# Comparing physical literacy scores by sex and movement behaviours across physical literacy profiles in French elementary school students (10 to 11 years old): an exploratory study from the 'EPLPA' project

Henri Moncanis, Pierre-Yves de Müllenheim, Nicolas Hauw

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Henri Moncanis, Pierre-Yves de Müllenheim, Nicolas Hauw. Comparing physical literacy scores by sex and movement behaviours across physical literacy profiles in French elementary school students (10 to 11 years old): an exploratory study from the 'EPLPA' project. 2026. hal-05458082

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# Comparing physical literacy scores by sex and movement behaviours across physical literacy profiles in French elementary school students (10 to 11 years old): an exploratory study from the 'EPLPA' project

Henri Moncanis¹, Pierre-Yves de Müllenheim ¹, Nicolas Hauw ¹ *

¹ APCoSS, UCO-IFEPSA - Les Ponts-de-Cé, France

*Corresponding author

Correspondence: nhauw@uco.fr

## ABSTRACT

Many studies support the idea that physical literacy can be a functional tool for promoting an active lifestyle. However, few scientific studies have been conducted in France to quantify the level of physical literacy, especially among children. Ninety-nine fifth-grade students (Avrillé, Maine-et-Loire) were assessed using the CAPL-2 scale across the following dimensions: physical (i.e., aerobic capacity, motor skills, trunk muscle strength), behavioural (i.e., physical activity level), affective (i.e., motivation and confidence), and cognitive (i.e., knowledge and understanding). CAPL-2 total scores, combinations of CAPL-2 subscores by sex and combinations of movement behaviour metrics across physical literacy profiles ('Beginning', 'Progressing', 'Achieving', 'Excelling') were compared using nonparametric analyses. Boys appeared stochastically superior to girls for CAPL-2 total score (P = 0.002). Boys and girls differed in terms of combinations of scores related to the CAPL-2 dimensions (P = 0.005), with scores that were generally stochastically superior for boys, specifically regarding affective dimension. Physical literacy profiles differed in terms of combinations of movement behaviour metrics (P < 0.001), in particular when considering step-based metrics and percentage of wear time spent in moderate-to-vigorous physical activity. These exploratory results encourage to conduct studies that would severely test sex differences in CAPL-2 scores and physical literacy profiles differences in movement behaviour metrics.

**Key words:** physical literacy; physical activity; CAPL-2; ActiGraph™ wGT3X-BT, sex
# INTRODUCTION

The transition from childhood to adolescence is a pivotal period for establishing a healthy and sustainable lifestyle (Graber et al., 2014). It is marked by different physiological, cognitive, and socio-affective changes that may result in instability or vulnerability in the young person's future life (Signorini et al., 2017). It is now well documented that a physically active lifestyle provides numerous health benefits during adolescence (Biddle et al., 2019). These include improvements in cardiorespiratory and muscular fitness, bone and cardiometabolic health, as well as positive effects on weight status. It is all the more important to develop an active lifestyle during adolescence as this could predict physical activity in adulthood (Telama et al., 1997). However, trends in physical activity show a deficit in teenagers, with more than four out of five school-going adolescents aged between 11 and 17 not being sufficiently physically active in 2016 (Guthold et al., 2020). In France, 37% of children aged between 6 and 10 do not comply with physical activity recommendations, compared with 73% of teenagers aged between 11 and 17 (Verdot, 2020). However, an English study reported that reductions in physical activity occurred earlier (around the age of 7) and less during adolescence (Farooq et al., 2018).

In order to improve physical activity among adolescents and children, it is necessary to focus on qualitative approaches (Biddle et al., 2019). This implies increasing physical activity in all its forms, focusing on quality rather than quantity, particularly through physical literacy (Guthold et al., 2020). This concept is emerging as a promising alternative to internalize the value of physical activity in one's life course. Physical literacy has emerged as a key determinant of commitment to physical activity and the reduction of sedentary behaviour (Clark et al., 2022; Öztürk et al., 2023; Stevens-Smith, 2016). It is defined as the motivation, confidence, physical competence, knowledge and understanding that an individual possesses, enabling them to value and take responsibility for lifelong physical activity (Tremblay, Costas-Bradstreet, et al., 2018). This concept is based on four dimensions (Edwards et al., 2018): physical (i.e., physical skills); behavioural (i.e., engagement in physical activity); affective (i.e., motivation and confidence in physical activity); and cognitive (i.e., knowledge and understanding of physical activity). These dimensions make it possible to adopt a holistic approach to physical literacy journey, going beyond the 'quantitative' assessment of a young person's physical activity.

The lens of physical literacy offers new perspectives to analyse physical activity behaviours. For example, girls are in proportion more physically inactive than boys worldwide ((Guthold et al., 2020)). However, when considering the concept of physical literacy, scientific findings regarding sex differences have been mixed, with several studies that found no difference (Caldwell et al., 2020; Vuletic et al., 2023; Zhang et al., 2023) and other studies that observed significant differences (Barnett et al., 2023; Hadier et al., 2024; Li et al., 2022; Longmuir, Gunnell, et al., 2018; Muñoz et al., 2024). Globally higher physical literacy profiles are
associated with a more active and less sedentary lifestyle (Clark et al., 2022). This work was confirmed by a study that aimed at identifying physical literacy profiles and examining their link with physical activity patterns (Brown et al., 2020). However, to our knowledge, no study has investigated the link between physical literacy profiles and various movement behaviour metrics, and only a few studies have sought to measure physical literacy levels in young populations (Kaioglou et al., 2020; Li et al., 2022; Nezondet et al., 2023; Tremblay, Longmuir, et al., 2018), with no studies conducted in French children.

