Original Research

Gross motor development, physical activity and anthropometry of Grade 1’s in a South African school

Monique de Milander, Elna van der Merwe, Bianca Barnard, Robynne Verster

Received: 29 July 2024; Accepted: 11 Mar. 2025; Published: 21 Nov. 2025

Copyright: © 2025. The Author(s). Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

Background: Gross motor difficulties can limit physical activity (PA) participation, contributing to unhealthy body composition.

Aim: This study profiled Grade 1 learners’ gross motor development, PA and anthropometry and explored relationships between these variables.

Setting: A cross-sectional design was followed, including Grade 1 learners (33 girls [58%]; 24 boys [42%]) from one primary school.

Methods: The Test of Gross Motor Development-3 (TGMD-3) evaluated gross motor development, while the Physical Activity Questionnaire-Young Children (PAQ-YC) determined PA participation. Anthropometry measurements were taken using standardised techniques.

Results: Participants predominantly portrayed average gross motor skills (49.1%), ball skills (50.9%) and locomotor skills (64.9%). Participation in a variety of PA types was evident, with outdoor play having the highest median (240 min per week). Participants spent 270 min (median) on screen time per week. Body mass index (BMI) results classified 31.6% of participants as overweight or obese. Central obesity (waist-to-height ratio) was identified in 29.8% of participants, while fat percentage, respectively, categorised 19.3% and 22.8% as overweight and obese. BMI significantly correlated with gross motor index (p = 0.0350; rho = −0.280) and ball skills (p = 0.0351; rho = −0.280), while fat percentage was significantly associated with gross motor index (p = 0.0046).

Conclusion: Participants portrayed average gross motor skills and sufficient PA levels, although screen time was high. Unhealthy body composition is significantly related to poorer gross motor skills.

Contribution: Alarmingly high incidences of unhealthy body composition negatively affect motor skills. Intervention programmes should therefore be implemented to improve young children’s body composition.

Keywords: gross motor development; physical activity; anthropometry; overweight; obesity; inactivity.

Introduction

Gross motor development is a prerequisite for motor proficiency that refers to the ability to accomplish a wide variety of motor skills with adequate success (Barnett et al. 2016; Lopes, Saraiva & Rodriques 2018). Gross motor development, according to Ulrich (2019), can be seen as the use of the large, force-producing muscles of the trunk, arms and legs to execute a motor skill to achieve a specific movement, task or goal. Gross motor skills involve the upper and lower limbs (large muscle groups) to accomplish movement actions of locomotion, such as jumping, running and galloping, in addition to object manipulation, such as catching, throwing and striking (Goodway, Ozmun & Gallahue 2021), as well as being able to use the hands and feet in synchronisation (Ashori, Norouzi & Jalil-Abkenar 2018).

Optimal development of these gross motor skills can facilitate and encourage learners’ participation in daily physical activity (PA) (Burns et al. 2019). According to Niemistö et al. (2019), learners who participate in regular PA have higher levels of motor competence, compared to learners who do not participate in PA. Furthermore, higher PA levels during childhood aid in the development of the neuromuscular system, which is essential for the development of gross motor skills (Hulteen et al. 2018). Engagement in structured PA also promotes gross motor skills and supports motor development in the long term (Dapp, Gashaj & Roebers 2021).

A study carried out by Van Niekerk, Du Toit and Pienaar (2016) in Potchefstroom, South Africa, showed a relationship between motor proficiency and PA levels in a group of 239 learners between the ages of 13 and 14 years. Motor proficiency was assessed using the Bruininks Oseretsky Test of Motor Proficiency 2 (BOT-2; Bruininks & Bruininks 2005) while the International Physical Activity Questionnaire (IPAQ; Craig et al. 2002) measured participants’ PA levels. Correlations were reported between the BOT-2 total score and the PA levels of the entire group, and for boys and girls, respectively. The body coordination skill of jumping in place and the strength test items showed strong correlations with PA in all the groups (Van Niekerk et al. 2016). A study by Gu (2016) in the southern United States included 256 participants consisting of 129 boys and 127 girls, with a mean age of 5.37 years. The results revealed that motor proficiency was significantly related to moderate to vigorous PA for some domains, specifically for object control and locomotor skills. Gu (2016) is of the opinion that motor skill development during early childhood, may result in the participation of wide-ranging PA, thus leading to a lower risk of obesity-related behaviours.

