TY - JOUR AU - Daskalaki, Elena AU - Parkinson, Anne AU - Brew-Sam, Nicola AU - Hossain, Md Zakir AU - O'Neal, David AU - Nolan, Christopher J AU - Suominen, Hanna PY - 2022 DA - 2022/4/8 TI -当前无创可穿戴技术在1型糖尿病生理信号监测中的潜力:文献综述JO - J Med Internet Res SP - e28901 VL - 24 IS - 4kw - 1型糖尿病KW -可穿戴传感器KW -大数据KW -消费者健康信息学KW -移动健康KW -调查AB -背景:监测1型糖尿病(T1D)患者的血糖等参数可以加强急性血糖管理和疾病长期并发症的诊断。对于大多数T1D患者，胰岛素输送的测定是基于一个单一的测量参数-葡萄糖。到目前为止，可穿戴传感器已经能够无缝、无创、低成本地监测多种生理参数。目的:本文献综述的目的是探讨一些可以通过无创、可穿戴传感器监测的生理参数是否可以用于加强T1D的管理。方法:通过对市场上现有设备的全面审查，编制了一份生理参数清单，这些参数可以使用2020年可用的可穿戴传感器进行监测。使用与T1D相关的搜索词结合确定的生理参数进行文献调查。所选的出版物仅限于人类研究，这些研究至少有其摘要。PubMed和Scopus数据库被询问。总共保留了77篇文章，并根据以下两个轴进行了分析:通过比较T1D患者和健康对照参与者发现的这些参数与T1D之间的关系，以及通过进一步分析在T1D队列中开展的研究中发现的关系，发现了T1D增强的潜在领域。 Results: On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. Conclusions: Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions. SN - 1438-8871 UR - //www.mybigtv.com/2022/4/e28901 UR - https://doi.org/10.2196/28901 UR - http://www.ncbi.nlm.nih.gov/pubmed/35394448 DO - 10.2196/28901 ID - info:doi/10.2196/28901 ER -