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CN113349766B - 对于生理参数进行动态葡萄糖曲线响应的系统、装置和方法 - Google Patents

对于生理参数进行动态葡萄糖曲线响应的系统、装置和方法 Download PDF

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CN113349766B
CN113349766B CN202110572460.1A CN202110572460A CN113349766B CN 113349766 B CN113349766 B CN 113349766B CN 202110572460 A CN202110572460 A CN 202110572460A CN 113349766 B CN113349766 B CN 113349766B
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CN113349766A (zh
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加里·艾伦·哈伊特
内森·克里斯托夫·克鲁泰
迈克尔·洛夫
欧文·萨特里亚·布迪姆安
丹尼尔·米尔弗雷德·伯恩斯坦
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Abbott Diabetes Care Inc
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Abstract

本发明涉及对于生理参数进行动态葡萄糖曲线响应的系统、装置和方法。提供了用于向生理变化和/或活动提供一致的和可靠的葡萄糖响应信息的方法、装置和系统以改善血糖控制和健康管理。

Description

对于生理参数进行动态葡萄糖曲线响应的系统、装置和方法
本申请是申请日为2016年7月8日的题为“对于生理参数进行动态葡萄糖曲线响应的系统、装置和方法”的中国专利申请No.201680052614.1的分案申请。
技术领域
相关申请
本申请涉及2016年3月11日提交的美国临时申请号62/307,346、2015年7月10日提交的美国临时申请号62/191,218和2016年3月11日提交的标题为“用于膳食信息收集、膳食评估和分析数据相关性的系统、装置和方法(Systems，Devices，and Methods For Mealinformation Collection，Meal Assessment，and Analyte Data Correlation)”的美国临时申请号62/307,344，其各自的公开内容出于所有目的通过引用并入本文。
援引并入
出于所有目的，本文所述的专利、申请和/或出版物，包括以下专利、申请和/或出版物，通过引用并入本文：美国专利号4,545,382；4,711,245；5,262,035；5,262,305；5,264,104；5,320,715；5,356,786；5,509,410；5,543,326；5,593,852；5,601,435；5,628,890；5,820,551；5,822,715；5,899,855；5,918,603；6,071,391；6,103,033；6,120,676；6,121,009；6,134,461；6,143,164；6,144,837；6,161,095；6,175,752；6,270,455；6,284,478；6,299,757；6,338,790；6,377,894；6,461,496；6,503,381；6,514,460；6,514,718；6,540,891；6,560,471；6,579,690；6,591,125；6,592,745；6,600,997；6,605,200；6,605,201；6,616,819；6,618,934；6,650,471；6,654,625；6,676,816；6,730,200；6,736,957；6,746,582；6,749,740；6,764,581；6,773,671；6,881,551；6,893,545；6,932,892；6,932,894；6,942,518；7,041,468；7,167,818；和7,299,082；美国公开申请号2004/0186365，现在美国专利号7,811,231；2005/0182306，现在美国专利号8,771,183；2006/0025662，现在美国专利号7,740,581；2006/0091006；2007/0056858，现在美国专利号8,298,389；2007/0068807，现在美国专利号7,846,311；2007/0095661；2007/0108048，现在美国专利号7,918,975；2007/0199818，现在美国专利号7,811,430；2007/0227911，现在美国专利号7,887,682；2007/0233013；2008/0066305，现在美国专利号7,895,740；2008/0081977，现在美国专利号7,618,369；2008/0102441，现在美国专利号7,822,557；2008/0148873，现在美国专利号7,802,467；2008/0161666；2008/0267823；和2009/0054748，现在美国专利号7,885,698；美国专利申请序列号11/461,725，现在美国专利号7,866,026；12/131,012；12/393,921、12/242,823，现在美国专利号8,219,173；12/363,712，现在美国专利号8,346,335；12/495,709；12/698,124；12/698,129；12/714,439；12/794,721，现在美国专利号8,595,607；和12/842,013和美国临时申请号61/238,646、61/246,825、61/247,516、61/249,535、61/317,243、61/345,562和61/361,374。
背景技术
在某些个体中检测和/或监测葡萄糖水平或其它分析物(例如，乳酸盐、氧、A1C等)对他们的健康是至关重要的。例如，监测葡萄糖水平对患有糖尿病的个体以及具有指示糖尿病发病的病况的那些尤其重要。糖尿病患者通常监测葡萄糖水平以确定他们的葡萄糖水平是否维持在临床安全范围内，并且也可以使用该信息来确定是否和/或何时需要胰岛素以降低他们体内的葡萄糖水平或何时需要额外的葡萄糖以提高他们体内的葡萄糖水平。
随着葡萄糖监测装置和系统以方便且无痛苦的方式提供实时葡萄糖水平信息的发展，持续期望将这种监测装置和系统整合到日常生活和活动中以改善葡萄糖控制。更具体地，强烈期望确定日常活动如运动、用药、用餐等对葡萄糖水平波动的影响，并提供可行的、个性化健康有关的信息以紧密地控制葡萄糖变化。此外，强烈期望通过考虑影响包括运动和用餐的日常活动中的药物治疗的参数，提供准确评估正确的药物剂量测定的药物剂量测定的准确性同时减少这种测定中的误差。
发明内容
本公开的实施方案包括多阶段葡萄糖响应模式测定和动态调整或修改以个性化与特定患者或用户有关的特定活动和外部参数的葡萄糖响应。在某些实施方案中，分析模块被提供为可由任何处理器控制的装置执行的软件应用(“App”)，并且具体地，具有用于接收、分析、转移、传输、显示或输出可操作信息的通信能力的智能电话，例如包括基于测定的葡萄糖响应模式的治疗推荐。在某些实施方案中，以持续实时为基础智能且动态地调整考虑到特定活动或活动组合、膳食摄入量、药物摄入量或用户或患者的日常活动特异性的任何其它外部参数测定的葡萄糖响应模式(当通过App接收和分析额外的活动特定的或外部参数特定的数据时)。
本公开的实施方案包括具有与配置为执行App的智能电话通信的基于传感器的装置的整体网络，并且可选地具有提供网络云配置的一个或多个后端服务器终端的数据通信网络，其配置为执行App的功能进行分析，例如与基于传感器的装置进行直接数据通信时，并且将分析结果提供给智能手机，或者配置为以更被动的方式进行操作，例如执行数据备份功能或数据储存库功能(用于智能电话和/或基于传感器的装置)。而且，可选地包括在整体网络中的是一个或多个药物装置，例如胰岛素泵或胰岛素注射器笔(injector pen)，其配置为从智能手机、从一个或多个后端服务器终端或直接从基于传感器的装置接收分析数据。
本公开的实施方案包括数据收集阶段，在此期间例如在预定的时间段内从一个或多个基于传感器的装置，通过手动用户输入或从药物递送装置收集用户或患者特定信息。当确定关于患者或用户的足够量的信息时，因为它涉及葡萄糖响应和葡萄糖变化(例如，最少5天、6天、1周、10天、14天，或者天数或天数部分(portion of day)的任何一种或多种组合)，在某些实施方案中在智能手机上执行的App可以提示用户或患者已经确定或识别了特定的葡萄糖响应模式，并准备好用于响应分析的用户输入。为了达到这一点，在某些实施方案中，App分析来自基于传感器的装置和其它接收到的用户或患者特定参数的数据或信息，并将所接收的数据分类为数据分析的一部分以确定葡萄糖响应模式，此后利用从一个或多个基于传感器的装置或其它用户或患者特定参数接收到的附加实时信息来连续且动态地更新响应模式。以这种方式，在某些实施方案中，当用户输入用户期望参与的活动或参数时(例如，包括约1000英尺的斜坡的90分钟跑，或者在建立的时间段期间如12小时、18小时、24小时或其它合适的时间段采取的步数)，使用动态葡萄糖响应模式识别能力的App配置为通知用户或患者该活动将导致特定的葡萄糖响应(例如，葡萄糖水平的降低(活动后)为约25mg/dL)。
此外，在某些实施方案中，除了所执行的身体活动驱动的分析之外，该App还可以被配置为提供推荐，诸如例如提供在参与活动之前和/或在活动之后的固定时间内的特定时间要消耗的食物类型和量的列表，以便在跨越活动之前、期间和活动之后的设定的时间段内使葡萄糖波动超过预定范围最小化。在某些实施方案中，App配置为使用推荐执行上面描述的类似分析，其中代替待执行的身体活动，分析涉及要消耗的药物、食物、饮料或其一种或多种组合的量。以这种方式，在某些实施方案中，用户或患者可以在消耗食物和/或饮料或施用药物之前采取行动。
