The impact of real-time sensor technology on quality of life for adults with type 1 diabetes: A Dutch national survey
Bibliographic record
Abstract
AIMS: To examine the impact of real-time continuous glucose monitoring (RT-CGM) on quality of life in Dutch adults with type 1 diabetes, inside/outside automated insulin delivery (AID) systems. METHODS: In this cross-sectional retrospective observational study, RT-CGM users completed an online survey including (adapted) validated questionnaires, study-specific items and open-ended questions. RESULTS: Of 893 participating adults, 69% used the RT-CGM as part of AID. The overall sample reported improvements in quality of life related to RT-CGM use (irrespective of initial indication), particularly with respect to physical health, emotional wellbeing and energy. Merits for sleep, intimacy and cognitive diabetes load lagged somewhat behind, mostly when RT-CGM was not integrated in AID. Users of AID had significantly larger improvements in overall quality of life, fatigue and diabetes-specific distress than users of sensor-augmented pump or Open Loop treatment. In regression analyses, user evaluations were associated with perceptions of benefit and burden. In qualitative content analysis, benefits (e.g. life 'normalization', increased perceptions of control) outweighed burdens (e.g. technology frustrations, confrontation with diabetes). CONCLUSIONS: RT-CGM positively impacted the quality of life of adults with type 1 diabetes. This justifies a (re-)consideration of broader access. Increased support to maximize device benefits and minimize burdens is also warranted.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".