I-Corps: A real-time dietary decision support system for the prevention and management of diabetes.

这个i-Corps项目的更广泛的影响/商业潜力是改善糖尿病和糖尿病前期人群的饮食依从性和健康结果。近3000万美国成年人患有II型糖尿病。II型糖尿病是一个严重的公共卫生问题，其并发症包括心血管疾病、肾功能衰竭、失明和截肢，极大地影响患者的健康和生活质量。为了有效地自我管理自己的疾病，患者面临着通过遵循饮食指导和防止过度体重增加来控制血糖水平的艰巨任务。数字健康产品通过提供实时、互动的自我管理辅助，有可能提高患者对这些建议的遵从性。然而，现有的移动糖尿病自我管理应用程序仅限于作为血糖跟踪器的功能。拟议项目的目标是开发一个主动的饮食相关决策支持系统，该系统是个性化的，可以持续监测血糖浓度。软件和传感器技术的集成将产生一种更有效和更个性化的方法来预防II型糖尿病的疾病进展。这个i-Corps项目研究了使用可穿戴传感器的生物输入来消除对卡路里跟踪的需求的商业可行性，并开发个性化和动态的实时食物相关决策支持。我们的专利算法使用连续的血糖读数作为生物反馈，帮助糖尿病患者决定吃什么和什么时候吃。以前的研究已经证明，使用便携式血糖仪改善超重非糖尿病人群的自我调节饮食行为，可以改善血糖控制和持续减肥。这项拟议的技术将把这种基于证据的生物行为干预方法实时转化为2型糖尿病患者和糖尿病前期患者。用户界面将是一个带有图形输出的移动平台，以帮助进行“即时”决策，并监控他们的血糖变异性。该平台将提供患者的汇总统计数据，可以与他们的医疗保健提供者共享。

The broader impact/commercial potential of this I-Corps project is to improve dietary compliance and health outcomes in diabetic and pre-diabetic populations. Almost 30 million Americans adults have Type II diabetes. Type II diabetes is a serious public health concern, as its complications, which include cardiovascular disease, kidney failure, blindness and amputations, greatly affect patients' health and quality of life. To effectively self-manage their disease, patients face the difficult task of controlling their glucose levels by following dietary guidance and preventing excessive weight gain. Digital health products have the potential to improve patient adherence to these recommendations by providing real-time, interactive self-management aid. Yet, existing mobile diabetes self-management applications are limited to their function as glucose trackers. The goal of the proposed project is to develop a proactive diet-related decision-making support system that is personalized to continuously monitored glucose concentrations. The integration of software and sensor technology will result in a more efficient and individualized method to prevent disease progression in Type II diabetics.This I-Corps project examines the commercial feasibility of using biological inputs from wearable sensors to eliminate the need for calorie-tracking and develop personalized and dynamic real-time food-related decision support. Our proprietary algorithms use continuous glucose readings as biological feedback to assist diabetic patients with decisions about what and when to eat. Previous studies have demonstrated improved glycemic control and sustained weight loss using portable glucometers to improve self-regulated eating behavior in overweight non-diabetic populations. The proposed technology will translate this evidence-based biobehavioral intervention approach to Type 2 and pre-diabetic patients in real-time. The user interface will be a mobile platform with graphical outputs to assist with 'in-the-moment' decision-making as well as to monitor their glucose variability. The platform will provide summary statistics for patients that can be shared with their healthcare providers.