Accelerometer thresholds: Accounting for body mass reduces discrepancies between measures of physical activity for individuals with overweight and obesity
Bibliographic record
Abstract
The objective of this study was to explore whether accelerometer thresholds that are adjusted to account for differences in body mass influence discrepancies between self-report and accelerometer-measured physical activity (PA) volume for individuals with overweight and obesity. We analyzed 6164 adults from the National Health and Nutrition Examination Survey between 2003-2006. Established accelerometer thresholds were adjusted to account for differences in body mass to produce a similar energy expenditure (EE) rate as individuals with normal weight. Moderate-, vigorous-, and moderate- to vigorous-intensity PA (MVPA) durations were measured using established and adjusted accelerometer thresholds and compared with self-report. Durations of self-report were longer than accelerometer-measured MVPA using established thresholds (normal weight: 57.8 ± 2.4 vs 9.0 ± 0.5 min/day, overweight: 56.1 ± 2.7 vs 7.4 ± 0.5 min/day, and obesity: 46.5 ± 2.2 vs 3.7 ± 0.3 min/day). Durations of subjective and objective PA were negatively associated with body mass index (BMI) (P < 0.05). Using adjusted thresholds increased MVPA durations, and reduced discrepancies between accelerometer and self-report measures for overweight and obese groups by 6.0 ± 0.3 min/day and 17.7 ± 0.8 min/day, respectively (P < 0.05). Using accelerometer thresholds that represent equal EE rates across BMI categories reduced the discrepancies between durations of subjective and objective PA for overweight and obese groups. However, accelerometer-measured PA generally remained shorter than durations of self-report within all BMI categories. Further research may be necessary to improve analytical approaches when using objective measures of PA for individuals with overweight or obesity.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".