A survey of reported changes in diet and activity with the <scp>FreeStyle</scp> Libre flash glucose monitor: a pilot study
Bibliographic record
Abstract
Abstract Introduction: Flash glucose monitors (FGMs) are increasingly popular with people with diabetes. We aimed to see if the use of the FreeStyle Libre affected users’ behaviours regarding dietary choices and exercise. Methods: An online questionnaire was developed using Voxco Survey Platform 2018 and was completed between 18 May and 14 August 2018. The questionnaire consisted of yes/no and 6‐point Likert scale questions to assess the effects of FGM on diet and exercise patterns. Anyone with diabetes who had been using the FGM for at least four weeks prior to study commencement was eligible to participate. Results: Forty‐one participants completed the questionnaire; the majority were < 18 years old (29/41; 70.7%), and had type 1 diabetes (37/41; 90.2%). As a result of using the FGM, 58.6% (17/29) of paediatric participants indicated they noticed specific foods affected their blood glucose levels that they were not previously aware of, and 31.0% (9/29) indicated that they changed their carbohydrate choices to lower glycaemic index foods. In all, 72.4% (21/29) of paediatric participants indicated that they felt more confident in participating in exercise, 20.7% (6/29) indicated that they initiated new exercise, and 13.8% (4/29) indicated that they increased their amount of exercise as a result of using the FGM. Conclusions: In this study of primarily paediatric patients, some participants reported improved confidence to participate in exercise, as well as making positive dietary changes as a result of using the FGM. Further study is required to confirm if the FGM may have positive and sustained effects on these parameters. Copyright © 2022 John Wiley & Sons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".