term music training for 6 months significantly improved behavior and changed the growth of synapses, showing that music is a means of fostering brain plasticity (Moreno et al., 2009). As noted above, Shen et al. (2020) also showed that street-dance training can improve children’s cognitive flexibility, and, similarly, Kosmat and Vranic (2017) found that elders with predispositions toward cognitive decline showed significantly better results in terms of their cognitive flexibility and their working memory after 10 weeks of classic dance, compared to elders who did not dance.

Regarding our finding of working memory improvement from the aerobic dance program, it is important to note that working memory is the process of storing and processing information (Shen et al., 2020). In our intervention, participants were asked to retain each of the dance movements they had performed within a long choreographic routine consisting of all four dance movements. When children were learning the movements, they needed to recall the name of the movement, its characteristics, its location in space, etc., and they had to store this information in the brain and call it up when they needed to use the movement. These processes likely contributed to their working memory gains on our Digit Recall test. Previous studies showed that dance provides continuous sensorimotor stimuli, including a variety of whole-body movements, and individuals must memorize and recall long movement sequences (Cortese & Rossi-Arnaud, 2010; Jola et al., 2013; Merom et al., 2013). Visuo-spatial working memory performance has been associated with frontal lobe brain activation (Klingberg et al., 2002; van Ewijk et al., 2015). A recent study showed that PA, in general, could improve EFs, attentional resources, and information processing by improving white matter integrity in the frontal lobes (Meijer et al., 2020). Importantly, our results agree with these earlier studies suggesting that dance can improve working memory capacity (Diamond & Ling, 2016; Eggenberger et al., 2015; Tomporowski & Pesce, 2019).

Regarding our participant’ gains in inhibition as measured by Stroop corrected word numbers and corrected error numbers, inhibition has been seen as the first domain of executive functioning to appear during childhood, showing its most significant development at 6–10 years, and it may be most sensitive to environmental factors around the age of 6–12 years (Jurado & Rosselli, 2007). As inhibition has also been linked to working memory (Davidson et al., 2006), it was not surprising that we found gains in both inhibition and working memory, as have others (Hillman et al., 2014; Kamijo et al., 2011; Shen et al., 2020). In aerobic dance training, children must inhibit some motor movements while engaging in others. As noted above, neuro-imaging studies have observed a relationship between PA generally and gains in inhibition, as mediated by improved integrity of frontal lobe white matter structures (Bellgrove et al., 2004; Botvinick et al., 2004; Lin et al., 2014; Voss et al., 2013).

Limitations and Directions for Future Research

Among limitations in this study that should be addressed in future research, our CG followed their normal routine during school without practicing any PE sessions, meaning that we cannot be certain that the relative gains in our EG were due specifically to the aerobic dance program versus general PA and PE instruction that occurs in other PE programs. Also, we requested that all participants minimize their PA outside of school, perhaps exaggerating the difference between an active EG and a sedentary CG. Of course, there was no control (beyond this simple request) over participants’ actual PA behavior outside of school. It would be interesting to conduct neuro-imaging assessments before and after the dance intervention to affirm or disaffirm the assumption that this activity directly affected white matter integrity in the frontal lobes. Other cognitive variables and measures of EF might also be assessed in future studies (e.g., selective attention, planning, time sense, and others) to determine whether aerobic dance benefits extend to these EFs and better design and integrate aerobic dance activity into PE curricula. Additionally, a post-intervention retention test might be used to test persistence of these gains over time. Finally, consideration should be given to the effect of training at a specific time of day on physical (Souissi et al., 2012) and cognitive (Elghoul et al., 2014) performances, particularly as some research as suggested that exercise early in the day may have the greatest cognitive benefits for academic functioning through the day, at least for adolescents (Mezcua-Hidalgo et al., 2019).

Conclusion

In the present study, we showed that an aerobic dance program spanning eight weeks at the rate of two sessions per week is likely to improve aspects of executive functioning (cognitive flexibility, inhibition, and working memory) in primary school children aged 9–11 years. These results suggest that it is important to integrate dance activities of this type into the school PE curricula, particularly because an aerobic dance program is easy for teachers to design and implement within a PE class (Rokka et al., 2019). Such a program is apt to improve to primary school children’s general PA with the added benefit of enhancing their EF.

