The Effect of Aerobic Training and Increasing Nonexercise Physical Activity on Cardiometabolic Risk Factors
Bibliographic record
Abstract
PURPOSE: Epidemiological studies suggest that sedentary behavior is an independent risk factor for cardiovascular mortality independent of meeting physical activity guidelines. However, limited evidence of this relationship is available from prospective interventions. The purpose of the present study is to evaluate the combined effect of aerobic training and increasing nonexercise physical activity on body composition and cardiometabolic risk factors. METHODS: Obese adults (N = 45) were randomized to 6 months of aerobic training (AERO), aerobic training and increasing nonexercise physical activity (~3000 steps above baseline levels; AERO-PA), or a control (CON) group. The AERO and AERO-PA groups performed supervised aerobic training (3-4 times per week). The AERO-PA group wore Fitbit One accelerometers and received behavioral coaching to increase nonexercise physical activity. RESULTS: There was a larger increase in fitness in the AERO-PA group (0.27 L·min-1; confidence interval (CI), 0.16 to 0.40 L·min-1) compared with the AERO group (0.09 L·min-1; CI, -0.04 to 0.22 L·min-1) and the CON group (0.01; CI, -0.11 to 0.12 L·min-1). Although significant findings were not observed in the entire study sample, when the analysis was restricted to participants compliant to the intervention (n = 33), we observed significant reductions in waist circumference, percent weight loss, body fat, 2-h glucose, and 2-h insulin in comparison to the CON group (P < 0.05), but not the AERO group. Furthermore, linear regression models showed that change in steps was associated with 21% and 26% of the variation in percent weight loss and percent fat loss, respectively. CONCLUSIONS: Increasing nonexercise physical activity with aerobic training may represent a viable strategy to augment the fitness response in comparison to aerobic training alone and has promise for other health indicators.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".