The present article reports on analyses that have been exploratory conducted to compare girls and boys in terms of physical literacy scores and to compare physical literacy profiles in terms of movement behaviour metrics. These analyses have been based on data from the first cohort of children related to the scientific project called ‘Evaluation and Promotion of Physical Literacy in Avrillé’ (EPLPA), which aims to quantify the level of physical literacy among the town's CM2 children over three cohorts. As recently stated for exploratory studies conducted in sport sciences (Ditroilo et al., 2025), the general interest of this study is to provide results highlighting potentially interesting research directions related to physical literacy in French children.

# METHODS

## Participants

A convenience sample of 99 children enrolled in the final year of primary school (10-11 years old) was recruited from six primary schools located in the department of Maine-et-Loire, France, which participate in the “Evaluation and Promotion of Physical Literacy in Avrillé” (EPLPA) program. The study protocol was orally presented to all the children from the visited schools and submitted in writing to their respective parents. Only children who completed an assent form and provided parental or guardian consent were included in the study. The study was approved by the Catholic University of the West Research Ethics Committee and the local Inspectorate of Education in Nantes.

## Physical literacy assessment

We assessed physical literacy following the procedure described in the CAPL-2 (Longmuir, Gunnell, et al., 2018), consisting of getting a physical literacy score (i.e., 100 points). Three domains of the CAPL-2 (physical competence, daily behaviour, and motivation and confidence) are equally weighted (i.e., 30 points each) whereas one domain (knowledge and understanding) has a weaker relative contribution to total physical literacy score (i.e., 10 points).
Briefly, physical competence score was based on three tests: the Canadian Agility and Movement Skill Assessment (CAMSA, 10/10 points), which is a timed course combining fundamental and complex movements such as catching, running, throwing and shooting (Longmuir et al., 2017); the Progressive Aerobic Cardiovascular Endurance Run (PACER, 10/10 points), which is a running test allowing the characterization of aerobic capacity (Meredith et al., 2010); the abdominal plank (10/10 points), which consists of holding a static position as long as possible to characterize isometric strength of the trunk muscles (Boyer et al., 2013). Daily behaviour score was based on two metrics: the average daily step count obtained over a 7-day accelerometer-based measurement (25/25 points); and the self-reported number of days with moderate-to-vigorous physical activity (MVPA) during the seven days preceding the time of assessment (5/5 points). Average daily step count was obtained using an ActiGraph wGT3X-BT accelerometer (ActiGraph LLC, Pensacola, FL) that was worn during awake time only (from getting out of bed to going to sleep). The accelerometer was placed at the right hip thanks to an elastic belt and recorded data at a 90-Hz sampling rate. The accelerometer had to be removed during water-based activities (e.g., swimming, showering). Children had to complete a logbook to indicate when they removed and then reused the accelerometer. After completing the measurement, accelerometer data were downloaded and exported to .agd files with 1-s epochs using ActiLife 6 software and the normal filter. Subsequent analysis was performed using the {activAnalyzer} R package (de Müllenheim, 2025a). Data were further accumulated to 15-s epochs. Only epochs between 6:00 a.m. and 12:00 p.m. were considered for analysis. Nonwear periods were defined as periods with 0 activity count (vector magnitude) lasting 60 minutes or more, with an allowance of time frames of activity that lasted ≤2 minutes and for which there were at least 30 minutes with 0 count before and after the times frames to confirm the nonwear period. Epochs were considered as both abnormal and nonwear periods when there was ≥15000 counts/min for the vertical axis (≥3750 counts/15s) as in Esliger et al. (2005). A day of measurement was valid when there were at least 10 hours of wear time and a whole accelerometer measurement was valid when there were at least four valid days to compute average daily step count. When there was only three valid days, a step count for a fourth day was obtained by randomly sampling a step count among the three available days. The motivation and confidence score was obtained from a 4-part questionnaire (CSAPPA, 7.5 points for each part (Hay, 1992; MacDonald et al., 2018)) to allow the determination of the levels of intrinsic motivation for physical activity, perceived physical activity competence, perceived predilection for sports, and perceived adequacy with sports. Finally, the knowledge and understanding score (PLKQ (Longmuir, Woodruff, et al., 2018)) was based on four questions (1/1 point each) and a gap-fill text (6/6 points). Total physical literacy score and each CAPL-2 domain score were further associated with one of the following profiles: 'Beginning',
‘Progressing’, ‘Achieving’, ‘Excelling’. CAPL-2 results were obtained using the {capl} R package (J. D. Barnes & Guerrero, 2021; J. Barnes & Guerrero, 2022).

All the CAPL-2 tests and measurements were managed by a group of 35 sport sciences students who were trained in order to ensure consistency and objectivity during test and measurement implementation and scoring. All questionnaires and physical tests data were collected over four half-days. The questionnaires were administered in classrooms or in a dedicated, quiet and studious area, and the physical tests were completed on sport fields, playgrounds, and in gyms. All data were collected between March and May 2024.

## Movement behaviours characterization

We used the counts and steps related to the 15-s epochs from the wGT3X-BT accelerometer recordings to compute a wide range of movement behaviour metrics basing on algorithms implemented in the {activAnalyzer} R package (de Müllenheim, 2025a). The metrics, related to four different categories, were the followings:

*   **Activity volume metrics (daily averages):** total vertical axis counts; total vector magnitude (VM) counts; vertical axis counts per minute (CPM); VM CPM; sedentary minutes; light physical activity (LPA) minutes; moderate-to-vigorous physical activity (MVPA) minutes; percentages of wear time spent sedentary, in LPA and MVPA; MVPA minutes to sedentary minutes ratio; total step count. Time spent in a given physical activity intensity category was determined by summing the minutes spent at and above a specific count-based cut-point using the VM. For LPA, the cut-point ≥3 counts/s (i.e., 45 counts/15s or 180 CPM) was used (Hänggi et al., 2013), while for MVPA, the cut-point ≥57 counts/s (i.e., 855 counts/15s or 3420 CPM) was used (Hänggi et al., 2013), as recommended (Migueles et al., 2017). Therefore, sedentary time was related to epochs with less than 45 counts/15s.

