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Abstract

Introduction:

Physical literacy encompasses the confidence, motivation and competence to engage in physical activity. While physical literacy is often self-reported, its relationship to actual motor skill performance remains unclear. This study explored the association between children’s self-perceived physical literacy and their objectively measured motor skills.

Method:

Twenty-eight neurotypical children aged 8–12 years completed two self-report measures of physical literacy: Physical Literacy Assessment for Youth Self and Physical Literacy for Children Questionnaire (PL-C Quest). Participants also completed the Bruininks-Oseretsky Test of Motor Proficiency – Second Edition (BOT-2). Spearman’s rho correlations were conducted to examine the relationships between the PLAYself, Physical Literacy for Children Questionnaire and Bruininks-Oseretsky Test of Motor Proficiency – Second Edition.

Results:

One statistically significant positive correlation was found between the Bruininks-Oseretsky Test of Motor Proficiency – Second Edition Body Coordination composite and the Physical Literacy Assessment for Youth Self Relative Rankings – Physical Literacy subscale (ρ = 0.41, p < 0.05). No other significant correlations were identified between the Bruininks-Oseretsky Test of Motor Proficiency – Second Edition and either of the two physical literacy self-report tools.

Conclusion:

Most self-reported physical literacy scores were not significantly associated with motor skill performance, suggesting a minimal relationship overall. However, children with stronger coordination reported higher scores in movement and sport. Combining self-report and performance-based tools may provide a more holistic understanding of children’s physical literacy and occupational participation.

Keywords

Children physical literacy gender motor skills self-report performance-based occupational therapy

Introduction

Physical literacy (PL) is a holistic concept that encompasses the skills, knowledge and behaviours essential to give individuals the confidence and motivation to lead active lives (Australian Sports Commission, 2022). This concept is gaining increased recognition across various disciplines, including child development, kinesiology, public health and physical education (Caldwell et al., 2021; Cornish et al., 2020; Diao et al., 2024). According to the Physical Literacy Consensus Statement for England (Foweather et al., 2024), PL is a person’s physical, social, cognitive and affective relationship with movement and is impacted by individual, social and environmental factors. In addition, the Australian Physical Literacy Framework (APLF) provides a unified vision, common language and consistent understanding of what PL is and how it can be developed (Australian Sports Commission, 2022). This framework measures PL using four interconnected domains – physical, psychological, social and cognitive – which together encompass 30 distinct elements that contribute to an individual’s overall PL (see Figure 1; Australian Sports Commission, 2022; Barnett et al., 2023; Clark et al., 2024).

Figure 1.

Australian Physical Literacy Framework: Domains (Australian Sports Commission, 2022). Reproduced with permission.

With the growing interest in PL, several self-report measures have been developed to assess children’s self-perceived PL, including the Physical Literacy Assessment for Youth Self (PLAYself; Jefferies et al., 2021), the Physical Literacy for Children Questionnaire (PL-C Quest) (Barnett et al., 2022) and components of the Canadian Assessment of Physical Literacy (CAPL; Longmuir et al., 2018). These self-report tools offer valuable insights into a child’s perception of their own physical abilities, motivation and self-awareness – factors that contribute to engagement and confidence in everyday activities (Barnett et al., 2023). Despite growing recognition of PL’s role in supporting children’s development and its importance as a foundational skill for successful occupational performance, its integration into healthcare settings, including occupational therapy, remains limited (Clark et al., 2024). Research suggests that many occupational therapists are unfamiliar with PL and its potential application in practice (Clark et al., 2024). Additionally, there is limited guidance on how health professionals can effectively incorporate PL principles into interventions for children (Clark et al., 2024; Cornish et al., 2020).

Motor skills

Children’s motor skill performance is a common area of assessment and intervention for paediatric occupational therapists (Brown, 2012). Motor skills refer to how an individual moves their body parts, in addition to the manner in which they engage with objects whilst participating in activities (American Occupational Therapy Association, 2023). These skills are typically categorised into fine and gross motor areas (Brown, 2012). Motor skills are an important underlying ability that supports an individual’s engagement in daily occupations, including self-care, learning and play, as well as social integration and well-being (Brown, 2012; Sutapa et al., 2021). When assessing motor skills, occupational therapists frequently use standardised tests to comprehensively evaluate a child’s motor abilities. It is recommended to use a standardised norm-referenced test (Radanović et al., 2021), such as the Bruininks-Oseretsky Test of Motor Proficiency – Second Edition (BOT-2; Bruininks and Bruininks, 2005), Test of Gross Motor Development – Third Edition (TGMD-3; Ulrich, 2019), Movement Assessment Battery for Children – Second Edition Checklist (MABC-2; Henderson et al., 2007) and the Peabody Developmental Motor Scales – Second Edition (PDMS-2; Folio and Fewell, 2000).

