Accelerometer-based physical activity levels, fundamental movement skills and weight status in British preschool children from a deprived area

Following institutional ethics approval from Coventry University (P45654) and informed consent, children from 11 preschools in North Warwickshire, England participated in this study; of these preschools, five were private and six were on a primary school site. Children’s assent was gained through the desire to be involved in the testing and those unwilling to participate were removed from the study. The participants were a convenience sample and included 185 preschool children (99 boys, 86 girls), aged 3–4 years. North Warwickshire was chosen as it incorporates preschools that are considered to have the highest levels of deprivation in the County [58]. The preschools selected were all in an area of SES deprivation.

Height was measured to the nearest millimetre, using a portable stadiometer (Leicester Height Measure, Leicester, UK). Body mass was measured to the nearest 0.1 kg using portable weighing scales (Tanita BF350, Tokyo, Japan); the children were lightly dressed (t-shirt and light trousers/skirt) and barefoot, or in socks. BMI was calculated as kilograms per square meter [53]. BMI was compared to a BMI-for-age chart to determine whether the child was of normal weight or overweight (≥ 95th percentile); this is recommended as a reasonable measure for assessing overweight in children [2, 4, 11, 28]. Waist circumference (WC) was measured to the nearest centimetre, midway between the lowest rib and the iliac crest [30], using a non-elastic flexible tape measure and the child in a standing position. WC was compared to standardised international cut-off points and weight status categorised as overweight/obese or normal weight [33].

PA was measured using a GENEActiv waveform triaxial accelerometer (ActivInsights Ltd., Cambridge, UK). The accelerometer measured at 10 epochs (s) and a sampling frequency of 100 Hz [25, 41, 55]. The accelerometer was attached using a watch strap and positioned over the dorsal aspect of the dominant wrist, midway between the radial and ulnar styloid process. The participants wore the accelerometers for four consecutive days; this included 2 weeks and two weekend days [52]. All children received a letter to take home describing how and when they should wear the GENEActiv accelerometers. Non-wear time was defined as 90-min windows of consecutive zero or nonzero counts [7]. The amount of wear time and percentage (%) of wear time that each child spent in different intensities of PA were calculated for weekdays and weekend days. It is recommended that 4 days, including one weekend day, is acceptable for measuring PA [52]. Due to the age of the participants and the difficulty in children wearing accelerometers for sustained periods of time, children were included in the final data analysis if the accelerometer had been worn for 3 days, including one weekend day and for a minimum of 6 h each day [5, 47, 52]. Of the 185 children sample, accelerometer data was recorded for 178 children; seven children’s data were not useable due to the children either not wearing the accelerometers or technical difficulties with them recording data.

For every epoch (number of seconds), movement data (activity counts) were added, logged, processed and analysed. Accumulated activity counts were categorised in terms of intensity: sedentary, light, moderate and vigorous PA [1]. The following cut points for 3–4-year-olds were used to determine PA intensity: dominant hand < 8.1 cpm for sedentary activity, 8.1–9.3 cpm for LPA and 9.3+ cpm for MVPA [47]^. For the non-dominant hand, < 5.3 cpm for sedentary activity, 5.3–8.6 cpm for LPA and 8.6+ cpm for MVPA [47]. Using the GENEActiv post-processing software, the raw 100 Hz triaxial GENEActiv data were summed into a signal vector magnitude and expressed in 10-s epochs [18]. Children were classified as either meeting (sufficiently active) or not meeting (insufficiently active) the UK recommended 180 min a day of PA for 0–5-year-olds [10].

An adapted version of the Test of Gross Motor Development-2 (TGMD-2) was employed as a measure of FMS [54], with the removal of the underhand roll and the addition of skipping. Skipping was included because it is a skill that benefits children’s physical fitness, improving their balance and muscle coordination, whilst being an enjoyable low-cost activity [16, 48]. TGMD-2 is a process-orientated test that examines a subset of locomotor and object control skills [3, 23]. The TGMD-2 relates to activities that preschool children participate in and it assesses gross motor development amongst 3- to 11-year-olds [23]. The TGMD-2 has been described as having an established validity and reliability amongst preschool children, with a test-retest reliability of 0.82–0.95 [23, 38].