Physical activity significantly affects anthropometry measures (Seo et al. 2019) and Van Gent, Pienaar and Noorbhai (2020) observed that learners who are more physically active, tend to have a more favourable body composition. Recent findings indicate that learners who participate in regular PA have a healthy body weight, compared to learners who do not participate in regular PA (López-Gil et al. 2020). Results of a recent systematic review indicated that preschool children between the ages of two to seven, who participated in vigorous PA, showed lower percentages of body fat, weight status, fat mass, fat mass indexes and skinfold thicknesses (Wiersma et al. 2020). This systematic review and meta-analyses examined associations between accelerometer-derived PA and varying adiposity outcomes, while 56 articles were included in the review and 48 in the meta-analyses (Wiersma et al. 2020).

Young children are building their basic movement repertoire as a foundation to do more specialised movement skills to assist them in taking part in a variety of physical activities as they get older and is, therefore, a unique period of life (Smith, Fisher & Hamer 2015). Furthermore, Naidoo et al. (2022) state that motor proficiency is an important aspect of developing gross motor skills, if these gross motor skills can develop early in life, it can contribute positively to participation in sports, improve academic skills and also general well-being and the quality of life of the young child. According to Armoon and Karimy (2019), an inactive lifestyle leads to obesity, therefore, children who are inactive have the chance of being twice as likely to become obese adults. In South Africa, research on gross motor development, PA participation and anthropometry measures of Grade 1 learners, have primarily been carried out in the North-West province (Pienaar & Kemp 2014; Pienaar et al. 2022) as well as a study in Cape Town, Western Cape province on a group of younger children (Tomaz et al. 2019) and the information on these measures of young children is outdated in the Free State province (De Milander 2011). The aim of the study was to determine the gross motor development, PA participation and anthropometry of Grade 1 learners and to establish if relationships exist between these measures for children in a school in Mangaung, situated in the Motheo District of the Free State province, South Africa.

Research methods and design

Study design

This was an empirical study, which consisted of a descriptive cross-sectional design, making use of quantitative methods to collect data. Information was gathered on the gross motor proficiency levels, PA participation, as well as anthropometry measures of participants.

Study population

The study sample included only one public primary school within a 5 km radius of the University of the Free State, Free State province of South Africa. A 5 km radius was selected to ensure that the researchers could easily access the school. During the planning phase of the study coronavirus disease 2019 (COVID-19) still impacted research at schools, thus influencing our choice to only include one school. Taking COVID-19 into consideration and the level of study (as this study formed part of an honours research project) including one school was not ideal but was deemed sufficient at the time. The chosen school was situated in a high economic environment and categorised as a quintile 5 school. Quintile 1 schools are in the most economically disadvantaged geographical areas, whereas quintile 5 schools are in the most affluent geographical areas (Graven 2013; Hall & Giese 2008).

Children

A non-random convenience sampling method was used for this study to select the participants, as the identified school was willing to participate in the study. All Grade 1 learners were invited to take part in the study; however, only 57 parents or guardians provided consent for their children to take part in the study. Fifty-seven (N = 57) participants, including boys (n = 24) and girls (n = 33) with an average age of 7.2 years and a standard deviation of 0.2703449 formed part of the study. Thirteen of these children were 6-years of age, while the other 44 had already turned 7. On the parent consent form, parents were asked to indicate any disabilities or health issues their child might have, such as visual or hearing impairments, epilepsy and others. This information is important as it could impact the child’s ability to participate fully in PA. Inclusion criteria included Grade 1 learners, who were enrolled in the school during 2022 and were between the ages of 6 years and 7 years and 11 months, whose parents provided consent, and who gave assent themselves. Children were excluded if their test forms were incomplete and if they were absent on the day of testing.

Parents

An information letter and consent form were sent to the parents of each learner at the school. The letter contained information regarding the study and explained how the questionnaire should be completed. In addition to this, the consent form asked for the parent’s permission to allow their child to participate in the study. Parents were given 1 week to complete the questionnaire and 75 parents returned a signed consent letter, as well as the completed questionnaire in time.

Procedure

The principal of the identified school gave permission to conduct the study after school hours. A pilot study was first conducted at the school with three Grade 1 learners. This was carried out to determine the time it took to administer the Test of Gross Motor Development-3 (TGMD-3) and anthropometric measurements, as well as to establish if the identified test venues or areas within the school environment were appropriate for all assessments and measurements. The pilot study also enabled the researchers to determine if the Physical Activity Questionnaire for Young Children (PAQ-YC) was understandable to the parents and if the questionnaires received back, were completed correctly. No changes were made to either the identified test venues or areas or the questionnaire, and the data of the identified three participants were included in the data set. Thereafter, the testing procedure commenced for a period of 1 week. The researchers, with the appropriate consent, took video recordings of the participants’ TGMD-3 execution. The videos were used to analyse the participants’ gross motor performance and to accurately score their execution. Anthropometric measurements were taken in a private area in the presence of an adult, whom the participants were familiar with. An attendance record was created, based on the class lists obtained from the school to ensure that every learner, who gave their assent, underwent testing. In addition, the PAQ-YC was sent home to parents to complete the questionnaire, according to the participant’s PA for the past 7 days and when completed the parents sent the questionnaire back to the school after a week for the researchers to collect.

