Gaps in Knowledge and the Need for Patient-Partners in Research Related to Physical Activity and Type 1 Diabetes: A Narrative Review
Bibliographic record
Abstract
Regular physical activity (PA) is a cornerstone in the management of complications associated with type 1 diabetes (T1D). Most national guidelines advocate for regular PA for persons living with T1D, however the evidence to support these recommendations has not be reviewed recently. Additionally, in an era of patient-centered care and patient oriented research, the role of patient partners in the area of PA and T1D interventions has never been explored. The purpose of this narrative review is to overcome these two gaps in the literature. Here we review selected epidemiological evidence and identify gaps in research that would add important information to guide practitioners and future guidelines. We also provide an overview of patient-oriented research projects co-developed with persons living with T1D. Significant gaps in the field include: (1) a lack of adequately powered prospective cohort studies using serial measures of PA and hard chronic disease end-points; (2) no multi-centered, highly powered, randomized controlled trials of PA, and long-term health outcomes; (3) little data on the role of new technologies to support PA-related behavior change, and (4) no trials that involved patients in the design and execution of PA-based clinical trials. This review provides a template for scientists and patient partners to develop future research priorities and agendas in the field.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".