Despite lower haemoglobin A1c with second‐generation automated insulin delivery systems, mental burden remains high for all adults with type 1 diabetes: A <scp>BETTER</scp> registry analysis
Bibliographic record
Abstract
OBJECTIVE: We aim to compare second-generation automated insulin delivery systems (AIDs) with other treatment modalities regarding both glucose management outcomes and person-reported outcomes/experiences (PROs/PREs, measured by the Hypoglycemia Fear Survey, Hypoglycemic Confidence Scale, Hyperglycemia Avoidance Scale, Diabetes Distress Scale, Pittsburgh Sleep Quality Index, Well-being Scale, and treatment satisfaction) among adults with type 1 diabetes. RESEARCH DESIGN AND METHODS: Cross-sectional analysis of the Canadian BETTER type 1 diabetes registry. Adult participants were divided into five groups: second-generation AIDs, first-generation AIDs, continuous glucose monitoring (CGM) + pump, CGM + multiple daily injections (MDI), and non-CGM using MDI or pump. Generalized linear models were used to assess differences between the second-generation AID group and each of the other groups, with p < 0.05 considered significant. RESULTS: Among 1731 participants (69.2% females), mean age was 45.3 ± 15.3 years with 24.7 ± 16.0 years of diabetes. The second-generation AID group had the highest proportion of achieving target haemoglobin A1c ≤ 7% (58.1%) compared to 40.6% in the first-generation AID group, 40.5% in the Pump + CGM group, 37.3% in the MDI + CGM group, and 32.3% in the non-CGM group, even after adjustment for multiple imbalanced characteristics (p < 0.001). While second-generation AID users reported higher treatment satisfaction, no other differences in measures of PROs/PREs were found between second-generation AID and other groups. Elevated diabetes distress (55%) and poor sleep quality (63%) remained common across all treatment groups, and even among those who had reached the optimal HbA1c target (48% and 60%, respectively). CONCLUSIONS: In real-world settings, second-generation AIDs were associated with lower haemoglobin A1c and higher treatment satisfaction but not better PROs/PREs. Mental burden remained high despite the use of advanced diabetes technologies and optimal glucose management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".