NCT05054907

Using Wearable Device to Improve Quality of Palliative Care

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 75 (estimated)
• Sponsor: National Taiwan University Hospital · Academic / Other
• Sex: All
• Age: 20 Years – 105 Years
• Healthy volunteers: Not accepted

Summary

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

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.

Conditions

• Terminal Cancer
• End Stage Cancer

Timeline

• Start date: 2021-09-23
• Primary completion: 2022-12-31
• Completion: 2023-04-30
• First posted: 2021-09-23
• Last updated: 2022-11-09

Locations

2 sites across 1 country: Taiwan