Measuring instruments

Test of gross motor development–3

The TGMD-3 was used to determine children’s gross motor development. The TGMD-3 is used for children aged 3–10 years and 11 months (Ulrich 2019). The TGMD-3 consists of two sub-components, locomotion skills (running, galloping, hopping, skipping, jumping and sliding) and ball skills (two-hand striking of a stationary ball, stationary dribbling, catching, kicking, overhand throwing, one-handed striking of a self-bounced ball and underhand throwing) (Ulrich 2019). Each motor skill was performed twice, and evaluation occurred based on the presence (achieved: one point) or absence (not achieved: zero points) of three to five performance standards. The total test scores obtained for both motor skills were converted to a standard score and the standard scores were used to calculate age-gender-specific norms (Ulrich 2019). According to Capio, Eguia and Simons (2015), this test had a high internal consistency (α > 0.80) for typically developing individuals.

The tests were conducted by the researchers and performed in the school hall and each TGMD-3 sub-test was prepared before the arrival of the participants. Each sub-test was set up in accordance with the test manual guidelines. Four participants were selected from their class and accompanied to the school hall. The researchers explained a few gross motor activities to the child and asked them to execute it. Each sub-test was thoroughly discussed and physically demonstrated to the learner. Recordings of the child’s execution of these activities were made by means of a video recording. The recording was from the side or back of the body to minimise identifiable features. Each child was given a practice round to ensure that they understood what was expected of them and the test took around 15 min – 20 min per learner to conduct. The learner was then taken to the stage and behind the curtains, his or her anthropometry measurements were taken before they were accompanied back to the classroom.

Physical activity questionnaire for young children

The Physical Activity Questionnaire for Young Children (PAQ-YC) was developed by Amor-Barbosa et al. (2021) for the paediatric population. To develop the questionnaire, these researchers used the published versions of various ages, in addition to the published literature, regarding the type of physical activity a typical 5–7-year-old child will participate in. The purpose of the questionnaire is to identify children who comply with the international PA recommendations compared to those children who are inactive (Amore-Barbosa et al. 2021). The PAQ-YC consists of 11 questions and aims to determine the overall amount of PA completed by a 5–7-year-old child. Its design allows one to report activities performed during a typical week at school while commuting, and during leisure time. Question 1 reported on extramural activities, Question 2 on PA participation at home, Question 3 on outdoor play, Question 4 on making use of active transport modes, Question 5 on activities of a sedentary nature and Question 6 on time spent in front of a screen. Question 7 was reported on participation in various sports. Question 8 was a yes or no question asking if physical education lessons are regularly participated in at school. Questions 9 and 10 reported on recess and lunch break activities, while the last question (Question 11) provided an opportunity to indicate if the child was ill and could consequently not participate in activities as usual. The questionnaire was completed by the child’s parent or guardian where they reported their child’s PA levels, according to the previous 7 days (Amor-Barbosa et al. 2021). All recorded activities should be a representation of a typical week for the child, except if something out of the ordinary happened that prevented them from completing their daily activities (Amor-Barbosa et al. 2021). Furthermore, PA was quantified for each question by means of type, duration, intensity and frequency. Higher durations indicated greater overall physical activity levels and/or time spent on the said activity. Questions 1 to 7 produced numerical data, while data from questions 9 to 11 were categorical in nature. A Delphi survey determined the content validity of the questionnaire, while the reliability of this questionnaire has not yet been determined. It was however found to be adequate, relevant, comprehensive and comprehensible, regarding the items and general characteristics (Amor-Barbosa et al. 2021).