Methods of assessment

There is significant emphasis on incorporating the perspective of the client/child in occupational therapy practices to further encourage client-centred approaches (Mroz et al., 2015). While previously this involved gathering information through proxy reports from parents/caregivers, such as through the MABC-2 (Henderson et al., 2007) and the Developmental Coordination Disorder Questionnaire (Wilson et al., 2009), there is now a recognised importance in directly hearing the voice of the child (Cordier et al., 2016). Using self-report measures alongside objective norm-referenced assessments allows clinicians to gain a comprehensive and nuanced understanding of a child’s skills, incorporating both subjective experiences and objective performance (Brown, 2012; Kennedy et al., 2013). This combined approach enables clinicians to tailor interventions more effectively to address individual needs and goals, ultimately facilitating better outcomes in motor skill development and participation in daily occupations (Brown, 2012).

Previous studies have explored the correlation between children’s self-reported perceptions of their motor skills and their actual performance measured by standardised tests. Some research indicates that children often struggle to accurately self-report their motor competencies, though accuracy improves with age (Crane et al., 2017; Kennedy et al., 2013; Washburn and Kolen, 2018). Other studies have found moderate correlations between self-reported motor skill capabilities and performance on gross motor skill tests, but not on fine motor skill tests (Brown, 2012; Lalor et al., 2016).

Relationship between PL and motor skills

Motor skills are a foundational component of PL. PL not only encompasses the ability to perform motor skills but also includes the understanding of how and when to apply those skills in different contexts (Lundvall, 2015). Physically literate individuals not only possess the motor skills necessary to perform physical activities but similarly understand concepts such as balance, coordination and timing, which are essential for effective movement (Carl et al., 2022). It is also important to note that PL as a concept does not solely focus on children’s physical and motor abilities but also incorporates the cognitive, social, affective, and psychological non-motor domains of a child’s occupational and developmental skill repertoire (Australian Sports Commission, 2022; Foweather et al., 2024). An Australian study found that participating in a PL intervention program led to enhancements in children’s ball-handling skills and fine motor control (Telford et al., 2022). Likewise, studies by Brandelli et al. (2022) and Britton et al. (2023) highlighted the importance of children establishing a solid foundation in PL to effectively participate in physical activity (PA). Given children’s daily play, self-care, education and social participation occupations are in part underpinned by their physical, social, cognitive and affective skill domains and affected by their individual, social and environmental factors, there is no doubt that PL is an integral component of children’s occupational development (Carl et al., 2022; Foweather et al., 2024; Weir et al., 2024).

As the empirical literature presents contrasting views on children’s ability to accurately perceive their motor skills through self-report scales compared to standardised motor skill assessments, further investigation is needed. PL could potentially play a crucial role in informing and enhancing data gathering for children with suspected motor problems. By incorporating PL assessments, which include not only motor skills but also the understanding of movement concepts, confidence and motivation to engage in physical activities, occupational therapists can gain a more comprehensive understanding of a child’s overall physical capabilities and challenges. This holistic approach allows for more personalised and effective intervention strategies, informing occupational therapy practices, aiding in goal setting and enhancing engagement and performance in daily occupations for children with motor skill challenges.

Therefore, this study aimed to examine the relationship between self-reported PL and performance-based motor skills in neurotypical children aged 8–12. One research question was posed: Are the child-reported physical literacy factors (as measured by the PLAYself and the PL-C Quest) significantly correlated with a child’s motor skill performance on a standardised motor skill test (as measured by the BOT-2)?

Methods

Study design

This study employed a correlational, cross-sectional design to determine the relationship between a child’s self-perceived PL and their performance on a standardised motor skill assessment.

Participant recruitment

A convenience sampling approach was employed to recruit children from three government primary schools in Victoria, Australia. Eligibility criteria required participants to be between 8 and 12 years old, neurotypical as reported by their parents/caregivers, have no known physical, mental or developmental disabilities and have a working knowledge of the English language. Consent was required from both the parent/caregiver and the child to be eligible to partake in the study. Ethics approval for this study was obtained from the Monash University Human Research Ethics Committee (MUHREC) on 04/12/2023 (Project No: 40409) and the Department of Education and Training (DET) Victoria on 02/01/2024 (Project No: 23-10-179).

As this study utilised correlation analyses to answer the proposed research question, the statistical power for the study was calculated using the online shareware program titled ‘Sample size calculators for designing clinical research’ by Hulley et al. (2013; https://sample-size.net/correlation-sample-size/). For a simple correlation r (r = 0.5) using a two-sided test with 80% power (β = 0.2) and significance level set at 5% (α = 0.05), the study required a minimum of 29 participants, which justified the target sample size identified for participant recruitment (Hulley et al., 2013). Therefore, a size of 28–30 participants was needed as this would sufficiently provide the statistical power for the completion of the proposed correlation analyses.

