The Use of Sequential Multiple Assignment Randomized Trials (SMARTs) in Physical Activity Interventions: Protocol for a Systematic Review
Bibliographic record
Abstract
Introduction Adaptive interventions involve a sequence of treatments tailored to individual responses, requiring multiple treatment decisions throughout an individual’s treatment pathway. Effective management of many chronic health conditions requires interventions adapted to individual performance. Physical activity (PA) is central to risk reduction therapies for chronic conditions. PA interventions are complex, multidimensional, and tailored to individual responses over time, requiring flexible study designs. However, PA intervention evidence is dominated by standard trial designs for non-adaptive interventions. A sequential, multiple assignment, randomised trial (SMART) design was developed to build adaptive interventions. SMARTs are factorial designs with sequential settings. Despite their potential for developing flexible interventions, SMARTs are relatively new in PA research. This review examines the state of SMART designs in PA interventions, focusing on study characteristics, design, and analysis methods. Methods and analysis A systematic review of SMARTs, wherein the intervention consisted of a PA intervention, will be conducted (June 2023). The following electronic databases were searched: PubMed, Embase, PsychINFO, CENTRAL, and CINAHL. The reference lists of all the identified studies will be reviewed to identify additional studies for inclusion. The titles and abstracts were independently screened by two review authors for selection. Any disagreement regarding inclusion was resolved by discussion or by referral to a third assessor. The methodological quality was assessed using the Cochrane Risk of Bias 2 tool. This review will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Ethics and dissemination As this systematic review will only collect secondary data, ethical approval is not required. These findings will be disseminated through academic conferences and peer-reviewed journals.
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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 | 0.102 | 0.148 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.019 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.098 | 0.015 |
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".