Anthropometry

The anthropometric measurements were sensitive in nature and were taken in a private area on stage and behind the curtains. The measurements were taken in the presence of the teacher’s assistant and the International Society for the Advancement of Kinanthropometry (ISAK) procedures were used. Instruments and anthropometry measurement tools included a measuring tape to measure the participant’s waist and hip circumferences in centimetres, a skinfold calliper to measure the width of the skinfold in millimetres, a SECA stadiometer was used to measure the height in centimetres, as well as an electronic weighing scale to measure the weight of the participant in kilograms. The following anthropometric measurements were taken to determine the body composition of the participants: triceps skinfold, biceps skinfold, subscapular skinfold and supraspinal skinfold, as well as height, weight and circumferences of the waist and hips. The right side of the body was used when the anthropometric measurements were taken and a qualified anthropometrist took each measurement twice in succession. A third measurement was only taken if the difference between the first two measurements was greater than 5% for skinfolds and more than 1% for circumferences. The mean value of the two measurements was used, whereas the median value was used when three measurements were taken. High validity and reliability of this measure were observed by Rumbo-Rodríguez et al. (2021), which indicated an intraclass correlation coefficient of 0.982. Anthropometric measurements were also used to calculate fat percentage, body mass index (BMI) and waist-to-height ratio (WHtR). Body mass index was calculated by dividing the weight of each participant in kilograms by their squared height in meters. Fat percentage was determined using the following equations for boys and girls respectively: (12.74*log Sum 4SF) − (21.47*Log Height) +87.82; (13.99*log Sum 4SF) − (21.42*Log Height) +85.65. Waist-to-height ratio was calculated by dividing the waist circumference in centimetres by the height in centimetres. The International Obesity Task Force (IOTF) cut-off values were used for fat percentage with the 85th percentile line indicating the overweight boundary and the 95th percentile line implying obesity (McCarthy et al. 2006). Six-year-old boys with a body fat percentage of 19.5% were classified as overweight, while a percentage of 22.7% or above, indicated obesity. Girls of this age were placed in the overweight category if their body fat percentage was 23.0% and a fat percentage of 26.2% or above placed them in the obese category. A body fat percentage of 20.4% indicated overweight and 24.1% or higher implied obesity for 7-year-old boys. Seven-year-old girls were categorised as overweight or obese if they had body fat percentages of 24.5% and 28.0% or above, respectively. At-risk categories for BMI were also established at the 85th (overweight) and 95th (obesity) percentiles, while a WHtR of > 0.5 indicated central obesity (Coe & Lobstein 2012). These new age-specific BMI value cut-offs for children make use of standard deviation and percentile frameworks, which ensures consistency and enables usage in the clinical setting (Coe & Lobstein 2012).

Data analysis

The data were statistically analysed by members of the Department of Biostatistics, University of the Free State. Frequencies and percentages for categorical data and medians and interquartile ranges were used. Differences between chronological age and the age equivalents indicated by the locomotor and ball skills tests were calculated and described by means of the Signed Rank test. The Fisher’s exact test was used to determine significant relationships between categorical variables of gross motor development, PA participation and anthropometry. This analysis was executed as the sample size was relatively small and expected frequencies in more than 20% of the categories, were less than five. Spearman rank order correlations were calculated to determine associations between gross motor skill, PA and anthropometry variables. Rho values indicate effect sizes whereas values closer to 1 or -1 indicate a large or strong effect. Median and interquartile ranges were used as summary statistics, as the numerical data within the dataset were distributed skew. A probability level of 0.05 or less was accepted to indicate statistical significance. Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows (Copyright 2013 SAS Institute Inc., SAS and all other SAS Institute Inc. product or service names are registered trademarks of SAS Institute Inc., Cary, NC, US).

Ethical considerations

After receiving approval from the Health Sciences Research Ethics Committee (HSREC) of the University of the Free State (clearance no: UFS-HSD2022/0111/2607) and the Free State Department of Basic Education, the principal of the school gave permission to conduct the study after school hours. The parents or legal guardians of all the Grade 1 learners who took part in the study gave consent for their children to participate. This included the video recording of gross motor skills and the taking of anthropometric measurements. Each learner agreeing to partake in the study completed an assent form, which was also verbally explained to them in their home language (Afrikaans). Assent forms indicated that participation in the study was voluntary, thus learners could withdraw at any time. Learners who took part in the study were provided with a report concerning their motor proficiency levels, BMI and PA results. The results of the study were stored on a password-protected computer and all the information was treated confidentially.

Results

Table 1 portrays the descriptive statistics for the gross motor, physical activity and anthropometry measurements for the whole participant group. Overall gross motor skills (including locomotor and ball skills) were average with each median being at or above the 75th percentile. It is evident that participants participated in a variety of physical activities each week and that outdoor play (240 min) had the highest median of all physical activity groupings. Of all activities recorded (active and sedentary), time spent on screen time showed the highest median (270 min) per week. It is noteworthy that the median BMI and WHtR values mostly fell just below the at-risk category, while the body fat percentage was within the at-risk category.

