What training and support do people need to make optimal use of automated insulin delivery systems: Interview study
Bibliographic record
Abstract
INTRODUCTION: Despite increased use of automated insulin delivery (AID) systems, limited attention has been paid to users' training and support needs. Indeed, most guidance has been driven by clinician opinion rather than users' experiences and needs. We explored the experiences of adults with type 1 diabetes initiated onto AID to make recommendations for supporting optimal use of AID systems in routine clinical practice. METHODS: We conducted interviews with n = 22 individuals who used the Tandem t:slim X2™ with Control-IQ technology AID system for ≥6 and ≤12 months. Data were analysed thematically. RESULTS: Users reported wide-ranging clinical and quality-of-life benefits. However, to actualise these benefits, they highlighted a need for bespoke training which took account of their learning preferences and strengths, pace of learning and prior insulin pump experience, together with ongoing support from healthcare professionals. Thematic analysis uncovered four overlapping support needs: (1) support reviewing data and making adjustments to the system's settings, (2) emotional/psychological support to help sustain motivation and/or adopt more passive self-management roles, (3) support to unlearn unhelpful behaviours and/or establish new habits and routines; and (4) educational support. CONCLUSIONS: AID systems are an exciting advance in diabetes treatment. However, to achieve optimal gains, users would benefit from initial training and ongoing glycaemic, educational, and psychosocial support tailored to their preferred ways of learning and personal circumstances. Consideration should be given to adapting existing education and training packages for AID system users to incorporate behavioural science techniques and upskilling diabetes professionals in behaviour change approaches and motivational interviewing.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".