从24小时腕部运动中学习人类健康与疾病

中文摘要 AI 辅助

人类的大部分健康状况和功能是在临床场景之外，通过日常生活中的运动展现出来的。腕部佩戴的加速度计能够连续捕捉这些运动，但其丰富的信号常被简化为一小部分预定义的行为汇总指标。在此，我们提出Sensori，一种自监督基础模型，可直接从24小时的原始三轴腕部运动中学习通用健康表征。我们在来自英国、中国和美国的四个基于人群的队列中开发并评估了该模型，这些队列包含122640名参与者，贡献了683617人天的自由生活记录数据。Sensori将每日运动浓缩为一种表征，可捕捉多样的运动行为、人口统计学特征、健康维度和身体功能。在独立队列中的评估表明，这些表征无需重新训练即可跨人群和测量设置泛化。当与常见临床协变量结合时，Sensori显著改善了102种符合条件疾病中52种的患病率分类（AUROC中位增量为0.060，范围0.012-0.242），以及87种符合条件疾病中26种的发病风险预测（Uno's C指数中位增量为0.064，范围0.025-0.172），其中神经和精神疾病的增益最大。这些发现确立了24小时腕部运动是一种丰富且可扩展的健康信息来源，具有支持人群规模被动健康监测和疾病预测的潜力。

英文摘要

Much of human health and function unfolds beyond the clinic, through the movements of everyday life. Wrist-worn accelerometers capture these movements continuously, yet their rich signals are often reduced to a small set of predefined behavioural summary measures. Here, we present Sensori, a self-supervised foundation model that learns general-purpose health representations directly from 24 hours of raw tri-axial wrist movement. We developed and evaluated the model across four population-based cohorts from the United Kingdom, China and the United States, comprising 122,640 participants contributing 683,617 person-days of free-living recordings. Sensori condensed each day of movement into a representation that captured diverse movement behaviours, demographic characteristics, health axes and physical function. Evaluation in independent cohorts showed that these representations generalised across populations and measurement settings without retraining. When added to common clinical covariates, Sensori significantly improved prevalent disease classification for 52 of 102 eligible conditions (median delta AUROC, 0.060; range, 0.012-0.242) and incident disease risk prediction for 26 of 87 eligible conditions (median delta Uno's C-index, 0.064; range, 0.025-0.172), with the largest gains for neurological and psychiatric disorders. These findings establish 24-hour wrist movement as a rich and scalable source of health information, with the potential to support passive health monitoring and disease prediction at population scale.

发表机构

• Big Data Institute, University of Oxford（牛津大学大数据研究所）
• Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford（牛津大学纳菲尔德骨科、风湿病学与肌肉骨骼科学系）
• Oxford University Hospitals（牛津大学医院）
• Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）
• Department of Engineering Science, University of Oxford（牛津大学工程科学系）
• Department of Computer Science, University of Oxford（牛津大学计算机科学系）
• Department of Statistics, University of Oxford（牛津大学统计系）
• Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center（北京大学医学部公共卫生学院流行病学与生物统计学系）
• Peking University Center for Public Health and Epidemic Preparedness and Response（北京大学公共卫生与疫情防控中心）

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