Impact of a 12‐week high‐intensity interval training intervention on cardiac structure and function after COVID‐19 at 12‐month follow‐up
Bibliographic record
Abstract
Abstract In patients previously hospitalised for COVID‐19, a 12‐week high‐intensity interval training (HIIT) intervention has previously been shown to increase left ventricular mass (LVM) immediately after the intervention. In the present study, we examined the effects of the same HIIT scheme on LVM, pulmonary diffusing capacity, symptom severity and functional capacity at 12‐month follow‐up. In this investigator‐blinded, randomised controlled trial, 12 weeks of a supervised HIIT scheme (4 × 4 min, three times a week) was compared to standard care (control) in patients recently discharged from hospital due to COVID‐19. At inclusion and at 12‐month follow‐up, LVM was assessed by cardiac magnetic resonance imaging (cMRI, primary outcome), while pulmonary diffusing capacity for carbon monoxide ( D LCOc , secondary outcome) was examined by the single‐breath method. Symptom severity and functional status were examined by the Post‐COVID‐19 Functional Scale (PCFS) and King's Brief Interstitial Lung Disease (KBILD) questionnaire score. Of the 28 patients assessed at baseline, 22 completed cMRI at 12‐month follow‐up (12.4 ± 0.6 months after inclusion). LVM was maintained in the HIIT but not the standard care group, with a mean between‐group difference of 9.68 [95% CI: 1.72, 17.64] g ( P = 0.0182). There was no differences in change from baseline to 12‐month follow‐up between groups in D LCOc % predicted (−2.45 [−11.25, 6.34]%; P = 0.578). PCFS and KBILD improved similarly in the two groups. In individuals previously hospitalised for COVID‐19, a 12‐week supervised HIIT scheme resulted in a preserved LVM at 12‐month follow‐up but did not affect pulmonary diffusing capacity or symptom severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".