Next Article in Journal
Adolescents’ Addictive Phone Use: Associations with Eating Behaviors and Adiposity
Next Article in Special Issue
Home-Based Stair Climbing as an Intervention for Disease Risk in Adult Females; A Controlled Study
Previous Article in Journal
Reflective Functioning of Refugee Mothers with Children Born of Conflict-Related Sexual Violence
Previous Article in Special Issue
Prevalence and Factors Associated with Thinness in Rural Polish Children
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 17
Issue 8
10.3390/ijerph17082874
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

What Keeps Them Physically Active? Predicting Physical Activity, Motor Competence, Health-Related Fitness, and Perceived Competence in Irish Adolescents after the Transition from Primary to Second-Level School

by
Una Britton
1,*,
Johann Issartel
1,
Jennifer Symonds
2 and
Sarahjane Belton
1
1
School of Health & Human Performance, Faculty of Science & Health, Dublin City University, D09 V209 Dublin 9, Ireland
2
School of Education, Social Sciences & Law, University College Dublin, D04 V1W8 Dublin 4, Ireland
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(8), 2874; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17082874
Submission received: 4 April 2020 / Revised: 13 April 2020 / Accepted: 16 April 2020 / Published: 21 April 2020
(This article belongs to the Collection Physical Activity and Public Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
Physical activity (PA) decreases with age. The school transition is noted for significant changes in PA behaviour. Motor competence (MC), health-related fitness (HRF), and perceived competence (PC) are generally positively associated with PA. The aim of this study was to examine longitudinal cross-lagged relationships between PA, MC, HRF, and PC across the school transition from final year of primary school to first year of second-level school in Irish youth. PA (accelerometery), object-control and locomotor MC (TGMD-III), PC (perceived athletic competence subscale of the Self-Perception Profile for Adolescents), and HRF (20 m shuttle run, horizontal jump, vertical jump, push-ups, curl-ups) were measured in final year of primary school (6th class) and first year of second-level school (1st year). In the sixth class, 261 participants (53% female; mean age 12.22 ± 0.48 years) were tested. In first year, 291 participants (48% female; mean age: 13.20 ± 0.39 years) were tested. In total, 220 participants were involved in the study at both timepoints. Cross-lagged regression in AMOS23, using full information maximum likelihood estimation, was conducted to test reciprocal and predictive pathways between variables. The full cross-lagged model showed acceptable fit (χ2 = 69.12, df = 8, p < 0.01, NFI = 0.93, CFI = 0.94). HRF was the strongest predictor of future PA (β = 0.353), and also predicted PC (β = 0.336) and MC (β = 0.163). Object-control MC predicted future PA (β = 0.192). Reciprocal relationships existed between object-control MC and PA, and between object-control MC and PC. HRF was the strongest predictor of PA. Object-control MC also predicted PA. PA promotion strategies should target the development of HRF and object-control MC in primary school to reduce the decline in PA frequently observed after the school transition.

1. Introduction

Physical activity (PA) is associated with positive health outcomes in youth [1,2], but globally there is a trend for decreasing PA with age [1,3,4]. Despite recognition of the importance of PA, and subsequent public health initiatives targeting the problem of youth physical inactivity, there is little to suggest this decline in PA is being reversed. A key period where substantial changes in PA are often reported is the transition from primary to second-level school [4,5]. In Irish youth, students in second-level school have been found to be significantly less active than their primary school counterparts [6,7]. In addition, a significant sex difference is frequently reported, with males more active than females [8,9]. The school transition in Ireland, as in several other countries, occurs at the age of approximately 12 years, often coinciding with the onset of puberty. Notwithstanding the significant physiological changes that occur at this time, changing school environment, as an independent factor, has been shown to impact on PA behaviours [5,10,11].
Numerous factors are associated with youth engagement in PA [12,13]. A seminal model published by Stodden and colleagues in 2008 [13], which includes motor competence (MC), health-related fitness (HRF), and perceived competence (PC) as factors associated with PA, has since guided a body of research within the PA domain seeking to understand pathways that promote engagement in PA. Within this model MC is positioned as a primary determinant of PA, with HRF and PC proposed to mediate the relationship between MC and PA [13]. Pathways between each of the variables are hypothesised to be reciprocal and developmental, in that each variable has an impact on each other variable and the strength of these associations is likely to change with age [12,13].
MC is generally used to describe goal-directed movement requiring coordination of the body [14]. An important aspect of MC is proficiency in fundamental movement skills (FMS) [13]. FMS are considered the basic building blocks needed for future PA [15] and include locomotor skills, such as running, skipping and jumping, and object-control skills such as kicking, throwing and striking [16]. Longitudinally, MC has been shown to be positively associated with PA, with low-MC children often becoming low-active adolescents [17,18,19]. It is generally accepted that children have the capacity to master the basic components of most FMS by the age of between 6 and 9 years [16]. Recent research however shows that many young people are not reaching the expected level of FMS mastery, both nationally [20,21] and internationally [22]. Sex differences in MC are also frequently reported, with females consistently found to be less competent in motor skills than males [8,17,23]. Considering the separate components that make up MC, females consistently score lower in object-control MC compared to males [23,24]. Sex differences have not been found to be as pronounced in locomotor MC with some studies finding no sex difference in locomotor MC [23,24], while others report females as more proficient [25].
HRF, a proposed mediator in Stodden et al.’s (2008) [13] model, generally refers to a multidimensional construct consisting of cardiorespiratory endurance (CRE), muscular strength (MS), muscular endurance (ME), and flexibility [26]. Across all components of HRF, males have consistently been shown to outperform females [27,28]. Often, researchers choose just one component of HRF as a measure of fitness, with CRE most frequently assessed [7,29,30] due to the well-established positive association between CRE and health [31]. There is a growing body of evidence that supports a positive association for MS and ME with health [32], indicating that MS and ME, in addition to CRE should be measured in youth. Longitudinally, childhood HRF is found to be a positive predictor of adolescent PA [33,34], while cross-sectionally, HRF is also positively associated with MC in youth [14]. Current trends for HRF report that the majority of youth (>60%) are achieving acceptable levels of CRE [9,28]. That being said, Tomkinson et al. (2018) [35] found that for every year from the age of nine, the percentage of males and females achieving a healthy CRE level decreased by 3% and 7%, respectively. Considering the strong evidence for a positive association between HRF and health in youth, and cognisant of the drop-off in PA frequently reported during adolescence, establishing the role of HRF at this stage of development may be an important step in understanding how and why youth disengage from PA.
PC refers to an individual’s ability to effectively master a task [36] and, like HRF, is positioned as a mediator of the MC-PA relationship within Stodden et al.’s (2008) [13] conceptual model. Cross-sectionally, positive associations between PC and self-report PA in youth have frequently been reported [37,38]. When assessing PA using objective measures however, the same positive association has not always been found [39,40]. In relation to MC, a positive association between PC and MC has been identified in youth, with some longitudinal studies finding that MC positively predicts PC in older adolescents [41]. Sex and age have been reported to influence PC levels in youth. Males are frequently found to have higher PC levels than females [8,42], and PC has been found to be higher in younger compared to older youth [8]. In some cases, PC has been found to be negatively affected by school transition [43]. However, a review of the effects of school transition on psychological development generally highlighted how there are numerous interacting factors which determine whether the school transition has a positive or negative impact on individuals [44]. Research on the development of PC in the physical domain within an Irish setting is scarce, particularly in relation to changes during the school transition. Considering the significant impact that the school transition may have on PC [43] and the role that PC may play in promoting future PA [13], it is important to establish the nature of the relationship between PC and PA over the course of the school transition, with a view to reducing the negative impact of this transition on future PA.
Individual pathways outlined in Stodden et al.’s (2008) [13] conceptual model have been tested, both cross-sectionally and longitudinally. However, the complexity of the proposed model, particularly the hypothesised reciprocal pathways, requires a longitudinal design including all the variables identified by Stodden et al. (2008) [13] to fully understand the predictive and reciprocal nature of the pathways. In addition, despite the frequently reported differences between males and females in PA, MC, HRF and PC, the model does not account for sex differences in its hypothesised pathways. The current study aims to evaluate the pathways outlined in Stodden et al.’s (2008) [13] conceptual model, taking account of potential differences by sex, during a critical period in a child’s life, the transition from primary to second-level school. Using cross-lagged panel analysis this study aims to gain an understanding of the primary predictors of PA post school transition. The main research hypotheses are; (1) MC and HRF in the sixth class will predict MVPA in first year, (2) sex differences will exist in the nature of the predictive pathways between variables, (3) relationships between variables across the school transition will be reciprocal.

