Background: Heart rate recovery (HRR), defined as the fall in HR during the first minute after exercise, is a marker of vagal tone, which is a powerful predictor of mortality in patients with coronary artery disease and in older patients. Whether exercise training (ET) modifies HRR in elderly patients recovering from acute myocardial infarction (AMI) is still unknown. Therefore, this study aims at evaluating the effect of ET on HRR in elderly AMI patients.
Methods: This was a prospective observational study including 268 older patients after AMI (217 men, 51 women), subdivided in two groups: Group A (n = 104), enrolled in an ET program; Group B (n = 164), discharged with generic instructions to continue physical activity. At baseline and at 3-month follow-up, all Group A and 54/164 Group B patients underwent a cardiopulmonary exercise stress test, whereas 110/164 Group B patients underwent an exercise stress test.
Results: After completion of the ET program, in Group A we observed an improvement in oxygen consumption at peak exercise (VO2peak; from 14.7 +/- 1.3 to 17.6 +/- 1.9 mL/kg/min, p < .001), in the rate of increase of ventilation per unit of increase of carbon dioxide production (VE/VCO2slope; from 34.2 +/- 3.8 to 30.4 +/- 3.0, p < .001), and in HRR (from 13.5 +/- 3.7 to 18.7 +/- 3.5 beats/min, p < .001). The changes in VO2peak and in VE/VCO2slope after ET were correlated with the improvement of HRR (r = -0.865, p < .01; r = -0.594, p < .01, respectively). No changes in these parameters were observed in Group B patients.
Conclusions: In older AMI patients, ET results in HRR improvement, which was correlated to the improvement in cardiopulmonary parameters. These findings may shed additional light on the possible mechanisms of the beneficial prognostic effects of ET in this patient population.
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http://dx.doi.org/10.1093/gerona/61.7.713 | DOI Listing |
PLoS One
January 2025
Ambulatory Healthcare Services, Academic Affairs, Abu Dhabi Health Services (SEHA), Abu Dhabi, UAE.
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PLoS One
January 2025
College of Education for the Future, Beijing Normal University, Zhuhai, Guangdong, China.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.
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January 2025
Cornell University, Ithaca, USA.
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Division of Rheumatology, Department of Pediatrics, The Warren Alpert Medical School of Brown University, 593 Eddy Street, Providence, RI, 02903, USA.
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Service de Physiologie Pédiatrique-Centre du Sommeil-CRMR Hypoventilations Alvéolaires Rares, INSERM NeuroDiderot, Université Paris-Cité, AP-HP, Hôpital Robert Debré, Paris, France.
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