Physical Activity Is Associated With Improved Glycemic Outcomes in Newly Diagnosed Youth With Type 1 Diabetes: 4T Exercise Program
Bibliographic record
Abstract
OBJECTIVE: The Teamwork, Targets, Technology, and Tight Range (4T) Exercise Program evaluated physical activity patterns across the first year of type 1 diabetes diagnosis and whether physical activity was associated with changes in glucose outcomes in the 24 h following physical activity. RESEARCH DESIGN AND METHODS: The 4T Exercise Program started newly diagnosed youth with type 1 diabetes on a continuous glucose monitoring (CGM) system and physical activity tracker around 1 month postdiagnosis. A subset of youth opted to participate in up to four quarterly structured exercise education sessions to increase their knowledge around safe physical activity. RESULTS: Ninety-eight youth with type 1 diabetes (median [interquartile range (IQR)] age of 13 [12-15] years, 45% female, 44% non-Hispanic White) completed the study. Compared with sedentary days, days with ≥10 min of vigorous-intensity physical activity were associated with an increase in time in range (TIR) of 2.3% (1.4-3.2%; P < 0.001), a decrease in time above range (TAR) of 3.1% (2.2-4.0%; P < 0.001), and an increase in time below range (TBR) of 0.8% (0.6-0.9%; P < 0.001) in the 24 h following physical activity. From 1-3 months to 10-12 months postdiagnosis, the median (IQR) step count increased by 1,134 (445-1,519) steps per day (P < 0.001), while daily moderate-to-vigorous physical activity increased by 11 (2-23) min per day (P < 0.001). CONCLUSIONS: In the 24 h following physical activity as compared with sedentary days, TIR improved, TAR was lower, and TBR remained within clinical target recommendations. For youth with new-onset type 1 diabetes, each structured exercise education session was associated with a further 0.79% increase in TIR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".