37-LB: Feasibility of a Remote Clinical Trial in People with Type 2 Diabetes—Findings from the MOTIVATE T2D Trial
Bibliographic record
Abstract
The execution of clinical trials can be expensive and present logistical challenges regarding recruitment and retention of participants. Innovative research design fostering convenience by eliminating research facility visits may enhance recruitment and retention. We examined the feasibility of a remote clinical trial in people living with type 2 diabetes (T2D) . People with recently diagnosed T2D were recruited across the UK and Canada to the MOTIVATE T2D trial (NCT04653532) ; a feasibility randomised controlled trial investigating two exercise and physical activity interventions. Participants received a self-testing kit, via mail, at baseline and post-intervention (6 months) . Measures included, HbA1c, lipid profile, anthropometrics and blood pressure and 14-day flash glucose and physical activity monitoring. Between Jan 2021 and Jan 2022 286 patients were eligible, of whom 110 (UK n=63, Can n=47, male n=58, white n=95) consented. Mean journey time from research facilities was ≤1h in 18%, 1-2h in 50% and >2h in 33% of participants. Availability of outcome data is presented in Table 1. Remote testing resulted in benefits to recruitment and good participant retention and protocol adherence. As such, remote clinical trials are feasible in people with T2D and future clinical trials should consider a remote clinical trial based approach as an alternative to conventional designs. Disclosure A. P. Davies: None. K. Hesketh: None. J. Low: None. V. S. Sprung: None. H. Jones: None. A. M. Mcmanus: None. M. Cocks: None. Motivate team: n/a. Funding UK Medical Research Council (MR/T032189/1) , Canadian Institute for Health Research (UCD-170587)
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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 | 0.042 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".