Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 75
- 试验地点
- 2
- 主要终点
- Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment
研究概览
简要总结
This study is going to use wearable devices and smartphones to collect physical data from terminal patients and build a survival predicting model for terminal patients with machine learning. Investigators hypothesize that continuous physical data monitoring could offer a hint to better predictability in end-of-life care.
详细描述
The study aim to examine the feasibility of utilizing wearable devices and smartphones in palliative patients in Taiwan. In addition, investigators try to identify the relationship between mobile health data and disease progression and establish a predicting model to the emergent medical need and death of patients, via machine learning.
This is a single-arm observational study using wearable devices and smartphones in terminal cancer patients. Investigators planned to enroll 75 patients who receive palliative care. After obtaining consent from the patients or their legally authorized surrogate decision-makers, a baseline assessment will be conducted, with a guide to use wearable devices and phone apps.
Investigators will keep regular follow-up for 52 weeks or until the participants' death. Assessment will be conducted every week, face-to-face or by telephone contact. A routine assessment includes symptoms and functionality in the past week, and vital signs and facial photograph will be recorded if possible. Physical data measured from wearable devices would be recorded continuously. The emergent medical needs of patient, including emergency department visit, unplanned admission and death of participants will be recorded if happen.
The primary outcome is the predictive performance (sensitivity and specificity) of the machine-learning model using wearable device data and symptoms assessment. The secondary outcomes are symptoms, including pain, dyspnea, diarrhea, constipation, nausea, vomiting, insomnia, depression, anxiety and fatigue. Users' opinion and comment to using experience will also be recorded.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 105 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment
时间窗: From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Other clinical assessments are performed every week. Death or survival is recorded at the time the case closed.
Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patients' death or survival within specific time range. The primary outcome is to evaluate the Area Under the Receiver Operating Characteristic curve (AUC - ROC) of the machine-learning model in predicting patients' survival.
Area Under the Receiver Operating Characteristic curve (AUC-ROC) of machine-learning model to predict unexpected medical needs using wearable device parameters and clinical assessment
时间窗: From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Other clinical assessments are performed every week. Events are recorded upon happening or afterwards.
Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patient's unexpected medical needs (which is defined as emergency department visit or unplanned admission to hospital). The primary outcome is to evaluate Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model in predicting unexpected medical needs.
次要结局
- Correlation between Australia-modified Karnofsky Performance Status (AKPS) and wearable device parameters(From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Functional status assessed every week.)
- Correlation between palliative care phase and wearable device parameters(From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Palliative care phase assessed every week.)
- Correlation between symptoms and wearable device parameters(From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Symptoms assessed every week.)