The 24-Hour Movement Paradigm: An integrated approach to the measurement and promotion of daily activity in cancer clinical trials
Bibliographic record
Abstract
Increased physical activity (PA), improved sleep, and decreased sedentary behavior (SB) are essential components of supportive care for cancer survivors. However, researchers and health care professionals have achieved limited success in improving these behaviors among cancer survivors. One potential reasoning is that, over the past two decades, guidelines for promoting and measuring PA, sleep, and SB have been largely siloed. With greater understanding of these three behaviors, health behavior researchers have recently developed a new paradigm: the 24-Hour movement approach. This approach considers PA, SB, and sleep as movement behaviors along a continuum that represent low through vigorous intensity activity. Together these three behaviors form the sum of an individual's movement across a 24-hour day. While this paradigm has been studied in the general population, its usage is still limited in cancer populations. Here, we seek to highlight (a) the potential benefits of this new paradigm for clinical trial design in oncology; (b) how this approach can allow for greater integration of wearable technology as a means of assessing and monitoring patient health outside the clinical setting, improving patient autonomy through self-monitoring of movement behavior. Ultimately, implementation of the 24-Hour movement paradigm will allow health behavior research in oncology to better promote and assess critical health behaviors to support the long-term well-being for cancer patients and survivors.
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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 | 0.264 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".