一项研究发现,智能观察数据加上血液测试可以准确预测2型糖尿病的风险,比传统方法早。 A study finds smartwatch data combined with blood tests can accurately predict type 2 diabetes risk earlier than traditional methods.

《自然》的一项新研究表明,将智能观察数据——类似心率、活动率和睡眠模式——与常规血液测试结合起来,可以准确地预测胰岛素抗药性,这是2型糖尿病的前身。 A new study in Nature shows that combining smartwatch data—like heart rate, activity, and sleep patterns—with routine blood tests can accurately predict insulin resistance, a precursor to type 2 diabetes. 研究人员利用机器学习来自1,100多人的数据,在比传统方法早于传统方法确定代谢风险方面实现了很高的准确性。 Using machine learning on data from over 1,100 people, researchers achieved high accuracy in identifying metabolic risk earlier than traditional methods. 这种方法利用现实世界的生理信号,可促成广泛、低成本的筛选和早期干预。 The approach, which leverages real-world physiological signals, could enable widespread, low-cost screening and early intervention. 广泛使用虽然很有希望,但需要不同群体之间的验证、明确的临床准则以及解决隐私和获取问题。 While promising, broader use requires validation across diverse groups, clear clinical guidelines, and solutions to privacy and access concerns.