Physical activity self-reports: past or future?
Bibliographic record
Abstract
The measurement of physical activity (PA) is fundamental to health-related research, practice and policy. For decades, self-report measures have provided unique insights into the role of PA for human health and society. In fact, studies, in which participants reported their behaviours—or the behaviours of others—using diaries, logs, questionnaires and recalls, have historically provided the evidence that underpins global PA guidelines.1 Self-reports have been used extensively in various settings, including population surveillance, observational and intervention studies and routine assessment as part of healthcare. The field of PA measurement is rapidly evolving. We have a wealth of measurement instruments and achieved remarkable advancements in the use of device-based information such as raw accelerometry, novel algorithms for pattern recognition and worldwide initiatives for data harmonisation.2 3 The technological evolution has changed the practice of PA self-reports as well, and led to electronic surveys and ecological momentary assessments (EMAs) for the measurement of PA in natural environments and in ‘real time’. Despite significant improvements, an established standard for the measurement of PA does not exist due to the complexity of the behaviour.4 PA is multifaceted and encompasses different domains (eg, leisure, occupation, transport, household), dimensions (eg, frequency, duration, intensity, …
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".