The aim of this study was to evaluate whether knowledge on concussions and self-reported risk-taking and sensation-seeking behavior are associated with helmet wear amongst certified Canadian ski and snowboard instructors. A total of 612 participants completed an online cross-sectional survey. Participants were asked about helmet use (answer options: always; most of the time; sometimes; rarely; never) and knowledge on concussion. Risk-taking behavior was self-reported (answer options: more cautious vs. more risky), and they completed the Sensation Seeking Scale Form V. Demographics (sex, age, education, nationality), certified winter sport (skiing and/or snowboarding), level of certification (4 levels, from beginner to expert), years of experience, average skiing time per season, and history of concussion were also recorded. Self-assessment of risk-taking was significantly associated with helmet wear (p = .04) but was not included in the multivariate model, which included only the variables level of p < .01. The final prediction model shows that self-assessment of sensation-seeking behavior (OR = 1.12; 95% confidence interval [CI: 1.05, 1.20]), certification level (OR = 5.75; 95% CI [1.85, 17.88]), and sex (OR = .22; 95% CI [.06, .76]) were significantly associated with helmet wear. Thus, a smaller proportion of men with a certification level of 3 (intermediate to expert level) who reported more sensation-seeking behaviors are wearing a helmet compared with others. In contrast, knowledge on concussion was not associated with the frequency of wearing a helmet. These findings may help in the development of prevention campaigns that focus on the main factors playing a role in helmet wear to more effectively convince nonwearers to adopt the safest practice.

Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.

Letters } Correspondance footprint of both cigarette production and the health care burden of tobacco-related illness.Producing just 1 cigarette takes 3.7 L of water and 3.5 g of oil, making cigarette production responsible for 0.2% of global carbon emissions. 2 Additionally, tobacco and cigarette production reduces the capability of agricultural land to produce food for consumption, increasing food insecurity in vulnerable populations and contributing to deforestation. 2,3 Every health care activity has an environmental impact. Every procedure, test, and treatment consumes energy and resources, and produces waste. 4 By enabling our adolescent patients to stop smoking, we can substantially improve their health, and also reduce the carbon emissions that would have been associated with tobacco production and tobacco-related illness.

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