Jennifer E. Vena

Background: Suboptimal diet quality and physical inactivity are key risk factors for chronic disease and disability in Canada. However, the lack of high-quality population-level data hinders the development of evidence-based strategies to support improvements in diet quality, movement behaviors (physical inactivity, activity, and sleep), and health. The lack of data is also a barrier to developing capacity in diet and physical activity assessment and epidemiology in Canada. Objective: This protocol describes the development of the largest known repository of dietary intake and movement behavior data in Canada by drawing upon an existing longitudinal cohort study, the Canadian Partnership for Tomorrow’s Health (CanPath). In the short-term, the data will be used to examine associations between system factors (eg, retail food environments) and dietary intake. In the longer-term, data will be available to pursue a range of research questions, including longitudinal associations between diet, movement behavior, and health outcomes. Methods: Participants in CanPath (>330,000 adults) who can complete online questionnaires are eligible and will be asked to complete a baseline web-based questionnaire including questions on demographic characteristics and screeners capturing dietary intake and movement behaviors. Subsequently, participants will be invited to complete an online 24-hour dietary recall using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24-Canada-2018) and an online 24-hour activity recall using Activities Completed Over Time in 24 Hours (ACT24). Repeat recalls will be administered 1-2 weeks later. A subset of participants will be invited to complete 2 additional ASA24-Canada-2018 and Activities Completed Over Time in 24 Hours recalls 6 months later. One year after baseline, participants will be invited to complete past-year diet and movement behavior questionnaires. In Québec, dietary intake and movement behavior data are from 3000 CanPath participants enrolled in the NutriQuébec study. Participant addresses will be linked to geospatial data on the food, built, and social environment. Results: Data collection began in 2025. As of manuscript acceptance (November 4, 2025), 3171 participants had been recruited. Data processing and cleaning will be completed in 2027, and analyses will occur in 2028. It is anticipated that dietary intake and movement behavior data will be available for up to 100,000 adults. Conclusions: This protocol outlines the collection of detailed data on dietary intake and movement behavior in a large cohort spanning all provinces in Canada. In addition to allowing examination of a range of research questions related to diet, movement behavior, and health, the combination of assessment tools will support methodological research, including expanding analytical strategies to mitigate the effects of error in dietary and movement behavior data. This effort will also build capacity in the collection, processing, and harmonization of dietary and movement behavior data among cohorts and provide a training ground for emerging researchers. International Registered Report Identifier (IRRID): PRR1-10.2196/71680

BACKGROUND Suboptimal diet quality and physical inactivity are key risk factors for chronic disease and disability in Canada. However, the lack of high-quality population-level data hinders the development of evidence-based strategies to support improvements in diet quality, movement behaviors (physical inactivity, activity, and sleep), and health. The lack of data is also a barrier to developing capacity in diet and physical activity assessment and epidemiology in Canada. OBJECTIVE This protocol describes the development of the largest known repository of dietary intake and movement behavior data in Canada by drawing upon an existing longitudinal cohort study, the Canadian Partnership for Tomorrow’s Health (CanPath). In the short-term, the data will be used to examine associations between system factors (eg, retail food environments) and dietary intake. In the longer-term, data will be available to pursue a range of research questions, including longitudinal associations between diet, movement behavior, and health outcomes. METHODS Participants in CanPath (>330,000 adults) who can complete online questionnaires are eligible and will be asked to complete a baseline web-based questionnaire including questions on demographic characteristics and screeners capturing dietary intake and movement behaviors. Subsequently, participants will be invited to complete an online 24-hour dietary recall using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24-Canada-2018) and an online 24-hour activity recall using Activities Completed Over Time in 24 Hours (ACT24). Repeat recalls will be administered 1-2 weeks later. A subset of participants will be invited to complete 2 additional ASA24-Canada-2018 and Activities Completed Over Time in 24 Hours recalls 6 months later. One year after baseline, participants will be invited to complete past-year diet and movement behavior questionnaires. In Québec, dietary intake and movement behavior data are from 3000 CanPath participants enrolled in the NutriQuébec study. Participant addresses will be linked to geospatial data on the food, built, and social environment. RESULTS Data collection began in 2025. As of manuscript acceptance (November 4, 2025), 3171 participants had been recruited. Data processing and cleaning will be completed in 2027, and analyses will occur in 2028. It is anticipated that dietary intake and movement behavior data will be available for up to 100,000 adults. CONCLUSIONS This protocol outlines the collection of detailed data on dietary intake and movement behavior in a large cohort spanning all provinces in Canada. In addition to allowing examination of a range of research questions related to diet, movement behavior, and health, the combination of assessment tools will support methodological research, including expanding analytical strategies to mitigate the effects of error in dietary and movement behavior data. This effort will also build capacity in the collection, processing, and harmonization of dietary and movement behavior data among cohorts and provide a training ground for emerging researchers. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/71680

