Moayedi Yasbanoo, Foroutan Farid, Gao Yuan, Kim Ben, De Luca Enza, Brum Margaret, Brahmbhatt Darshan H, Duhamel Joe, Simard Anne, McIntosh Christopher, Ross Heather J
Ted Rogers Centre for Heart Research, University of Toronto, ON, Canada (Y.M., F.F., Y.G., B.K., E.D.L., M.B., D.H.B., J.D., A.S., C.M.I., H.J.R.).
Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada (Y.M., D.H.B., H.J.R.).
Circ Heart Fail. 2025 Jun;18(6):e012204. doi: 10.1161/CIRCHEARTFAILURE.124.012204. Epub 2025 May 9.
Heart failure (HF) is a highly prevalent condition characterized by exercise intolerance, an important metric for ambulatory prognostication. However, current methods to assess exercise capacity are often limited to tertiary HF centers, lacking scalability or accessibility. Wearable devices can enable near-continuous dynamic biometrics including exercise tolerance.
Leveraging the capabilities of Apple Watch and a custom application, the TRUE-HF (Ted Rogers Understanding of Exacerbations in Heart Failure) Apple cardiopulmonary exercise testing study aims to investigate whether HealthKit data from Apple Watch can estimate cardiorespiratory fitness, as compared with the gold standard peak oxygen uptake from cardiopulmonary exercise testing. The TRUE-HF study will evaluate the potential impact of wearable technology in the functional assessment of ambulatory patients with HF. The primary end point is to use HealthKit variables to estimate a TRUE-HF peak oxygen uptake. We outline key features of this trial designed to reduce the burden of wearable technology. In addition, we highlight the benefits of various machine learning analyses, with a particular focus on transformer models for the wearable space.
Using cutting-edge wearable technology and machine learning analytics, TRUE-HF may provide state-of-the-art assessment of functional capacity by measuring participant-generated free-world data.
URL: https://www.clinicaltrials.gov; Unique identifier: NCT05008692.
心力衰竭（HF）是一种高度普遍的病症，其特征为运动不耐受，这是门诊预后的一项重要指标。然而，目前评估运动能力的方法通常仅限于三级心力衰竭中心，缺乏可扩展性或可及性。可穿戴设备能够实现近乎连续的动态生物特征识别，包括运动耐量。
利用苹果手表和一个定制应用程序的功能，TRUE-HF（泰德·罗杰斯对心力衰竭加重的理解）苹果心肺运动测试研究旨在调查与心肺运动测试的金标准峰值摄氧量相比，来自苹果手表的健康数据是否能够估算心肺适能。TRUE-HF研究将评估可穿戴技术在门诊心力衰竭患者功能评估中的潜在影响。主要终点是使用健康数据变量来估算TRUE-HF峰值摄氧量。我们概述了该试验旨在减轻可穿戴技术负担的关键特征。此外，我们强调了各种机器学习分析的益处，特别关注可穿戴领域的变压器模型。
通过使用前沿的可穿戴技术和机器学习分析，TRUE-HF可能通过测量参与者生成的真实世界数据，提供功能能力的先进评估。
