Psychometric Properties of a Virtual Physical Literacy Assessment for Preschool-Aged Children

Abstract

Physical literacy is a pillar of life-long physical activity participation. This study examined the psychometric properties of a virtual assessment protocol for multiple physical literacy sub-components among preschool-aged children (3−5 years). Baseline data were from the Canadian PLAYshop randomized controlled trial involving 130 participants aged 3−5 years and their parents. Children’s fundamental movement skills (FMS), motivation, confidence, enjoyment, physical activity, and age were measured. Acceptable inter-rater reliability (10% of videos) for TGMD-3 skills (ICC = 0.88−0.97) and internal consistency for parental-reported children’s motivation/enjoyment/confidence (α = 0.72) and just the confidence items (2 items; α = 0.66) but not for FMS (α = 0.51) was observed. Positive correlations of physical activity and age with specific FMS skills provided preliminary support for convergent validity. Overall, findings indicate initial support for the PLAYshop’s virtual assessment protocol. However, FMS should not be combined into a total FMS score, and the self-reported children’s enjoyment score should be interpreted with caution.

Keywords

physical literacy pre-school fundamental movement skills

Introduction

Physical activity plays a large role in chronic disease prevention (Powell et al., 2019; Warburton & Bredin, 2017), while also supporting mental well-being (Powell et al., 2019). Although there is strong evidence for the benefits of physical activity, physical inactivity rates remain a significant public health problem that begins in early life (Aubert et al., 2021; Strain et al., 2024). Physical activity routines, practices, and habits that are established in childhood can carry forward later in life (Aubert et al., 2021). Therefore, childhood is a critical period for establishing a physically active lifestyle that can lead to lifelong preventative health benefits.

Physical literacy is seen as a pillar of lifetime physical activity, particularly with regard to sport participation (Cairney et al., 2019; Caldwell et al., 2020; Edwards et al., 2017; Sport for Life, 2019; Stodden et al., 2008). Physical literacy is a multidimensional construct that has been defined as ‘the motivation, confidence, physical competence, knowledge and understanding to value and take responsibility for engagement in physical activities for life’ (International Physical Literacy Association, 2017). The competence and confidence that comes from developing fundamental movement skills (FMS) at an early age can impact life-long physical activity and physical fitness levels (Stodden et al., 2008). Developing these foundational skills enables the development of more complex skills needed in later sport participation (International Physical Literacy Association, 2017; Jones et al., 2020; Stodden et al., 2008). For example, actively playing with a ball with basic instruction regarding catching, trapping, receiving, and throwing provides opportunities to build competence and confidence for more complex skills related to sports (e.g., baseball; Sport for Life, 2019). As a result, increases in initiatives to promote physical literacy within education, community, and public health settings to promote physical activity and subsequent health are growing worldwide (Cairney et al., 2019; Edwards et al., 2017; Sport for Life, 2019; World Health Organization, 2018).

Early childhood, particularly the preschool years (3–5 years), is thought to be a key period for physical literacy development because it has great potential to increase physical activity, including sport participation, throughout childhood and beyond (Cairney et al., 2018; Carson et al., 2022; Edwards et al., 2017; Sport for Life, 2019). Nurturing early childhood development through early physical literacy programs provides benefits for children across physical, social-emotional, and cognitive development domains (Porter et al., 2023). Selecting motor-engaging and cognitively challenging activities for children (e.g., cognitive and movement-based skills) compared to those activities geared only towards aerobic performance, particularly in the first 6 years of life, is critical to maximizing engagement and benefit (Cairney et al., 2016). However, a lack of evidence on physical literacy exists in this age group, with many studies focusing on school-age children rather than preschool-aged children (Cairney et al., 2019). More specifically, while a number of studies have focused on FMS in this age group (Buckler et al., 2023; Cairney et al., 2019; Carl et al., 2022; Porter et al., 2023), it has been argued that focusing on this single sub-component of physical literacy does not meet the holistic definition of physical literacy (Cairney et al., 2019; Carl et al., 2022; Porter et al., 2023).

In order to further understand physical literacy in preschool-aged children, it is important to consider how to holistically measure physical literacy. The preschool-aged group has unique challenges compared to other age groups. For instance, it has been noted that it is not developmentally appropriate to measure the knowledge and understanding sub-component within the cognitive domain of physical literacy in this age group (Cairney et al., 2018). Furthermore, tools are lacking for this specific age group (Cairney et al., 2018). Consequently, the Preschool Physical Literacy Assessment Tool (PrePLAy) was developed to assess preschool-aged children’s physical literacy in childcare settings, including movement competences, coordinate movements, and motivation and enjoyment (Cairney et al., 2018). However, only initial psychometric properties have been established (Cairney et al., 2018). Currently, there is no single tool or protocol that exists for the holistic assessment of physical literacy in the home setting among preschool-aged children. As part of the PLAYshop intervention (Carson et al., 2022; Hwang et al., 2023), a virtual assessment protocol for specific sub-components of physical literacy (i.e., FMS and motivation, enjoyment, and confidence) among preschool-aged children was development for remote administration in the home setting during COVID-19 using existing tools (Brown & Lalor, 2009; Cairney et al., 2018; Ulrich, 2019). Knowledge and understanding of physical activity was not assessed due to the young age group (Cairney et al., 2018). The FMS tools were selected because they had established psychometric properties (Brown & Lalor, 2009; Ulrich, 2019). The motivation, enjoyment, and confidence tools were selected because they were the only tool in this age group (Cairney et al., 2018) or similar tools had been used in this age group in different fields (Hall et al., 2016). Therefore, the present virtual assessment protocol is a partial operationalization of the multidimensional construct of physical literacy, assessing key sub-components of physical literacy appropriate for preschool-aged children.