Pagano AP, et al. World J Mens Health. 2024 Jun;42:e64. https://doi.org/10.5534/wjmh.240022

The design and quality of the neighbourhood built environment can encourage health-supportive behaviours and support cardiometabolic health. However, despite the relationships between demographic and behavioural risk factors of metabolic syndrome being investigated by many studies, only some studies have directly estimated the associations between the built environment and metabolic syndrome. Using data from Canada, we examined the associations between the neighbourhood built environment and metabolic syndrome. Data from Alberta’s Tomorrow Project participants, conducted in Alberta, Canada, was used (n = 6718). Metabolic syndrome was defined as the presence of at least three clinical risk factors among lipid levels, blood pressure, and waist circumference. The normalised difference vegetation index was used to quantify the greenness of each participant’s neighbourhood. Built attributes of participants’ neighbourhoods associated with supporting physical activity, including dwelling density, intersection density, and the number of points of interest, were obtained via the Canadian Urban Environmental Health Research Consortium. Increases in the number of points of interest and total active living environment-friendliness of the neighbourhood were associated with having fewer metabolic syndrome risk factors (b = −0.11, 95% CI −0.16, −0.07 and b = −0.03, 95% CI −0.05, −0.01, respectively) and lower odds of metabolic syndrome (OR = 0.89, 95% CI 0.84, 0.094 and OR = 0.97, 95% CI 0.95, 0.99, respectively). Furthermore, higher dwelling density was associated with having fewer metabolic syndrome risk factors (b = −0.05, 95% CI −0.09, −0.01). Our findings highlight the importance of urban design to prevent and potentially manage metabolic syndrome and improve population health.

Studies suggest that fatty acid status influences breast cancer etiology, yet the roles of individual fatty acids in breast cancer risk are unclear, specifically when central adiposity and menopausal status are considered. This study examined the associations of fatty acid status with breast cancer risk including location, menopausal status, and waist-to-hip ratio as key variables. Prediagnostic plasma phospholipid fatty acids were measured in women with breast cancer (n = 393) and age-matched controls (n = 786) from a nested case-control prospective study within Alberta's Tomorrow Project (ATP) and British Columbia Generations Project (BCGP) cohorts. Binary logistic regression models were used to evaluate associations of fatty acids and breast cancer risk with subgroup analysis for menopausal status and waist-to-hip ratio. Women from BCGP had a higher n–3 (ɷ-3) fatty acid status compared with the ATP (6.4% ± 0.08% vs. 5.3% ± 0.06%; P < 0.001), so subsequent analysis was blocked by cohort. Overall, fatty acids had inconsistent associations with risk. In the ATP among premenopausal women, total long-chain n–3 fatty acids (OR_Q4vsQ1 = 1.78; 95% CI: 0.58, 5.43; P-trend = 0.007, P-interaction = 0.07) were positively associated with breast cancer risk, whereas in BCGP, DHA (OR_Q4vsQ1 = 0.66; 95% CI: 0.28, 1.53; P-trend = 0.03, P-interaction = 0.05) and total long-chain n–3 fatty acids (OR_Q4vsQ1 = 0.66; 95% CI: 0.28, 1.54; P-trend = 0.03) were associated with decreased cancer risk when the waist-to-hip ratio was <0.85. Our findings suggest that regional variations in fatty acid status influence breast cancer risk, resulting in positive associations of total long-chain n–3 fatty acids in premenopausal ATP women and negative associations of these fatty acids in BCGP women with a waist-to-hip ratio below guidelines. This study highlights the complexity and difficulty in using fatty acid status to predict breast cancer risk in diverse populations without the consideration of other risk factors.