Esophageal Cancer, Head and Neck Cancer, Lung Cancer
Conditions
Brief summary
In this study, the investigators obtain wearable disease based biomarkers from patients diagnosed with cancer and undergoing chemotherapy, and simultaneously measure patient self-reported adverse events through an app to evaluate chemotherapy completion rates, emergency room visits, and frequency of CTCAE adverse events. The investigators will develop an artificial intelligence-based algorism that can predict patients' side effects based on biomarkers alone.
Detailed description
In this study, patients diagnosed with lung, head and neck, and esophageal cancers and undergoing chemotherapy will be measured for self-reported side effects using a wearable device to collect biomarkers through an app, and the association between patient quality of life and side effects and biomarkers obtained from the wearable device will be analyzed. On the other hand, blood (EDTA 3cc) for pharmacogenomics testing will be tested once at any point during the study period as an indicator associated with side effects after chemotherapy.
Interventions
OTHERFitbit smartwatch
Patients wear the wearable to check their biomarkers and use the application to assess their quality of life and side effects at one-week intervals.
Sponsors
Sehhoon Park
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 20 years of age or older * Must have diagnosed with lung, head and neck, or esophageal cancer, 1. scheduled to receive their first treatment in preoperative or postoperative chemotherapy. 2. scheduled for systemic chemotherapy due to recurrence or metastasis. * Eastern Cooperative Oncology (ECOG) performance status of 0 to 2. * Patients who have access to a smartphone and can use the mobile app on their own. * Understand the purpose of the study and agree to participate in the study.
Exclusion criteria
* Patients who, in the judgment of the researcher, have difficulty using the application. * Patients who are judged by the medical staff to be unable to participate in the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Developing artificial intelligence prediction algorism | Through study completion, an average of 30 months | PRO data and treatment information collected from the wearable are used to evaluate correlations through methods such as linear regression to determine valid variables, utilizing LSTM models, etc. |
Countries
South Korea
Contacts
Primary ContactSehhoon Park, MD
Outcome results
None listed