Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.

翻译：摘要：客观的数字数据在众多领域中仍较为稀缺，但正是这些数据能够推动研究，从而变革医疗保健标准。虽然消费者级可穿戴设备和智能手机的数据更易获取，但针对确诊患者所使用的临床级设备，同样迫切需要类似的数据。糖尿病领域可穿戴医疗设备的普及为这一领域及其他相关领域的独特研发创造了条件。然而，开源数据集的匮乏构成了进展的主要障碍。为促进更广泛的糖尿病相关问题研究，并加速稳健计算解决方案的开发，我们提供了DiaTrend数据集。该数据集由来自可穿戴医疗设备的密集纵向数据组成，包括来自54名糖尿病患者的共计27,561天的连续血糖监测数据和8,220天的胰岛素泵数据。该数据集有助于开发新型分析解决方案，以减轻糖尿病患者的疾病负担，并增进对门诊环境中慢性病管理的认识。