Changes in Objective and Self-Reported Measures of Physical Capacity After an Intervention in Obese Older Women
Bibliographic record
Abstract
The aim of this study was to determine if objective and self-reported measures of physical capacity are two equivalent methods to detect changes following an intervention in obese older women. 36 obese women aged between 55 and 75 years participated in a 3-month study with the aim of improving physical capacity by caloric restriction and/or resistance training. Physical capacity was measured objectively with 10 different tests and self-reported with the SF-36 physical functioning score (SF-36 PF score). Then the performance-to-objective tests were computed using quartiles to provide a baseline global physical capacity score. The mean percentage of change of the 10 tests as well as the SF-36 PF score were also calculated after the study. Body composition was measured by dual-energy X-ray (DXA) absorptiometry. The baseline global physical capacity score and the SF-36 PF score were significantly correlated at baseline (r = 0.43; P < 0.01). Eight out of the 10 objective tests of physical capacity improved after the intervention, while no improvement was observed for the SF-36 PF score. On average, percentage of change in physical capacity was 4.1 +/- 5.9% for the SF-36 PF score and 11.1 +/- 2.9% for the objective measures. However, no significant correlation was observed between percentage of changes between the two approaches after the intervention (r = 0.32; P = 0.07). Based on these results, the method used to quantify physical capacity after an intervention may have major implications on the outcomes. The methods used should be carefully analyzed in regard to the objective of the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".