*   **Step accumulation metrics (daily averages):** 60-, 30- 20-, 5- and 1-min maximum and peak step accumulation. Maximum or peak step accumulation related to a given time window refer to the average accumulation of steps over 1-min epochs maintained over the best (in terms of step count) consecutive (maximum) or non-necessarily consecutive (peak) minutes included in the considered time window.

*   **Intensity distribution metrics (daily averages):** intensity gradient; the count/15-s values at and above which the most active 8 hours (M1/3), 120 minutes (M120), 60 minutes (M60), 30 minutes (M30), 15 minutes (M15) and 5 minutes (M5) were accumulated over the day. The intensity gradient was the slope of the regression line depicting the link between the log of the accumulated time in a given intensity band (here based on VM counts/15s) and the log of the midpoint of the intensity band (Rowlands et al.,
2018). As intensity gradient was originally developed using raw acceleration data at the wrist and we were aware of no work guiding the definition of intensity bands when using counts data recorded at the hip, we arbitrary used the following intensity bands in counts/15s: 0-125, 126-250, ..., 2,376-2,500, >2,500.

* Sedentary time accumulation metrics: daily average of number of sedentary breaks (this actually corresponded to the mean daily total number of sedentary bouts detected over the valid days); power-law exponent alpha computed from all the sedentary bouts from the valid days of the week as described in Chastin & Granat (2010) with lower values depicting a higher prevalence of sedentary bouts of relatively long duration; median bout duration computed from all the sedentary bouts from the valid days of the week; usual sedentary bout duration (bout duration at which 50% of total sedentary time is accumulated) computed from all the sedentary bouts of valid days of the week as described in Bellettiere et al. (2021); Gini index (Chastin & Granat, 2010) computed from all the sedentary bouts from the valid days of the week (it provides information on the equality with which the different bout durations contributed to total sedentary time, a value of 1 revealing perfect inequality and a value of 0 revealing perfect equality). Any series of 15-s epochs (≥1 epoch) with less than 45 counts/15s was considered as a sedentary bout.

## Statistical analysis

All data processing and statistical analyses were performed using R programming language v4.4.1 (R Core Team, 2024) and the RStudio environment (v2024.04.2: "Chocolate Cosmos"). In the results section, descriptive statistics for numeric variables are firstly shown as medians (25th – 75th percentile) since distributions were in general non-gaussian. Results are also shown as means ± SD to allow the access to potentially useful information for sample size calculation in future studies. Categorical variables are shown as counts and percentages.

We conducted a first series of exploratory analyses consisting of comparing CAPL-2 scores between sexes (i.e., girls and boys). These analyses more precisely concerned CAPL-2 total physical literacy score, CAPL-2 domain scores (physical competence, daily behaviour, etc.) and CAPL-2 items scores (PACER, CAMSA, etc.). For CAPL-2 total score, we tested the stochastic equality between girls and boys using a Brunner-Munzel test that was complemented with the relative effect (stochastic superiority), that is the probability that the score of a randomly sampled individual from one group (e.g., boys) is superior to the score of a randomly sampled individual from a second group (e.g., girls). For CAPL-2 domain and item scores respectively, we conducted nonparametric multivariate comparisons (W. W. Burchett et al., 2017) to investigate as to whether girls and boys differ in terms of sets of CAPL-2 scores.
Multivariate analysis was complemented with relative effects (W. W. Burchett et al., 2017) that depicted, for a given CAPL-2 score, the probability that an individual randomly sampled from a particular group (e.g., girls) has a higher value than an individual randomly sampled from both groups (i.e., girls and boys).

We also conducted a second series of exploratory analyses consisting of comparing movement behaviour metrics across the global physical literacy profiles (i.e., 'Beginning', 'Progressing', 'Achieving', 'Excelling'). These series of analyses more precisely consisted of comparisons based on metrics depicting the daily average behaviour over a week. Sedentary time accumulation metrics, that were not daily averages, were also used for this set of analyses. The rationale of this set of comparisons was to investigate to what extent the hierarchy of the different physical literacy profiles can be retrieved when considering several facets of movement behaviour at a time (e.g., is the 'Beginning' profile the one that has the worst results both when considering sedentary and MVPA times?) and not only the daily average step count as currently used in the CAPL-2 total score. For this analysis, as there were a number of movement behaviour metrics that could be theoretically used to perform physical literacy profiles comparisons and that these metrics could be highly correlated (that is, some metrics could not add further discriminant information), we conducted analysis to reduce the number of metrics to be considered for further analysis with the rule to keep at least one metric for the following metric categories: activity volume, step accumulation, intensity distribution and sedentary time accumulation. From the metrics available and previously described above in the manuscript, a first selection was performed regarding activity volume metrics to keep only VM CPM (because VM CPM are in principle related to total VM counts as well as vertical axis total counts and CPM, and VM is more likely to represent all movements than the vertical axis), percentages of wear time spent in the different intensity categories (that were deemed more appropriate than absolute times to allow comparisons of participants who did not have similar total wear times), MVPA/sedentary time ratio and total step count. Then, all the retained metric variables were centered and scaled to perform principal component analysis (PCA) leading to the production of a PCA biplot that approximatively depicted the direction and the strength of the relationships between all the considered metrics. Basing on the PCA biplot, a first set of variables was retained. After looking at the strength of the Spearman rank correlations among the retained variables, we further eliminated some metrics to keep a list of metrics achieving Spearman rank correlations below a threshold arbitrary set at 0.85.

