Development and Applications of Daily-use Fall Risk Assessment Device to Prevent Elderly People from Falling
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 20
- 试验地点
- 1
- 主要终点
- Precision assessment of falling risk prediction
研究概览
简要总结
Study Objectives Objective 1: Compare the fall risk assessment results between balance sensors, traditional tests, and clinical diagnoses.
Objective 2: Improve the feasibility of using sensors to assess fall risk among older patients in the hospital.
The investigators select Hong Kong as the region for the experiment. Specifically, the community clinics and daytime hospitals are the actual onsite locations for experimenting. The specific venues of these locations need an electrical power supply and a flat ground for conducting the device test. Patients will be recruited for the development and testing of a device for fall risk assessment, study participants will be involved in balancing assessments, and questionnaire surveys, their medical records will be accessed. And during these tests and questionnaire surveys, the investigators will take photos, videos, and or audio recordings.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Device Feasibility
- 盲法
- None
入排标准
- 年龄范围
- 60 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Older adults aged 60 years and above;
- •Understand Cantonese or Mandarin or English;
- •Clinical patients in the clinics.
排除标准
- •Unable to give written informed consent (e.g., illiterate or with cognitive impairment);
- •Inability to stand for 30 seconds without any assistance.
研究组 & 干预措施
Intervention Group
Older adults with zero time of falls in the past twelve months will be treated as the reference group. Older adults with at least one time of falls in the past twelve months will use the device to assess their balance ability by attending three rounds of the tests.
干预措施: iBalance (Device)
结局指标
主要结局
Precision assessment of falling risk prediction
时间窗: six months
A primary outcome is the precision assessment of falling risk prediction both from device and clinical diagnose. The specific measurement variables for this primary outcome are the classification results of high/low falling risk based on the results (a composite result) of the device test and participants' actual falling incident indicators, including the number of falls, the severity of falling incidents after the device test, and medical history of the participants (eg. the history of the emergency, surgery, and diagnosed disease in the past three years), as well as the diagnosis results of the fall risk by clinical professionals. These results can be shown through the sensitivity and specificity analysis of the device evaluation.
次要结局
- Feasibility of fall risk assessment by using balance sensor in clinical settings(six months)
研究者
Prof. Xi Ning
Head of Department; Chair Professor of Robotics and Automation; Director of Advanced Technologies Institute, The University of Hong Kong
The University of Hong Kong