2. Materials and Methods

2.1. Participants

Data were collected from participants in the 6th class of primary school (January–April 2017), and one year later in 1st year of second-level school (January–May 2018). Participant recruitment began with contacting second-level schools which fit the criteria of having a maximum of three main feeder primary schools. Two second-level school principals consented for their schools to be involved in the study. Following this, the main feeder primary schools for both consenting second-level schools were contacted. Six primary school principals (three feeder schools for each second-level school) provided consent. Parental consent and participant assent were obtained in the 6th class of primary school for each participating student. At baseline, 261 participants were tested (53% female and 47% males; mean age 12.22 ± 0.48 years). Of these, 41 participants were not available for testing in 1st year of second-level school as they did not transfer into the catchment area schools selected for data collection. In 1st year, 291 participants (48% female and 52% male; mean age: 13.20 ± 0.39 years) were tested. The sample in 1st year included an additional 71 students who had not attended the feeder primary schools. These additional participants provided assent, and parental consent, prior to data collection in 1st year. In total, 335 participants were involved in the study for at least one timepoint, and 220 of these participated at both timepoints. Ethical approval for all measures was obtained from the authors’ institutional ethics committee (ethical approval number: DCUREC2016_109).

2.2. Measures

Actigraph (Actigraph LLC, Pensacola, FL, USA) accelerometers (models: GT1M, GT3X, GT3X+, wGT3X-BT), set to capture data in 10-s epochs, were used to measure PA [45]. Participants wore the accelerometer for nine consecutive days during waking hours. To account for subject reactivity the first and last days of the wear period were excluded from analysis [46]. A reminder text message was sent each morning to participants who had provided a contact phone number to increase compliance [47]. Minutes of moderate-vigorous PA (MVPA) per day were calculated using Evenson et al. (2008) cut-points [48] using Actilife (Actigraph LLC, Pensacola, FL, USA) version 6.13.3. Non-wear time was identified as greater than 20 min of zero-counts, and count values of <0 and ≥15,000 per minute were excluded [45]. Similar to other studies measuring PA across school transition, greater than or equal to 8 h was considered a valid day [5,49]. The minimum number of valid days required for inclusion in analysis were two week- and one weekend day [5].
MC was assessed using skills selected from the 3rd edition of the Test of Gross Motor Development(TGMD-III [50] and the Victoria Department of Education (1996) manual [51]: (1) object control skills: kick, catch, overhand throw, one-hand strike, and two-hand strike, (2) locomotor skills: run, skip, horizontal jump, and vertical jump. Each skill is made up of movement components. The presence or absence of a component is scored with a 1 or 0 respectively. Participants were videoed performing each skill and these videos were later scored by trained researchers.
Harter’s Self-Perception Profile for Adolescents (SPPA) [52] was used to measure perceived athletic competence (PAC). Athletic competence is one of nine dimensions in the SPPA, with the others being scholastic competence, social competence, physical appearance, job competence, romantic appeal, behavioural conduct, close friendship, and global self-worth. Each dimension is measured using five items scored with a structured alternative format scale designed to reduce social desirability [52]. Each item has two criteria, e.g., “Some kids feel that they are better than others their age at sports BUT other kids don’t feel they can play as well”. Participants first select which criteria they fit best with, then score this as either “sort of true for me” or “really true for me”, creating a four-point scale. In the PAC dimension, items regard how well participants feel they do at sport, and how athletic they are in general. Higher scores indicate more positive self-perceptions [52]. At both timepoints the PAC subscale showed satisfactory internal reliability (Cronbach’s alpha coefficient; 0.83 and 0.87), composite reliability (CR; 0.85 and 0.88), and convergent validity (Mean Variance Extracted: 0.49 and 0.56) in 6th class and 1st year respectively. Values greater than or equal to 0.70 for Cronbach’s alpha and CR indicate acceptable reliability, while values greater than or equal to 0.50 for mean variance extracted indicate acceptable convergent validity [53].
HRF was measured using five tests (20 metre shuttle run, horizontal jump, vertical jump, push-ups and curl-ups). A previous confirmatory factor analysis (CFA) showed these five tests give a fair representation of HRF in youth [54]. Protocols for each HRF test can be found in manuals for FITNESSGRAM [55] and EUROFIT [56], and in the HELENA study [27,57] (Table 1).

2.3. Data Processing

Due to the longitudinal study design, a missing data analysis was conducted. All variables had some degree of missingness at both timepoints. In 6th class 220 participants (65%) had missing data on at least one variable. In 1st year 295 participants (88%) had missing data on at least one variable. A large proportion of this missing data was due to accelerometer non-compliance. Excluding MVPA, 68% of participants in 6th class had complete data, while 34% of participants had complete data in 1st year. Little’s “missing completely at random” (MCAR) test was conducted to determine the nature of missingness for data at each timepoint. Little’s MCAR test was insignificant for data in 6th class (Little’s MCAR χ2 (33) = 22.05, p = 0.93) and 1st year (Little’s MCAR χ2 (37) = 52.28, p = 0.05) indicating that missing data was MCAR [59]. Given the random nature of the missing data, a path analysis using full information maximum likelihood estimation in AMOS version 23 (IBM Corp. Armonk, NY, USA) was chosen as a means to estimate cross-lagged and autoregressive pathways between variables in 6th class and 1st year [60]. Maximum likelihood estimation is recommended for longitudinal data with missing values that are MCAR as it uses all available data for each participant to estimate model pathways [61,62].
Confirmatory factor analyses (CFA) were conducted in AMOS version 23 to create latent variables for locomotor MC, object-control MC, PAC, and HRF. First, the measurement models for each latent variable in 6th class were tested. All items tested for each variable were included in the initial CFA’s. Following statistical and theoretical analyses, catch was removed from the object-control MC latent variable due to both its relatively low loading (0.43; [63]), and the nature of its assessment within the TGMD. The success criteria for the catch include using two hands to catch a tennis ball. For older children and adolescents, advanced execution of the skill generally results in catching with one hand. Therefore, if evaluating the catch as directed in the TGMD manual, participants with more advanced skill levels who catch the ball with one hand will ultimately fail on the “catch with two hands” criteria. This indicates a clear issue with the inclusion of catch within the object-control MC latent variable when evaluating this skill in older children and adolescents. The removal of the catch resulted in an improvement in model fit in the object-control MC latent variable. All individual items were retained for locomotor MC, PAC, and HRF. Each of the latent variables showed acceptable model fit in 6th class (Table 2). Following this, measurement invariance from 6th class to 1st year was assessed and showed all latent variables to be invariant over time (Table 3).