本领域的技术人员在阅读本公开的细节(如下面更充分地描述的)后将清楚本公开的这些和其它特征、目的和优点。
附图说明
图1是根据本公开的一个实施方案的整体葡萄糖响应数据分析系统；
图2A是根据本公开的一个实施方案的图1的分析模块的框图；
图2B显示了根据本公开的一个实施方案的结合图1的分析模块(执行数据分类、模式识别和动态更新)的信息流；
图3是根据本公开的一个实施方案的数据输入接口(interface，界面)111(图2A)的示例性屏幕截图；
图4是显示根据本公开的一个实施方案的确定日间活动对过夜葡萄糖水平的影响的例程(routine，例行程序)的流程图；
图5是显示根据本公开的一个实施方案的确定日间活动对过夜葡萄糖水平的影响的另一例程的流程图；
图6是显示根据本公开的一个实施方案的基于绝对过夜葡萄糖水平的特定活动的葡萄糖响应模式识别和表征的流程图；
图7是显示根据本公开的一个实施方案的基于昼夜葡萄糖水平变化的特定活动的葡萄糖响应模式识别和表征的流程图；
图8是显示根据本公开的一个实施方案的基于昼夜葡萄糖水平比例的特定活动的葡萄糖响应模式识别和表征的流程图；
图9显示了根据本公开的一个实施方案的用于训练和通知的过程流程；
图10显示了根据本公开的另一个实施方案的用于训练和通知的过程流程；
图11显示基于表2中所示测定的数据集，在具有活动的天相对于相应R值上进行线性拟合分析；
图12显示基于表2中所示测定的数据集，在δ中值葡萄糖(Gδ(X天))相对于活动水平(步数)上进行线性拟合分析；
图13显示基于表4中所示测定的数据集，相对于具有活动的天绘制的R值；
图14显示基于表4中所示测定的数据集，相对于活动度量(Act(X天))绘制δ中值葡萄糖(Gδ(X天))并进行线性拟合分析；以及
图15显示基于表6中所示测定的数据集，通过相对于具有显著活动的天的活动度量(Act)绘制中值葡萄糖比例(Gactd2nr(X天))进行线性拟合分析。
具体实施方式
在详细描述本公开之前，应当理解，本公开不限于所描述的特定实施方案，因此当然可以变化。还应该理解的是，本文使用的术语仅仅是为了描述特定实施方案的目的，而不意图是限制性的，因为本公开的范围将仅由所附权利要求来限定。
在提供数值范围的情况下，应当理解，除非上下文另外明确指出，否则在该范围的上限和下限之间的每个中间值到下限单位的十分之一以及在该范围内的任何其它规定值或中间值包含在本公开中。这些较小范围的上限和下限可以独立地包括在较小范围内，规定范围内的任何明确排除的限制也包含在本公开内。在规定范围包括一个或两个限制的情况下，排除那些包括的限制中的任一个或两个的范围也包括在本公开中。
除非另外定义，否则在此使用的所有技术和科学术语具有与由本公开所属领域的普通技术人员通常所理解的相同的含义。虽然与本文所述的那些相似或等同的任何方法和材料也可用于本公开的实践或测试，但现在描述优选的方法和材料。本文提及的所有出版物通过引用并入本文，以公开和描述与引用的出版物有关的方法和/或材料。
必需注意，如本文和所附权利要求书中所用，单数形式“一个(a)”、“一种(an)”和“该(the)”包括复数指示物，除非上下文另有明确规定。
提供本文讨论的出版物仅仅是为了在本申请的提交日期之前的它们的公开。本文中的任何内容都不应被解释为承认本公开由于先前公开而无权先于这样的公开。此外，所提供的出版日期可能与需要独立确认的实际出版日期有所不同。
如本领域的技术人员在阅读本公开后将显而易见的是，本文描述和示出的每个单独的实施方案具有离散的组件和特征，在不背离本公开的范围或精神的情况下其可以容易地与其它多个实施方案中的任何一个的特征分离或组合。
本文示出的附图不一定按比例绘制，为了清楚起见放大了一些组件和特征。
图1是根据本公开的一个实施方案的整体葡萄糖响应数据分析系统。参照该图，在某些实施方案中，葡萄糖响应数据分析系统100包括移动电话110，该移动电话110包括在移动电话110中被编程为App(例如作为从服务器150的数据网络140上下载的可执行文件安装)的用户界面110A和分析模块110B。如下面进一步详细讨论的，在某些实施方案中，通过App将数据调理(data conditioning)、分析和动态葡萄糖响应模式识别和/或更新葡萄糖响应模式识别作为一个或多个可执行例程(routine，例行程序)来执行。
返回参考图1，还示出了各自与移动电话110数据通信的活动监视器130A、心率监视器130B和葡萄糖监视器130C，或者可替代地或者除了各自通过数据网络140与服务器150数据通信之外。以这种方式，在某些实施方案中，每个活动监视器130A、心率监视器130B、葡萄糖监视器130C被编程为将所监视的信息通信至服务器150以用于存储和/或分析，或者通信至移动电话110以用于从每个活动监视器130A、心率监视器130B、葡萄糖监视器130C接收的任何一个或两个原始数据存储、分析以及随后的通信，和/或将处理的来自每个活动监视器130A、心率监视器130B、葡萄糖监视器130C的数据或信息通过数据网络通信至服务器150以用于存储和/或进一步分析。
还参考图1，在葡萄糖响应数据分析系统100中还示出的是药物递送装置120，其通过数据网络140与移动电话110、服务器150或活动监视器130A、心率监视器130B、葡萄糖监视器130C中的一个或多个进行数据通信。虽然未示出，但在某些实施方案中，App的例程和功能的操作可以在药物递送装置120中实现，其中药物递送装置120从一个或多个活动监视器130A、心率监视器130B、葡萄糖监视器130C直接接收数据或信息，并且执行葡萄糖响应模式识别和分析，并且例如基于来自监测数据的测定的葡萄糖响应模式(例如，生理监测状况，和/或食物和/或饮料的消耗，以及药物摄入量)来修改药物递送曲线(例如，基础胰岛素递送率、确定推注胰岛素剂量)(考虑到所提出的身体活动和/或食物或饮料消耗)。
在某些实施方案中，移动电话110包括集成在移动电话110内的一个或多个活动监视器130A、心率监视器130B、葡萄糖监视器130C。例如，在某些实施方案中，移动电话110包括能够监视移动电话110用户的移动的加速度计和/或陀螺仪，例如保持跟踪或记录所采取的步的数量，参与的身体活动(当例如使用臂带使移动电话110在身体上或附近时)，例如所采取的步数、奔跑、慢跑、冲刺、各自具有强度的程度或水平。在某些实施方案中，移动电话110被设置为腕表配置，在这种情况下，除了加速度计或陀螺仪之外，移动电话110还包括心率监视器。在移动电话110被配置为腕表的某些实施方案中，移动电话110结合葡萄糖传感器(体内、真皮、透皮或光学的)，使得葡萄糖水平的实时监测功能结合到移动电话110中。
再次参考葡萄糖响应数据分析系统100，在某些实施方案中，集线器装置(hubdevice)(未示出)可结合到葡萄糖响应数据分析系统100中，该葡萄糖响应数据分析系统100被配置为与活动监视器130A、心率监视器130B、葡萄糖监视器130C中的一个或多个通信以进行数据接收、存储以及随后通过数据网络140与葡萄糖响应数据分析系统100中的其它装置通信，或者与葡萄糖响应数据分析系统100中的其它装置(例如，移动电话110和/或药物递送装置120)直接通信。在某些实施方案中，集线器装置被配置为收集来自活动监视器130A、心率监视器130B、葡萄糖监视器130C中的一个或多个的信息的通过中继装置(passthrough relay device)或适配器，并且实时地或者在数据收集的特定时间段之后，将所收集的数据传输或发送到服务器150、移动电话110和/或药物递送装置120。在某些实施方案中，集线器装置在物理上体现为用户或患者保持靠近身体并与佩戴在身体上的活动监视器130A、心率监视器130B、葡萄糖监视器130C直接通信的小型、离散的密钥卡型(key fobtype)或加密狗型(dongle type)装置。此外，虽然在葡萄糖响应数据分析系统100中示出了三个活动监视器130A、心率监视器130B、葡萄糖监视器130C，但在本公开的范围内，提供附加传感器以监视用户的其它或相关参数。例如，用于通过一个或多个传感器进行监测或测量的参数包括但不限于出汗水平、温度水平、心率变异性(HRV)、神经活动、眼球运动、言语等等。在葡萄糖响应数据分析系统100的某些实施方案中，这些监测参数中的每一个或多个被用作移动电话110的分析模块110B的输入参数，如下面进一步详细讨论的。
图2A是根据本公开的一个实施方案的图1的分析模块110B的框图。如某些实施方案所示，移动电话110的分析模块110B包括数据输入接口111，用于接口或接收来自移动电话110外部或内部和移动电话110内的一个或多个活动监视器130A、心率监视器130B、葡萄糖监视器130C监视器的数据输入。经由数据输入接口接收的数据和/或信息被提供给葡萄糖响应训练单元112。在某些实施方案中，葡萄糖响应训练单元112根据数据的类型以及与数据相关联的参数的类型或多种类型将接收到的输入数据分类成相应的类别。例如，如果数据类型与诸如90分钟跑步的身体活动相关联，则除了持续时间之外，与数据相关联的参数还包括跑步强度(跑步、慢跑，冲刺)的水平，在某些实施方案中这可以使用监测的心率信息(如果可用的话)或跑步的步幅(pace of run)、有氧或无氧跑步、竞争性或非竞争性(训练)跑步或与身体活动相关联的任何其它合适类别(例如，跑步)来测定。在某些实施方案中，可以使用与身体活动相关联的其它类型的数据，诸如在建立的时间段期间采取的步的数量。
利用从一个或多个活动监视器130A、心率监视器130B、葡萄糖监视器130C接收的分类数据(图1)，检索(或从葡萄糖监视器130C(图1)接收)对应葡萄糖水平信息的时间，并且葡萄糖响应训练单元112基于例如App中提供的用于在移动电话110上执行的分析工具来执行动态葡萄糖响应模式识别。此外，在某些实施方案中，葡萄糖响应训练单元112被配置为基于来自一个或多个监视器(图1)的实时信息动态地和连续地更新测定的葡萄糖响应模式。
