Objective Measurement of Physical Activity and Diet—New Technologies and Standards: The NIH Genes, Environment and Health Initiative for Technology Development
Bibliographic record
Abstract
This symposium addressed the ongoing development of new technologies for the objective measurement of physical activity and diet and efforts to provide best practice guidelines for scientists developing, evaluating and using existing and new technologies for the objective measurement of physical activity. The research projects discussed and the workshop overview presented are components of the Genes, Environment, and Health Initiative (GEI) of the National Institutes of Health. The rationale, plans and progress of the GEI physical activity and diet initiative were presented. Detailed presentations described 2 projects focused on the use of mobile phone based systems designed to collect, process and store data; 1 uses multiple wireless accelerometers to detect body movement and the other uses a camera built into a mobile phone and advanced software to quantify dietary intake. Given the rapid development of new accelerometer-based physical activity measurement devices and analytical approaches, it is important that best practices be used by scientists and practitioners using theses devices. An overview of a “best practices” workshop held in July 2009 was presented. The presentations and discussions during this symposium made evident the progress, potential and challenges of implementing advanced technologies to enhance the measurement of physical activity and diet.
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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 | 0.101 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".