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Introduction: Youth initiation may drive differences in smoking prevalence across Canadian provinces. Provincial differences in initiation relate to tobacco control strategies and public health funding, but have also been attributed to population characteristics. We test this hypothesis by examining the extent to which seven characteristics--immigration, language, family structure, education, income, home ownership and at-school status--explain differences in initiation across provinces. Methods: We used data from 16 897 youth aged 12 to 17 years in the Canadian Community Health Survey collected from 2015 to 2018. To examine the proportion of provincial differences explained by population characteristics, we compared average marginal effects (AMEs) from partially and fully adjusted models regressing "having ever initiated" on province and other characteristics. We also tested interactions to examine differences in the association between population characteristics and initiation across provinces. Results: Initiation varied from 4% in British Columbia to 10% in Quebec. Being born in Canada, speaking French, not living in a two-parent household, being in the lowest household income quintile, having parents without postsecondary education, living in rented accommodation and not being in school were each associated with initiation. Taking these results into consideration, the AME of residing in another province compared with Quebec was attenuated by between 3% and 9%. Family structure and household income were more strongly associated with initiation in the Atlantic region and Manitoba, but not in Quebec. Conclusion: Differences in initiation between Quebec and other provinces are unlikely to be substantially explained by their demographic or socioeconomic composition. Reprioritizing tobacco control and public health funding are likely key in attaining the "tobacco endgame" across provinces. [ABSTRACT FROM AUTHOR]

Introduction: School-based health-promoting interventions (HPIs) aim to support youth development and positively influence modifiable lifestyle behaviours. Identifying factors that contribute to or hinder the perceived success of HPIs could facilitate their adaptation, improve implementation and contribute to HPI sustainability. The objective of this study was to identify factors in three domains (school characteristics, characteristics of the HPI and factors related to planning and implementing the HPI) associated with perceived success of HPIs among school principals in elementary schools. Methods: Data were drawn from Project PromeSS, a cross-sectional survey of school principals and/or nominated staff members in a convenience sample of 171 public elementary schools in Quebec, Canada. School board and school recruitment spanned three academic school years (2016-2019). Data on school and participant characteristics, HPI characteristics, variables related to HPI planning and implementation and perceived success of the HPI were collected in two-part, structured telephone interviews. Descriptive statistics were used to characterize schools and study participants. Twentyeight potential correlates of perceived HPI success were investigated separately in multivariable linear regression modelling. Results: Participants generally perceived HPIs as highly successful. After controlling for number of students, language of instruction, school neighbourhood and school deprivation, we identified five correlates of perceived success, including lower teacher turnover, higher scores for school physical environment, school/teacher commitment to student health, principal leadership and school being a developer (vs. adopter) of the HPI. Conclusion: If replicated, these factors should be considered by HPI developers and school personnel when planning and implementing HPIs in elementary schools. [ABSTRACT FROM AUTHOR]

Whether physical activity (PA) tracking devices are associated with PA motivation in young adults is largely unknown. We compared total PA minutes per week, total minutes walking/week, meeting moderate-to vigorous PA guidelines, and past-year activity tracking across motivation cluster profiles among 799 young adults. Participants with "self-determined" profiles reported the highest total PA minutes/week followed by participants with "low intrinsic," "controlled self-determined," and "high external" profiles. A behavior regulation profile X activity tracking frequency interaction was not significant. Behavior regulation profiles may need to be considered in PA interventions using activity trackers. [ABSTRACT FROM AUTHOR]

In addition to its recognized role in bone health, recent studies point to vitamin D functions in other tissues, including the pancreas. We tested the association between the vitamin D status and glucose and lipid homeostasis in a school-based, cross-sectional survey of a representative sample of youth. We measured fasting plasma insulin, glucose, total cholesterol (TC), triglycerides (TG), HDL cholesterol (HDL-C) apolipoproteins (apo) A1 and B, and 25-hydroxyvitamin D [25(OH)D] concentrations in 878 boys and 867girls. The 25(OH)D concentrations (mean [+ or -] SD) were 45.9 [+ or -] 12.2 nmol/L in boys and 45.9 [+ or -] 13.0 nmol/L in girls. More than 93% of youth had suboptimal ( doi: 10.3945/jn.109.112250.

