T1-DEXi-T Study: Does exercise training improve the time in range following acute bouts of exercise?
Bibliographic record
Abstract
Building on our recently funded T1DHome study, we are excited to propose an expansion of the recent T1DEXi study by investigating the impact of regular exercise training and detraining on various blood glucose metrics following acute bouts of exercise in adult men and women with T1D. We refer to this proposed study as the T1-DEXI Training Study (T1-DEXi-T study). Our proposed work aims to inform new clinical guidelines for exercise for adults living with type 1 diabetes. While existing guidelines mention an exercise “prescription” they are not well tailored for adults living with type 1 diabetes who may benefit from a more targeted exercise training prescription that should improve cardiometabolic and skeletal muscle health along with enhancing global TIR metrics for this patient population. While the T1DEXi study serves as a critical database, it was not a training intervention and no objective measures of cardiometabolic and skeletal muscle health were made. The T1DHome follow-up study is a natural progression for this important first step in improving the health and wellbeing through regular exercise of people living with type 1 diabetes but the CGM TIR metrics analyses and exercise wearable portion of this study is not yet funded. We propose that the Leona M. and Harry B. Helmsley Charitable Trust offer research support for the use of CGM, exercise wearables (Garmin) and deep data analytics for the first phase of the T1DHome study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".