在某些实施方案中，葡萄糖响应模式的准确性随着更长时间段(和/或具有更高分辨率/监测频率)内增加的数据集而改善。然而，个人对输入的葡萄糖响应可能随时间而改变。某些实施方案通过在某个预定时间段过去之后“重置”或清除数据集来解决这个问题。在其它实施方案中，App识别超过设定的数据收集持续时间可能引起葡萄糖响应模式的准确性的误差，在这种情况下，当该时间点已经到达时，App被配置为重置并进入数据收集时段，在此期间葡萄糖响应反馈的用户驱动的分析被禁用了至少监测数据对于分析和测定新的葡萄糖响应模式所需的最少天数或小时数。如下面进一步详细描述的，在某些实施方案中，App被配置成建立“遗忘”窗口，在此期间持续更新葡萄糖响应反馈的用户驱动的分析。在某些实施方案中，“遗忘”窗口包括由App设置的或基于用户输入的一个或多个预定时间段，或者可备选地，基于葡萄糖响应反馈动态地修改。
返回参考图2A，在某些实施方案中，葡萄糖响应训练单元112的输出被提供给数据输出接口113，其可操作地连接到移动电话110的用户界面110A用于显示、输出或以其它方式用通知或提示移动电话110的用户App已完成初始或初步分析，并可操作以分析对输入的葡萄糖响应，例如用户或患者期望识别对应的葡萄糖响应的所采取的步的数量、骑自行车、跑步、远足(hike)、进餐等，以便及时采取行动(矫正性(corrective)或前摄性(proactive))来维持葡萄糖控制和最小化不希望的葡萄糖波动。
图2A显示了根据本公开的一个实施方案的结合图1的分析模块110B执行数据分类、模式识别和动态更新的信息流。参考图2A，在某些实施方案中，执行App的移动电话110的分析模块110B(图1、图2A)被配置为分类(220)接收的输入数据(210)，例如活动类型、强度水平、持续时间、位置、高度信息、葡萄糖水平、心率信息、心率变异性(HRV)信息、氧饱和度水平、出汗水平、温度水平、药物摄入量信息、药物类型、药物施用持续时间、对应于药物施用的当日时间(time of day)信息、碳水化合物摄入量信息、酒精消耗量信息或对应于接收的输入数据的特定监视条件的任何其它相关度量。
使用所接收的信息，在某些实施方案中，葡萄糖响应训练单元112(图2A)执行动态葡萄糖响应模式识别，并在接收到新的或附加数据时更新模式(220)。如下面进一步详细讨论的，在某些实施方案中，在基于所测定的模式输出葡萄糖响应曲线(230)之前，移动电话110中的分析模块110B的葡萄糖响应训练单元112确保已经分析了足够的输入数据。一旦该点已经达到并且已经接收和分析在至少最小持续时间内监测到的信息，则在某些实施方案中，当它确定可能对用户有用的信息时App被配置为生成对用户的通知(例如，作为移动电话110的用户界面110A上的输出提示)。通知可以自动进行，例如警报通知；或者在使用App时由用户检索，例如从菜单访问信息；或者当用户下一次与App交互时显示。有用的信息的实例是用户的葡萄糖水平在他们前一天期间锻炼后过夜通常会20％更低。用户可以使用该信息来确保他们不会经历夜间低葡萄糖，例如在这段时间减少他们的胰岛素覆盖(coverage)，或者通过在睡前吃零食。
在本公开的另一方面中，当它检测到确保输入更多信息的某些情况时，App提示用户输入上下文信息。输入的信息被分析输入数据的例程使用以测定葡萄糖响应模式。App包含检测情况的例程，例如当发生进食或发生活动时，并在检测到这些情况时通知用户。用户通知的实施方案包括图标显示、听觉或文本输出通知或振动通知中的一种或多种，其被配置为提示用户提供关于检测到的状况的更多信息。一个或多个状况的实例包括检测到的运动、检测到的葡萄糖增加或降低的变化率超过或加速超过设定的阈值、检测到的尖峰(spike)或心率变化、出汗或温度水平。可备选地，App可以在用户下次与App或智能手机进行交互时提供通知，而不是报警类型通知。
再次参考图2A，在某些实施方案中，分析模块110B的葡萄糖响应训练单元112被配置为基于葡萄糖度量执行动态葡萄糖响应模式识别，其表征特定用户或患者的特定活动或事件的影响，例如在活动期间和之后发生的特定活动或事件(例如，进餐或药物摄入)的当日特定时间段(for specific time of day period)的影响。不同的葡萄糖度量如平均或中值葡萄糖水平可以用作葡萄糖度量。在某些实施方案中，与平均葡萄糖水平相比，使用中值葡萄糖信息不易受离群值(outlier)葡萄糖数据影响。
在某些实施方案中，葡萄糖响应训练单元112测定例如在特定活动之后的过夜时间期间，例如从下午10点到上午3点，或从上午3点到上午8点，或从下午10点到上午8点，连续监测的葡萄糖水平的中值。在某些实施方案中，葡萄糖响应训练单元112使用在白天时间段(例如，从上午8点至下午10点，从上午8点至下午6点，从上午9点至下午5点，从下午5点至下午10点或任何其它合适的白天时间段范围)测定的中值葡萄糖水平。在某些实施方案中，参照特定活动测定中值葡萄糖信息，使得在活动开始后的一段时间内(活动开始后2小时)特定持续时间(例如，12小时)测定中值葡萄糖水平。在某些实施方案中，用于测定中值葡萄糖水平的相对开始时间和持续时间段根据活动的类型和/或与活动相关的或与用户或患者相关联的其它参数而变化。
尽管所公开的实施方案集中于在夜间影响葡萄糖水平的白天时间期间的活动，但在本公开的范围内，类似的分析适用于由固定的一天中的时间(times-of-day)定义的任何时间段，例如早上的活动(例如，上午5点到下午12点)，影响晚餐后的葡萄糖水平(例如，下午6点到下午10点)。可备选地，在本公开的范围内的本文公开的分析适用于由定期发生的事件定义的时间段。例如，活动数据集从定义每天为上午5点至早餐的时间段生成，其中每天的早餐时间不同，并且由用户输入或生成的指示确定，或者通过处理葡萄糖数据以确定用餐开始的算法确定或通过记录的速效胰岛素输注确定。在WO 2015/153482(2015年3月30日提交的国际申请号PCT/US2015/023380)中公开了算法上检测用餐开始的示例性实施方案，其被转让给本申请的受让人，并且其公开内容通过引用其全部内容并入本文用于所有目的。
此外，影响的时间段可同样被定义为在检测到用餐时开始的时间段，诸如晚餐开始直到午夜。而且，在本公开的范围内，提供了一种混合方法，其中活动时间段被确定为固定的一天时间段，而影响的时间段由特定的用餐开始时间来确定。在本公开的范围内，包括对多个时间段的影响，例如早餐后、午餐后、晚餐后和过夜。而且，分析可以延伸到多天的时间段；例如，测定在第一天的早晨阶段发生的活动如何影响随后一天的葡萄糖水平。
此外，在本公开的范围内，可以使用两种或多种活动类型来分析。非限制性实例要求a)用户进入App的用户界面(UI)(例如，分析模块110B的数据输入界面111(图2A))，与它们进行的活动有关的情境信息，或b)使用一个或多个传感器来区分不同类型的活动，或c)用于区分不同类型的活动的可替代检测技术。对于上述用户输入的信息方法(a)，App被配置为呈现用户界面(例如，如图3所示)以允许用户输入活动信息。在某些实施方案中，用户可以从检查表或自由文本输入输入信息。此外，App被配置为检测测量的活动何时超过预定义的阈值并提示用户输入这个信息。对于使用一个或多个传感器来检测不同活动的方法(方法(b))，可以使用计步器、心率传感器和位置传感器的组合，其中一个或多个阈值和定义的逻辑被配置为识别身体运动、强度、速度和高度变化。最后，对于使用可替代检测技术的方法(方法(c))，例如可以使用位置传感器来检测用户何时在举重健身房，以使测量的活动可以与无氧活动相关联。
当活动类型属性与测量的活动度量相关联时，可以针对每种活动类型执行下面描述的分析。例如，如果使用两种活动类型如有氧和无氧，下面描述的分析可用于测定有氧活动对未来葡萄糖水平的影响，并独立地测定无氧活动对未来葡萄糖水平的影响。在本公开的范围内，可以实现活动和分析时间段的一个或多个组合，例如具有两种活动类型的天(day)指示新的活动类型。
在某些实施方案中，葡萄糖响应训练单元112测定多个白天时间段的葡萄糖中值水平、活动和其它相关参数，并且测定白天时间段之后的相关过夜时间段的中值葡萄糖水平。在某些实施方案中，葡萄糖响应训练单元112测定无活动的天的当天时间段的葡萄糖中值水平。更具体地，在某些实施方案中，葡萄糖响应训练单元112被配置为与用户或患者确认显著的活动(例如，锻炼事件，在一天时间段内(12小时、18小时、24小时或其它合适的时间段)采取的步数量、跑步、骑自行车、徒步等)在没有在无显著活动的这些天中发生。使用在具有显著活动的那些天与没有显著活动的那些天之间隔开的时间段，在某些实施方案中，葡萄糖响应训练单元112分析所接收的输入数据(参见图2A)以表征特定活动对过夜葡萄糖水平的影响以生成动态葡萄糖响应模式-即评估用户或患者身体如何对特定活动作出反应，并且当用户决定参加具有相同或相似参数如持续时间、强度水平等的相同活动时生成或向用户或患者提供适当的治疗推荐。
图3是根据本公开的一个实施方案的数据输入接口111(图2A)的示例性屏幕截图。参考图3，在某些实施方案中，定制的数据输入屏幕被呈现给用户进行信息输入以供App进行分析。在非限制性实例中，将用户界面(例如，执行App的移动电话的用户界面)上的一组单选按钮(radio button)植入(seed with)一个或多个默认活动相关参数如步数、跑步、慢跑、远足、骑自行车、游泳、睡眠和/或食物/饮料相关的参数，例如咖啡、含糖酒精、不含糖酒精、谷物、培根、烤面包等，使用由用户添加随时间作为新的定制答案/反馈或响应而改变的选项。这允许用户快速输入最常见或最常用的活动类型，而不会失去输入其它类型的自定义数据的灵活性。
在本公开的范围内，App为用户或患者提供了多种手段来输入关于膳食和活动的信息。患者可以主动(proactively)输入该信息。这特别适用于其中可以输入进餐的照片的进餐输入。这对于用户或患者来说可能是一种更方便和有趣的方式来进入和查看进餐信息。在与本申请同时提交的标题为“用于膳食信息收集、膳食评估和分析数据相关性的系统、装置和方法(Systems，Devices，and Methods For Meal information Collection，Meal Assessment，and Analyte Data Correlation)”的临时专利申请号______中，可以找到另外的细节[代理人案卷号A0130.0134.P2]。如上所述，在某些实施方案中，App可检测膳食或活动情况(episode)并提示患者更多信息，如通过引用其全部内容并入本文用于所有目的WO 2015/153482中所公开的。
