The fit-active profile to better reflect the benefits of a lifelong vigorous physical activity participation: mini-review of literature and population data
Bibliographic record
Abstract
Physical activity is favourably considered for its effect on metabolic fitness and body composition. This observation is generally supported by observational studies and is concordant with endurance-trained individuals’ metabolic and morphological profiles. However, in some contexts, the measurement of physical activity habits may not provide an adequate representation of its benefits. In this paper, we review relevant literature on the respective effects of fitness and physical activity on anthropometric and metabolic variables and the informative potential of a classification based on aerobic fitness and activity indicators. The relevance to defining a profile based on both fitness and activity is reinforced by data from the Quebec Family Study showing that, in both men and women, “fit-active” individuals displayed a much more favourable morphological and metabolic profile than “unfit-inactive” individuals. Moreover, these benefits seemed to be more related to variations in fitness than in physical activity. In summary, evidence suggests that a profile combining information on aerobic fitness and physical activity may better reflect the lifelong impact of physical activity on body composition and health. Novelty: The fit-active profile better reflects the long-term benefits of vigorous physical activity participation on health. The reported benefits seem to be more related to variations in aerobic fitness than to those in physical activity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".