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Use CPET data to predict the intervention effect of aerobic exercise on young hypertensive patients

机译:使用CPET数据预测有氧运动对年轻高血压患者的干预效果

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The incidence of hypertension has recently shown a significant increase in young people, with aerobic exercise intervention being recognized as an effective approach to decrease blood pressure (BP). However, BP response to aerobic exercise can be highly individualized, and no research has been conducted on predicting the effect of aerobic exercise intervention for reducing BP in young hypertensive patients. In this work, we use the data generated from a cardiopulmonary exercise test (CPET) in young hypertensive patients (before aerobic exercise intervention) to derive information from multiple cardiopulmonary metabolic indices. The data, presented as time series, are then analyzed by a machine learning method to predict the effect of aerobic exercise intervention in lowering BP. This study provides several novel insights for making personalized aerobic exercise intervention programs for young adults with stage I hypertension.
机译:高血压的发病率最近显示出年轻人的显着增加,有氧运动干预被认为是降低血压(BP)的有效方法。然而,对有氧运动的血压反应可以高度个体化,并且尚无关于预测有氧运动干预对降低年轻高血压患者血压的作用的研究。在这项工作中,我们使用年轻高血压患者心肺运动测试(CPET)产生的数据(有氧运动干预之前)从多个心肺代谢指标中获取信息。然后,通过机器学习方法分析以时间序列表示的数据,以预测有氧运动干预对降低血压的影响。这项研究提供了一些新颖的见解,可为患有I期高血压的年轻成年人制定个性化有氧运动干预计划。

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