# App-Assisted Day Reconstruction to Reduce Treatment Burden and Logistic Toxicity in Cancer Patients

> **NIH NIH R41** · DAYNAMICA, INC. · 2022 · $399,865

## Abstract

PROJECT SUMMARY
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, we 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, we leverage our 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...

## Key facts

- **NIH application ID:** 10483109
- **Project number:** 1R41CA271962-01
- **Recipient organization:** DAYNAMICA, INC.
- **Principal Investigator:** Yingling Fan
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $399,865
- **Award type:** 1
- **Project period:** 2022-06-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10483109

## Citation

> US National Institutes of Health, RePORTER application 10483109, App-Assisted Day Reconstruction to Reduce Treatment Burden and Logistic Toxicity in Cancer Patients (1R41CA271962-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10483109. Licensed CC0.

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