601-P: Exploring Strategies to Manage Hyperglycemia-Related Anxiety during Competition in Elite Athletes with Type 1 Diabetes—A Qualitative Analysis
Bibliographic record
Abstract
Introduction & Objective: Elite athletes with type 1 diabetes (T1D) face unique challenges managing their blood glucose levels, as competitions can cause unpredictable fluctuations. The impact of hyperglycemia-related anxiety on their performance and diabetes management is important, yet research is limited. This study explores strategies used to prevent hyperglycemia-related anxiety during competition and highlights additional approaches they can use. Methods: Elite athletes >18 years old with T1D who self-reported hyperglycemia-related anxiety during competitions were recruited to participate in virtual semi-structured interviews. A discussion guide was used to direct the 30-60 minute conversation using open-ended questions. Interview content was analysed using an Interpretative Phenomenological Analysis approach. Results: Ten elite athletes with T1D (average age: 25 ± 3 years; duration of TID: 12 ± 8 years; # of competitions per year: 27 ± 19; training time per week: 12 ± 6 hours) reported the strategies they currently use during competition to manage hyperglycemia-related anxiety. These include: carefully managing insulin and nutrition intake, leveraging technology, practicing relaxation techniques, establishing routines and maintaining adequate sleep hygiene. Additional approaches that could be implemented include: addressing the psychological burden, ensuring support teams have sufficient tools and resources, promoting self-awareness and establishing peer mentorship networks. Conclusion: Elite athletes with T1D use physiological and psychological interventions to mitigate hyperglycemia-related anxiety during competition, emphasising the need for further support and education. Targeted strategies and individualized approaches are necessary to optimize performance and well-being in this population. Disclosure A. Katz: None. A. Shulkin: None. M. Fortier: None. A. Housni: None. M.K. Talbo: None. E. Sesbreno: None. J.E. Yardley: Speaker's Bureau; Dexcom, Inc. Research Support; LifeScan Diabetes Institute. J.C. Kichler: None. R. Rabasa-Lhoret: Other Relationship; Abbott, AstraZeneca, Bayer Inc., Boehringer-Ingelheim, Dexcom, Inc. Research Support; Diabetes Canada. Other Relationship; Eli Lilly and Company. Research Support; Cystic Fibrosis Canada, Canadian Institutes of Health Research, FFRD - Fondation Francophone pour la Recherche du Diabète. Other Relationship; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Other Relationship; Novo Nordisk, GlaxoSmithKline plc. Consultant; HLS Therapeutics Inc., Insulet Corporation. Speaker's Bureau; CPD Networks. Other Relationship; Medtronic. Consultant; Pfizer Inc. Speaker's Bureau; Tandem Diabetes Care, Inc. Other Relationship; Sanofi. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; SFD - Société Francophone du Diabète. A. Brazeau: Other Relationship; Dexcom, Inc. Research Support; Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF), Diabète québec, Fonds de recherche du Québec en Santé. Funding Chaire J.A DeSFve
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".