Effects of multicomponent training on the intrinsic capacity of community-dwelling older adults: quasi-experimental study protocol
Bibliographic record
Abstract
Objective: This is a protocol for assessing the effects of multicomponent exercise on the intrinsic capacity of older adults. Methods: Older adults (≥ 60 years old) will be selected for a multicomponent training program in Porto Alegre, RS, Brazil to evaluate the 5 domains of intrinsic capacity: vitality (handgrip strength, body mass index, and nutrition) sensory perception (self-reported questions), psychology (the 15-item Geriatric Depression Scale), cognition (the Montreal Cognitive Assessment) and locomotion (the sit-to-stand test and the Timed Up and Go test). The composite intrinsic capacity score will be obtained by summing the domains, with total scores ranging from 0 to 10 points. After 12 weeks of the multicomponent exercise intervention, the participants will be reassessed. Student’s t-test and ANOVA will be used to compare the effects of different types of training on intrinsic capacity. This study was approved by the research ethics committee of the involved institution. Expected results: After the 12-week multicomponent exercise intervention, we expect scores for composite intrinsic capacity and its domains, especially locomotion, to increase. Relevance: The risk of dependence, falls, and mortality increases with reduced intrinsic capacity, indicating a need for interventions to limit these negative outcomes. Multicomponent exercise, a simple, widely recommended, and effective strategy with good adherence, is designed to prevent intrinsic capacity decline in older people and improve their health and functionality.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".