It is important to understand the psychometric properties, including validity and reliability, of these existing tools for the PLAYshop intervention because they are being used in different context (i.e., virtual) and setting (i.e., home). Therefore, the objective of this study was to examine the psychometric properties of this virtual assessment protocol for multiple physical literacy sub-components among preschool-aged children, including inter-rater reliability, internal consistency reliability, and convergent validity. Inter-rater reliability was defined as the consistency in scores across different observers; whereas; internal consistency reliability was defined as the consistency of results across items of the same test (Trochim & Donnelly, 2006). It is important to note that in this study total FMS (locomotor skills + ball skills + balance) and motivation/enjoyment/confidence variables were considered formative constructs (e.g., composite of multiple measures) not reflective constructs (e.g., underlying latent variable), therefore internal consistency reliability can provide insight as to whether these multiple measures should form a composite score (Coltman et al., 2008; Petter et al., 2007). Finally, convergent validity was defined as the degree to which two constructs are related to each other that theoretically should be related (Trochim & Donnelly, 2006). We hypothesized that there would be: (1) acceptable inter-rater reliability for FMS, (2) acceptable internal consistency reliability for parental-report children’s motivation/enjoyment/confidence, and (3) a positive correlation between physical activity, age, and FMS supporting convergent validity.

Materials and Methods

Study Design

This study utilized baseline data collected as part of PLAYshop randomized controlled trial (NCT05255250). PLAYshop is a parent-focused physical literacy intervention for early childhood (Carson et al., 2022). Further details on the PLAYshop intervention have been previously published (Carson et al., 2022, 2025).

Participants

Participants were preschool-aged children (3−5 years) and their parents from small (population between 1,000 and 29,999), medium (population between 30,000 and 99,999), and large population (population of ≥100,000) centres (Statistics Canada, 2017) of Alberta and British Columbia. Families were recruited through paid social media ads on Facebook. Participants were ineligible if the child had a developmental delay, disorder, or condition that could affect their gross motor development or limit their ability to be physically active. Participants were also ineligible if the parents did not comfortably speak or read English, did not have access to an electronic device with a camera and microphone (e.g., smartphone), or if they participated in a previous PLAYshop study. In total, 130 parents or guardians (parents hereafter) provided written informed consent. The sample size of 130 participants was based on a sample size calculation for the primary outcome of the RCT (Carson et al., 2022). Ethics approval was obtained from the University of Alberta (00093764) and the University of Victoria (16−444) Research Ethics Boards. Participating parents provided written consent.

Overall Procedures

At baseline, parents completed an online questionnaire via REDCap, an online data capture tool (Harris et al., 2009). Then, the parents and children attended a virtual session where children participated in a FMS assessment. Trained research staff led the virtual sessions and guided the participants through each skill using a pre-recorded treasure hunt adventure video. As part of the treasure hunt, parents and children watched a demonstration video before each of the FMS were assessed (i.e., horizontal jump, hop, overhand throw, underhand throw, one-leg balance). Children’s skills were filmed via parent’s smartphones, tablets, or laptops/computers and the virtual session was recorded for later scoring using a virtual meeting platform. Research staff used specific features of the virtual meeting platform to ensure the best visuals were captured (e.g., gallery view option for a larger screen capture of the skills). When filming, parents were instructed to ensure that the child’s full body was visible and that the camera was positioned to capture the entire skill. Special attention was also given to the filming angle, ensuring that the child was standing in a position that could best capture each performance criteria of the FMS assessment. For example, overhand throw and underhand throw skills were filmed from the side-view of the child, with them standing in the centre of the frame. Efforts were made to keep the filming angles consistent among all participants. This virtual session took approximately 20−30 min. Verbal consent was obtained from parents prior to the virtual session being recorded.

Measures

Physical Literacy Sub-Components

FMS were measured using a sub-set of the Test of Gross Motor Development – third Edition (TGMD-3; Ulrich, 2019), and the Movement Assessment Battery for Children-Second Edition (MABC-2; Brown & Lalor, 2009). Five different FMS were selected (locomotor skills: horizontal jump, hop; ball skills: overhand throw, underhand throw; one leg balance) as it was not possible to complete all TGMD-3 or MABC-2 skills because of space and equipment limitations in participant homes. The five skills selected required minimal space and simple equipment and aligned with the PLAYshop workshop content. Furthermore, it was found in a previous study that the locomotor and ball skills were significantly correlated with total motor skills (Kuzik et al., 2020), and large effect sizes (r = 0.5−0.7; Cohen, 1992) were observed. For the TGMD-3 skills, children were given one practice and two test trials for each of the skills and parents were asked not to provide any help, including verbal cues or skill demonstrations while their child was performing the skills (Ulrich, 2019). Each skill consisted of four components that were scored as demonstrated (i.e., 1) or not demonstrated (i.e., 0). For each TGMD-3 skill, the score for both skills were summed, ranging from 0 to 8, with higher scores indicating more advanced motor skills. One researcher scored 100% of the videos, while 10% of the videos, which were randomly selected and concurrently scored by a second researcher to assess inter-rater reliability. Prior to scoring the study videos, both researchers underwent training by scoring pilot videos until inter-rater reliability was at 80% or higher. For the one-leg balance skill, children completed two practice trials (one per leg) and then two test trials (one per leg). Balance was scored as a timed trial of total seconds balancing on one leg to the nearest 10th of a second up to a maximum of 30 s. For one-leg balance, the final score represents the longest time on either leg (Brown & Lalor, 2009).

