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Prediction of habitual physical activity level andweight status from fundamental movement skill levelElizabeth Sarah Bryanta, Rob S. Jamesa, Samantha Louise Bircha & Mike Duncana

a Biomolecular & Sports Science, Coventry University, Coventry, UKPublished online: 19 May 2014.

To cite this article: Elizabeth Sarah Bryant, Rob S. James, Samantha Louise Birch & Mike Duncan (2014): Prediction ofhabitual physical activity level and weight status from fundamental movement skill level, Journal of Sports Sciences, DOI:10.1080/02640414.2014.918644

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Prediction of habitual physical activity level and weight status fromfundamental movement skill level

ELIZABETH SARAH BRYANT, ROB S. JAMES, SAMANTHA LOUISE BIRCH, & MIKEDUNCAN

Biomolecular & Sports Science, Coventry University, Coventry, UK

(Accepted 23 April 2014)

AbstractFundamental movement skills (FMS) have been assessed in children in order to investigate the issues of the low proportionof children who meet physical activity (PA) guidelines and rising levels of obesity. The aim of this research was to identifywhether previous or current FMS level is a better predictor of PA levels and weight status in children. In January 2012(year 1), 281 children were recruited from one primary school in the West Midlands, UK. Children performed eight FMSthree times, which were videoed and assessed using a subjective checklist. Sprint speed and jump height were measuredobjectively. Height and mass were measured to calculate the body mass index to determine the weight status. Skinfoldcalliper readings were used to calculate body fat percentage. One year later, in January 2013, all these tests were repeated onthe same children, with the additional collection of PA data via the use of pedometers. Following multiple linear regression,it was identified that prior mastery in FMS was a better predictor of current PA, whereas current FMS was a better predictorof current weight status. Overall, FMS mastery is needed in childhood to be able to participate in PA and maintain a healthyweight status.

Keywords: fundamental movement skill, children, physical activity, skill mastery, obesity

Introduction

Higher levels of physical activity (PA) are related tomultiple positive health outcomes in childrenand adults (Eriksen, Curtis, Grønbæk, Helge, &Tolstrup, 2013). PA levels have been tracked fromchildhood to adolescence (Kelder, Perry, Klepp, &Lytle, 1994) and from adolescence into adulthood(Kjønniksen, Torsheim, & Wold, 2008). In addition,other cardiovascular disease risk factors such ashypertension and high cholesterol also track fromchildhood into adulthood and are linked to low PA(Wilkinson, Diamond, & Miller, 2011). Therefore, itis important to target children as a preventive mea-sure for later life disease related to physical inactivityand obesity.

In order to address the low proportion of childrenmeeting the PA guidelines and increasing obesity,the assessment and development of fundamentalmovement skills (FMS) in childhood have beenexamined (McKenzie et al., 2002; Okely & Booth,2004). FMS form the prerequisites for sport-specificand PA skills (Stodden et al., 2008). Understandingthis relationship is therefore important for interven-tion planning. A review by Lubans, Morgan, Cliff,

Barnett, and Okley (2010) highlighted five studiesthat elucidated that an increased weight status hasnegative effects on FMS mastery. In all of thesestudies, the body mass index (BMI) was used todetermine the weight status. BMI has been docu-mented to hold limitations, particularly in children(Jackson et al., 2002). However, using alternativemethods, such as measuring body composition (fatmass), avoids the limitations of assessing BMI inpaediatric populations and is arguably a more impor-tant measure in children (Doak et al., 2013).

