1301-P: Factors Predicting Diabetes Distress over 18 Months in Teens with Type 1 Diabetes (T1D)
Bibliographic record
Abstract
Background: Teens with T1D are burdened by physical, social, and emotional challenges that can lead to diabetes distress, impacting self-care and health outcomes. We assessed diabetes distress and associated factors in teens with T1D over 18 months. Methods: At baseline, 6, 12, and 18 months, teens (N=301), ages 13-17 years, completed the Problem Areas in Diabetes Survey - Pediatric version (PAID-Peds), a validated 20-item survey measuring diabetes distress over the past month. Higher scores (range 0-100) indicate more distress; ≥41 = high distress. Mixed linear models assessed factors associated with changing diabetes distress over time. Results: At baseline, teens (49% male, 78% white) were 15.0±1.3 (M±SD) years old with T1D duration 6.5±3.7 years; 84% were from 2-parent families. Youth checked BG 4.5±1.9 x/day; 59% were pump-treated; A1c was 8.5±1.1%. At baseline, 45% of teens endorsed high diabetes distress and females were disproportionately affected (54% vs. 36% of males, p<.01). In follow-up, 75% of teens with high baseline distress and 24% of teens with low baseline distress endorsed high distress at ≥50% of follow-up time points. High baseline distress strongly predicted subsequent distress (OR 9.6 [95% CI: 5.6, 16.5], p<.0001). Female sex and increasing A1c predicted increasing distress. In teens with high baseline distress, decreasing BG monitoring predicted increasing distress. In those without high baseline distress, only increasing A1c predicted increasing distress. Factors such as zBMI, race, family structure, household income, parental education, pump use, or use of continuous glucose monitors did not predict distress over time. Discussion: A substantial proportion of teens with T1D experience high diabetes distress. Rising A1c is a major factor associated with worsening diabetes distress over time. Interventions targeting teens with diabetes distress and/or increasing A1c may positively impact self-care and simultaneously help to manage distress and improve A1c. Disclosure D.E. McGill: None. L.K. Volkening: None. P.V. Commissariat: None. R.M. Wasserman: None. B. Anderson: None. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (R01DK095273, K12DK094721, P30DK036836), JDRF (2-SRA-2014-253-M-B)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".