Enrichment of the Canadian Partnership for Tomorrow’s Health Study: Protocol for Administering Multiple Online Dietary and Movement Behavior Assessment Tools in a Longitudinal Cohort Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".