2.4. Analysis

Mean values for each variable at each time point were calculated and independent samples t-tests were conducted to analyse sex differences for each variable in 6th class, and in 1st year. Effect sizes were calculated to determine the magnitude of any differences.
A cross-lagged regression model was specified consisting of four latent (locomotor MC, object-control MC, AC, and HRF) and one observed (MVPA) variable, each measured in 6th class and again in 1st year. Full panel analyses in AMOS, using maximum likelihood estimation, was used to test all hypotheses simultaneously. The model included autoregressive paths for each variable over time and cross-lagged paths from each variable in 6th class to each other variable in 1st year. As measurement invariance was satisfied the full cross-lagged regression model was specified with observed variables to allow for a more parsimonious model fit [64]. Variables that had a correlation of ≥0.30 within timepoints were allowed to correlate. Goodness of fit for the full model, was examined using a variety of measures including the Chi-square (χ2) test, the Comparative Fit Index (CFI), the Normed Fit Index (NFI), and the Root Mean Square Error of Approximation (RMSEA). A statistically insignificant χ2 indicates a good fitting model, although a statistically significant χ2 need not necessarily signify poor model fit, given that large samples have been shown to give erroneous χ2 results [65]. CFI and NFI values of >0.9 show acceptable model fit [66,67], while values of >0.95 for CFI are considered to show superior fit [68]. RMSEA and CFI are two of the most frequently reported fit statistics [59]. In models with few degrees of freedom (df) interpretation of RMSEA can, however, result in rejecting a good-fitting model [68].
Using the multigroup analysis, a Chi-square (χ2) difference test was conducted on the full model to identify any sex differences in pathways. A significant χ2 test indicates variance between sexes for model fit [67]. The table of critical ratios was also inspected. Values >1.96 for a given pathway indicate a significant difference between groups for this pathway [60].

3. Results

The mean values and sex differences for each variable at both timepoints are presented in Table 4. In both sixth class and first year, males were significantly more active, had significantly higher HRF levels, and were significantly better in object-control MC (Table 4). Males also had significantly higher PAC than females in first year, but not in sixth class (Table 4). Similarly, males were significantly better in locomotor MC in first year, but not in sixth class (Table 4). The magnitude of the differences between males and females can be interpreted from the effect size (d) (Table 4). According to Cohen (1992) [69] effect sizes of 0.20–0.49 are small, 0.50–0.79 are medium, and ≥0.80 are large.
Analysis of the full cross-lagged model showed a reasonable fit (χ2 = 69.12, df = 8, p < 0.01, NFI = 0.93, CFI = 0.94, RMSEA = 0.15 (CI: 0.12–0.19)). Pathways with standardised regression coefficients greater than 0.10 are shown in Figure 1.
Each variable in sixth class significantly and positively predicted itself in first year (Figure 1). HRF and object-control MC were highly stable over time, while all other variables showed moderate stability over time. MVPA was the least stable over time.
HRF was a moderate and significant predictor of MVPA (β = 0.353) and PAC (β = 0.336) in first year, and significantly predicted first year locomotor MC (β = 0.163), though this association was small. Other than the associations between sixth class HRF and first year MVPA and PAC, all other pathways from sixth class to first year variables were small (0.10–0.29). Reciprocal relationships between object-control MC and MVPA, and object-control MC and PAC were found. In addition, object-control MC in sixth class was a significant predictor of HRF (β = 0.125) and locomotor MC (β = 0.165) in first year. Locomotor MC in sixth class negatively predicted MVPA in first year, though this association was small and insignificant (β = −0.175). PAC in sixth class was a predictor of locomotor MC in first year (β = 0.100).
For variance in first-year variables, the overall model predicted 81% variance in HRF, 61% variance in PAC, 44% variance in object-control MC, 39% variance in locomotor MC, and 34% variance in MVPA. Excluding autoregressive pathways, HRF was the only 6th class variable to exhibit moderate associations with multiple variables in 1st year. Object-control MC also contributed to a variance in multiple factors in first year, but these associations were small.
Significant differences in model fit for males and females were found following a χ2 difference test (χ2 = 48.02, df = 25, p < 0.004). Inspection of the critical ratios table showed differences in regression coefficients for several pathways in the model (Table 5).
A moderate autoregressive pathway was found for MVPA in males, but the same pathway in females was extremely weak. A strong autoregressive pathway was found for object-control MC in females, but the same pathway for males was small. A small positive pathway from sixth class MVPA to first year object-control MC was found for males, but for females the pathway was negative, and much stronger. Similarly, the pathway from sixth class MVPA to first year locomotor MC in females was moderate and negative, whereas for males there was no visible pathway between 6th class MVPA and first year locomotor MC (Figure 2).

4. Discussion

The purpose of this study was to identify pathways between PA, MC, HRF and PAC, across the transition from primary to second-level school in a cohort of adolescent Irish youth. Building on previous research [8] which identified baseline HRF as a strong mediator between MC and PA, the current research sought to better understand the predictive role of each of the variables identified in Stodden et al.’s (2008) [13] model. A significant finding from this research is the role of HRF in predicting future PA, PAC and MC. Even when accounting for baseline PA, HRF was the strongest predictor of 1st year MVPA. HRF also positively predicted 1st year PAC and locomotor MC. Britton et al. (2019) [8] demonstrated that pathways were stronger in the direction of HRF to MC and PA rather than in the reverse direction. Results from the current study support these findings providing evidence for the important role of HRF in predicting future PA. Indeed, contrary to Stodden et al.’s (2008) [13] model, where MC is hypothesised as the driving force for engaging in PA, in this sample, HRF is the primary factor positively predicting MVPA post school-transition.
A positive association between HRF and PA in youth has frequently been reported, both cross-sectionally [1,70,71] and longitudinally [33,72]. Jaakkola et al. (2016) [72] found both MC and fitness in early adolescence to have a significant impact on self-report PA in young adulthood, with the association with moderate PA stronger for fitness than for MC. In the current sample, compared to MC, HRF acted as a much stronger predictor of future PA. While MC is undoubtedly an important factor for PA engagement, it is possible that HRF is more important in promoting involvement in PA. Involvement in PA can of course in turn subsequently allow for development of MC [73] and further enhancements in HRF [1]. In a study examining adolescents involvements with the ball during recreational soccer matches, Ré et al. (2016) [74] found that HRF, along with agility, was the strongest predictor of involvements with the ball, while soccer specific technical skills did not significantly contribute to an individual’s involvement. At a recreational level, having higher levels of HRF may enable youth to be more engaged within a PA setting, as demonstrated by Ré et al. (2016) [74], which may then allow for subsequent development of MC, as well as promoting higher levels of MVPA.
In comparison to other cross-sectional studies examining associations between HRF and MC [75], the longitudinal nature of the present study allows for analysis of predictive pathways between variables and clearly shows that HRF is a stronger predictor of MVPA compared to MC. Considering the potential importance of HRF in enabling youth to engage in MVPA, as demonstrated from the current findings, and the numerous health benefits associated with HRF [31], it is essential that practitioners seek to promote HRF development in youth. However, in the same way that repetitively striking a ball from a tee, such as outlined in MC test manuals, would not be recommended as a method to develop MC in youth, simply mimicking HRF tests as a means of developing HRF should also not be encouraged. Novel and fun programmes that develop HRF with a view to promoting lifelong engagement in PA are more likely to be successful [76], rather than overly prescriptive methods of HRF promotion. Programmes that take this more holistic approach seem to be more likely to succeed in their goals of increasing HRF, PA and MC [77].
In addition to HRF, object-control MC was the only other variable in sixth class that significantly and positively predicted MVPA in first year. Similar to the current results, Barnett et al. (2009) [17] found that object-control, but not locomotor MC, at age 10 significantly predicted self-report PA six years later. Object-control MC appears to be more stable over time compared to locomotor MC [24]. The relationship between object-control MC and MVPA in the current sample was reciprocal, supporting the hypothesis outlined in Stodden et al.’s (2008) [13] conceptual model. This is in keeping with a previous study which reported a positive reciprocal relationship in older adolescents for object-control MC and self-report MVPA [78]. Consistent with Barnett et al.’s (2011) findings, the current study found no reciprocal relationship between MVPA and locomotor MC. In the current sample object-control MC, more so than locomotor MC, was predictive of future PA. Object-control MC encompasses skills such as kicking, throwing, and striking. These skills are required for participation in many of Ireland’s most popular youth sports [79]. Despite other forms of activity available to young people which require locomotor proficiency (cycling, walking, running), it is likely that activities which require object-control MC are more popular in an Irish adolescent population, and that, without proficiency in this component of MC, youth are likely to disengage from popular sports and thus fail to be sufficiently physically active for health. In addition to a reciprocal relationship between object-control MC and MVPA, a reciprocal relationship was also found between object-control MC and PAC. While PAC did not predict MVPA in this sample, similar to other studies using objective measures of PA [40], it did positively predict object-control MC. In fact, this reciprocal relationship was slightly stronger in the direction of PAC predicting object-control MC rather than the reverse. It may be that higher levels of PAC provide young people with the confidence to develop their skills, and that, in time the knock-on effect of higher PAC on MC would then promote future engagement in MVPA. Further research is required to fully investigate this pathway in adolescence.

