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Cognitively challenging physical activity benefitsexecutive function in overweight childrenClaudia Crova a , Ilaria Struzzolino a , Rosalba Marchetti a , Ilaria Masci a , GiuseppeVannozzi a , Roberta Forte a & Caterina Pesce aa Department of Human Motion and Sport Science , Italian University Sport and Movement ,Rome , ItalyPublished online: 09 Sep 2013.

To cite this article: Claudia Crova , Ilaria Struzzolino , Rosalba Marchetti , Ilaria Masci , Giuseppe Vannozzi , Roberta Forte& Caterina Pesce , Journal of Sports Sciences (2013): Cognitively challenging physical activity benefits executive function inoverweight children, Journal of Sports Sciences, DOI: 10.1080/02640414.2013.828849

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Cognitively challenging physical activity benefits executive function inoverweight children

CLAUDIA CROVA, ILARIA STRUZZOLINO, ROSALBA MARCHETTI, ILARIA MASCI,GIUSEPPE VANNOZZI, ROBERTA FORTE, & CATERINA PESCE

Department of Human Motion and Sport Science, Italian University Sport and Movement, Rome, Italy

(Accepted 15 July 2013)

AbstractThis study tested the association between aerobic fitness and executive function and the impact of enhanced, cognitivelychallenging physical activity on executive function in overweight and lean children. Seventy children aged 9–10 years wereassigned to either a 6-month enhanced physical education programme including cognitively demanding (open skill)activities or curricular physical education only. Pre- and post-intervention tests assessed aerobic capacity (Leger test) andtwo components of executive function: inhibition and working memory updating (random number generation task). Indicesof inhibition and memory updating were compared in higher- and lower-fit children and intervention effects were evaluatedas a function of physical activity programme (enhanced vs. curricular) and weight status (lean vs. overweight). Resultsshowed better inhibition in higher- than lower-fit children, extending the existing evidence of the association betweenaerobic fitness and executive function to new aspects of children’s inhibitory ability. Overweight children had morepronounced pre- to post-intervention improvements in inhibition than lean children only if involved in enhanced physicaleducation. Such intervention effects were not mediated by aerobic fitness gains. Therefore, the cognitive and socialinteraction challenges inherent in open skill tasks, even though embedded in a low-dose physical activity programme,may represent an effective means to promote cognitive efficiency, especially in overweight children.

Keywords: cognition, executive function, body weight, aerobic fitness, open skill

Introduction

Association of children’s cognition with weight status andphysical fitness

The prevalence of childhood overweight and obesitycontinues to rise, year after year, in many developedcountries. In Europe, it is as high as 35% amongschool-age children (Jackson-Leach & Lobstein,2006), and in the United States, the prevalencerates have tripled in the past 30 years (Lytle, 2012).Overweight and obese children are at a higher risk ofexperiencing adverse health-related outcomes in theshort and long term, both in the physical and mentaldomains, such as cardiovascular disease, type 2 dia-betes and poor academic achievement (WHO,2013).

While some studies showed no relation betweenelevated body mass index (BMI) and various cogni-tive performances in children and adolescents (e.g.Gunstadt et al., 2008), several other studies demon-strated that overweight or obesity is associated withpoor cognitive performance (e.g. Shore et al., 2008).

It is still an issue of debate if overweight is indepen-dently associated with decreased cognitive perfor-mance in children (Li, Dai, Jackson, & Zhang,2008), or this negative association is covariationalin nature or mediated by other factors. Cognitivedifferences between overweight and lean childrenseem to be explained by socio-environmental con-founders (Datar, Sturm, & Magnabosco, 2004) andmay also depend on low global self-worth in over-weight children (Franklin, Denyer, Steinbeck,Caterson, & Hill, 2006). Mediational chains maybe inferred from piecemeal evidence on the associa-tions of overweight with inactivity, inactivity with lowphysical fitness and the latter with poor cognition. Inyouth, overweight has been proved to be a markerand a predictor of chronic inactivity (Metcalf et al.,2010), and low physical activity (PA) levels havebeen proved to be associated with low aerobic fitness(Aires et al., 2011).

The linkage between aerobic fitness and cognitionhas been demonstrated by a growing body ofresearch with children and adolescents (Fedewa &

Correspondence: Caterina Pesce, Department of Human Motion and Sport Science, Italian University Sport and Movement, Rome, Italy. E-mail: caterina.pesce@uniroma4.it

Journal of Sports Sciences, 2013http://dx.doi.org/10.1080/02640414.2013.828849

© 2013 Taylor & Francis

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Ahn, 2011; Hillman, Erickson, & Kramer, 2008;Tomporowski, Davis, Miller, & Naglieri, 2008).Low-fit children, as compared to high-fit ones, havedifferences at brain structural and cognitive func-tional levels, including memory flexibility associatedwith smaller hippocampal volume (Chaddock et al.,2010; Chaddock, Hillman, Buck, & Cohen, 2011),lower efficiency of cognitive functions responsiblefor cognitive and action control (Pontifex et al.,2011; Stroth et al., 2009) and poorer academic per-formances (Castelli, Hillman, Buck, & Erwin, 2007;Wittberg, Northrup, Cottrell, & Davis, 2010).