Objectives. We sought to document the sequence and timing of milestones in the process of smoking cessation by prospectively studied cessation milestones among novice adolescent smokers. Methods. Participants, aged 12 to 13 years in 1999 (n=1293), completed self-report questionnaires every 3 months during the school year over 5 years. We ascertained time after first puff to attain 5 cessation milestones among 319 participants who initiated cigarette smoking during follow-up. Results. The cumulative probability of first reports of a serious desire to quit and perceived permanent cessation was 25% at 1.5 months (95% confidence interval [CI]=1.5, 2.5) after the first puff. The first serious quit attempt occurred at 2.5 months (95% CI=2.5, 5.4), lack of confidence about quitting followed at 18.4 months (95% CI=18.4, 26.8), and awareness of the difficulty of quitting occurred at 32.2 months (95% CI=19.2, 38.4). Conclusions. Desire and attempts to quit began soon after smoking onset. Novice smokers progressed through several stages in their perception of the difficulty of quitting. Increased understanding of the cessation process may help in developing effective tobacco control interventions for novice smokers. (doi:10.2105/AJPH.2007.128629)

Introduction: An association between socioeconomic status (SES) and smoke-free private spaces among smokers could be due to heavier smoking among low SES smokers. We assessed whether quantity smoked or SES are independently associated with smoke-free homes or cars in daily smokers. Method: Data were drawn from a cross-sectional telephone survey (2011–2012) of 750 daily smokers age ≥18 years in Quebec, Canada (45% response). Multivariable logistic regression was used to model the independent association between (a) number of cigarettes smoked per day, and (b) each of educational attainment, annual household income, or active employment status and smoke-free homes or cars. Results: Participants were 41.0 years old on average, 57% were female. Median (IQR) number of cigarettes smoked per day was 14 (10, 20). Forty-eight percent of participants reported smoke-free homes; 34% reported smoke-free cars. Quantity smoked was strongly associated with both smoke-free homes and cars. Income and education (but not actively employed) were associated with smoke-free homes. None of the SES indicators were associated with smoke-free cars. Conclusions: Interventions targeting smokers to promote smoke-free homes and cars should incorporate components to help smokers reduce quantity smoked or preferably, to quit. Interventions targeting smoke-free homes will also need to address SES inequalities by education and income. Our data suggest that reduction in quantity smoked may help smokers reduce SHS exposure in cars, but that an inequality lens may not be relevant. [ABSTRACT FROM AUTHOR]

Introduction: Most studies modeling adolescent cigarette smoking trajectories use age as the time axis, possibly obscuring depiction of the natural course of cigarette smoking. We used a simulated example and real data to contrast smoking trajectories obtained from models that used time since smoking onset or calendar time (age) as the time axis.Methods: Data were drawn from a longitudinal investigation of 1293 grade 7 students (mean age 12.8 years) recruited from 10 high schools in Montreal, Canada in 1999-2000, who were followed into young adulthood. Cigarette consumption was measured every 3 months during high school, and again at mean ages 20.4 and 24.0. Analyses using time since onset of smoking as the time metric was restricted to 307 incident smokers; analysis using calendar time included 645 prevalent and incident smokers. Smoking status and nicotine dependence (ND) were assessed at mean ages 20.4 and 24.0. Simulated data mimicked the real study during high school.Results: Use of different time metrics resulted in different numbers and shapes of trajectories in the simulated and real datasets. Participants in the calendar time analyses reported more ND in young adulthood, reflecting inclusion of 388 prevalent smokers who had smoked for longer durations.Conclusions: Choosing the right time metric for trajectory analysis should be balanced against research intent. Trajectory analyses using the time since onset metric depict the natural course of smoking in incident smokers. Those using calendar time offer a snapshot of smoking across ages during a given time period.Implications: This study uses simulated and real data to show that trajectory analyses of cigarette smoking that use calendar time (e.g., age) versus time since onset as the time axis metric tell a different story. Trajectory analyses using the time since onset metric depict the natural course of smoking in incident smokers. Those using calendar time offer a snapshot of smoking across ages during a given time period. Choosing the right time metric should be balanced against research intent. [ABSTRACT FROM AUTHOR]