这里所述的研究得到了国家卫生研究院、国家老龄研究所 (U2CAG054397、P30 AG024978、P30 AG008017、R01 AG042191、R01 AG024059)、英特尔、国家卫生研究院基金会的资助和罗伯特伍德约翰逊基金会。

所有与会者均提供书面知情同意。生活实验室的参与者被要求过他们的生活, 因为他们通常会允许纵向观察他们的生活活动和模式, 在他们的余生。如果他们愿意, 他们可以随时撤退。这项研究协议获得了俄勒冈州健康 & 的认可。科学大学 (大观音) 机构审查委员会 (生命实验室大观音 IRB #2765)。
1. 准备
<ol><li>在部署之前, 请将所有设备联机添加到控制台库存系统。为每个设备或传感器指定一个名称以及其序列号和 MAC 地址, 使其存储在控制台库存系统中。</li>
	<li>在每个设备上放置一个 QR 码标记, 以便在在家中部署传感器和设备的主位置的特定分配。</li>
	<li>部署前, 所有要安装的传感器和设备都将通过扫描传感器或设备上的 QR 代码分配给主页。这将带来一个网站, 允许传感器或设备分配到该特定的家庭。</li>
	<li>使用包含集线器配置管理工具的 SD 卡安装集线器计算机。</li>
	<li>打包所有现在清点的传感器和设备, 将安装 SD 卡的集线器计算机放入安装套件 (框) 中进行家庭部署。</li>
	<li>验证参与者的主页是否有 Internet 服务提供商。</li>
</ol>2. 家庭部署
<ol><li>通过在集线器计算机中插入无线转换器和主网络协调员接口来设置中心计算机。将以太网电缆插入中心计算机。最后, 将集线器计算机的电源线插入中央定位室的电源插座, 并将以太网电缆连接到家庭的 Internet 连接。 注意: 配置管理工具将确保它使用的是最新版本的软件。</li>
	<li>将启用 Internet 的设备 (笔记本电脑、tablet、手机) 连接到集线器计算机的 swireless 网络以访问本地控制面板网站。控制面板网站将显示中心计算机的状态以及安装在家里的任何传感器 (图 3)。</li>
	<li>运行软件配置工具, 确保安装了相应的软件。通过导航到 "控制面板" 并运行 "更新" 来完成此项。</li>
	<li>导航到 "控制面板" 以检查集线器计算机是否正在与主服务器通信。请确保允许从每个已安装的设备和传感器收集数据的服务都已运行。</li>
	<li>将传感器添加到主页, 从运动传感器开始。从控制面板打开传感器放置网站开始。 注: 如果家庭需要16多个运动传感器, 请将路由器转换器连接到中心计算机, 并将其添加到家庭或个人区域网络, 也称为 "PAN"。一旦将扩展适配器 (如果需要) 添加到 PAN 中, 将其从集线器计算机中移除, 并将其插入到整个家庭的网点, 在家庭周围创建一个网络, 将移动传感器数据发送到中心计算机。</li>
	<li>在传感器放置网站, 创建一个虚拟地板计划的家庭, 包括所有的房间和出口门。请确保选择传感器线作为添加到平面图中的区域之一。将传感器的虚拟表示形式添加到平面图中。最后, 将家庭区域的虚拟表示形式链接到其他彼此-以反映家庭物理布局的方式以及传感器的虚拟表示形式。</li>
	<li>通过使用传感器放置工具并在传感器电池附近的物理按下按钮, 将每个连续传感器添加到 PAN 称为个人区域网络。然后, 开始将每个传感器附加到在虚拟平面图中表示的家庭中的房间或区域。</li>
	<li>继续将物理传感器附加到家庭墙上。在每个房间 (厨房、卧室、浴室、起居室) 中放置每面墙传感器, 确保传感器只捕获该房间内的活动, 不从另一个区域接收活动 (例如, 避免有人在走廊上漫步由传感器在走廊旁边的一个房间里捡到的)。 注意: 传感器放置工具允许您识别和创建房间之间的路径。</li>
	<li>在直线通道 (走廊或其他区域) 中安装四个受限域 (天花板) 传感器, 以便在天花板上捕捉行走速度, 而不需要改变速度。<ol>
<li>空间这些步行速度传感器61厘米 (2 英尺) 分开。</li>
		<li>记录传感器放置网站中受限字段传感器之间的确切距离。</li>
	</ol></li>
	<li>在每个出口门上安装门传感器, 再次使用传感器放置网站上的平面图来表示其物理位置。</li>
	<li>将碉堡添加到平底锅中, 确认设备已分配给主库存。然后打开它的盖子之一激活设备。因为碉堡与集线器计算机通信, 所以请确保它足够接近中心计算机, 以便检测到它的信号。 注: 碉堡经常保存在厨房或浴室中, 根据参与者的喜好。</li>
	<li>要设置刻度, 请导航到 "控制面板" 的 "设备" 选项卡中的 "缩放" 页。<ol>
<li>在刻度上, 按侧电源按钮10秒钟。刻度应显示确认消息。</li>
		<li>一旦刻度显示在设备列表中, 请单击控制面板右侧的 "设置" 按钮以启动安装过程。</li>
		<li>在 "控制面板" 中提示时输入参与者的高度和粗细。</li>
		<li>如果参与者没有起搏器, 请在 "控制面板" 中切换 "起搏器" 按钮, 通知其可以收集 bioimpedence 数据的刻度。</li>
		<li>将刻度放在具有平坦的、实心表面 (通常位于浴室) 的位置。</li>
		<li>让参与者权衡自己, 确认刻度是记录其初始重量进入控制面板。</li>
	</ol></li>
	<li>通过打开可穿戴的控制面板设置页面, 并按下设备背面的复位按钮十次, 设置手腕磨损的耐磨设备。<ol>
<li>在 "控制面板" 中的设备列表中显示该设备后, 单击控制面板右侧的 "设置" 按钮以启动安装过程。</li>
		<li>设置帐户后, 使用安装页面上的滚轮工具校准时间。</li>
		<li>通过将可穿戴与集线器计算机同步来完成安装。单击 "控制面板" 中的 "同步" 按钮以确认设备连接正确, 并将时间设置为与集线器计算机相同的时间。</li>
		<li>在控制台上注明佩戴者拟佩戴的手腕。 注意: 不同的设备可能需要不同的过程取决于制造商。还可以部署其他传感器和设备, 并将其集成到数据流中, 如计算机使用软件和驱动传感器。接下来将给出添加这些程序的步骤。</li>
	</ol></li>
	<li>在参与者的计算机上安装商用计算机使用监视软件并记录其电子邮件地址。这些电子邮件地址用于发送和接收每周的在线健康和活动调查。<ol>
<li>验证参与者的计算机操作系统是否与商用计算机使用监视软件兼容。</li>
		<li>使用在 USB 闪存驱动器上承载的安装程序, 在参与者的计算机上安装软件。</li>
		<li>通过打开任务管理器并检查软件是否在应用程序列表中, 验证该软件是否在计算机上运行。</li>
		<li>在控制台库存系统中, 将软件程序与参与者的配置文件相关联。 注: 见所使用的特定计算机使用的材料表(其他商用监控软件可以替代)。</li>
	</ol></li>
	<li>为参与者设置驱动传感器<ol>
<li>验证参加者的汽车是在1996年后进行的, 并且汽车是由驱动传感器设备软件支持的。</li>
		<li>在移动设备上安装驱动监视设备的应用程序, 并使用该应用程序设置适配器。</li>
		<li>当汽车关闭后, 将适配器插入汽车的车载诊断 (坏蛋) 端口。</li>
		<li>等待应用程序识别并连接到适配器。这应该需要 2-4 分钟。</li>
		<li>把车钥匙插入点火器。(如果汽车有无钥匙点火, 按下汽车的启动按钮)。把钥匙转到开关电源的位置而不启动引擎。</li>
		<li>等待应用程序完成设置适配器。</li>
		<li>在控制台库存系统中, 从应用程序中添加参与者的帐户信息, 以允许适配器的数据使用商用软件的应用程序编程接口 (API) 传输到 ORCATECH 服务器。 注: 请参阅所使用的特定驱动监视设备的材料表。</li>
	</ol></li>
</ol>3. 系统确认
<ol><li>一旦所有设备都在家里的最后位置, 请通过导航到控制面板来确认集线器计算机是否正常工作。检查集线器计算机能否与主服务器通信以传输数据, 以及为每个设备类型收集数据的服务是否正在运行。</li>
	<li>通过导航到 "控制面板" 上的 "数据收集" 页, 查看数据是否从每个设备流。</li>
	<li>在每个房间中安装的运动传感器附近走动, 以确认每个传感器正在收集有关最近移动的数据。通过查看移动传感器数据的实时图来检查运动传感器。</li>
	<li>通过打开和关闭碉堡的每个隔间门, 检查碉堡几次。查看 "控制" 面板上的 "数据收集" 页, 查看最近的活动是否已被测量和收集。</li>
	<li>通过权衡自己或参与者来检查刻度。通过导航到 "控制面板" 的 "缩放设备" 页中的 "同步" 列, 确认此数据正确同步和传输。</li>
	<li>通过导航到 "控制面板" 的 "可穿戴设备" 页面中的 "同步" 列, 检查可穿戴设备是否正确同步和传输数据。</li>
</ol>

