Evaluating the effectiveness of the D1 Now intervention to improve outcomes among young adults with type 1 diabetes: Protocol for a cluster randomised controlled trial
Bibliographic record
Abstract
Abstract Background Young adults living with type 1 diabetes (T1D) often experience sub-optimal outcomes due to competing life demands, disruptions in care, reduced clinic attendance, and difficulties in self-management. To improve outcomes among this population, we developed and piloted the D1 Now intervention using a user-centred and theory-informed approach. This protocol describes a cluster randomised controlled trial (RCT) to test the effectiveness and cost-effectiveness of the D1 Now intervention. Methods A cluster RCT seeking to recruit 348 young adults (aged 18–25) with T1D from 12 hospital diabetes centres in Ireland. Centres will be randomised to receive either standard care or the D1 Now intervention, which includes two components: an agenda-setting tool and a support worker. The primary outcome is the change in HbA1c from baseline to 12-month follow-up. Secondary outcomes include patient-reported psychosocial outcomes, clinical outcomes, self-management outcomes and healthcare utilisation. We will collect data through blood samples, online patient surveys, and patient records at baseline and 12 months. Additionally, we will conduct a cost-effectiveness evaluation and a mixed-methods process evaluation. Discussion We anticipate that the D1 Now intervention will be both effective and cost-effective in improving clinical and psychosocial outcomes for young adults compared to standard care. The findings from the process evaluation will shed light on how the intervention works (or not) and how implementation into health services (if warranted) can be optimised. If effective, D1 Now will offer a sustainable model of care to support engagement and self-management for young adults living with T1D. Trial registration ISRCTN Identifier: ISRCTN28944606 . Date applied 01 May 2025
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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 | 0.087 | 0.079 |
| Meta-epidemiology (narrow) | 0.009 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.085 | 0.016 |
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".