Self-Monitoring Technologies for Type 2 Diabetes and the Prevention of Cardiovascular Complications: Perspectives from End Users
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to explore facilitators and barriers to the adoption of self-monitoring devices in individuals with type 2 diabetes mellitus (T2DM). METHODS: Individuals with T2DM who were currently using one or more devices to monitor their disease participated in focus groups. Transcripts of focus group meetings were coded into themes by two reviewers using NVivo qualitative software. RESULTS: Twenty-eight adults with T2DM reported using a blood glucose meter, and almost half reported monitoring their blood pressure. Few individuals consistently monitored other aspects of their cardiovascular health. Four major themes impacting device use/disuse were identified: knowledge gaps, relationships with health care providers, environment, and personal experience. Knowledge barriers included lack of information regarding diabetes and the associated risk of complications. Perceptions of inconvenience, pain, and financial restrictions were important factors influencing the adoption, use, and abandonment of self-monitoring devices. Community-run programs, as well as dieticians and pharmacists, were identified as important resources for accessing information related to T2DM. CONCLUSIONS: We identified the need for development of accessible and relevant education material; improved communication of disease-specific information between patients and providers, as well as providers and community resources; and strategies to improve the convenience and cost of monitoring devices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".