Information packages containing explanatory statements and consent forms for both parents/caregivers and children, and a demographic questionnaire were distributed to 130 recipients across six classrooms (grades 3–6) in three state government schools. A total of 34 responses were received, resulting in a response rate of 26.15%. Six participants were excluded from the study: five due to the child being reported to have a known diagnosis and one for incorrectly filling out the self-report surveys. Acknowledgement letters were sent to the parents of the six excluded participants. The final sample included 28 participants.

Instrumentation

Data were gathered through two self-report questionnaires: Physical Literacy Assessment for Youth Self (PLAYself) (Jefferies et al., 2021) and Physical Literacy for Children Questionnaire (PL-C Quest; Barnett et al., 2022), as well as one standardised performance-based motor skills assessment: the Bruininks-Oseretsky Test of Motor Proficiency – Second Edition (BOT-2; Bruininks and Bruininks, 2005). The rationale for selecting two child self-report PL scales was that they each measured different aspects of PL as a theoretical construct. The PLAYself examines four aspects of physical literacy (e.g. environment, PL self-descriptions, relative rankings of literacies and fitness; Jefferies et al., 2021) while the PL-C Quest measures four physical literacy domains (e.g., physical, psychological, social and cognitive; Barnett et al., 2022). Before completing the questionnaires and motor skills assessment, all parents/caregivers who expressed interest in their child participating in the study completed a demographic questionnaire.

The researchers created a demographic questionnaire to assess the eligibility of child participants for the study. The demographic questionnaire included questions regarding the child’s age, gender identity, grade level, neurodiversity status (e.g. based on parental/caregiver report, was the child participant neurodivergent or non-neurodivergent), and any previous diagnoses of physical, mental or developmental disabilities. Two paediatric occupational therapists with over 10 years of professional experience reviewed the demographic questionnaire, and it was also field-tested with three parents of school-age children. Field-testing the demographic questionnaire referred to the process of the authors trialling and evaluating the questionnaire in real-world conditions to ensure it functioned effectively before using it in the current study (Willis, 2020). The steps in the field testing involved administering it to a small, representative sample of the target population (those being the three parents of school-age children), noting how parent respondents interpreted and responded to each demographic questionnaire item, identifying potential issues (such as confusing wording, unclear formatting of items, or ambiguous questions), and collecting feedback on the demographic questionnaire’s layout, length, clarity, level of complexity and overall usability (Solans-Domènech et al., 2019). No changes were made to the demographic form after the review and field-testing.

The Physical Literacy Assessment for Youth Self (PLAYself) is a self-report questionnaire designed to assess children’s perceptions of their own PL and is intended for children aged 7 years and above (Jefferies et al., 2021). This tool can be administered independently or alongside other PLAY tools. The assessment consists of 28 questions divided into four categories: environment (6 items), PL self-descriptions (12 items), relative rankings of literacies (9 items) and fitness (1 item) (Caldwell et al., 2020). However, the fitness component is not factored into the overall PLAYself PL score and is only evaluated when used in combination with other PLAY tools. The questionnaire prompts children to respond on a five-point Likert scale, indicating their level of agreement with a statement or how well they believe they perform a given task. Completion typically takes between 5 and 10 minutes, with higher scores reflecting a stronger self-perception of PL.

The PLAYself’s Environment category assessed a child’s degree of confidence in different play environments (such as land, water, ice and snow) during the spring, summer, fall and winter (Kriellaars et al., 2013). Each of the PLAYself’s Environment category items used the following Likert-type scale: Never tried = 0; Not so good = 25; OK = 50; Very good = 75 and Excellent = 100 (Kriellaars et al., 2013). Examples of items on the PLAYself Environment category included ‘How good are you at doing sports and activities in and on the water?’ and ‘How good are you at doing sports and activities outdoors?’ (Kriellaars et al., 2013: 8).

The PLAYself’s Physical Literacy Self-Description category examined a child’s self-efficacy and how it connected to their PA participation (Kriellaars et al., 2013). Each of the PLAYself’s Physical Literacy Self-Description category items used the following Likert-type scale: Not true at all = 0; Not usually true = 33; True = 67 and Very true = 100 (Kriellaars et al., 2013). Examples of items on the PLAYself Physical Literacy Self-Description category included ‘It doesn’t take me long to learn new skills, sports or activities’ and ‘I think I have enough skills to participate in all the sports and activities I want’ (Kriellaars et al., 2013: 8).