Footnotes

Acknowledgements

We would like to thank the principal of the school, all the children who participated in this study, and the university teacher who participated in the design of our intervention program.

Author contributions

Conception or design of the work: Khawla Zinelabidine, Yousri Elghoul and Sonia SahliData collection: Khawla ZinelabidineData analysis and interpretation: Khawla Zinelabidine and Ghada JouiraDrafting the article: Khawla Zinelabidine and Yousri ElghoulFinal approval of the version to be submitted: Sonia Sahli

Declaration of Conflicting Interests

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

Funding

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

Ethical Approval

A signed consent form was obtained from the participants themselves or their parents/guardians. The present study was conducted according to the Declaration of Helsinki and the protocol was fully approved by the local Ethics Committee.Children gave their assent to participate in this study, and we obtained written consent from their parents or legal guardians. The study was conducted according to the Declaration of Helsinki, and the protocol was fully approved by the local Ethics Committee.

ORCID iDs

Khawla Zinelabidine

Yousri Elghoul

Author Biographies

Khawla zinelabidine is a PhD student at the High Institute of Sports and Physical Education of Sfax, University of Sfax, Tunisia. Her main field is executive function and motor exercise. She has experience working with children on the topic of physical activity.

Yousri Elghoul is currently an assistant professor at the High Institute of Sports and Physical Education of Sfax, University of Sfax, Tunisia. His main field is motor behavior and the motor learning process. He has experience working with young populations on the topic of task difficulty perception, physical activity, and relative cognitive changes during practice and learning.

Ghada jouira is currently a Doctor in sciences of physical activities and sports. Her main field is postural balance in athletes with intellectual disabilities, biomechanics and sport injuries.

Sonia sahli is an associate professor and a director of Research Laboratory Education, Motricité, Sport et Santé (EM2S) LR19JS01 High Institute of Sport and Physical Education of Sfax, University of Sfax, Tunisia. Her main field is postural balance, biomechanics and physical activity.

References

Abdelkarim

Ammar

Chtourou

Wagner

Knisel

Hökelmann

Bös

(2017). Relationship between motor and cognitive learning abilities among primary school-aged children. Alexandria Journal of Medicine, 53(4), 325–331. https://doi.org/10.1016/j.ajme.2016.12.004

Alesi

Giordano

Giaccone

Basile

Costa

Bianco

(2020). Effects of the enriched sports activities-program on executive functions in Italian children. Journal of Functional Morphology and Kinesiology, 5(2), 26. https://doi.org/10.3390/jfmk5020026

Anderson

(2002). Assessment and development of executive function (EF) during childhood. Child Neuropsychology, 8(2), 71–82. https://doi.org/10.1076/chin.8.2.71.8724

Bellgrove

M. A.

Hester

Garavan

(2004). The functional neuroanatomical correlates of response variability: Evidence from a response inhibition task. Neuropsychologia, 42(14), 1910–1916. https://doi.org/10.1016/j.neuropsychologia.2004.05.007

Benítez‐Porres

López‐Fernández

Raya

J. F.

Álvarez Carnero

Alvero‐Cruz

J. R.

Álvarez Carnero

(2016). Reliability and validity of the PAQ‐C questionnaire to assess physical activity in children. Journal of School Health, 86(9), 677–685. https://doi.org/10.1111/josh.12418

Best

(2010). Effects of physical activity on children’s executive function: Contributions of experimental research on aerobic exercise. Developmental Review, 30(4), 331–351. https://doi.org/10.1016/j.dr.2010.08.001

Bidzan-Bluma

Lipowska

(2018). Physical activity and cognitive functioning of children: A systematic review. International Journal of Environmental Research and Public Health, 15(4), 800. https://doi.org/10.3390/ijerph15040800

Biederman

Monuteaux

M. C.

Doyle

A. E.

Seidman

L. J.

Wilens

T. E.

Ferrero

Morgan

C. L.

Faraone

S. V.

(2004). Impact of executive function deficits and attention-deficit/hyperactivity disorder (ADHD) on academic outcomes in children. Journal of Consulting and Clinical Psychology, 72(5), 757–766. https://doi.org/10.1037/0022-006X.72.5.757

Botvinick

M. M.

Cohen

J. D.

Carter

C. S.

(2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8(12), 539–546. https://doi.org/10.1016/j.tics.2004.10.003

10.

