<i>Mo</i> bile Heal <i>t</i> h B <i>i</i> ometrics to Enhance Exercise and Physical Acti <i>v</i> ity <i>A</i> dherence in <i>T</i> yp <i>e</i> 2 Diabetes (MOTIVATE-T2D): protocol for a feasibility randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Exercise and physical activity (PA) are fundamental to the treatment of type 2 diabetes. Current exercise and PA strategies for newly diagnosed individuals with type 2 diabetes are either clinically effective but unsuitable in routine practice (supervised exercise) or suitable in routine practice but clinically ineffective (PA advice). Mobile health (mHealth) technologies, offering biometric data to patients and healthcare professionals, may bridge the gap between supervised exercise and PA advice, enabling patients to engage in regular long-term physically active lifestyles. This feasibility randomised controlled trial (RCT) will evaluate the use of mHealth technology when incorporated into a structured home-based exercise and PA intervention, in those recently diagnosed with type 2 diabetes. METHODS AND ANALYSIS: This feasibility multicentre, parallel group RCT will recruit 120 individuals with type 2 diabetes (diagnosis within 5-24 months, aged 40-75 years) in the UK (n=60) and Canada (n=60). Participants will undertake a 6-month structured exercise and PA intervention and be supported by an exercise specialist (active control). The intervention group will receive additional support from a smartwatch and phone app, providing real-time feedback and enabling improved communication between the exercise specialist and participant. Primary outcomes are recruitment rate, adherence to exercise and loss to follow-up. Secondary outcomes include a qualitative process evaluation and piloting of potential clinical outcome measures for a future RCT. ETHICS AND DISSEMINATION: The trial was approved in the UK by the South East Scotland Research Ethics Committee 01 (20/SS/0101) and in Canada by the Clinical Research Ethics Board of the University of British Columbia (H20-01936), and is being conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. Results will be published in peer-reviewed journals and presented at national and international scientific meetings. TRIAL REGISTRATION NUMBERS: ISRCTN14335124; ClinicalTrials.gov: NCT04653532.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.013 |
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".