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L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Neuropsychiatric+Inventory+Assessing+psychopathology+in+dementia+patients.">The Neuropsychiatric Inventory Assessing psychopathology in dementia patients.</a> Neurology. 48, 10-16 (1997).</li><li>Teng, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Utility+of+the+Functional+Activities+Questionnaire+for+distinguishing+mild+cognitive+impairment+from+very+mild+Alzheimer's+disease.">Utility of the Functional Activities Questionnaire for distinguishing mild cognitive impairment from very mild Alzheimer's disease.</a> Alzheimer disease and associated disorders. 24 (4), 348 (2010).</li><li>Wild, K. V., Mattek, N., Austin, D., Kaye, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term="Are+You+Sure?"+Lapses+in+Self-Reported+Activities+Among+Healthy+Older+Adults+Reporting+Online.">"Are You Sure?" Lapses in Self-Reported Activities Among Healthy Older Adults Reporting Online.</a> Journal of Applied Gerontology. , (2015).</li></ol>

作者没有什么可透露的。

我们已经描述了一个基本的系统或平台, 能够持续地进行基于家庭和社区的遥感和报告显著的健康和福祉措施。该系统主要用于目前的研究。
在可能的情况下, 系统使用开放源码工具和传感器或设备, 利用可用的 api 和软件开发工具包 (SDK)。该系统的设计是技术 "不可知性", 使各种各样的传感器或设备可以 "插入" 或纳入必要的。所选择的感知域 (例如, 运动移动性测量, 药...