对于使用胰岛素或服用其它葡萄糖改变药物(glucose-altering medication)的用户或患者，App可被配置为自动检索关于这些药物使用的用户/患者特定数据，或允许患者手动输入进入系统。
在本公开的范围内，App被配置为通过提供膳食/活动分析输出来促进实验和理解。在某些实施方案中，输出被呈现为智能电话上或者从服务器检索的网络浏览器上的一个或多个报告。一个或多个报告列出了由葡萄糖漂移(glucose excursion)定义的进餐情况。进餐情况的列表可以通过该情况的日期-时间(date-time)或者通过葡萄糖漂移的严重程度(例如，通过峰值葡萄糖水平测量的)，通过漂移过程中的葡萄糖变化或通过葡萄糖和漂移持续时间定义的面积进行分类。分析输出报告中的每一行都包括与进餐情况相关的信息。在某些实施方案中，报告包括与该进餐情况、日期时间以及一个或多个进餐严重性度量相关联的一个或多个照片或其它文本条目。在某些实施方案中，报告还包括在进餐的一段时间内的任何相关的活动信息。这个列表上太多信息可能太杂乱，不实用。因此，在某些实施方案中，App向用户或患者提供操纵信息的呈现，例如选择行并且呈现具有更详细的信息屏幕的弹出窗口。这种详细的信息屏幕还提供了与进餐情况相关的葡萄糖曲线图。以这种方式，对葡萄糖水平影响最大的膳食可以通过易于查看的呈现突出显示，以提供更好地理解某些食物对其葡萄糖水平的影响，使得用户或患者可以避免或限制不利于他们的健康的食物。
在某些实施方案中，App还被配置成学习食物和活动如何影响未来的葡萄糖水平。当在上述可定制检查表上选择食物和活动时，葡萄糖数据与这些选择相关联，并且多个葡萄糖数据集可以与单个条目类型相关联。而且，多个葡萄糖数据集可以与一个或多个膳食条目类型和一个或多个活动条目类型的组合相关联。可以一种或多种不同的方式处理葡萄糖数据集，以表征该情况对葡萄糖水平的影响。
在某些实施方案中，测定来自所有数据集的中值葡萄糖水平，并将其与所有捕获的葡萄糖数据的时间段的中值进行比较。可备选地，这种方法可以应用于单独的一天时间段，例如早餐前、早餐后、午餐后、晚餐后和就寝后。随着时间的推移，App被配置为以一定程度的置信度估计对于任何给定条目类型或条目类型组合的血糖影响。例如，一个特定的活动类型“骑自行车上山”1小时或更多小时的活动可能与接下来的24小时患者胰岛素敏感性20％增加相关-胰岛素抗性的改变容易与中值葡萄糖的变化相关联。当系统检测到统计学置信水平超过某个预定量时，系统可以进行这种关联。此信息可能会在接下来的24小时内改变用于推注计算器(bolus calculator)中使用的参数。可备选地，App可以检测与骑车相关的活动，并且例如在就寝时提醒患者，以便他们可以在那天晚上吃零食以避免低血糖。
由App提供的另一种类型的输出报告包括活动列表，该活动列表可以在活动之后的时间段(例如，24小时)内通过中值葡萄糖水平进行分类。该列表可以说明哪些活动对未来葡萄糖水平具有最大的影响。此外，另一种类型的报告可以与所述相同的方式呈现食物和活动组合的列表。这些方法可以容易地扩展到其它传感器数据和其它上下文输入，例如疾病、饮酒、咖啡消费等。
图4是显示根据本公开的一个实施方案的确定日间活动对过夜葡萄糖水平的影响的例程的流程图。参考图4，在一个实施方案中，测定日间活动对过夜葡萄糖水平的影响包括生成度量以定义在预定时间段(例如，2周、一个月或任何其它合适的时间段)内没有显著活动的所有天的过夜葡萄糖水平(410)。此后，生成度量以在预定时间段内限定具有显著活动的每一天的过夜葡萄糖水平(420)。在本公开的范围内，测定具有或没有显著活动的天是基于超过定义的阈值的一个或多个活动度量(例如，在24小时的时间段内超过阈值的步的数量)。返回参考图4，在产生度量来定义没有显著活动的所有天的过夜葡萄糖水平，以及多个度量来定义具有显著活动的每一天的过夜葡萄糖水平之后，定义具有显著活动的每一天的过夜葡萄糖水平的多个度量中的每一个使用没有显著活动的所有天的度量来修改(430)。然后，测定具有显著活动的天的每个修改的度量与没有显著活动的所有天的度量之间的相关性(440)，并且此后，给定活动水平，测定对活动水平的过夜葡萄糖水平的影响并基于所测定的相关性呈现给用户(450)。
图5是显示根据本公开的一个实施方案的确定日间活动对过夜葡萄糖水平的影响的另一例程的流程图。参考图5，在一个实施方案中，测定日间活动对过夜葡萄糖水平的影响包括生成度量以定义在预定时间段(例如，2周、一个月或其它合适的时间段)内没有显著活动的所有天的昼夜的葡萄糖水平变化(510)。此后，生成多个度量以定义具有显著活动的每个相应天的昼夜的葡萄糖水平变化(520)。对于具有显著活动的每天的昼夜的葡萄糖水平变化的度量和没有显著活动的所有天的昼夜的葡萄糖水平变化的度量，使用没有显著活动的天的昼夜的葡萄糖水平变化的度量修改定义具有显著活动的天的昼夜的葡萄糖水平变化的每天度量(530)。然后，测定具有显著活动的天的每个修改的度量与没有显著活动的所有天的度量之间的相关性关系(540)。利用测定的相关性，对于给定的活动水平，测定基于所测定的相关性的活动水平对过夜葡萄糖水平的影响并将其呈现给用户(550)。
图6是显示根据本公开的一个实施方案的基于绝对过夜葡萄糖水平的特定活动的葡萄糖响应模式识别和表征的流程图。参考图6，基于从活动监视器130A、心率监视器130B、葡萄糖监视器130C中的一个或多个接收的输入数据，分析模块110B(图2A)的葡萄糖响应训练单元112测定是否已经经由数据输入接口111(图2A)接收到足够量的数据。在某些实施方案中，足以执行葡萄糖响应模式和表征分析的数据量是基于在具有显著活动的预定天数以及没有显著活动的预定天数(统称为“X”)内接收的数据。在某些实施方案中，基于活动持续时间、活动持续期间燃烧的卡路里、活动强度水平、活动是有氧活动还是无氧活动、或活动类型(例如，竞争性活动或非竞争性训练活动)中的一种或多种来测定特定活动是否适合作为显著活动。例如，在某些实施方案中葡萄糖响应训练单元112测定来自一个或多个活动监视器130A、心率监视器130B、葡萄糖监视器130C(图1)的输入数据1)具有显著活动的3天，和没有显著活动的3天提供足够数量的数据用于分析。
在可替代实施方案中，数据充足性的测定是基于估计的血糖模式的确定程度，而不是数据的预定天数或数据量。
参考图6，使用测定分析所需的输入数据的天数(610)，葡萄糖响应训练单元112(图2A)测定针对测定的没有显著活动的天数(Gwo)所有过夜葡萄糖中值水平的中值葡萄糖水平(620)。在某些实施方案中，没有显著活动的天数(Gwo)被定义为在此期间活动测量值低于预定义阈值的天数，例如在预定的当日时间期间(12小时、18小时或其它合适的时间段)10,000步。在某些实施方案中，无显著活动的天数(Gwo)的所有过夜葡萄糖中值水平的中值葡萄糖水平根据活动类型而变化。
此后，如图6所示，对于具有显著活动的每一天(X天)，测定δ中值葡萄糖水平(Gδ(X天))(630)，其中δ中值葡萄糖水平(Gδ(X天))是具有显著活动的特定天G(X天)的过夜葡萄糖中值和对于没有显著活动的测定的天数(Gwo)的所有过夜葡萄糖中值水平的中值葡萄糖水平之间的差异。即：
(Gδ(X天))＝G(X天)-(Gwo)
在某些实施方案中，同时测定没有显著活动(Gwo)的测定的天数的所有过夜葡萄糖中值水平的中值葡萄糖水平(620)和每天δ中值葡萄糖水平(Gδ(X天))(630)。换句话说，步骤620和630可以串联执行，或相对于彼此并联执行。
还参考图6，测定具有显著活动的天(X天)的中值葡萄糖水平(Gδ(X天))和那天的活动度量(Act(X天))之间的相关性关系(640)，并且将相关性拟合为预定函数(650)。在某些实施方案中，相关性关系包括线性函数，其中具有显著活动(Gδ(X天))的天的δ中值葡萄糖水平是活动度量(Act(X天))的线性函数。在本公开的范围内，相关性关系包括具有显著活动(Gδ(X天))的天的δ中值葡萄糖水平与活动度量(Act(X天))之间的恒定偏移关系(constant offset relationship)、指数关系、对数关系或多项式关系。
在某些实施方案中，活动度量(Act(X天))对于用户或患者参与的特定活动是预先确定的，并且是基于例如由分析模块110B的葡萄糖响应训练单元112(图2A)执行的输入数据分类220(图2B)。在某些实施方案中，活动度量(Act(X天))根据与活动相关联的一个或多个参数而变化，包括例如活动持续时间、强度水平、活动类型、与活动相关联的心率数据等。在某些实施方案中，活动度量(Act(X天))包括诸如每小时步数的“步进速率”，或者在预定的或固定的持续时间内的步数。
在某些实施方案中，应用最小二乘技术来将相关性关系拟合成数据集。例如，可以将最小二乘法应用于数据集以测定线性关系的斜率和偏移，所述线性关系定义了具有显著活动的天的δ中值葡萄糖水平(Gδ(X天))和活动度量(Act(X天))之间的相关性。在某些实施方案中，随后由App应用线性关系来预测或预期显著锻炼对过夜葡萄糖水平的影响。换句话说，通过已知的或测定的活动度量(Act(X天))，App通过将活动度量(Act(X天))乘以线性相关性关系的斜率并加上偏移量(其中斜率和偏移量是例如通过最佳拟合分析测定的参数)来估计具有显著活动的天的所得δ中值葡萄糖水平。在某些实施方案中，最佳拟合分析随着从活动监视器130A、心率监视器130B、葡萄糖监视器130C(图1)收集或接收的数据集的每个修订或添加而更新。可备选地，在某些实施方案中，在数据集收集的预定时间段之后更新最佳拟合分析。
在某些实施方案中，测定具有显著活动的每天测定的一组比例(R)。这些比例被计算为具有显著活动的天的δ中值葡萄糖水平(Gδ(X天))除以活动度量(Act(X天))。然后，计算该组比例的中值或平均值。然后，通过将该组比例(R)的中值乘以当前活动度量(Act(X天))来测定活动的影响。可备选地，在本公开的范围内，例如使用最小二乘技术来将该组比例(R's)拟合成最小二乘拟合线来应用曲线拟合方法。
