Promoting sustainable healthy diet: pre-registered protocol
Bibliographic record
Abstract
Background: Changing current dietary patterns into sustainable healthy diets (i.e. healthy diets with low environmental impact and socio-economically fair) is urgent. So far, few eating behavior change interventions have addressed all the dimensions of sustainable healthy diets at once and using cutting edge methods from the field of digital health behavior change. Objective: Primary objectives of this pilot study are to assess the feasibility and effectiveness of an individual behavior change intervention towards the adoption of a more environmentally sustainable healthy diet as a whole, and changes on specific relevant food groups, food waste and obtaining food from fair sources. Secondary aims include (i) the identification of mechanisms of action potentially mediating the effect of the intervention on behaviors; (ii) the identification of potential spillover effects and covariations between different food outcomes; (iii) the identification of the role of socio-economic status in regards of behavior changes.Methods: We will run a series of ABA n-of-1 trials over a year, with the first A phase corresponding to a 2-week baseline evaluation, the B phase to a 22-week intervention, and the second A phase a 24-week post-intervention follow-up phase. We plan to enroll twenty-one participants from low, middle and high socio-economic status, seven from each socio-economic group. The intervention will consist on sending text messages and providing brief individualized online feedbacks sessions based on app-based regular assessments of eating behavior. Text messages will contain brief educational messages on human health, environmental and socio-economic effects of dietary choices, motivational messages to encourage the adoption of sustainable healthy diets providing tips to achieve their own behavioral goals and links to recipes. Both quantitative and qualitative data will be collected. Quantitative data (e.g., eating behaviors, motivation) will be collected through self-reported questionnaires, on several weekly burst spread over the study. Qualitative data will be collected through 3 individual semi-structured interviews, at the beginning, at the end of the intervention period, and at the end of the study. Analyses will be performed at both the individual and group level depending on the outcome and objective. Results: The study is planned to start in September 2022. Final results are expected by September 2023.Conclusion: The results of this pilot study will be useful to design future larger interventions on individual behavior change for sustainable healthy diets.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.256 | 0.115 |
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".