B. Owen

Smith

Lubans

D. R.

J. Y. Y.

Lonsdale

(2014). Self-determined motivation and physical activity in children and adolescents: A systematic review and meta-analysis. Preventive Medicine, 67, 270–279. https://doi.org/10.1016/j.ypmed.2014.07.033.

11.

Ciomag

R.-V.

Dinciu

C.-C.

(2013). Aerobics - modern trend in the university educational domain. Procedia - Social and Behavioral Sciences, 92(2013), 251–258. https://doi.org/10.1016/j.sbspro.2013.08.668

12.

Clark

Prior

Kinsella

(2002). The relationship between executive function abilities, adaptive behaviour, and academic achievement in children with externalising behaviour problems. Journal of Child Psychology and Psychiatry, 43(6), 785–796. https://doi.org/10.1111/1469-7610.00084

13.

Cortese

Rossi-Arnaud

(2010). Working memory for ballet moves and spatial locations in professional ballet dancers. Applied Cognitive Psychology, 24(2), 266–286. https://doi.org/10.1002/acp.1593

14.

Cosnefroy

(2010). L’apprentissage autorégulé : Perspectives en formation d’adultes. Savoirs, 23(2), 9–50. https://doi.org/10.3917/savo.023.0009

15.

Crocker

P. R. E.

Bailey

D. A.

Faulkner

R. A.

Kowalski

K. C.

McGrath

(1997). Measuring General Levels of Physical activity: Preliminary evidence for the Physical Activity Questionnaire for Older Children. Medicine and Science in Sports and Exercise, 29(10), 1344–1349. https://doi.org/10.1097/00005768-199710000-00011

16.

Davidson

M. C.

Amso

Anderson

L. C.

Diamond

(2006). Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia, 44(11), 2037–2078. https://doi.org/10.1016/j.neuropsychologia.2006.02.006

17.

de Bruin

Eggenberger

Schumacher

Angst

Theill

(2015). Does multicomponent physical exercise with simultaneous cognitive training boost cognitive performance in older adults? A 6-month randomized controlled trial with a 1-year follow-up. Clinical Interventions in Aging, 10, 1335–1349. https://doi.org/10.2147/CIA.S87732

18.

de Greeff

J. W.

Bosker

R. J.

Oosterlaan

Visscher

Hartman

(2018). Effects of physical activity on executive functions, attention and academic performance in preadolescent children: A meta-analysis. Journal of Science and Medicine in Sport, 21(5), 501–507. https://doi.org/10.1016/j.jsams.2017.09.595

19.

Diamond

(2000). Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development, 71(1), 44–56. https://doi.org/10.1111/1467-8624.00117

20.

Diamond

(2013). Executive functions. Annual Review of Psychology, 64(01), 135–168. https://doi.org/10.1146/annurev-psych-113011-143750

21.

Diamond

Ling

D. S.

(2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48. https://doi.org/10.1016/j.dcn.2015.11.005

22.

Drygas

Skiba

Bielecki

Puska

(2001). Physical activity estimation among the inhabitants of six European countries Project “Bridging East-West Health Gap”. Med Sportiva, 5(Suppl 2), 119–128.

23.

Elghoul

Frikha

Masmoudi

Chtourou

Chaouachi

Chamari

Souissi

(2014). Diurnal variation of cognitive performance and perceived difficulty in dart-throwing performance in 9–10-year-old boys. Biological Rhythm Research, 45(5), 789–801. https://doi.org/10.1080/09291016.2014.921409

24.

Ellis

L. K.

Rothbart

M. K.

Posner

M. I.

(2004). Individual differences in executive attention predict self-regulation and adolescent psychosocial behaviors. Annals of the New York Academy of Sciences, 1021(1), 337–340. https://doi.org/10.1196/annals.1308.041

25.

Faul

Erdfelder

Lang

A.-G.

Buchner

(2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175–191.

26.

Fournet

Achachi

Roy

Besnard

Lancelot

Le Gall

Bouvard

(2019). Impaired executive function in everyday life: a predictor of OCD relapse? Journal of Behavioral and Brain Science, 09(3), 90–107. https://doi.org/10.4236/jbbs.2019.93008

27.