Parental-reported children’s motivation, enjoyment, and confidence was assessed in the baseline questionnaire using items from the Preschool Physical Literacy Assessment (PrePLAy) (Cairney et al., 2018). The PrePLAy includes four questions related to perceived motivation (1 item), enjoyment (1 item), and confidence (2 items) of their child’s active play on a scale of 1−5, ranging from strongly disagree, disagree, neutral, agree, to strongly agree. The scores of these four items were summed, with higher scores indicating greater perceived children’s motivation/enjoyment/confidence during active play (Cairney et al., 2018). Of note, this tool was developed for early childhood educators (Cairney et al., 2018) but was used by parents in this study.

Children’s enjoyment was self-reported at the beginning of the virtual session using an adapted Five Degrees of Happiness Likert scale for children (Hall et al., 2016). The research staff asked the child: [Child name], before we start playing I wanted to ask you a question. When you are doing active play like running, jumping, throwing, chasing, or dancing, which one of these faces looks most like you? Are you like this face and thinks it’s okay but you rather be doing something else? Or are you more like this face and you really love it and it’s your favourite thing to do. Or are you like one of the faces in the middle? You can point to face you think is most like you. The enjoyment scale ranged from 1 to 5 with a higher rating indicating greater enjoyment for active play.

Physical Activity

Children’s physical activity, including light-intensity physical activity (LPA), moderate-to vigorous-intensity physical activity (MVPA) and total physical activity (TPA) were accelerometer-derived from Actigraph wGT3X-BT devices. For data to be included, participants needed ≥4 days with ≥1,440 total 15 s intervals (equivalent to 6 hr) of wear time each day (Hinkley et al., 2012). A weekend day was not required for the ≥4 days. Non-wear time was defined as ≥80 consecutive 15 s intervals of zero counts (equivalent to ≥20 min of consecutive zeros counts; Esliger et al., 2005). For children who napped, this time was assumed to be removed with non-wear time. LPA was defined as 26−419 counts per 15 s, and MVPA as ≥420 counts per 15 s (Janssen et al., 2013).

Participant Characteristics

Children’s age was calculated using the date of baseline questionnaire completion and the date of birth reported in the questionnaire. Children’s sex (male, female) and race/ethnicity (White, other) were parental reported. The ‘other’ category consisted of 13 other race/ethnicities as well as multiracial children. These categories were collapsed into one group due to lower frequencies across the various race/ethnicity groups.

Statistical Analyses

Data were analyzed using STATA 17 software and statistical significance was set at p <.05. Descriptive statistics including mean, standard deviation, and frequencies were calculated for all variables. For inter-rater reliability, intraclass coefficients (ICCs) were calculated for the TGMD-3 FMS using a two-way mixed-effects model with absolute agreement for single measurements (ICC(3,1)). The following ICC cut-points were used. <0.50 = poor; 0.5−<0.75 = moderate; 0.75−0.9 = good; and >0.90 = excellent (Koo & Li, 2016). An ICC ≥0.75 was defined as acceptable reliability. For internal consistency reliability, Cronbach’s alphas were calculated for FMS as well as parental-reported motivation/enjoyment/confidence (4 items) and parental-reported confidence (2 items). The following α cut-points were used: <0.50 = unacceptable; 0.5−<0.6 = poor; 0.60−<0.70 = acceptable; 0.70−<0.90 = good; and ≥0.90 = excellent (Kim, 2016; Kline, 2000). An α of ≥0.60 was defined as acceptable reliability, meaning for these variables it is acceptable for the multiple measures to form a composite score. For convergent validity, Spearman’s rank correlation coefficients (r_s) were calculated between TPA, MVPA, and LPA and each physical literacy measure. Spearman rank-order correlations were used instead of Pearson correlations because some variables did not have normal distributions. Similarly, Spearman’s rank correlation coefficients (r_s) were calculated between age and each physical literacy measure. Finally, Spearman’s rank correlation coefficients (r_s) were calculated between parental-reported enjoyment (1 item) and self-reported enjoyment (1 item). The following cut-points were used for validity: 0.10 = small effect size, 0.30 = medium effect size and 0.50 = large effect size (Cohen, 1992).

Results

Of the 130 participants that consented to participate, one was excluded because they had no physical literacy data, leaving a final sample of 129. Participant descriptive characteristics are represented in Table 1. The average age of child participants was 4.2 (±0.8) years and 51.6% were female.

Table 1.

Descriptive Characteristics of Participating Children and Parents

Participant characteristics
Children’s age (years; n = 129)	4.2 (0.8)
Children’s sex (assigned at birth; n = 128)
Male	48.4 (62)
Female	51.6 (66)
Children’s race/Ethnicity (n = 129)
White	68.2 (88)
Other	31.8 (41)

Note. Values are mean (standard deviation) for continuous variables (children’s age) and percentage (frequency) for categorical variables (children’s sex, children’s race/ethnicity).

Findings for inter-rater reliability are displayed in Table 2. The inter-rater reliability for the four TGMD-3 FMS were all acceptable and ranged from ICC = 0.88−0.97. Findings for internal consistency reliability are shown in Table 3. Internal consistency reliability for total FMS (TGMD-3 and one-leg balance) was α = 0.51. Similarly, internal consistency reliability for just TGMD-3 locomotor skills (2 skills; α = 0.50), TGMD-3 ball skills (2 skills; α = 0.51) and all four TGMD-3 FMS (α = 0.59) were all below an alpha of 0.60. Acceptable internal consistency was observed for parental-reported children’s motivation/enjoyment/confidence (4 items; α = 0.72) and just the confidence items (2 items; α = 0.66).

Table 2.