由于评估方法的内在缺陷, 目前的临床研究充满了对数据的可靠性和有效性的限制。面试受到临床医生和病人能够协调时间表的时间的限制。为考试分配的时间受志愿人员在一届会议上合理要求做的限制。这些简短的、间距很大的会话-即使通过偶尔的电话或 Internet 查询增加--严重限制了在一段时间内检测功能或福祉有意义变化的可能性。当前的测试会话主要由要求提供难以召回和验证的信息组成 (例如, "您是否记得服药？") 或人工任务的性能 (例如, "站起来, 坐得和你一样快"能 ";"记住这十个字")。评估通常是为了限制测试到测试的可变性, 而实际上性能本身的可变性可能是关键的诊断功能。此外, 这些短暂的时间活检是在人工条件下进行的, 而不是在日常生活的正常流动中进行的。因此, 它们的生态有效性是有限的。最后, 目前的范式内在不能提供相互依存的关键事件或结果 (例如睡眠、社会化、体力活动) 的直接联系, 因为这些数据没有被召回的时间戳过。
克服这些缺点的方法是开发可嵌入在家庭或社区中的系统, 利用普适计算和传感技术、无线通信和高频多域数据的进步。分析。这一领域的技术和经验正在增长, 许多系统已经开发出来, 但在部署、特点或纵向经验方面有1、2、3、4的限制。在这篇手稿中, 我们描述了一项协议, 作为一种手段, 提供实时、连续和纵向的基于家庭的健康相关数据评估, 以改进目前健康评估范式的局限性。俄勒冈老龄 & 中心;技术 (ORCATECH) 开发了一个基于普适计算和传感技术的基于家庭的系统, 以提供对健康相关活动和行为的连续、实时的评估。将评估纳入国内, 以使对实际活动的主要不显眼和持续监测大大克服当前的限制。首先, 由于核心系统被嵌入到参与者的生命空间中, 作为其环境环境的一部分, 它本来就很方便。当一个人最安心时, 在被动收集方法的情况下, 如有必要, 在不负担参与者的情况下, 可以收集需要离散反应的评估。第二, 在人的正常生命空间中, 提供了收集与生态相关的即时数据的机会, 而不是简单地测试功能的做作措施, 而是日常认知。例如, 潜在的记忆衰竭, 一个常见的抱怨难以在临床 naturalistically 测试, 可以在家里评估的日常药物服用行为的自动跟踪, 从而窃听日常认知, 以及关键的表现已知对认知变化敏感的度量。第三, 由于数据是数字和时间戳的, 因此, 对多个相互关联的度量方法进行时间调整是很方便的。例如, 时间在电话和时间外面 (社会参与或撤退措施), 计算机使用 (测量开始, 精神运动活动和认知作用) 和其他措施, 已经显示改变与功能下降 (睡眠行为、体重、行走速度) 可以增加传感器网络的灵敏度, 以区分可能不明显的细微变化。重要的是, 健康和生命事件对认知和功能的影响 (例如, 每周报告的疼痛, 药物变化, 低情绪) 也可以链接到这个数据流, 因为它们发生。最后, 传统的测试和查询可以通过计算机或相关接口 (例如, 平板电脑, 智能手机) 来呈现, 提供无与伦比的机会, 同时将旧式测试性能与新的数字派生度量值进行比较来自相同的测试, 如响应或暂停时间、学习曲线和测试内的可变性。这种新的方法将当前的评估转化为更方便、不显眼、连续、多域和自然主义。最终, 基于家庭传感器的评估技术和方法的基本平台, 提供了一个系统, 可以调整和缩放, 以解决与健康和福祉有关的广泛的具体研究问题, 并指出优势超过目前接受的罕见诊所或电话评估的做法。
下面的协议概述了部署此平台以进行不显眼的家庭行为和与健康相关的数据收集的过程。在开发这一平台时, 一个关键目标是提供一套基本的评估功能, 提供必要的数据, 以推断健康和福祉的一般领域 (物理、认知、社会、情感) 以及更具体的行为 (例如服药、走路、与睡眠有关的活动、生理活动)。该平台的发展已经遵循了几个原则, 包括使用最被动的不引人注意的传感方法, 尽量减少直接用户与技术的接触, 是技术的 "不可知性" (即使用最好的设备或技术解决方案, 而不是要求特定的方法或产品), 耐用 (长期评估) 和可伸缩性, 并尽量减少实际的维护。
所描述的平台在过去十二年中发展了, 重要的是从 "数字幼稚" 到早期使用者的一系列最终用户的了解。定期调查和重点小组是通知这一发展5、6、7的关键。数以百计的志愿人员允许这些系统在其家中持续部署长达十一年, 并根据技术的进步、研究界要求的新功能能力来实施迭代修改, 并在技术已部署的家庭中, 个人的关键恒定输入。总的来说, 这些志愿者在社区中形成了一个 "生活" 实验室, 我们称之为 "生命实验室" , 在那里他们的家园和每天收集的连续数据提供了关于健康、活动和人生历程。
