SBIR Phase I: A WEARABLE PATCH TO MEASURE KEY BREASTFEEDING METRICS

这个SBIR第一阶段项目的更广泛的商业影响是实时测量与母乳喂养相关的最关键的指标，即乳量和吸奶模式。强烈建议在婴儿出生后的前六个月进行纯母乳喂养。虽然美国的母乳喂养率超过80%，但只有25%的女性能够达到六个月的目标。过早停止母乳喂养的主要原因是母亲对牛奶供应的焦虑，由于缺乏准确、廉价和方便的手段来评估婴儿的牛奶摄入量，加剧了这种焦虑。目前的护理标准是在喂养前和喂养后称重婴儿，需要昂贵的临床秤来检测1%-2%的体重变化，训练有素的工作人员，给婴儿紧紧地裹在被子里，仔细操作，并定期校准。即使到那时，这仍然是一项乏味的运动，因此不能提供每天牛奶摄入量的质量代表性指标。因此，需要一种“母乳喂养体温计”--一种准确、易用的设备，可在临床和家庭环境中用于多次喂养。该项目开发的技术可用于快速识别和干预高危婴儿的喂养问题。该项目旨在开发并验证一种低成本可穿戴贴片实时测量乳量和吸奶模式的准确性。这种贴片通过检测乳房形状和电特性的变化来进行操作，每天可以使用1-12次喂养。数据被报告给移动应用程序进行跟踪和分析，并与婴儿喂养和生长的其他关键指标相集成。将构建和测试几个补丁配置，以优化设计和分析算法。最高精确度的设计将在一项涉及母乳喂养母婴的更大规模的研究中得到进一步验证。这一奖项反映了NSF的法定使命，并已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。

The broader/commercial impact of this SBIR Phase I Project is to measure the most critical metrics associated with breastfeeding in real time, namely milk volume and suck patterns. Exclusive breastfeeding in the first six months of life is highly recommended. While U.S. breastfeeding initiation rates exceed 80%, only 25% of women are able to reach the six-month goal. The leading cause of premature breastfeeding cessation is maternal anxiety about milk supply exacerbated by the absence of an accurate, inexpensive, and convenient means to assess infant milk intake. The current standard-of-care, weighing an infant before and after feeding, requires an expensive clinical scale to detect a 1-2% weight change, trained staff, tight swaddling of the infant, careful operation, and regular calibration. Even then, it’s still a tedious exercise and thus does not provide a quality representative measure of daily milk intake. There is therefore a need for a “thermometer for breastfeeding” – an accurate, easy-to-use device to be used for multiple feedings in both clinical and home settings. The technology developed in this project can be used for rapid identification and intervention for infants at high risk of feeding problems.This project aims to develop and validate the accuracy of a low-cost wearable patch measuring milk volume and suck patterns in real time. The patch operates by detecting changes in the shape and electrical properties of the breast and can be used for 1-12 feedings per day. Data are reported to a mobile app for tracking and analysis, as well as integration with other key metrics of infant feeding and growth. Several patch configurations will be built and tested to optimize the design and analysis algorithm. The design with highest accuracy will be further validated in a larger study involving breastfeeding mother-infant dyads.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.