Intraclass Correlation Coefficients (ICCs) for TGMD-3 Fundamental Movement Skills (FMS; n = 13)

FMS	ICC (95% confidence intervals)
Horizontal jump	0.877 (0.592−0.963)
Hop	0.898 (0.660−0.969)
Overhand throw	0.951(0.839−0.985)
Underhand throw	0.973 (0.913−0.992)
Overall locomotor	0.918 (0.725–0.975)
Overall ball	0.973 (0.910–0.992)
Total	0.967 (0.892−0.990)

Note. TGMD-3 = Test of Gross Motor Development – Third Edition.

Table 3.

Internal Consistency Reliability for Fundamental Movement Skills (FMS) and Parental-Reported Children’s Motivation, Enjoyment, and Confidence

Physical literacy	Alpha values
TGMD-3 locomotor (2 skills; n = 116)	0.503
TGMD-3 ball (2 skills; n = 119)	0.511
TGMD-3 (4 skills; n = 116)	0.591
TGMD-3 + balance (5 skills; n = 109)	0.510
Parental-reported children’s motivation/enjoyment/confidence (4 items; n = 127)	0.719
Parental-reported children’s confidence (2 items; n = 128)	0.661

Notes. TGMD-3 = Test of Gross Motor Development – Third Edition. Sample sizes vary across measures as data was not available in all children across all assessment components.

Findings for convergent validity are displayed in Table 4. Total physical activity was significantly positively correlated with all FMS (r_s = 0.20−0.24), except horizontal jump. MVPA was also significantly positively correlated with all FMS (r_s = 0.20−0.22), except horizontal jump and overhand throw. LPA was significantly positively correlated with underhand throw, overhand throw, and balance (r_s = 0.19−0.22). MVPA was significantly positively correlated with parental-reported measure of children’s motivation/enjoyment/confidence (r_s = 0.22). Overall, effect sizes for convergent validity with physical activity were small-medium. For age, significant positive correlations with all FMS (r_s = 0.27-0.64) were observed, with small-medium to large effect sizes. However, age was not significantly correlated with self-reported children’s enjoyment (r_s = 0.07) or parental-reported children’s motivation/enjoyment/confidence (r_s = −0.03). Lastly, parental-reported children’s enjoyment and self-reported children’s enjoyment items were not significant (r_s = −0.07; p = .470), though they were positively correlated.

Table 4.

Convergent Validity for Physical Literacy With Physical Activity and age

	Horizontal jump	Hop	Overhand throw	Underhand throw	Balance	Parental-reported motivation/Enjoyment/Confidence (4 items)	Self-reported enjoyment (1 item)
LPA	0.128 (−0.060, 0.303) n = 113	0.126 (−0.062, 0.303) n = 110	0.224 (0.038, 0.390) n = 113	0.211 (0.028, 0.384) n = 111	0.192 (0.004, 0.373) n = 105	0.077 (−0.111, 0.257) n = 113	0.130 (−0.058, 0.306) n = 113
MVPA	0.103 (−0.088, 0.278) n = 113	0.217 (0.028, 0.386) n = 110	0.183 (−0.002, 0.356) n = 113	0.196 (0.009, 0.368) n = 111	0.209 (0.015, 0.383) n = 105	0.223 (0.043, 0.394) n = 113	0.154 (−0.060, 0.325) n = 113
TPA	0.137 (−0.051, 0.312) n = 113	0.198 (0.012, 0.372) n = 110	0.239 (0.056, 0.405) n = 113	0.241 (0.057, 0.409) n = 111	0.221 (0.034, 0.399) n = 105	0.153 (−0.033, 0.328) n = 113	0.148 (−0.040, 0.322) n = 113
Age (months)	0.266 (0.091, 0.424) n = 121	0.641 (0.519, 0.735) n = 117	0.283 (0.109, 0.438) n = 121	0.514 (0.366, 0.634) n = 119	0.635 (0.509, 0.733) n = 112	−0.030 (−0.203, 0.145) n = 127	0.066 (−0.114, 0.241) n = 122

Note. Values are r_s (95% Confidence Interval). Bolded values indicate statistically significance (p < 0.05). MVPA = Moderate to Vigorous Physical Activity; LPA = Light Physical Activity; TPA = Total Physical Activity.

Discussion

The current study examined the reliability and validity of a virtual assessment protocol for specific sub-components of physical literacy (i.e., FMS and motivation/enjoyment/confidence) among preschool-aged children. In summary, the findings indicate initial support for this physical literacy assessment protocol. Specifically, in terms of reliability, acceptable (defined as ICC ≥0.75) inter-rater reliability was observed for all TGMD-3 FMS and acceptable internal consistency reliability (defined as α ≥ 0.60) was observed for parental-reported children’s motivation/enjoyment/confidence supporting hypotheses one and two. In terms of convergent validity, small-medium effect sizes were observed for the significant positive associations between TPA and the physical literacy measures, with the exception of horizontal jump. Small-medium to large effect sizes were observed for the significant positive associations between age and FMS. These convergent validity findings provide preliminary support hypothesis three. Given the number of correlations examined, findings should be interpreted cautiously, with greater emphasis placed on the overall pattern and direction of associations rather than individual statistically significant results.