Chronic exercise effects on children’s executive function

Growing evidence from intervention studies hasshown PA benefits on relevant dimensions of chil-dren’s mental health (Biddle & Asare, 2011), includ-ing cognitive functioning (Fedewa & Ahn, 2011;Tomporowski, Lambourne, & Okumura, 2011).Particularly, PA improves executive functions, thehigher level cognitive control functions (Diamond& Lee, 2011; Tomporowski et al., 2008), whosedevelopment is central to both cognitive and emo-tional self-regulation and, therefore, closely linked togoal-oriented behaviours, school readiness and suc-cess (Blair & Diamond, 2008).

While there is general consensus that PA maybenefit cognition, findings concerning the cognitiveoutcomes of programmes of enhanced school-basedPA are inconsistent (Tomporowski et al., 2008) andthe search for potential mediators and moderatorsacting on the exercise–cognition relationship is stillongoing. A working model including mediators andmoderators has been developed within gerontologi-cal research and subsequently extended to develop-mental research (Tomporowski et al., 2011). Itproposes that not only physical fitness, but also psy-chosocial and health factors, including overweightstatus, may play a relevant mediating or moderatingrole. Davis et al. (2007, 2011) performed chronicexercise studies on overweight children and foundselective effects of PA on executive functions, sinceonly one measure of executive function (i.e. plan-ning), and no other executive and nonexecutive cog-nitive performance measures responded to exercisetraining. Moreover, only high-dose and not low-doseexercise elicited significant cognitive benefits ascompared to no-exercise. The authors suggestedthat there might be a threshold effect, but without acausal chain involving changes in weight status andfitness, since they found a lack of change in degree ofoverweight and similar improvements in aerobic fit-ness in the two exercise groups. In their conclusions,they proposed the existence of a direct path betweenexercise and cognitive performance due to neuralstimulation by movement (Davis et al., 2011).

Tomporowski et al. (2008) first addressed thepotential role played by engagement in goal-directedmovement actions involvingmental effort. Best (2010)further developed this issue proposing that there maybe at least two pathways, beyond the metabolic char-acteristics of exercise, by which PA impacts high-levelcognition: the cognitive effort required to performcomplex movements and the cognitive demandsinherent in active games and sport actions. Whilesome acute exercise studies support this view (e.g.Pesce, Crova, Cereatti, Casella, & Bellucci, 2009),others do not (e.g. Best, 2012) and it still remains tobe tested if cognitive engagement is a relevant factorfor chronic exercise. Recently, it has been claimed thatboth mental and physical activities, especially aerobictraining can influence the process of neurogenesis andsuggested that their combination is more beneficial formental health than either training alone. The additivebenefits of physical andmental training seem to be dueto different mechanisms incresasing the number ofcells that mature and survive as functional neurons,respectively (Curlick & Shors, 2012). PA interventiontypes used in exercise–cognition studies with childrenare heterogeneous (e.g. aerobic resistance, perceptual–motor training or their combination; see the review byFedewa & Ahn, 2011) and presumably involve differ-ent levels of cognitive engagement. Nevertheless, noneof those studies have verified whether movement andsport activities joining physical and cognitive chal-lenges may determine more pronounced changes incognitive functioning than physical exercise per se.

The aim of the present study was twofold. First,we examined the association between children’s phy-sical fitness and two executive function subfamilies,inhibition of mental routines and working memoryupdating, whose development is considered to be anessential step in the process that enables children tosolve complex problems (Pennequin, Sorel, &Fontaine, 2010). Second, we assessed if a pro-gramme of enhanced school physical education(PE) impacts inhibition and memory updating abilityin overweight and lean children. We hypothesisedthat due to the relationship existing between over-weight, chronic inactivity and poor cognition(Metcalf et al., 2010; Shore et al., 2008), overweightchildren, as compared to lean peers, may more likelybenefit from the enhanced PE programme. The lat-ter involved additional skill-based, cognitively chal-lenging exercise training (i.e. open skill tennisactivities). Introducing children to tennis play waschosen because learning novel coordination of grossmotor skill such as playing tennis is conceived asdependent on the achievement of cognitive processes(Keller & Ripoll, 2006), and point games involvesituation uncertainty and time pressure, thus repre-senting a cognitively complex form of PA. Also, anaspect of tennis that may specifically contribute to

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executive function development is the formulationand implementation of strategies (e.g. that of hittingthe ball where the opponent is not staying). Childrenwith poor baseline cognition seem to profit fromtable tennis training for improving executive func-tion (Tsai, 2009). However, it remains unclear towhat extent these benefits are induced by the meta-bolic or the cognitive demands of this open skillactivity. In our study, we also tested if aerobic fitnessgains mediate the effects of the tennis-based PEprogramme on cognition.