This study adhered to the TGMD-2 guidelines with the addition of skipping and the removal of the underhand roll [54]. Prior to data collection, a senior member of the research team, who had previous experience of delivering the TGMD-2 protocol, trained the field tester (primary researcher). The children were assessed in small groups (2–3) and the tests were administered by one tester to ensure consistency. The tests took part in an outside area or an adjacent primary school’s hall. The skills were physically demonstrated and verbally explained to ensure all children had the same clear information on the different skills. If any child did not understand a task correctly then they were provided with a further verbal description and asked to repeat the trial of the skill again [14]. The children all had a practice attempt prior to being scored on their two tests. All children were videoed completing the skills, using a camcorder (Sony, Tokyo, Japan) at standard frame rate, allowing the skills to be analysed after the occasion. All 12 skills were assessed in a standardised order and the testing took between 30 and 35 min per group. The skills were always performed in the following order: run, gallop, hop, leap, horizontal jump, skip, slide (locomotor skills), followed by two-handed strike of a stationary ball, stationary bounce of a ball, catch, kick and overhand throw (object control skills) [3, 23].

The children’s FMS competency was assessed using the guidelines of the TGMD-2 protocol [54]. All video analyses were completed by the field tester (primary researcher). Inter-tester reliability was established prior to the commencement of testing, using pre-coded videos of ten children, there was 84.5% agreement across the 12 skills (range = 81.7–88.4%); this was similar to work by Foulkes et al. (2015) [14]. Intra-tester reliability was also performed again using pre-coded videos of an additional ten children, with the test-retest completed 1 week apart, 93.9% agreement was determined across the 12 skills (range = 90–98%). There is no specified minimum level of percentage agreement, however, 80–85% has previously been deemed acceptable [57]. If at any point the assessor was unsure whether a child had performed a criterion or not, then both the assessor and trainer viewed the videos and agreed on a score [14]. For both trials of the skill, the run, gallop, hop, jump, slide, strike, catch, kick and throw were all scored out of 4 and the leap, skip and bounce were out of 3; zero represented absence of components for a skill. The scores were totalled over two attempts to provide the locomotor, object control and total gross motor skill score for a child (total FMS) [3, 23].

The percentage of time and mean amount of time (min) spent in sedentary, LPA and MVPA were determined. Data for total, locomotive and object control FMS were analysed separately. The participants were split equally into tertiles for their total FMS, locomotive FMS and object control FMS. This was to identify whether being in the lower, middle or higher FMS tertile for these measurements impacted on PA, BMI and WC. The use of tertiles in this manner has been recommended [12, 59] in the context of motor competence research with children as the most effective way to understand whether health-related variables differ as a consequence of FMS being low, moderate or high in nature. The effect of any differences in the FMS measurements on PA, BMI and WC was analysed using a univariate ANOVA, with WC, BMI or PA as the dependent variable and gender and the specific FMS tertile as the fixed factors. The Bonferroni post hoc test was used to analyse any differences. Independent two tailed t tests were also performed to identify if there were any significant differences between each FMS between boys and girls. Pearson’s correlation coefficients were performed between the overall total FMS scores, locomotor FMS and object control FMS and the percentage of time in MVPA during the weekdays and the weekend days, to see if there was a relationship. Pearson’s correlation coefficient was calculated for each combination of BMI or WC against total, locomotor or object control FMS. Pearson’s correlation requires parametric data; therefore, arcsine transformation was conducted on the percentage time data (non-parametric) prior to statistical analysis [60]. The Statistical Package for Social Sciences (Version 20, SPSS Inc., Chicago, Ill, USA) was used for statistical analysis and the alpha level was set a priori at P = 0.05.

Using wrist-worn accelerometers can be logistically and practically challenging with preschool children [8]. In the current study, although a cut-off of 6 h per day was employed, the participants, in general, far exceeded this value with mean wear times being over 600 min/day for both weekdays and weekends. This may be considered a comparatively low wear time, however, achieving a greater amount of days and higher wear time in preschool children is challenging. The preschool children were drawn from a deprived area of the UK. This may have resulted in differences in PA levels and FMS competency rate in the study, compared to an area of higher SES, therefore future research comparing both high and low SES groups would be welcome. That said, the focus on low SES children was important as these children face greater barriers to becoming physically active and when older they face higher rates of obesity and associated comorbidities [35]. It is important to emphasise that the participant’s preschools were all in a deprived area, however, the samples were probably not homogenous in terms of their home social and physical environment, as not all children were necessarily from a low SES household. This study did not assess the interaction between familiar environments, PA levels of parents, ponderal status of parents or genetics of the children, but these would be key areas to focus on in future research to identify if they are contributing factors to a preschool child’s FMS competency. Also, none of the preschool children met the UK-recommended PA guidelines, but the majority were of a healthy weight. This in itself is a key consideration and may indicate a window of opportunity for PA intervention before overweight/obesity has set in. Finally, a single accelerometer may not capture all PA at these ages, as preschool children may play a lot on the ground without displacement and when they walk, they may not move their upper limbs with a regular pattern, therefore the wrist-worn GENEActiv accelerometer may not capture all movements, thereby potentially underestimating total physical activity. However, use of a single accelerometer has previously been calibrated for use in this population [47].