Davoudi Anis, Ozrazgat-Baslanti Tezcan, Tighe Patrick J, Bihorac Azra, Rashidi Parisa
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5696-5699. doi: 10.1109/EMBC44109.2020.9176227.
Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group.
重症监护患者在重症监护病房住院期间会经历不同程度的疼痛,通常需要使用镇痛药和镇静剂。这类药物一般会使重症监护患者本就久坐不动的身体活动状况恶化,导致恢复延迟。因此,不仅要将疼痛程度降至最低,还要优化镇痛策略,以最大限度地提高重症监护病房患者的活动能力和活动量,这一点很重要。目前,我们在细粒度层面上对疼痛与身体活动之间的关系缺乏了解。在本研究中,我们考察了护士评估的疼痛评分与使用可穿戴加速度计设备测量的身体活动之间的关系。我们发现,在白天和夜间,与低疼痛报告之前相比,高疼痛报告之前的平均、标准差和最大身体活动计数均显著更高,而两个疼痛报告组之间的静息时间百分比没有显著差异。在使用提取的身体活动特征检测到的患者聚类中,对于疼痛报告组的预测,在调整后的逻辑回归分析中具有显著性。