Method

Participants

Two primary schools of the same urban district ofthe Municipality of Rome (Italy) were involved inthe study. Six gender-balanced classes of childrenaged 9–10 years were selected according to teacherand class availability and randomly assigned either toan experimental, enhanced PE programme or to atraditional PE programme. All children providedverbal assent before involvement in the study andtheir parents provided informed written consent inconformity with the laws of the country. The studyprotocol was approved by the institutional ethicscommittee. Of the total 144 children involved inthe intervention, 70 who were present at school onboth pre- and post-intervention testing days withoutany diagnosed disorder of cognition and physicalcondition hindering them to participate in a schoolPE programme represented the actual sample. Thecharacteristics of children belonging to the experi-mental (n = 37) and to the control group (n = 33)are reported in Table I.

Prior to the intervention, body height andmass weremeasured for BMI (kg · m–2) calculation. Age-refer-enced cut-off values of BMI were used to identify over-weight and lean children (range of cut-off values for9.0–10.5-year-old males: 19.10–20.20; for co-agedfemales: 19.07–20.29; Cole, Bellizzi, Flegal, & Dietz,2000). Twenty-six of the seventy students (37.1%)were classified as overweight. Children’s aerobic fit-ness was determined by their performance on an aero-bic fitness field test (20 m shuttle run test, Léger &Lambert, 1982) described in the Physical assessmentparagraph. Children whose estimated VO2max fellabove the 70th percentile (n = 12,VO2max = 49.64 ± 1.74 mL · kg−1 · min−1) or belowthe 30th percentile (n= 14, VO2max = 36.74 ± 2.55mL· kg−1 · min−1) were considered as higher-fit and lower-fit, respectively, according to age-referenced aerobicfitness norms (Chaddock et al., 2011; Shvartz &Reibold, 1990).

Content and delivery in the enhanced and curricularphysical education programmes

The enhanced PE programme lasted 21 weeks withone curricular PE class per week plus two additionalhours of skill-based and tennis-specific training. Thecurricular programme consisted of only one PE classper week and was focused on the development offundamental motor skills and coordinative abilities,bodily expression and deliberate play, in accordancewith the Ministerial Programmes for the ItalianPrimary School. The additional weekly PA sessionin the enhanced PE programme included: (1) a firsttraining hour aimed at developing fundamentalmotor skills and perceptual–motor adaptation abil-ities in situational games preceded by warm-up andfollowed by static stretching; (2) a second hour dedi-cated to object control skills in tennis and specificallyto learning main tennis shoots and playing individualor team point games. Among open skill sports, ten-nis was considered particularly appropriate to

Table I. Baseline and post-intervention characteristics(n, mean ± SD) of the sample of 9–10-year-old children assignedto the enhanced or curricular PE intervention types.

Specialist-ledenhanced PE

(S-led)

Generalist-ledcurricular PE

(G-led)

N 37 33Age (years) 9.6 ± 0.5 9.6 ± 0.5GenderMales (n) 20 15Females (n) 17 18

Weight statusLean (n) 23 21Overweight (n) 14 12

BMI (kg · m−2)Pre 18.9 ± 3.2 19.3 ± 3.6Post 18.7 ± 3.4 19.5 ± 3.7

Estimated VO2max

(mL · kg−1 · min−1)Pre 43.4 ± 4.5 42.5 ± 4.8Post 44.3 ± 4.0 44.4 ± 4.3

RNG indices of inhibitionTurning point index Pre 70.3 ± 17.6 72.3 ± 16.8(improvement↑) Post 79.8 ± 14.4 79.0 ± 17.0Adjacency score Pre 43.8 ± 17.2 41.8 ± 15.9(improvement↓) Post 36.6 ± 12.9 33.2 ± 15.9Runs score Pre 2.9 ± 2.9 2.7 ± 3.0(improvement↓) Post 2.2 ± 2.1 2.3 ± 2.1RNG indices of working memory updatingRedundancy score Pre 3.0 ± 1.9 3.3 ± 3.3(improvement↓) Post 3.9 ± 2.7 5.1 ± 3.3Coupon score Pre 22.3 ± 9.1 23.7 ± 11.3(improvement↓) Post 25.4 ± 12.0 22.4 ± 14.2Mean repetition gap Pre 9.1 ± 0.7 8.9 ± 1.1(improvement↑) Post 9.0 ± 0.6 8.6 ± 0.9

Note: BMI: body mass index; VO2max: maximal oxygen consump-tion; and RNG: random number generation.

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challenge motor control and executive functionjointly. In fact, the object control skills in this sportrely on internal models used for both eye–hand coor-dination and cognitive (predictive and control) func-tions with a common substrate in the cerebellum(Iacoboni, 2001). The development of the cerebel-lum, in turn, is strictly linked to that of the prefrontalcortex and many cognitive tasks require both(Diamond, 2000).

To estimate exercise intensity and duration inthe two PE programmes, heart rate (HR) wasrecorded by means of HR monitors (Polar S610i;Polar Electro Oy, Kempele, Finland) during onecurricular PE session and one enhanced PE ses-sion on a subsample of students (n = 10). Thepercentage of physically active time, that of activetime spent in moderate-to-vigorous PA (MVPA)and average HR are shown in Table III. The phy-sically active time was computed by subtractingfrom the PE time during which children wore theHR monitors, the time devoted to physically non-engaging activities, as when the teacher took roll orput breaks to give instruction and feedback,arrange people or equipment and allow recoverybetween PA phases. The percentage of physicallyactive time spent in MVPA was operationalised asHR > 139 bpm (Wang, Pereira, & Mota, 2005). t-Tests for independent samples showed a signifi-cant difference in physically active time (t = 7.71,P < 0.001), but no significant difference in propor-tion of active time spent in MVPA (P = 0.940) andaverage HR (P = 0.996) between traditional andenhanced PE sessions.