There are few longitudinal studies that have lookedat assessing the relationship between childhood FMSand future PA level (Barnett, Van Beurden, Morgan,Brooks, & Beard, 2008). Barnett et al. (2008) andOkely, Booth, and Patterson (2001) both reportedthat very small variations in adolescent PA could bepredicted from childhood FMS competency. Incontrast, McKenzie et al. (2002) did not identify arelationship between childhood FMS and future PAlevels. None of these three studies reassessed FMScompetency when they measured the future PA levels.It is therefore not known whether children needto gain FMS mastery in order to participate and

Correspondence: Elizabeth Sarah Bryant, Biomolecular & Sports Science, Coventry University, Coventry, UK. E-mail: bryante2@uni.coventry.ac.uk

Journal of Sports Sciences, 2014http://dx.doi.org/10.1080/02640414.2014.918644

© 2014 Taylor & Francis

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maintain participation in PA, or whether an increasein PA participation will develop and increase masteryof FMS (Graf et al., 2004; Hume et al., 2008). Suchinformation is needed to inform physical educationpractice and health policy in the UK. The objective ofthis study will be to predict the variation of PA (aver-age daily steps) and weight status (body fat percentage[BF%] and BMI) at time point 2 from the FMSmeasures at time points 1 and 2. The aim of thisstudy was to identify whether FMS measured oneyear earlier or FMS measured at the same timepoint is a better predictor of PA levels and weightstatus in children. This study is novel as it will assessFMS at two time points and identify which time pointcan best predict variation in PA, which other studieshave failed to do (Barnett et al., 2008; Graf et al.,2004; Okley et al., 2001). Based on Stodden et al’s(2008) theoretical model, it would be hypothesisedthat current FMS will be a better predictor of PA, asthe model suggests that PA is a direct consequence ofmotor competence (FMS level).

Method

Sample

Following institutional ethical consent, the presentstudy began in January 2012 (year 1). A sample of292 children were recruited from one primary schoolin the West Midlands, UK. Informed consent fromparents/guardians was gained from 281 children(129 boys, 152 girls) who then participated in thestudy. Year groups two–six (age 6–11 years) wereassessed with an overall mean (±SD) age of8.9 years (± 1.4 years). From the sample, 84% ofthe children were White British, 15% were Asian and1% was Afro-Carrabin, and this is a representativesample of the ethnic diversity for the West Midlands(Coventry City Council, 2013). The local council’slead advisory teacher for Physical Education assistedin the selection of the chosen school. This was toensure that the school was broadly representative(was within the 50–59% bracket of electoral wardsfor deprivation and socio-economic status within thecity and nationally (Coventry City Council, 2012))of primary schools within the city. The school wasalso within the mid-range of electoral wards fordeprivation and socio-economic status for the cityin question. Between the years of testing, childrenhad their scheduled physical education lessons buthad not received any additional PA programmesduring or between testing periods.

The data collection was repeated one year laterin 2013 (year 2). There was a 90% follow-up rateof 252 (116 boys and 136 girls) children (90%follow-up rate), with a mean age of 9.8 years (±1.4 years).

Anthropometric measurement

Table I displays the proportion of normal and over-weight/obese children in year 1 (2012) and year 2(2013) determined by BMI and BF%.

BMI. Height (cm) and mass (kg) were recorded to thenearest cm and 0.1 kg, respectively, using a stadiometer(SECA Instruments, Ltd, Hamburg, Germany) andelectronic weighing scales (SECA Instruments, Ltd),respectively. BMI was calculated as kg·m−2; the 1990reference curves controlling for age and sexwere used toidentify overweight and obese children (Cole, Freeman,& Preece, 1995). Children were classed as overweight ifthey were in the 85th centile and obese if in the 95thcentile (Cole et al., 1995).

Body fat percentage (BF%). BF%was calculated usingskinfold assessment from two sites – tricep and medialcalf. Two measures from these two sites were takenfollowing guidelines from Thompson, Gordon, andPescatello (2009) and according to the InternationalSociety for the Advancement of Kinanthropometrycriteria. The Slaughter et al. (1998) skin fold equation(BF% = (0.735 × (tricep + medial calf)) + 1) was usedto calculate each participant’s BF%. The same trainedresearcher took the skin fold measurements each yearto maintain consistency, and the technical error ofmeasurement at the tricep and medial calf was 4.57%and 4.12%, respectively, showing appropriate intrates-ter reliability. BF reference curves controlling for ageand sex were used to identify overweight and obesechildren (McCarthy et al., 2006). Children who fellinto the 85th centile were classed as overweight andchildren who fell into the 95th centile were obese(McCarthy et al., 2006).