The PLAYself also asks the child to rank the importance of three different types of literacy: literacy (reading and writing), numeracy (mathematics and arithmetic) and physical literacy (movement, activities and sports; Kriellaars et al., 2013). This section of the PLAYself is referred to as the Relative Ranking of Physical Literacy. The three types of literacies are rated in three contexts (in school, at home with family and with friends) on the following Likert-type scales: Strongly disagree = 0; Disagree = 33; Agree = 67 and Strongly agree = 100 (Kriellaars et al., 2013). This allows for a comparison of how a child ranked each literacy and a ‘literacy with a higher score means it is more relevant to the child’ (Kriellaars et al., 2013: 18).

On the PLAYself, the child is asked to rate their overall fitness answering the following item: ‘My fitness is good enough to let me do all the activities I choose’ (Kriellaars et al., 2013: 21). An agree – disagree response scale is used to answer this item to determine the PLAYself Fitness Score. According to Kriellaars et al. (2013), the Fitness Score is not used when calculating the total PLAYself Physical Literacy Score. ‘The PLAYself Physical Literacy Score is the overall measure of the child’s self-perceived physical literacy’ (Kriellaars et al., 2013: 21) and is calculated by adding up the Environment, Physical Literacy Self-Description and Relative Ranking of Physical Literacy (Literacy, Numeracy, Physical Literacy) category items and calculating an average score out of 100. ‘The maximum score of 100 represents high self-perceived physical literacy’ (Kriellaars et al., 2013: 21).

The PLAYself demonstrates strong psychometric properties, with established construct and convergent validity, as significant relationships have been found between its subscales and other related measures (Jefferies et al., 2021). The tool has shown excellent test–retest reliability (ICC = 0.81–0.84) and strong internal consistency, with Cronbach’s alpha coefficients reported between 0.80 (Brandelli et al., 2022) and 0.87 (Jefferies et al., 2021). Rasch analysis has further confirmed its unidimensionality and the absence of item bias (Jefferies et al., 2021). Similarly, Barnett et al. (2023) reported good test–retest reliability (ICC = 0.85).

The Physical Literacy for Children Questionnaire (PL-C Quest) is a self-report tool designed to assess children’s self-perceived PL and is suitable for those aged 5–12. Incorporating pictorial aids, the PL-C Quest evaluates all 30 components of PL outlined in the Australian Physical Literacy Framework (Australian Sports Commission, 2019) across four key domains: physical (12 items), psychological (7 items), social (4 items) and cognitive (7 items; Barnett et al., 2023).

The PL-C Quest’s physical domain refers to ‘the skills and fitness a person acquires and applies through movement’ (Sports Australia, 2019: 8), and examples of elements it includes are coordination, flexibility, agility, strength and reaction time. The psychological domain of the PL-C Quest is described as an individual’s feelings and perceptions about movement, and how these influence their confidence and drive to engage in PA, and includes the elements of confidence, motivation and self-perception, to name a few (Sports Australia, 2019). Next, the PL-C Quest’s social domain refers to how a person engages and interacts with others in the context of PA or movement (Sports Australia, 2019). Components of the PL-C Quest’s social domain include relationships, collaboration, ethics and society and culture. Finally, the PL-C Quest’s cognitive domain is a ‘person’s understanding of how, why and when they move’ (Sports Australia, 2019: 8) and can include several skills such as reasoning, rules, content knowledge and perceptual awareness. Children respond to statements by selecting the degree to which they identify with them on a 4-point scale. The assessment typically takes 10–12 minutes to complete, with higher scores reflecting a greater perceived level of PL in the child (Australian Sports Commission, 2019).

The PL-C Quest has demonstrated strong psychometric properties, including test–retest reliability (ICC = 0.83 and ICC = 0.90 over a 16-day interval), excellent internal consistency with a Cronbach’s alpha of 0.94 and good construct validity as supported by confirmatory factor analysis (Barnett et al., 2022; Diao et al., 2024).

The Bruininks-Oseretsky Test of Motor Proficiency – Second Edition (BOT-2) is a standardised performance-based assessment tool designed for individuals aged 4–21 years that measures fine and gross motor proficiency in four motor areas: fine manual control, manual coordination, body coordination, and strength and agility (Bruininks and Bruininks, 2005). These motor skill proficiency areas are further subdivided into eight subsets, comprising a total of 53 items (Jírovec et al., 2019). The BOT-2 assessment is available in two forms: the short form and the complete form, with the latter chosen for this study as it offers a more comprehensive measure of children’s motor proficiency. Administering the complete form typically takes approximately 40–60 minutes (Deitz et al., 2007).

