Multidimensional school features associated with physical activity among youth at risk of obesity: an exploratory principal component and generalized estimating equation analysis.

BACKGROUND: Schools may be high-leverage points for the promotion of physical activity (PA), yet little is known about school built and social environments among youth at high risk of obesity. PURPOSE: To characterise school built and social environments that may be salient for PA and to examine associations between school PA environments and PA in youth at risk of obesity. METHODS: Data from youth attending 206 schools (314 youth in 2005-2008, and 129 youth in 2008-2010) within the QUALITY cohort study, a longitudinal investigation of youth at high risk of obesity were used. Features of schools, based on built, policy/programming and social environments were identified using principal components (PC) analysis. Gender-stratified generalized estimating equation (GEE) models were used to explore associations between school features and accelerometer measured mean counts per minute (MCPM), mean daily moderate-to-vigorous physical activity (MVPA) and the odds of meeting MVPA guidelines cross-sectionally and prospectively using 90% confidence intervals. RESULTS: Nine PCs were identified. Associations were observed between PA and 7 of the 9 PCs. The social environment seemed to be particularly important. Social Norms to Promote PA was associated with an increase in girls' baseline MCPM and MVPA. High Willingness to Promote PA was associated with boys' MCPM, MVPA, and odds of meeting MVPA guidelines, at both baseline and follow-up. CONCLUSION: School built and social contexts may be associated with PA cross-sectionally and over time. Further studies are necessary to confirm the direction and magnitude of effects and to establish their relevance to school-based health promotion efforts.

Associations of neighborhood walkability with moderate to vigorous physical activity: an application of compositional data analysis comparing compositional and non-compositional approaches.

BACKGROUND: We compared the relation between neighborhood features and moderate to vigorous physical activity (MVPA) using linear regression analysis and the more novel compositional data analysis (CoDA). Compositional data analysis allows us to take the time children allocate to different movement behaviours during a 24-hour time period into account. METHODOLOGY: Data from youth participants (n = 409) in the QUALITY (QUebec Adipose and Lifestyle InvesTigation in Youth) cohort were included. Time spent in MVPA, light physical activity, sedentary behavior, and sleep ("24-hour movement behaviours") was measured using accelerometers. Neighborhood data were collected using a geographic information system and through direct observation. In CoDA models, we used orthogonal logratio coordinates, which allows for the association of neighbourhood walkability with MVPA to be estimated with respect to the average composition of all other behaviours within a 24-hour time frame. In baseline linear regression models, MVPA was regressed cross-sectionally on neighborhood walkability. All models were stratified by sex, and controlled for BMI z-scores, pubertal development, seasonal variation, parental education, and neighbourhood safety. RESULTS: Based on CoDA, girls who lived in more walkable neighborhoods had 10% higher daily MVPA (95% CI: 2%, 19%), taking into account all other movement behaviours. Based on linear regression, girls who resided in more walkable neighborhoods engaged in 4.2 (95% confidence interval [CI]: 1.2, 6.6) more minutes of MVPA per day on average than girls residing in less walkable neighborhoods. CONCLUSIONS: Unlike with traditional linear models, all movement behaviours were included in a single model using CoDA, allowing for a more complete picture of the strength and direction of the association between neighbourhood Walkability and MVPA. Application of CoDA to investigate determinants of physical activity provides additional insight into potential mechanisms and the ways in which people allocate their time.

Identification of two aptamers binding to Legionella pneumophila with high affinity and specificity.

Legionella pneumophila (Lp) is a water borne bacterium causing Legionnaires' Disease (LD) in humans. Rapid detection of Lp in water system is essential to reduce the risk of LD outbreaks. The methods currently available require expert skills and are time intensive, thus delaying intervention. In situ detection of Lp by biosensor would allow rapid implementation of control strategies. To this end, a biorecognition element is required. Aptamers are considered promising biorecognition molecules for biosensing. Aptamers are short oligonucleotide sequence folding into a specific structure and are able to bind to specific molecules. Currently, no aptamer and thus no aptamer-based technology exists for the detection of Lp. In this study, Systemic Evolution of Ligands through EXponential enrichment (SELEX) was used to identify aptamers binding specifically to Lp. Ten rounds of positive selection and two rounds of counter-selection against two Pseudomonas species were performed. Two aptamers binding strongly to Lp were identified with KD of 116 and 135 nM. Binding specificity of these two aptamers to Lp was confirmed by flow cytometry and fluorescence microscopy. Therefore, these two aptamers are promising biorecognition molecules for the detection of Lp in water systems.