Overall, findings of the present study regarding inter-rater reliability align with previous research using the TGMD tools. For example, in a systematic review of 51 studies focused on educator-led physical literacy and/or physical activity interventions for children aged 3−5 years (Buckler et al., 2023), seven studies were reported to have utilized a portion of the TGMD tools and three reported on inter-rater reliability (Brian et al., 2017a, 2017b; Hardy et al., 2010). For these three studies, acceptable inter-rater reliability for the total scores of selected skills (92%−98% and ICC = 0.9) were reported (Brian et al., 2017a, 2017b; Hardy et al., 2010). Similar to these studies, we did not include all TGMD-3 skills. Specifically, 9 skills were excluded due to space and equipment restrictions, including run, gallop, skip, slide, two-hand strike, one-hand strike, bounce, catch, and kick. Additionally, in a systematic review of the reliability of TGMD-2 and TGMD-3 in 3−11 year olds, inter-rater reliability was summarized for individual skills (Rey et al., 2020). Similar to the present study, acceptable reliability (defined as ICC ≥0.75) was also found for overhand throw and underhand throw, however, hop and horizontal jump showed conflicting levels of inter-rater reliability (Rey et al., 2020). In the present study, our inter-rater reliability was also lower for horizontal jump but still within the acceptable range. The authors concluded that these skills (i.e., horizontal jump, hop) may be more difficult to access and further scoring and interpretation clarity may be needed (Rey et al., 2020).

Internal consistency reliability was also examined for the TGMD-2 or TGMD-3 tools in the previously mentioned review and acceptable internal consistency reliability (defined as α ≥ 0.6) in both subscales (i.e., locomotor and ball skills) was reported across 14 studies (Rey et al., 2020). However, none of the studies utilized a sub-set of the TGMD tool. Furthermore, one-leg balance has been found to be correlated with the total test score (r = 0.73) for the MABC-2 but it is not a TGMD skill (Brown & Lalor, 2009). As previously noted total FMS was considered a formative construct in this study, given locomotor, ball, and balance skills were being combined. Therefore, internal consistency reliability is less of a concern, given different aspects of the construct are being measured (Coltman et al., 2008; Petter et al., 2007). Though we also found alpha levels to be below 0.60, when just considering locomotor or ball skills. The lower internal consistency reliability scores in our study may be due to the limited number of FMS selected, despite the significant correlations and large effect sizes observed with total motor skills in a previous study (Kuzik et al., 2020). Overall, these findings suggest that the FMS in the PLAYshop virtual assessment protocol should be treated separately versus combined into a total composite score for future analyses.

A novel aspect of our study is the inclusion of motivation, enjoyment, and confidence physical literacy sub-components, rather than focusing on FMS alone. In the recent review of educator led physical literacy or physical activity interventions for preschool-aged children (Buckler et al., 2023), only one study included a measure to assess a physical literacy sub-component other than physical competence (O’Dwyer et al., 2013). Consistent with the present study, previous research indicates that the PrePLAy tool demonstrates acceptable internal consistency reliability (α = 0.84) for motivation/enjoyment/confidence when assessed by early childhood educators (Cairney et al., 2018). It should be noted, similar to the FMS, the PrePLAy scale is a formative construct, but the internal consistency findings suggest in can be treated as a composite score. Overall, findings suggest that it may be appropriate for the motivation/enjoyment/confidence scale of the PrePLAy tool to be assessed by either parents or early childcare educators.

In regard to convergent validity, evidence from a meta-analysis in preschool-aged children (3−6 years) found significant associations between FMS and MVPA and FMS and TPA, with small to medium effect sizes reported (r = 0.20) (Jones et al., 2020). Therefore, findings of the present study regarding the significant correlations, with small-medium effect sizes, between TPA, MVPA, and FMS align with previous research, with the exception of horizontal jump. The significant positive correlations between underhand throw, overhand throw, and balance, and LPA could be related to the fact that the ball skills and balance involve more standing and stepping compared to locomotor skills within the TGMD-3. The modest strength of associations observed in this study and the previous literature (Jones et al., 2020) suggests that physical activity may capture limited or specific aspects of FMS.

In terms of parental-reported children’s motivation/enjoyment/confidence, convergent validity findings regarding MVPA align with a meta-analysis in children and youth that found a medium effect size between affective judgment (i.e., enjoyment) and physical activity (Nasuti & Rhodes, 2013). Although enjoyment is not explicitly stated in the definitions of physical literacy, previous literature has linked enjoyment to intrinsic motivation and autonomous regulated behaviour (Shearer et al., 2021). The convergent validity findings for children’s self-reported enjoyment as well as the lack of significant correlation between the children and parental enjoyment measures suggests the parent tool may be a more accurate assessment of children’s motivation/enjoyment/confidence compared to the self-reported enjoyment measure in this age group. It is important to acknowledge that the parental-reported tool still reflects parental perceptions rather than the children’s direct experiences. Though face scales have previously been used in the pediatric pain field with preschool-aged children, the scale we adapted was originally tested in a sample of children aged 9−11 years (Gulur et al., 2009; Hall et al., 2016; Wong & Baker, 1988). Developmental limitations in young children’s ability to self-report enjoyment experiences (Cairney et al., 2018), which could have resulted in children misunderstanding the question or how the scale works, may explain study findings. Thus, future studies may want to only measure motivation/enjoyment/confidence via proxy report in line with the PrePLAy tool (Cairney et al., 2018) or engage in further work (e.g., interviews with parents and children) to test and refine the self-report tool. Finally, regarding age, findings suggest that motivation, enjoyment, and confidence for active play may be similar across the 3−5-year age group. Further research should examine whether age-related changes in motivation, enjoyment, and confidence occur from early childhood to late childhood.

