In-hospital physical activity measured with a new Bosch accelerometer sensor system

Extended hospital stays and readmissions are associated with high costs and burden of disease. Effective ways to predict patient outcomes, select those at risk of adverse outcomes, and to prevent these accordingly, are lacking. Physical activity measured by accelerometry is a promising surrogate parameter of health, but it is mainly evaluated in highly selected patient groups and with few algorithms. We used a new wrist-worn Bosch sensor platform to measure various characteristics of physical activity in 58 patients of various ages treated on internal medicine wards over a period of 30 days, with a goal of developing algorithms to predict duration of hospitalization and 30-day readmission. New algorithms are required to increase the accuracy of the predictions and to understand the significance of physical activity in hospitalized patients. To facilitate research in this area, and to allow the public to explore these issues, all data generated in the study are published here as a public dataset.

延长住院时间及再次入院与高昂的医疗费用及疾病负担密切相关。目前，预测患者预后、筛选出易受不良预后影响的患者群体以及据此采取预防措施的有效方法尚显不足。通过加速度计测量的身体活动是衡量健康状态的有前景的替代指标，然而，该指标主要在经过高度选择的病人群体和有限的算法中进行评估。本研究采用新型的Bosch腕带式传感器平台，对内科病房中58名不同年龄段的病人30天内的身体活动特性进行测量，旨在开发预测住院时长及30天再次入院率的算法。为提高预测的准确性并揭示身体活动在住院病人中的重要性，迫切需要新的算法。为促进该领域的研究，并使公众能够探讨这些问题，本研究中生成的一切数据均公开发布，以形成公共数据集。