Patient Perspectives on Managing Type 1 Diabetes During High-Performance Exercise: What Resources Do They Want?
Bibliographic record
Abstract
OBJECTIVE: Athletes with type 1 diabetes face unique challenges that make it difficult for health care providers to offer concise recommendations for diabetes management. Moreover, little is known about patient preferences for diabetes management during high-level and competitive exercise. We undertook a qualitative study to understand patient perspectives on managing type 1 diabetes during exercise. METHODS: A qualitative design using focus groups was selected. Samples of 5-10 participants per group were recruited to participate in one of three 1.5-hour sessions focusing on experiences in managing diabetes, supports, and desired resources. Sessions were audiotaped and transcribed verbatim. Data were analyzed iteratively among team members. RESULTS: The study included 21 participants (10 male and 11 female) with a mean age of 41 years. Most participants used trial and error to manage their blood glucose around exercise. Frequent monitoring of blood glucose was a common strategy and a challenge during exercise. Hypoglycemia after exercise and adrenaline-fueled hyperglycemia during exercise were the most prevalent concerns. Most participants relied on themselves, an endocrinologist, or the Internet for support but said they would prefer to rely more on peers with type 1 diabetes and mobile apps. Peer support or mentorship was strongly supported with recommendations for moving forward. CONCLUSION: This study highlights the individualized nature of balancing glycemic control in athletes and athletes' heavy self-reliance to develop strategies. Expanding the availability of resources such as peer mentoring and mobile apps could potentially support athletes with type 1 diabetes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".