Fundamental movement skill assessment

Subjective measurement. FMS was assessed using theprocess orient checklist (POC) taken from the New

Table I. Mean (±SD) BMI and BF values, and percentage ofnormal and overweight/obese children, in year 1 (2012) and year2 (2013) determined by 85th and 95th centiles according to Cole,Bellizzi, Flegal, and Dietz (2000) for BMI and McCarthy, Cole,Fry, Jebb, and Prentice (2006).

Normalweight (%) Overweight/obese (%) Mean (±SD)

BMIYear 1 70 30 17.5 (2.9)Year 2 69 31 17.7 (3.4)BF%Year 1 78 22 14.6 (2.9)Year 2 60 40 21.9 (8.5)

Note: BMI, body mass index; BF, body fat.

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South Wales “Move it Groove it; Physical activity inprimary schools: Summary Report” (2003). Thechecklist was comprised of eight individual FMS(Sprint Run, Side Gallop, Hop, Kick, Catch,Overarm Throw, Vertical Jump and StaticBalance), and each skill was broken down into fiveor six components (e.g. sprint run is broken downinto six components; 1: Land on ball of foot, 2: Non-support foot bent at least 90° in recovery, 3: Highknee lift, thigh parallel to ground, 4: Head and trunkstable eyes forwards, 5: Elbows bent at 90°, 6: Armsdrive forward and back, opposite legs).

Each skill was demonstrated to the children once by aresearcher, with no coaching points between attempts,in accordance with “Move it Groove it” (Department ofEducation and Training NSW, 2000) guidelines. Eachchild performed each skill three times with no feedback.Trained researchers (according to the guidelines(Department of Education and Training NSW,2000)) assisted in the recording of the FMS. The eightFMS split into locomotor (sprint, hop and gallop) wererecorded on the sagittal plane and static (balance, jump,catch, kick and throw) skills on the coronal plane(Knudson & Morrison, 2002). The performance ofeach FMS was video recorded at 50 frames per second(Sony video camera, Sony, UK) and subsequently ana-lysed using Quintic biomechanics analysis software(Quintic Consultancy Ltd, Sutton Coldfield, UK).The POC was used to determine the mastery level ofthe skill. Due to the eight FMS consisting of five or sixcomponents, a percentage was calculated to make thescore for the individual scores to be comparable. Forexample, for the sprint run, if three out of six compo-nents were present when the skill was performed theywould be given 50%mastery of that skill. After all threeattempts, each skill was analysed and an averagepercentage of mastery was calculated and used in theanalysis. Prior to analysis, inter-rater reliability was con-ducted in year 1 and year 2 producing a score of 90.3%and intra-rater reliability was 97.6% demonstratinggood reliability (Jones, Okely, Caputi, & Cliff, 2010).

Objective measurement. A 10-metre sprint run wastimed using smart speed gates (Fusion Sport,Coopers Plains, Australia). Two laser gates were setup 10 metres apart, with the participant having a flyingstart to ensure that sprint speed was measured inde-pendently of the acceleration phase. Vertical jumpheight was measured using a myo test (Myo testTECH, France). The height and mass of each partici-pant was entered into the myo test. The device wasattached to a belt on the waist of the participant. Whenthe belt beeped, three consecutive jumps were per-formed. The myo test produced the best jump heightfor the participant, which was recorded and used foranalysis.