Following the metric selection step, we compared the physical literacy profiles for the retained metrics using nonparametric multivariate analysis complemented with relative effects (W. W. Burchett et al., 2017) as described above for the between-sex comparisons of CAPL-2 scores. All analyses were based on data from participants who had both at least four valid days for the
accelerometer-based measurement of movement behaviours and a valid CAPL-2 profile. Contrary to the previous analysis that compared girls and boys based on the CAPL-2 only, no imputation of a fourth valid day of measurement was allowed here. Sex difference for CAPL-2 total score was considered significant when the *P* value associated to the Brunner-Munzel test statistic was ≤ 0.05 (based on t-approximation). Regarding sex and physical literacy profile comparisons performed using nonparametric multivariate analysis, observed differences were considered significant when the *P* value associated to the ANOVA type statistic (based on F-approximation) was ≤ 0.05. When there was an overall significant difference between the compared groups, multiple comparisons were performed to determine for which combinations of CAPL-2 scores or metrics there were significant differences between groups, again using the ANOVA type statistic (based on F-approximation) while maintaining the overall Type I error rate ≤ 5%.

As the present work is an exploratory study, inferential analyses described above should be considered as a mean to highlight potentially interesting research directions and not as a mean to support a scientific claim. Indeed, in spite of the efforts made here to control the family-wise error rate, the exploratory nature of the present study prevents a good control of the Type I error rate with no control on the Type II error rate (Ditroilo et al., 2025).

## Reproducibility materials

Data processing, data joining, and statistical analysis have been implemented in a reproducible analytical pipeline based on R programming language. We have provided explanations to run the pipeline in the GitHub repository of the project: https://github.com/pydemull/EPLPA.

# RESULTS AND DISCUSSION

## Global descriptive analysis

Among the 99 children (53 girls [54%], 10.3 ± 0.5 yrs old) who were included in the study, only 71 (39 girls [55%]) correctly completed all the CAPL-2 measurements. Only 47 participants (23 girls [50%]) had both a valid CAPL-2 profile and ≥4 valid days of accelerometer wear. Four of these participants had the 'Beginning' profile, 14 the 'Progressing' profile, 9 the 'Achieving' profile and 20 the 'Excelling' profile.

The CAPL-2 scores from all participants and by sex are shown in Table 1 and Figure 1. We observed that children had in general good results regarding total physical literacy, physical competence, motivation and confidence, and knowledge and understanding scores, with
median scores clearly above the average. However, they had only moderate results for daily behaviour, with a median score below the average regarding average daily step count. Moreover, very few children (23%) knew what is necessary to improve sport skill.

The movement behaviour metrics from all participants and by CAPL-2 profile are shown in Table 2 and Figure 3. The movement behaviour metrics with which physical literacy profiles were compared were the daily percentages of wear time spent sedentary and in MVPA, step count, 60-min maximum step accumulation, intensity gradient, number of sedentary breaks, and usual sedentary bout duration. Children spent on average about 42% of accelerometer wear time sedentary and about 11% of this time in MVPA.

## Comparisons of CAPL-2 scores between sexes

The relative effects depicting the differences in CAPL-2 scores between sexes are shown in Table 1. The combinations of CAPL-2 domain and item scores for which girls and boys significantly differed are shown in Figure 2 and Supplemental material 1, respectively. From a descriptive perspective, we observed that boys were stochastically superior to girls for all domain scores except for knowledge and understanding. Regarding item scores, boys were also stochastically superior to girls for most scores, excluding knowledge about physical activity comprehension and understanding, cardiorespiratory fitness and sport skill improvement. Relative effect was the highest for total physical literacy score (0.71), close to the maximum theoretical relative effect.

From an inferential perspective, we found a significant sex difference for total physical literacy score ($t_{(69)} = 3.29, P = 0.002$), the combination of domain scores ($F_{(3,55,242.06)} = 3.99, P = 0.005$), and the combination of item scores ($F_{(8,42,575.14)} = 3.65, P < 0.001$). The combinations of domain scores for which sexes significantly differed were those which included motivation and confidence score, including motivation and confidence score alone. There was a number of combinations of item scores for which sexes significantly differed. The simplest significant combinations of item scores included at least six items from the following: PACER shuttle run, average daily step count, self-reported number of days with MVPA, intrinsic motivation, competence, adequacy, cardiorespiratory fitness definition, physical activity comprehension and understanding, and sport skill improvement knowledge.

These results invite to mainly consider, in future studies, total physical literacy score as well as motivation and confidence domain score as outcomes to highlight the superiority of boys over girls in terms of physical literacy. The results also suggest to use combinations of PACER shuttle run performance along with most of the items of the other CAPL-2 domains to confirm that girls and boys have different physical literacy profiles when considering the subdimensions of physical literacy. Such confirmatory studies could help to identify the list of domains and
items of the CAPL-2 on which particular efforts should be made to reduce sex inequalities in
physical literacy.

Comparison of movement behaviour metrics between physical literacy profiles

The relative effects depicting the differences of movement behaviour metrics between the
physical literacy profiles are shown in Table 2 and the combinations of movement behaviour
metrics for which physical literacy profiles significantly differed are shown in Figure 4. From a
descriptive perspective, the two best physical literacy profiles ('Achieving' and 'Excelling') had
the highest relative effects regarding physical activity metrics and the lowest relative effects
regarding sedentary behaviour when analysed using daily percentage of wear time spent
sedentary. The 'Beginning' profile markedly differed from the other profiles in terms of relative
effects for most metrics, with a clear stochastic superiority over the other profiles for the
percentage of wear time spent sedentary, and a clear stochastic inferiority for the percentage
of wear time spent in MVPA, the step-based metrics, and the intensity gradient. Surprisingly,
the 'Beginning' profile also had the highest relative effect regarding the number of breaks of
sedentary time. Finally, percentage of wear time spent in MVPA and step-based metrics were
the metrics for which the order of the compared groups was aligned with the hierarchy of the
physical literacy profiles.

From an inferential perspective, physical literacy profiles significantly differed in terms of sets of movement behaviour metrics (F<sub>(8.25, 74.87)</sub> = 6.29, P < 0.001), with the 'Beginning' profile being the single profile that had a set of movement behaviour metrics significantly different from all the other physical literacy profiles, while the 'Progressing' profile differed only from the 'Excelling' profile. Several combinations of movements behaviour metrics caused significant differences between the physical literacy profiles. The simplest combinations included two metrics: daily step count with 60-min maximum step accumulation, and daily 60-min maximum step accumulation with daily percentage of MVPA.