Since classroom generalist teachers never partici-pated in specific teacher training for PE and had notspecialised skills to deliver tennis play experiences,

specialist teachers licensed to teach PE were involvedin the enhanced PE programme. Therefore, PA con-tent could not be decoupled from teachers’ deliveryskills. To take into consideration these qualitative char-acteristics of teaching, curricular and enhanced PElessons led by generalists and specialists, respectively,were videotaped twice by means of non-participantvideo observations. Two independent observers ana-lysed videos twice each, one week apart. They codedevents every 20 s to classify them as teacher’sbehavioural categories according to the ObservationSystem for Content Development-Physical Education(OSCD-PE) (Rink, 2006; Table II). To operationalisethe cognitive demands of the movement tasks, it wasassumed that they are higher when children areprompted to improve their motor performance usingcorrective feedback (“Refining” task), or must copewith increased task complexity and response variety(“Extending” task) or autonomously apply theirmotor skills in games (“Applying” task), than whenthe teacher informs, conducts or organises the learningsituation. A satisfactory reliability of the categorisationoutcomes (i.e. intra- and inter-observer agreement,indicated by a percentage of agreement [Agreements/(Agreements + Disagreements) × 100] ≥0.80) wasreached. As shown in Table III, the difference betweenspecialist-led enhanced PE and generalist-ledcurricular PE was that the specialist informed anddisciplinarily conducted children less frequently thanthe generalist (χ2 = 53.07, P < 0.001 and χ2 = 3.98,P = 0.046, respectively) but performed refining,extending and applying tasks more frequently(χ2 = 60.25, P < 0.001; χ2 = 9.42, P = 0.002; andχ2 = 8.33, P = 0.012, respectively), while the fre-quency of organisation of events was similar(P = 0.878).

Table II. Category definitions for PE tasks according to the Observation System for Content Development-PE (OSCD-PE) (Rink, 2006).

PE tasks

Informing An informing task states or presents a motor task. It is usually the first task and merely describes what the students are to do.For instance: “Bounce the ball eight consecutive times on place”

Refining A refining task is aimed at improving a student’s motor performance through corrective feedback, appraisals, initiations orresponses on how to perform better, in order to reach the level of performance quality according to predefined cues.Refining tasks are, for instance correcting performance individually (“maintain low body position while bouncing”),making the task easier (bouncing the ball on place before doing it while walking), stopping and focusing the entire class ona relevant cue (looking for spaces to move in while bouncing).

Extending An extending task seeks a variety of responses or adds complexity to a previous task through manipulation of task andenvironmental constraints, for instance equipment modification (bouncing balls of different size/weight), spatialarrangements (constraining the space of the course with obstacles), rule changes and expansion of a number of differentactions (changing bouncing speed/walking direction or alternating bouncing/throwing according to signals).

Applying An applying task asks students to use their motor skill in an applied, competitive or assessment setting, shifting the focus fromhow to move to using a skill differently than learnt. For instance, activities are played against opponents, with varyingdegrees of complexity (bouncing a ball while walking, avoiding an opponent who tries tapping).

Conduct A conduct task is a disciplinary behaviour that structures, directs or reinforces the behaviour code of a specific situation.Organisation An organisation behaviour is a management behaviour that structures, directs or reinforces the arrangement of people, time

or equipment to create appropriate conditions for learning.

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Physical assessment

Following a familiarisation session, the participants per-formed the 20 m shuttle run test (Léger & Lambert,1982) twice, prior to and after the intervention period.This field test allows estimating the maximum aerobicperformance. Participants were instructed to run anumber of stages (levels), each lasting about 1 minand comprising a number of 20 m laps (shuttles),paced by auditory signals (beeps). At each stage, therequired running speed increased, starting at a speed of8.5 km · h−1 and increasing by 0.5 km · h−1 everyminute. The test was scored in number of stages per-formed until the participants could no longer maintainthe requested speed or stopped due to exhaustion. Theparticipants performed the test in small groups (n = 6)on the sport field outside the school in themorning.Theexperimenters and experimental conditions were equalfor all participants and the recommendations reportedin the literature were followed (The Cooper Institute,2010). The maximal oxygen consumption (VO2max)was estimated as: [31.025 + (3.238 × velo-city) – (3.248 × age) + (0.1536 × age × velocity)]where velocity (km · h−1) was inferred from the numberof stages performed.

