SCH: Movement as a Vital Sign in Preterm Infants

在美国，每年有十分之一的活产婴儿，或者说大约38万名婴儿早产，死亡率为27%。近三分之一的幸存者患有终生神经系统疾病，包括脑瘫、自闭症和精神疾病，这些疾病给个人和社会带来了巨大的代价。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。这项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，以促进我们对正常和异常神经成熟的理解，并开发及时的临床干预措施，以改善早产的健康结果和降低成本。该研究项目旨在表明，婴儿的身体动作是神经系统功能完整性的重要标志。在目标1中，研究小组将量化常规获得的数据集中从出生(29周胎龄)到新生儿重症监护病房(NICU)出院的约3个月的运动次数。他们假设运动模式的复杂性随着年龄的增长而增加，以此作为成熟的标志，他们将检查几个月来运动信号的演变熵。目标2将量化运动与呼吸和心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的特征来在线预测呼吸暂停发作的机器学习算法。作为回报，这些算法将揭示增强临床洞察力的生理特征和机制。初步数据保证结果支持我们的论点，即新生儿的运动是一个重要的生命体征。总而言之，这种量化方法将促进对新生儿神经系统成熟的理解，以及运动如何稳定或扰乱新生儿的心肺控制。该团队还确定了多项推广活动，以促进科学研究、技术和STEM教育的整合，包括与一家为少数群体服务的机构合作举办的运动科学、数据和技术研讨会，以增加少数群体在运动、数据和计算科学中的代表性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.