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Physical fitness, motor skill and physical activity relationships in Grade 4 to 6 children
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Richard Larouche1,2, M.Sc.; Charles Boyer1, M.A.; Mark Stephen Tremblay1,2,3, Ph.D., FACSM;
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Patricia Longmuir1,3, Ph.D.
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Research Institute, 401 Smyth Road, Ottawa, Canada, K1H 8L1
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Healthy Active Living and Obesity Research Group, Children’s Hospital of Eastern Ontario
School of Human Kinetics, University of Ottawa, 125 University Private Ottawa, Canada, K1N
Department of Pediatrics, University of Ottawa, 401 Smyth Road, Ottawa, Canada, K1H 8L1
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E-mail
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(
[email protected]); Mark S Tremblay (
[email protected]); Patricia E Longmuir
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(
[email protected])
addresses:
Richard
Larouche
(
[email protected]);
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Author for correspondence:
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Richard Larouche, M.Sc., Ph.D. candidate
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Children’s Hospital of Eastern Ontario Research Institute
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401 Smyth Road, Room R242
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Ottawa, ON, K1H 8L1
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Phone: 1-613-737-7600 ext 4191
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Fax: 1-613-738-4800
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E-mail:
[email protected]
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Charles
Boyer
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Abstract
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The present study sought to quantify the relationships among physical activity (PA), health-
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related fitness and motor skill in children (Grades 4 to 6), and to determine whether specific tests
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of fitness or motor skill are independently associated with objectively-measured PA level. 491
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students (56.4% female) wore a Digi-Walker pedometer for 7 consecutive days. Standardized
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protocols were used to assess health-related fitness (BMI percentile, waist circumference, 20m
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shuttle run, plank, handgrip, and trunk flexibility). Motor skill was evaluated using a validated
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obstacle course. Pearson correlations (with Holm adjustments for multiple comparisons) initially
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assessed associations among PA, health-related fitness and motor skill. Multi-variable linear
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regression was used to determine which factors were significantly associated with daily step
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counts, while adjusting for gender, age, testing season and socio-economic status. Step counts
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were significantly correlated with predicted aerobic power (r=0.30), obstacle course time (r=-
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0.27), obstacle course score (r=0.20), plank isometric torso endurance (r=0.16), and handgrip
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strength (r=0.12), but not with waist circumference (r=-0.10), trunk flexibility (r=0.10) or
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overweight status (rho=-0.06). In the multi-variable model, predicted aerobic power, obstacle
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course time, testing season, gender and the predicted aerobic power by gender interaction were
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significantly associated with step counts, explaining 16.4% of the variance. Specifically, the
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relationship between predicted aerobic power and step counts was stronger in girls. These
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findings suggest that aerobic fitness and motor skill are independently associated with children’s
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PA. Future longitudinal studies should evaluate whether interventions to enhance aerobic fitness
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and motor skill could enhance daily PA among children of this age.
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Keywords: children, pedometer, health-related fitness, motor proficiency, correlates, multi-
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variable model
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Résumé
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Cette étude visait à quantifier les associations entre l’activité physique (AP), la condition
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physique et la motricité chez les enfants de la 4è à la 6è année et à déterminer si certains tests de
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condition physique ou de motricité sont associés indépendamment à l’AP mesurée
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objectivement. 491 étudiants (56,4% de filles) ont porté un podomètre Digi-Walker pendant 7
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jours consécutifs. Des protocoles standardisés ont été employés pour mesurer la condition
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physique (percentile d’IMC, circonférence de taille, test navette, planche, force de préhension et
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flexibilité du tronc). Les habiletés motrices ont été évaluées avec un parcours d’obstacles validé.
