The current study documents the performance of school-age children on static and dynamic balance, as well as motor co-ordination tests. The stimulus materials used were simple to develop, not time-consuming and children participated willingly, demonstrating their suitability. Furthermore, the tests were inexpensive to develop and could be easily administered by trained testers. The developed motor measures were culturally appropriate and psychometrically sound with moderate to excellent reliability levels. Moderate to strong correlations of the motor scores with executive function scores provided evidence of convergent validity; on the other hand, weak associations with verbal memory demonstrated evidence of discriminant validity. Consistent with Bronfenbrenner’s bioecological model (Bronfenbrenner and Ceci 1994), we were able to identify proximal and distal influences on motor proficiency in school-age children.
Influence of background characteristics
The superior performance of girls on the tests of dynamic balance is similar to what has been reported among South African (Portela 2007; du Toit and Pienaar 2002), Nigerian (Toriola and Igbokwe 1986) and Australian (Livesey et al. 2007) children. And congruent with the conclusions of Largo and colleagues (2003), gender differences on the various tasks varied in size and direction. Despite the differences observed in the current study, our findings do not however support the suggestion by Livesey and colleagues (2007) that separate gender-specific norms be used in the assessment of motor abilities in school-aged children. Reported differences between boys and girls within the studied age-group may have resulted from differences in cultural expectations – the socialising influences of parents and teachers – and environmental practices, as has been emphasized by others (Bénéfice et al. 1999; Thomas and French 1985; Munroe and Munroe 1975). In many rural communities such as the one in which the current study was conducted, girls are socialised to perform household activities from a young age. To successfully perform some of these tasks, such as fetching water from the river, requires balance.
Nutritional status was an important determinant of motor performance as it had moderate effects on balance and co-ordination. Children with growth retardation achieved lower scores on the composite motor test scores, similar to what has been reported in varied contexts from studies among younger (Bénéfice et al. 1999; Bénéfice et al. 1996; Abubakar et al. 2008b), older (Chang et al. 2010) and children of comparable ages (Chowdhury et al. 2010; Kar et al. 2008). The negative impact of poor nutritional status on motor performance may be attributed to deficiency in muscular strength (Malina and Little 1985), low energy levels (Dufour 1997) and slower motor development ((Malina 1984). Given that the negative impact of chronic undernutrition is long-term (Hoorweg and Stanfield 1976), and that stunting has a particularly strong effect on early gross motor development (Pollit et al. 1994), opportunities for interventions to specifically improve children’s nutritional status, should be explored.
Contrary to our expectations, children from the least wealthy households had lower scores than their counterparts from wealthier households only on the balance composite score. Furthermore, children from households with moderate wealth levels performed the worst on the Stork Balance Test and on the Overall Motor Index. The moderate effects sizes recorded suggested only modest differences among the various groups, demonstrating that socioeconomic conditions did not have such a major influence on children’s motor performance. These findings are in contrast to those reported in studies among populations with similar socioeconomic characteristics (Chowdhury et al. 2010). We offer the following explanations for our findings. As both nutritional status and household resources showed similar effect sizes in their associations with motor outcomes, it may be that the two are inextricably linked. For one, poorer households have fewer resources at their disposal and are therefore more likely to make poor nutrition-related choices. Second, our findings that nutritional status had a more pervasive role than SES may be related to the measure of stunting used. Height-for-age as a measure of chronic undernutrition may in itself be indicative of the cumulative effects of poor nutrition which impacts outcomes from a young age. Infant data from an earlier study in this area (Abubakar et al. 2008b) suggested that SES (conceptualised as distal factors) had less of an impact on child outcome than proximal factors (such as anthropometric status). Among our school-age population, we anticipated that SES would play a more influential role as the impact of outside environments surpasses that of immediate environments. The specific pathways through which poor SES and nutritional status affect outcome remain an area for further study.
Schooling effects were consistently larger than those of the other background influences suggesting that school exposure exerted a much stronger influence on child outcomes. Our findings have precedence in this setting where previous studies have reported strong consistent effects of school attendance on children’s performance (Alcock et al. 2008; Holding et al. 2004). Superior performance in children with greater exposure to school may, as has been postulated elsewhere (Bénéfice and Ba 1994), be attributed to the positive effects of attending school; the ability to follow instructions, pay attention to tasks and increased opportunities for practice.
With area of residence, the pattern of motor performance observed in the current study was unexpected as children living in the more rural areas had significantly lower scores on the Hopping in Squares Test. These findings were in stark contrast to reports from elsewhere which demonstrate that rural children consistently outperform their urban counterparts on tests of motor abilities (Portela 2007), since they have much more open play areas and they are more likely to engage in outdoor activities for longer periods of time (Loucaides et al. 2004). It should be noted that a much wider (and significant) variance in the mean scores of three tests for rural children in the current study possibly affected the significance levels recorded and may have jeopardized the validity of the obtained results (Glass et al. 1972). Perhaps we did not observe the expected differences in performance due to the widely disparate numbers of children in the two groups, reflecting a misclassification according to area of residence. Furthermore, our data failed to suggest that area of residence was a confounder on school attendance. Secondly, because we did not have a truly urban population, variations in the living conditions of children residing in rural and peri-urban areas may have been too subtle to create any real differences.