Impact of glycemic variability on cognitive impairment, disordered eating behaviors and self-management skills in patients with type 1 diabetes: study protocol for a cross-sectional online study, the Sugar Swing study
Bibliographic record
Abstract
BACKGROUND: People living with type 1 diabetes (PWT1D) are at increased risk for impairments in brain function, which may impact on daily life. Cognitive impairments in PWT1D might contribute to increasing eating disorders, reducing self-management skills, and deteriorating glycemic control. Glycemic variability may be a key determinant of disordered eating behaviors, as well as of cognitive impairments. The main objective of this study is to better understand the impact of glycemic variability in disordered eating behaviors and cognitive impairment, and its consequences on self-management skills in PWT1D. METHOD: We aim to recruit 150 PWT1D with 50% of men and women in this cross-sectional study. Participants will record their glycemic variability over a 10-day period using a continuous glucose monitoring system (CGMS) and track their dietary intakes using image-assisted food tracking mobile application (2 days). Over four online visits, eating behaviors, diabetes self-management's skills, anxiety disorders, depression disorder, diabetes literacy and numeracy skills, cognitive flexibility, attention deficit, level of interoception, and impulsivity behaviors will be assessed using self-reported questionnaires. Cognitive functions (i.e., attention, executive functions, impulsivity, inhibition and temporal discounting), will be measured. Finally, medical, biological and sociodemographic data will be collected. To further our understanding of the PWT1D experience and factors impacting glycemic self-management, 50 PWT1D will also participate in the qualitative phase of the protocol which consist of individual in-depth face-to-face (virtual) interviews, led by a trained investigator using a semi-structured interview. DISCUSSION: This study will contribute to highlighting the consequences of blood sugar fluctuations (i.e., "sugar swings"), in daily life, especially how they disrupt eating behaviors and brain functioning. A better understanding of the mechanisms involved could eventually allow for early detection and management of these problems. Our study will also seek to understand the patients' point of view, which will allow the design of appropriate and meaningful recommendations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05487534. Registered 4 August 2022.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".