First, all data were tested for normality (i.e., Kolmogorov–Smirnov test) and preliminary analyses were performed to ensure no violation of the assumptions (i.e., homogeneity of variances). Second, descriptive statistics for MVPA were calculated separately for boys, girls, and the total sample. An independent-samples t-test was conducted to compare the MVPA percentages of each day between boys and girls. Third, the intraclass correlations (ICC) and 95% confidence intervals were calculated to inspect the reliability between repeated measures with the same instrument. We also inspected the correlation between the MVPA measurement via accelerometry and MVPA measurement via observational momentary time sampling methods using the Pearson product–moment correlation coefficient. Fourth, we analyzed the agreement between methods using the Bland and Altman method [24]. Finally, to estimate the regression equation of MVPA measurement via accelerometry, we performed a multiple regression analysis. Different predictors, such as age, MVPA measurement via observational momentary time sampling and sex were tested. However since, age was not a significant predictor, the final model only included the estimation of MVPA measurement via observational momentary time sampling and sex as main predictors. Heteroscedasticity was tested using the Breusch–Pagan test. Data analysis was performed using IBM SPSS v24 (IBM Corp., Armonk, NY, USA). The significance level was set at p < 0.05.

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