Individual, social, environmental, and physical environmental correlates with physical activity among Canadians: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>The identification of various individual, social and physical environmental factors affecting physical activity (PA) behavior in Canada can help in the development of more tailored intervention strategies for promoting higher PA levels in Canada. This study examined the influences of various individual, social and physical environmental factors on PA participation by gender, age and socioeconomic status, using data from the 2002 nationwide survey of the Physical Activity Monitor.</p> <p>Methods</p> <p>In 2002, 5,167 Canadians aged 15–79 years, selected by random-digit dialling from household-based telephone exchanges, completed a telephone survey. The short version of the International Physical Activity Questionnaire was used to collect information on total physical activity. The effects of socio-economical status, self-rated health, self-efficacy, intention, perceived barriers to PA, health benefits of PA, social support, and facility availability on PA level were examined by multiple logistic regression analyses.</p> <p>Results</p> <p>Self-efficacy and intention were the strongest correlates and had the greatest effect on PA. Family income, self-rated health and perceived barriers were also consistently associated with PA. The effects of the perceived health benefits, education and family income were more salient to older people, whereas the influence of education was more important to women and the influence of perceived barriers was more salient to women and younger people. Facility availability was more strongly associated with PA among people with a university degree than among people with a lower education level. However, social support was not significantly related to PA in any subgroup.</p> <p>Conclusion</p> <p>This study suggests that PA promotion strategies should be tailored to enhance people's confidence to engage in PA, motivate people to be more active, educate people on PA's health benefits and reduce barriers, as well as target different factors for men and women and for differing socio-economic and demographic groups.</p

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Sedentary behaviour and biomarkers for cardiovascular disease and diabetes in mid-life: the role of television-viewing and sitting at work.

Knowledge of sedentary behaviour associations with health has relied mainly on television-viewing as a proxy and studies with other measures are less common. To clarify whether sedentary behaviour is associated with disease-risk, we examined associations for television-viewing and sitting at work

The petrogenesis of sodic island arc magmas at Savo volcano, Solomon Islands

Savo, Solomon Islands, is a historically active volcano dominated by sodic, alkaline lavas, and pyroclastic rocks with up to 7.5 wt% Na2O, and high Sr, arc-like trace element chemistry. The suite is dominated by mugearites (plagioclase–clinopyroxene–magnetite ± amphibole ± olivine) and trachytes (plagioclase–amphibole–magnetite ± biotite). The presence of hydrous minerals (amphibole, biotite) indicates relatively wet magmas. In such melts, plagioclase is relatively unstable relative to iron oxides and ferromagnesian silicates; it is the latter minerals (particularly hornblende) that dominate cumulate nodules at Savo and drive the chemical differentiation of the suite, with a limited role for plagioclase. This is potentially occurring in a crustal “hot zone”, with major chemical differentiation occurring at depth. Batches of magma ascend periodically, where they are subject to decompression, water saturation and further cooling, resulting in closed-system crystallisation of plagioclase, and ultimately the production of sodic, crystal and feldspar-rich, high-Sr rocks. The sodic and hydrous nature of the parental magmas is interpreted to be the result of partial melting of metasomatised mantle, but radiogenic isotope data (Pb, Sr, Nd) cannot uniquely identify the source of the metasomatic agent. Electronic supplementary material The online version of this article (doi:10.1007/s00410-009-0410-9) contains supplementary material, which is available to authorized users