Clinical Trial Details
— Status: Enrolling by invitation
Administrative data
NCT number |
NCT06322992 |
Other study ID # |
R41CA271962 |
Secondary ID |
|
Status |
Enrolling by invitation |
Phase |
|
First received |
|
Last updated |
|
Start date |
May 14, 2024 |
Est. completion date |
May 2025 |
Study information
Verified date |
May 2024 |
Source |
Daynamica, Inc. |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
The goal of this observational study is to develop a mobile app for cancer patients
undergoing treatments. In Aim 3, patients will rate the quality of the app using the Mobile
App Rating Scale (MARS) and their satisfaction with the three key app features. The outcome
of this project will be a final prototype app with 70% of patients indicating an overall MARS
score of 4.0 or more and satisfaction with the three features.
Description:
Each year, more than 1.7 million new cancer patients in the U.S. undergo intense, multimodal
treatments that that create numerous logistical challenges in managing treatment and everyday
life priorities. In the current cancer care system, "logistic toxicity"-the toxic effects
imposed by the logistical burden of carrying out cancer treatment-related tasks on patient
well-being-has been largely unmeasured and unaddressed. Current methods for measuring
logistic toxicity generate retrospective assessments intended for researchers. They do not
offer timely information that empower patients to solicit assistance from care providers,
employers, family, and friends. Nor do they empower providers to explore the increasingly
available treatment options for patient- centered cancer care. This proposal aims to apply a
new method-app-assisted day reconstruction-to develop the first digital health tool to enable
remote patient monitoring of logistic toxicity, which is the necessary first step towards
developing effective care interventions for addressing it.
Our product is both conceptually and technically innovative. Conceptually, the investigators
apply the day reconstruction method-a method initially created by well-being researchers for
collecting more accurate data on daily life experiences-to collect activity engagement and
well-being information related to cancer treatment tasks. Technically, the investigators
leverage the existing patented technology and new machine learning techniques to enable novel
integration of objective mobile sensing with subjective patient input. Mobile sensing and
machine learning will constitute the "assist" that the app provides for day reconstruction in
relation to logistic toxicity, significantly reducing recall errors and the need for manual
input. The "assist" will also prompt patients to provide information such as subjective
well-being ratings that are not detectable by mobile sensing or machine learning, generating
more accurate and comprehensive measures of logistic toxicity than existing methods.
The project has three specific aims, including (1) an initial system design based upon input
from cancer patients and cancer care stakeholders, (2) prototype development and initial
tests, and (3) field tests of the app among 60 diverse patients undergoing treatment for
cancer. In Aim 3, patients will rate the quality of the app using the Mobile App Rating Scale
(MARS) and their satisfaction with the three key app features: 1) the app's ability to
capture out-of-home treatment-related activities and trips, 2) the ease of the interface for
inputting home-based treatment-related activities and well-being ratings, and 3) the
usefulness of the logistic toxicity summary report. The outcome of this project will be a
final prototype app with 70% of patients indicating an overall MARS score of 4.0 or more and
satisfaction with the three features. In Phase II, the team will test the efficacy of the
app-both separately and in conjunction with care coordination, telemedicine, and home-based
treatments-in reducing logistic toxicity and improving treatment outcomes in a randomized
controlled trial.