Gathercole

S. E.

Brown

Pickering

S. J.

(2003). Working memory assessments at school entry as longitudinal predictors of national curriculum attainment levels. Educational and Child Psychology, 20(3), 109–122.

28.

Giguere

(2011). Dancing thoughts: An examination of children’s cognition and creative process in dance. Research in Dance Education, 12(1), 5–28. https://doi.org/10.1080/14647893.2011.554975

29.

Hillman

C. H.

Castelli

D. M.

Buck

S. M.

(2005). Aerobic fitness and neurocognitive function in healthy preadolescent children. Medicine and Science in Sports and Exercise, 37(11), 1967–1974. https://doi.org/10.1249/01.mss.0000176680.79702.ce

30.

Hillman

C. H.

Pontifex

M. B.

Castelli

D. M.

Khan

N. A.

Raine

L. B.

Scudder

M. R.

Drollette

E. S.

Moore

R. D.

C.-T.

Kamijo

(2014). Effects of the FITKids randomized controlled trial on executive control and brain function. Pediatrics, 134(4), e1063–e1071. https://doi.org/10.1542/peds.2013-3219

31.

Howie

E. K.

Schatz

Pate

R. R.

(2015). Acute effects of classroom exercise breaks on executive function and math performance: a dose-response study. Research Quarterly for Exercise and Sport, 86(3), 217–224. https://doi.org/10.1080/02701367.2015.1039892

32.

Iermakov

Podrigalo

Alekseev

Rovnaya

(2016). Studying of interconnections of morphological functional indicators of students, who practice martial arts. Physical education of students, 20(1), 64–70. https://doi.org/10.15561/20755279.2016.0109

33.

Jaakkola

Liukkonen

Laakso

Ommundsen

(2008). The relationship between situational and contextual self-determined motivation and physical activity intensity as measured by heart rates during ninth grade students’ physical education classes. European Physical Education Review, 14(1), 13–31. https://doi.org/10.1177/1356336X07085707

34.

Jola

McAleer

Grosbras

M.-H.

Love

S. A.

Morison

Pollick

F. E.

(2013). Uni- and multisensory brain areas are synchronised across spectators when watching unedited dance recordings. I-Perception, 4(4), 265–284. https://doi.org/10.1068/i0536

35.

Jurado

M. B.

Rosselli

(2007). The elusive nature of executive functions: A review of our current understanding. Neuropsychology Review, 17(3), 213–233. https://doi.org/10.1007/s11065-007-9040-z

36.

Kamijo

Pontifex

M. B.

O’Leary

K. C.

Scudder

M. R.

C.-T.

Castelli

D. M.

Hillman

C. H.

(2011). The effects of an afterschool physical activity program on working memory in preadolescent children. Developmental Science, 14(5), 1046–1058. https://doi.org/10.1111/j.1467-7687.2011.01054.x

37.

Kattenstroth

J.-C.

Kalisch

Holt

Tegenthoff

Dinse

H. R.

(2013). Six months of dance intervention enhances postural, sensorimotor, and cognitive performance in elderly without affecting cardio-respiratory functions. Frontiers in Aging Neuroscience, 5, 5. https://doi.org/10.3389/fnagi.2013.00005.

38.

Kaya

Çamligüney

Ramazanoǧlu

Aldemir

(2011). The effects of dance education on motor performance of children. Educational Research and Reviews, 16(19), 979–982. https://doi.org/10.5897/ERR11.179

39.

Kimura

Hozumi

(2012). Investigating the acute effect of an aerobic dance exercise program on neuro-cognitive function in the elderly. Psychology of Sport and Exercise, 13(5), 623–629. https://doi.org/10.1016/j.psychsport.2012.04.001

40.

Klingberg

Forssberg

Westerberg

(2002). Increased brain activity in frontal and parietal cortex underlies the development of visuospatial working memory capacity during childhood. Journal of Cognitive Neuroscience, 14(1), 1–10. https://doi.org/10.1162/089892902317205276

41.

