Associations between training characteristics and change in peak oxygen consumption following exercise training in patients with heart failure with preserved ejection fraction
Bibliographic record
Abstract
Abstract Introduction In heart failure with preserved ejection fraction (HFpEF), moderate continuous training (MCT) and high-intensity interval training (HIIT) are both effective in increasing peak oxygen uptake (peak V̇O2). Purpose The aim of this study was to investigate the association of training characteristics (i.e. average sessions/week, average duration/week, mean intensity) and change in peak V̇O2 following 3 months of MCT and HIIT in patients with HFpEF. Methods Among 120 patients who were randomized to MCT (5x40 min/week at 35–50% heart rate reserve [HRR]) or HIIT (3x38 min/week at 80–90% HRR), those who completed 3-month follow-up (N=107) were considered for this analysis. Training duration and heart rates [HR] were recorded with a smartphone application, evaluated with a customized software and manually checked for plausibility. If HR measurements were classified as invalid/unreliable (e.g. very strong fluctuations), patients were excluded from analysis. Intensities were calculated as average % HRR of total sessions in MCT and the average of the highest % HRR values of all intervals in HIIT. Associations between training characteristics and change in peak V̇O2 were evaluated using univariate and multivariate regression analyses. Individual HR-V̇O2 relationships were used to calculate and compare energy expenditure (MET-minutes) in MCT and HIIT. Results After excluding 16 patients due to invalid/unreliable HR data, 91 patients (67% female, 69±7 years) were included in this analysis. On average, MCT patients (N=45) performed 4.0±1.2 sessions/week (162±52 min/week) at 47.4±6.7% HRR, while HIIT patients (N=46) performed 2.4±0.8 sessions/week (96±40 min/week) at 81.8±11.8% HRR. Peak V̇O2 was improved by 1.70±2.35 ml/kg/min in MCT and 1.46±2.98 ml/kg/min in HIIT (difference: 0.24 [95% CI, −0.87 to 1.34], p=0.67). The associations between training characteristics and change in peak V̇O2 are shown in Fig.1. Mean % HRR was not significantly associated with the change in peak V̇O2 in the HIIT group, whereas in MCT, mean duration/week and mean intensity were of similar relative importance (standardized coefficients) and explained up to 26% of the variation in change in peak V̇O2 (Table 1). Average weekly MET-minutes above rest were 451±260 for MCT and 389±375 for HIIT (difference: 62 [95% CI, −71 to 195], p=0.36). After adjustment for MET-minutes, the difference in change in peak V̇O2 between groups diminished to 0.09 ml/kg/min (95% CI, −0.97 to 1.16; p=0.98). Conclusions Weekly duration and mean % HRR had a similar predictive ability for the change in peak V̇O2 following MCT with, interestingly, lower change in peak V̇O2 with increasing intensity. In HIIT, mean % HRR was not significantly associated with the change in peak V̇O2. After adjusting for energy expenditure, the difference in change in peak V̇O2 between training modes diminished, suggesting that MCT and HIIT were similarly effective. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Commission, Framework Program 7
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".