This study addressed an important gap in the literature as no previous protocol existed for assessing physical literacy among preschool-aged children in the home setting. Other physical literacy assessment tools exist but are not applicable for preschool-aged children. For example, Passport for Life, which is used in a physical education setting and administered by teachers, applies to grades 3 to 9 students and assesses aspects of physical literacy including active participation as well as living, fitness, and movement skills (PHE Canada, 2020). Furthermore, the Canadian Assessment of Physical Literacy (CAPL), which combines assessments of motivation and confidence, physical competence, knowledge and understanding, and physical activity (Longmuir et al., 2015), applies to children ages 8−12 years. The Physical Literacy Assessment for Youth (PLAY) combines various tools that assess movement skills/competence, cognitive domain, environment, and fitness (Longmuir et al., 2015). However, these tools apply to children and youth ages 7−14 years old (Caldwell et al., 2021). Recently, the PrePLAy tool was developed to assess preschool-aged children’s physical literacy, however, the tool was developed for educators to assess physical literacy in the childcare setting and only initial psychometric properties have been established (Cairney et al., 2018). The lack of tools likely reflects the challenges of holistically measuring physical literacy in this age group or sub-components beyond FMS (Cairney et al., 2018).

Another novel aspect of this study was that our physical literacy protocol involved a virtual assessment. To our knowledge, no other physical literacy virtual assessments exist for children, to date. The virtual assessment protocol was originally developed to enable assessment during COVID-19 restrictions. However, now that the restrictions have been lifted, using a virtual format still provides several benefits. For example, it is possible to access a larger number of participants outside of the local setting, such as in different provinces/states and regions where there is adequate internet bandwidth. Another strength of the virtual assessment protocol is the cost and time efficiencies associated with travel for both families and research staff. For instance, families do not have to leave their home to participate in the study, which reduces participant burden. Furthermore, video-recorded TGMD-3 assessments allow for detailed scrutiny, slow motion replay, and the ability to replay the performance multiple times (Rey et al., 2020).

This study had a number of novel aspects (e.g., preschool-aged children, virtual format, inclusion of multiple physical literacy sub-components) but there were also limitations. For instance, despite the several benefits of the virtual format, the accuracy of scoring could have been impacted by not having standardized settings, sub-optimal camera angle, poor camera quality, positioning of the children (i.e., relying on verbal cues instead of physically positioning the child), or variation in the home equipment used. For example, children sometimes moved out of frame or threw objects out of the frame during the assessment, which led to additional trials or incomplete scoring for some measures. However, these instances were rare and could also occur during video-recorded in-person sessions. The virtual format also limited the FMS included in the assessment protocol due to space and equipment restrictions, which may explain the lower internal consistency reliability finding. Additionally, the large number of correlations conducted increased the risk of Type 1 error. Therefore, emphasis was placed on patterns of associations and effect sizes rather than individual statistical significance. Furthermore, inter-rater reliability was assessed using a 10% subset of videos. Finally, this study utilized a relatively small convenience sample that had access to technology, which may impact the generalizability of findings. It is important to note that recruitment did occur across two provinces, and almost all Canadian adults of child-rearing age own smartphones (Statistics Canada, 2023). Furthermore, the proportion of participating children who were identified as White aligns with nationally representative data for race/ethnicity (70%; Statistics Canada, 2022). Future directions of the PLAYshop intervention will consider wider inclusion criteria compared to this trial, including children who experience a disability, families without access to an electronic device, and families who do not comfortably speak and read English, to expand the generalizability of findings.

Conclusions

Prior to the intervention PLAYshop, no virtual or in-person early childhood physical literacy assessment tool or protocol existed for the home setting. Findings indicate initial support for the virtual assessment protocol used in the present study. However, findings also indicate that FMS scores of these select skills should not be combined into a total composite FMS score. Lastly, the self-reported children’s enjoyment score should be interpreted with caution for this age group. Additional reliability and validity testing may be required to ensure the protocol is broadly generalizable across diverse early childhood populations.

Footnotes

Acknowledgements

The authors would like to thank all participating parents and children. We would also like to thank Victor Han for his help with FMS assessments.

ORCID iDs

Joshua Li

Morgan Potter

Jayleen Hills

Valerie Carson

Ethical Considerations

Ethics approval was obtained from the University of Alberta (00093764) and the University of Victoria (16−444) Research Ethics Boards.

Consent to Participate

Participating parents provided written consent.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Study data were collected and managed using REDCap electronic data capture tools hosted and supported by the Women and Children’s Health Research Institute at the University of Alberta. This research was funded by the Stollery Children’s Hospital Foundation through the Women and Children’s Health and Research Institute.

Declaration of Conflicting Interests

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Valerie Carson, upon reasonable request.*

References

Aubert

Brazo-Sayavera

González

S. A.

Janssen

Manyanga

Oyeyemi

A. L.

Picard

Sherar

L. B.

Turner

Tremblay

M. S.

(2021). Global prevalence of physical activity for children and adolescents: Inconsistencies, research gaps, and recommendations—A narrative review. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 81. https://doi.org/10.1186/s12966-021-01155-2

Brian

Goodway

J. D.

Logan

J. A.

Sutherland

(2017a). SKIPing with teachers: An early years motor skill intervention. Physical Education and Sport Pedagogy, 22(3), 270–282. https://doi.org/10.1080/17408989.2016.1176133

Brian

Goodway

J. D.

Logan

J. A.

Sutherland

(2017b). SKIPing with head start teachers: Influence of T-SKIP on object-control skills. Research Quarterly for Exercise & Sport, 88(4), 479–491. https://doi.org/10.1080/02701367.2017.1375077

Brown

Lalor

(2009). The movement assessment battery for children—Second edition (MABC-2): A review and critique. Physical & Occupational Therapy in Pediatrics, 29(1), 86–103. https://doi.org/10.1080/01942630802574908

Buckler

E. J.

Faulkner

G. E.

Beauchamp

M. R.

Rizzardo

DeSouza

Puterman

(2023). A systematic review of educator-led physical literacy and activity interventions. American Journal of Preventive Medicine, 64(5), 742–760. https://doi.org/10.1016/j.amepre.2023.01.010

