Clinical Exercise Physiology, Ball State University, Muncie, IN, USA; Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, IL, United States of America.
Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, IL, United States of America; Veterans Affairs Palo Alto Healthcare System and Stanford University, Palo Alto, CA, USA.
Prog Cardiovasc Dis. 2024 Mar-Apr;83:36-42. doi: 10.1016/j.pcad.2024.02.009. Epub 2024 Feb 27.
Cardiorespiratory fitness (CRF) is a well-established biomarker that has applications to all adults across the health and disease spectrum. Despite overwhelming evidence supporting the prognostic utility of CRF, it remains vastly underutilized. CRF is optimally measured via cardiopulmonary exercise testing which may not be feasible to implement on a large scale. Therefore, it is prudent to develop ways to accurately estimate CRF that can be applied in clinical and community settings. As such, several prediction equations incorporating non-exercise information that is readily available from routine clinical encounters have been developed that provide an adequate reflection of CRF that could be implemented to raise awareness of the importance of CRF. Further, technological advances in smartphone apps and consumer-grade wearables have demonstrated promise to provide reasonable estimates of CRF that are widely available, which could enhance the utilization of CRF in both clinical and community settings.
心肺适能(CRF)是一种经过充分证实的生物标志物,适用于健康和疾病谱中的所有成年人。尽管有压倒性的证据支持 CRF 的预后效用,但它的应用仍然远远不足。CRF 可以通过心肺运动测试来最佳测量,但在大规模实施可能不可行。因此,开发能够在临床和社区环境中应用的准确估计 CRF 的方法是明智的。因此,已经开发了一些包含易于从常规临床接触中获得的非运动信息的预测方程,这些方程提供了对 CRF 的充分反映,可以用来提高对 CRF 重要性的认识。此外,智能手机应用程序和消费级可穿戴设备的技术进步已经证明有希望提供广泛可用的合理的 CRF 估计值,这可以提高 CRF 在临床和社区环境中的利用率。