Modelling Missteps to Improve Fall Risk Assessment.
试验速览
- 阶段
- 不适用
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Community ambulation
研究概览
简要总结
The long-term goals of the project are: 1) Preventing falls before they occur, by significantly improving our ability to monitor fall risk and develop early and sensitive markers for this risk, based on tripping and near falls and other physiological signs, 2) automatically diagnosing falls within seconds from the time of the incident, without the need for an emergency / distress button or making a phone call.
详细描述
All subjects will be asked to come to the Center for the Study of Movement, Cognition and Mobility (CMCM), where they will undergo baseline testing. This initial evaluation is designed 1) to assess each subject's mobility, fall risk and related functions, and 2) to obtain more specific information that will be used to inform and update the model of falls and missteps detection.
The study is divided into 3 sections:
- First session in Gait Lab (CMCM) for an overall assessment of subject health (see below).
- Using the system ("monitoring ADL period") in daily life for 4 months (system: Owlytics Healthcare's app+wearable wristband & insoles).
- A concluding session where the mobility tests performed at the beginning of the study are repeated to assess the changes that occurred during the a period in which the monitoring system is used.
During the first session medical data will be recorded, such as demographics (age, gender, years of education, etc.), habits (physical activity, leisure activities, dietary habits), daily life activities, health-related behaviors (e.g., alcohol consumption and smoking history) and so.
Medical examination will include standardized walking tests (usual-walking and dual-task walking), eye examination, hearing test, balance tests, etc. In addition, to assess cognitive abilities standard Neuropsychological Battery will be used.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Prevention
- 盲法
- None
入排标准
- 年龄范围
- 65 Years 至 90 Years（Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •65-90 years old;
- •Ambulatory without help from another person (with or without use of walking aid);
- •Able to follow simple directions (Mini Mental State Examination score >21);
- •Community-living or assisted living housing for elderly.
排除标准
- •Subjects who will not be able to wear the devices for more than a 1 week period during the 4 months following their baseline evaluation (planning on traveling out of town, etc.);
- •Patients who are not able to deal with the device and do not family member or therapist who is willing to help with the system;
- •A state of health that does not allow participation in research and testing, or who has not agreed to participate in the study, or is unable to understand and follow simple instructions.
研究组 & 干预措施
Using Digital wearable system for fall detection
Digital wearable system (Owlytics Healthcare's app) enables a 24/7 health-tracking service, collecting personal health data from wearable wristbands and insoles. The data is analyzed by machine-learning algorithms that can detect abnormal physiological patterns. This allows the prediction and prevention of potentially harmful health events (such as falls).
干预措施: Digital wearable system (Behavioral)
结局指标
主要结局
Community ambulation
时间窗: One week post monitoring period
A body-worn small lightweight device (6-Axis Logging Accelerometer) that will be worn by the subject for 7 days to monitor ADL.
Physical Activity
时间窗: One week post monitoring period
The International Physical Activity Questionnaires (IPAQ-short). It will quantify the health-related physical activity.
The frequency of falling
时间窗: One week post monitoring period
The subjects are asked to fill in monthly Fall log (Frequency and circumstances of falls if occurred)
次要结局
- Improve in motor function(One week post monitoring period)
- Changes in endurance(One week post monitoring period)