Overall, these results invite to mainly consider combinations of physical activity volume and
step accumulation metrics to confirm that physical literacy profiles differ in terms of device-
measured movement behaviours. Future studies could also further investigate the hypothesis
that physical literacy profiles provide information about the likelihood to engage in physically
activity and to avoid sedentary behaviour.

General comment

Our study highlighted sex differences, as well as physical literacy profile differences, when
considering various physical literacy domains and movement behaviour metrics respectively,
suggesting, if the results are confirmed, the need of multi-component interventions to reduce
sex inequalities and to improve movement behaviour profile. Although some authors have shown that multi-component interventions do not consistently produce better outcomes than single-component approaches (Bernal et al., 2020), this does not diminish the importance of grounding future approaches in holistic interventions targeting the widest possible range of determinants across the various dimensions of physical literacy, with particular attention to their links with academic learning (Jerebine et al., 2024).

## Limitations

The present exploratory study has several limitations. First, we had no information on the sociodemographic characteristics of the participants. Future studies should consider such information to control potential sources of bias when comparing sexes for a given CAPL-2 score. Second, our sample of participants was not representative of the diversity of French children, since all participants came from the same city. The fact that we have recruited participants from different schools which were associated to different typical socio-cultural profiles might have limited this problem. Nevertheless, we observed comprehension difficulties among some children, particularly with regard to items related to the affective and cognitive dimensions of physical literacy. While the CAPL-2 was designed for 8-12 years old, the use of pictorial items, like what Barnett et al. (2022) proposed, could optimize the measurement of these dimensions. Third, some physical literacy profiles included very few participants with, for example, only four participants in the 'Beginning' group. Fourth, we made relatively arbitrary choices in selecting certain movement behaviour metrics for comparing physical literacy profiles with an unknown impact on the final results. Finally, it is important to point out that we were unable to use the accelerometer logbooks because some of them had been incorrectly completed or lost. In a future study, it would be valuable to use these logbooks to get information about the nature and volume of physical activity performed during nonwear periods.

## CONCLUSION

In the present exploratory study, we mainly observed that boys were stochastically and significantly superior to girls for total physical literacy score. Girls and boys significantly differed in terms of sets of CAPL-2 domain scores, in particular due to a superiority of boys over girls in the motivation and confidence domain. Girls and boys also significantly differed in terms of sets of CAPL-2 item scores, in particular when considering both the PACER shuttle run and most of the item scores from the daily behaviour, motivation and confidence, and knowledge and understanding domains. Moreover, physical literacy profiles were significantly different in
terms of movement behaviour metrics, in particular when considering step-based metrics and daily percentage of MVPA. Importantly, as the present work is an exploratory study, all effects identified as significant should be considered only as promising research directions and should be confirmed in future studies with adequate error control and statistical power.

## Acknowledgements

Our acknowledgements go to all the teachers, principals, and sport sciences students for their involvement and the city of Avrillé for its organizational and financial support. We also wish to thank all the children for their commitment to the study.

## Author contribution

NH and HM conceived the idea for the research, trained the evaluators and set up the study. PYM built the code to analyse data and to make the figures and tables. All authors contributed to the drafting of the manuscript, read it and approved the final version.

## Conflict of interest disclosure

This work received no funding, and authors declare no conflict of interest.

## SOFTWARE

The results of the study were obtained using several R packages:

* Analysis of CAPL-2 score: `{capl}` (J. D. Barnes & Guerrero, 2021; J. Barnes & Guerrero, 2022).

* Analysis of accelerometer data: `{activAnalyzer.batch}` (de Müllenheim, 2025b); `{activAnalyzer}` (de Müllenheim, 2025a).

* Initial tasks of the data analysis workflow (import, tidy, and transform data): `{tidyverse}` (Wickham & RStudio, 2023); `{tidyselect}` (Henry et al., 2024).

* Figures: `{ggplot2}` (Wickham, 2016); `{Hmisc}` (Harrell & Dupont, 2025); `{patchwork}` (Pedersen, 2024); `{ggrain}` (Judd et al., 2024).

* Tables: `{flextable}` (Gohel et al., 2024).

* Descriptive statistics: `{skimr}` (Waring et al., 2022); `{gtsummary}` (Sjoberg et al., 2025).

* Brunner-Munzel test: `{rankFD}` (Konietschke et al., 2022).

* Nonparametric multivariate analysis: `{npmv}` (W. Burchett & Ellis, 2025).
* Principal component analysis: {stats} (R Core Team, 2024); {factoextra} (Kassambara & Mundt, 2020).

* Correlation analysis: {correlation} (Makowski et al., 2025).

* Analytical pipeline implementation: {targets} (W. Landau, 2021), {tarchetypes} (W. M. Landau et al., 2025).

* Package dependencies recording: {groundhog} (Simonsohn & Gruson, 2025).

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Barnes, J., & Guerrero, M. (2022). *capl: Compute and visualize CAPL-2 scores and interpretations* (Version 1.42) [R 4.4.3]. https://cran.r-project.org/web/packages/capl/index.html

Barnett, L. M., Jerebine, A., Keegan, R., Watson-Mackie, K., Arundell, L., Ridgers, N. D., Salmon, J., & Dudley, D. (2023). Validity, Reliability, and Feasibility of Physical Literacy Assessments Designed for School Children: A Systematic Review. *Sports Med*, *53*(10), 1905-1929.
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TABLES