Figure 1 represents the different types of activities participants spend time on in the category of extramural activities under the supervision of a coach, the most frequent activity was hockey (46%), followed by tennis (11%), while the 12% was equally distributed between rugby and dancing (6%). Children’s physical activities at home included activities such as running (26%), cycling (24%) and jumping on a trampoline (21%). Outdoor play was mostly performed outside the house (79%), while only 21% of participants indicated playing in a park. Active transport consisted mostly of walking (38%), walking in a shopping mall (35%) and cycling (27%). The most frequent passive activity was arts and crafts (60%), followed by 36% of participants indicating doing homework and only 4% indicating playing with blocks, cars and dolls. For screen time, watching television was the most frequent (60%), while 19% indicated using a cell phone and 12% playing video games. Physical education activities such as a variety of sports were most frequent, indicating 54% of participants preferred hockey, while 24% played mini tennis and 9% rugby. Physical education lessons were regularly participated in by all the participants as PE is compulsory in the South African basic education school curriculum. During recess children most frequently played with one another (48%), followed by playing rugby (20%) and drawing activities (13%). During lunch break activities 38% of participants indicated sitting and talking to each other, while 24% played on the playground and 16% spent time on their cell phones.

FIGURE 1: Frequency of physical activity type.

Table 2 depicts the percentage of participants in the different descriptive categories for gross motor skills and anthropometry. The results for the BMI measures indicated that, although 68.4% of the participants were in the healthy range, 31.6% fell within the overweight and obese range. When observing the WHtR results, 29.8% of participants showed central obesity. The fat percentage results indicate that 19.3% and 22.8% of participants are overweight and obese, respectively. Gross motor index (49.1%), ball skills (50.9%) and locomotor skills (64.9%) were predominantly in the average category.

Table 3 reflects the correlations between gross motor skills, PA and anthropometry variables. Gross motor index significantly correlated with locomotor skills (p ≤ 0001; rho = 0.758) and ball skills (p ≤ 0001; rho = 0.839), while locomotor and ball skills also significantly correlated with one another (p = 0.0190; rho = 0.310). The anthropometry variables (BMI, fat percentage and WHtR) also correlated significantly, while several significant correlations with medium to large effect sizes occurred between the different PA activities. Significant negative correlations were noticed for gross motor index (p = 0.0350) and ball skills (p = 0.0351) with BMI, indicating an inverse relationship where a higher BMI is related to a lower motor skill score. No significant correlations occurred between PA and the anthropometry and/or gross motor skill variables. Gross motor index and fat percentage, as well as time spent on PA at home and WHtR, showed almost significant correlations of p = 0.0515 and p = 0576, respectively. Another correlation of almost significant nature was observed between screen time and fat percentage (p = 0.0524).

Table 4 portrays the associations between anthropometry categories and gross motor index, locomotor, and ball skill descriptive categories. Overall, associations were not significant, except for fat percentage and gross motor skills, which were significantly associated (p = 0.0046) with one another.

Discussion

The aim of the study was to investigate the gross motor development, PA participation and anthropometry profiles of Grade 1 learners and to establish if a relationship exists between these measures.

In this study, the majority of participants had average locomotor abilities, ball skills and gross motor skills. This is in contrast to a study conducted by Bardid et al. (2016) on 1614 Belgian children between the ages of 3 and 8 years, which indicated that the children had lower levels of motor competency than the current reference sample, specifically for ball skills (Bardid et al. 2016). The score distribution of the Belgian sample was skewed, with 37.4% scoring below average and only 6.9% scoring above average.

With regard to the gross motor skills of learners in South Africa, a study on 259 learners, between the ages of 3 and 6 years indicated varying results (ranging from very poor to very superior, between learners from low and high socio-economic-environments) (Tomaz et al. 2019). Research from high-income countries indicates that children from low-income backgrounds display poorer GMS proficiency, compared to higher-income peers (Tomaz et al. 2019). Overall, the learners from the study of Tomaz et al. (2019) demonstrated good gross motor skills, with only 7% of all children attaining a descriptive ranking below average, which is comparable with this study, because none of the learners were below average, regarding their gross motor index. It is important to note that this study was from a high economic background. According to Shumway-Cook and Woollacott (2001), the relationship between motor skills and physical activity could be a result of neurological or physiological reasons. Any movement requires coordinated movements, which are biomechanical and neuro-muscular in nature that need to provide activation, sequencing, timing and scaling of muscle activity (Shumway-Cook & Woollacott 2001). Thus, if children have better motor skills, they will have more opportunities to choose to take part in a variety of physical activities, compared to peers with less motor skills, because of better activation and sequencing of their movement patterns.

In a study by Pienaar and Kemp (2014), using a stratified randomised sample of 816 Grade 1 children (419 boys and 397 girls), with a mean age of 6 years and 8 months in the North-West province of South Africa, almost half of the participants (49.63%) showed below average motor skills, while 48.16% portrayed average motor skills. The Bruininks-Oseretsky Test of Motor Proficiency-2 Short Form was used to test participants’ motor proficiency (Pienaar & Kemp 2014), where the results indicated motor skill difficulties, something which was not seen in this study. However, it is important to note the Bruininks-Oseretsky Test of Motor Proficiency – 2 Short Form also includes fine motor activities while the TGMD-3 only consist of gross motor activities. It is important to observe that interventions should be implemented for children with motor skills difficulties. These interventions can assist children to be more competent and interested in taking part in a variety of physical activities (Melvin Chung, Cheah & Hazmi 2023).

