Accuracy of Energy Expenditure Estimation by Activity Monitors Differs with Ethnicity
Bibliographic record
Abstract
The aim of this project is to explore the accuracy of 2 activity monitors (SenseWear Armband & Actical) to estimate energy expenditure during rest and light to moderate intensity exercises in 2 ethnic groups. 18 Caucasian and 20 Black adults (age: 26.8±5.2 years; body mass index: 23.9±3.0 kg/m(2)) wore the 2 devices simultaneously during 3 standardised activities: 30-min rest, 45-min of treadmill at 40% of their V˙O2peak and 45-min of stationary cycling at 50% of their V˙O2peak. Energy estimated with the 2 devices was compared to indirect calorimetry measurements. Both devices overestimated energy expenditure during rest (SenseWear: 36% in Black vs. 16% in Caucasian; Actical: 26% vs. 11%, p<0.01 between groups) and treadmill (SenseWear: 50% vs. 25%; Actical: 67% vs. 32%, p<0.01 between groups). Both devices significantly underestimated energy expenditure during stationary cycling (SenseWear: 24% vs. 26%; Actical: 58% vs. 70%, p=NS between groups). Equations used to estimate energy expenditure from accelerometer data is less precise among Black adults than Caucasian adults. Ethnic-specific formulas are probably required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".