Using Wearable Device to Improve Quality of Palliative Care

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care

Conditions
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Terminal Cancer, End Stage Cancer

Participation Criteria
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Eligibility Criteria
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Inclusion criteria Age: 20 years old or older Clinical diagnosis: cancer in terminal stage. Exclusion criteria - Cannot cooperate with use of wearable devices or smartphones.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 105 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Wearable devices + Smartphone<br>The only arm in the study. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment | 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. | 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. | 
| 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 | 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. | 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. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Correlation between symptoms and wearable device parameters | The severity of symptoms will be recorded by symptoms assessment scale (SAS). Investigators will explore the correlation between the wearable device parameters and symptoms. | 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. | 
| Correlation between Australia-modified Karnofsky Performance Status (AKPS) and wearable device parameters | The functional status will be assessed by Australia-modified Karnofsky Performance Status (AKPS) during the follow-up. Investigators will explore the correlation between 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 | Evaluation of palliative care phases from the Palliative Care Outcomes Collaboration (PCOC) system will be assessed regularly. Investigators will explore the correlation between the palliative care phases and other parameters (wearable device parameters, symptoms, medical condition). | 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. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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palliative, cancer, survival prediction, artificial intelligence, wearable devices