Do moderate- to vigorous-intensity accelerometer count thresholds correspond to relative moderate- to vigorous-intensity physical activity?
Bibliographic record
Abstract
We aimed to predict % maximal oxygen consumption at absolute accelerometer thresholds and to estimate and compare durations of objective physical activity (PA) among body mass index (BMI) categories using thresholds that account for cardiorespiratory fitness. Eight hundred twenty-eight adults (53.5% male; age, 33.9 ± 0.3 years) from the National Health and Nutrition Examination Survey 2003–2004 were analyzed. Metabolic equivalent values at absolute thresholds were converted to percentage of maximal oxygen consumption, and accelerometer counts corresponding to 40% or 60% maximal oxygen consumption were determined using 4 energy expenditure prediction equations. Absolute thresholds underestimated PA intensity for all adults; however, because of lower fitness, individuals with overweight and obesity work at significantly higher percentage of maximal oxygen consumption at the absolute thresholds and require significantly lower accelerometer counts to reach relative moderate and vigorous PA intensities compared with those with normal weight (P < 0.05). However, moderate-to-vigorous physical activity (MVPA) durations were shorter when using relative thresholds compared with absolute thresholds (in all BMI groups, P < 0.05), and they were shorter among individuals with obesity compared with those with normal weight when using relative thresholds (P < 0.05). Regardless of the thresholds used, a greater proportion of individuals with normal weight met the PA guideline of 150 min·week–1 of MVPA compared with individuals with obesity (absolute: 21.3% vs 6.7%; Yngve: 4.0% vs 0.2%; Swartz: 10.7% vs 3.9%; Hendelman: 4.7% vs 0.2%; Freedson: 6.4% vs 0.5%; P < 0.05). Current absolute thresholds of accelerometry-derived PA may overestimate MVPA for all BMI categories when compared with relative thresholds that account for cardiorespiratory fitness. Given the large variability in our results, more work is needed to better understand how to use accelerometers for evaluating PA at the population level.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".