1. The Effects of an Aerobic Exercise Intervention on Daily Activity Patterns
Bibliographic record
Abstract
The purpose of this study was to examine the effects of participation in an aerobic exercise intervention on daily activity occurring outside of the structured exercise sessions. Participants were randomized into one of the following 4 conditions: 1) No-exercise, 2) Low volume, low intensity exercise (LVLI), 3) High volume, low intensity exercise (HVLI), 4) Low volume, high intensity (LVHI). Physical activity was measured over 7 days with an accelerometer at baseline and during week 8 of the intervention. Activity was defined as: sedentary behaviour (SED; < 100 counts/minute), light physical activity (LPA; 100 to 1951 counts/minute), moderate-to-vigorous physical activity (MVPA; ≥1952 counts/minute), and total physical activity (TPA; LPA + MVPA). Activity was quantified as average total minutes per day of each SED, LPA, MVPA, and TPA. A one-way ANOVA was used to determine if time spent in SED, LPA, MVPA, and TPA changed from baseline to week 8. Seventy-one participants (No-exercise; n=12, LVLI n=17, HVLI n=24, LVHI; n=18,) with a mean age of 54 y and waist circumference of 110 cm completed 8 weeks of the intervention. There were no significant differences in SED, LPA, MVPA, or TPA between groups at baseline. There was no significant change in SED, LPA, MVPA, or TPA at week 8 compared to baseline (p>0.05). Similarly, there were no significant differences in activity variables between exercise conditions. Our observations suggest that daily activity patterns do not change with the implementation of an exercise intervention in men and women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".