<table><caption>Table 1. CAPL-2 scores and relative effects complementing nonparametric comparisons between sexes</caption><thead><tr><th>Score</th><th>All participants<br>N = 99</th><th>Girls<br>N = 53</th><th>Boys<br>N = 46</th><th>N Girls<br>(Min. / Max. Theo.<br>Rel. Eff.)</th><th>N Boys<br>(Min. / Max.<br>Theo. Rel. Eff.)</th><th>Rel. Eff.<br>Girls</th><th>Rel. Eff.<br>Boys</th></tr></thead><tbody><tr><td>Physical<br>competence<br>(/30)</td><td>24.0 (16.5 - 27.0)<br>21.6 ± 7.0</td><td>19.5 (15.0 -<br>25.5)<br>19.7 ± 7.1</td><td>24.8 (19.5 - 30.0)<br>23.7 ± 6.5</td><td>51<br>(0.26/0.74)</td><td>46<br>(0.24/0.76)</td><td>0.36</td><td>0.64</td></tr><tr><td>PACER shuttle<br>run (/10)</td><td>8.0 (5.0 - 10.0)<br>7.3 ± 2.8</td><td>6.0 (4.0 - 9.0)<br>6.2 ± 2.8</td><td>10.0 (7.0 - 10.0)<br>8.5 ± 2.3</td><td>51<br>(0.26/0.74)</td><td>46<br>(0.24/0.76)</td><td>0.32</td><td>0.68</td></tr><tr><td>CAMSA (/10)</td><td>7.9 (7.1 - 8.6)<br>7.7 ± 1.2</td><td>7.7 (6.4 - 8.2)<br>7.5 ± 1.2</td><td>8.2 (7.5 - 8.9)<br>8.0 ± 1.2</td><td>52<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.41</td><td>0.59</td></tr><tr><td>Plank (/10)</td><td>8.0 (5.0 - 10.0)<br>7.0 ± 3.1</td><td>7.0 (4.5 - 10.0)<br>6.8 ± 3.0</td><td>9.0 (5.0 - 10.0)<br>7.3 ± 3.1</td><td>52<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.45</td><td>0.55</td></tr><tr><td>Daily<br>behaviour (/30)</td><td>15.0 (11.0 - 19.5)<br>15.1 ± 6.0</td><td>14.0 (8.0 -<br>18.5)<br>14.0 ± 6.1</td><td>16.0 (13.5 - 20.5)<br>16.5 ± 5.7</td><td>40<br>(0.28/0.72)</td><td>32<br>(0.22/0.78)</td><td>0.39</td><td>0.61</td></tr><tr><td>Average daily<br>step count (/25)</td><td>11.0 (8.0 - 16.0)<br>11.8 ± 5.4</td><td>10.0 (6.0 -<br>14.5)<br>11.0 ± 5.6</td><td>11.0 (10.0 - 16.0)<br>12.7 ± 5.2</td><td>40<br>(0.28/0.72)</td><td>32<br>(0.22/0.78)</td><td>0.41</td><td>0.59</td></tr><tr><td>Self-reported<br>number of days<br>with MVPA (/5)</td><td>4.0 (2.0 - 5.0)<br>3.4 ± 1.7</td><td>3.0 (2.0 - 4.0)<br>2.9 ± 1.6</td><td>5.0 (3.0 - 5.0)<br>3.9 ± 1.5</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.33</td><td>0.67</td></tr><tr><td>Motivation and<br>confidence<br>(/30)</td><td>25.2 (21.7 - 27.1)<br>24.6 ± 3.8</td><td>23.2 (20.7 -<br>25.4)<br>23.2 ± 3.8</td><td>26.7 (24.5 - 28.8)<br>26.2 ± 3.1</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.31</td><td>0.69</td></tr></tbody></table>
<table><thead><tr><th>Score</th><th>All participants<br>N = 99</th><th>Girls<br>N = 53</th><th>Boys<br>N = 46</th><th>N Girls<br>(Min. / Max. Theo.<br>Rel. Eff.)</th><th>N Boys<br>(Min. / Max.<br>Theo. Rel. Eff.)</th><th>Rel. Eff.<br>Girls</th><th>Rel. Eff.<br>Boys</th></tr></thead><tbody><tr><td>Intrinsic<br>motivation (/7.5)</td><td>7.0 (6.0 - 7.5)<br>6.4 ± 1.3</td><td>6.5 (5.5 - 7.0)<br>6.1 ± 1.3</td><td>7.0 (6.5 - 7.5)<br>6.9 ± 1.0</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.32</td><td>0.68</td></tr><tr><td>Competence<br>(/7.5)</td><td>6.0 (5.0 - 7.0)<br>5.9 ± 1.3</td><td>5.5 (4.5 - 6.0)<br>5.5 ± 1.2</td><td>6.5 (6.0 - 7.5)<br>6.3 ± 1.4</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.31</td><td>0.69</td></tr><tr><td>Predilection<br>(/7.5)</td><td>6.8 (5.5 - 7.5)<br>6.5 ± 1.3</td><td>6.8 (5.4 - 7.5)<br>6.3 ± 1.4</td><td>7.2 (6.1 - 7.5)<br>6.8 ± 1.0</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.45</td><td>0.55</td></tr><tr><td>Adequacy (/7.5)</td><td>6.1 (4.8 - 6.8)<br>5.8 ± 1.4</td><td>5.4 (4.8 - 6.2)<br>5.4 ± 1.4</td><td>6.5 (5.4 - 7.5)<br>6.2 ± 1.1</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.34</td><td>0.66</td></tr><tr><td><strong>Knowledge<br>and<br>understanding<br>(/10)</strong></td><td><strong>8.0 (7.0 - 9.0)<br>7.6 ± 1.7</strong></td><td><strong>8.0 (7.0 - 9.0)<br>7.7 ± 1.8</strong></td><td><strong>7.5 (7.0 - 9.0)<br>7.5 ± 1.7</strong></td><td><strong>53<br>(0.27/0.73)</strong></td><td><strong>46<br>(0.23/0.77)</strong></td><td><strong>0.56</strong></td><td><strong>0.44</strong></td></tr><tr><td>PA<br>comprehension<br>and<br>understanding<br>(/6)</td><td>6.0 (6.0 - 6.0)<br>5.4 ± 1.2</td><td>6.0 (6.0 - 6.0)<br>5.4 ± 1.3</td><td>6.0 (6.0 - 6.0)<br>5.4 ± 1.1</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.52</td><td>0.48</td></tr><tr><td>Daily PA<br>guidelines (/1)</td><td>51 (52%)</td><td>26 (49%)</td><td>25 (54%)</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.48</td><td>0.52</td></tr><tr><td>Cardiorespirator<br>y fitness<br>definition (/1)</td><td>71 (72%)</td><td>44 (83%)</td><td>27 (59%)</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.63</td><td>0.37</td></tr><tr><td>Muscular<br>strength and<br>endurance<br>definition (/1)</td><td>72 (73%)</td><td>37 (70%)</td><td>35 (76%)</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.48</td><td>0.52</td></tr></tbody></table>
<table><thead><tr><th>Score</th><th>All participants<br>N = 99</th><th>Girls<br>N = 53</th><th>Boys<br>N = 46</th><th>N Girls<br>(Min. / Max. Theo.<br>Rel. Eff.)</th><th>N Boys<br>(Min. / Max.<br>Theo. Rel. Eff.)</th><th>Rel. Eff.<br>Girls</th><th>Rel. Eff.<br>Boys</th></tr></thead><tbody><tr><td>Improve sport<br>skill (/1)</td><td>23 (23%)</td><td>16 (30%)</td><td>7 (15%)</td><td>53<br>(0.27/0.73)</td><td>46<br>(0.23/0.77)</td><td>0.55</td><td>0.45</td></tr><tr><td>Physical<br>literacy (/100)</td><td>71.5 (60.3 - 79.2)<br>68.6 ± 12.8</td><td>63.2 (56.2 -<br>74.4)<br>65.1 ± 12.5</td><td>76.5 (65.7 - 79.7)<br>72.9 ± 12.0</td><td>39<br>(0.00/1.00)</td><td>32<br>(0.00/1.00)</td><td>0.29</td><td>0.71</td></tr></tbody></table>

