SCH: Movement as a Vital Sign in Preterm Infants

在美国，每年有十分之一的活产婴儿或大约38万婴儿早产，死亡率为27%。近三分之一的幸存者终生患有神经系统疾病，包括脑瘫、自闭症和精神疾病，给个人和社会带来巨大代价。尽管在新生儿重症监护方面取得了进展，但在监测早产儿神经功能成熟方面进展甚微。该建议解决了在新生儿重症监护病房持续监测神经功能的迫切需要，以提高我们对正常和异常神经成熟的理解，并制定及时的临床干预措施，以改善健康结果并降低早产的成本。该研究计划旨在表明婴儿的身体运动是神经系统功能完整性的重要标志。在目标1中，研究小组将量化从出生（29周孕龄）到从新生儿重症监护病房（NICU）出院的3个月内常规获得的数据集的运动次数。他们假设，随着年龄的增长，动作模式的复杂性会增加，这是成熟的标志，他们将在几个月内检查动作信号的熵的演变。目的2将量化运动发作与呼吸和心脏节律的强烈生理相互作用，并将运动与呼吸暂停事件和心肺不稳定联系起来。目标3旨在开发利用运动和其他生理事件特征的机器学习算法，用于在线预测呼吸暂停发作。作为回报，这些算法将揭示生理特征和机制，增强临床洞察力。初步数据有望支持我们的论文，即新生儿的运动是一个重要的生命体征。综上所述，这种定量方法将有助于理解新生儿神经系统的成熟，以及运动如何稳定或破坏新生儿的心肺控制。该团队还确定了多项外展活动，以促进科学研究、技术和STEM教育的整合，包括与少数族裔服务机构合作举办的运动科学、数据和技术研讨会，以增加少数族裔在运动、数据和计算科学领域的代表性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Each year in the United States, one in ten live births or roughly 380,000 babies are born prematurely, with a mortality of 27%. Nearly one third of those surviving suffer from lifelong neurological conditions including cerebral palsy, autism, and psychiatric disorders with substantial personal and societal costs. Despite advances in neonatal intensive care, little progress has been made in monitoring maturation of neurological function in preterm infants. This proposal addresses an urgent need for continuous monitoring of neurological function in the neonatal intensive care unit to advance our understanding of normal and abnormal neurological maturation and to develop timely clinical interventions to improve the health outcomes and reduce costs of prematurity. The research program aims to show that bodily movements of infants are a vital marker for the functional integrity of the nervous system. In Objective 1 the team will quantify movement bouts in routinely obtained data sets over ~3 months, from birth (29 weeks gestational age) until discharge from the neonatal intensive care unit (NICU). Pursuing the hypothesis that complexity of movement patterns increases with age as a sign of maturation, they will examine the evolving entropy of movement signals over several months. Objective 2 will quantify the strong physiological interactions of movement bouts with the respiratory and cardiac rhythms, and correlate movement to apneic events and cardiorespiratory instability. Objective 3 seeks to develop machine learning algorithms using features of movement and other physiological events for online prediction of apneic episodes. In return, the algorithms will reveal physiological features and mechanisms that enhance clinical insights. Preliminary data promise results that support our thesis that movement in newborn infants is an important vital sign. Taken together, this quantitative approach will advance understanding maturation of the neonatal nervous system and how movement can stabilize or disrupt cardiorespiratory control in neonates. The team has also identified multiple outreach activities to foster the integration of scientific research, technology and STEM education, including a workshop on Movement Science, Data, and Technology, in collaboration with a minority-serving institution to increase minority representation in the movement, data, and computational sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.