Cognitive assessment

Prior to and after the intervention period, the parti-cipants also performed the random number genera-tion (RNG) task. This is a child-appropriate test thattaps executive cognitive function and is feasible alsowith children (Towse & McIachlan, 1999). The par-ticipants were tested individually in a quiet area ofthe school. They were told that the RNG is a game

involving numbers and were instructed to verballygenerate a random sequence of numbers between 1and 10 to each beat of a 70-beat sequence with aninter-beat interval of 1.5 s. Randomness wasexplained by means of an age-appropriate instructionincluding a “hide-and-seek” type game (Towse &McIachlan, 1999). Prior to data collection by tape-recording, participants performed a familiarisationtrial of 70 numbers and could ask questions con-cerning the test. Both the omission of a numbergeneration in correspondence of one tone and theproduction of numbers lower than 1 (0) or higherthan 10 (11, 12, etc.) were considered as errors anddiscarded. If errors exceeded a predefined maximumthreshold of five, the entire block was repeated. Therandomness of the sequence of numbers was mea-sured by means of 18 different indices described byTowse and Neil (1998). Among those, six indiceswere selected as they reflect two components ofexecutive function: inhibition of mental routines(turning point index [TPI], adjacency score [Adj]and runs score [Runs]) and working memory updat-ing (redundancy score [Red], coupon score[Coupon] and mean repetition gap [MeanRG]).

The TPI is the ratio between the real frequency ofturning points between ascending and descendingseries of numbers (e.g. the response change betweenthe digits “2” and “5” in a hypothetical sequence “9,7, 2, 5, 6, 8”) generated by the participant and theirtheoretical frequency in random responses. Turningpoints in random responses are assumed to be2/3 × (n – 2), where n is the number of digits to begenerated. A TPI lower than the optimal value of100 indicates that participants produced more orfewer turning points than theoretically expected.The Adj measures the relative frequency of pairs ofadjacent ascending or descending numbers (e.g. 7–8or 4–3) as compared to the total number of responsepairs produced by the participant. It ranges between0% and 100% and reflects the habitual tendency tocount forward or backward. The Runs score is anindex of variability of the number of digits in succes-sive ascending or descending runs. Counting from 1to 9 and from 9 to 1 along the whole sequence ofgenerated numbers leads to the highest Run value,whereas alternating ascending and descending pairsof digits as “4, 7, 9, 2” will lead to lowest scores oreven to null if this alternation is produced through-out the sequence.

The Red index reflects the unbalance of responsealternative frequencies in a sequence that derivesfrom a more frequent usage of given numbers thanexpected based on the theoretical frequency of eachdigit in random responses. In the present experi-ment, a perfect equality of response alternative fre-quencies corresponds to the generation of eachnumber, from 1 to 10, seven times each and would

Table III. Physical education (PE) task characteristics, studentorganisation modes and exercise intensity in the specialist-ledenhanced and generalist-led curricular PE programmes.

Specialist-ledenhanced PE (S-led)

Generalist-ledcurricular PE (G-led)

PE tasks (eventsevery 20 s)

Informing (%) 2.2*** 45.7Refining (%) 43.4*** 8.6Extending (%) 9.0** 0.0Applying (%) 7.4* 0.0Conduct (%) 7.2* 16.1Organisation (%) 30.8 29.6Exercise intensityPhysically active

time (%)58.3*** (±0.7) 46.2 (±3.4)

Active time inMVPA (%)

49.6 (±16.3) 48.5 (±26.2)

Average heart rate(mean ± SD)

150.5 (±6.4) 150.4 (±16.0)

Note: ***P < 0.001; **P < 0.01; *P < 0.05; MVPA: moderate-to-vigorous physical activity.

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lead to a Red score of 0% (no redundancy), whereasrepeating the same digit along the whole sequencewould lead to a Red score of 100% (complete redun-dancy). The Coupon score measures the mean num-ber of digits generated until the entire set ofalternatives has been used. In the case that the parti-cipant omits to generate one of the available num-bers throughout the whole sequence, the Couponscore will be the highest, that is equal to the numberof digits composing the sequence (i.e. 70). TheMeanRG is the mean number of responses givenuntil each digit reoccurs calculated for all digitsthroughout the whole sequence (e.g. in the sequence“2, 8, 4, 6, 2, 9, 7, 8”, the digits “2” and “8” reoccurwith a mean gap equal to 4). If the participant reg-ularly varies all possible digits throughout thesequence, then the MeanRG is high, whereas repeat-ing one or more items much more frequently thantheoretically expected leads to a low MeanRG value.

Preliminary analyses

The six RNG indices were first submitted to bivari-ate correlation analysis (Pearson’s r) to determinewhether they tap the two distinct constructs of inhi-bition and working memory as in adults (Miyakeet al., 2000). Results confirmed this distinction(Table IV), showing a high correlation among thethree inhibition indices (TPI, Adj and Runs) andbetween two of the three working memory updatingindices (Red and meanRG), but an inverse correla-tion between inhibition and working memory updat-ing indices. The sign of the r coefficient was positiveor negative consistently with the meaning of theindices: high levels of TPI, but low levels of Adjand Runs correspond to a high ability to inhibit,avoiding the production of stereotyped strings andprepotent associates, and high levels of MeanRG,but low levels of Red and Coupon correspond to ahigh ability to update working memory and employequality of responses using alternation among num-bers. Based on the results of the correlational

analysis, TPI, Adj and Runs were merged into anaverage index of inhibition and Red and MeanRGinto an average index of memory updating.Consistent with the pattern of correlations betweenindividual indices (Table IV), the average indices ofinhibition and memory updating were negativelycorrelated (r = −0.372, P = 0.002). Before aver-aging, all indices were standardised and Adj, Runsand Red were reversed. Also average pre-to-postchanges (delta, Δ) in inhibition and updating werecomputed from standardised Δ values of TPI, Adj,Runs, Red and MeanRG.

