Blood Glucose Monitoring and Sharing Amongst People With Diabetes and Their Facilitators: Cross-sectional Study of Methods and Practices
Bibliographic record
Abstract
BACKGROUND: The last two decades have witnessed a burgeoning rise in the prevalence of diabetes globally. It has already reached epidemic proportions in Saudi Arabia, with reported high risk among women. As a result, diabetes monitoring and self-management programs are being highly prioritized for diabetes control and management. OBJECTIVE: To investigate measuring and sharing practices of the self-monitoring of blood glucose (SMBG) among patients with type 1 or 2 diabetes using insulin. METHODS: A cross-sectional study was conducted on a sample of 203 patients attending primary care clinics at a tertiary care center. The questionnaire assessed the measuring, recording, and sharing of SMBG practices of patients having diabetes with their physicians. The methods used for recording and sharing were categorized into paper-based and electronic-based. In addition, the determinants of the different methods used and frequency of sharing were analyzed. RESULTS: The overall monitoring prevalence was 95% (193/203), and 57% (117/203) of participants shared the SMBG results. Among the 193 individuals that performed self-monitoring, 138 (72%) performed daily monitoring, and 147 (76%) recorded their blood sugar levels. Almost 55% (81/147) used paper-based materials like notebooks and paper for recording, while the rest (66/147, 45%) used digital devices like laptops and smartphones. A shift towards the use of digital devices and smart applications was observed in patients below 50 years of age. The digitally recorded blood glucose measurements were being shared thrice more often than the recordings made on paper or in notebooks (OR [odds ratio] 2.8; P=.01). Patients >50 years of age (OR 2.3; P=.02), with lesser formal education, married (OR 4.2; P<.001), with smaller family size (OR 2.6; P=.01), having type 2 diabetes (OR 4.1; P<.001) and any comorbid conditions (OR 2.6; P=.01) were associated with higher odds of using paper-based sharing methods. Only the female gender and type 2 diabetes were associated with increased frequency of sharing, while uncontrolled diabetes, the presence of other comorbidities, and duration of diabetes did not show any influence. CONCLUSIONS: Good monitoring and optimal sharing practices were found. Sharing using electronic devices can be emphasized. Diabetes self-management programs can incorporate the use of digital technology in training sessions. Digital literacy and its applications in health care may enhance SMBG practices resulting in better diabetes control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".