Non-Intrusive Automated Portable Data Collection System for Aging Surveys

用于老龄化调查的非侵入式自动便携式数据收集系统

• 批准号: 8314307
• 负责人: Amy Papadopoulos
• 金额: $ 61.38万
• 依托单位: AFRAME DIGITAL, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-15 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8314307
• 关键词: Accounting; Acute; Address; Adherence; Affect; Aging; Agreement; Area; Beds; Behavior; Behavior Therapy; Blood Pressure; Caregivers; Caring; Centers for Disease Control and Prevention (U.S.); Charge; Chronic; Chronic Disease; Congestive Heart Failure; Data; Data Collection; Dementia; Devices; Diabetes Mellitus; Diet and Nutrition; Disease; Early treatment; Economics; Effectiveness; Elderly; Evaluation; Health; Health Personnel; Health Policy; Healthcare; Household; Human; Hypertension; Individual; Institutes; Insurance Carriers; Intervention; Learning; Location; Measures; Monitor; Nurses; Pain; Participant; Patient Self-Report; Patients; Pattern; Phase; Physical Function; Physicians; Physiological; Play; Population; Reporting; Research Personnel; Sampling; Sleep; Stress; Survey Methodology; Surveys; System; Time; United States; Visiting Nurse; Weight; Wireless Technology; Wrist; aged; base; care burden; cost; data collection methodology; falls; instrument; interest; phase 2 study; population based; population survey; sample collection; sensor; touchscreen

DESCRIPTION (provided by applicant): As the percentage of the U.S. population aged 65 and older grows from the 12.9 percent reported in 2009 to the estimated 19 percent projected in 20301, it is becoming increasingly important that the many factors affecting the health and well being of this portion of the population are understood and addressed. Methods of surveying this population and gathering longitudinal data regarding aspects of their lives, such as activity level sleep patterns, physiological data, and behavior patterns are needed and have been highlighted as an area of interest by the National Institute of Aging. Such data are not only useful to academic researchers, but to insurers, health care providers, health and health policy analysts and, on a more intimate level, by caregivers. However, cost and adherence play key roles in the ability to collect such data, so an easy-to-use, low-cost automatic data collection system is required; one which can and will be utilized by the elderly on a regular basis and which is capable of capturing a wide range of key health indicators. This Phase II application aims to demonstrate the effectiveness of a portable and real-time survey system capable of collecting activity level, location, and sleep patterns on a continual basis from a wrist-based device in a watch form factor, as well as physiological data such as blood pressure and weight from easy-to-use wireless devices, and self-reported data from easy, touch-screen interfaces regarding a number of varying topics including pain level, diet and nutrition, or stress. The application aims to prove the following hypothesis: The proposed monitoring system provides an effective means of collecting real-time survey data (including physiological, activity, sleep, and self- report dat) from elderly individuals for an extended period of time and is capable of recognizing deviations from individualized baseline norms that could be indicative of illness or need for intervention. During the study, 30 reasonably healthy elderly individuals and 60 elderly individuals with congestive heart failure (CHF) will be monitored over six months, during which a number of episodes of acute CHF exacerbation are expected to occur; the system will learn to recognize and alert upon such episodes. CHF was chosen so that the effectiveness of the system in flagging deviations from an individual's system-learned baseline could be better demonstrated. All study participants will be monitored by a visiting nurse/physician once each week. Moreover, 30 of the CHF participants and the 30 other participants will also be monitored using the proposed system configured to provide real-time data to the nurse in charge of each participant. The specific aims of the project will be to establish that: 1) the platform can and will be used by an elderly population for an extended (six-month) period of time, 2) the watch device is effective for collecting longitudinal sleep and activity data, and 3) the system is effective in providing useful survey data to monitoring nurses while reducing the burden of care. PUBLIC HEALTH RELEVANCE: As the percentage of the U.S. population aged 65 and older grows from the 12.9 percent reported in 2009 to the estimated 19 percent projected in 20301, it is becoming increasingly important that the many factors affecting the health and well being of this portion of the population are understood and addressed. Methods of surveying this population and gathering longitudinal data regarding many aspects of their lives, such as activity level, sleep patterns, physiological data, and behavior patterns are needed and have been highlighted as an area of interest by the National Institute of Aging. The monitoring system proposed for evaluation during this Phase II study is expected to provide an effective means of easily collecting real-time survey (including physiological, activity, sleep and self-report) data from elderly individuals for an extended period of time as well as automatically recognize deviations from individualized baseline norms indicating the possibility of illness or need for intervention.

描述（由申请人提供）：随着美国65岁及以上人口的比例从2009年报告的12.9%增长到2030年预计的19%，了解和解决影响这部分人口健康和福祉的许多因素变得越来越重要。对这一人群进行调查，收集他们生活各方面的纵向数据，如活动水平、睡眠模式、生理数据和行为模式，这些都是必要的，并且已经被国家老龄化研究所强调为一个感兴趣的领域。这些数据不仅对学术研究人员有用，而且对保险公司、卫生保健提供者、卫生和卫生政策分析人员有用，在更亲密的层面上，对护理人员也有用。然而，成本和依从性在收集这些数据的能力中起着关键作用，因此需要一种易于使用、低成本的自动数据收集系统；一种老年人能够并将经常使用的工具，它能够收集各种关键的健康指标。该二期应用旨在展示便携式实时调查系统的有效性，该系统能够通过腕表形式的腕带设备持续收集活动水平、位置和睡眠模式，以及通过易于使用的无线设备收集血压和体重等生理数据，并通过简单的触摸屏界面收集有关疼痛水平、饮食和营养或压力等一系列不同主题的自我报告数据。该应用程序旨在证明以下假设：提出的监测系统提供了一种有效的方法，可以在较长一段时间内收集老年人的实时调查数据（包括生理、活动、睡眠和自我报告数据），并能够识别与个性化基线规范的偏差，这些偏差可能是疾病或需要干预的指示。在研究期间，将对30名相当健康的老年人和60名患有充血性心力衰竭（CHF）的老年人进行为期6个月的监测，在此期间预计会发生多次急性CHF加重发作；系统将学会识别并对此类事件发出警报。选择CHF是为了更好地证明系统在标记偏离个人系统学习基线方面的有效性。所有研究参与者将由一名来访护士/医生每周监测一次。此外，30名CHF参与者和其他30名参与者也将使用拟议的系统进行监测，该系统配置为向负责每位参与者的护士提供实时数据。该项目的具体目标将是确定：1)该平台可以并且将被……使用