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Un modèle de régression multiple a été utilisé pour identifier les facteurs associés
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significativement au nombre de pas par jour en contrôlant pour le genre, l’âge, la saison et le
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niveau socioéconomique. Le nombre de pas par jour était significativement corrélé avec la
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puissance aérobie estimée (r=0,30), le temps pour compléter le parcours d’obstacles (r=-0,27), le
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score sur le parcours d’obstacles (r=0,20), l’endurance musculaire évaluée avec la planche
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(r=0,16) et la force de préhension (r=0,12), mais pas avec la circonférence de taille (r=-0,10), la
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flexibilité du tronc (r=-0,10) ou l’embonpoint (rho=-0,06). Dans le modèle multivarié, la
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puissance aérobie estimée, le temps pour compléter le parcours d’obstacles, la saison, le genre et
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l’interaction entre la puissance aérobie et le genre étaient significativement associés au nombre
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de pas par jour, expliquant 16,4% de la variance. La relation entre la puissance aérobie estimée et
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le nombre de pas par jour était plus forte chez les filles. Ces résultats suggèrent que la puissance
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aérobie et la motricité sont indépendamment associées avec l’AP des enfants. De futures études
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longitudinales devraient évaluer si des interventions visant à améliorer la puissance aérobie et la
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motricité peuvent accroître l’AP des enfants de cet âge.
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Mots clés : enfant, podomètre, condition physique, habiletés motrices, corrélats, modèle
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multivarié
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INTRODUCTION Population studies indicate that most North American children fail to meet current
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physical activity (PA) recommendations of 60 minutes of daily moderate-to-vigorous physical
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activity (Colley et al. 2011; Troiano et al. 2008). Moreover, during the last few decades
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significant decreases in childhood physical fitness and large increases in the prevalence of
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overweight and obesity have been reported in many countries (Lobstein et al. 2004; Tomkinson
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et al. 2003; Tremblay et al. 2010). Even in children and youth, insufficient physical activity and
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poor cardiovascular fitness are independent cardiovascular disease risk factors (Ekelund et al.
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2012). These risk factors are also likely to track from childhood to adulthood (Bao et al. 1994).
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This epidemiological evidence clearly suggests that PA should be promoted at an early
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age. While some controlled trials have been shown to achieve short-term increases in PA (van
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Sluijs et al. 2007), little is known regarding the long-term effectiveness of PA interventions
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(Trudeau and Shephard 2005). This underscores a need for a better understanding of the
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correlates of PA, and the extent to which they vary with characteristics such as gender and age
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(Bauman et al. 2002, 2012). The correlates of PA can be divided into different ‘levels of
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influence’ (e.g. individual factors, interpersonal factors, built environment, etc.) following an
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ecological approach (Bauman et al. 2012). Within this categorization, one’s physical fitness and
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motor skill would fit among the individual factors.
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An earlier literature review found generally weak associations between self-reported PA
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and aerobic fitness (Morrow and Freedson 1994). The limited validity and reliability of self-
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report instruments has been proposed as an explanation for these findings (Welk et al. 2000).
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Recently, the use of objective measures of PA has become increasingly common, and a recent
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systematic review reported a consistent low-to-moderate association between accelerometry-
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measured PA and cardiovascular fitness (Dencker and Andersen 2011). In parallel, another
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systematic review concluded that there is strong evidence supporting an association between PA
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and fundamental motor skills (Lubans et al. 2010). However, earlier studies were generally
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concerned with how PA influences fitness outcomes (or motor skill) and have typically assessed
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the link between PA and different outcomes separately. Hence, there remains a lack of data
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examining multiple health-related fitness and motor skill variables as correlates of PA levels
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within a single multi-variable model.
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Therefore, the primary objective of the current study was to determine which indicators
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of health-related fitness and motor skill are independently associated with pedometer-determined
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PA in a multi-variable model adjusted for potential confounders (age, gender, season of the year
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and school area socioeconomic status). Of note, motor skill was assessed in a dynamic setting
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using a recently developed and validated obstacle course. The second objective was to determine
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the strength of the associations between PA and indicators of physical fitness and motor skill in
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children.
