Field evaluation of energy expenditure in women using Tritrac accelerometers
Bibliographic record
Abstract
PURPOSE: To investigate the use of Tritrac accelerometers to measure energy expenditure (EE) of various activities for women in the field setting, as compared with portable indirect calorimetry. METHODS: Twenty women (age 20-29) performed a choreographed routine of six activities (walking, jogging, stair climbing, walking on an incline, stationary cycling, and arm ergometry) while wearing a Tritrac-R3D accelerometer (Hemokinetics Inc., Madison WI) and the Cosmed K4b(2) portable metabolic cart (Cosmed, Rome, Italy). RESULTS: Comparing the mean error scores (K4b(2) - Tritrac), the Tritrac overestimated the EE (kcal x min(-1)) of walking (-1.45) and jogging (-1.75), whereas underestimating the EE of stair climbing (2.76), stationary cycling (2.75), and arm ergometry (1.20). Walking on an incline showed the lowest mean error score (-0.11). Intraclass correlations were moderate for walking (r = 0.568, < 0.05), jogging (r = 0.666, < 0.05), and stairs (r = 0.503, < 0.05) but for the other activities ranged from r = 0.290 ( > 0.05) to r = 0.480 ( < 0.05). The raw data from the Tritrac was applied to a previously developed nonlinear model to adjust the Tritrac scores to the standard of whole-room indirect calorimetry. This resulted in statistically significant improvements in the agreement between the adjusted Tritrac value and the K4b for walking, jogging, and walking on an incline ( < 0.05). CONCLUSION: When compared with portable indirect calorimetry, the Tritrac overestimates the EE of walking and jogging, whereas underestimating that of stair climbing, stationary cycling, and arm ergometry. This limits the use of such a technique to measure EE in the field. The main issues appear to be the type and intensity of the activity and the need for movement in order for the Tritrac to register EE. Activity specific linear regression equations are proposed as a tool to improve the measurement of EE using the Tritrac in the field.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".