Differences Between Males and Females

Substantial differences in some of the pathways depicted in the model were seen between males and females in the current study. For males, MVPA in sixth class was a strong predictor of first year MVPA, whereas for females this pathway was virtually non-existent. This supports previous research nationally [80] and internationally [81] which has found PA to be more stable in males compared to females over time. In the current study, PA was less stable across the school transition compared to HRF, MC and PAC. PA is a behaviour, whereas HRF and MC are individual attributes [82]. Compared to HRF and MC, PA is more likely to be influenced by psychosocial factors [83], which can be magnified during major transitions such as the school transition [81]. It is possible that the instability in PA seen in females during the school transition reflects a greater impact of school transition on females compared to males. In research on the effects of the school transition on adolescents, Anderson et al. (2000) [84] found that the impact of the transition on self-perceptions was greater on females. It may be that the same holds true for PA behaviours during this transitional period.
While MVPA was less stable in females than males across the transition, the opposite was found for object-control MC, indicating that object-control MC is less likely to change in females compared to males across the school transition. A longitudinal study in Irish youth reported that, between the ages of 10 and 18, females were more likely to drop out of organised sport and less likely to take up a new sport compared to males [79]. Popular youth sports in Ireland, such as Gaelic games, soccer, and basketball [79] provide the opportunity to develop many object-control skills. If females are more likely to drop out of sport during the adolescent years [79], then it is likely they will not have the opportunity to further develop their object-control MC, potentially explaining the higher stability in this MC component over time.
Contrary to expectation, sixth class MVPA negatively predicted first year MC for females. In comparison, sixth class MVPA did not have a substantial effect on first year MC for males. MC has been identified as an important factor for social acceptance within peer groups for males [85]. For females however, no positive association is seen between MC and popularity with peers [85]. In fact, earlier studies found that sport competency was associated with being the least popular member in female peer groups [86,87]. This could potentially explain how MVPA in sixth class negatively predicted MC in first year for females in this sample. Female participants who were relatively active in primary school may recognise that being competent in sports or motor skills is not in fact conducive to being accepted within their peer group, and as a result may depress their abilities simply to “fit in”.
That being said, HRF in sixth class was a positive predictor of MC for females in first year. HRF in females, and males, is highly stable over time. In comparison, MVPA for females is highly unstable. This lack of stability in MVPA compared to HRF for females may in part explain how MVPA could negatively predict MC while HRF positively predicts MC. Notwithstanding the significant mean change in MVPA in females across the school transition, significant intra-individual change is also occurring, as indicated by the low stability of MVPA. Thus, females who were highly active in sixth class may be low active in first year and vice versa. This may then have a knock-on effect on their execution of MC in first year. In comparison, HRF in females is much more stable indicating that fitness levels attained by sixth class of primary school carry through into first year of second-level school. In addition, compared to MVPA which is a singular variable, HRF is a multifaceted construct [26,88]. Having high levels of HRF indicates that an individual displays fitness across a range of different activity-types (e.g., activities requiring CRE, MS and ME). Higher levels of HRF in sixth class likely provide individuals with the tools to engage in numerous activities in first year that require proficiency in various FMS, thus enabling the development of MC [82]. It is unlikely that females who were active in 6th class lose the capacity to successfully execute the basic components of many object-control and locomotor skills in first year. It is more likely that the perceived value of MC and PA within the female peer group impacts on the desire of some female adolescents to appear competent in what is often perceived as a male environment [85].

5. Conclusions

This is the first study to explore the predictive pathways between all of the variables depicted in Stodden et al.’s (2008) [13] model across the school transition. In addition, the use of CFA to check for measurement invariance in each variable over time in this study is a crucial yet often ignored practice in longitudinal analyses. Poor accelerometer compliance, and the subsequent loss of PA data, along with a high degree of missing data in general, is a limitation of this study, not uncommon in longitudinal research in this field. However, the use of full information maximum likelihood path analysis allowed for analysis on pathways between variables from primary to second-level school to be conducted, despite missing data.
The findings from the current study point to the importance of HRF for future PA engagement in adolescents. Whereas MC is commonly positioned as one of the primary predictors of PA, this study found HRF, measured as a composite, to be more important in predicting PA across the transition from primary to second-level school. Object-control proficiency, as a component of MC, was however found to have a positive effect on PA, and other factors shown to be associated with positive PA behaviour. From this perspective it would seem that developing HRF and object-control MC in primary school may be crucial factors to consider as we strive to address the frequently reported decrease in PA observed across the school transition.