The BOT-2 assessment demonstrates appropriate validity and reliability (Brown, 2012; Deitz et al., 2007), with good internal consistency (Cronbach’s alpha coefficient = 0.89), and excellent test–retest (ICC = 0.99, p < 0.001) and inter-rater reliability (ICC = 0.99, p < 0.001) (Kim and Kim, 2022; Wuang and Su, 2009). Evidence of the BOT-2’s validity has been reported in the peer-reviewed literature (Brown, 2019a, 2019b; Spironello et al., 2010; Wuang et al., 2009).

Procedure

Approval for participant recruitment was granted by the Victorian DET, followed by permission from the Assistant Principals of three state government schools in Victoria, Australia. Classroom teachers distributed information packages containing explanatory statements and consent forms for children and parents/caregivers, and a demographic questionnaire to potential participants. Written consent was obtained from both parents/caregivers and children before data collection began. Classroom teachers gathered the completed forms and provided them to the researchers. Once collected, eligible children (n = 28) completed the PLAYself and PL-C Quest in small groups, while the BOT-2 was administered individually on different days at their schools. These assessments took place during non-core academic periods. Before completing the PLAYself, PL-C Quest and BOT-2, verbal consent was also obtained from each child. At the commencement of data collection, each participant was assigned a unique numerical code to ensure confidentiality throughout the scoring and data-analysis process. After scoring was completed, summary reports outlining the child’s assessment results were shared with parents/caregivers.

To ensure that the setup, administration, interpretation, and scoring of the BOT-2 were done in a standardised and correct manner, the following steps were taken. The first author administered the BOT-2 to all 28 child participants and was knowledgeable on the topics of child development, standardised assessment and physical literacy. Moreover, the first author had completed university units related to the use of standardised assessments, research methods, motor skill development of children and occupational development of children. The first author had also completed an extensive literature review on the topic of PL before commencing any data collection. In addition, the first author received mentorship from the second two authors, who were experienced paediatric occupational therapists, where she administered the BOT-2 to three volunteer school-age children while observed by the second two authors and received feedback on the administration and scoring of the performance tasks. Finally, the first author also viewed the administration training video provided by Pearson Assessments and followed the administration and scoring instructions provided in the BOT-2 manual.

Data analysis

Data analysis was conducted using the Statistical Package for Social Sciences (SPSS), version 27.0 (International Business Machines Corporation, 2020). Descriptive statistics were used to summarise the participant demographics and assessment scores for the PLAYself, PL-C Quest and BOT-2. Categorical data were reported using frequencies and percentages, while numerical data were summarised using means, standard deviations (SD), ranges and interquartile ranges (IQR). Raw scores were analysed for the PLAYself and PL-C Quest, whereas scaled scores were used for the BOT-2 subscales and composite scales, as only scaled scores are applicable for calculating the BOT-2 composite scores. Spearman-rho correlation analyses were conducted to examine relationships between children’s self-reported PL and their motor skill performance (as measured by the BOT-2). Significant correlation coefficients above 0.70 were classified as strong, those ranging from 0.40 to 0.69 were considered moderate, and values below 0.39 were categorised as weak (Schober et al., 2018). Statistical significance was set at p < 0.05 (Harvey, 2018; Wood et al., 2014).

Results

Participant demographic information

The final study sample consisted of 28 children. Of these, 16 participants (57.14%) identified as female, while 12 (42.86%) identified as male. Their ages ranged from 8 to 11 years (M = 9.39 years, standard deviation (SD) = 1.197). All participants were enrolled in state public primary schools in Victoria, Australia, across Grades 3–6. In terms of grade distribution, 13 (46.4%) children were in Grade 3, four (14.3%) in Grade 4, seven (25%) in Grade 5 and four (14.3%) in Grade 6.

Descriptive statistics: PLAYself, PL-C Quest and BOT-2

Table 1 presents the descriptive statistics for the PLAYself and PL-C Quest raw subscale scores, as well as the BOT-2 scaled subscale and composite scores. It includes the mean, SD, range and interquartile ranges.

Table 1.

Descriptive statistics for the PLAYself and PL-C Quest subscale raw scores and the BOT-2 subscale and composite scaled scores (N = 28).

