利用不受监督的自由生活数据来估计心肺健康:系统性审查和元分析
Alexios Dosis1,2, Aron Berger Syversen3, Mikolaj R Kowal1,2
1Leeds Institute of Medical Research, University of Leeds, Clinical Sciences Building, St James's University Hospital, Beckett Street, Leeds, LS97LN, United Kingdom, 44 07754226212.
JMIR mHealth and uHealth
|January 27, 2026
概括
使用可穿戴数据估计心肺呼吸能力 (CRF) 对无法执行最大测试的人来说是有前途的. 虽然初步结果令人鼓舞,但由于数据的变化,需要更多的研究来临床使用.
科学领域:
- 评估心肺呼吸系统的健康状况
- 可穿戴技术是可穿戴的技术.
- 机器学习在医疗保健中的应用
背景情况:
- 传统的心肺呼吸能力 (CRF) 评估可能不适合脆弱的个人.
- 使用自由生活的可穿戴数据进行CRF估计提供了潜在的更具代表性和可访问性的方法.
- 随着时间的推移捕获的可穿戴数据可以提高CRF评估的临床可用性.
研究的目的:
- 从自由生活的可穿戴数据系统地审查估计CRF的证据.
- 评估为此目的开发的模型的性能和质量.
- 评估这些新型CRF估计方法的临床实施潜力.
主要方法:
- 在四个数据库 (MEDLINE,Embase,Scopus,arXiv) 中按照PRISMA指南进行系统的文献搜索.
- 包括从连续自由生活可穿戴数据中开发CRF估计模型的研究;排除基于实验室的研究.
- 使用随机效应模型和费舍尔Z转换组合性能指标的元相关性分析.
主要成果:
- 18项涉及31,072名参与者的研究符合资格标准;平均年龄为46.9岁.
- 在8项研究中使用了机器学习模型;聚合的元相关性估计为0.83 (95% CI 0.77-0.88).
- 观察到高异质性 (I2=97%) 和数据分析中偏差风险,对数据处理清晰度存在担忧.
结论:
- 从可穿戴数据中预测和测量CRF值之间存在一个有希望的初步协议.
- 高异质性和缺乏外部验证,阻止了临床实施的最终结论.
- 连续可穿戴数据流是一个有价值的资源,有可能显著推进CRF测量和监测.
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