Strategies to reduce hyperglycemia-related anxiety in elite athletes with type 1 diabetes: A qualitative analysis
Bibliographic record
Abstract
OBJECTIVE: Managing blood glucose levels is challenging for elite athletes with type 1 diabetes (T1D) as competition can cause unpredictable fluctuations. While fear of hypoglycemia during physical activity is well documented, research on hyperglycemia-related anxiety (HRA) is limited. HRA refers to the heightened fear that hyperglycemia-related symptoms will impair functioning. This study investigates current strategies employed to mitigate HRA during competition and the development of alternative approaches. RESEARCH DESIGN AND METHODS: Elite athletes with TID, aged >14 who self-reported HRA during competition were recruited. Elite athletes were defined as individuals exercising >10 hours per week whose athletic performance has achieved the highest competition level. 60 to 90-minute virtual semi-structured interviews were analyzed using an Interpretative Phenomenological Analysis. RESULTS: Ten elite athletes with T1D (average age 25 ± 3 years; T1D duration 12 ± 8 years; number of competitions per year 27 ± 19; training time per week 12 ± 6 hours) reported the strategies they currently use to mitigate HRA. These strategies include managing insulin and nutrition intake, embracing social support networks, using technology, practicing relaxation techniques, establishing routines, performing pre-competition aerobic exercise, and maintaining adequate sleep hygiene. Several additional approaches that could be implemented were identified including establishing targeted support networks, developing peer-reviewed resources on HRA, ensuring support teams have sufficient tools, and improving existing technology. CONCLUSIONS: Elite athletes with T1D use physiological and psychological strategies to mitigate HRA during competition. This finding highlights the need for increased support and education for these athletes, and advancements in technology. A multidisciplinary approach involving healthcare professionals, athletic staff, and peer mentors could help integrate personalized anxiety management and diabetes care strategies into training regimens, enhancing both mental resilience and performance outcomes for athletes with T1D.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".