Participant acceptability and feasibility of virtual and digital health solutions in a phase IIa COPD trial
Bibliographic record
Abstract
Introduction: Understanding participant experience is crucial for improving participant satisfaction, retention, data quality, and hereby trial efficiency. This is especially important when incorporating novel procedures like digital patient solutions and virtual study elements. Methods: This study explored participant experiences in the CRESCENDO phase-II COPD study, focusing on feasibility and acceptance of remote coached spirometry. Semi-structured qualitative interviews (n=21) and study participant feedback questionnaire (SPFQ)(n=83) were conducted in Bulgaria, Canada, Poland, Spain, UK, and USA. Interview data was analysed using thematic analysis. Results: Participants (mean age 66.8year, 52%male) were generally satisfied with the trial. Digital and virtual elements were appreciated and easy to use, though frequent technical issues were considered bothersome. When functioning properly, participants reported minimal differences between in-person and remote spirometry coaching. Most preferred in-person visits due to positive interactions with study staff, which greatly influenced satisfaction. The high concordance between the interviews and SPFQ validated the interview results and confirmed the utility of the SPFQ to measure participant experience. Conclusions: This study highlights the potential of digital and virtual elements in clinical trials while addressing technical challenges. Despite a small interview sample and potential selection bias, findings reveal cultural and individual differences in experiences and preferences. Practical recommendations include allowing participants to "design" their trial procedures, such as chosing between in-person or virtual visits.
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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 | 0.167 | 0.262 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".