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METHODS Participants. 784 students in grades 4 through 6 from 7 different schools in the Ottawa
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(Canada) region were invited to participate in the Canadian Assessment of Physical Literacy
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(CAPL) study. The study was approved by the Children’s Hospital of Eastern Ontario Research
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Ethics Board and both participating school boards. All students assented to study participation
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and written informed consent was provided by a parent/guardian. Consent and assent were
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obtained from 679 (53.2% female) participants, a response rate of 86.6%. Of these, 71.3% (n =
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484) were recruited in rural schools and 28.7% (n = 195) in urban/suburban schools.
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Measures. Data collection was performed during the fall of 2011 and winter/spring 2012.
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The primary outcome was steps per day measured by a Digi-Walker SW200 pedometer (Deep
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River, ON) that was worn over the iliac crest for 7 consecutive days. Participants completed a
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daily log sheet recording their daily step counts as well as the time the pedometer was worn
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during waking hours. Digi-walker pedometers have high validity and reliability for measuring
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steps taken (Crouter et al. 2003) and have previously been used for population-based studies of
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PA levels in Canadian children and youth (Craig et al. 2010).
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Cardiovascular fitness was measured with the Progressive Aerobic Cardiovascular
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Endurance Run (PACER) test (Welk and Meredith 2008); the 15m course was used with most
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participants, and results were converted to be equivalent to the 20m course for analysis with the
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conversion chart available at http://www.fitnessgram.net/PACER_conversion.pdf. Initially, an
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examiner explained and then demonstrated performance of the PACER test. Children were
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instructed to continue running as long as possible, even if other children have stopped. The
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PACER test was conducted in small groups (1 to 8 children per examiner depending on available
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gymnasium space) to allow more individual attention. Upper body muscle strength was assessed
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with a Takei handgrip dynamometer (A5001), with maximal grip strength assessed alternately,
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twice with each hand (Canadian Society for Exercise Physiology 2003). Trunk flexibility was
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evaluated with the sit and reach test (Canadian Society for Exercise Physiology 2003). Torso
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muscular endurance was measured with the plank isometric position test, in which participants
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were asked to maintain a straight body position (as shown in Figure 1) for as long as possible.
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The plank test has substantial convergent validity with other measures of strength and substantial
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intra-rater, inter-rater and test-retest reliability in 8 to 12 year old children (Boyer et al. 2013).
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Body composition measurements were performed following the Canadian Society for
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Exercise Physiology (2003) protocols. Specifically, height was measured to the nearest 0.1 cm,
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without footwear, using a portable stadiometer (SECA, Hamburg, Germany). Weight was
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measured to the nearest 0.1 kg with a digital scale (A&D Medical, Milpitas, CA) with the
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participants wearing light clothing without footwear. Waist circumference was measured to the
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nearest 0.5 cm at the mid-point between the top of the iliac crest and the bottom of the last
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floating rib, and at the end of a normal expiration.
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Motor skills were assessed with a newly developed obstacle course that was developed to
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facilitate in-school testing of groups of children in the context of physical education classes
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(Figure 2). Existing motor skills assessments, such as the Test of Gross Motor Development
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(Ulrich 1985) or Movement ABC (Henderson et al. 2007), are relatively more time-consuming;
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thereby undermining their feasibility in this context. These traditional assessment batteries
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require the separate performance of each individual skill, with initiation of the skill from a static
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position. In contrast, the obstacle course assessment is a dynamic setting, incorporating both
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object control and locomotor skills while the child moves through a timed and scored obstacle
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course. The time needed to complete the obstacle course is measured with a stopwatch, and the
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score is determined with a systematic observation grid that contains 20 process- and product-
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oriented motor skill criteria. Pilot data indicate modest convergent validity with the Movement
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ABC test for the time and score components (Longmuir et al. 2013). Testing also showed
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substantial inter-rater reliability for the time component, as well as inter-rater reliability and
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intra-rater reliability for the score (Longmuir et al. 2013). Participants completed the obstacle
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course 5 times; the first 3 trials were practice trials and the last 2 trials were timed and scored.
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All health-related fitness and motor skill assessments were conducted by trained research staff.