A more recent study, conducted by Pienaar et al. (2022) analysed the level of gross motor skills among typically developing 5–8-year-old children. The study of Pienaar et al. (2022) involved an available sample of 636 children and consisted of 291 boys and 345 girls, with a mean age of 6 years and 8 months in the North-West province of South Africa. The researchers determined the state of developmental differences in four Fundamental Motor Skills (FMS), using the Test of Gross Motor Development-2. Two locomotor- (running and jumping) and ball skills (kicking and catching) were evaluated by using, among other things, the process assessment approach. The four locomotor and ball skills are mostly used by South African children, as the TGMD-3 also included skills not so familiar to South African children. The results were similar to those of this study, indicating an average mastery of gross motor skills (Pienaar et al. 2022). According to these researchers (Pienaar et al. 2022), mastery levels ranged between average and good for ball skills and locomotor skills.

Overall physical activity levels

Physical activity consists of various indicators for children, including overall physical activity levels, organised sports (extramural activities), active play, active transport and passive activities (Naidoo et al. 2022). Furthermore, these indicators were relevant to the PAQ-YC questionnaire used in this study, and the results of each indicator are discussed separately.

The findings of a study conducted by Burns et al. (2019) correlate with our study findings as our study population had a high level of overall PA participation (extramural activities, PA at or at home, outdoor play, active transport and PE at school) but only an average gross motor index. The study by Burns et al. (2019) included 409 school-aged children with an average age of 8 years, from first to fifth grades, while boys (n = 205) and girls (n = 204) were chosen from five elementary schools in the Mountain West region of the United States. The WHO (2022) recommends 420 min per week being active. According to Wrotniak et al. (2006), a lot of children do not meet the recommended active minutes per week as indicated by the WHO (2022), and as children get older physical activity levels decline. In contrast, this study indicates that the participants comply with the recommendations as stated by the WHO with a median of 600 min of overall PA per participant per week (sum of active activities in Table 1). This might be a result of the fact that the school presents PE classes during the week, and in addition provides organised sports such as rugby, netball, hockey and tennis. The results also indicate that some of the children took part in sports not presented by the school such as dancing and karate, which is characteristic of high-resourced environments.

A recent study conducted by Gericke et al. (2024) used a subsample of the Exercise, Arterial Modulation and Nutrition I Youth South Africa study (ExAMIN Yourth SA). A total of 299 children took part, which consisted of 150 boys and 149 girls aged 5–8 with a mean age of 6.83. The results indicated that only 66% of the sample complied with the 60 min of daily moderate-vigorous PA as recommended. The results further indicated that moderate to vigorous PA is positively associated with some motor skills, which concurs with this study.

The Healthy Active Kids South Africa Report Card (HAKSA) provides the most recent and best available evidence on PA among South African children and adolescents aged 3 to 18 years (Naidoo et al. 2022). The report card stated that although there is an absence of current nationally representative data, they recognise that 60% to 73% of children did meet the recommended PA guidelines in regionally based samples, which correspond to the overall PA levels found in this study (Naidoo et al. 2022). This is similar to the results of this study where most participants reported high levels of overall PA, independent of whether it was obtained through activities such as running, cycling, trampoline jumping and even walking the dog.

Naidoo et al. (2022) indicated that approximately 20%–26% of children partake in organised sports (extramural activities) in South Africa. This study indicated that children spend 135 min per week on organised sports, which included hockey (46%), tennis (11%), rugby (6%) and dancing (6%). According to Kokstejn et al. (2019), being physically active develops fundamental movement skills and is generally the basic component to developing more complex and advanced motor skills that will assist children to continue to take part in games, sports, as well as lifetime activities. This indicates that physical activity is a prerequisite for taking part in sports as children get older.

In this study, the main themes that emerged for active play in a high socio-economic environment at home were running (26%), cycling (24%) and jumping on a trampoline (21%). In South Africa, children mostly play at home (79%) because of the fact that there is no access to desirable parks, swimming pools and playing fields, and another barrier is safety concerns at the few parks that are available. These can all be barriers for children to take part in physical activities in public. At school, during break time, the three most popular activities were playing rugby (14%), playing with friends (31%) and climbing on the jungle gym (9%) as reported by the parents. However, it must be taken into consideration that the school has basic equipment, such as a jungle gym and outdoor facilities (play area), and children bring equipment to school, such as rugby balls, netball balls, hockey sticks, etc. for them to play with. The facilities are suitable according to their age and the school fields are kept neat and big enough for any form of physical activity to take place.