Physical activity (PA)

PA was assessed using a sealed, piezo-electric ped-ometer (New Lifestyles, NL2000, Montana, USA)worn over four days (two weekdays and two weekenddays (Riddoch et al., 2004)). Four days of monitor-ing is a sufficient length of time to determine habi-tual PA levels in children (Trost, Pate, Freedson,Sallis, & Taylor, 2000). Children were given a ped-ometer on a Wednesday with an explanation anddemonstration of how to use it and when to recordtheir steps. Each child was provided with an instruc-tion sheet informing them how to use the ped-ometers, it also included a table to record theirdaily step counts. The pedometers were given out aday early to allow the children to practise using itbefore the data collection commenced. From theserecordings, the average daily step, average weekendstep and average weekday step counts were calcu-lated for the analysis (Table II).

Statistical analysis. Regression relationships wereanalysed between all 20 variables and age. For the15 out of 20 variables that were significantly relatedto age, unstandardised age residuals were calculatedto remove age as a confounding factor (James et al.,2005). Once this was completed, the Q-Q plot andthe kurtosis and skewness values for each variablewere assessed, with normal distribution being indi-cated as between <1 and >−1 (Kline, 2005). Thetwo variables that were not normally distributed(kick year 1 and speed year 1) were initially arcsinetransformed: y1 = 2 × arcsine(

py) (Black, 1999).

Arcsine transformation caused kick 1, but notspeed year 1, to become normally distributed.Square root transformation still caused the valuesfor speed 1 to be non-normal, but closer to thedesirable kurtosis and skewness values (0.882 and1.493, respectively). A further consideration of theQ-Q plot identified that speed 1 showed two valuesthat were large outliers. These two participants wereremoved from the data set. Once these wereremoved, values for kurtosis and skewness becamenormal. Discriminant function analysis was carriedout on the complete data set to identify whether boysand girls were significantly different groups andshould therefore be analysed separately. For boys

Table II. Mean (±SD) step counts for daily, weekend and week-day habitual PA.

GenderDaily steps(±SD)

Weekend steps(±SD)

Weekday steps(±SD)

Combined 8820 (3724) 8005 (4316) 9603 (4053)Boys 9712 (3816) 8819 (4427) 10,514 (4236)Girls 8064 (3488) 7314 (4116) 8830 (3741)

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and girls, the Wilks’ lambda value (0.514,P = < 0.001) was significant, indicating that boysand girls were significantly different and should beanalysed separately. Multiple linear regression ana-lysis, with use of the enter method, was used toassess the multicollinearity of the variables in thedata set. Values were checked for their variable infla-tion factor (VIF) value. All variables had a VIF <2,which identified that a stepwise multiple linearregression analysis would be an appropriate methodto analyse the data sets. The statistical package forsocial sciences (SPSS Inc., Version 20) was used forall analysis, and statistical significance was set atP = 0.05 a priori.

Results

Table I displays that over the year that the childrenhave been tested, the number of children of a normalweight has decreased and the number of overweight/obese has increased; however, this change is notsignificant (P = > 0.05).

Table II displays that boys are significantly moreactive than girls (P = < 0.05) and that both boys andgirls are more active on weekdays compared to week-end days; however this is only significant for boys(P = < 0.05).

Multiple linear regression with a stepwise methodwas used to produce models to predict variation inthe dependent variable (Tables III and IV). In eachcase, the FMS variable that predicted the highestamount of variation was retained in the model.Overall, for both boys and girls, current FMS was abetter predictor of current weight status (BMI andBF%). However, for both boys and girls, previousFMS mastery levels were better predictors of futurePA levels.

As mentioned in the “Statistical Analysis” sectionin the “Method” section, boys and girls were identi-fied as being significantly different groups when

measuring FMS. Below are the results for boys andgirls when controlled for age.

