| Resumo: | The assessment of cardiorespiratory fitness (CRF) is of paramount importance in the field of human health and sports sciences. The maintenance of satisfactory cardiorespiratory fitness levels is related with the prevention of cardiovascular disease (Ortega et al., 2008), diabetes and obesity (Dwyer et al., 2009), and school-based interventions have proved a positive effect in promoting cardiorespiratory fitness (Minatto et al., 2016). Since the more direct measurement of CRF (VO2max) is complex and expensive, a variety of indirect tests have been used in field-based protocols such as the 20-m endurance shuttle run (PACER). International normative data for the PACER is well established (Tomkinson et al., 2016). Both percentile and average values show that PACER values are expected to increase from 9-to 17 years of age, although more for boys than girls. Furthermore, CRF values in youth are expected to track into adulthood. In this presentation we will show that individual developmental pathways of PACER can be quite distinct from the widely used normative data. Two hundred and twenty nine children (56% boys) were followed longitudinally from age 9 to 15. Multilevel modelling of changes was conducted in HLM 6.0 software. Ordinary least square (OLS) regressions were used to estimate each child’s linear regression equation for the PACER test. Children were clustered into three groups according to their rate of change (slope values), thus representing distinct developmental pathways (Low, Average and High Rate of Change). These three developmental pathways were tested on a hierarchical linear regression (measures within persons), resulting on a very good model fit. Outputs were compared with the normative data setting. Results showed that three groups of children with similar rate of change on their developmental PACER performance can be found, but these pathways do not fully copy with the normative tables’ information. In conclusion, we suggest that developmental pathways, using longitudinal information, should be preferably used for predicting present and future outcomes.
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