传感技术的一个基本平台构成了整个系统的主干, 用于捕获连续的基于家庭的数据。随后对该平台的元素进行了描述。对核心平台进行修改 (可以添加或删除元素), 其基础是收集用户态度的过程中获得的信息, 以及使用研究平台对研究感兴趣的信念和结果度量。由于数据通信协议是标准化的, 因此系统设计为允许将这些协议遵循的任何设备合并到网络中。
这里描述的基本平台是基于生命实验室中志愿者的使用案例, 他们同意在自己的家中部署平台, 以收集自然活动和他们正常生活活动的行为数据多年 (最长当前连续部署 = 11 年)。
集线器计算机和以太网/WiFi 连接允许从系统设备中收集数据并将其传输回 ORCATECH 的安全服务器, 而不受参与者的干扰。在系统安装时, 集线器计算机配置为特定参与者和家庭设置, 使用便携式计算机或 tablet 以及连接到集中式数字参与者管理系统的控制面板。其他数据收集设备 (如传感器、MedTracker 和刻度) 可以通过与集线器计算机的通信方式进行配置。
ORCATECH 控制台和远程技术管理系统是一个称为 "控制台" 的自定义数字技术和数据管理系统, 它允许参与者在家技术配置和系统设置, 以及远程技术管理家庭, 包括安全的数据收集和监测。此外, 为了便于在每个家庭都有独特布局的社区中部署系统, 使用基于 tablet 接口的图形工具自动记录各种传感器的位置, 并将其有效的物理周边到其他传感器 (图 2)。在对系统进行远程监视的过程中, 这一点很重要。
被动红外 (红外) 运动传感器在系统安装过程中被数字分配给给定的家庭, 通过无线 USB 转换器与中心计算机通信。每个房间都有一个传感器, 可以感觉到房间内的运动, 参与者从房间到房间的过渡。一个直 "传感器线" 的四传感器是放置在天花板上的走廊或其他地区的参与者步行定期以一致的速度。这种传感器线允许不显眼地收集步行速度每天很多次。其他度量可以从这些活动传感器 (如驻留时间或房间转换数) 派生。门接触传感器被放置在所有外部门周围的家庭, 以检测参与者的进出从家里, 并在冰箱, 以确定一般频率的食物进入。
为了从收集设备的被动系统中获得最佳的数据, 需要在线每周健康和活动自报告。这些数据对于分析与传感器收集的数据有关的家庭事件的参与者报告至关重要。网上每周自我报告调查可以完成任何计算设备 (例如, 笔记本电脑, 片剂, 智能手机) 与互联网连接, 以查询参加者的家庭出游, 访客在家里, 健康变化, 空间变化在家庭, 孤独, 抑郁和疼痛水平。每周的数据收集依赖于相对较短的记忆窗口, 它提供了更高的数据分辨率和准确性的可能性, 例如每年或半年的检查。此外, 这种自我报告过程还允许调查人员检查潜在认知障碍的被动指标, 如完成调查的时间变化、点击次数变化、报告准确性增加的困难。在自由文本响应中的日期或减值标记。作为基本平台的一部分, 我们安装了一个七天的电子药丸盒, 记录了指定的一天的车厢是否打开, 以及每天打开的时间。这提供了药物依从性的信息, 以及如果药物服用的一致性下降, 可能表明认知下降。
浴室还安装了一个无线数字 bioimpedence 秤, 它还收集脉冲、身体成分测量、脉搏波速度、环境温度和环境二氧化碳水平, 提供了参与者每日体重的数据。这些数据可以与其他报告的事件 (例如, 健康状况、药物) 以及其他被动的行为指标 (如协议依从性和时间使用频率) 相关。
在我们的参与者驾驶的情况下, 我们在他们的车辆上安装一个驱动传感器。此传感器提供有关驾驶习惯的信息, 如频率、定时、持续时间和行程距离, 以及硬停止或硬加速度的频率。
腕部佩戴的可穿戴设备收集进出家庭的物理活动数据。一些品牌和型号的 "服饰" 已被用于生活实验室的房子。
根据项目的不同, 使用 ORCATECH 平台的调查人员可以选择用其他数据收集组件补充基本传感器集。过去测试过的例子包括一个电话传感器, 通过固定电话活动来监控社会化, 开发和实施平衡测试数字平衡板, 为参与者提供周期性认知任务的平板电脑, 以完成在自己的家里, 和一个自动短信系统, 以评估药物提醒的功效通过电话。
为了处理 ORCATECH 生命实验室生成的各种数据, 定制的信息和数据系统用于收集、注释、维护和分析丰富的活动和健康数据。ORCATECH 开发了一个自定义系统, 用于参与者管理、自报告数据收集和处理, 以及从所有系统设备和传感器连续收集数据。该系统依靠分布式 NoSQL 卡珊德拉服务器群集存储传感器数据和 lambda 体系结构, 使用卡夫卡和火花, 使我们的数据处理能力更接近实时处理。使用 REST API, 数据被转移到标准数据分析平台和统计软件程序中进行数据分析。

