Does Insulin Delivery Technology Change Our Relationship with Foods? A Scoping Review
Bibliographic record
Abstract
Introduction: Automated insulin delivery (AID) systems reduce burden and improve glycemic management for people with type 1 diabetes (PwT1D) by automatically adjusting insulin as a response to measured glucose levels. There is a lack of evidence on AID and nutrition variables such as dietary intake, eating behaviors, and disordered eating. Objectives: This scoping review aims to provide a summary of the literature regarding AID and nutrition variables and to identify gaps that require further investigation. Methods: Two researchers conducted a blinded search of Medline (OVID) and PubMed for studies, including AID use (compared to non-AID use) and nutrition variables. Studies from January 2000 to July 2023 were included, as were PwT1D of all ages. Results: A total of 3132 articles were screened for appropriateness. After exclusions, 7 studies were included (2017–2023): 4 qualitative, 1 crossover, 1 randomized controlled, and 1 observational. Studies included adolescents ( n = 1), adults ( n = 3) or both ( n = 2), and all ages ( n = 1). In quantitative studies, AID was associated with lower eating distress (−0.43 ± 0.12, P = 0.004) and higher quality of life (3.1, 95% confidence interval [CI]: 0.8–5.4, P = 0.01), but not grams of carbohydrates at meals (1.0; 95% CI: −0.7 to 3.0; P = 0.24) and snacks (0.004; 95% CI: −0.8 to 0.8; P = 0.99) compared to non-AID use. In qualitative studies, AID increased the frequency and portions of food intake and led to less dietary control from parents. AID users reported eating foods higher in energy density. PwT1D were less worried about achieving accurate carbohydrate counting (CC) when using AID. Conclusions: AID use appears to influence eating behaviors, dietary patterns, and CC, although evidence was limited. AID may reduce food management burden due to the perception that AID can correct for CC inaccuracy. Significance: Further research needs to determine if AID allows for simplification of CC and improves eating behaviors while maintaining glycemic stability.
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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 | 0.021 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".