Author Contributions

Conceptualization, U.B., S.B., J.I.; methodology, U.B., S.B., J.I.; Formal analysis; U.B., J.S.; Investigation, U.B.; Writing—original draft preparation, U.B.; Writing—review and editing; U.B., S.B., J.I., J.S.; Data curation, U.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

The authors would like to thank the students and staff of the participating schools for their involvement in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bailey, D.P.; Boddy, L.M.; Savory, L.A.; Denton, S.J.; Kerr, C.J. Associations between cardiorespiratory fitness, physical activity and clustered cardiometabolic risk in children and adolescents: The HAPPY study. Eur. J. Pediatr. 2012, 171, 1317–1323. [Google Scholar] [CrossRef] [Green Version]
  2. Ekelund, U.; Luan, J.; Sherar, L.B.; Esliger, D.W.; Griew, P.; Cooper, A. Moderate to Vigorous Physical Activity and Sedentary Time and Cardiometabolic Risk Factors in Children and Adolescents. J. Am. Med. Assoc. 2012, 307, 704–712. [Google Scholar] [CrossRef] [Green Version]
  3. Inchley, J.; Kirby, J.; Currie, C. Physical Activity Among Adolescents in Scotland: Final Report of the PASS Study; Child and Adolescent Health Research Unit (CAHRU): Edinburgh, Scotland, 2008. [Google Scholar]
  4. De Meester, F.; Van Dyck, D.; De Bourdeaudhuij, I.; Deforche, B.; Cardon, G. Changes in physical activity during the transition from primary to secondary school in Belgian children: What is the role of the school environment? BMC Public Health 2014, 14, 261. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Marks, J.; Barnett, L.M.; Strugnell, C.; Allender, S. Changing from primary to secondary school highlights opportunities for school environment interventions aiming to increase physical activity and reduce sedentary behaviour: A longitudinal cohort study. Int. J. Behav. Nutr. Phys. Act. 2015, 12, 59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Gavin, A.; Keane, E.; Callaghan, M.; Molcho, M.; Kelly, C.; Nic Gabhainn, S. The Irish Health Behaviour in School-Aged Children (HBSC) Study 2014; Department of Health and Children: Dublin, Ireland, 2014; ISBN 9781908358028. [Google Scholar]
  7. Woods, C.B.; Tannehill, D.; Quinlan, A.; Moyna, N.; Walsh, J. The Children’s Sport Participation and Physical Activity Study (CSPPA); Research Report No 1.; School of Health and Human Performance, Dublin City University: Dublin, Ireland; Irish Sports Council: Dublin, Ireland, 2010. [Google Scholar]
  8. Britton, Ú.; Issartel, J.; Belton, S. Small Fish, Big Pond: The role of health-related fitness and perceived athletic competence in mediating the physical activity-motor competence relationship during the transition from primary to secondary school. J. Sport Sci. 2019, 37, 2538–2548. [Google Scholar] [CrossRef] [PubMed]
  9. Belton, S.; Issartel, J.; McGrane, B.; Powell, D.; O’Brien, W. The Y-PATH programme: A consideration for physical literacy in Irish youth, and implications for physical education in a changing landscape. Irish Educ. Stud. 2018, 38, 193–211. [Google Scholar] [CrossRef]
  10. Lau, E.Y.; Dowda, M.; McIver, K.L.; Pate, R.R. Changes in Physical Activity in the School, Afterschool, and Evening Periods During. J. Sch. Health 2017, 87, 531–537. [Google Scholar] [CrossRef]
  11. Taymoori, P.; Berry, T.R.; Lubans, D.R. Tracking of physical activity during middle school transition in Iranian adolescents. Health Educ. J. 2011, 71, 631–641. [Google Scholar] [CrossRef]
  12. Robinson, L.E.; Stodden, D.F.; Barnett, L.M.; Lopes, V.P.; Logan, S.W.; Rodrigues, L.P.; D’Hondt, E. Motor Competence and its Effect on Positive Developmental Trajectories of Health. Sports Med. 2015, 45, 1273–1284. [Google Scholar] [CrossRef]
  13. Stodden, D.F.; Goodway, J.D.; Langendorfer, S.J.; Roberton, M.A.; Rudisill, M.E.; Garcia, C.; Garcia, L.E. A Developmental Perspective on the Role of Motor Skill Competence in Physical Activity: An Emergent Relationship. Quest 2008, 60, 290–306. [Google Scholar] [CrossRef]
  14. Cattuzzo, M.T.; Dos Santos Henrique, R.; Ré, A.H.N.; de Oliveira, I.S.; Melo, B.M.; de Sousa Moura, M.; de Araújo, R.C.; Stodden, D. Motor competence and health related physical fitness in youth: A systematic review. J. Sci. Med. Sport 2014, 19, 123–129. [Google Scholar] [CrossRef] [PubMed]
  15. Clark, J.; Metcalfe, J. The mountain of motor development: A metaphor. In Motor Development: Research and Reviews; Clark, J.E., Humphrey, J., Eds.; NASPE Publications: Reston, VA, USA, 2002; Volume 2, pp. 163–190. [Google Scholar]
  16. Gallahue, D.; Ozmun, J. Understanding Motor Development: Infants, Children, Adolescents, Adults; Jones & Bartlett Learning: Burlington, MA, USA, 2012. [Google Scholar]
  17. Barnett, L.M.; van Beurden, E.; Morgan, P.J.; Brooks, L.O.; Beard, J.R. Childhood Motor Skill Proficiency as a Predictor of Adolescent Physical Activity. J. Adolesc. Heal. 2009, 44, 252–259. [Google Scholar] [CrossRef] [PubMed]
  18. Lloyd, M.; Saunders, T.J.; Bremer, E.; Tremblay, M.S. Long-term importance of fundamental motor skills: A 20-year follow-up study. Adapt. Phys. Act. Q. 2014, 31, 67–78. [Google Scholar] [CrossRef] [PubMed]
  19. Lopes, V.P.; Rodrigues, L.P.; Maia, J.A.R.; Malina, R.M. Motor coordination as predictor of physical activity in childhood. Scand. J. Med. Sci. Sports 2011, 21, 663–669. [Google Scholar] [CrossRef]
  20. Belton, S.; O’ Brien, W.; Meegan, S.; Woods, C.; Issartel, J. Youth-Physical Activity Towards Health: Evidence and background to the development of the Y-PATH physical activity intervention for adolescents. BMC Public Health 2014, 14, 122. [Google Scholar] [CrossRef]
  21. O’Connor, S.; Whyte, E.F.; Gibbons, B.; Feeney, O.; Luc, S.; Ní Chéilleachair, N. Fundamental movement skill proficiency in juvenile Gaelic games. Sport Sci. Health 2018, 14, 161–172. [Google Scholar] [CrossRef]
  22. Mitchell, B.; McLennan, S.; Latimer, K.; Graham, D.; Gilmore, J.; Rush, E. Improvement of fundamental movement skills through support and mentorship of class room teachers. Obes. Res. Clin. Pract. 2013, 7, 230–234. [Google Scholar] [CrossRef]
  23. O’ Brien, W.; Belton, S.; Issartel, J. Fundamental movement skill proficiency amongst adolescent youth. Phys. Educ. Sport Pedagog. 2015, 21, 557–571. [Google Scholar] [CrossRef]
  24. Barnett, L.M.; van Beurden, E.; Morgan, P.J.; Brooks, L.O.; Beard, J.R. Gender differences in motor skill proficiency from childhood to adolescence: A longitudinal study. Res Q Exerc Sport 2010, 81, 162–170. [Google Scholar]
