Efficacy, effectiveness, and behavior change trials in exercise research
Bibliographic record
Abstract
BACKGROUND: The widespread incorporation of behavioral support interventions into exercise trials has sometimes caused confusion concerning the primary purpose of a trial. The purpose of the present paper is to offer some conceptual and methodological distinctions among three types of exercise trials with a view towards improving their design, conduct, reporting, and interpretation. DISCUSSION: Exercise trials can be divided into "health outcome trials" or "behavior change trials" based on their primary outcome. Health outcome trials can be further divided into efficacy and effectiveness trials based on their potential for dissemination into practice. Exercise efficacy trials may achieve high levels of exercise adherence by supervising the exercise over a short intervention period ("traditional" exercise efficacy trials) or by the adoption of an extensive behavioral support intervention designed to accommodate unsupervised exercise and/or an extended intervention period ("contemporary" exercise efficacy trials). Exercise effectiveness trials may emanate from the desire to test exercise interventions with proven efficacy ("traditional" exercise effectiveness trials) or the desire to test behavioral support interventions with proven feasibility ("contemporary" exercise effectiveness trials). Efficacy, effectiveness, and behavior change trials often differ in terms of their primary and secondary outcomes, theoretical models adopted, selection of participants, nature of the exercise and comparison interventions, nature of the behavioral support intervention, sample size calculation, and interpretation of trial results. SUMMARY: Exercise researchers are encouraged to clarify the primary purpose of their trial to facilitate its design, conduct, and interpretation.
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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 | 0.585 | 0.722 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".