Cairney

Bedard

Dudley

D. A.

Kriellaars

(2016). Towards a physical literacy framework to guide the design, implementation and evaluation of early childhood movement-based interventions targeting cognitive development. Annals of Applied Sport Science, 3(4), 1073. https://doi.org/10.47739/2379-0571/1073

Cairney

Clark

H. J.

James

M. E.

Mitchell

Dudley

D. A.

Kriellaars

(2018). The preschool physical literacy assessment tool: Testing a new physical literacy tool for the early years. Frontiers in Pediatrics, 6(1), 138. https://doi.org/10.3389/fped.2018.00138

Cairney

Dudley

Kwan

Bulten

Kriellaars

(2019). Physical literacy, physical activity and health: Toward an evidence-informed conceptual model. Sports Medicine, 49(3), 371–383. https://doi.org/10.1007/s40279-019-01063-3

Caldwell

H. A. T.

Di Cristofaro

N. A.

Cairney

Bray

S. R.

MacDonald

M. J.

Timmons

B. W.

(2020). Physical literacy, physical activity, and health indicators in school-age children. International Journal of Environmental Research and Public Health, 17(15), 5367. https://doi.org/10.3390/ijerph17155367

10.

Caldwell

H. A. T.

Di Cristofaro

N. A.

Cairney

Bray

S. R.

Timmons

B. W.

(2021). Measurement properties of the physical literacy assessment for youth (PLAY) tools. Applied Physiology, Nutrition, and Metabolism, 46(6), 571–578. https://doi.org/10.1139/apnm-2020-0648

11.

Carl

Barratt

Topfer

Cairney

Pfeifer

(2022). How are physical literacy interventions conceptualized?—A systematic review on intervention design and content. Psychology of Sport and Exercise, 58(1), 102091. https://doi.org/10.1016/j.psychsport.2021.102091

12.

Carson

Boyd

Potter

Rhodes

Liu

Naylor

P. J.

(2022). Protocol for the PLAYshop randomised controlled trial: Examining efficacy of a virtually delivered parent-focused physical literacy intervention for early childhood on child-specific and family-specific outcomes. BMJ Open, 12(12), Article e066962. https://doi.org/10.1136/bmjopen-2022-066962

13.

Carson

Potter

Hwang

Buckler

E. J.

Boyd

Rhodes

R. E.

Liu

Moldenhauer

Hills

Naylor

P. J.

(2025). PLAYshop randomized controlled trial: Efficacy and implementation of a virtually delivered parent-focused physical literacy intervention for early childhood. Scandinavian Journal of Medicine & Science in Sports, 35(11), Article e70161. https://doi.org/10.1111/sms.70161

14.

Cohen

(1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155

15.

Coltman

Devinney

T. M.

Midgley

D. F.

Venaik

(2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262. https://doi.org/10.1016/j.jbusres.2008.01.013

16.

Edwards

L. C.

Bryant

A. S.

Keegan

R. J.

Morgan

Jones

A. M.

(2017). Definitions, foundations and associations of physical literacy: A systematic review. Sports Medicine, 47(1), 113–126. https://doi.org/10.1007/s40279-016-0560-7

17.

Esliger

D. W.

Copeland

J. L.

Barnes

J. D.

Tremblay

M. S.

(2005). Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. Journal of Physical Activity and Health, 2(3), 366–383. https://doi.org/10.1123/jpah.2.3.366

18.

Gulur

Rodi

S. W.

Washington

T. A.

Cravero

J. P.

Fanciullo

G. J.

McHugo

G. J.

Baird

J. C.

(2009). Computer face scale for measuring pediatric pain and mood. Journal of Pain, 10(2), 173–910. https://doi.org/10.1016/j.jpain.2008.08.005

19.

Hall

Hume

Tazzyman

(2016). Five degrees of happiness: Effective smile-face likert scales for evaluating with children. In Proceedings of the 15th International Conference on Interaction Design and Children, Manchester, UK, 21−24 June, 2016.

20.

Hardy

L. L.

King

Kelly

Farrell

Howlett

(2010). Munch and move: Evaluation of a preschool healthy eating and movement skill program. International Journal of Behavioral Nutrition and Physical Activity, 7(1), 80. https://doi.org/10.1186/1479-5868-7-80

21.

Harris

P. A.

Taylor

Thielke

Payne

Gonzalez

Conde

J. G.

(2009). Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381. https://doi.org/10.1016/j.jbi.2008.08.010

22.

Hinkley

O’Connell

Okely

A. D.

Crawford

Hesketh

Salmon

(2012). Assessing volume of accelerometry data for reliability in preschool children. Medicine & Science in Sports & Exercise, 44(12), 2436–2444. https://doi.org/10.1249/MSS.0b013e3182661478

23.

Hwang

Boyd

Naylor

P. J.

Rhodes

R. E.

Liu

Moldenhauer

Wright

Buckler

E. J.

Carson

(2023). Piloting the virtual PLAYshop program: A parent-focused physical literacy intervention for early childhood. Children, 10(4), 720. https://doi.org/10.3390/children10040720

24.

International Physical Literacy Association . (2017). International physical literacy association. https://www.physical-literacy.org.uk/

25.

Janssen

Cliff

D. P.

Reilly

J. J.

Hinkley

Jones

R. A.

Batterham

Ekelund

Brage

Okely

A. D.

(2013). Predictive validity and classification accuracy of actiGraph energy expenditure equations and cut-points in young children. PLoS One, 8(11), Article e79124. https://doi.org/10.1371/journal.pone.0079124

26.

Jones

Innerd

Giles

E. L.

Azevedo

L. B.

(2020). Association between fundamental motor skills and physical activity in the early years: A systematic review and meta-analysis. Journal of Sport and Health Science, 9(6), 542–552. https://doi.org/10.1016/j.jshs.2020.03.001

27.