返回参考图6，在某些实施方案中，分析所需的天数(610)可以通过相关性的质量来测定(650)。例如，在某些实施方案中，线性线拟合分析提供指示这样的线拟合质量的度量(例如，相关系数R2或具有显著活动的天的δ中值葡萄糖水平(Gδ(X天)的标准误差估计)。在某些实施方案中，如果线拟合质量度量超过特定值，例如(但不限于)，当相关系数R2值大于0.9或对于线性拟合具有显著活动的天的δ中值葡萄糖水平(Gδ(X天)的标准误差小于10％时，该数据集被确定为是足够的(610)。如果线性拟合被确定为无效，则在某些实施方案中，App被配置为继续分析数据集(即，继续训练)，并且每一天线性拟合都被更新以测定其是否有效。当线性拟合被确定为有效时，则在某些实施方案中，分析结果例如在分析模块110B的数据输出接口113(图2A)处被呈现给用户。
作为非限制性实例，下表1示出了根据本公开的某些实施方案使用采取的步数作为活动针对葡萄糖响应模式识别和表征收集的数据集。
表1.活动与无活动数据的14天
从上表1可以看出，在两周时间内，有6天具有活动(确定为超过阈值水平的步数-例如在24小时内采取10000步)，包括第1天、第4天、第5天，第6天，第9天和第13天。还可以看出，在两周时间内，有8天没有活动(确定为低于阈值水平(在24小时内10000步)的步数)，包括第2天、第3天、第7天、第8天，第10天，第11天和第12天。
给定14天中的每一天的日间中值葡萄糖水平以及14天中的每一天的相应的过夜中值葡萄糖水平，对于没有显著活动的天(Gwo)，所有过夜中值葡萄糖水平的中值葡萄糖水平是通过采取来自表1的第2天、第3天、第7天、第8天，第10天，第11天和第12天的过夜中值葡萄糖水平的中值测定，其为143.5mg/dL。此外，对于具有活动的每一天(例如，第1天、第4天、第5天，第6天，第9天和第13天)，通过从相应的过夜中值葡萄糖水平(G(X天))减去测定为143.5mg/dL的没有显著活动的天(Gwo)的所有过夜中值葡萄糖水平的中值葡萄糖水平来测定δ中值葡萄糖(Gδ(X天))。例如，对于第1天(活动)，δ中值葡萄糖(Gδ(第1天))为117mg/dL减去143.5mg/dL(没有显著活动的天(Gwo)的所有过夜中值葡萄糖水平的中值葡萄糖水平)结果是δ中值葡萄糖(Gδ(第1天))为-26.5。类似地，对于第4天(活动)，δ中值葡萄糖(Gδ(第4天))为-18.5(125mg/dL减去143.5mg/dL)。对于第5天(活动)，δ中值葡萄糖(Gδ(第5天))为-32.5(111mg/dL减去143.5mg/dL)。对于第6天(活动)，δ中值葡萄糖(Gδ(第6天))为-23.5(120mg/dL减去143.5mg/dL)。对于第9天(活动)，δ中值葡萄糖(Gδ(第9天))为-12.5(131mg/dL减去143.5mg/dL)。最后，对于第13天(活动)，δ中值葡萄糖(Gδ(第13天))为-38.5(105mg/dL减去143.5mg/dL)。
使用如上所述测定的具有活动的每天的δ中值葡萄糖(Gδ(X天))，通过将测定的δ中值葡萄糖(Gδ(X天))除以活动度量(Act(X天))测定具有活动的每天的相应R值(对于具有活动的相应天)。例如，第1天的R值为-0.002(-26.5除以12,503步(第1天的活动度量)。以这种方式，测定具有活动的天的R值，并且如下表2所示所得值(具有相应的δ中值葡萄糖水平(Gδ(X天))。
表2
基于如上表2中所示测定的数据集，在具有活动的天相对于相应R值(如图11中所示)上进行线性拟合分析：
可备选地，R值的中值或平均值可用于表示血糖模式。此外，可以在δ中值葡萄糖(Gδ(X天))相对于活动水平(步数)上进行线性拟合分析，并且如图12所示：
其中可以看出相关系数R2是0.9125，证明了可接受的相关性，并且其中线性拟合分析提供了具有-0.0026的斜率的10.811的偏移。这条线表示血糖模式。
使用图12，当用户决定执行将导致15,000步的特定活动时，从线性拟合分析可以看出，这样的活动将导致葡萄糖水平降低约28mg/dL。使用该信息，如果用户期望保持更严格的血糖控制，并且知道执行15,000步可以将葡萄糖水平降低约28mg/dL，则用户可以采取主动(proactive)行动来抵消活动的影响(例如，15,000步)，例如通过在参与活动之前或期间消费更多的食物和/或饮料。
在替代实施方案中，活动度量被转换成两个值：显著活动或不显著活动。在这种情况下，过夜葡萄糖中值水平与显著活动的天或没有显著活动的天相关联，其中显著活动被定义为当活动测量超过预定义的阈值(例如，超过10,000步的步数的那天)时。更具体地，参照表1，将与显著活动的天相关的所有过夜时间段的中值葡萄糖测定为(第1天、第4天、第5天、第6天、第9天、和第13天)118.5mg/dL，以及与非显著活动(第2天、第3天、第7天、第8天、第10天、第11天、第12天、和第14天)相关的所有过夜时间段的中值葡萄糖水平为143.5mg/dL。然后，通过从118.5mg/dL(与显著活动的天相关的所有过夜时间段的中值葡萄糖)减去143.5mg/dL(作为与非显著活动相关的所有过夜时间段的中值葡萄糖水平)来测定中值活动的降低，其导致-25mg/dL。然后，百分比中值降低为17.42％(-25mg/dL除以143.5mg/dL)。在这种方法中，通过使用用于测定两个不同群体的平均值是否不同的标准统计测试可以测定是否已经收集了数据集的足够天数。例如，通过确认每个中值过夜葡萄糖测定(具有和不具有活动)的标准偏差低于预定阈值，例如20mg/dL。参照表1，具有显著活动的天(第1天、第4天、第5天、第6天、第9天、和第13天)的标准偏差为8.864mg/dL，而没有显著活动的天(第2天、第3天、第7天、第8天、第10天、第11天、第12天、和第14天)的标准偏差为7.08mg/dL。
再次参考图6，使用上述的葡萄糖响应模式识别和表征，在某些实施方案中，App被配置为当检测到随后的显著活动时向用户输出：“与没有显著活动的天相比，对于具有显著活动的天，过夜葡萄糖水平倾向于25mg/dL更低”。可备选地，该结果可能以百分比形式显示，对于此实例，17％更低。在本公开的范围内，上述技术可以扩展到诸如三个或四个级别的任何级别的量化。
在某些实施方案中，使用上述例程结合图6，分析模块110B(图2A)的葡萄糖响应训练单元112使用特定参数识别对特定活动的一致葡萄糖响应。然后，用户或患者利用该信息来修改或调整治疗方案、消耗的进食或者活动类型在给定基础生理状态的情况下进行，以维持严格的血糖控制并改善健康状况。
图7是显示根据本公开的一个实施方案的基于昼夜葡萄糖水平变化的特定活动的葡萄糖响应模式识别和表征的流程图。参考图7，类似于图5的步骤510，基于从活动监视器130A、心率监视器130B、葡萄糖监视器130C中的一个或多个接收的输入数据，分析模块110B(图2A)的葡萄糖响应训练单元112测定是否已经经由数据输入接口111(图2A)接收到足够量的数据(710)。然后，分析模块110B的葡萄糖响应训练单元112测定对于没有显著活动的天(确定为提供足够量的数据的天数)的葡萄糖中值(Gd2n(X天))的所有昼夜变化的中值(Gwo(δ))(720)。
更具体地，通过从第二预定当天时间段(例如，从上午10点到下午6点)(G夜间(X天))内的中值葡萄糖水平减去第一预定当天时间段(例如，从上午8点到下午10点)(G日间(X天))内的中值葡萄糖水平来测定没有显著活动的葡萄糖中值的每个昼夜变化(Gd2n(X天))(720)。即：
(Gd2n(X天))＝G夜间(X天)-G日间(X天)
在本公开的范围内，可以改变第一和第二预定当天时间段的时间段和范围，使得一个比另一个长，或者可备选地，两个时段是相同的长度。在某些实施方案中，基于特定事件如进餐事件或与患者相关的其它指标来测定每一天的第一和第二预定时间段。
返回参考图7，使用测定的对于没有显著活动的天的中值葡萄糖的所有昼夜变化的中值(Gwo(δ))(720)，在某些实施方案中葡萄糖响应训练单元112通过从没有显著活动的葡萄糖中值的昼夜的变化(Gd2n(X天))中减去没有显著活动的天的葡萄糖中值的所有昼夜变化的中值(Gwo(δ))测定δ中值葡萄糖水平(Gδ(X天))(730)。在某些实施方案中，同时地而不是顺序地测定对于没有显著活动的天的中值葡萄糖的所有昼夜变化的中值(Gwo(δ))(720)和对于具有显著活动的每天的δ中值葡萄糖水平(Gδ(X天))(730)的测定。在替代实施方案中，可以在对于没有显著活动的天的中值葡萄糖的所有昼夜变化的中值(Gwo(δ))(720)之间测定具有显著活动(730)的每一天的δ中值葡萄糖水平(Gδ(X天))。
此后，测定对于具有显著活动的每天(X天)(740)的δ中值葡萄糖(Gδ(X天))和活动度量(Act(X天))之间的相关性关系。类似于结合图6执行的例程，在某些实施方案中，活动度量(Act(X天))对于用户或患者参与的特定活动是预先确定的，并且因此可以基于由分析模块110B(图2A)的葡萄糖响应训练单元112执行的输入数据分类(图2B)。类似地，在某些实施方案中，活动度量(Act(X天))根据与活动相关联的一个或多个参数而变化，包括例如活动持续时间、强度水平、活动类型、与活动相关联的心率数据。
再次，类似于结合图6执行的例程，参考图7，一旦测定具有显著活动的天(X天)的δ中值葡萄糖水平(Gδ(X天))和该天的活动度量(Act(X天))之间的相关性关系(740)，相关关系(例如，其中具有显著活动的天的δ中值葡萄糖水平(Gδ(X天))被表示为活动度量(Act(X天))的线性函数)被用于生成具有显著活动的天的下一个过夜时间段的具有显著活动的天的δ中值葡萄糖水平(Gδ(X天))的估值，并将分析结果显示给用户。即，将相关性拟合为预定函数(750)，并且将所得的关系输出给用户。