Statistical analysis

To answer the question concerning the associationbetween aerobic fitness and executive cognitive effi-ciency, the average indices of inhibition and workingmemory updating obtained from the RNG task per-formed at pre-intervention time were compared inhigh-fit and low-fit children by means of indepen-dent-samples t-tests. Also, it was tested whether high-fit and low-fit children differed in gender (χ2 test), ageand BMI (independent-samples t-tests).

Since entire classes and not individuals could berandomly assigned to the curricular or enhanced PEprogramme, we first tested for baseline differences inBMI, VO2max estimated from 20 m shuttle run per-formance and RNG indices in children assigned todifferent PE programmes that might influence theintervention outcomes. To this aim, pre-interventionvalues of those variables were submitted to multi-variate analysis of variance (MANOVA) and subse-quent ANOVAs with PE programme (enhanced vs.curricular) as factor.

To answer the question regarding the effects of theenhanced, cognitively challenging PE programme, ageneral linear model was applied to the Δ indices ofinhibition and memory updating with weight status(lean vs. overweight), PE programme (enhanced vs.curricular) and school (nested within PE pro-gramme) as factors and baseline VO2max as

Table IV. Bivariate correlations (Pearson’s r) between RNG indices of inhibition and working memory updating.

Inhibition Working memory updating

TPI Adj Runs Red Coupon MeanRG

TPI 1Adj −0.869*** 1Runs −0.681*** 0.666*** 1Red 0.349** −0.456*** −0.252* 1Coupon 0.187 −0.198 −0.142 0.184 1MeanRG −0.307* 0.400** 0.207 −0.889*** −0.052 1

Note: ***P < 0.001; **P < 0.01; *P < 0.05; TPI: turning point index; Adj: adjacency score; Runs: runs score; Red: redundancy score;Coupon: coupon score; MeanRG: mean repetition gap.

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covariate. Post hoc analyses were performed bymeans of planned pairwise comparisons (t-tests).The change score method was considered moreappropriate to tap differential intervention effects inthe present nonequivalent control group design thanentering the post-treatment measure as the depen-dent and the baseline measure as the covariate (i.e.regressor variable method) (Allison, 1990). In factthe latter, treating the pretest like any other controlvariable, seems to underadjust for pre-existing differ-ences, whereas the change score method assigns tothe pretest a special status. In the present study,baseline cognitive differences could not be excluded:(1) between intervention groups, since participantswere not individually randomised to the two inter-ventions, and (2) between lean and overweight chil-dren, since weight status – a factor included in theanalysis model – is potentially linked to children’scognitive function (Shore et al., 2008).

Mixed-model ANOVAs with PE programme andtesting time (pre vs. post) as factors were run onBMI and VO2max data to control for pre- to post-intervention changes. In the case of significanteffects of the two PE programmes, mediation analy-sis was performed (McKinnon, Fairchild, & Fritz,2007) to test whether pre–post intervention changes(Δ) in BMI or VO2max mediated positive cognitiveoutcomes. This analysis was applied to the relation-ship between PE programme (independent variable)and pre–post Δ in inhibition or memory updatingindices (dependent variables) using pre–post Δ inBMI or estimated VO2max as mediator. For eachmediation, three regression analyses were performedto assess the effects of: (1) the independent variableon the dependent variable, that is if the two PEprogrammes differently influenced executive func-tion, (2) the independent variable on the mediator,that is if the two programmes determined differentialBMI changes or fitness gains and (3) the indepen-dent variable and the mediator on the dependentvariable, that is whether introducing the BMI changeor fitness gain as a mediator reduces the direct effectof PE programme on executive function to a non-significant level. This third regression allows obtain-ing two coefficients relating the mediator to thedependent variable and the independent variable tothe dependent variable, respectively, after account-ing for the mediator. Mediation significance andeffect size were determined with the Sobel test(McKinnon et al., 2007).

Results

The t-tests performed on pre-intervention averageindices of inhibition and working memory updatingshowed a significant difference between high-fit andlow-fit children for inhibition, t = 2.217, P = 0.020,

but not for memory updating (P = 0.851). High-fitchildren had a higher average inhibition value thanlow-fit peers (0.43 ± 0.40 vs. –0.61 ± 0.74), derivingfrom higher TPI (81.7 ± 15.4 vs. 67.0 ± 18.0), lowerAdj (33.2 ± 14.3 vs. 45.0 ± 18.4) and lower Runsscores (2.1 ± 1.6 vs. 5.2 ± 5.5). Also, high-fit andlow-fit children significantly differed in BMI(17.9 ± 2.6 vs. 21.5 ± 3.5; t = 3.087, P = 0.005)and age (9.4 ± 0.52 vs. 10.0 ± 3.16; t = 3.465,P = 0.003), but not in gender.