Kosmat

Vranic

(2017). The efficacy of a dance intervention as cognitive training for the old-old. Journal of Aging and Physical Activity, 25(1), 32–40. https://doi.org/10.1123/japa.2015-0264

42.

Kowalski

K. C.

Crocker

P. R.

Donen

R. M.

(2004). The physical activity questionnaire for older children (PAQ-C) and adolescents (PAQ-A) manual. College of Kinesiology, University of Saskatchewan, 87(1), 1–38.

43.

Kowalski

K. C.

Crocker

P. R. E.

Faulkner

R. A.

(1997). Validation of the physical activity questionnaire for older children. Pediatric Exercise Science, 9(2), 174–186. https://doi.org/10.1123/pes.9.2.174

44.

Kremenitzer

J. P.

(1990). Aerobic fitness dancing in the elementary schools. Journal of Physical Education, Recreation & Dance, 61(6), 89–90. https://doi.org/10.1080/07303084.1990.10604562

45.

Kulinna

P. H.

Stylianou

Dyson

Banville

Dryden

Colby

(2018). The effect of an authentic acute physical education session of dance on elementary students’ selective attention. Biomed Research International, 2018, 1–8. https://doi.org/10.1155/2018/8790283

46.

Ledochowski

Andrade

B. F.

Toplak

M. E.

(2019). A novel unstructured performance-based task of executive function in children with attention-deficit/hyperactivity disorder. Journal of Clinical and Experimental Neuropsychology, 41(5), 445–459. https://doi.org/10.1080/13803395.2019.1567694

47.

Lezak

M. D.

Howieson

D. B.

Loring

D. W.

Fischer

J. S.

(2004). Neuropsychological assessment: Oxford University Press.

48.

Lin

H.-Y.

Gau

S. S.-F.

Huang-Gu

S. L.

Shang

C.-Y.

Y.-H.

Tseng

W.-Y. I.

(2014). Neural substrates of behavioral variability in attention deficit hyperactivity disorder: Based on ex-gaussian reaction time distribution and diffusion spectrum imaging tractography. Psychological Medicine, 44(8), 1751–1764. https://doi.org/10.1017/S0033291713001955

49.

Mavridis

Filippou

Rokka

Bousiou

Mavridis

(2004). The effect of a health-related aerobic dance program on elementary school children: Teviot Scientific. https://doi.org/10.1111/j.1750-8606.2011.00191.x

50.

McClelland

M. M.

Cameron

C. E.

(2012). Self-regulation in early childhood: improving conceptual clarity and developing ecologically valid measures. Child Development Perspectives, 6(2), 136–142. https://doi.org/10.1111/j.1750-8606.2011.00191.x

51.

Meijer

Königs

Bruijn

A. G. M.

Visscher

Bosker

R. J.

Hartman

Oosterlaan

(2020). Cardiovascular fitness and executive functioning in primary school‐aged children. Developmental Science, 24, e13019. https://doi.org/10.1111/desc.13019

52.

Merom

Cumming

Mathieu

Anstey

K. J.

Rissel

Simpson

J. M.

Morton

R. L.

Cerin

Sherrington

Lord

S. R.

(2013). Can social dancing prevent falls in older adults? A protocol of the dance, aging, cognition, economics (DAnCE) fall prevention randomised controlled trial. BMC Public Health, 13(1), 1–9. https://doi.org/10.1186/1471-2458-13-477

53.

Metcalf

B. S.

Hosking

Jeffery

A. N.

Voss

L. D.

Henley

Wilkin

T. J.

(2011). Fatness leads to inactivity, but inactivity does not lead to fatness: A longitudinal study in children (EarlyBird 45). Archives of Disease in Childhood, 96(10), 942–947. https://doi.org/10.1136/adc.2009.175927

54.

Mezcua-Hidalgo

Ruiz-Ariza

Suárez-Manzano

Martínez-López

E. J.

(2019). 48-hour effects of monitored cooperative high-intensity interval training on adolescent cognitive functioning. Perceptual and Motor Skills, 126(2), 202–222. https://doi.org/10.1177/0031512518825197

55.

Moore

J. B.

Hanes

J. C.

Barbeau

Gutin

Treviño

R. P.

Yin

(2007). Validation of the physical activity questionnaire for older children in children of different races. Pediatric Exercise Science, 19(1), 6–19. https://doi.org/10.1123/pes.19.1.6

56.