Descriptive statistics are medians (Q1 - Q3), means ± SD or counts (%) of participants who obtained a score of 1/1. Min./ Max. Theo. Rel. Eff. = Minimum / maximum theoretical relative effect. A relative effect can be only between 0 and 1 and depicts the probability, for an individual randomly sampled from the considered group, to have a higher value than the one from an individual randomly sampled from both groups (girls and boys) or from the other group for Physical literacy score only. Minimum and maximum theoretical relative effects are respectively the lowest and highest effect sizes than could be expected for a given group and score based on the number of girls and boys available for the considered score. Grey cells highlight the highest relative effects among girls and boys.
<table><caption>Table 2. Movement behaviour metrics and relative effects complementing nonparametric comparisons between physical literacy profiles</caption><thead><tr><th>Metric</th><th>All participa<br>nts<br>N = 47</th><th>Beginn<br>ing<br>N = 4</th><th>Progres<br>sing<br>N = 14</th><th>Achievi<br>ng<br>N = 9</th><th>Excelli<br>ng<br>N = 20</th><th>N<br>Beginni<br>ng<br>(Min. /<br>Max.<br>Theo.<br>Rel. Eff.)</th><th>N<br>Progres<br>sing<br>(Min. /<br>Max.<br>Theo.<br>Rel. Eff.)</th><th>N<br>Achievi<br>ng<br>(Min. /<br>Max.<br>Theo.<br>Rel. Eff.)</th><th>N<br>Excelli<br>ng<br>(Min. /<br>Max.<br>Theo.<br>Rel. Eff.)</th><th>Rel. Eff.<br>Beginning</th><th>Rel. Eff.<br>Progres<br>sing</th><th>Rel.<br>Eff.<br>Achievi<br>ng</th><th>Rel. Eff.<br>Excelling</th></tr></thead><tbody><tr><td>% Wear<br>time</td><td>41.2<br>(36.9 -<br>47.9)</td><td>50.6<br>(44.1 -<br>53.9)</td><td>41.4<br>(36.1 -<br>47.7)</td><td>40.4<br>(33.5 -<br>47.1)</td><td>41.1<br>(37.2 -<br>46.1)</td><td>4<br>(0.04/0.9<br>6)</td><td>14<br>(0.15/0.8<br>5)</td><td>9<br>(0.1/0.9<br>)</td><td>20<br>(0.21/0.<br>79)</td><td>0.72</td><td>0.51</td><td>0.44</td><td>0.47</td></tr><tr><td>SED</td><td>42.3 ±<br>8.1</td><td>49.0 ±<br>7.5</td><td>42.8 ±<br>9.0</td><td>40.5 ±<br>6.5</td><td>41.3 ±<br>8.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% Wear<br>time</td><td>11.4<br>(8.8 -<br>13.6)</td><td>6.8<br>(5.7 -<br>7.2)</td><td>9.9<br>(6.2 -<br>13.4)</td><td>11.7<br>(9.3 -<br>13.2)</td><td>12.2<br>(9.6 -<br>15.1)</td><td>4<br>(0.04/0.9<br>6)</td><td>14<br>(0.15/0.8<br>5)</td><td>9<br>(0.1/0.9<br>)</td><td>20<br>(0.21/0.<br>79)</td><td>0.13</td><td>0.44</td><td>0.57</td><td>0.58</td></tr><tr><td>MVPA</td><td>11.1 ±<br>3.8</td><td>6.5 ±<br>1.0</td><td>10.4 ±<br>4.1</td><td>12.1 ±<br>2.9</td><td>12.2 ±<br>3.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Step<br>count</td><td>9,495.8<br>(8,211.2 -<br>11,761.5)<br>9,767.4 ±<br>2,404.3</td><td>6,132.3<br>(5,801.<br>0 -<br>6,801.5<br>)<br>6,301.2<br>± 696.6</td><td>8,692.1<br>(6,835.4<br>-<br>9,002.5)<br>8,349.7 ±<br>1,515.7</td><td>10,269.<br>8<br>(9,495.<br>8 -<br>11,378.<br>3)<br>10,348.<br>1 ±<br>1,887.5</td><td>11,748.<br>5<br>(9,503.<br>4 -<br>12,408.<br>7)<br>11,191.<br>6 ±<br>2,131.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>60-min<br>max</td><td>41.9<br>(34.7 -<br>50.4)</td><td>24.4<br>(22.3 -<br>26.7)</td><td>37.3<br>(34.3 -<br>40.2)</td><td>38.1<br>(35.8 -<br>44.0)</td><td>48.6<br>(42.7 -<br>54.3)</td><td>4<br>(0.04/0.9<br>6)</td><td>14<br>(0.15/0.8<br>5)</td><td>9<br>(0.1/0.9<br>)</td><td>20<br>(0.21/0.<br>79)</td><td>0.05</td><td>0.36</td><td>0.48</td><td>0.70</td></tr></tbody></table>
<table><thead><tr><th>Metric</th><th>All participants<br>N = 47</th><th>Beginning<br>N = 4</th><th>Progressing<br>N = 14</th><th>Achieving<br>N = 9</th><th>Excelling<br>N = 20</th><th>N Beginning<br>(Min. / Max.<br>Theo.<br>Rel. Eff.)</th><th>N Progressing<br>(Min. / Max.<br>Theo.<br>Rel. Eff.)</th><th>N Achieving<br>(Min. / Max.<br>Theo.<br>Rel. Eff.)</th><th>N Exceeding<br>(Min. / Max.<br>Theo.<br>Rel. Eff.)</th><th>Rel. Eff. Beginning</th><th>Rel. Eff. Progressing</th><th>Rel. Eff. Achieving</th><th>Rel. Eff. Exceeding</th></tr></thead><tbody><tr><td>step<br>accum.</td><td>42.4 ±<br>11.1</td><td>24.5 ±<br>2.6</td><td>37.5 ±<br>6.4</td><td>41.7 ±<br>8.6</td><td>49.7 ±<br>9.9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity<br>gradient</td><td>-2.8<br>(-2.9 - -<br>2.7)<br>-2.8 ± 0.2</td><td>-3.0<br>(-3.1 - -<br>2.9)<br>-3.0 ±<br>0.1</td><td>-2.9<br>(-3.0 - -<br>2.7)<br>-2.8 ± 0.2</td><td>-2.7<br>(-2.7 - -<br>2.7)<br>-2.7 ±<br>0.1</td><td>-2.8<br>(-2.8 - -<br>2.6)<br>-2.7 ±<br>0.2</td><td>4<br>(0.04/0.9<br>6)</td><td>14<br>(0.15/0.8<br>5)</td><td>9<br>(0.1/0.9<br>)</td><td>20<br>(0.21/0.<br>79)</td><td>0.16</td><td>0.41</td><td>0.62</td><td>0.58</td></tr><tr><td>Number<br>of SED<br>breaks</td><td>301.2<br>(275.8 -<br>327.8)<br>306.4 ±<br>36.8</td><td>361.4<br>(332.9 -<br>402.2)<br>367.5 ±<br>56.1</td><td>300.0<br>(272.3 -<br>318.5)<br>297.0 ±<br>25.9</td><td>318.5<br>(293.7 -<br>342.8)<br>313.9 ±<br>36.2</td><td>294.6<br>(275.6 -<br>309.9)<br>297.5 ±<br>28.5</td><td>4<br>(0.04/0.9<br>6)</td><td>14<br>(0.15/0.8<br>5)</td><td>9<br>(0.1/0.9<br>)</td><td>20<br>(0.21/0.<br>79)</td><td>0.86</td><td>0.44</td><td>0.60</td><td>0.43</td></tr><tr><td>Usual<br>SED<br>bout<br>duration<br>(min)</td><td>2.2<br>(1.6 -<br>2.6)<br>2.2 ± 0.9</td><td>2.0<br>(1.4 -<br>2.4)<br>1.9 ±<br>0.7</td><td>2.3<br>(1.6 -<br>2.8)<br>2.3 ±<br>0.9</td><td>2.0<br>(1.5 -<br>2.6)<br>2.3 ±<br>1.2</td><td>2.2<br>(1.7 -<br>2.5)<br>2.3 ±<br>0.9</td><td>4<br>(0.04/0.9<br>6)</td><td>14<br>(0.15/0.8<br>5)</td><td>9<br>(0.1/0.9<br>)</td><td>20<br>(0.21/0.<br>79)</td><td>0.43</td><td>0.52</td><td>0.47</td><td>0.52</td></tr></tbody></table>

