Does Continuous Glucose Monitoring Use Prompt Greater Engagement in Self-Management? A Randomized Controlled Trial Focusing on Adults With Type 2 Diabetes
Bibliographic record
Abstract
Background: Continuous glucose monitoring (CGM) promotes glycemic benefits in adults with type 2 diabetes (T2D), including insulin users as well as noninsulin users, often with minimal professional support. To investigate whether these benefits may stem from increased user engagement in self-management, we conducted a randomized controlled trial comparing the impact of CGM versus self-monitoring of blood glucose (SMBG) on self-reported engagement and HbA1c in CGM-naïve adults with T2D. Methods: Potential participants completed the Impact of Glucose Monitoring on Self-Management Scale (IGMSS) and an HbA1c home test to confirm eligibility (>7.5%). N = 110 eligible participants were randomized to receive a FreeStyle Libre 3 (CGM arm) or a FreeStyle Precision Neo Blood Glucose Monitoring System (SMBG arm). The IGMSS and HbA1c home test were repeated after three months. Latent change score models estimated group differences in outcomes over time. Results: CGM users reported significantly greater engagement with T2D self-management than SMBG users (IGMSS total b = 0.61, P < .001), including greater gains on all three major subscales, capability (b = 0.76, P < .001), opportunity (b = 0.46, P = .001), and motivation (b = 0.66, P < .001). CGM users also saw a significant HbA1c drop of ~1% (9.2% to 8.3%, P < .001, d = .65), with less than half the reduction in SMBG users (8.9% to 8.4%, P = .065, d = .30). However, the effect of group on HbA1c change did not reach statistical significance ( P = .170), likely due to limited sample size. Conclusions: These findings suggest that introducing CGM to adults with T2D heightens users’ engagement with their own diabetes care and also improves glycemic control more than providing SMBG.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".