The study will assess the feasibility of using the ChronolifeTM smart t-shirt for home monitoring of vital parameters in cancer patients during their treatment course. This study will bring new insights to how wearables and biometric data can be used as a part of symptom recognition in cancer patients during treatment course in the quest of increasing patients' quality of life.
Collecting biometric sensor data by wearables is an example of real-time patient-generated health data that can provide vital and detailed objective information about patients. This may have the potential to improve quality of oncological treatment and increase patients' quality of life. Studies have shown that there may be a dissimilar perception on symptoms and side effects between patients and health care professionals. Wearables may help identifying symptoms earlier. A new design of a wearable is a smart t-shirt. A smart t-shirt has sensors embedded in the fabric which generate measurement flows. This new tool provide more precise information without recall and reporting bias which may have the potential to lead to a better and more accurate cancer treatment.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SUPPORTIVE_CARE
Masking
NONE
Enrollment
10
The patients will be asked to wear a smart shirt. The shirt is designed with multiple sensors and electrodes fully embedded which engender 6 different measurement flows continuously. The smart t-shirt system is washable, and patients can wash and dry this t-shirt anytime, thus the shirt can be worn repeatedly.
Rigshospitalet
Copenhagen, Denmark
Number of participants who could wear the smart t-shirt preferably 8 hours pr. day during the three weeks study period
To assess the feasibility of using the ChronolifeTM smart t-shirt based on the completion rate which is defined as the number of included patients using the smart t-shirt at least 12 hours pr. day during the three weeks study period.
Time frame: 2 weeks
Technical feasibility
To assess technical feasibility in a Danish healthcare system including data acquisition rate and data completeness.
Time frame: 2 weeks
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