Understanding Real-Life Falls in Amputees using Mobile Phone Technology

使用移动电话技术了解截肢者现实生活中的跌倒情况

基本信息

批准号：
9133378
负责人：
Arun Jayaraman
金额：
$ 33.36万
依托单位：
REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9133378
关键词：
3-Dimensional Accelerometer Age-Years Algorithms Amputation Amputees Car Phone Cause of Death Cellular Phone Classification Communication Communities Crowding Data Data Collection Data Quality Data Set Data Storage and Retrieval Detection Devices Elderly Emergency department visit Environment Environmental Risk Factor Etiology Event Fall injury Fall prevention Family Fright Goals Health Health Care Costs Hospitals Individual Injury Interview Knowledge Laboratories Lateral Lead Life Location Longitudinal Studies Lower Extremity Machine Learning Maps Medical Assistance Medical Care Costs Memory Methods Morbidity - disease rate Musculoskeletal Diseases Outcome Patients Persons Population Population Density Populations at Risk Prevalence Prevention strategy Prosthesis Prosthesis Design Protocols documentation Publications Quality of life Questionnaires Rain Real-Time Systems Recovery Rehabilitation therapy Reporting Research Running Side Surveys System Techniques Technology Time Vascular Diseases Visit Walking Weather Wireless Technology Work base cohort cost effective data exchange design diaries disability falls fear of falling health care quality high risk improved improved mobility information gathering mortality new technology novel prospective sensor social stigma stroke survivor trafficking willingness

项目摘要

DESCRIPTION (provided by applicant): Falls are a significant cause of death and serious injury and result in significant health-care costs. Individuals with a lower extremity amputation due to vascular disease are overwhelmingly elderly (at least 65 years of age) and are at especially high risk of falling. Successful fall prevention strategies depend on understanding how, why, when, and where individuals fall, and what types of falls (e.g., trip, slip, or lateral fll) are likely in a given population. Most studies on falls in amputees to date have relied surveys or questionnaires that are often completed a significant time after the fall and thus rely both on the individual's ability to remember the details of their fall and their willingness to be objective abut how and why they fell. Such approaches are susceptible both to inaccurate memories of the fall and to recall bias-for example, due to embarrassment about falling- and are especially unreliable in the elderly amputees. Mobile phones provide a simple, cost-effective method for detection and characterization of falls. Most available smart phones today have a tri-axial accelerometer, which provides highly accurate fall detection in real-time. Other available applications (or apps) can provide data on activity (running, walking etc.) and environment-such as the weather conditions or population density-that may have contributed to the fall and can pin-point the location of the fall-using GPS technology and highly accurate maps. Mobile phones also have inbuilt data storage and transfer capability, allowing for real-time acquisition and transmission of data. Additionally, mobile phones provide a simple means to contact the individual immediately after a suspected fall to confirm details of the fall (and to ascertain the need for medical assistance). Because mobile phone use is so widespread, there is no stigma associated with carrying such a device, which is likely to lead to high compliance. This study aims to use a mobile phone-based fall detection system in dysvascular amputees to detect falls, characterize the type of fall, analyze environmental conditions that may have contributed to the fall, and determine the longer-term consequences of each type of fall. Data acquired may be used to improve rehabilitation protocols or design better prostheses in order to prevent falls. This technology is ultimately transferrable to many populations with a high risk of falling-for example, the elderly, stroke survivors, or those with other musculoskeletal disorders or disabilities-leading to the design of specific fall prevention strategies for those populations.

描述（由申请人提供）：跌倒是死亡和重伤的一个重要原因，并会导致高昂的医疗费用。因血管疾病而下肢截肢的人绝大多数是老年人（至少 65 岁），跌倒的风险特别高。成功的跌倒预防策略取决于了解个人跌倒的方式、原因、时间和地点，以及特定人群中可能发生的跌倒类型（例如绊倒、滑倒或侧向跌倒）。迄今为止，大多数关于截肢者跌倒的研究都依赖于调查或问卷，这些调查或问卷通常在跌倒后很长时间内完成，因此都依赖于个人记住跌倒细节的能力，以及他们是否愿意客观地了解跌倒的方式和原因。这种方法很容易受到对跌倒的不准确记忆的影响，并且容易产生回忆偏差（例如，由于跌倒的尴尬），并且对于老年截肢者来说尤其不可靠。移动电话提供了一种简单、经济高效的跌倒检测和表征方法。当今大多数智能手机都配备三轴加速度计，可实时提供高度准确的跌倒检测。其他可用的应用程序（或多个应用程序）可以提供可能导致跌倒的活动（跑步、行走等）和环境（例如天气条件或人口密度）的数据，并可以使用以下方式精确定位跌倒的位置： GPS 技术和高精度地图。手机还具有内置的数据存储和传输功能，可以实时采集和传输数据。此外，手机提供了一种简单的方法，可以在疑似跌倒后立即联系个人，以确认跌倒的详细信息（并确定是否需要医疗援助）。由于移动电话的使用如此广泛，因此携带此类设备不会带来任何耻辱，这很可能会带来很高的合规性。本研究旨在对血管障碍性截肢者使用基于手机的跌倒检测系统来检测跌倒，描述跌倒类型，分析可能导致跌倒的环境条件，并确定每种跌倒类型的长期后果。获得的数据可用于改进康复方案或设计更好的假肢以防止跌倒。这项技术最终可以转移到许多跌倒风险较高的人群，例如老年人、中风幸存者或患有其他肌肉骨骼疾病或残疾的人群，从而为这些人群设计具体的跌倒预防策略。