Application of Self-Monitoring of Blood Glucose by Intermittently Scanned Continuous Glucose Monitoring to Lifestyle Improvement After Health Checkup
Bibliographic record
Abstract
Background: Although workers are required to undergo annual health examinations in Japan, many issues remain with encouraging examinees to visit medical institutions after the checkup and providing education in lifestyle improvements. We recommended self-monitoring of blood glucose to high-risk staff with impaired glucose tolerance to establish effective and practical health guidance. Methods: Subjects were staff who underwent regular health examinations in 2017 - 2018 and who showed hemoglobin A1c (HbA1c) ? 6.0% or HbA1c 5.6-5.9% and either body mass index (BMI) ? 25 kg/m 2 or waist circumference ? 85 cm for men or ? 90 cm for women. Subjects received instruction in, and performed self-monitoring of blood glucose using, intermittently scanned continuous glucose monitoring (isCGM) for 14 days. At that time, we did not provide specific lifestyle improvement guidance, only explaining that blood glucose can be measured repeatedly without finger-pricks. We compared metabolic parameters between isCGM and non-isCGM groups for the previous year, the next year, and 3 years later, and also examined relationships among daily number of measurements, average blood glucose level, and questionnaire responses. Results: In the isCGM group, positive behavioral changes were observed to improve lifestyle, and the proportion of subjects taking pharmacotherapies for lifestyle-related diseases increased significantly (P = 0.014). In addition, among staff who did not change medications, the isCGM group showed improvements in blood pressure and liver function as well as decreases in body weight and waist circumference, all of which were maintained even after 2 years. The more daily scans for blood glucose measurement, the higher the awareness of improving diet and exercise habits, and the greater the motivation to treat obesity and diabetes. Conversely, no relationships with average daily blood glucose were found. Conclusions: Blood glucose measurement by isCGM promoted improvements in lifestyle and metabolic parameters. Self-monitoring, but not blood glucose level, was the trigger for behavior changes and frequency of self-monitoring was associated with increased motivation to improve lifestyle. Health guidance that values the desire of subjects to be aware of their own changes should be kept in mind as a useful technique. J Endocrinol Metab. 2023;13(2):57-69 doi: https://doi.org/10.14740/jem875
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".