Considerations of Control Conditions Designs in Randomized Controlled Trials of Exercise Interventions for Cancer Survivors
Bibliographic record
Abstract
Background Given the multifaceted complexity in the nature of randomized controlled trials, identifying an appropriate and comparable control condition is an essential step to ensure methodological rigor, which allows for researchers to draw unambiguous conclusions concerning the efficacy of the intervention being studied. Objectives The objectives of this paper are to (a) review the current literature and analyze the control condition designs in exercise interventions targeted for cancer survivors; (b) provide an overview of the benefits and limitations of various types of control conditions used in exercise interventions; (c) discuss the considerations in the design of control conditions for exercise interventions; and (d) suggest recommendations for control condition design in future trials of behavioral interventions. Results The review of randomized controlled trials of exercise training interventions for cancer survivors revealed that the design of control conditions varied. The most commonly employed design could be classified into two major categories: (a) active controls including attention control, add-on controls, and dismantling controls; and (b) inactive controls including no-treatment, usual care, and wait-list control. Examples from the literature are presented. Four principal considerations concerning control condition design, including appropriateness, credibility, appeal, and comparability, are discussed. Recommendations on how to avoid some major threats to validity and potential biases are also provided. Conclusions Careful planning for the control group design is as important as for the intervention group. Researchers can use the considerations presented in the paper to assist in planning for the most appropriate control condition for their study.
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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 | 0.721 | 0.821 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".