例如，参照表1中所示的数据集，没有显著活动的天的葡萄糖中值的所有昼夜变化的中值(Gwo(δ))为-1.5。这来源于测定在没有显著活动情况下的葡萄糖中值的所有昼夜变化的中值(Gd2n(X天))。即，从表1可以看出，对于没有显著活动的每一天(第2天、第3天、第7天、第8天、第10天、第11天、第12天、和第14天)，通过从过夜葡萄糖水平中减去日间中值葡萄糖水平来测定葡萄糖中值的中值昼夜变化(Gd2n(X天))。例如，第2天的葡萄糖中值的昼夜变化的中值(Gd2n(第2天))为-14mg/dL(142mg/dL-156mg/dL)。第3天的葡萄糖中值的昼夜变化的中值(Gd2n(第3天))为8mg/dL(150mg/dL-142mg/dL)。第7天的葡萄糖中值的昼夜变化的中值(Gd2n(第7天))为17mg/dL(160mg/dL-143mg/dL)。第8天的葡萄糖中值的昼夜变化的中值(Gd2n(第8天))为6mg/dL(151mg/dL-145mg/dL)。第10天的葡萄糖中值的昼夜变化的中值(Gd2n(第10天))为1mg/dL(140mg/dL-139mg/dL)。第11天的葡萄糖中值的昼夜变化的中值(Gd2n(第11天))为-22mg/dL(139mg/dL-161mg/dL)。第12天的葡萄糖中值的昼夜变化的中值(Gd2n(第12天))为-11mg/dL(144mg/dL-155mg/dL)。最后，第14天的葡萄糖中值的昼夜变化的中值(Gd2n(第14天))为-4mg/dL(143mg/dL-147mg/dL)。这在下表3中示出。
表3
对于没有显著活动的天的葡萄糖中值的所有昼夜变化的中值(Gwo(δ))确定为-1.5的情况下，对于具有显著活动的每一天，可以通过将每天葡萄糖中值的中值昼夜变化减去没有显著活动的天的葡萄糖中值的所有昼夜变化的中值(Gwo(δ))来测定δ中值葡萄糖(Gδ(X天))。这在下表4中示出。
表4
从表4可以看出，对于具有显著活动的每一天，相应的R值通过将所测定的δ中值葡萄糖(Gδ(X天))除以具有活动的相应天的活动度量(Act(X天))来测定。
此外，在某些实施方案中，不是线性函数，生成对于具有显著活动的每天测定的一组比例(R)。比例R通过将具有显著活动的每一天的δ中值葡萄糖(Gδ(X天))除以相应的活动度量(Act(X天))来测定。然后，测定该组比例R的中值或平均值(在这种情况下，具有显著活动的天的R值的中值是-0.00199553198802936)。然后，可以通过将中值R乘以当前活动度量(Act(X天))来测定活动的效果。可备选地，可以使用例如最小二乘法来应用曲线拟合技术，以将该组比例(R's)拟合成一条线。
图13显示了相对于具有活动的天绘制的R值。
可备选地，R值的中值或平均值可用于表示血糖模式。此外，可以相对于活动度量(Act(X天))绘制δ中值葡萄糖(Gδ(X天))并进行线性拟合分析，得到如图14所示的曲线。
根据图14所示的线性拟合分析，相关系数R2为约0.86，线性拟合的偏移为2.687，且斜率为-0.0022。通过图14所示的分析，期望参与包括15,000步的活动的用户可以从图14中确定这样的活动将导致葡萄糖水平降低约30mg/dL。可备选地，App包括通过将当天的活动输入线性方程来估计即将到来的过夜Gδ(X天)的例程。然后，用户可以决定采取适当的行动(在活动期间或活动前消耗额外的食物/饮料)以更好地控制由活动引起的预期葡萄糖水平下降。
在替代实施方案中，活动度量(Act(X天))可被分类成两个值：显著活动或不显著活动。在这种情况下，过夜葡萄糖中值与显著活动的天或没有显著活动的天相关联，其中如果活动测量超过预定义的阈值(例如，对于一天的时间段大于10,000步)则测定显著活动。测定与具有显著活动的天相关的所有过夜时间段的中值葡萄糖水平的中值昼夜变化(Gd2n(X天))，以及测定与没有显著活动相关的所有过夜时间段的中值葡萄糖水平的中值昼夜变化(Gd2n(X天))，然后测定中值活动的下降。在某些实施方案中，使用统计技术测定数据充分性；例如通过验证每个中值计算的标准误差低于预定的阈值，例如20mg/dL。
例如，与具有显著活动的天相关的所有过夜时间段的中值葡萄糖水平的中值昼夜变化(Gd2n(X天))测定为-28.5mg/dL(采取第1天、第4天、第5天、第6天、第9天、和第13天的中值葡萄糖水平的昼夜变化的中值，其分别为-26、-25、-35、-31、-18和-39)，而与没有显著活动相关的所有过夜时间段的中值葡萄糖水平的中值昼夜变化(Gd2n(X天))测定为-1.5mg/dL(采取第2天、第3天、第7天、第8天、第10天、第11天、第12天和第14天的中值葡萄糖水平的昼夜变化的中值，其分别为-14、8、17、6、1、-22、-11和-4)。由此，葡萄糖水平的中值降低可测定为-27mg/dL(从-28.5mg/dL减去-1.5mg/dL)。
在这种情况下，当如下检测后续的显著活动时，由App向用户显示分析结果：“与没有显著活动的天相比，对于具有显著活动的天，葡萄糖水平倾向于27mg/dL更低”。在本公开的范围内，分析可以扩展到诸如三个或四个级别的任何级别的量化。
图8是显示根据本公开的一个实施方案的基于昼夜葡萄糖水平比例的特定活动的葡萄糖响应模式识别和表征的流程图。参考图8，与图8所示的例程相比，结合图7的由分析模块110B(图2A)的葡萄糖响应训练单元112执行的例程之间的差别在于代替使用没有显著活动(在图7的步骤720的天的葡萄糖中值水平的所有昼夜变化(Gd2n(X天))的中值(Gwo(δ))，图8中的例程在确定分析所需数据的天数(810)之后，测定没有显著活动的天(820)的葡萄糖中值水平(Gd2nr(X天))的所有昼夜比例的中值Gwod2nr。在某些实施方案中，通过第二预定的一天的时间段(例如，从下午10点到上午6点)内的中值葡萄糖水平(G夜间(X天))除以第一预定的一天的时间段(例如，从上午8点到下午10点)内的中值葡萄糖水平(G日间(X天))来测定没有显著活动的天的葡萄糖中值水平(Gd2nr(X天))的昼夜比例。即：
(Gd2nr(X天))＝G夜间(X天)/G日间(X天)
返回参考图8，测定没有显著活动的天的葡萄糖中值水平(Gd2nr(X天))的所有昼夜比例的中值(Gwo(δ))。然后，分析模块110B的葡萄糖响应训练单元112针对具有显著活动的每天通过具有显著活动(830)的每天的昼夜的比例(Gd2nr(X天))中的每一个减去没有显著活动的天的葡萄糖中值水平的所有昼夜比例的中值(Gwo(δ))测定δ中值葡萄糖(Gδ(X天))。在某些实施方案中，在测定分析所需的数据的天数(810)之后，同时地而不是依次地测定没有显著活动的天的葡萄糖中值(Gd2nr(X天))的所有昼夜比例的中值(Gwo(δ))(820)和具有显著活动的每天的δ中值葡萄糖(Gδ(X天))(830)。
再次参考图8，与图7步骤740类似，测定每一个的δ中值葡萄糖(Gδ(X天))和活动度量(Act(X天))之间的相关性关系(840)。这种相关性关系表明在显著活动之后，过夜的昼夜葡萄糖水平的比例的成比例下降。将具有显著活动的天的δ中值葡萄糖(Gδ(X天))与活动度量(Act(X天))的相关性拟合成预定函数(850)，并将所得的相关性信息输出给用户。
再次参考上面表1中所示的数据集，基于如下表5所示的没有显著活动的天的葡萄糖中值水平的昼夜比例的中值，结合图8所描述的分析导致葡萄糖中值水平的所有昼夜比例的中值Gwod2nr为0.989991680125287：
表5
然后，如下表6所示，可以通过具有显著活动的每天的葡萄糖中值(Gactd2nr(X天))的昼夜比例除以葡萄糖中值水平的每个昼夜比例的中值Gwod2nr(0.989991680125287)来测定具有显著活动的每天的中值水平葡萄糖的比例(Gactd2nr(X天))。
表6
从表6可以测定具有显著活动的天的中值葡萄糖比例(Gactd2nr(X天))的中值为0.814595。可备选地，可以通过相对于具有显著活动的天的活动度量(Act)绘制中值葡萄糖比例(Gactd2nr(X天))(如图15中所示)进行线性拟合分析。
可以看出，图15中的相关系数R2为约0.89，偏移为约1.03，且斜率为-0.00002(2E-05)。
图9显示了根据本公开的一个实施方案的用于训练和通知的过程流程。参考图9，在某些实施方案中，例如结合上述图4-图8描述的数据分析训练是以预定时间间隔(例如，每天一次)对接收到的输入数据集执行的。每次执行例程时，已经获取的新数据集被添加到所保持的数据集并用于数据分析训练，例如以测定活动与未来葡萄糖水平(例如，过夜葡萄糖水平)之间的相关性关系。
返回参考图9，除了向训练数据集添加新的数据集(910)之外，每次执行数据分析训练例程时，从训练集中去除较旧的数据(920)，诸如90天或更旧的或180天或更旧的或任何其它合适的时间段的数据。这允许数据分析训练例程适应从其中导出数据集的用户的变化的生理学(“遗忘”)。在某些实施方案中，“遗忘”子例程(subroutine)可被排除或可选。当已经包括数据分析训练过程(930)时，结合图4-图8如上所述检查训练充分性(940)，使得例如与相关性关系“拟合”的相关联的不确定性度量小于预定阈值。如果测定该训练是足够的(940)，则生成并输出结果的通知(950)。然而，如果测定训练不足，则不会生成或输出通知。可备选地，在某些实施方案中，当App测定训练不够时，不提供通知，而是可以提供指示训练仍不够的通知。
图10显示了根据本公开的另一个实施方案的用于训练和通知的过程流程。如图10所示，数据分析训练和通知例程类似于图9中所示和描述的例程，其中“遗忘”特征被重置(1010)或清除(1020)训练数据集代替。参考图10，在某些实施方案中，响应于例如在App的用户界面上的输入按钮的致动(actuation)来实现例程的开始重置(1010)和清除(1020)训练数据集以重置训练例程。在某些实施方案中，用户启动例程的重置(1010)和训练数据集清除(1020)以便通过App更新活动和未来葡萄糖水平之间的学习的相关性关系。
参考图10，当启动重置时，此后定期调用(invoke)数据训练和通知例程，并且类似于图9所示的例程，新数据集被添加到训练数据集(1030)，并且在训练过程完成之后(1040)，确定训练是否足够(1050)。当确定训练足够时，在某些实施方案中App产生并向用户输出通知(1060)。当确定训练不足时，则不向用户呈现通知，或者可备选地，由App生成指示训练不足的通知并呈现给用户。
在本公开的范围内，考虑了结合图9和图10描述的对数据集训练和通知例程的修改，其中重置(1010)/清除(1020)训练数据集(图10)特征和“遗忘”特征(图9)包含在相同分析例程中。而且，在某些实施方案中，定期发生重置，例如每年一次。可备选地，在某些实施方案中，在训练已经提供有效通知之后(即，当确定训练足够时)发生重置。