Numbers are medians (Q1 - Q3) and means ± SD. SED = sedentary, MVPA = moderate-to-vigorous physical activity. All metrics are daily averages except usual SED bout duration that was based on the entire week of measurement. Min./ Max. Theo. Rel. Eff. = Minimum / maximum theoretical relative effect. A relative effect can be only between 0 and 1 and depicts the probability, for an individual randomly sampled from the considered group, to have a higher value than the one from an individual randomly sampled from all groups (all physical literacy profiles). Minimum and maximum theoretical relative effects are respectively the lowest and highest effect sizes than could be expected for a given group and metric based on the number of participants available for the considered metric. Grey cells highlight the highest relative effects (>0.5) among the physical literacy profiles.
FIGURES

<figure><img src="image_2.png" /><figcaption>Figure 1. Distributions of the CAPL-2 scores by sex</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2. Significant CAPL-2 domain score combinations obtained during between-sex nonparametric multivariate comparisons</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3. Distributions of the movement behaviour metrics across physical literacy profiles</figcaption></figure>

SED = sedentary. MVPA = moderate-to vigorous physical activity. All metrics are daily averages except usual bout duration.
<figure><img src="image_5.png" /><figcaption>Figure 4. Significant movement behaviour metric combinations obtained during between-CAPL-2 profile nonparametric multivariate comparisons</figcaption></figure>

SED = sedentary; MVPA = moderate-to-vigorous physical activity.