Active transport to and from school can improve the physical activity levels, as well as the health status of children (Ter Goon 2016). Although South Africa is successful with between 60% and 66% of children using active transport to and from school (Naidoo et al. 2022), this study’s participants did not indicate using active transport, specifically to get to school. This might be as a result of the fact that participants are quite young and that parents might see active transport as a safety risk, and/or because of the school being situated in a high socio-economic environment where active transport might not be necessary as most parents have cars to take their children to school. A study was conducted by Ter Goon (2016) on 1136 children (548 boys and 588 girls) between the ages of 9 and 13 years, who attend public schools in Central Pretoria, South Africa. The results indicated that the majority (53.4%) of girls were transported to school by car, compared to the boys (46.5%). Furthermore, Ter Goon (2016) is of the opinion that children are being transported to school, instead of making use of active transport, such as walking, an occurrence frequently observed in schools in urban settings, which is similar to our findings. However, in this study, active transport, such as walking (38%) and cycling (27%) was indicated for journeys other than going to school and participants indicated walking in shopping malls (35%) as a form of active transport.

This study indicated that the participants indulge in alarmingly high levels of sedentary behaviour, such as screen time (television 60%, cell phone 19% and video games 12%) and sedentary play (arts and crafts 60%, homework 16%, blocks, cars and dolls 4%). According to Melvin Chung et al. (2023), the physical activity levels of children have been overestimated by their parents, in addition to healthcare providers, as well as educational professionals. This means that to date, a low level of physical activity is considered a major health problem, furthermore, not only affecting adults as previously believed but also for children. Melvin Chung and colleagues (2023) are of the opinion that it is because of the advancement of technology and social media, which they call the ‘digital childhood’ which started earlier. In addition, levels of physical inactivity or sedentary behaviour, particularly screen time or electronic media use, had exceeded recommended levels. Results of this study indicate that participants spend a total median of 450 min being sedentary per week after school hours, with 270 min spent in front of a screen and 180 min sitting at a table doing fine motor activities. This is less than the WHO’s (2022) allowed recommendation of not more than 840 min of screen time in a week, indicating that the study’s sample does comply with the recommendations. However, it is important to note that the upper quartile indicated 480 min and 360 min for screen time and passive activities, respectively. This implies that there are children who spend a lot of time being sedentary.

According to Naidoo et al. (2022), physical education is embedded in Life Orientation. The results indicated that 70% of schools offer formal and structured physical education; however, these sessions are not presented by specialists in physical education (PE). In addition, there is not enough evidence available to determine if PE is implemented as intended in the curriculum. Reasons for these challenges are because of curricular and budgetary restrictions, a lack of content knowledge and competencies, as well as lack of facilities (Naidoo et al. 2022). This study indicates that PE is still compulsory at the school.

Anthropometry (body composition)

Although this study’s population performed average, with regard to their gross motor development and were physically active, the population included an alarming amount of overweight (BMI: 15.8%; fat %:19.3%) and obese (BMI:15.8%; fat %:22.8%) children. Our overweight percentage is similar to an estimate made by the United Nations Children’s Fund (UNICEF), WHO and the World Bank, indicating that 13% of young South African children (with a range of 9.3 – 17.9%) would be overweight by 2018 (Di Cesare et al. 2019). The obesity incidence reported in our study is however greatly higher than the estimate of 1.7% (0.9% – 2.8%) for boys and 3.2% (2.2% – 4.4%) for girls from sub-Saharan Africa (Di Cesare et al. 2019). Despite participants having high levels of physical activity, they also spent a lot more than the recommended time being sedentary. Higher levels of sedentary behaviour have been linked to a less favourable body composition, according to Pan, Wang and Pan (2021), who observed that unhealthy diets and physical inactivity are systemic processes and environmental determinants that contribute to obesity. Participants of this study lived in a high socio-economic environment, which has been linked to a higher exposure to energy-dense foods and drinks, which have obesogenic effects (Pan et al. 2021).

Correlations between gross motor skills, physical activity and anthropometry

Gross motor skill variables significantly correlated with one another, as was the case for the PA variables and the anthropometry variables. No significant correlations were the case between PA and gross motor skills, or PA and anthropometry. Contradicting our results, several researchers indicated a correlation between PA and motor skills (Burns et al. 2019; Ma & Luo 2024; Niemistö et al. 2019). The study by Burns et al. (2019) concluded that children who spent more time being active, obtained higher TGMD-3 scores, compared to children who were less active. Their study involved 409 school-aged children (205 boys and 204 girls) with an average age of 8 years old, who were chosen from five elementary schools in the Mountain West region of the United States (Burns et al. 2019). Niemistö et al. (2019) also reported a positive correlation between motor competence and physical activity participation with higher motor proficiency seen in children who spent more time being active. These results stem from a project including younger children (mean = 5.4 years) from 37 childcare centres in Finland, of which 473 were boys and 481 were girls (Niemistö et al. 2019).