Boys (age-independent data)

Differences, between individuals, in jump height inyear 2 predicted more of the variation in BMI in year2 than in year 1 (6.3% compared with 3.1%).Variation in jump height in year 2 predicted moreof the inter-individual differences in BF% in year 2than in year 1 (13.8 compared with 12.6%).Differences between individuals, in the catch inyear 1, predicted more of the variation in averagedaily steps in year 2, whereas variation from anyFMS in year 2 did not predict any differences inaverage daily steps. Variation in the catch and sprintspeed in year 1 predicted more of the inter-indivi-dual differences in average weekday steps in year 2compared to the FMS in year 2 (17.6% compared to5.9%). Differences between individuals in FMS inyear 1 or in year 2 failed to predict variation inaverage weekend steps in year 2.

Girls (age-independent data)

Differences between individuals, in jump height in year2, predictedmore of the variation in BMI in year 2 thanin year 1 (12% and 5.3%, respectively). Variationin jump height in year 2 predicted more of inter-indi-vidual differences in BF% in year 2 than in year 1 (15%and 12.9%, respectively). Differences between indivi-duals, in the hop in year 1, predicted more of thevariation in average daily steps in year 2 (24.1% com-pared to 10.8%, respectively). Variation in the hop inyear 1 predicted more of the inter-individual differ-ences in average weekday steps in year 2 (21.8%compared to 10.9%, respectively). Differencesbetween individuals in jump height in year 1 predictedmore of the variation in average weekend steps in year2 (13.5% compared to 6.2%, respectively).

Table III. Multiple linear regression models that significantly predicted variation in the dependent variable via the use of age residuals offunctional movement skills in boys.

Dependent variable β slope P value % of variation Skills 1 β slope P value % of variation Skills 2

BMI 1 — — — — −1.694 0.037 3.1 Jump height 2BMI 2 — — — — −2.581 0.005 6.3 Jump height 2BF% 1 — — — — −2.375 0.01 5.3 Jump

–1.559 0.005 8.0 height 2–1.619 0.001 12.6 Balance 2

Jump 2BF% 2 −8.155 0.048 3.8 Catch 1 −10.967 0.000 13.8 Jump height 2Average daily steps 3623 0.029 6.7 Catch 1 — — — —

Average weekend steps — — — — — — — —

Average weekday steps 4307 0.006 11.2 Catch 1 3138 0.019 5.9 Run 29632 0.002 17.6 Speed 1

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Discussion

The purpose of this study was to identify whetherprevious or current FMS level best predicted futurePA levels and weight status. The results highlightthat previous FMS levels are the better predictor offuture PA levels, whereas current FMS levels arebetter predictors of current weight status whendetermined using BMI and BF%.

For BMI, figures have virtually stayed the same,with approximately 70% of children being classed asnormal weight and 30% of children as overweight orobese. This is representative of children in Englandaccording to The Health and Social CareInformation Centre, Lifestyles Statistics (2013)reports that 29.5% of children are overweight orobese.

In the present study, although individual differ-ences in current FMS do predict variation in currentPA levels, FMS one year earlier predicted a largerpercentage of the variation in PA levels in year 2.This suggests that mastery in FMS is needed for anincreased level of PA participation, rather thanincreased time spent in PA to increase FMS mastery.Previous research has failed to determine this due toonly assessing baseline measurements or not testingFMS when assessing PA in follow-up studies(Barnett et al., 2008; McKenzie et al., 2002). Theresults from the current study align with Stoddenet al.’s (2008) theoretical model. Stodden et al.’s(2008) model indicates that FMS needs to be learntand practised to influence an uptake and mainte-nance of PA, in agreement with the principle thatFMS should theoretically be a better predictor offuture PA levels. This model also supports our find-ings that current FMS will predict current weightstatus.