Scale scores	Whole sample mean	Whole sample SD	Whole sample range	Whole sample IQRs
Scale scores	Whole sample mean	Whole sample SD	Whole sample range	25	50	75
PLAYself subscales^a
Environment	359.21	84.39	425	325	350	400
Self-descriptions	815.93	186.94	901	707.75	785	901.75
Relative rankings – literacy	205.96	59.63	200	166	200	234
Relative rankings – Numeracy	229.75	54.86	167	175.25	233.5	300
Relative rankings – Physical literacy	255.07	51.28	200	209	267	300
Total percentage score	69.11	12.68	54.63	61.66	67.17	76.19
PL-C Quest subscales ^a
Physical	36.46	5.1	22	33	36	40.75
Psychological	22.96	3.93	19	21	23.5	26
Social	14.11	1.66	7	13	14	15.75
Cognitive	22.61	2.99	14	21	22.5	24.75
Total percentage score	80.12	8.56	38	75.21	78.75	86.46
	Whole sample mean	Whole sample SD	Whole sample range	Whole sample IQRs
	Whole sample mean	Whole sample SD	Whole sample range	25	50	75
BOT-2 subscales ^b
Fine motor precision	13.64	4.43	15	10.25	14	17.5
Fine motor integration	13.54	4.1	17	10.5	14.5	16.75
Manual dexterity	13.04	3.88	16	10.25	13	15.75
Upper-limb coordination	15.04	3.28	13	13	15	18
Bilateral coordination	18.32	2.87	8	16	20	20
Balance	18.64	4.29	18	17	19	22
Running speed and agility	15.5	4.24	17	12.5	16	18
Strength	16.39	4.04	14	13.25	15.5	19
BOT-2 composites ^b
Fine manual control	27.18	7.38	28	22.25	28.5	32.75
Manual coordination	28.07	5.68	24	25	29	31.75
Body coordination	36.96	6.56	26	32.75	38	42
Strength and agility	31.89	7.32	28	27.25	32	36

PLAYself: Physical Literacy Assessment for Youth (self); PL-C Quest: Physical Literacy for Children Questionnaire; BOT-2: Bruininks-Oseretsky Test of Motor Proficiency – Second Edition; SD: standard deviation; IQR: interquartile range.

Raw score; ^bScaled score.

Correlations between the PLAYself, PL-C Quest and the BOT-2 subscale, total and composite scores

Spearman rho correlations between the raw subscale scores of the PLAYself and the PL-C Quest and the scaled subscale and composite scores of the BOT-2 are included in Table 2. The BOT-2 Body Coordination composite was moderately positively correlated with the PLAYself Relative Rankings – Physical Literacy subscale (rho = 0.41, p < 0.05). Notably, none of the other BOT-2 subscales or composites were found to be significantly correlated with any of the other PLAYself or PL-C Quest subscale or total scores.

Table 2.

Spearman’s rho correlation coefficients between the Physical Literacy PLAYself and PL-C Quest subscale raw scores and the BOT-2 subscale and composite scaled scores (N = 28).

Physical Literacy Scale Scores		BOT-2 subscales^b								BOT-2 composites^b
		Fine Motor Precision	Fine Motor Integration	Manual Dexterity	Upper-Limb Coordination	Bilateral Coordination	Balance	Running Speed and Agility	Strength	Fine Manual Control	Manual Coordination	Body Coordination	Strength and Agility
PLAYself subscales^a
Environment	rho	−0.09	0.16	0.08	0.11	−0.03	0.17	0.02	0.36	0.02	0.18	0.11	0.14
	p	0.67	0.41	0.68	0.58	0.87	0.4	0.91	0.06	0.91	0.35	0.57	0.48
Self-descriptions	rho	−0.25	0.2	−0.14	0.05	0.27	0.26	0.22	0.28	−0.06	−0.04	0.33	0.23
	p	0.19	0.32	0.47	0.82	0.16	0.19	0.27	0.15	0.77	0.83	0.08	0.23
Relative rankings	rho	−0.21	−0.07	−0.09	−0.23	0.09	0.07	−0.03	−0.11	−0.17	−0.18	0.15	−0.12
Literacy	p	0.3	0.73	0.64	0.24	0.64	0.72	0.9	0.58	0.38	0.37	0.45	0.56
Relative rankings	rho	−0.07	0.24	−0.1	−0.18	0.31	0.21	0.09	0.06	0.03	−0.14	0.3	0.07
Numeracy	p	0.73	0.21	0.63	0.37	0.11	0.29	0.65	0.77	0.88	0.47	0.12	0.74
Relative rankings	rho	−0.06	0.15	−0.17	−0.09	0.29	0.32	0.11	0.12	00.00	−0.09	0.41*	0.1
Physical literacy	p	0.77	0.45	0.38	0.63	0.14	0.09	0.57	0.53	00.99	0.65	0.03	0.62
Total percentage score	rho	−0.3	0.17	−0.2	−0.04	0.3	0.27	0.18	0.2	−0.13	−0.12	0.37	0.16
	p	0.12	0.39	0.32	0.85	0.12	0.17	0.36	0.3	0.53	0.53	0.06	0.42
		BOT-2 subscales^b								BOT-2 composites^b
		Fine Motor Precision	Fine Motor Integration	Manual Dexterity	Upper-Limb Coordination	Bilateral Coordination	Balance	Running Speed and Agility	Strength	Fine Manual Control	Manual Coordination	Body Coordination	Strength and Agility
PL-C Quest subscales^a
Physical	rho	0.05	0.19	−0.08	0.14	0.11	0.1	0.25	0.31	0.1	0.01	0.19	0.26
	p	0.8	0.33	0.68	0.47	0.57	0.61	0.2	0.11	0.63	0.98	0.33	0.19
Psychological	rho	−0.34	−0.2	−0.36	−0.04	−0.21	−0.1	0.01	0.08	−0.32	−0.29	−0.1	0.05
	p	0.08	0.32	0.06	0.84	0.27	0.61	0.97	0.68	0.1	0.14	0.61	0.81
Social	rho	−0.01	0.33	−0.19	−0.05	0.13	−0.03	0.2	0.07	0.15	−0.14	0.06	0.12
	p	0.95	0.08	0.34	0.79	0.52	0.9	0.31	0.74	0.45	0.48	0.77	0.53
Cognitive	rho	−0.07	−0.19	−0.21	−0.16	−0.03	−0.16	0.12	0.14	−0.14	−0.26	−0.08	0.13
	p	0.72	0.32	0.29	0.41	0.87	0.42	0.54	0.49	0.46	0.19	0.69	0.51
Total percentage score	rho	−0.12	−0.02	−0.27	−0.01	−0.03	−0.03	0.19	0.21	−0.1	−0.21	0.06	0.19
	p	0.54	0.94	0.17	0.97	0.9	0.87	0.33	0.28	0.61	0.29	0.78	0.34

