T1-REDEEM—An RCT to Reduce Diabetes Distress in Adults with Type 1 Diabetes
Bibliographic record
Abstract
Diabetes distress (DD) refers to the fears, worries and burdens associated with the diabetes experience. DD is common, is distinct from depression, and has been associated with glycemic level and disease management. T1-REDEEM (Type1-Reducing Distress and Enhancing Effective Management) was an RCT that compared two interventions to reduce DD among adults with type 1 diabetes (T1D). After baseline assessment, participants were randomized to either KnowIt, a diabetes education and management program, or OnTrack, which focused on recognizing, normalizing and addressing DD. Both programs involved a 1-day group workshop followed by four 1-hour online group meetings over 3 months. Recruitment occurred at clinic and community sites in three Western U.S. states and Toronto, Canada. Inclusion criteria were T1D ≥ 12 months, elevated DD (≥ 2.0 on the Diabetes Distress Scale), age ≥ 21 years, HbA1C ≥ 7.5% (58 mmol/mol), and English speaking. Regression models analyzed change in DD and HbA1C at 3 and 9 months; missing values were imputed. Nine-month retention was 87% (N = 301 at baseline). DD was significantly reduced in both study arms (p < .001) at 3 months, with modest additional improvements at 9 months (p < .02). Between-group differences were nonsignificant. However, significant group-by-baseline level interactions for key variables revealed for whom the two interventions were most effective: OnTrack led to significantly greater reductions in DD than KnowIt for those with higher DD, poorer emotion regulation, poorer problem-solving skills, and higher knowledge at baseline; KnowIt led to significantly greater DD reductions for those with lower diabetes knowledge at baseline. There was a modest decrease in HbA1C at 3 months (p = .05) that did not differ by group. Findings suggest that effective interventions to address DD must focus on the emotional side of diabetes, especially for participants with high DD, poor emotion regulation skills, poor problem-solving abilities, and different levels of diabetes knowledge. Disclosure L. Fisher: Consultant; Self; Eli Lilly and Company, Abbott, Merck & Co., Inc. D.M. Hessler: Consultant; Self; Eli Lilly and Company. W. Polonsky: Consultant; Self; Abbott, AstraZeneca, Dexcom, Inc., Sanofi, Novo Nordisk Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Servier, Ascensia Diabetes Care, Merck & Co., Inc., MannKind Corporation, Glooko, Inc., Roche Diabetes Care Health and Digital Solutions. U. Masharani: None. S. Guzman: Consultant; Self; TypeZero Technologies, Inc.. Board Member; Self; Behavioral Diabetes Institute. Consultant; Self; Becton, Dickinson and Company. V. Bowyer: None. L.A. Strycker: None.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".