Feasibility of a blended digital behavior change intervention promoting sustainable diets over a year: a series of pilot N-of-1 trials
Bibliographic record
Abstract
Background: Sustainable diets are healthy dietary patterns with low environmental impact, that are economically fair and affordable. This study aimed to evaluate the feasibility, acceptability, and potential impact of a pilot blended digital intervention aimed at promoting behavior change towards sustainable diets. Methods: We conducted a series of ABA (A phase: 2-week baseline evaluation; B phase: 22-week intervention; A phase: 24-week post-intervention, follow-up) N-of-1 trials over a year. Intervention involved (i) text messages containing brief educational information, motivational messages or links to recipes, and (ii) individualized online feedback sessions. Intensive longitudinal quantitative data on eating behaviors were collected daily for 15 weeks distributed over the year. A weekly composite score for sustainable diets was calculated to evaluate changes in the overall dietary quality, considering health and environmental sustainability. Qualitative data was collected through three individual semi-structured interviews: at baseline, at the end of the intervention, and 6 months later, to gain further insight into the study objectives. Analyses were performed at both the group and individual levels.Results: This pilot trial involved 12 participants. Feasibility and acceptability were high, with a 100% retention rate over a year, 75% attendance at all feedback sessions, and an average response rate of 86% to the intensive longitudinal eating behavior questionnaires. At the group level, the intervention had a positive and significant effect on the overall diet composition. Compared to baseline, and on a 10-point scale, participants showed a significant increase of 1.25 and 1.85 points during the intervention and follow-up phases, respectively. At the individual level, 92% of the participants demonstrated a significant increase in daily fruit and vegetable consumption between the baseline and follow-up phases, and 58% significantly reduced their intake of red and processed meat as well as ultra-processed foods. Beyond diet composition, participants reported improvements in other crucial aspects of sustainable diets, including reducing food waste, opting for minimally packaged and in-season foods, and prioritizing food from fair sources, as assessed with the qualitative interviews.Conclusion: This pilot study provides valuable knowledge for designing, optimizing, and implementing future large-scale interventions targeting individual behavior change for sustainable diets in a holistic way.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".