The results of the analyses performed on pre-intervention BMI, VO2max and RNG data did notshow any significant difference between the groupsassigned to the curricular or enhanced PE pro-gramme (MANOVA: P = 0.644, ANOVAs:0.191 ≥ P ≥ 0.910). Results of the analyses run onpre–post BMI and VO2max data showed a post-inter-vention improvement in VO2max independently ofPE programme type, F(1,68) = 6.28, P = 0.015,and no change in BMI (P = 0.193) (see Table I).

Adjusted analyses from general linear models ofpre–post Δ inhibition and working memory updat-ing, with VO2max at pretest as covariate and schoolnested within group, showed a significant PEProgramme × Weight Status interaction for Δ inhibi-tion, F(1, 63) = 5.57, P = 0.021, ηp

2 = 0.08, but notfor Δ memory updating (P = 0.666). Post hoc ana-lyses (Figure 1) showed that overweight childreninvolved in enhanced PE obtained a significantlyhigher improvement of inhibitory ability than theirlean peers, t = 1.80, P = 0.040, and their overweightpeers involved in curricular PE, t = 1.75, P = 0.046,while lean and overweight children assigned to cur-ricular PE showed a similar (P = 0.248) and onlyaveragely pronounced improvement of inhibitoryability.

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Figure 1. Pre- to post-intervention improvement in inhibitoryability, as reflected in the standardised Δ inhibition index derivedfrom TPI, Adj and Runs scores of the Random NumberGeneration (RNG) task, in lean and overweight children as afunction of physical activity programme. Zero corresponds to anaveragely pronounced improvement.

Note: *P < 0.05.

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Mediation analyses were applied to the subsampleof overweight children, who showed differentialeffects of PE programmes on inhibition. Results didnot show any significant mediation by pre–post Δ inaerobic fitness. Inhibitory ability of overweight chil-dren improved more pronouncedly following theenhanced PE programme without the influence ofaerobic fitness gains, since the direct relationshipbetween the independent and the dependent variabledid not significantly change after accounting for thishypothesised mediator.

Discussion

The aims of the present study were to test: (1) theassociation between children’s physical fitness andtwo core executive functions, that are the ability toinhibit mental routines and update working memoryand (2) the effectiveness of cognitively challenging,enhanced PE for improving inhibition and memoryupdating in overweight and lean children. On thewhole, the results indicate that inhibitory ability isbetter in high-fit children and enhanced in overweightchildren after participating in PE with additional, cog-nitively challenging exercise training. These findingsadd to the still scarce, but emerging evidence thatcognitively challenging PA may effectively promotethe development of embodied cognition and particu-larly of executive functions, which are considered acornerstone of development (Best, 2010).

The positive association between children’s physi-cal fitness and RNG indices of inhibition extends toa new aspect of children’s cognition the evidencethat being physically fit is beneficial to cognitivefunctioning at a preadolescent age. The existenceof association between fitness and high-level cogni-tion is well documented in children, particularly asconcerns memory (Chaddock et al., 2010, 2011) anda specific form of inhibition, executive interferencecontrol (Pontifex et al., 2011; Stroth et al., 2009).During childhood, inhibition seems to be more mul-tifaceted than other executive functions (Huizinga,Dolan, & Van der Molen, 2006). The inhibition ofprepotent mental routines, as reflected in RNGindices of randomness used in this study, is a formof deliberate inhibition that may enable children tothink about other possibilities and to shift betweenthem, thus representing an essential first step in sol-ving complex problems (Pennequin et al., 2010).Thus, identifying the effects of physical exercise onthis component of inhibitory ability is developmen-tally relevant.

While the cross-sectional nature of these observa-tions limits causal inference, the results of the inter-ventional study overcome this limitation. Theyconfirm the hypothesis proposed by Davis et al.(2007, 2011) that overweight children are more

likely to benefit from exercise to improve cognitivefunctioning. Overweight children, compared to theirlean peers, showed a higher improvement of inhibi-tory ability after the intervention period, but only ifthey were involved in the enhanced PE programme(Figure 1).

In our study, PA quantity and quality weremanipulated in combination with the enhanced PEprogramme including two additional hours per weekof perceptual–motor skill training and tennis play.Thus, our intervention outcome might be accountedfor by at least three types of mechanisms: (1) themetabolic adaptations and sympathetic arousalincrements resulting from the enhanced quantity ofexercise training (Hillman et al., 2008), (2) theneural stimulation resulting from coordinativelyand cognitively challenging movement tasks (Best,2010) and (3) the psychological activation derivingfrom social interaction with peers during PA (Daviset al., 2011), which seems to increase PA motivationof overweight youths (Salvy et al., 2009).