<table border="0" cellpadding="0" cellspacing="0" style="width:672px;" width="671">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Raspberry Pi 3 Model B</td>
			<td style="width:123px;">Raspberry Pi Foundation</td>
			<td style="width:167px;">Raspberry Pi 3 Model B</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Motion Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3041-HA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Door/Window Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3011-HA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Curtain Motion Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3045-HA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">iSort</td>
			<td style="width:123px;">TimerCap</td>
			<td style="width:167px;">iSort</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Home Stealth USB Phone Recorder</td>
			<td style="width:123px;">Fiho</td>
			<td>Fi3001B</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Automatic Pro</td>
			<td style="width:123px;">Automatic</td>
			<td style="width:167px;">AUT-350C</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Body Cardio Scale</td>
			<td style="width:123px;">Nokia</td>
			<td>WBS04</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Activite/Steel Activity Monitor</td>
			<td style="width:123px;">Nokia</td>
			<td style="width:167px;">HWA01 STEEL</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Alta 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB406</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Charge 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB407</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Flex 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB403</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Zigbee USB Stick</td>
			<td style="width:123px;">Silicon Labs</td>
			<td style="width:167px;">ETRX3USB</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">WorkTime</td>
			<td style="width:123px;">Nestersoft</td>
			<td style="width:167px;">WorkTime Corporate</td>
			<td></td>
		</tr>
	</tbody>
</table>

methodology for establishing a community-wide life laboratory for capturing unobtrusive and continuous remote activity and health data

已经建立了一个端到端的技术套件, 用于不显眼和持续地监测老年人在较长时间内日常生活中发生的健康和活动变化。该技术被整合到一个系统中, 其中包含了最低限度的干扰原则, 同时在现实世界 (基于家庭的) 设置中生成安全、隐私保护、连续的客观数据, 数月到数年。该系统包括在整个家庭中放置的被动式红外线传感器、门接触传感器安装在外部门上、连接的生理监测设备 (如刻度)、药物盒和可穿戴 actigraphs。驾驶传感器也安装在参与者的汽车和计算机 (PC, 平板电脑或智能手机) 的使用被跟踪。数据是通过频繁的在线自报告选项进行标注的, 这些选择提供了关于难以通过传感器 (如疼痛、情绪、孤独) 以及数据引用到活动模式的数据的重要信息。口译 (如访客, 重新布置家具)。算法是利用获得的数据来确定功能领域的关键健康或疾病活动监测, 包括流动性 (例如, 房间过渡, 步骤, 步态速度), 生理功能 (如体重, 身体质量指数, 脉搏), 睡眠行为 (例如, 睡眠时间, 夜间上厕所), 服药依从性 (例如, 错过剂量), 社会参与 (例如, 时间花在家里, 时间情侣一起度过), 和认知功能 (例如, 计算机上的时间, 鼠标移动, 在线形式完成的特点, 驾驶能力)。这些功能的变化检测提供了一个敏感的标志, 用于健康监测的急性疾病 (如病毒流行), 以及早发现前驱痴呆综合症。该系统特别适合于监测临床干预在老年综合征的自然史研究和临床试验中的有效性。

ORCATECH 的技术套件使人们有可能收集一个独特的丰富的数据集关于人的生活模式, 因为他们的日常活动。传感器系统允许对他们自己家里的志愿者进行不显眼和连续的监测。该系统已用于许多研究, 其中包括数以百计的志愿者研究健康和功能的关键领域, 如步行速度和流动性, 服药行为, 情绪, 在家中或离家, 睡眠和计算机使用8,9<su...

