Bibliographic record
Abstract
PURPOSE OF REVIEW: This review focuses on recent literature examining the validity and reliability of the talk test for prescribing and monitoring exercise intensity. The utility of the talk test for high-intensity interval training and recently proposed exercise training guidelines for patients with atrial fibrillation is also examined. RECENT FINDINGS: In healthy adults and patients with cardiovascular disease, comfortable speech is likely possible (equivocal or last positive talk test stage) when exercise intensity is below the ventilatory or lactate threshold, and not likely possible (negative talk test stage) when exercise intensity exceeds the ventilatory or lactate threshold. The talk test can be used to produce exercise intensities (moderate-to-vigorous intensity, 40-80% (Equation is included in full-text article.)) within accepted Canadian Association of Cardiovascular Prevention and Rehabilitation and American College of Sports Medicine guidelines for exercise training, to monitor exercise training for patients with atrial fibrillation, and help avoid exertional ischemia. The talk test has been shown to be consistent across various modes of exercise (i.e., walking, jogging, cycling, elliptical trainer and stair stepper). It may not be practical for high-intensity interval training. SUMMARY: The talk test is a valid, reliable, practical and inexpensive tool for prescribing and monitoring exercise intensity in competitive athletes, healthy active adults and patients with cardiovascular disease. Healthcare professionals should feel comfortable in advocating its use in a variety of clinical and health-promotion settings.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".