Note. PLAYself: Physical Literacy Assessment for Youth (self); PL-C Quest: Physical Literacy for Children Questionnaire; BOT-2: Bruininks-Oseretsky Test of Motor Proficiency – Second Edition; rho: Spearman’s correlation coefficient; p: significance (two-tailed).

Correlation is significant at the p < 0.05 level (two-tailed).

Raw score; ^b Scaled score.

Discussion

Correlations between the PLAYself, PL-C Quest and the BOT-2 subscale, total and composite scores

A moderately positive correlation was found between the BOT-2 Body Coordination composite scale and the PLAYself Relative Rankings – Physical Literacy subscale. The BOT-2 Body Coordination composite scale score is calculated based on a child’s performance on bilateral coordination and balance tasks, where children perform specific motor skill tasks such as jumping jacks, tapping alternating hands and feet simultaneously and balance activities with and without movement and sight (Bruininks and Bruininks, 2005). The PLAYself Relative Rankings – Physical Literacy subscale measures a child’s perspective on the importance of movement, activities and sports in various contexts. This finding suggests that children who perform well in coordination and balance motor tasks are likely to value physical activities more, which could influence their motivation to engage in sports and physical activities.

A study by Caldwell et al. (2021) found a positive correlation between all subscales of the BOT-2 short form and two physical literacy (PL) instruments, the PLAYfun and PLAYbasic. All three of these tools are objective measures of physical competence that measure running, locomotor, object control, balance, stability and body control. A study by Silva-Santos et al. (2019) similarly found that children aged 3–6 years with well-developed gross motor coordination skills spent more time engaging in moderate-to-vigorous PA than those with low gross motor coordination. Likewise, following the examination of the sports participation of children with relatively high, average and low motor coordination, Fransen et al. (2014) determined that children with higher motor coordination participated in sports more often.

However, no significant correlations were found between the remaining BOT-2 subscale and composite scores and any of the PLAYself or PL-C Quest subscales. While no studies to date have evaluated the relationship between self-perceived PL and performance-based motor skills using the PLAYself, PL-C Quest and the BOT-2, this lack of correlation indicates an absence of association between a child’s self-perception of their movement abilities and their actual motor skill performance. In a similar study, Washburn and Kolen (2018) used the self-report component of the CAPL to measure a child’s perceived motor competence (PMC) and the Canadian Agility and Movement Skill Assessment to measure their actual motor competence (AMC). Washburn and Kolen (2018) reported that over half of the participants were unable to accurately perceive their motor competence; however, their ability increased with age.

This is consistent with the findings of Barnett et al. (2017), Kennedy et al. (2013) and Liong et al. (2015), who all found that a child’s perceptions of their own motor skills were not predictive of their actual motor skill performance based on self-report and performance-based motor assessments. However, Australian studies by Brown (2012), Humble et al. (2024), Kennedy et al. (2012) and Lalor et al. (2016) indicated that children more accurately estimated their gross motor abilities, compared to their fine motor abilities. By contrast, a British study with children aged 4–7 years found a direct alignment between PMC and AMC (Duncan et al., 2018). Similarly, a study with school-age children in China reported significant correlations between a child’s perceived level of PL and their actual level of PL (Li et al., 2020).

