运动训练模式与射血分数保留型心力衰竭患者峰值耗氧量变化的比较:OptimEx-Clin 试验的二次分析。

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European journal of preventive cardiology Pub Date : 2024-10-25 DOI:10.1093/eurjpc/zwae332
Stephan Mueller, Marina Kabelac, Isabel Fegers-Wustrow, Ephraim B Winzer, Andreas B Gevaert, Paul Beckers, Bernhard Haller, Frank Edelmann, Jeffrey W Christle, Mark J Haykowsky, Vandana Sachdev, Dalane W Kitzman, Axel Linke, Volker Adams, Ulrik Wisloff, Burkert Pieske, Emeline van Craenenbroeck, Martin Halle
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引用次数: 0

摘要

目的:运动训练(ET)是射血分数保留型心力衰竭(HFpEF)的一种有效疗法,但不同ET特征的影响尚不清楚。我们的目的是评估在中度持续训练(MCT,5×/周)或高强度间歇训练(HIIT,3×/周)的3个月期间,ET频率、持续时间、强度[心率储备百分比(%HRR)]和估计能量消耗(EEE)与HFpEF峰值耗氧量(V.M.O2)变化之间的关系:使用智能手机应用程序记录 ET 持续时间和心率(HR)。使用 ET 期间的心率数据和心肺运动测试期间的单个 HR-V̇O2 关系计算 EEE。通过线性回归分析评估了组间差异以及ET特征与峰值V̇O2变化之间的关联。MCT 后峰值 V̇O2 提高了 9.2 ± 13.2%,HIIT 后提高了 8.7 ± 15.9%(P = 0.67)。1 次 HIIT 的平均 EEE 相当于 1.42 次 MCT,经 EEE 调整后,MCT 和 HIIT 的平均差异为-0.1%(P = 0.98)。对于 MCT 和 HIIT,峰值 V̇O2 变化与 ET 频率(MCT:R2 = 0.103;HIIT:R2 = 0.149)和持续时间/周(MCT:R2 = 0.120;HIIT:R2 = 0.125;所有 P <0.05)呈正相关。在 MCT 中,平均 HRR%与峰值 V̇O2 变化呈负相关(R2 = 0.101;P = 0.034),而在 HIIT 中未发现显著的相关性(P = 0.234)。多元回归分析解释了峰值 V̇O2 变化的 1 ∼ 1/3:结论:在HFpEF中,3个月内等热量HIIT和MCT似乎同样有效。在每种模式中,增加 ET 频率或持续时间/周可能比增加 ET 强度更能有效改善峰值 V̇O2。
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Comparison of exercise training modalities and change in peak oxygen consumption in heart failure with preserved ejection fraction: a secondary analysis of the OptimEx-Clin trial.

Aims: Exercise training (ET) is an effective therapy in heart failure with preserved ejection fraction (HFpEF), but the influence of different ET characteristics is unclear. We aimed to evaluate the associations between ET frequency, duration, intensity [% heart rate reserve (%HRR)] and estimated energy expenditure (EEE) with the change in peak oxygen consumption (V̇O2) over 3 months of moderate continuous training (MCT, 5×/week) or high-intensity interval training (HIIT, 3×/week) in HFpEF.

Methods and results: ET duration and heart rate (HR) were recorded with a smartphone application. EEE was calculated using the HR data during ET and the individual HR-V̇O2 relationships during cardiopulmonary exercise testing. Differences between groups and associations between ET characteristics and peak V̇O2 change were assessed with linear regression analyses. Peak V̇O2 improved by 9.2 ± 13.2% after MCT and 8.7 ± 15.9% after HIIT (P = 0.67). The average EEE of 1 HIIT session was equivalent to ∼1.42 MCT sessions and when adjusted for EEE, the mean difference between MCT and HIIT was -0.1% (P = 0.98). For both MCT and HIIT, peak V̇O2 change was positively associated with ET frequency (MCT: R2 = 0.103; HIIT: R2 = 0.149) and duration/week (MCT: R2 = 0.120; HIIT: R2 = 0.125; all P < 0.05). Average %HRR was negatively associated with peak V̇O2 change in MCT (R2 = 0.101; P = 0.034), whereas no significant association was found in HIIT (P = 0.234). Multiple regression analyses explained ∼1/3 of the variance in peak V̇O2 change.

Conclusion: In HFpEF, isocaloric HIIT and MCT seem to be equally effective over 3 months. Within each mode, increasing ET frequency or duration/week may be more effective to improve peak V̇O2 than increasing ET intensity.

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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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