In contrast to our findings, PA significantly correlated with BMI and fat percentage in young Chinese children aged three to 5-years (He & Kang 2024). Similar to our findings, but in contrast with that of He and Kang, no significant correlation between three to 6-year-old children’s PA and BMI were recently reported (Ma & Luo 2024). Ma and Luo (2024) highlight that PA and BMI might be unrelated during the younger years, because of ongoing motor skills development, which in turn influences PA levels. Importantly, this study did observe a few correlations, which were almost of statistical significance, including fat percentage’s correlations with screen time (sedentary time) and the correlation between WHtR and participation in PA at home. Taking in mind this discrepancy in results, it is found that relationships between PA and anthropometry should be interpreted considering the child’s age and developmental level.

Fat percentage was significantly associated with the gross motor index in this study. Furthermore, BMI significantly and inversely correlated with overall gross motor skills and ball skills, linking unhealthy body composition with poorer motor skills. Several studies support this finding (Lima et al. 2019; Matarma et al. 2018; Raut & Tripathy 2020; Webster et al. 2021). Webster et al. (2021) reported that excess weight negatively impacts the total gross motor skills of 6-year-olds. Ball skills and locomotor skills significantly correlated with BMI in the study of Raut and Tripathy (2020), after measuring the fundamental movement skills and anthropometry of 48 boys between the ages of seven and ten. Both Webster et al. (2021) and Raut and Tripathy (2020) identified BMI to impact locomotor skills specifically, which was not the case in our study’s findings. A longitudinal study on 6–7-year-olds also underpinned a reciprocal relation between body fat and motor competence (Lima et al. 2019), while Matarma et al. (2018) concluded that being overweight, decreased the gross motor performance of 5–6-year-olds in Finland. In contrast, Ferreira et al. (2019) found that BMI did not play a mediating role in the relationship between 6 and 10-year-olds’ sports participation and motor competence, except if the child’s BMI was classified as obese, with the mediation still being of small nature. A recent systematic review (Barnett et al. 2022) emphasises the negative association between weight status and motor competence in children, while also highlighting the importance of good motor competence as the foundation for fitness and physical activity. Their recommendation to include various variables (motor competence, physical activity, weight status, perceived motor competence and health-related fitness) in future studies, supports the combination of variables included in this study. Consequently, this indicates that our findings can contribute to the existing literature.

Limitations and recommendations

Only one school took part in the study and therefore did not reflect the demographics of South Africa. Furthermore, the study was based on a small number of children; therefore, it can only be generalised to children in this specific area of the Free State. Lastly, the PA levels were determined using a proxy measure (the parent) and were not based on objectively measured PA levels. A replication of this study in different provinces and regions in South Africa is recommended to provide more robust results. The results of our study can be used to raise awareness that in this high social economic school, there is an alarming number of children classified as being overweight and obese and that this must be addressed to ensure the children’s long-term health.

Conclusion

Physical activity patterns of the children were determined by the PA questionnaire, as it provided an understanding of the makeup of the PA levels of young children. Gross motor skills were average for most of the participants and they achieved adequate physical activity levels. Unfortunately, participants still spend a lot of time being inactive. Only half of the children were in the healthy population range, regarding body fat percentage, while the overweight and obese categories for BMI are of concern. Lastly, significant inverse relationships were the case between gross motor skills and body composition.

Acknowledgements

This article is partially based on the author’s undergraduate thesis entitled ‘Gross motor development, physical activity participation and anthropometry measurement in Grade 1 learners’ towards the degree of B-Biokinetics in the Department of Exercise and Sport Sciences, University of the Free State, South Africa in 2022, with supervisors Dr Monique de Milander and co-supervisor Dr Elna van der Merwe.

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

M.d.M., B.B, and R.V. were the researchers responsible for obtaining all the data for the study. B.B and R.V. compiled the data and wrote the original report for their undergraduate study. M.d.M. served as the supervisor and E.v.d.M the co-supervisor of the study, providing guidance and advice. M.d.M. and E.v.d.M redid the article and analysis of the data, structure and content of the article, as well as completing the final writing and editing for publication.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Raw data were generated at the university and are the property of the University of the Free State. Derived data supporting the findings of this study are available and have been added as supplementary material.

The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.

References