Watch this Scientific Journal Video about Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data at JoVE.com

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

建立社区范围的生命实验室以捕获不显眼和持续的远程活动和健康数据的方法

不显眼的传感器和普适计算技术纳入老年人的日常生活中, 使有意义的健康和活动变化连续数月持续记录, 提供生态上有效的、高频率的、用于研究或临床使用的多领域数据。

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

methodology-for-establishing-community-wide-life-laboratory-for

Research

JoVE Journal

Behavior

8.1K 次观看.  Oregon Health & Science University. 不显眼的传感器和普适计算技术纳入老年人的日常生活中, 使有意义的健康和活动变化连续数月持续记录, 提供生态上有效的、高频率的、用于研究或临床使用的多领域数据。

视频: Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Nest Building as an Indicator of Health and Welfare in Laboratory Mice

The minimization and alleviation of suffering has moral and scientific implications. In order to mitigate this negative experience one must be able to identify when an animal is actually in distress. Pain, illness, or distress cannot be managed if unrecognized. Evaluation of pain or illness typically involves the measurement of physiologic and behavioral indicators which are either invasive or not suitable for large scale assessment. The observation of nesting behavior shows promise as the basis of a species appropriate cage-side assessment tool for recognizing distress in mice. Here we demonstrate the utility of nest building behavior in laboratory mice as an ethologically relevant indicator of welfare. The methods presented can be successfully used to identify thermal stressors, aggressive cages, sickness, and pain. Observation of nest building behavior in mouse colonies provides a refinement to health and well-being assessment on a day to day basis.

We demonstrate the utility of nest building behavior in laboratory mice as an indicator of welfare. Nest scoring is a sensitive technique that is altered by temperature, illness, and aggression. The time to integrate into nest test (TINT) is a simple cage-side assessment that can detect postoperative pain.

The minimization and alleviation of  suffering has moral and scientific implications. In order to mitigate this  negative experience one must be able to ...

科研

行为学

Barnes Maze Testing Strategies with Small and Large Rodent Models

Spatial learning and memory of laboratory rodents is often assessed via navigational ability in mazes, most popular of which are the water and dry-land (Barnes) mazes. Improved performance over sessions or trials is thought to reflect learning and memory of the escape cage/platform location. Considered less stressful than water mazes, the Barnes maze is a relatively simple design of a circular platform top with several holes equally spaced around the perimeter edge. All but one of the holes are false-bottomed or blind-ending, while one leads to an escape cage. Mildly aversive stimuli (e.g.&#160;bright overhead lights) provide motivation to locate the escape cage. Latency to locate the escape cage can be measured during the session; however, additional endpoints typically require video recording. From those video recordings, use of automated tracking software can generate a variety of endpoints that are similar to those produced in water mazes (e.g.&#160;distance traveled, velocity/speed, time spent in the correct quadrant, time spent moving/resting, and confirmation of latency). Type of search strategy (i.e.&#160;random, serial, or direct) can be categorized as well. Barnes maze construction and testing methodologies can differ for small rodents, such as mice, and large rodents, such as rats. For example, while extra-maze cues are effective for rats, smaller wild rodents may require intra-maze cues with a visual barrier around the maze. Appropriate stimuli must be identified which motivate the rodent to locate the escape cage. Both Barnes and water mazes can be time consuming as 4-7 test trials are typically required to detect improved learning and memory performance (e.g.&#160;shorter latencies or path lengths to locate the escape platform or cage) and/or differences between experimental groups. Even so, the Barnes maze is a widely employed behavioral assessment measuring spatial navigational abilities and their potential disruption by genetic, neurobehavioral manipulations, or drug/ toxicant exposure.

The dry-land Barnes maze is widely used to measure spatial navigation ability in response to mildly aversive stimuli. Over consecutive days, performance (e.g.&#160;latency to locate escape cage) of control subjects improves, indicative of normal learning and memory. Differences between rats and mice necessitate apparatus and methodology changes that are detailed here.