Kim

S. Y.

(2016). Fundamentals and extensions of structural equation modeling. Hakjisa.

28.

Kline

(2000). A handbook of psychological testing (2nd ed.). Routledge.

29.

Koo

T. K.

M. Y.

(2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

30.

Kuzik

Naylor

P. J.

Spence

J. C.

Carson

(2020). Movement behaviours and physical, cognitive, and social-emotional development in preschool-aged children: Cross-sectional associations using compositional analyses. PLoS One, 15(8), Article e0237945. https://doi.org/10.1371/journal.pone.0237945

31.

Longmuir

P. E.

Boyer

Lloyd

Yang

Boiarskaia

Zhu

Tremblay

M. S.

(2015). The Canadian assessment of physical literacy: Methods for children in grades 4 to 6 (8 to 12 years). BMC Public Health, 15(1), 767. https://doi.org/10.1186/s12889-015-2106-6

32.

Nasuti

Rhodes

R. E.

(2013). Affective judgment and physical activity in youth: Review and meta-analyses. Annals of Behavioral Medicine, 45(3), 357–376. https://doi.org/10.1007/s12160-012-9462-6

33.

O’Dwyer

M. V.

Fairclough

S. J.

Ridgers

N. D.

Knowles

Foweather

Stratton

(2013). Effect of a school-based active play intervention on sedentary time and physical activity in preschool children. Health Education Research, 28(6), 931–942. https://doi.org/10.1093/her/cyt097

34.

Petter

Straub

D. W.

Rai

(2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656. https://doi.org/10.2307/25148814

35.

PHE Canada . (2020). Passport for life. https://phecanada.ca/teaching-tools/passport-for-life

36.

Porter

J. E.

Dabkowski

Prokopiv

Missen

Barbagallo

James

(2023). An exploration into early childhood physical literacy programs: A systematic literature review. Australian Journal of Early Childhood, 48(1), 34–49. https://doi.org/10.1177/18369391221118698

37.

Powell

K. E.

King

A. C.

Buchner

D. M.

Campbell

W. W.

DiPietro

Erickson

K. I.

Hillman

C. H.

Jakicic

J. M.

Janz

K. F.

Katzmarzyk

P. T.

Kraus

W. E.

Macko

R. F.

Marquez

D. X.

McTiernan

Pate

R. R.

Pescatello

L. S.

Whitt-Glover

M. C.

(2019). The scientific foundation for the physical activity guidelines for Americans, 2nd edition. Journal of Physical Activity and Health, 16(1), 1–11. https://doi.org/10.1123/jpah.2018-0618

38.

Rey

Carballo-Fazanes

Varela-Casal

Abelairas-Gómez

ALFA-MOV Project Collaborators . (2020). Reliability of the test of gross motor development: A systematic review. PLoS One, 15(7), Article e0236070. https://doi.org/10.1371/journal.pone.0236070

39.

Shearer

Goss

H. R.

Boddy

L. M.

Knowles

Z. R.

Durden-Myers

E. J.

Foweather

(2021). Assessments related to the physical, affective and cognitive domains of physical literacy amongst children aged 7-11.9 years: A systematic review. Sports Medicine - Open, 7(1), 37. https://doi.org/10.1186/s40798-021-00324-8

40.

Sport for Life . (2019). Developing physical literacy: building a new normal for all Canadians. https://sportforlife.ca/portfolio-item/developing-physical-literacy-building-a-new-normal-for-all-canadians/

41.

Statistics Canada . (2017). Population centre and rural area classification 2016. https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=339235

42.

Statistics Canada . (2022). The Canadian census: A rich portrait of the country’s religious and ethnocultural diversity. https://www150.statcan.gc.ca/n1/en/daily-quotidien/221026/dq221026b-eng.pdf?st=3RAzEiU9

43.

Statistics Canada . (2023). So long landline, hello smartphone. https://www.statcan.gc.ca/o1/en/plus/3582-so-long-landline-hello-smartphone

44.

Stodden

D. F.

Goodway

J. D.

Langendorfer

S. J.

Roberton

M. A.

Rudisill

M. E.

Garcia

L. E.

(2008). A developmental perspective on the role of motor skill competence in physical activity: An emergent relationship. Quest, 60(2), 290–306. https://doi.org/10.1080/00336297.2008.10483582

45.

Strain

Flaxman

Guthold

Semenova

Cowan

Riley

L. M.

Bull

F. C.

Stevens

G. A.

Country Data Author Group . (2024). National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: A pooled analysis of 507 population-based surveys with 5.7 million participants. Lancet Global Health, 12(8), e1232–e1243. https://doi.org/10.1016/S2214-109X(24)00150-5

46.

Trochim

W. M.

Donnelly

J. P.

(2006). The research methods knowledge base (3rd ed.). Atomic Dog.

47.

Ulrich

(2019). Test of gross motor development – Third edition (TGMD-3). APA PsycTests. https://doi.org/10.1037/t87935-000

48.

Warburton

D. E. R.

Bredin

S. S. D.

(2017). Health benefits of physical activity: A systematic review of current systematic reviews. Current Opinion in Cardiology, 32(5), 541–556. https://doi.org/10.1097/HCO.0000000000000437

49.

Wong

D. L.

Baker

C. M.

(1988). Pain in children: Comparison of assessment scales. Pediatric Nursing, 14(1), 9–17. https://doi.org/10.1097/00006416-198701000-00003

50.

World Health Organization . (2018). Global action plan on physical activity 2018–2030: More active people for a healthier world. https://www.who.int/publications/i/item/9789241514187