Preventing the ‘24-hour Babel’: the need for a consensus on a consistent terminology scheme for physical activity, sedentary behaviour and sleep
Bibliographic record
Abstract
Clear, concise, and consistent. These qualities (the 3Cs) are critical for effective scientific communication. Plain language enables easy comparisons between study findings, ensures construct uniformity, and enhances communication of scientific discoveries to audiences beyond academia. Health research has a growing interest in the inter-relationships of physical activity (PA), sedentary behaviour (SB) and sleep. These behaviours occupy most of the 24-hour cycle and are associated with a plethora of health outcomes.1 Consensus statements suggest how to analyse these behaviours.2 Some PA/SB guidelines have incorporated sleep using mostly cross-sectional evidence.1 However, these consensus statements and guidelines fail to acknowledge the interactive and reciprocal relationships between PA, SB and sleep. As more scientists enter this field (figure 1), terminology is becoming inconsistent and confusing. Here, we outline the disparate vocabulary for the 24-hour cycle, and make the case for a consensus project to address how we collectively think about and refer to PA, SB and sleep using the 3Cs principle. Figure 1 PubMed search hits from 2010 to 2020 for each term for collectively defining physical activity, sedentary behaviour and sleep. Health research has a well-defined vocabulary for behaviours which occupy most of the 24-hour cycle. PA is any bodily movement which increases energy expenditure above 1.5 metabolic equivalents (METs).3 SB is any behaviour performed from a seated/lying position that requires ≤1.5 METs.4 …
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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 | 0.423 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.014 | 0.066 |
| Scholarly communication | 0.031 | 0.068 |
| Open science | 0.018 | 0.036 |
| Research integrity | 0.026 | 0.066 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".