An Evidence-Based Nurse-Led Intervention to Reduce Diabetes Distress Among Adults With Type 1 Diabetes and Diabetes Distress (REDUCE): Development of a Complex Intervention Using Qualitative Methods Informed by the Medical Research Council Framework
Bibliographic record
Abstract
BACKGROUND: Diabetes distress refers to the negative emotional reaction to living with the demands of diabetes; it occurs in >40% of adults with type 1 diabetes (T1D). However, no interventions to reduce diabetes distress are specifically designed to be an integral part of diabetes care. OBJECTIVE: This study aims to modify and adapt existing evidence-based methods into a nurse-led group intervention to reduce diabetes distress among adults with T1D and moderate to severe diabetes distress. METHODS: The overall framework of this study was informed by the initial phase of the Medical Research Council's complex intervention framework that focused on undertaking intervention identification and development to guide the adaptation of the intervention. This study took place at 2 specialized diabetes centers in Denmark from November 2019 to June 2021. A total of 36 adults with T1D participated in 10 parallel workshops. A total of 12 diabetes-specialized nurses were interviewed and participated in 1 cocreation workshop; 12 multidisciplinary specialists, including psychologists, educational specialists, and researchers, participated in 4 cocreation workshops and 14 feedback meetings. Data were analyzed by applying a deductive analytic approach. RESULTS: The intervention included 5 biweekly 2.5-hour small group sessions involving adults with T1D and diabetes distress. Guided by a detailed step-by-step manual, the intervention was delivered by 2 trained diabetes specialist nurses. The intervention material included visual conversation tools covering seven diabetes-specific sources derived from the 28-item Type 1 Diabetes Distress Scale for measuring diabetes distress: (1) powerlessness, (2) self-management, (3) fear of hypoglycemia, (4) food and eating, (5) friends and family, (6) negative social perception, and (7) physician distress. The tools are designed to kick-start awareness and sharing of diabetes-specific challenges and strengths, individual reflections, as well as plenary and peer-to-peer discussions about strategies to manage diabetes distress, providing new perspectives on diabetes worries and strategies to overcome negative emotions. Diabetes specialist nurses expressed a need for a manual with descriptions of methods and detailed guidelines for using the tools. To deliver the intervention, nurses need increased knowledge about diabetes distress, how to support diabetes distress reduction, and training and supervision to improve skills. CONCLUSIONS: This co-design study describes the adaptation of a complex intervention with a strong evidence base, including detailed reporting of the theoretical underpinnings and core mechanisms.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".