  25. Barnett, L.M.; Morgan, P.; van Beurden, E.; Beard, J. Perceived sports competence mediates the relationship between childhood motor skill proficiency and adolescent physical activity and fitness: A longitudinal assessment. Int. J. Behav. Nutr. Phys. Act. 2008, 5, 40. [Google Scholar] [CrossRef] [Green Version]
  26. Caspersen, C.J.; Powell, K.E.; Christenson, G.M. Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public Health Rep. 1985, 100, 126–131. [Google Scholar] [PubMed]
  27. Ortega, F.B.; Ortega, F.B.; Artero, E.G.; Ruiz, J.R.; España-Romero, V.; Jiménez-Pavón, D.; Vicente-Rodriguez, G.; Moreno, L.A.; Manios, Y.; Béghin, L.; et al. Physical fi tness levels among European adolescents: The HELENA study. Br. J. Sports Med. 2011, 45, 20–29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Santos, R.; Mota, J.; Santos, D.A.; Silva, A.M.; Baptista, F.; Sardinha, L.B. Physical fitness percentiles for Portuguese children and adolescents aged 10–18 years. J. Sports Sci. 2014, 32, 1510–1518. [Google Scholar] [CrossRef] [PubMed]
  29. Barnett, L.M.; van BeurdenN, E.; Morgan, P.J.; Brooks, L.O.; Beard, J.R. Does Childhood Motor Skill Proficiency Predict Adolescent Fitness? Med. Sci. Sport. Exerc. 2008, 40, 2137–2144. [Google Scholar] [CrossRef] [Green Version]
  30. Kriemler, S.; Zahner, L.; Schindler, C.; Meyer, U.; Hartmann, T.; Hebestreit, H.; Brunner-La Rocca, H.P.; van Mechelen, W.; Puder, J.J. Effect of school based physical activity programme (KISS) on fitness and adiposity in primary schoolchildren: Cluster randomised controlled trial. BMJ 2010, 340, c785. [Google Scholar] [CrossRef] [Green Version]
  31. Ortega, F.; Ruiz, J.R.; Castillo, M.J.; Sjöström, M. Physical fitness in childhood and adolescence: A powerful marker of health. Int. J. Obes. 2008, 32, 1–11. [Google Scholar] [CrossRef] [Green Version]
  32. Smith, J.J.; Eather, N.; Morgan, P.J.; Plotnikoff, R.C.; Faigenbaum, A.D.; Lubans, D.R. The Health Benefits of Muscular Fitness for Children and Adolescents: A Systematic Review and Meta-Analysis. Sports Med. 2014, 44, 1209–1223. [Google Scholar] [CrossRef]
  33. Aires, L.; Andersen, L.B.; Mendonça, D.; Martins, C.; Silva, G.; Mota, J. A 3-year longitudinal analysis of changes in fitness, physical activity, fatness and screen time. Acta Paediatr. Int. J. Paediatr. 2010, 99, 140–144. [Google Scholar] [CrossRef]
  34. Larsen, L.R.; Kristensen, P.L.; Junge, T.; Rexen, C.T.; Wedderkopp, N. Motor Performance as Predictor of Physical Activity in Children: The CHAMPS Study-DK. Med. Sci. Sports Exerc. 2015, 47, 1849–1856. [Google Scholar] [CrossRef]
  35. Tomkinson, G.R.; Carver, K.D.; Atkinson, F.; Daniell, N.D.; Lewis, L.K.; Fitzgerald, J.S.; Lang, J.J.; Ortega, F.B. European normative values for physical fitness in children and adolescents aged 9-17 years: Results from 2 779 165 Eurofit performances representing 30 countries. Br. J. Sports Med. 2018, 52, 1445–1456. [Google Scholar] [CrossRef] [Green Version]
  36. Feltz, D.L. Self-confidence and sports performance. Exerc. Sport Sci. Rev. 1988, 16, 423–457. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. De Meester, A.; Maes, J.; Stodden, D.; Cardon, G.; Goodway, J.; Lenoir, M.; Haerens, L. Identifying profiles of actual and perceived motor competence among adolescents: Associations with motivation, physical activity, and sports participation. J. Sports Sci. 2016, 34, 2027–2037. [Google Scholar] [CrossRef] [PubMed]
  38. Masci, I.; Schmidt, M.; Marchetti, R.; Vannozzi, G.; Pesce, C. When Children’s Perceived and Actual Motor Competence Mismatch: Sport Participation and Gender Differences. J. Mot. Learn. Dev. 2018, 6, S440–S460. [Google Scholar]
  39. Crane, J.R.; Naylor, P.J.; Cook, R.; Temple, V.A. Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten? J. Phys. Act. Health 2015, 12, 954–961. [Google Scholar] [CrossRef] [PubMed]
  40. Kavanaugh, K.; Moore, J.B.; Hibbett, L.J.; Kaczynski, A.T. Correlates of subjectively and objectively measured physical activity in young adolescents. J. Sport Heal. Sci. 2015, 4, 222–227. [Google Scholar] [CrossRef] [Green Version]
  41. Cantell, M.H.; Smyth, M.M.; Ahonen, T.P. Two distinct pathways for developmental coordination disorder: Persistence and resolution. Hum. Mov. Sci. 2003, 22, 413–431. [Google Scholar] [CrossRef]
  42. Piek, J.P.; Baynam, G.B.; Barrett, N.C. The relationship between fine and gross motor ability, self-perceptions and self-worth in children and adolescents. Hum. Mov. Sci. 2006, 25, 65–75. [Google Scholar] [CrossRef]
  43. Arens, A.K.; Yeung, A.S.; Craven, R.G.; Watermann, R.; Hasselhorn, M. Does the timing of transition matter? Comparison of German students’ self-perceptions before and after transition to secondary school. Int. J. Educ. Res. 2013, 57, 1–11. [Google Scholar] [CrossRef]
  44. Symonds, J.E.; Galton, M. Moving to the next school at age 10–14 years: An international review of psychological development at school transition. Rev. Educ. 2014, 2, 28–30. [Google Scholar] [CrossRef] [Green Version]
  45. Esliger, D.W.; Copeland, J.L.; Barnes, J.D.; Tremblay, M.S. Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. J. Phys. Act. Heal. 2005, 2, 366–383. [Google Scholar] [CrossRef]
  46. Dossegger, A.; Ruch, N.; Jimmy, G.; Braun-Fahrländer, C.; Mäder, U.; Hänggi, J.; Hofmann, H.; Puder, J.J.; Kriemler, S.; Bringolf-Isler, B. Reactivity to accelerometer measurement of children and adolescents. Med. Sci. Sports Exerc. 2014, 46, 1140–1146. [Google Scholar] [CrossRef] [Green Version]
  47. Belton, S.; O’ Brien, W.; Wickel, E.E.; Issartel, J. Patterns of noncompliance in adolescent field-based accelerometer research. J. Phys. Act. Health 2013, 10, 1181–1185. [Google Scholar] [CrossRef] [PubMed]
  48. Evenson, K.R.; Catellier, D.J.; Gill, K.; Ondrak, K.S.; McMurray, R.G. Calibration of two objective measures of physical activity for children. J. Sports Sci. 2008, 26, 1557–1565. [Google Scholar] [CrossRef] [PubMed]
  49. Jago, R.; Page, A.S.; Cooper, A.R. Friends and physical activity during the transition from primary to secondary school. Med. Sci. Sports Exerc. 2012, 44, 111–117. [Google Scholar] [CrossRef]
  50. Ulrich, D. Test of Gross Motor Development-3, 3rd ed.; Pro-Ed: Austin, TX, USA, 2016. [Google Scholar]
  51. Victoria Department of Education. Fundamental Motor Skills. A Manual for Classroom Teachers; Community Information Service, Department of Education: Melbourne, Australia, 1996; 52p. [Google Scholar]
  52. Harter, S. The Self-Perception Profile for Adolescents: Manual and Questionaires; The University of Denver, Department of Psychology: Denver, CO, USA, 2012; 47p. [Google Scholar]