The highest predictor of variation in PA was ingirls, whereby individual differences in the hop per-formance in year 1 predicted 24.1% of the variationin the average daily steps in year 2. Hop is a loco-motor skill and Vandaele, Cools, de Decker, and deMartelaer (2011) highlighted that children have a

lower mastery level of locomotor skills when com-pared to other FMS. Locomotor skills are harder tomaster because they involve moving the whole bodymass from one point to another whilst simulta-neously coordinating all parts and sides of the bodyto produce the movement (Westendorp, Houwen,Hartman, and Visscher, 2011). The hop is one ofthe harder FMS to master, due to the shift of bodymass on to one leg. This not only doubles the weighton that one leg, but decreases the base of support,therefore making it harder for the centre of gravity tostay within the latter and for the child to remain onbalance (Burkett, 2010). The difficulty is furtherexacerbated by the increased motor control andcoordinative factors required to swing the restingleg and arms to aid the movement of the hop. Bydeveloping the skills to be able to master the hop, itis more likely that individuals will have mastery ofthe other FMS and therefore engage in more PA.This is however speculative as to why hop perfor-mance one year prior to PA assessment contributesto the prediction of 24.1% of the variation of PA ingirls.

In boys, individual differences in FMS in year 1predicted 17.6% of the variation in average weekdaysteps one year later, with catch and sprint speedcontributing to the model. Both Okely and Booth(2004) and Vandaele et al. (2011) have shown thatboys have a higher mastery of object control skillscompared to girls. Boys are more likely to engage inball games, organised games and utilise spacearound them (Blatchford, Baines, & Pellegrini,2003; Okely et al., 2001). Therefore, for boys topartake and maintain participation in these organisedactivities, mastery of object control skills, such as thecatch, is essential. In both boys and girls, the predic-tion of variation in average weekday steps, by varia-tion in FMS, is higher than prediction of averageweekend steps. The current study has indicatedthat children are more physically active in the weekcompared to weekends and that boys are more phy-sically active than girls, which is consistent with pre-vious research (Rowlands, Pilgrim, & Eston, 2008).

Table IV. Multiple linear regression models that significantly predicted variation in the dependent variable via the use of age residuals offunctional movement skills in girls.

Dependent variable β slope P value % of variation Skills 1 β slope P value % of variation Skills 2

BMI 1 — — — — −3.146 0.000 12.0 Jump height 2BMI 2 — — — — −4.117 0.000 15.3 Jump height 2BF% 1 −1.403 0.009 6.3 Throw 1 −3.166 0.000 12.9 Jump height 2BF% 2 — — — — −10.702 0.000 15.0 Jump height 2Average daily steps 3452 0.001 16.3 Jump height 1 2564 0.002 10.8 Hop 2

3178 0.000 24.1 Hop 1Average weekend steps 4309 0.002 13.5 Jump height 1 2586 0.014 6.2 Jump height 2Average weekday steps 3890 0.001 14.6 Hop 1 2983 0.002 10.9 Hop 2

3016 0.000 21.8 Jump height 1

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Rowland et al. (2008) analysed PA patterns inBritish primary school children and identified that adecrease in PA at the weekend was due to a drop inlight intensity exercise lasting more than five minutesand a drop in frequency of high-intensity PA bouts.Westerterp and Plasqui (2004) highlighted that it isthese low- and high-intensity activities, rather thanmoderate PA, that are the main contributors to achild’s PA status. Mota, Santos, Guerra, Ribeiro,and Duarte (2003) assessed PA levels in childrenbut took into consideration PA in school and PAafter school/evenings. In their study, girls’ PA waspredominantly made up of PA from the school day,whereas PA for boys was mainly executed afterschool. This suggests that boys are more likely toparticipate in school sports clubs compared to girls.

When the initial regression analysis was conductedon all 20 variables, 15 were significantly influencedby age. For these 15 independent variables, agecould be masking the true variation of the dependentvariables. Therefore, age residuals were analysed forthese 15 variables to ensure that all analyses under-taken in this study considered the effects of FMSindependently of the effects of age. Barnett et al.(2008) reported that object control skills can predict12.7% of variation in moderate-to-vigorous PA(MVPA) in adolescents. It was identified that“grade” significantly affected the variables measured.When using school grade, there could be up to 12months between children from the same “grade,”and therefore this could have been masking thetrue effect of the object control skills on MVPA.However, like the current study, if the effect of agewas controlled for, a higher prediction of PA mayhave been reported. This is important because recentresearch has identified relative age effects in theperformance of some tests of physical performance,including vertical jump, in a non-sport-specificpopulation of British children (Sandercock et al.,2013). Future research examining whether there isa relative age effect in performance of FMS may beof particular interest to teachers and coaches.

