Various address georeferencing (AG) tools are currently available. But little is known about the quality of each tool. Using data from the EPIPorto cohort we compared the most commonly used AG tools in terms of positional error (PE) and subjects' misclassification according to census tract socioeconomic status (SES), a widely used variable in epidemiologic studies. Participants of the EPIPorto cohort (n = 2427) were georeferenced using Geographical Information Systems (GIS) and Google Earth (GE). One hundred were randomly selected and georeferenced using three additional tools: 1) cadastral maps (gold-standard); 2) Global Positioning Systems (GPS) and 3) Google Earth, single and in a batch. Mean PE and the proportion of misclassified individuals were compared. Google Earth showed lower PE than GIS, but 10% of the addresses were imprecisely positioned. Thirty-eight, 27, 16 and 14% of the participants were located in the wrong census tract by GIS, GPS, GE (batch) and GE (single), respectively (p<0.001). Misclassification according to SES was less frequent but still non-negligible −14.4, 8.1, 4.2 and 2% (p<0.001). The quality of georeferencing differed substantially between AG tools. GE seems to be the best tool, but only if prudently used. Epidemiologic studies using spatial data should start including information on the quality and accuracy of their georeferencing tools and spatial datasets.
Practice of sports in early adolescence may be important to promote maintenance of high PA. Urban environment in the vicinity of residence did not affect changes in the practice of leisure-time PA.
We aimed to assess the validity of a single question to evaluate leisure-time physical activity (PA) in adolescents. We included 209 participants (57.4% girls) aged 14-18 years from Porto, Portugal, evaluated as part of the SALTA project. A self-reported question with four answer options, designed for the EPITeen study, was used to classify the intensity level of usual leisure-time activities. Actigraph accelerometers were used to objectively measure total PA during 7 consecutive days. Since the accelerometers measured PA as a continuous variable, hierarchical cluster analysis was used to identify clusters of individuals with similar level of objectively measured PA. Correlations between self-reported and objective measures were evaluated through polychoric correlations. In girls, we found higher mean time on sedentary activities among those describing their leisure-time PA as "sitting", and an increase on the time spent on light and moderate activities with increasing intensity of PA on self-reported classification. A similar trend was found in boys, but not reaching statistical significance. The correlation between the two measures of PA was 0.42 for girls and 0.46 for boys. We found an acceptable correlation between our single question and the objectively measured PA, showing that, although the single question is not adequate to quantify the intensity of the physical activity, it allows to rank adolescents according to leisure-time physical activity.
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