Pilot and feasibility studies in exercise, physical activity, or rehabilitation research
Bibliographic record
Abstract
BACKGROUND: Clinical trials of physical activity and rehabilitation interventions can be challenging. Pilot or feasibility studies can be conducted prior to a definitive randomized controlled trial (RCT), to improve the chances of conducting a high-quality RCT of a physical activity intervention. MAIN BODY: Physical activity interventions or trials present unique challenges at the population, intervention, comparator and outcome levels. At each level, we present guidance for researchers on the design considerations for pilot or feasibility studies of physical activity interventions. When it comes to defining study population, physical activity trials often exclude participants with certain health conditions or other characteristics (e.g., age, gender) because of uncertainty of the safety of the exercise intervention or presumed differences in responsiveness, at the expense of trial generalizability. A pilot trial could help investigators determine refined inclusion and exclusion criteria to balance safety, adequate recruitment, and generalizability. At the intervention level, because exercise can be a complex intervention, pilot trials allow investigators to evaluate participant adherence and instructor fidelity to the intervention and participant experience. At the comparator level, control group dissatisfaction and post-randomization drop-out can occur, because of the desire to be randomized to the exercise group, and the difficulty with blinding to group allocation; an active control or deception could be used. Finally, at the outcome level, there should be an emphasis on the pilot or feasibility outcomes such as recruitment rate, adherence to exercise, and retention or fidelity, than the efficacy of the exercise intervention. CONCLUSION: Physical activity and rehabilitation researchers can use pilot and feasibility studies to enhance the rigor of future trials, while also publishing the results of their pilot work to move the field forward. Researchers in this field are encouraged to use published reporting guidelines for pilot and feasibility studies and to consider the challenges discussed in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".