The first hypothesis of a causal chain in which thecognitive benefits are mediated by metabolic changesinduced in the brain by the higher exercise dose ofthe enhanced PE programme is not well supportedby our data for three reasons. First, there was nochange in BMI after the intervention. Second, aero-bic fitness gains did not mediate the more pro-nounced improvement of inhibitory ability inoverweight children assigned to the enhanced PEprogramme. Third, the 3 h PE of the enhancedprogramme corresponded to a low exercise dose ofabout 100 min · week−1 devoted to exercise training.In the study by Davis et al. (2007), only overweightchildren involved in high-dose (200 min) exercisetraining per week, but not the low-dose group(100 min · week−1) improved on cognition.

Most probably, the relatively high complexity ofthe perceptual–motor tasks in the enhanced PE pro-gramme may account for the more pronounced cog-nitive improvement. Given that cognitivedevelopmental trajectories are interwined withthose of children’s perception and action, the con-struction of perception–action representations pro-moted by tennis may represent a way in whichcognition derives structure from action (Rakison &Woodward, 2008). This hypothesis is supported bythe results of a meta-analysis showing that both per-ceptual–motor training and PE programmes signifi-cantly benefit children’s cognition, even though lesspronouncedly than aerobic training (Fedewa & Ahn,2011). Also, the existence of interplay between phy-sical exercise and movement-related cognitiveengagement is grounded on the evidence thatchanges in brain structure following exercise arenot broadly distributed, but localised to specificbrain regions whose activity is related to movement

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production (Anderson, McCloskey, Mitchell, &Tata, 2009).

The role played by the cognitive challenge inher-ent in tennis training within the enhanced PE pro-gramme may be explained referring to evidenceshowing that the potential for neurogenesis that isindirectly prepared by exercise-induced metabolicchanges can be better exploited if exposed to cogni-tive enrichment (Fabel et al., 2009). Mental trainingvia skill learning increases neuroplasticity, particu-larly when training goals and task complexity areadequately challenging (Carey, Bhatt, & Nagpal,2005; Curlick & Shors, 2012). As specifically con-cerns executive functions, it has been demonstratedthat they must be continually challenged by incre-ments in task difficulty to promote improvements inchildren (Diamond & Lee, 2011).

The selective effect on inhibitory function is in linewith a study demonstrating the effectiveness of tabletennis training for improving inhibitory function ofchildren with executive function deficit (Tsai, 2009).Also, Keller and Ripoll (2006) suggested that whenchildren achieve new tennis skills, specific cognitiveprocesses of selection and activation–inhibition areoperative for new coordination to emerge. Our ten-nis-based intervention included playful technicaltraining, categorised as “Refining” and “Extending”tasks and requiring cognitive engagement to masterincreasing perceptual–motor task complexity, andeasy game situations, categorised as “Applying”tasks and requiring the inhibition of routines tocope with contextual interference and unexpectedevents. Carey et al. (2005) called for careful judg-ment in incorporating the proper level of cognitiveengagement and contextual interference in motorskill training as a distinguishing feature of outstand-ing teaching and coaching. In our study, the cogni-tive demands of the tennis-based interventionrepresented a cognitively appropriate challengepoint to promote inhibitory function developmentin overweight children. The finding that gains ininhibition were not paralleled by gains in memoryupdating performance indices is attributable to thefact that given children’s tennis inexperience, nodeliberate tactical practice was applied. Tacticaltraining would require continuous updating of infor-mation in working memory to compare perceivedinformation from ongoing game situations with tac-tical knowledge retrieved from long-term memory.

Finally, the third explanatory hypothesis of the pre-sent intervention outcomes concerns the role of socialinteraction and motivating peer influence. Best(2012) argued that social interaction demands morethan cognitive challenges may be responsible forshort-term effects of ball game playing on executivefunctions. This explanation might also apply to theoutcomes of our chronic exercise study, since

children assigned to the enhanced PE programmespent more time in situational games with peersthan those assigned to the traditional PE programme.

The present study has limitations which should beaddressed. First, the fact that children were sampledfrom schools of the same district limits the generali-sability of the intervention outcomes. Second, thegold standard of randomised controlled trials wasnot applied because not individuals, but schoolclasses could be randomly assigned to the enhancedor curricular PE programmes. Lastly, we did not testfor potential mediators of intervention effects differ-ent from physical fitness mechanisms, such as moti-vation or self-efficacy (Tomporowski et al., 2011).More research is needed to identify further mechan-isms that may account for the cognitive benefits ofqualitatively different types of PA.

In conclusion, our study adds to the extant literatureindicating that PA represents an important method ofaiding aspects of mental functioning that are central tocognitive development. More interestingly, it highlightsthe relevance of cognitively and socially challenging PAfor overweight children who are at risk of poor cognitionand over-represented among disadvantaged populations(Lambourne & Donnelly, 2011). These findings carrysignificant educational and public health implications,since exercise programmes for overweight childrenusually range from basic PA to programmes specificallytailored to enhance physical fitness (Zenzen & Kridli,2009), whereas programmes that focus on specific cog-nitive and social interaction demands are still under-represented. More interventional research with specialchildren’s populations is needed which focuses not onlyon the quantitative, but also on the qualitative character-istics of physical exercise to contribute to an evidence-based model of quality PA to inform educational andhealth care policies (Pesce, 2012).

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