Diabetes technologies and hypoglycemia: insights from person-reported experience
Bibliographic record
Abstract
Background: Managing type 1 diabetes (T1D) requires daily insulin interventions.Advances like insulin pumps and continuous glucose monitoring (CGM) can reduce the burden of multiple daily injections (MDI) and capillary blood glucose (CBG) checks and improve glycemic outcomes.Yet, hypoglycemia remains a significant barrier, increasing the management burden and fear of hypoglycemia (FOH).Limited evidence exists on how diabetes technology impacts FOH and hypoglycemia beyond glucose metrics.This thesis aimed to ( 1) explore what strategies people with T1D use to prevent nocturnal hypoglycemia in the context of diabetes technologies use, (2) evaluate the frequency and consequences of clinically significant hypoglycemia, (3) review the current literature on the impact of diabetes technologies on FOH, (4) evaluate the impact of automated insulin delivery (AID) on hypoglycemia frequency and worries and behaviors related to FOH, and (5) examine the characteristics of people with T1D who perceive FOH as a barrier to achieve optimal glycemic levels.Methods: Data from the T1D BETTER registry was used to answer objectives 1, 2, and 5 crosssectionally and objective 4 prospectively.For objective 3, a systematic review and meta-analysis (SRMA) with studies from 2000-2023 was conducted.Regression models examined associations between nocturnal hypoglycemia (NH) prevention strategies and diabetes technology use; consequences of level 2 (L2H, glucose < 3.0 mmol/L, self-managed) or level 3 hypoglycemia (L3H, needing external help) and gender identity; perception of FOH as a barrier to glycemic management and participant traits (e.g., gender, HbA1c, hypoglycemia history).To evaluate the impact of starting an AID system on FOH, linear mixed models assessed changes in Hypoglycemia Fear Survey-II (HFS-II) scores and hypoglycemia frequency compared to baseline.Results: Sample sizes ranged from 830 to 1300 participants (objectives 1, 2, and 5).On average, participants were 44 years old, had T1D for 26 years, and two-thirds identified as women.One-third met the HbA1c target (≤7.0%).Hypoglycemia was common: 66% reported ⩾1 symptomatic NH, and 81% reported ⩾1 L2H episode in the past month.Women reported more frequent L2H, persistent fatigue (OR [95% CI]: 1.95 [1.16, 3.28]), and anxiety (1.70 [1.05, 2.75]) after hypoglycemia.Results showed 43% perceived FOH as a barrier to glycemic management, Contribution to original knowledgeThis thesis is presented in manuscript format and consists of nine chapters.Chapter 1 is a general introduction explaining the thesis' rationale and objectives while giving a brief overview of the research focus.Chapter 2 presents a current and comprehensive review of the relevant literature on type 1 diabetes, including treatment targets, types of therapies and technology used, and hypoglycemia.Chapters 3 through 7 are manuscripts bridged with connecting statements 1 to 4.Four manuscripts are original work, and one is a systematic review and meta-analysis.Of the manuscripts, Chapters 3 to 5 have been published in Diabetes Research and Clinical Practice (DRCP) and eClinicalMedicine.Chapter 7 was submitted for publication to Diabetes Care, and Chapter 6 will be submitted shortly to Diabetes Technology & Therapeutics.Chapters 8 and 9 present an overall discussion and conclusion of the work presented throughout this thesis, addressing the strengths and limitations of the research, its implications, and future directions.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".