  53. Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Mena, J.A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 2012, 40, 414–433. [Google Scholar] [CrossRef]
  54. Britton, Ú.; Issartel, J.; Fahey, G.; Conyngham, G.; Belton, S. What is health-related fitness? Investigating the underlying factor structure of fitness in youth. Eur. Phys. Educ. Rev. 2019. [Google Scholar] [CrossRef]
  55. Plowman, S.A.; Mahar, M.T. FITNESSGRAM/ACTIVITYGRAM Reference Guide, 4th ed.; The Cooper Institute: Dallas, TX, USA, 2013. [Google Scholar]
  56. Council of Europe Testing Physical Fitness: Eurofit; Council of Europe, Committee for the Development of Sport: Strasbourg, France, 1983; pp. 1–18.
  57. Ortega, F.; Artero, E.; Ruiz, J.; Vicente-Rodriguez, G.; Bergman, P.; Hagströmer, M.; Ottevaere, C.; Nagy, E.; Konsta, O.; Pez, R.-L.; et al. Reliability of health-related physical fitness tests in European adolescents. The HELENA Study. Int. J. Obes. 2008, 32, S49–S57. [Google Scholar] [CrossRef] [Green Version]
  58. Coulson, M.; Archer, D. Practical Fitness Testing; A & C Black Publishers Ltd.: London, UK, 2009. [Google Scholar]
  59. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics, 5th ed.; Allyn and Bacon: Boston, MA, USA, 2007. [Google Scholar]
  60. Arbuckle, J.L. IBM® SPSS® AmosTM 23 User’s Guide; StataCorp LLC: College Station, TX, USA, 2014; ISBN 9781597180955. [Google Scholar]
  61. Enders, C.K.; Bandalos, D.L.; Enders, C.K. The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models. Educ. Psychol. Pap. Publ. 2001, 8, 430–457. [Google Scholar] [CrossRef]
  62. Shin, T.; Davison, M.; Long, J. Maximum Likelihood Versus Multiple Imputation for Missing Data in Data in Small Longitudinal Samples With Nonnormality. Psychol. Methods Am. Psychol. Assoc. 2016, 22, 426–449. [Google Scholar] [CrossRef]
  63. Brown, T.A. Confirmatory Factor Analysis for Applied Research; Guilford Press: New York, NY, USA, 2006. [Google Scholar]
  64. Harju, L.K.; Hakanen, J.J.; Schaufeli, W.B. Can job crafting reduce job boredom and increase work engagement? A three-year cross-lagged panel study. J. Vocat. Behav. 2016, 95–96, 11–20. [Google Scholar] [CrossRef]
  65. Byrne, B. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 3rd ed.; Routledge: New York, NY, USA, 2010. [Google Scholar]
  66. Schumacker, E.R.; Lomax, G.R. A Beginner’s Guide to Structural Equation Modeling; Erlbaum: Hillsdale, NJ, USA, 1996. [Google Scholar]
  67. Kline, R. Principles and Practice of Structural Equation Modeling, 4th ed.; Sage Publications Ltd.: London, UK, 2015. [Google Scholar]
  68. Hu, L.; Bentler, P. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  69. Cohen, J. A Power Primer. Psychol. Bull. 1992, 112, 155–159. [Google Scholar] [CrossRef] [PubMed]
  70. Burgi, F.; Meyer, U.; Granacher, U.; Schindler, C.; Marques-Vidal, P.; Kriemler, S.; Puder, J.J. Relationship of physical activity with motor skills, aerobic fitness and body fat in preschool children: A cross-sectional and longitudinal study (Ballabeina). Int. J. Obes. 2011, 35, 937–944. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Haugen, T.; Ommundsen, Y.; Seiler, S. The Relationship Between Physical Activity and Physical Self-Esteem in Adolescents: The Role of Physical Fitness Indices. Pediatr. Exerc. Sci. 2013, 25, 138–153. [Google Scholar] [CrossRef] [Green Version]
  72. Jaakkola, T.; Yli-Piipari, S.; Huotari, P.; Watt, A.; Liukkonen, J. Fundamental movement skills and physical fitness as predictors of physical activity: A 6-year follow-up study. Scand. J. Med. Sci. Sports 2016, 26, 74–81. [Google Scholar] [CrossRef] [PubMed]
  73. Jaakkola, T.; Hakonen, H.; Kankaanpää, A.; Joensuu, L.; Kulmala, J.; Kallio, J.; Watt, A.; Tammelin, T.H. Longitudinal associations of fundamental movement skills with objectively measured physical activity and sedentariness during school transition from primary to lower secondary school. J. Sci. Med. Sport 2019, 22, 85–90. [Google Scholar] [CrossRef]
  74. Ré, A.H.N.; Cattuzzo, M.T.; Ré, A.H.N.; Cattuzzo, M.T.; Henrique, S.; Stodden, D.F.; Nicolai, A.H.; Cattuzzo, M.T. Physical characteristics that predict involvement with the ball in recreational youth soccer. J. Sport Sci. 2016, 34, 1716–1722. [Google Scholar] [CrossRef]
  75. Stodden, D.F.; True, L.K.; Langendorfer, S.J.; Gao, Z. Associations Among Selected Motor Skills and Health-Related Fitness: Indirect Evidence for Seefeldt’s Proficiency Barrier in Young Adults? Res. Q. Exerc. Sport 2013, 84, 397–403. [Google Scholar] [CrossRef]
  76. Belton, S.; O’Brien, W.; McGann, J.; Issartel, J. Bright spots, physical activity investments that work: Workplace Challenge. Br. J. Sports Med. 2018, 52, 1026–1028. [Google Scholar]
  77. Lai, S.K.; Costigan, S.A.; Morgan, P.J.; Lubans, D.R.; Stodden, D.F.; Salmon, J.; Barnett, L.M. Do School-Based Interventions Focusing on Physical Activity, Fitness, or Fundamental Movement Skill Competency Produce a Sustained Impact in These Outcomes in Children and Adolescents? A Systematic Review of Follow-Up Studies. Sports Med. 2014, 44, 67–79. [Google Scholar] [CrossRef]
  78. Barnett, L.M.; Morgan, P.J.; Van Beurden, E.; Ball, K.; Lubans, D.R. A reverse pathway? Actual and perceived skill proficiency and physical activity. Med. Sci. Sports Exerc. 2011, 43, 898–904. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Hardie Murphy, M.; Rowe, D.A.; Woods, C.B. Impact of physical activity domains on subsequent physical activity in youth: A 5-year longitudinal study. J. Sports Sci. 2017, 35, 262–268. [Google Scholar] [CrossRef] [PubMed]
  80. Murphy, M.H.; Rowe, D.A.; Woods, C.B. Sports Participation in Youth as a Predictor of Physical Activity: A 5-Year Longitudinal Study. J. Phys. Act. Heal. 2016, 13, 704–711. [Google Scholar] [CrossRef] [PubMed]
  81. Anderssen, N.; Wold, B.; Torsheim, T. Tracking of Physical Activity in Adolescence. Res. Q. Exerc. Sport 2005, 76, 119–129. [Google Scholar] [CrossRef]
  82. Hands, B.; Larkin, D.; Parker, H.; Straker, L.; Perry, M. The relationship among physical activity, motor competence and health-related fitness in 14-year-old adolescents. Scand. J. Med. Sci. Sports 2009, 19, 655–663. [Google Scholar] [CrossRef]
  83. Rowland, T.W. Children’s Exercise Physiology; Human Kinetics: Champaign, IL, USA, 2005. [Google Scholar]
  84. Anderson, L.; Jacobs, J.; Schramm, S.; Splittgerber, F. School transitions: Beginning of the end or a new beginning? Int. J. Educ. Res. 2000, 33, 325–339. [Google Scholar] [CrossRef]