Moreno

Marques

Santos

Castro

S. L.

Besson

(2009). Musical training influences linguistic abilities in 8-year-old children: More evidence for brain plasticity. Cerebral Cortex, 19(3), 712–723. https://doi.org/10.1093/cercor/bhn120

57.

Murrock

C. J.

Graor

C. H.

(2014). Effects of dance on depression, physical function, and disability in underserved adults. Journal of Aging and Physical Activity, 22(3), 380–385. https://doi.org/10.1123/japa.2013-0003

58.

Myer

G. D.

Faigenbaum

A. D.

Edwards

N. M.

Clark

J. F.

Best

T. M.

Sallis

R. E.

(2015). Sixty minutes of what? A developing brain perspective for activating children with an integrative exercise approach. British Journal of Sports Medicine, 49(23), 1510–1516. https://doi.org/10.1136/bjsports-2014-093661

59.

Norouzi

Hosseini

Vaezmosavi

Gerber

Pühse

Brand

(2019). Zumba dancing and aerobic exercise can improve working memory, motor function, and depressive symptoms in female patients with fibromyalgia. European journal of sport science, 20, 981–991. https://doi.org/10.1080/17461391.2019.1683610

60.

Oppici

Rudd

Buszard

Spittle

(2020101675). Efficacy of a 7-week dance (RCT) PE curriculum with different teaching pedagogies and levels of cognitive challenge to improve working memory capacity and motor competence in 8–10 years old children. Psychology of Sport and Exercise, 50(2020), 101675. https://doi.org/10.1016/j.psychsport.2020.101675

61.

Pantelić

Kostić

Mikalački

Đurašković

Čokorilo

Mladenović

(2007). The effects of a recreational aerobic exercise model on the functional abilities of women. Facta Universitatis-Series: Physical Education and Sport, 5(1), 19–35.

62.

Papaioannou

Marsh

H. W.

Theodorakis

(2004). A multilevel approach to motivational climate in physical education and sport settings: an individual or a group level construct? Journal of Sport & Exercise Psychology, 26(1), 90–118. https://doi.org/10.1123/jsep.26.1.90

63.

Predovan

Julien

Esmail

Bherer

(2019). Effects of dancing on cognition in healthy older adults: a systematic review. Journal of Cognitive Enhancement, 3(2), 161–167. https://doi.org/10.1007/s41465-018-0103-2

64.

Raver

C. C.

Jones

S. M.

Li-Grining

Zhai

Bub

Pressler

(2011). CSRP’s impact on low-income preschoolers’ preacademic skills: Self-regulation as a mediating mechanism. Child Development, 82(1), 362–378. https://doi.org/10.1111/j.1467-8624.2010.01561.x

65.

Reader

M. J.

Harris

E. L.

Schuerholz

L. J.

Denckla

M. B.

(1994). Attention deficit hyperactivity disorder and executive dysfunction. Developmental Neuropsychology, 10(4), 493–512. https://doi.org/10.1080/87565649409540598

66.

Reitan

(1979). Manual for administration of neuropsychological test batteries for adults and children: Reitan Neuropsychology Laboratory

67.

Rokka

Kouli

Bebetsos

Goulimaris

Mavridis

(2019). Effect of dance aerobic programs on intrinsic motivation and perceived task climate in secondary school students. International Journal of Instruction, 12(1), 641–654. https://doi.org/10.29333/iji.2019.12141a

68.

Rudd

Buszard

Spittle

O'Callaghan

Oppici

(2021). Comparing the efficacy (RCT) of learning a dance choreography and practicing creative dance on improving executive functions and motor competence in 6-7 years old children. Psychology of Sport and Exercise, 53, 101846. https://doi.org/10.1016/j.psychsport.2020.101846

69.

Sánchez-cubillo

Periáñez

J. A.

Adrover-Roig

Rodríguez-sánchez

J. M.

RÍOS-LAGO

Tirapu

Barceló

(2009). Construct validity of the trail making test: role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. Journal of the International Neuropsychological Society, 15(3), 438–450. https://doi.org/10.1017/S1355617709090626

70.

