Developing Artificial Intelligence-based Algorism to Predict Side Effects and Symptoms From Chemotherapy

July 20, 2023 updated by: Sehhoon Park

Developing Artificial Intelligence-based Algorism Based on Life-log Data Acquired From Wearable Device for the Prediction of Chemotherapy-induced Side Effects and Symptom

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Patients undergoing chemotherapy after being diagnosed with lung, head and neck, or esophageal cancer.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Neoadjuvant, Adjuvant Chemotherapy Arm
Patients diagnosed with lung, head and neck, or esophageal cancer and undergoing Neoadjuvant, Adjuvant chemotherapy.
Other: Fitbit 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.
Palliative Chemotherapy Arm
Patients diagnosed with lung, head and neck, or esophageal cancer and undergoing Palliative chemotherapy.
Other: Fitbit 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Developing artificial intelligence prediction algorism
Time Frame: 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.
Through study completion, an average of 30 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sehhoon Park

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 8, 2023

Primary Completion (Estimated)

December 31, 2025

Study Completion (Estimated)

June 30, 2026

Study Registration Dates

First Submitted

June 14, 2023

First Submitted That Met QC Criteria

June 30, 2023

First Posted (Actual)

July 10, 2023

Study Record Updates

Last Update Posted (Actual)

July 21, 2023

Last Update Submitted That Met QC Criteria

July 20, 2023

Last Verified

July 1, 2023

More Information

Terms related to this study

Other Study ID Numbers

  • 2023-02-057

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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