Dose-Response Relationship between Endurance Training Prescription Variables and Increases in Aerobic Performance of Healthy and Unhealthy Middle and Very Old Individuals Aged 70 Years and Older: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: The objectives of this systematic review and meta-analysis were to quantify the effectiveness of endurance training (ET) on aerobic performance (i.e., peak oxygen uptake (VO2peak)) in healthy and unhealthy middle and very old adults aged 70 years and older, and to provide dose–response relationships of training prescription variables (in terms of frequency, and volume). Methods: Several scholarly databases (i.e., PubMed/MEDLINE, SpringerLink, ScienceDirect Journals, and Taylor & Francis Online—Journals) were searched, identifying randomized controlled studies that investigated the effectiveness of ET on VO2peak in older adults. Standardized mean differences (SMD) were calculated. Results: In terms of changes differences between experimental and control group, ET produced significant large effects on VO2peak performance (SMD = 2.64 (95%CI 0.97–4.31)). The moderator analysis revealed that “health status” variable moderated ET effect onVO2peak performance. More specifically, ET produced larger SMD magnitudes on VO2peak performance in healthy compared with unhealthy individuals. With regard to the dose–response relationships, findings from the meta-regression showed that none of the included training prescription variables predicted ET effects on VO2peak performance. Conclusions: ET is an effective mean for improving aerobic performance in healthy older adults when compared with their unhealthy counterparts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".