Eccentric Training in Pulmonary Rehabilitation of Post-COVID-19 Patients: An Alternative for Improving the Functional Capacity, Inflammation, and Oxidative Stress
Bibliographic record
Abstract
The purpose of this narrative review is to highlight the oxidative stress induced in COVID-19 patients (SARS-CoV-2 infection), describe longstanding functional impairments, and provide the pathophysiologic rationale that supports aerobic eccentric (ECC) exercise as a novel alternative to conventional concentric (CONC) exercise for post-COVID-19 patients. Patients who recovered from moderate-to-severe COVID-19 respiratory distress demonstrate long-term functional impairment. During the acute phase, SARS-CoV-2 induces the generation of reactive oxygen species that can be amplified to a "cytokine storm". The resultant inflammatory and oxidative stress process causes organ damage, particularly in the respiratory system, with the lungs as the tissues most susceptible to injury. The acute illness often requires a long-term hospital stay and consequent sarcopenia. Upon discharge, muscle weakness compounded by limited lung and cardiac function is often accompanied by dyspnea, myalgia, anxiety, depression, and sleep disturbance. Consequently, these patients could benefit from pulmonary rehabilitation (PR), with exercise as a critical intervention (including sessions of strength and endurance or aerobic exercises). Unfortunately, conventional CONC exercises induce significant cardiopulmonary stress and increase inflammatory and oxidative stress (OS) when performed at moderate/high intensity, which can exacerbate debilitating dyspnoea and muscle fatigue post-COVID-19. Eccentric training (ECC) is a well-tolerated alternative that improves muscle mass while mitigating cardiopulmonary stress in patients with COPD and other chronic diseases. Similar benefits could be realized in post-COVID-19 patients. Consequently, these patients could benefit from PR with exercise as a critical intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".