In addition, Barnett et al. (2008) reported overallgender differences in FMS but didn’t analyse themseparately when conducting the regression analyses.In the current study, discriminative function analysisidentified males and females as being significantlydifferent groups, and once analysed separately largervariations were explained for each gender group. Forexample, FMS in year 1 predicted 7.2% of variationin average daily steps when boys’ and girls’ data werecombined. When analysed separately, 24.1% and17.6% of variation in average daily steps was pre-dicted in girls and boys, respectively. Therefore,prior studies that have analysed the data combiningboth genders may not be reporting the true effects ofvariation in FMS (Barnett et al., 2008).

Children in the current study were at a pre-pub-ertal stage of maturation and theoretically should bebiologically the same (Malina, 2001; Thomas &French, 1985). The current study identified thatboys were significantly better at the kick and catch,whereas girls were better at balancing; this is consis-tent with previous research (Bryant, Duncan, &Birch, 2013; Okely & Booth, 2004; Vandaele et al.,2011). Children are socialised into specific types ofPA based on their gender (Blakemore and Centers,2005), suggesting why some skills are more impor-tant for predicting variation in PA depending ongender. It is however important to stress that equalopportunities need to be provided for both gendersso that all FMS can be learned, practised anddeveloped.

The present study holds limitations; the first beingthe use of pedometers in children. Adherence tostrict wear time criteria can be more problematic inchildren compared to adults, and pedometry is notable to distinguish the intensity and type of PA. Toovercome this issue, children were given diaries torecord with parents when they did not wear thepedometer and any children who had values <1000steps or >40,000 steps per day were excluded fromthe analysis (Rowe, Mahar, Raedeke, & Lore, 2004).Identifying the intensity and type of the activitiescompleted would be beneficial. However, for thepresent study, the measurement of habitual PA wasthe objective, of which Riddoch et al. (2004) identi-fied pedometery to be a valid tool to obtain thisobjective. An additional limitation of the study isthe method used to predict BF%. The sample ofchildren would have been a mixture of pre-pubertaland pubertal. Thus, perhaps an age- and a sex-spe-cific equation would be more appropriate for futurework (Rodríguez et al., 2005) compared to Slaughteret al.’s (1998) equation which does not take theseindividual characteristics. However, the methodused in the current study has been validated in theliterature (Parker, Reilly, Slater, Wells, & Pitsiladis,2003). Furthermore, this method was the mostappropriate due to it only needing two skinfoldstaken that were non-invasive and time efficient withthe large sample size.

The limitations of the use of BMI in paediatricpopulations have been identified. However, it wasincluded in the present study for comparative pur-poses with other studies. In addition, BMI is therecommended method to classify weight status bythe International Obesity Task Force, and in theUK it is the way in which policy and governmentassess weight status in children.

The current study has identified that previousFMS are better predictors of current PA levels.Therefore, FMS needs to be learned and practicedfor future success in participation and maintenance

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of PA. The results from this study suggest that FMSshould be tested in primary schools; so weaknessescan be identified in children so they can be improvedvia intervention. Bryant et al. (2013) highlighted thatchildren aged 8–10 years old have a decrease inmastery level in specific skills; therefore, this wouldbe a good target age for intervention. These resultsshould be used to influence physical education andhealth policy in the UK to help decrease obesitylevels by increasing PA levels. Not only would asustained level of PA benefit obesity but would alsohave other health benefits such as health-related fit-ness and mental well-being of the child.

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