以所描述的方式，根据本公开的实施方案，为1型糖尿病患者、2型糖尿病患者以及糖尿病前期患者提供工具以监测生理状况，同时参与日常例程，并且随着时间推移，例如可在用户或患者的移动电话上执行的App对可能影响用户或患者的葡萄糖水平波动的各种类型的活动和参数提供一致的葡萄糖响应。这样的工具将允许用户或患者修改饮食、锻炼例程或知道特定饮食、锻炼或活动如何影响葡萄糖水平波动的其它日常活动，并主动采取行动以维持期望的血糖控制并避免有害的血糖漂移。
本公开的实施方案包括数据收集的方面，包括检测特定活动并提示用户或患者输入与检测到的活动有关的附加信息，以便使数据收集更稳健。例如，使用活动监视器130A，当在移动电话110上执行的App检测到预定时间段内连续运动，在某些实施方案中，App被配置为生成并向用户界面110A输出询问(query)以提示用户或患者确认检测到的活动正在发生，和/或添加与检测到的活动相关的附加信息(在某些实施方案中，其可以在检测到活动的终止时生成并输出到用户界面110A)。
以这种方式，根据本公开的实施方案，提供了稳健的生理参数监测系统和动态葡萄糖响应模式以向生理或其它参数和活动提供一致的且可靠的葡萄糖响应。
在不脱离本公开的实施方案的范围和精神的情况下，本公开的结构和操作方法中的各种其它修改和改变对于本领域技术人员来说将是显而易见的。尽管已经结合特定实施方案描述了本公开，但应当理解，所要求保护的本公开不应该不适当地限于这样的特定实施方案。意图是随附权利要求限定本公开的范围，并且由此涵盖这些权利要求及其等同物范围内的结构和方法。

Claims (101)

1.一种识别葡萄糖响应模式的方法，包括：
在预定时间段内接收活动度量信息和葡萄糖水平信息，所述预定时间段包括多个天；
将具有显著活动度量的所述预定时间段内的天的过夜葡萄糖水平信息分类为第一时间段，并且将没有显著活动度量的所述预定时间段内的天的过夜葡萄糖水平信息分类为第二时间段；
确定所述第一时间段内的天的所有过夜葡萄糖水平的中值葡萄糖G(X天)；
确定所述第二时间段内的天的所有过夜葡萄糖水平的中值葡萄糖Gwo；
通过从所述中值葡萄糖G(X天)减去所述中值葡萄糖Gwo而确定所述第一时间段内每天的δ中值葡萄糖水平Gδ(X天)；
确定每天所确定的δ中值葡萄糖水平与对应于所述第一时间段内该天的活动度量信息的活动度量之间的相关性；
将所确定的相关性拟合成预定函数；和
对于给定的活动水平，基于所确定的相关性测定活动水平对过夜葡萄糖水平的影响。
2.根据权利要求1所述的方法，其中所述第一时间段内的天包括具有活动度量超过预定阈值的天，并且进一步地，其中所述第二时间段内的天包括具有活动度量低于所述预定阈值的天。
3.根据权利要求2所述的方法，其中所述活动度量包括在24小时时间段期间燃烧的卡路里的量。
4.根据权利要求2所述的方法，其中所述活动度量包括在24小时时间段期间记录的步数。
5.根据权利要求2所述的方法，其中所述活动度量包括以下一种或多种：活动的持续时间、活动的强度水平、与活动相关的心率数据、或活动类型。
6.根据权利要求1所述的方法，其中确定相关性包括识别每个确定的δ中值葡萄糖与相应活动度量之间的关联。
7.根据权利要求1所述的方法，进一步包括在用户界面上输出与测定的影响相关联的信息。
8.根据权利要求7所述的方法，其中输出的信息包括血糖模式。
9.根据权利要求1所述的方法，其中接收葡萄糖水平信息包括用葡萄糖传感器生成对应于监测的葡萄糖水平的信号。
10.根据权利要求9所述的方法，其中所述葡萄糖传感器生成对应于来自真皮流体的监测的葡萄糖水平的信号。
11.根据权利要求9所述的方法，其中所述葡萄糖传感器生成对应于来自间质液的监测的葡萄糖水平的信号。
12.根据权利要求9所述的方法，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的分析物响应酶。
13.根据权利要求12所述的方法，其中所述分析物响应酶与设置在所述工作电极上的聚合物化学结合。
14.根据权利要求12所述的方法，其中所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
15.根据权利要求14所述的方法，其中所述介体与设置在所述工作电极上的聚合物交联。
16.根据权利要求9所述的方法，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
17.根据权利要求1所述的方法，其中所述预定函数包括以下之一：线性函数、恒定偏移关系、指数关系、对数关系或多项式关系。
18.根据权利要求1所述的方法，进一步包括确定基于所述第一时间段和所述第二时间段的分析所需的数据的多个天。
19.根据权利要求18所述的方法，其中确定分析所需的数据的多个天包括确定相关性的质量。
20.根据权利要求1所述的方法，进一步包括：
在预定时间段执行数据分析训练例程，所述例程包括：
将新数据集添加至训练数据集；
进行数据分析训练过程；
检查训练过程的充分性；和
产生和输出对应于训练过程充分性的通知。
21.根据权利要求20所述的方法，进一步包括去除比预定时间段更旧的数据集。
22.根据权利要求21所述的方法，其中所述预定时间段为90天或更多。
23.根据权利要求21所述的方法，其中所述预定时间段为180天或更多。
24.根据权利要求20所述的方法，进一步包括启动训练例程的重置和清除训练数据集。
25.根据权利要求20所述的方法，其中所述预定时间段为每日一次。
26.一种用于识别葡萄糖响应模式的装置，包括：
数据输入模块，用于接收预定时间段内的活动度量信息和葡萄糖水平信息，所述预定时间段包括第一时间段和第二时间段；
数据分析模块，可操作地连接至所述数据输入模块，并且配置成：
将具有显著活动度量的预定时间段内的天的过夜葡萄糖水平信息分类为第一时间段，并且将不具有显著活动度量的预定时间段内的天的过夜葡萄糖水平信息分类为第二时间段；
确定所述第一时间段内的天的所有过夜葡萄糖水平的中值葡萄糖G(X天)；
确定所述第二时间段内的天的全部过夜葡萄糖水平的中值葡萄糖Gwo；
通过从所述中值葡萄糖G(X天)减去所述中值葡萄糖Gwo而确定所述第一时间段内每天的δ中值葡萄糖水平Gδ(X天)；
确定每天所确定的δ中值葡萄糖水平Gδ(X天)与对应于所述第一时间段内该天的活动度量信息的活动度量之间的相关性；
将所确定的相关性拟合成预定函数；
对于给定的活动水平，基于所确定的相关性测定活动水平对过夜葡萄糖水平的影响；和
数据输出接口，可操作地连接至数据分析模块以输出与所确定的影响相关的信息。
27.根据权利要求26所述的装置，其中所述第一时间段内的天包括具有活动度量超过预定阈值的天，并且进一步地，其中所述第二时间段内的天包括具有活动度量低于所述预定阈值的天。
28.根据权利要求27所述的装置，其中所述活动度量包括在24小时时间段期间燃烧的卡路里的量。
29.根据权利要求27所述的装置，其中所述活动度量包括在24小时时间段期间记录的步数。
30.根据权利要求27所述的装置，其中所述活动度量包括以下一种或多种：活动持续时间、活动的强度水平、与活动相关的心率数据、或活动类型。
31.根据权利要求26所述的装置，其中分析所需的数据的多个天是基于在所述预定时间段内接收的活性度量信息和葡萄糖水平信息。
32.根据权利要求26所述的装置，其中输出的信息包括血糖模式。
33.根据权利要求26所述的装置，其中从定位与体液流体接触的葡萄糖传感器接收所述葡萄糖水平信息以生成对应于所述葡萄糖水平信息的信号。
34.根据权利要求33所述的装置，其中所述体液包括真皮流体。
35.根据权利要求33所述的装置，其中所述体液包括间质液。
36.根据权利要求33所述的装置，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的分析物响应酶。
37.根据权利要求36所述的装置，其中所述分析物响应酶与设置在所述工作电极上的聚合物化学结合。
38.根据权利要求36所述的装置，其中所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
39.根据权利要求38所述的装置，其中所述介体与设置在所述工作电极上的聚合物交联。
40.根据权利要求33所述的装置，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
41.根据权利要求26所述的装置，其中所述数据输出接口包括以下一种或多种的用户接口：移动电话、平板计算装置、服务器、膝上型计算机、或包括智能手表的可穿戴装置。
42.根据权利要求26所述的装置，其中所述预定函数包括以下之一：线性函数、恒定偏移关系、指数关系、对数关系或多项式关系。
43.根据权利要求26所述的装置，其中所述数据分析模块进一步配置成确定基于所述第一时间段和所述第二时间段的分析所需的数据的多个天。
44.根据权利要求43所述的装置，其中所述数据分析模块配置成确定分析所需的数据的多个天，确定相关性的质量。
45.根据权利要求26所述的装置，其中所述数据分析模块进一步配置成：
在预定时间段执行数据分析训练例程，所述例程包括：
将新数据集添加至训练数据集；
进行数据分析训练过程；
检查训练过程的充分性；和
产生和输出对应于训练过程充分性的通知。
46.根据权利要求45所述的装置，其中所述数据分析模块进一步配置成去除比预定时间段更旧的数据集。
47.根据权利要求46所述的装置，其中所述预定时间段为90天或更多。
48.根据权利要求46所述的装置，其中所述预定时间段为180天或更多。
49.根据权利要求45所述的装置，其中所述数据分析模块进一步配置成启动训练例程的重置和清除训练数据集。
50.根据权利要求45所述的装置，其中所述预定时间段为每日一次。
51.一种识别基于相对变化的葡萄糖响应模式的方法，包括：
在预定时间段内接收活动度量信息和葡萄糖水平信息，所述预定时间段包括多个天；
计算在所述预定时间段内多个天的昼夜之间葡萄糖水平信息的相对变化；