Implications for practice

This study highlights the importance of incorporating both self-reported and performance-based measures when assessing children’s PL and motor skills. While limited significant correlations were found between the PL assessments and the BOT-2 motor skill assessment overall, body coordination was positively associated with children’s perceived importance of movement, sports and activities. This suggests that occupational therapists should aim to encourage children’s perceptions and motivation around movement – particularly tasks involving balance and bilateral coordination – to support engagement in PA.

These findings indicate that although PL includes elements related to motor competence, it is not fully represented across all domains of the BOT-2. Using both self-report and standardised assessments together can provide therapists with a more holistic understanding of each child – not only in terms of their actual motor performance but also in relation to their confidence, motivation and values around movement. This integrated perspective can inform the development of more individualised, meaningful intervention plans that enhance occupational performance. Furthermore, the lack of alignment between children’s self-perceptions and their standardised test results reinforces the need for occupational therapists to consider both subjective and objective data. Doing so will enable therapists to tailor interventions more effectively, ensuring they address the unique developmental needs, goals and lived experiences of each child.

Limitations

The interpretation and generalisability of the study’s results are constrained by a number of methodological limitations. The sample was relatively small (n = 28) and recruited using a convenience sampling approach, which may have resulted in reduced statistical power. Although the participants varied in age and gender, the majority were drawn from a single geographic location and shared similar socio-economic and cultural backgrounds, potentially introducing geographical bias. Additionally, the reliance on self-report measures may have contributed to social desirability bias, as participants were asked to complete two questionnaires reflecting on their own abilities and behaviours.

Future research

Future research investigating the relationship between children’s PL and motor skills using a larger randomised sample recruited from a broader geographical region in Australia is recommended. Additionally, replicating this study with children who have known motor skill challenges or diagnoses such as developmental coordination disorder (DCD) would be beneficial.

Conclusion

This study examined the relationship between children’s self-reported PL skills and their association with performance-based motor skills. The results indicate that using self-report and performance-based tools in conjunction may provide a holistic view of a child’s PL abilities and perceptions. Paediatric occupational therapists can use these findings to inform their assessment and intervention practices, effectively addressing each child’s unique needs related to motor skills, confidence and motivation to participate in physical activities, thereby promoting their overall occupational performance.

Key findings

Children with stronger coordination valued movement and sports more in self-reported assessments.

Most self-reported PL scores were not significantly associated with actual motor performance.

When used together, self-report and performance-based tools offer a more holistic view of children’s PL.

What the study has added

This study highlights the importance of combining self-report physical literacy assessments with performance-based tools to gain a more holistic view of children’s motor skills, values and motivation for physical activity.

Footnotes

Acknowledgements

The authors would like to thank all the children and parents/caregivers who participated in this study.

Research ethics

Ethics Committee approval was obtained for the study from the Monash University Human Research and Ethics Committee (MUHREC) on 04/12/2023 (Project No: 40409) and the Department of Education and Training (DET) Victoria on 02/01/2024 (Project No: 23-10-179).

Consent

All participants provided written informed consent to complete the assessments in this study.

Patient and public involvement data

During the development, progress and reporting of the submitted research, Patient and Public Involvement in the research was not included at any stage of the research.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) declared no financial support for the research, authorship and/or publication of this article.

Contributorship

OV completed this study as part of the Bachelor of Occupational Therapy (Honours) degree under the supervision of TB and MY. TB and MY contributed to the conception and design of the project. OV led the data collection and analysis process. All authors contributed substantially to project planning, obtaining ethics committee approval and the preparation of the manuscript.

ORCID iDs

Ted Brown

Mong-Lin Yu

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Examining the relationship between self-reported physical literacy and performance-based motor skills in school-aged children

Abstract

Introduction:

Method:

Results:

Conclusion:

Keywords

Introduction

Motor skills

Methods of assessment

Relationship between PL and motor skills

Methods

Study design

Participant recruitment

Instrumentation

Procedure

Data analysis

Results

Participant demographic information

Descriptive statistics: PLAYself, PL-C Quest and BOT-2

Correlations between the PLAYself, PL-C Quest and the BOT-2 subscale, total and composite scores

Discussion

Correlations between the PLAYself, PL-C Quest and the BOT-2 subscale, total and composite scores

Implications for practice

Limitations

Future research

Conclusion

Key findings

What the study has added

Footnotes

Acknowledgements

Research ethics

Consent

Patient and public involvement data

Declaration of conflicting interests

Funding

Contributorship

ORCID iDs

References