Parkinson’s Critical Heart Rate Test: Applying the Critical Power Model for People with Parkinson’s Disease
Bibliographic record
Abstract
Aerobic exercise with the correct intensity can attenuate motor and non-motor symptoms of Parkinson's disease (PD) and improve the quality of life. However, a specific, validated, non-invasive, and outside the laboratory protocol that assesses physiological variables to prescribe optimal aerobic exercise intensity for people with PD is nonexistent. Therefore, this study aimed to propose a protocol, the Parkinson's critical heart rate test (Parkinson-CHR test), to determine the critical heart rate (CHR) in individuals with PD and verify its validity, reliability, and sensitivity. Fifteen people with idiopathic PD, who were able to practice exercises, were recruited to participate in the study (71.1 ± 6.6 years). The study consisted of two experiments: i) the first one aimed to assess the validity and reliability of the protocol, with participants performing the test twice at a one-week interval; ii) the second experiment aimed to investigate the protocol sensitivity, with individuals being evaluated before and after an 8-week training program according to Parkinson-CHR intensity. In experiment 1, no differences between test and retest were observed in the time to cover the distances (400, 800 and 1200 m), the total heart rate, the critical heart rate, and critical speed (p > 0.05). In experiment 2, there was a reduction in time to cover 400 and 800 m as well as in the total heart rate for all distances after the 8-week training program. The Parkinson-CHR test is a reliable, reproducible, inexpensive, and non-invasive protocol to assess, prescribe, and monitor aerobic exercise intensity in people with PD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".