Background: The distribution of adolescent moderate to vigorous physical activity (MVPA) across multiple contexts is unclear. This study examined indoor and outdoor leisure time in terms of being structured or unstructured and explored relationships with total daily MVPA. Methods: Between September 2012 and January 2014, 70 participants (aged 11–13 y) from 4 schools in Edinburgh wore an accelerometer and global positioning system receiver over 7 days, reporting structured physical activity using a diary. Time spent and MVPA were summarized according to indoor/outdoor location and whether activity was structured/unstructured. Independent associations between context-specific time spent and total daily MVPA were examined using a multivariate linear regression model. Results: Very little time or MVPA was recorded in structured contexts. Unstructured outdoor leisure time was associated with an increase in total daily MVPA almost twice that of unstructured indoor leisure time [b value (95% confidence interval), 8.45 (1.71 to 14.48) vs 4.38 (0.20 to 8.22) minute increase per hour spent]. The association was stronger for time spent in structured outdoor leisure time [35.81 (20.60 to 52.27)]. Conclusions: Research and interventions should focus on strategies to facilitate time outdoors during unstructured leisure time and maximize MVPA once youth are outdoors. Increasing the proportion of youth engaging in structured activity may be beneficial given that, although time spent was limited, association with MVPA was strongest.
Matthew Pearce, David H. Saunders, Peter Allison and Anthony P. Turner
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.