巴恩斯迷宫测试策略与小型和大型啮齿类动物模型

Spatial learning and memory of laboratory rodents is often assessed via navigational ability in mazes, most popular of which are the water and dry-land ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

协议进行数据采集和分析应用到自动人脸表情分析技术和时序分析感官评价

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse

Fatigue is a prominent symptom in many diseases and disorders and reduces quality of life for many people. The lack of clear pathogenesis and failure of current interventions to adequately treat fatigue in all patients leaves a need for new treatment options. Despite the therapeutic need and importance of preclinical research in helping identify promising novel treatments, few preclinical assays of fatigue are available. Moreover, the most common preclinical assay used to assess fatigue-like behavior, voluntary wheel running, is not suitable for use with some strains of mice, may not be sensitive to drugs that reduce fatigue, and has relatively low throughput. The current protocol describes a novel, non-voluntary preclinical assay of fatigue-like behavior, the treadmill fatigue test, and provides evidence of its efficacy in detecting fatigue-like behavior in mice treated with a chemotherapy drug known to cause fatigue in humans and fatigue-like behavior in animals. This assay may be a beneficial alternative to wheel running, as fatigue-like behavior and potential interventions can be assessed in a greater number of mice over a shorter time frame, thus permitting faster discovery of new therapeutic options.

Fatigue is a common, undertreated and frequently poorly-understood symptom in many diseases and disorders. New preclinical assays of fatigue may help to improve current understanding and future treatment of fatigue. To that end, the current protocol provides a novel means of measuring fatigue-like behavior in the mouse.

跑步机疲劳试验:疲劳类似的行为，为鼠标操作简单，高通量检测

Fatigue is a prominent symptom in many diseases and disorders and reduces quality of life for many people. The lack of clear pathogenesis and failure of ...

Introducing Clicker Training as a Cognitive Enrichment for Laboratory Mice

Establishing new refinement strategies in laboratory animal science is a central goal in fulfilling the requirements of Directive 2010/63/EU. Previous research determined a profound impact of gentle handling protocols on the well-being of laboratory mice. By introducing clicker training to the keeping of mice, not only do we promote the amicable treatment of mice, but we also enable them to experience cognitive enrichment. Clicker training is a form of positive reinforcement training using a conditioned secondary reinforcer, the "click" sound of a clicker, which serves as a time bridge between the strengthened behavior and an upcoming reward. The effective implementation of the clicker training protocol with a cohort of 12 BALB/c inbred mice of each sex proved to be uncomplicated. The mice learned rather quickly when challenged with tasks of the clicker training protocol, and almost all trained mice overcame the challenges they were given (100% of female mice and 83% of male mice). This study has identified that clicker training for mice strongly correlates with reduced fear in the mice during human-mice interactions, as shown by reduced anxiety-related behaviors (e.g., defecation, vocalization, and urination) and fewer depression-like behaviors (e.g., floating). By developing a reliable protocol that can be easily integrated into the daily routine of the keeping of laboratory mice, the lifetime experience of welfare in the mice can be improved substantially.

The development of new refinement strategies for laboratory mice is a challenging task that contributes towards fulfilling the 3R principle. This protocol introduces clicker training as a cognitive enrichment program for laboratory mice.

引进响片训练为实验室老鼠认知富集

Establishing new refinement strategies in laboratory animal science is a central goal in fulfilling the requirements of Directive 2010/63/EU. Previous ...

Methods of Pairing and Pair Maintenance of New Zealand White Rabbits (Oryctolagus Cuniculus) Via Behavioral Ethogram, Monitoring, and Interventions

New Zealand White (NZW) laboratory rabbits (Oryctolagus cuniculus), as well as their ancestors the European Rabbit, are a social species that exhibit numerous benefits to being housed accordingly. Although these rabbits are innately gregarious, certain behaviors can still arise when kept in captivity, which if left unchecked, can confound research results or lead to wounding, which in extreme cases can be severe. To prevent these issues, there must be a well-structured plan for the monitoring and maintenance of paired laboratory rabbits. The purpose of this protocol is to present effective procedures for establishing newly paired NZW rabbits as well as methods for successful maintenance. Multiple methods have been tested for the creation of newly paired female rabbits from the vendor, but the most efficacious technique emphasizes capitalizing on the stress bonding from transport, urine marking, pairing in a neutral cage with no forced sharing of resources and a system of monitoring and intervention. To determine the best method of housing paired rabbits in a standard caging environment, data were collected to generate a behavioral ethogram. Behaviors were then quantified as positive, neutral or negative and were tracked across the lifespan of the pair to determine which behaviors indicated pair success or failure. With the newfound knowledge of socially housed laboratory NZW rabbit behavior, enrichment intervention was applied to alleviate aggression and prevent wounding, thus resulting in a higher percentage of successful pairs. Through several years of trialing different pairing methods, the development of the ethogram and the resulting enrichment interventions, understanding of the highly complex social constructs that dominate pair housed rabbit behavior has dramatically increased and allowed for the provision of more species-specific care and increased standards of welfare.

Methods of Pairing and Pair Maintenance of New Zealand White Rabbits Oryctolagus Cuniculus Via Behavioral Ethogram, Monitoring, and Interventions

Though European rabbits are a social species, socially housing them can be challenging. Therefore, there must be a thorough understanding of behaviors and social structures of pair-housed laboratory rabbits. Here we present a protocol to identify pairing methods, species-typical hierarchy establishment behaviors and behaviors that warrant appropriate intervention.