  85. Grimminger, E. Sport motor competencies and the experience of social recognition among peers in physical education—A video-based study. Phys. Educ. Sport Pedagog. 2013, 18, 506–519. [Google Scholar] [CrossRef]
  86. Feltz, D.L. Athletics in the status system of female adolescents. Rev. Sport Leis. 1978, 3, 98–108. [Google Scholar]
  87. Williams, J.M.; White, K.A. Adolescent status systems for males and females at three age levels. Adolescence 1983, 2, 381–389. [Google Scholar]
  88. Utesch, T.; Bardid, F.; Busch, D.; Strauss, B. The relationship between motor competence and physical fitness from early 2 childhood to early adulthood: A meta-analysis. Sports Med. 2019, 49, 541–551. [Google Scholar] [CrossRef] [Green Version]
Ijerph 17 02874 g001 550
Figure 1. Autoregressive and cross-lagged pathways between variables in 6th class (T1) and 1st year (T2). Note: * Standardised regression coefficients significant at p < 0.05; ** p < 0.01; *** p < 0.001. Dashed line = non-significant pathway.
Figure 1. Autoregressive and cross-lagged pathways between variables in 6th class (T1) and 1st year (T2). Note: * Standardised regression coefficients significant at p < 0.05; ** p < 0.01; *** p < 0.001. Dashed line = non-significant pathway.
Ijerph 17 02874 g001
Ijerph 17 02874 g002 550
Figure 2. Pathways between variables from 6th class (T1) to 1st year (T2) that were statistically significantly different between males and females. Only significantly different pathways are shown. All other pathways were not statistically different by sex. Note: * Standardised regression coefficients significant at p < 0.05; ** p < 0.01; *** p < 0.001. Grey line = female pathway; black line = male pathway. Dashed line = non-significant pathway. Regression coefficients shown in italics = male values.
Figure 2. Pathways between variables from 6th class (T1) to 1st year (T2) that were statistically significantly different between males and females. Only significantly different pathways are shown. All other pathways were not statistically different by sex. Note: * Standardised regression coefficients significant at p < 0.05; ** p < 0.01; *** p < 0.001. Grey line = female pathway; black line = male pathway. Dashed line = non-significant pathway. Regression coefficients shown in italics = male values.
Ijerph 17 02874 g002
Table
Table 1. Measurement of health-related fitness (HRF).
Table 1. Measurement of health-related fitness (HRF).
HRF ComponentTestSource
CRE20 MSTFITNESSGRAM; EUROFIT
MSHJEUROFIT; HELENA Study
-VJ *HELENA Study
MEPush-upsFITNESSGRAM
-Curl-upsFITNESSGRAM
Note: CRE = cardiorespiratory endurance; MS = muscular strength; ME = muscular endurance; 20 MST = 20 metre shuttle run test; HJ = horizontal jump; VJ = vertical jump; FITNESSGRAM [55]. The HELENA Study [27]. * VJ was assessed using the Abalakov jump test protocol outlined in the HELENA study [27,57] and using a jump mat and belt [58] in place of an infrared jump platform.
Table
Table 2. Fit indices for latent variables in 6th class.
Table 2. Fit indices for latent variables in 6th class.
Variableχ2Dfp-ValueCFIRMSEALCIUCI
PAC5.5650.3520.9900.0400.0200.070
Object-Control *1.120.5761.0000.0000.0000.090
Locomotor2.6520.2660.9800.0310.0000.117
HRF14.2630.0030.9600.1060.0550.164
Note: * catch removed due to low loading. χ2 = Chi-square; Df = degrees of freedom; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation; LCI = lower confidence interval; UCI = upper confidence interval; PAC = perceived athletic competence; HRF = health-related fitness.
Table
Table 3. Measurement invariance for latent variables.
Table 3. Measurement invariance for latent variables.
Variableχ2Dfp-Value
PAC6.3340.176
Object-Control *7.3330.062
Locomotor5.29130.152
HRF7.78640.100
Note: * catch removed due to low loading; χ2 = Chi-square; Df = degrees of freedom; PAC = perceived athletic competence; HRF = health-related fitness.
Table
Table 4. Descriptive statistics for measured variables.
Table 4. Descriptive statistics for measured variables.
MaleFemaletp ad b
Testing PeriodnMeannMean
6th class MVPA (mins)5859.27 ± 24.557748.50 ± 16.822.870.0050.52
1st year MVPA (mins)4444.95 ± 18.835935.11 ± 12.123.040.0030.64
6th class HRF1281.32 ± 3.71133−1.27 ± 3.086.12<0.0010.76
1st year HRF1551.41 ± 3.43143−1.51 ± 2.658.22<0.0010.95
6th class PAC1222.92 ± 0.671252.75 ± 0.731.940.0530.24
1st year PAC1352.92 ± 0.701192.58 ± 0.793.6<0.0010.46
6th class object-control MC12027.91 ± 4.0111823.12 ± 5.557.62<0.0010.99
1st year object-control MC7728.88 ± 3.516224.90 ± 5.086.57<0.0010.92
6th class locomotor MC11929.29 ± 3.8511929.02 ± 3.640.550.5800.07
1st year locomotor MC9930.78 ± 3.218624.90 ± 5.082.460.0151.40
Note:ap-value for sex differences; b Cohen’s d effect size; MVPA = moderate-vigorous physical activity; HRF = health-related fitness; PAC = perceived athletic competence; MC = motor competence.
Table
Table 5. Pathways by sex.
Table 5. Pathways by sex.
MalesFemales
Pathwayβpβp
T1 MVPA–T2 MVPA0.590***0.019NS
T1 Object-Control–T2 Object-Control0.298**0.654***
T1 MVPA–T2 Object-Control0.144NS−0.329**
T1 MVPA–T2 Locomotor−0.004NS−0.358**
Note: β = standardised regression coefficient; * significant at p < 0.05; ** p < 0.01; ***p < 0.001; NS = non-significant; T1 = 6th class; T2 = 1st year, MVPA = moderate-vigorous physical activity.

Share and Cite

MDPI and ACS Style

Britton, U.; Issartel, J.; Symonds, J.; Belton, S. What Keeps Them Physically Active? Predicting Physical Activity, Motor Competence, Health-Related Fitness, and Perceived Competence in Irish Adolescents after the Transition from Primary to Second-Level School. Int. J. Environ. Res. Public Health 2020, 17, 2874. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17082874

AMA Style

Britton U, Issartel J, Symonds J, Belton S. What Keeps Them Physically Active? Predicting Physical Activity, Motor Competence, Health-Related Fitness, and Perceived Competence in Irish Adolescents after the Transition from Primary to Second-Level School. International Journal of Environmental Research and Public Health. 2020; 17(8):2874. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17082874

Chicago/Turabian Style

Britton, Una, Johann Issartel, Jennifer Symonds, and Sarahjane Belton. 2020. "What Keeps Them Physically Active? Predicting Physical Activity, Motor Competence, Health-Related Fitness, and Perceived Competence in Irish Adolescents after the Transition from Primary to Second-Level School" International Journal of Environmental Research and Public Health 17, no. 8: 2874. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17082874

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop