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Research Quarterly for Exercise and Sport

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The Association between PLAYfun and PhysicalActivity: A Convergent Validation Study

Emily Bremer, Jeffrey D. Graham, Chloe Bedard, Christine Rodriguez, DeanKriellaars & John Cairney

To cite this article: Emily Bremer, Jeffrey D. Graham, Chloe Bedard, Christine Rodriguez,Dean Kriellaars & John Cairney (2019): The Association between PLAYfun and PhysicalActivity: A Convergent Validation Study, Research Quarterly for Exercise and Sport, DOI:10.1080/02701367.2019.1652723

To link to this article: https://doi.org/10.1080/02701367.2019.1652723

Published online: 16 Oct 2019.

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The Association between PLAYfun and Physical Activity: A ConvergentValidation StudyEmily Bremer a, Jeffrey D. Grahama, Chloe Bedarda, Christine Rodrigueza,b, Dean Kriellaarsc,and John Cairney a,b

aMcMaster University; bUniversity of Toronto; cUniversity of Manitoba

ABSTRACTPurpose: The purpose of this study was to examine the convergent validity of the PLAYfun tool,a physical literacy-based measure of movement competence, by examining its association withobjectively measured physical activity in a sample of children and youth. Method: Participantsincluded 110 children between the ages of seven to 14 years attending a stratified randomsample of 27 afterschool programs across the province of Ontario, Canada. The PLAYfun toolwas administered to the participants on one occasion at their afterschool program and then theywere asked to wear a pedometer for seven consecutive days to measure their physical activitylevels. A series of multiple linear regression models were used to examine the associationbetween PLAYfun scores and physical activity, while controlling for age, sex, and time of year(season) in which the data were collected. Results: On its own, the PLAYfun average scoreaccounted for close to 13% of the variance in physical activity, R = .36, R2 = .13, p < .001. ThePLAYfun average score was also a significant independent predictor of physical activity,b (SE) = 145.98 (53.46), p < .01, when controlling for age, sex, and season in which the datawere collected, R2 = .30, F (4, 105) = 11.04, p < .001. Conclusion: Results from the present studyindicate that the PLAYfun tool is a significant predictor of objectively measured physical activity,supporting the convergent validity of the tool.

ARTICLE HISTORYReceived 7 September 2018Accepted 1 August 2019

KEYWORDSMovement skill; physicalliteracy; construct validity;children; measurement

There has been a recent resurgence in interest in theassociation between movement competence and physicalactivity, especially in school-aged children and youth(Engel, Broderick, van Doorn, Hardy, & Parmenter,2018; Holfelder & Schott, 2014; Hulteen, Morgan,Barnett, Stodden, & Lubans, 2018; Logan, KiplingWebster, Getchell, Pfeiffer, & Robinson, 2015;Robinson et al., 2015). Conceptually, this makes sense;a child or youth who lacks fundamental movement skillswill be less proficient and less confident in their abilitiesto engage in active play, sport and other forms of phy-sical activity (Bouffard, Watkinson, Thompson, Dunn, &Romanow, 1996; Hulteen et al., 2018; Wall, 2004) andtherefore will be less likely to actually engage in physicalactivity. Clinically, children with neurodevelopmentaldisabilities such as developmental coordination disorder,attention deficit hyperactivity disorder and autism spec-trum disorder, also frequently present poor or under-developed movement skills, which in turn is associatedwith lower levels of physical activity (Cairney, 2015;Cairney & Veldhuizen, 2013; MacDonald, Esposito, &Ulrich, 2011; Piek & Dyck, 2004; Quesada, Ahmed,

Fennie, Gollub, & Ibrahimou, 2018). Having the requi-site skills to engage in physical activity is critical, espe-cially as children grow older and the motor demands(complexity) of physical activity increase (Wall, 2004).

At the same time, while movement competence isviewed as an important predictor of physical activitythere is large variability in the strength of the relation-ship between movement skill and physical activity inschool-aged children with some research demonstrat-ing a strong relationship, but generally finding relation-ships in the weak to moderate range (Holfelder &Schott, 2014; Logan et al., 2015). This large variabilitymay be due in part to differences in the measurementof physical activity. For example, studies employingself-reported measures of physical activity have foundthat movement skill may account for a small (e.g., 3%;Barnett, van Beurden, Morgan, Brooks, & Beard, 2009;Okely, Booth, & Patterson, 2001) to large (e.g., 41%;Castelli & Valley, 2007) amount of the variance inphysical activity. In contrast, studies employing objec-tive measures of physical activity have generally foundthat movement skill accounts for a small (e.g., 1%;

CONTACT John Cairney john.cairney@utoronto.ca Faculty of Kinesiology and Physical Education, University of Toronto, 55 Harbord St., Toronto, ONM5S 2W6, CanadaEmily Bremer is now at the University of Toronto.

RESEARCH QUARTERLY FOR EXERCISE AND SPORThttps://doi.org/10.1080/02701367.2019.1652723

© 2019 SHAPE America

Hume et al., 2008) to moderate (e.g., 9%; Wrotniak,Epstein, Dorn, Jones, & Kondilis, 2006) amount of thevariance in physical activity.

However, another explanation for these low correla-tions concerns the measurement of movement compe-tence itself. Some measures of movement competencefocus on process-related assessments (i.e., form or howthe task is completed; e.g., Test of Gross MotorDevelopment [TGMD]; Ulrich, 2000) where otherstend to measure products or outcomes (i.e., distanceor time; e.g., Bruininks-Oseretsky Test of MotorProficiency [BOTMP]; Bruininks & Bruininks, 2005),both of which can limit the variability in scores. Forexample, the scoring system of the TGMD, now in itsthird edition and the most commonly used measure ofmovement skill (Logan, Ross, Chee, Stodden, &Robinson, 2017), may be one reason for the relativelysmall amount of variability in physical activity that isexplained by movement competence. For the variousskills evaluated in the TGMD, the assessor rates thechild using discrete, dichotomous categories (i.e.,whether the skill components are or are not present).The limited response options available reduces theamount of variability in rating a child or youth’s move-ment skill. The loss of information that results from“binning” respondents into a small number of discretecategories in turn has a negative effect when we exam-ine correlations with more continuous outcomes likephysical activity. Simply put, restricting range in mea-surement reduces our ability to explain variability ina phenomenon (Streiner, Norman, & Cairney, 2015).This may explain why movement competence measuresusing discrete ordinal response categories, account foronly a modest proportion of physical activity.

In contrast, the PLAYfun tool is an objective mea-sure of movement competence included in a suite ofmeasures collectively referred to as the PhysicalLiteracy Assessment for Youth (PLAY) tools(Canadian Sport for Life, 2013; https://play.physicalliteracy.ca/play-tools/playfun). When administeredtogether, the complete suite of tools measures the affec-tive, motivational and movement competence subdo-mains of physical literacy, based on the inclusion ofthese domains in virtually all definitions of physicalliteracy (Dudley, 2015; Edwards, Bryant, Keegan,Morgan, & Jones, 2017; Whitehead, 2001). Asa measure of movement competence, PLAYfun inten-tionally differs in several important ways from existingmeasures such as the TGMD by measuring movementcompetence within the holistic framework of physicalliteracy. First, each task on the scale is administeredusing a verbal instruction, which is repeated twice (e.g.,“I want you to jump from this pylon to the next. I want

you to jump as best you can. Please jump from here tothere. Ready? Jump now.”). Following this, no furtherinstruction is provided and participants are to performone attempt of the skill. The verbal phrasing of theinstructions is intentional and designed to assess ifa child or youth comprehends the movement skillbeing assessed. In this sense, PLAYfun includes anassessment of the “knowledge” domain of the physicalliteracy construct. Second, and more germane to thisstudy, is the scoring method for each of the 18 tasksincluded in the PLAYfun tool. The tool uses a holisticrubric, where participants are rated by a trained obser-ver on a modified visual analogue scale (VAS), which isguided by four quadrants: Initial (0.00–24.99),Emerging (25.00–49.99), Competent (50.00–74.99),and Proficient (75.00–100.00). Scores below 50 indicatethe individual is still developing that skill, whereasa score above 50 indicates the skill has been acquired.That is, while the midpoint represents the threshold ofcompetence, it is still possible to rate a participant fromalong a 100-point continuum from lowest to highest.The scale represents all people regardless of age (i.e.,children are not scored relative to other children thesame age). Moreover, a score of 100 on any given skillis indicative of the “very best in the world.” This isdesigned to intentionally create an end-point aversionbias so raters would not score a child at the very highestvalue, thereby encouraging greater use of the middleand upper middle ranges of the scale. Overall, sucha scoring system increases the range of possible scoresto evaluate a participants’ performance (Wewers &Lowe, 1990). In theory, this may result in strongerassociations with continuous measures of physicalactivity.

At present, the only published psychometric data onthe PLAY measures are for the PLAYfun andPLAYbasic (a five skill subset of PLAYfun) (Cairneyet al., 2018; Stearns, Wohlers, McHugh, Kuzik, &Spence, 2018). Previous work (Cairney et al., 2018)examined one aspect of the construct validity ofPLAYfun in children ages 7 to 14 years using confir-matory factor analysis, showing that the items measur-ing each of PLAYfun’s subdomains (running,locomotion, object control—upper body, object con-trol—lower body, and balance) loaded onto thehypothesized data structure. Moreover, PLAYfun scoresalso demonstrated hypothesized sex and age differenceswhen tested in this sample of children (Cairney et al.,2018). Stearns et al. (2018) recently reported good-to-excellent inter-rater reliability on both PLAYfun andPLAYbasic, and moderate-to-large convergent validitywhen these tools were compared against the CanadianAgility and Movement Skill Assessment obstacle course

2 E. BREMER ET AL.

(Stearns et al., 2018). While these are important com-ponents of construct validation, they are notexhaustive.

Another aspect of construct validation is convergentvalidity, which is tested by comparing two or moremeasures that are theoretically related to one another.For example, given that movement competence ishypothesized to be a predictor of physical activity inchildren and youth (Barnett et al., 2016; Hulteen et al.,2018; Lubans, Morgan, Cliff, Barnett, & Okely, 2010), ifPLAYfun is indeed measuring movement competence,then we would expect to see at least a weak to moderatecorrelation between it and a measure of physical activ-ity suitable for this population. Stearns et al. (2018)examined the relationship between PLAYfun and phy-sical activity using a subjective measure of physicalactivity and found small to medium correlations.However, they did not account for age or sex, and thesample was drawn from a Northern community thatmay not represent the typical demographic of Canadianchildren. Moreover, self-reported physical activity hasbeen shown to over-estimate physical activity whencompared against more objective measures (Adamo,Prince, Tricco, Connor-Gorber, & Tremblay, 2009).

Therefore, in this paper we examine the associationbetween PLAYfun and an objective measure (i.e., stepcounts measured by pedometer) of physical activity ina sample of children and youth in order to test theconvergent validity of the PLAYfun tool. We hypothe-size that there will be a significant positive associationbetween PLAYfun and physical activity, at least in theweak to moderate range as we observe with otherexisting measures.

Method

Study design and sample

This was a cross-sectional study in which participants7–14 years of age were assessed on their movementcompetence and then asked to wear a pedometer forseven consecutive days during waking hours. Data col-lection was completed in two waves (March toMay 2016; and December 2016) as part of a largerstudy (Cairney et al., 2018). The larger study included226 consented participants derived from a stratified,random sample of 27 afterschool programs across theprovince of Ontario, Canada (see Cairney et al., [2018]for complete site stratification and selection procedure).For practicality reasons, approximately 80% of theseparticipants (n = 182) were given pedometers to wear.This study was approved by the appropriateInstitutional Research Ethics Board. Parents of

participating children provided informed, written con-sent and all children provided informed, written assentprior to their participation.

Measures

Demographic informationParticipants’ sex and date of birth were reported by theafterschool program in which the participant attendedand were confirmed by the participant prior to theonset of testing.

Movement competenceMovement competence was assessed with the PLAYfuntool (Cairney et al., 2018; Canadian Sport for Life, 2013).PLAYfun consists of 18 distinct movement tasks, com-pleted by participants, and scored on a modified100 mm VAS. For each of the 18 tasks, participants aregiven an instruction to perform the skill and then askedto execute the skill one time. Participants are rated ontheir competence for each of the 18 tasks, using a holisticrubric outlined in the manual, with scores between0.00–24.99 indicating the skill is in the initial phase ofdevelopment, scores of 25.00–49.99 indicating the skill isemerging, scores of 50.00–74.99 indicating skill compe-tence, and scores of 75.00–100.00 indicating proficiency.Assessors use the rubric to first judge the quadrant inwhich the participant will score and then at which pointin the quadrant the score lies. Assessors then mark an“X” to indicate the participant’s score on the skill.A score of 100 is reserved for the “very best in theworld” at the given skill. Domain scores are calculatedfor running, locomotor, object control—upper body,object control—lower body, and balance by averagingthe scores for the tasks included in the respectivedomains. Lastly, the PLAYfun average score is the aver-age score from all 18 tasks (Canadian Sport for Life,2013). The 18 movement tasks included in PLAYfunare linked to health and physical education curricularlearning objectives, with the expectation that children inCanada achieve a score of 50 or greater in all 18 tasks bygrade five (i.e., approximately age 10) (ManitobaEducation and Training, 2001; Ontario Ministry ofEducation, 2015). Further information regarding thetasks included in the PLAYfun tool, along with completeadministration and scoring instructions can be found inthe PLAYfun manual (https://play.physicalliteracy.ca/play-tools/playfun) and in a previous publication exam-ining the factor structure of the tool (Cairney et al.,2018).

All assessors on the PLAYfun had previously estab-lished excellent interrater reliability (>.90), and the tool

RESEARCH QUARTERLY FOR EXERCISE AND SPORT 3

has previously demonstrated an acceptable model fitusing confirmatory factor analysis (RMSEA, 0.05; 90%confidence interval, 0.03–0.08; CFI, 0.95; TLI, 0.94)(Cairney et al., 2018). All PLAYfun assessments werecompleted during the afterschool period in the gymna-sium at each respective site.

Physical activityPhysical activity was assessed with a pedometer(PiezoRX, StepsCount, Ontario, Canada). ThePiezoRX pedometer has a 34 day memory and utilizesa uniaxial piezoelectric sensor. Pedometers with piezo-electric sensors have previously been validated for usein children (Saunders et al., 2014) . Participants weregiven the pedometer at their afterschool program, fol-lowing the PLAYfun assessment, and instructed to wearit on their right hip for the subsequent seven days fromthe time they woke up until going to bed, with theexception of water activities. Following the seven-daywear period, the pedometers were collected from theafterschool program by a research assistant.

Data cleaning procedures were applied to the ped-ometer data for inclusion in the study. Given the 34-day memory of the pedometer, participants did notcomplete daily step logs as daily step counts were storedin the pedometers’ internal memory. Pedometer datawere recorded from the device into a database bya research assistant upon collecting the pedometerfrom the afterschool programs. Since we did not havelogs to confirm wear time, a threshold of 1,000 stepswas set as the minimum number of steps needed to becounted as a valid day of data (Rowe, Mahar, Raedeke,& Lore, 2004). While 30,000 steps has previously beenused as a maximal threshold for steps per day (Roweet al., 2004), research has suggested that the truncationof data points beyond this threshold does not appear tobe warranted (Craig, Tudor-Locke, Cragg, & Cameron,2010). Furthermore, in our data, only six data pointswere above 30,000 steps, therefore we did not applymaximal step count thresholds to the data. Lastly, weconsidered the pedometer data valid if there wasa minimum of two days with at least 1,000 stepsper day. A two-day wear period has been shown to bereliable and valid in representing weekly activity levelswhen compared to steps taken the rest of the week(Craig et al., 2010).

Data analysis

Pedometer data cleaning rules described above wereapplied to obtain our final data set (i.e., includingonly participants with valid data). Student t-tests wereused to evaluate differences between demographic

characteristics and PLAYfun scores for participantswith and without valid pedometer data, as well asbetween males and females. A series of six multiplelinear regression models were used to examine therelationships between PLAYfun average score and indi-vidual subdomain scores on physical activity, adjustingfor other covariates. Given that previous research hasdemonstrated that PLAYfun scores vary by age and sex(Cairney et al., 2018), these variables were included inthe regression models. Further, as two samples of par-ticipants were included in this study, with physicalactivity measured in the spring and winter, we includedseason as a predictor in the regression models toaccount for possible seasonal variation in physicalactivity (Atkin, Sharp, Harrison, Brage, & Van Sluijs,2016; Carson & Spence, 2010). All analyses were com-pleted in SPSS 25 (IBM Corporation, 2017). A p-valueof .05 was set as the minimum threshold for statisticalsignificance.

Results

Valid pedometer data were obtained from 110 partici-pants (48% male; 60% of the participants who wereprovided with a pedometer). Participants were excludedfrom the final sample for either having zero days ofvalid data (n = 67) or only one day of valid data (n = 5).No participants wore their pedometer for more thanseven days. There were no significant differences (allp-values > .25) in age, sex, or PLAYfun scores betweenparticipants with and without valid pedometer data.

Table 1. Descriptive statistics of the sample.Statistic

Variable %, n

SexMale 48%, 53Female 52%, 57

Age distributionAge 7 9.10%, 10Age 8 20.00%, 22Age 9 19.10%, 21Age 10 17.30%, 19Age 11 20.90%, 23Age 12 9.10%, 10Age 13 2.70%, 3Age 14 1.80%, 2

M, SD (Min.-Max.)Age (years) 10.20, 1.70 (7.00–14.80)Average step count 12,665, 4,674 (2,739–30,902)†

PLAYfun average 46.50, 9.20 (19.70–67.50)Running 50.40, 8.20 (23.20–70.30)Locomotor 40.30, 11.10 (14.20–73.80)Object control—upper 50.10, 10.90 (20.00–74.00)††

Object control—lower 46.80, 14.70 (4.00–76.50)Balance 47.60, 13.50 (7.40–72.00)

M = mean, SD = standard deviation. † Significantly higher in the springcompared to winter data collection (p < .001); †† Males significantlyoutperformed females (p < .001).

4 E. BREMER ET AL.

Complete descriptive statistics for the sample are pre-sented in Table 1.

Significant regression equations were found for each ofthe six models. Specifically, the model including thePLAYfun average score explained approximately 30% ofthe variance in physical activity, R2 = .30, F (4, 105) = 11.04,p < .001, and both the PLAYfun average score and seasonwere significant predictors in the model (Table 2). Whenexamining the individual subdomains of PLAYfun, we sawthat the locomotor, R2 = .29, F (4, 105) = 10.46, p < .001,and balance, R2 = .29, F (4, 105) = 10.56, p < .001, sub-domains were significant independent predictors of physi-cal activity in their respective models (Table 3). In contrast,the subdomains of running, R2 = .265, F (4, 105) = 9.48,p < .001, object control—upper body, R2 = .263, F (4,105) = 9.37, p < .001, and object control—lower body,R2 = .26, F (4, 105) = 9.14, p < .001, were not significantindependent predictors of physical activity in their respec-tive models (Table 3). Seasonwas a significant independentpredictor in all models, with less physical activity takingplace in the winter. Sex was a significant independentpredictor in the balance model only, with females engagingin less physical activity than males.

We also found that a moderate amount of the variancein physical activity can be attributed directly to thePLAYfun tool, without age, sex, and season in the regres-sion models. Specifically, the PLAYfun average score was

a significant independent predictor of physical activity,accounting for approximately 13% of the variance,R = .36, R2 = .13, p < .001. The individual subdomainsof the PLAYfun tool were also significant independentpredictors of physical activity on their own: running,R = .23, R2 = .05, p < .05; locomotor, R = .31, R2 = .09,p < .001; object control—upper, R = .22, R2 = .05, p < .05;object control—lower, R = .28, R2 = .08, p < .01; andbalance, R = .35, R2 = .12, p < .001.

Discussion

Movement competence has long been positioned as animportant variable necessary for participation in physi-cal activity (Barnett et al., 2016; Lubans et al., 2010;Robinson et al., 2015) with previous research suggest-ing that the strength of the relationship between move-ment competence and physical activity is quite variable(Holfelder & Schott, 2014; Logan et al., 2015). Morerecently, physical literacy has been gaining traction asa prominent framework in which to understand parti-cipation in physical activity through the holistic inter-action of multiple domains including movementcompetence, confidence, motivation, and knowledgeand understanding. In theory, an individual needs todevelop proficiency in each of these domains in orderto engage in lifelong physical activity. The PLAYfuntool is a measure of physical literacy that predominatelyassesses the domain of movement competence, whilealso assessing aspects of knowledge and understandingand confidence. The results of this study indicate thatthe PLAYfun tool is a significant independent predictorof objectively measured physical activity in our sampleof children when controlling for age, sex, and seasonalvariation in activity, with this model accounting forapproximately 30% of the variance in physical activity.Importantly, the PLAYfun average score accounted forapproximately 13% of the variance in physical activityon its own, which supports the convergent validity ofthe PLAYfun tool.

Our results provide slightly stronger evidence thanStearns et al. (2018) who reported correlations of .30 to.40 between the PLAYfun average score and physicalactivity. The difference here may be due to Stearns’(2018) use of a subjective measure of physical activity;that they did not account for age and sex; or becausethey reported their correlations separately by season(fall and spring), rather than controlling for seasonalvariation. Further, Stearns et al. (2018) also reportedsignificant correlations for all subdomains of thePLAYfun with the exception of balance. In contrast,our models indicate that only locomotor and balanceare significant independent predictors of physical

Table 2. Effect of PLAYfun average score on physical activity.b (SE) p-value R-squared

PLAYfun average 145.98 (53.46) <.01Age −344.31 (275.90) .22Female −1009.29 (787.16) .20Winter −3968.15 (830.23) <.001 .30

b = unstandardized coefficient, SE = standard error.

Table 3. Effects of PLAYfun subdomain scores on physicalactivity.

b (SE) p-value R-squared

Running 86.77 (52.82) .10Age −71.45 (250.55) .78Female −1237.72 (799.62) .13Winter −4438.32 (822.09) <.001 .27Locomotor 94.65 (39.64) .02Age −181.77 (254.64) .48Female −1251.90 (779.07) .11Winter −4237.28 (818.86) <.001 .29Object control—upper 73.27 (47.21) .12Age −177.02 (285.91) .54Female −919.99 (870.76) .29Winter −4506.03 (820.46) <.001 .26Object control—lower 41.70 (31.84) .19Age −66.62 (259.31) .80Female −1240.72 (812.32) .13Winter −4259.37 (854.61) <.001 .26Balance 83.15 (33.93) .02Age −176.44 (252.25) .49Female −1643.73 (772.37) .04Winter −3858.24 (856.16) <.001 .29

b = unstandardized coefficient, SE = standard error.

RESEARCH QUARTERLY FOR EXERCISE AND SPORT 5

activity when controlling for age, sex, and seasonalvariation. Again, these differences could be due to thedifferences in measurement of physical activity andanalytical procedures. However, it may also be indica-tive of differences in the relationships between thesevariables in children in remote Northern Canadiancommunities in comparison to the more representativesample of Canadian children included in the currentstudy.

The strength of the associations between overallmovement competence and physical activity in thecurrent study generally align with previous research(Holfelder & Schott, 2014; Logan et al., 2015). It isworth noting that while the strengths of these relation-ships are still weak, the breadth of tasks included in thePLAYfun tool help to provide some of the most com-prehensive evidence supporting the relationshipbetween movement competence and physical activityto-date. Previous research on the association betweenmovement competence and physical activity has beenlimited by the measures and statistical analysesemployed. For example, approximately half of theresearch on this topic has used questionnaires to assessphysical activity (Holfelder & Schott, 2014; Logan et al.,2015), which is problematic as indirect assessments areknown to overestimate physical activity in children(Adamo et al., 2009). Previous work has also typicallyincluded assessments of movement competence thatonly distinguish between locomotor and object controlskills (e.g., TGMD) or have included a limited numberof skills (e.g., single item; Reed, Metzker, & Phillips,2004), which limits our understanding of movementcompetence, as a whole, on physical activity levels. Incontrast, the PLAYfun tool includes a greater numberand breadth of movement skills, across a variety ofdomains, along with a scoring system that allows fora larger degree of variability between participants.

In regard to analyses, many studies have reportedthe relationship between movement competence andphysical activity separately by sex, but have not con-trolled for sex or age in the analyses (Holfelder &Schott, 2014; Logan et al., 2015). This limits our under-standing of how the relationship between movementcompetence and physical activity may vary across thesevariables. Previous research has found differences inphysical activity (Colley et al., 2011) and movementskill competence (Barnett, van Beurden, Morgan,Brooks, & Beard, 2010; Hardy, Reinten-Reynolds,Espinel, Zask, & Okely, 2012) by sex, with males gen-erally outperforming females in both of these areas.Our results indicate that females were slightly less phy-sically active and less proficient than males in all

subdomains of the PLAYfun with the exception ofbalance scores. However, only differences in upperbody object control skills were statistically significant.The fact that the PLAYfun tool was able to significantlypredict physical activity, irrespective of sex, furtherdemonstrates its utility as an assessment of movementcompetence for school-aged children and youth.

Physical literacy may provide a useful framework inwhich to intervene on the physical activity levels ofchildren and youth. While our results indicate thatthe PLAYfun measure of movement competence, oneof the core pillars of physical literacy, accounts fora small amount of the variance in physical activitythere is still a large amount of unexplained variance.By taking into account children’s confidence, motiva-tion, and knowledge and understanding we may be ableto better explain the physical activity levels of childrenand youth and consequently create interventions thatdeliberately target the areas with the greatest influenceon activity levels. Future longitudinal and interventionstudies are needed to explore the impact of physicalliteracy, as a complete construct, on physical activity.

A limitation to the current study is that we wereunable to discern the intensity of physical activity(e.g., moderate to vigorous physical activity) fromthe pedometer data, which may provide importantinsight into further understanding the relationshipbetween PLAYfun and physical activity. A secondlimitation is that only 60% of possible participantscomposed our final sample, with the remainderbeing excluded for a lack of valid pedometer data.Given that this study was conducted in a community-run afterschool program setting, we were limited inour ability to engage directly with parents and care-givers in order to increase adherence to the physicalactivity tracking portion of the study. While this ped-ometer adherence rate is lower than most lab-basedstudies (e.g., Hume et al., 2008), it does align withadherence rates from large-scale surveillance studies(Craig et al., 2010). Third, we did not ask participantsto complete a previous day recall of their physicalactivity data nor was there a requirement to haveone weekday and one weekend day of pedometerwear time. The pedometer used in this study hada 34-day recall, which meant that we did not needparticipants to record their steps each day; however,the recall function does not discern the day of weekmaking the differentiation between weekdays andweekends a logistical challenge. It is important tonote though that previous research has not foundsystematic intra-individual differences in pedometer-measured physical activity between days of the week

6 E. BREMER ET AL.

(Craig et al., 2010). Further, given the diverse range ofsites included in this study, the limited contact wehad with participants’ parents, and the low pedometeradherence, it was not feasible to have participantsrecord recall information and may have furtherreduced our sample by making day of week an inclu-sion criteria. A fourth limitation is that we wereunable to collect anthropometric measures of heightor leg length, which may be important covariates toconsider given that children with longer legs will takefewer steps per minute to cover the same distance astheir peers (Beets, Agiovlasitis, Fahs, Ranadive, &Fernhall, 2010). Finally, ideally we would have useda clustered approach to our data analysis given thelarge number of sites included in the study; yet, wewere unable to do this due to small cell sizes.

A strength to our study is the inclusion of indepen-dent variables in our regression models that are knownto influence both movement competence and physicalactivity including, age, sex, and seasonal variation;along with an objective measure of physical activity.With specific regard to PLAYfun, this is the first peer-reviewed study to report associations of this measurewith objectively measured physical activity. This studyalso includes a sample of participants who were drawnfrom a representative sample of afterschool programsacross Ontario, Canada.

In conclusion, these findings support the convergentvalidity of the PLAYfun tool as a measure of movementcompetence and as an independent predictor of physi-cal activity. Further work is needed to investigate howother aspects of physical literacy impact physical activ-ity participation in children and youth.

What does this article add?

This is the first study to examine the convergentvalidity of the PLAYfun tool using an objective mea-sure of physical activity. PLAYfun is one measure ina suite of tools known as the Physical LiteracyAssessment for Youth; together the suite of PLAYtools was designed to assess physical literacy.PLAYfun is unique from more traditional assess-ments of movement competence as it includes 18tasks that are scored on a 100 mm visual analoguescale, allowing for a large degree of variability inscores. Results from this study demonstrate thatscores on PLAYfun are positively associated withphysical activity in children and youth. Overall,these findings support the convergent validity ofPLAYfun. Future studies could add to this work byexamining the relationships between these variables

over time, as well as accounting for additional vari-ables (e.g., motivation, confidence, knowledge andunderstanding) that are known to impact participa-tion in physical activity.

Acknowledgments

EB was supported by a Vanier Canada Graduate Scholarship.JG was supported by a Canadian Institutes of HealthResearch Postdoctoral Fellowship. CB was supported bya Canadian Institutes of Health Research Doctoral Award.

Funding

This work was supported by funding from Sport for LifeSociety, Ontario Trillium Foundation, and the Governmentof Ontario. The funding sources did not have any involve-ment in the study design, data collection, analysis and inter-pretation of data, or writing of the manuscript.

ORCID

Emily Bremer http://orcid.org/0000-0003-1488-1032John Cairney http://orcid.org/0000-0003-2856-3967

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