将具有显著活动度量的所述预定时间段内的天的昼夜之间葡萄糖水平信息的相对变化分类为第一时间段，并且将没有显著活动度量的所述预定时间段内的天的昼夜之间葡萄糖水平信息的相对变化分类为第二时间段；
从所述活动度量信息确定所述第一时间段内每天的活动度量，并且从所述葡萄糖水平信息确定所述第一时间段内的葡萄糖度量；
从所述活动度量信息确定所述第二时间段内每天的活动度量，并且从所述葡萄糖水平信息确定所述第二时间段内的葡萄糖度量；
确定所述第一时间段内所确定的葡萄糖度量的中值葡萄糖；
通过从所述第二时间段内的第二预定当天时间段内的中值葡萄糖水平G夜间(X天)减去所述第二时间段内的第一预定当天时间段内的中值葡萄糖水平G日间(X天)来确定所述第二时间段内所确定的葡萄糖度量的中值葡萄糖昼夜变化Gd2n(X天)；
确定所述第二时间段内的天的所有中值葡萄糖昼夜变化Gd2n(X天)的中值葡萄糖Gwo(δ)；
通过从所述第一时间段内的第二预定当天时间段内的中值葡萄糖水平G夜间(X天)减去所述第一时间段内的第一预定当天时间段内的中值葡萄糖水平G日间(X天)来确定所述第一时间段内所确定的葡萄糖度量的中值葡萄糖昼夜变化Gd2n(X天)；
通过在所述第一时间段内的中值葡萄糖昼夜变化Gd2n(X天)与所述第二时间段内的中值葡萄糖Gwo(δ)之间取差值而确定所述第一时间段内每天的δ中值葡萄糖水平Gδ(X天)；
确定所述第一时间段内每天所确定的δ中值葡萄糖水平Gδ(X天)与对应于所述第一时间段内该天的活动度量信息的活动度量之间的相关性；
将所确定的相关性拟合成预定函数；和
对于给定的活动水平，基于所确定的相关性测定活动水平对过夜葡萄糖水平的影响。
52.根据权利要求51所述的方法，其中确定所述第二时间段内的所述葡萄糖度量包括确定在所述第二时间段内每天的昼夜之间的葡萄糖水平信息的相对变化。
53.根据权利要求51所述的方法，其中确定所述第二时间段内的所述葡萄糖度量包括确定在所述第二时间段内每天的日间葡萄糖水平信息与夜间葡萄糖水平信息之间的比率。
54.根据权利要求51所述的方法，其中所述第一时间段内的天包括具有活动度量超过预定阈值的天，并且进一步地，其中所述第二时间段内的天包括具有活动度量低于所述预定阈值的天。
55.根据权利要求54所述的方法，其中所述活动度量包括在24小时时间段期间燃烧的卡路里的量。
56.根据权利要求54所述的方法，其中所述活动度量包括在24小时时间段期间记录的步数。
57.根据权利要求54所述的方法，其中所述活动度量包括以下一种或多种：活动的持续时间、活动的强度水平、与活动相关的心率数据、或活动类型。
58.根据权利要求51所述的方法，其中确定相关性包括识别每个确定的δ中值葡萄糖与相应活动度量之间的关联。
59.根据权利要求51所述的方法，进一步包括在用户界面上输出与测定的影响相关联的信息。
60.根据权利要求59所述的方法，其中输出的信息包括血糖模式。
61.根据权利要求51所述的方法，其中接收葡萄糖水平信息包括用葡萄糖传感器生成对应于监测的葡萄糖水平的信号。
62.根据权利要求61所述的方法，其中所述葡萄糖传感器生成对应于来自真皮流体的监测的葡萄糖水平的信号。
63.根据权利要求61所述的方法，其中所述葡萄糖传感器生成对应于来自间质液的监测的葡萄糖水平的信号。
64.根据权利要求61所述的方法，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的分析物响应酶。
65.根据权利要求64所述的方法，其中所述分析物响应酶与设置在所述工作电极上的聚合物化学结合。
66.根据权利要求64所述的方法，其中所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
67.根据权利要求66所述的方法，其中所述介体与设置在所述工作电极上的聚合物交联。
68.根据权利要求61所述的方法，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
69.根据权利要求51所述的方法，其中所述预定函数包括线性函数。
70.根据权利要求51所述的方法，进一步包括确定基于所述第一时间段和所述第二时间段的分析所需的数据的多个天。
71.根据权利要求51所述的方法，进一步包括：
在预定时间段执行数据分析训练例程，所述例程包括：
将新数据集添加至训练数据集；
进行数据分析训练过程；
检查训练过程的充分性；和
产生和输出对应于训练过程充分性的通知。
72.根据权利要求71所述的方法，进一步包括去除比预定时间段更旧的数据集。
73.根据权利要求72所述的方法，其中所述预定时间段为90天或更多。
74.根据权利要求72所述的方法，其中所述预定时间段为180天或更多。
75.根据权利要求71所述的方法，进一步包括启动训练例程的重置和清除训练数据集。
76.根据权利要求71所述的方法，其中所述预定时间段为每日一次。
77.一种用于识别葡萄糖响应模式的装置，包括：
数据输入模块，用于接收预定时间段内的活动度量信息和葡萄糖水平信息，所述预定时间段包括多个天；
数据分析模块，可操作地连接至所述数据输入模块，并且配置成：
计算在所述预定时间段内多个天的昼夜之间葡萄糖水平信息的相对变化；
将具有显著活动度量的预定时间段内的天的昼夜之间葡萄糖水平信息的相对变化分类为第一时间段，并且将不具有显著活动度量的预定时间段内的天的昼夜之间葡萄糖水平信息的相对变化分类为第二时间段；
从所述活动度量信息确定所述第一时间段内每天的活动度量，并且从所述葡萄糖水平信息确定所述第一时间段内的葡萄糖度量；
从所述活动度量信息确定所述第二时间段内每天的活动度量，并且从所述葡萄糖水平信息确定所述第二时间段内的葡萄糖度量；
确定所述第一时间段内所确定的葡萄糖度量的中值葡萄糖；
通过从所述第二时间段内的第二预定当天时间段内的中值葡萄糖水平G夜间(X天)减去所述第二时间段内的第一预定当天时间段内的中值葡萄糖水平G日间(X天)来确定所述第二时间段内所确定的葡萄糖度量的中值葡萄糖昼夜变化Gd2n(X天)；
确定所述第二时间段内的天的所有中值葡萄糖昼夜变化Gd2n(X天)的中值葡萄糖Gwo(δ)；
通过从所述第一时间段内的第二预定当天时间段内的中值葡萄糖水平G夜间(X天)减去所述第一时间段内的第一预定当天时间段内的中值葡萄糖水平G日间(X天)来确定所述第一时间段内所确定的葡萄糖度量的中值葡萄糖昼夜变化Gd2n(X天)；
通过在所述第一时间段内的中值葡萄糖昼夜变化Gd2n(X天)与所述第二时间段内的中值葡萄糖Gwo(δ)之间取差值而确定所述第一时间段每天的δ中值葡萄糖水平Gδ(X天)；
确定所述第一时间段内每天所确定的δ中值葡萄糖水平Gδ(X天)与对应于所述第一时间段内该天的活动度量信息的活动度量之间的相关性；
将所确定的相关性拟合成预定函数；
对于给定的活动水平，基于所确定的相关性测定活动水平对过夜葡萄糖水平的影响；和数据输出接口，可操作地连接至数据分析模块以输出与所确定的影响相关的信息。
78.根据权利要求77所述的装置，其中所述数据分析模块配置成确定所述第二时间段内的所述葡萄糖度量，确定在所述第二时间段内每天的昼夜之间的葡萄糖水平信息的相对变化。
79.根据权利要求77所述的装置，其中所述数据分析模块配置成确定所述第二时间段内的所述葡萄糖度量，确定在所述第二时间段内每天的日间葡萄糖水平信息与夜间葡萄糖水平信息之间的比率。
80.根据权利要求77所述的装置，其中所述第一时间段内的天包括具有活动度量超过预定阈值的天，并且进一步地，其中所述第二时间段内的天包括具有活动度量低于所述预定阈值的天。
81.根据权利要求80所述的装置，其中所述活动度量包括在24小时时间段期间燃烧的卡路里的量。
82.根据权利要求80所述的装置，其中所述活动度量包括在24小时时间段期间记录的步数。
83.根据权利要求80所述的装置，其中所述活动度量包括以下一种或多种：活动的持续时间、活动的强度水平、与活动相关的心率数据、或活动类型。
84.根据权利要求77所述的装置，其中所述数据分析模块配置成确定相关性，识别每个确定的δ中值葡萄糖与相应活动度量之间的关联。
85.根据权利要求77所述的装置，其中输出的信息包括血糖模式。
86.根据权利要求77所述的装置，其中从定位与体液流体接触的葡萄糖传感器接收所述葡萄糖水平信息以生成对应于所述葡萄糖水平信息的信号。
87.根据权利要求86所述的装置，其中所述体液包括真皮流体。
88.根据权利要求86所述的装置，其中所述体液包括间质液。
89.根据权利要求86所述的装置，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的分析物响应酶。
90.根据权利要求89所述的装置，其中所述分析物响应酶与设置在所述工作电极上的聚合物化学结合。
91.根据权利要求90所述的装置，其中所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
92.根据权利要求91所述的装置，其中所述介体与设置在所述工作电极上的聚合物交联。
93.根据权利要求86所述的装置，其中所述葡萄糖传感器包括多个电极，所述电极包括工作电极，所述工作电极包括结合至设置在所述工作电极上的聚合物的介体。
94.根据权利要求77所述的装置，其中所述预定函数包括线性函数。
95.根据权利要求77所述的装置，其中所述数据分析模块进一步配置成确定基于所述第一时间段和所述第二时间段的分析所需的数据的多个天。
96.根据权利要求77所述的装置，其中所述数据分析模块进一步配置成：
在预定时间段执行数据分析训练例程，所述例程包括：
将新数据集添加至训练数据集；
进行数据分析训练过程；
检查训练过程的充分性；和
产生和输出对应于训练过程充分性的通知。
97.根据权利要求77所述的装置，其中所述数据分析模块进一步配置成去除比预定时间段更旧的数据集。
98.根据权利要求77所述的装置，其中所述预定时间段为90天或更多。
99.根据权利要求77所述的装置，其中所述预定时间段为180天或更多。
100.根据权利要求77所述的装置，其中所述数据分析模块进一步配置成启动训练例程的重置和清除训练数据集。
101.根据权利要求77所述的装置，其中所述预定时间段为每日一次。
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