Piersanti Agnese, Littero Martina, Del Giudice Libera Lucia, Marcantoni Ilaria, Burattini Laura, Tura Andrea, Morettini Micaela
CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy.
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.
Sensors (Basel). 2025 Jun 28;25(13):4038. doi: 10.3390/s25134038.
This study exploited the skin temperature signal derived from a wrist-worn wearable device to define potential digital biomarkers for glycemia levels. Characterization of the skin temperature signal measured through the Empatica E4 device was obtained in 16 subjects (data taken from a dataset freely available on PhysioNet) by deriving standard metrics and a set of novel metrics describing both the current and the retrospective behavior of the signal. For each subject and for each metric, values that correspond to when glycemia was inside the tight range (70-140 mg/dL) were compared through the Wilcoxon rank-sum test against those above or below the range. For hypoglycemia characterization (below range), retrospective behavior of skin temperature described by the metric (standard deviation of the series of coefficient of variation) proved to be the most effective both in daytime and nighttime (100% and 50% of the analyzed subjects, respectively). On the other side, for hyperglycemia characterization (above range), differences were observed between daytime and nighttime, with current behavior of skin temperature, described by (deviation from the reference value of 32 °C), being the most informative during daytime, whereas retrospective behavior, described by (standard deviation of the series of means), showed the highest effectiveness during nighttime. Proposed variability features outperformed standard metrics, and in future studies, their integration with other digital biomarkers of glycemia could improve the performance of applications devoted to non-invasive detection of glycemic events.
本研究利用从腕戴式可穿戴设备获取的皮肤温度信号来定义血糖水平的潜在数字生物标志物。通过推导标准指标和一组描述信号当前及回顾性行为的新指标，对16名受试者（数据取自PhysioNet上免费提供的数据集）使用Empatica E4设备测量的皮肤温度信号进行了特征分析。对于每个受试者和每个指标，通过Wilcoxon秩和检验，将血糖处于严格范围内（70 - 140 mg/dL）时对应的数值与该范围之上或之下的数值进行比较。对于低血糖特征（低于范围），指标（变异系数系列的标准差）所描述的皮肤温度回顾性行为在白天和夜间均被证明是最有效的（分别为100%和50%的分析受试者）。另一方面，对于高血糖特征（高于范围），白天和夜间观察到差异，由（与32°C参考值的偏差）描述的皮肤温度当前行为在白天最具信息量，而由（均值系列的标准差）描述的回顾性行为在夜间显示出最高有效性。所提出的变异性特征优于标准指标，在未来研究中，将其与其他血糖数字生物标志物相结合可能会提高致力于血糖事件无创检测的应用性能。
