Associations between training characteristics and change in peak oxygen consumption following exercise training in patients with heart failure with preserved ejection fraction
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».