Efficacy of technology-based personalised feedback on diet quality in young Australian adults: results for the advice, ideas and motivation for my eating (Aim4Me) randomised controlled trial
Bibliographic record
Abstract
Abstract Objective: Web-based dietary interventions could support healthy eating. The Advice, Ideas and Motivation for My Eating (Aim4Me) trial investigated the impact of three levels of personalised web-based dietary feedback on diet quality in young adults. Secondary aims were to investigate participant retention, engagement and satisfaction. Design: Randomised controlled trial. Setting: Web-based intervention for young adults living in Australia. Participants: 18–24-year-olds recruited across Australia were randomised to Group 1 (control: brief diet quality feedback), Group 2 (comprehensive feedback on nutritional adequacy + website nutrition resources) or Group 3 (30-min dietitian consultation + Group 2 elements). Australian Recommended Food Score (ARFS) was the primary outcome. The ARFS subscales and percentage energy from nutrient-rich foods (secondary outcomes) were analysed at 3, 6 and 12 months using generalised linear mixed models. Engagement was measured with usage statistics and satisfaction with a process evaluation questionnaire. Results: Participants ( n 1005, 85 % female, mean age 21·7 ± 2·0 years) were randomised to Group 1 ( n 343), Group 2 ( n 325) and Group 3 ( n 337). Overall, 32 (3 %), 88 (9 %) and 141 (14 %) participants were retained at 3, 6 and 12 months, respectively. Only fifty-two participants (15 % of Group 3) completed the dietitian consultation. No significant group-by-time interactions were observed ( P > 0·05). The proportion of participants who visited the thirteen website pages ranged from 0·6 % to 75 %. Half (Group 2 = 53 %, Group 3 = 52 %) of participants who completed the process evaluation (Group 2, n 111; Group 3, n 90) were satisfied with the programme. Conclusion: Recruiting and retaining young adults in web-based dietary interventions are challenging. Future research should consider ways to optimise these interventions, including co-design methods.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".