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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：
9341305
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 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9341305
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 prevention Family Fright Geography Goals 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 Phonation Population Population Density Populations at Risk Prevalence Prevention strategy Prospective cohort 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 Telephone Time Vascular Diseases Visit Walking Weather Wireless Technology Work base cost effective data exchange design diaries disability fall risk falls fear of falling health care quality high risk high risk population improved improved mobility mortality new technology novel portability prospective public health relevance sensor social stigma stroke survivor 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）：福尔斯是导致死亡和严重伤害的重要原因，并导致大量的医疗保健费用。由于血管疾病而导致下肢截肢的患者绝大多数是老年人（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如何、为什么、何时和在哪里跌倒，以及什么类型的福尔斯（例如，绊倒、滑倒或侧向跌倒）在给定群体中是可能的。迄今为止，大多数关于截肢者福尔斯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一段时间内完成，因此既依赖于个人的能力，以记住他们的下降和他们的意愿是客观的细节如何和为什么他们下降的细节。这种方法容易受到跌倒的不准确记忆和回忆偏差的影响-例如，由于跌倒的尴尬-并且在老年截肢者中特别不可靠。移动的电话为福尔斯的检测和表征提供了一种简单的、具有成本效益的方法。如今，大多数可用的智能手机都具有三轴加速度计，其实时提供高度准确的跌倒检测。其他可用的应用程序（或应用程序）可以提供活动数据（跑步，步行等）。和环境--如天气状况或人口密度--这些因素可能是导致坠落的原因，并可以使用GPS技术和高度精确的地图来确定坠落的位置。移动的电话还具有内置的数据存储和传输能力，允许实时采集和传输数据。此外，移动的电话提供了一种简单的手段，在怀疑跌倒后立即联系个人，以确认跌倒的细节（并确定是否需要医疗援助）。由于移动的电话使用如此广泛，携带这种设备不会带来耻辱，这可能会导致高依从性。本研究旨在使用移动的基于手机的跌倒检测系统在血管障碍截肢者检测福尔斯，描述跌倒的类型，分析可能导致跌倒的环境条件，并确定每种类型跌倒的长期后果。所获得的数据可用于改进康复方案或设计更好的假体，以防止福尔斯。这项技术最终可用于许多高跌倒风险人群，例如老年人、中风幸存者或其他肌肉骨骼疾病或残疾人群，从而为这些人群设计特定的跌倒预防策略。