Predicting doubly labeled water energy expenditure from ambulatory activity
Bibliographic record
Abstract
The purpose of this study was to evaluate the potential for using accelerometer-determined ambulatory activity indicators (steps per day and cadence) to predict total energy expenditure (TEE) and physical activity energy expenditure (PAEE) derived from doubly labeled water (DLW). Twenty men and 34 women (20-36 years of age) provided complete anthropometric, accelerometer, resting metabolic rate (RMR), and DLW data. TEE and PAEE were determined for the same week that accelerometers were worn during waking hours. Accelerometer data included mean steps per day, peak 30-min cadence (average steps per minute for the highest 30 min of the day), and time spent in each incremental cadence band: 0 (nonmovement), 1-19 (incidental movement), 20-39 (sporadic movement), 40-59 (purposeful steps), 60-79 (slow walking), 80-99 (medium walking), 100-119 (brisk walking), and 120+ steps·min(-1) (indicative of all faster ambulatory activities). Regression analyses were employed to develop sex-specific equations for predicting TEE and PAEE. The final model predicting TEE included body weight, steps per day, and time in incremental cadence bands and explained 79% (men) and 65% (women) of the variability. The final model predicting PAEE included peak 30-min cadence, steps per day, and time in cadence bands and explained 76% (men) and 46% (women) of the variability. Time in cadence bands alone explained 39%-73% of the variability in TEE and 30%-63% of the variability in PAEE. Prediction models were stronger for men than for women.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".