新西兰白兔的配对和配对维护方法 (穴兔家兔) 通过行为 Ethogram、监视和干预

New Zealand White (NZW) laboratory rabbits (Oryctolagus cuniculus), as well as their ancestors the European Rabbit, are a social species that exhibit ...

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional (2-D) images. In everyday life, however, humans interact predominantly with real-world solid objects, not images. Currently, we know very little about whether images of objects trigger similar behavioral or neural processes as do real-world exemplars. Here, we present methods for bringing the real-world into the laboratory. We detail methods for presenting rich, ecologically-valid real-world stimuli under tightly-controlled viewing conditions. We describe how to match closely the visual appearance of real objects and their images, as well as novel apparatus and protocols that can be used to present real objects and computerized images on successively interleaved trials. We use a decision-making paradigm as a case example in which we compare willingness-to-pay (WTP) for real snack foods versus 2-D images of the same items. We show that WTP increases by 6.6% for food items displayed as real objects versus high-resolution 2-D colored&#160;images of the same foods -suggesting that real foods are perceived as being more valuable than their images. Although presenting real object stimuli under controlled conditions presents several practical challenges for the experimenter, this approach will fundamentally expand our understanding of the cognitive and neural processes that underlie naturalistic vision.

We describe methods for presenting real-world objects and matched images of the same objects under tightly-controlled experimental conditions. The methods are described in the context of a decision-making task, but the same real-world approach can be extended to other cognitive domains such as perception, attention, and memory.

在受控实验室条件下呈现真实物体的方法

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

使用移动眼动追踪器捕捉联合视觉注意力的方法

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful system for supporting the early detection of individuals at-risk of psychosis at a large scale, by leveraging electronic health records (EHRs). This risk calculator has been externally validated twice and is undergoing feasibility testing for clinical implementation. Integration of this risk calculator in clinical routine should be facilitated by prospective feasibility studies, which are required to address pragmatic challenges, such as missing data, and the usability of this risk calculator in a real-world and routine clinical setting. Here, we present an approach for a prospective implementation of a real-time psychosis risk detection and alerting service in a real-world EHR system. This method leverages the CogStack platform, which is an open-source, lightweight, and distributed information retrieval and text extraction system. The CogStack platform incorporates a set of services that allow for full-text search of clinical data, lifespan-inclusive, real-time calculation of psychosis risk, early risk-alerting to clinicians, and the visual monitoring of patients over time. Our method includes: 1) ingestion and synchronization of data from multiple sources into the CogStack platform, 2) implementation of a risk calculator, whose algorithm was previously developed and validated, for timely computation of a patient&#39;s risk of psychosis, 3) creation of interactive visualizations and dashboards to monitor patients&#39; health status over time, and 4) building automated alerting systems to ensure that clinicians are notified of patients at-risk, so that appropriate actions can be pursued. This is the first ever study that has developed and implemented a similar detection and alerting system in clinical routine for early detection of psychosis.

We demonstrate how to deploy a real-time psychosis risk calculation and alerting system based on CogStack, an information retrieval and extraction platform for electronic health records.

使用 CogStack 基于电子健康记录的实时精神病风险检测和警报系统的实施

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful ...

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

"Implementation" of new initiatives in healthcare settings typically encompasses two distinct components: a "clinical intervention" plus accompanying "implementation strategies" that support putting the clinical intervention into day-to-day practice. A novel clinical intervention, for example, might consist of a new medication, a new protocol, a new device, or a new program. As clinical interventions are not self-implementing, however, they nearly always require effective implementation strategies in order to succeed. Implementation strategies set out to engage healthcare providers, staff and patients in ways that increase the likelihood of the new initiative being successfully adopted, a process that often involves behavior change and new ways of thinking by participants. One of the challenges in studying implementation is that it can be difficult to collect data about the status and progress of implementation, including participants' own perspectives and experiences concerning implementation to date. This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score, or PRIUS. The PRIUS method allows for the efficient and systematic capture of qualitative and quantitative data that can provide a detailed and nuanced account of implementation over time and from multiple viewpoints. This longitudinal method can enable researchers, as well as implementation leaders and organizational stakeholders, to monitor implementation progress more closely, conduct formative evaluation, identify improvement opportunities, and gauge the effect of any implementation changes on a rolling basis.

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score (PRIUS). The PRIUS method allows for the efficient and systematic capture of data over time and from multiple viewpoints in healthcare settings.

参与者报告的实施更新和评分（PRIUS）:一种随时间推移捕获实施相关数据的新方法

"Implementation" of new initiatives in healthcare settings typically encompasses two distinct components: a "clinical intervention" plus accompanying ...