Predicting energy expenditure from physical activity, heart rate and anthropometry in female Indian tea pluckers
Bibliographic record
Abstract
The objective of this study was to test a methodological procedure for estimating energy expenditure for a population of Indian female tea pluckers. Subjects (N=40; age=20–50 y) working on a tea estate in West Bengal, India participated in the study. Each subject wore an Actigraph accelerometer, Polar heart rate monitor and CosMed K4b2 metabolic analyzer during a 90‐minute period to assess minute‐by‐minute physical activity (PA), heart rate (HR) and energy expenditure (EE), respectively. The testing period was meant to replicate a normal tea picker's work day which included 2 periods of rest, 3 periods of picking while carrying weight (0, 5, and 10kg) and 3 periods of walking while carrying weight (15, 20 and 25kg). An EE prediction equation was generated using a branched method that first distinguishes times at rest from non‐rest (picking and walking) using accelerometer counts. Resting EE was estimated from age and BMI, while minute‐by‐minute non‐resting EE, was estimated from HR, age and BMI. Predicted EE will be used to evaluate the efficiency of performing work (weight of tea plucked/kcal EE) relative to iron status in an independent sample of 248 tea pluckers. We conclude that energy expenditure can be accurately predicted with a single branched equation based on PA, HR, age and BMI for a specific population participating in a known set of activities. Supported by the Mathile Institute and Micronutrient Initiative.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".