More real-world trials are needed to establish if web-based physical activity interventions are effective
Bibliographic record
Abstract
Despite the positive health benefits of physical activity, physical inactivity remains highly prevalent. To address this public health issue, population-based interventions that can effectively reach large numbers of people at low cost are needed. Numerous randomised controlled trials (RCT) have examined the effectiveness of web-based physical activity interventions, and overall, these intervention studies have found to increase participants’ physical activity levels. Few studies, however, have examined how well these interventions work in ‘real world’ or ecologically valid settings, where there are no repeated contacts with research staff, comprehensive assessments or incentives. A recent systematic review examined mobile health (mHealth) clinical trial study methodology for trials conducted in 2014 and 2015 and did not identify a single ecological trial, yet RCTs were dominant (80%, 51/71). To address this, we conducted two studies using the same web-based physical activity interventions: a RCT and a randomised ecological trial (RET).
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".