Schmidt

Jäger

Egger

Roebers

C. M.

Conzelmann

(2015). Cognitively engaging chronic physical activity, but not aerobic exercise, affects executive functions in primary school children: A group-randomized controlled trial. Journal of Sport & Exercise Psychology, 37(6), 575–591. https://doi.org/10.1123/jsep.2015-0069

71.

Shen

Zhao

Huang

Liu

Fang

(2020). Promotion of street-dance training on the executive function in preschool children. Frontiers in Psychology, 11(2817). https://doi.org/10.3389/fpsyg.2020.585598

72.

Sibley

B. A.

Etnier

J. L.

(2003). The relationship between physical activity and cognition in children: a meta-analysis. Pediatric Exercise Science, 15(3), 243–256. https://doi.org/10.1515/ijsl.2000.143.183

73.

Souissi

Chtourou

Chaouachi

Dogui

Chamari

Souissi

Amri

(2012). The effect of training at a specific time-of-day on the diurnal variations of short-term exercise performances in 10- to 11-year-old boys. Pediatric Exercise Science, 24(1), 84–99. https://doi.org/10.1123/pes.24.1.84

74.

Stevens

M. C.

Skudlarski

Pearlson

G. D.

Calhoun

V. D.

(2009). Age-related cognitive gains are mediated by the effects of white matter development on brain network integration. Neuroimage, 48(4), 738–746. https://doi.org/10.1016/j.neuroimage.2009.06.065

75.

Stroop

J. R.

(1935). Studies of interference in serial verbal reactions. Journal of experimental psychology, 18(6), 643–662. https://doi.org/10.1037/h0054651

76.

Tapert

S. F.

Baratta

M. V.

Abrantes

A. M.

Brown

S. A.

(2002). Attention dysfunction predicts substance involvement in community youths. Journal of the American Academy of Child and Adolescent Psychiatry, 41(6), 680–686. https://doi.org/10.1097/00004583-200206000-00007

77.

Tomporowski

P. D.

Pesce

(2019). Exercise, sports, and performance arts benefit cognition via a common process. Psychological Bulletin, 145(9), 929–951. https://doi.org/10.1037/bul0000200

78.

Ursache

Blair

Raver

C. C.

(2012). The promotion of self-regulation as a means of enhancing school readiness and early achievement in children at risk for school failure. Child Development Perspectives, 6(2), 122–128. https://doi.org/10.1111/j.1750-8606.2011.00209.x

79.

van den Berg

Saliasi

de Groot

R. H. M.

Chinapaw

M. J. M.

Singh

A. S.

(2019). Improving cognitive performance of 9-12 Years old children: just dance? A randomized controlled trial. Frontiers in Psychology, 10, 174. https://doi.org/10.3389/fpsyg.2019.00174

80.

van der Niet

A. G.

Smith

Oosterlaan

Scherder

E. J. A.

Hartman

Visscher

(2016). Effects of a cognitively demanding aerobic intervention during recess on children’s physical fitness and executive functioning. Pediatric Exercise Science, 28(1), 64–70. https://doi.org/10.1123/pes.2015-0084

81.

van Ewijk

Weeda

W. D.

Heslenfeld

D. J.

Luman

Hartman

C. A.

Hoekstra

P. J.

, et al (2015). Neural correlates of visuospatial working memory in attention-deficit/hyperactivity disorder and healthy controls. Psychiatry Research: Neuroimaging, 233(2), 233–242. https://doi.org/10.1016/j.pscychresns.2015.07.003

82.

Voss

M. W.

Heo

Prakash

R. S.

Erickson

K. I.

Alves

Chaddock

, et al (2013). The influence of aerobic fitness on cerebral white matter integrity and cognitive function in older adults: results of a one-year exercise intervention. Human Brain Mapping, 34(11), 2972–2985. https://doi.org/10.1002/hbm.22119

83.

Yetti

Syarah

E. S.

Pramitasari

Syarfina

Susanti

(2019). The influence of the dance creativity on executive functions of early childhood. cognitive Development, 14, 15. https://doi.org/10.2991/icade-18.2019.59.

84.

Yli-Piipari

(2011). The development of students ́physical education motivation and physical activity: A 3.5-year longitudinal study across grades 6 to 9. University of Jyväskylä.