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Data Treatment. Pedometer data were excluded if participants failed to meet the
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following established criteria: 1) between 1,000 and 30,000 steps/day (Rowe et al. 2004); 2) at
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least 10 hours of data/day (Eisenmann et al. 2007) and 3) at least 3 days of valid data meeting the
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aforementioned threshold values (Tudor-Locke et al. 2005). 535 participants returned their
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pedometer log sheet (78.8% of the total sample) and application of the thresholds led to the
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exclusion of 44 participants. Thus, acceptable pedometry data were available from 491
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participants (72.3% of the total sample), including 214 boys and 277 girls. Average daily step
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counts (hereafter referred to as step counts) were used as the measure of PA in the analyses. As
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recommended in the Fitnessgram / Activitygram Reference Guide (Welk and Meredith 2008),
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the Léger equation (Léger et al. 1988) was used to estimate aerobic power based on PACER test
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results. For the handgrip test, the sum of the best trial for each hand was computed. Similarly, the
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best performances were used in the analyses for the sit and reach and the obstacle course. For the
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obstacle course, the time and score components were considered to be independent because both
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higher and lower scores could be associated with slower times. For example, a participant who
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drops the ball or makes a mistake hopping/jumping (i.e., lower score) will take more time to
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complete the course, as will someone who goes more slowly in order to concentrate on accurate
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skill performance. Body mass index (BMI) was computed from measured height and weight (e.g.
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kg/m2) and converted into percentiles based on the Centers for Disease Control (2000) growth
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charts.
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As a proxy for socioeconomic status (SES), the median household income of the census
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tract for each school location was obtained from the 2006 Canadian census (Statistics Canada
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2006). Four schools were located in census tracts in which the median income was lower than
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the Ottawa-Gatineau region average (low SES schools, n = 251 participants) and three were
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located in census tracts with above average median income (high SES schools, n = 240).
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Variables found to be skewed (handgrip, plank time, waist circumference and obstacle
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course time) were transformed into their natural logarithm in order to achieve quasi-normal
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distributions. The BMI percentile distribution was highly skewed to the left even after log-
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transformation; therefore participants were categorized by weight status (overweight: 85th
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percentile or higher; non-overweight: less than 85th percentile).
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Statistical analyses. To examine the study participants for potential biases in our sample,
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t-tests and chi-square tests were used to investigate differences between participants who
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provided acceptable pedometry data versus those who were given a pedometer, but either did not
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return their log sheet or had step counts that did not meet inclusion criteria. Differences between
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children who did not receive a pedometer and those who provided acceptable pedometer data
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were examined the same way. Second, Pearson correlations were used to assess the relationship
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among average daily step counts and the health-related fitness and motor skill variables (a
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Spearman correlation was used for weight status). Holm’s (1979) method was used to adjust for
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the multiple comparisons of step counts with these 8 variables (weight status, waist
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circumference, predicted aerobic power, plank, handgrip, trunk flexibility, obstacle course time
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and obstacle course score). Third, a linear regression analysis with backward variable selection
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using the General Linear Model (GLM) procedure (IBM SPSS 19) was used to determine which
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fitness and motor skill variables were significantly associated with step counts, while adjusting
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for gender, age, season of the year and school area SES (dichotomized as high vs. low).
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Variables and interaction terms with p values > 0.05 were manually removed from the model.
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Effect sizes were determined with the proportion of explained variance (R2), using the
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conventions proposed by Cohen (1988) (0.01 = small effect; 0.06 = moderate effect; 0.15 = large
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effect). Within the final model, the independent effect size of each predictor was determined with
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the partial eta-squared (η2) statistic with Cohen’s (1988) conventions (0.01 = small effect; 0.06 =
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medium effect; 0.14 = large effect).
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RESULTS: First, differences between the participants who provided acceptable pedometry data and
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those who did not were examined. Participants who did not provide acceptable pedometry data
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(n = 141) took more time to complete the obstacle course (17.7 vs. 16.5 seconds; t = 3.75; p
