# Topic 412: CHAMP-WARE: Continuous Health Monitoring and Predictions using a Wearables-Agnostics Platform for Cancer Patients, Phase I

> **NIH NIH N43** · INTELLIGENT AUTOMATION, INC. · 2020 · $399,980

## Abstract

Commercially available off-the-shelves (COTS) wearables that objectively track
physiological variables offer a rich source of information about a patient’s health to
clinicians and oncology researchers, to facilitate early adverse-event detection and
subsequent management, which can decrease healthcare costs and improve patient quality
of life. The passive, continuously measured data streams generated by current or future
COTS sensors will allow direct/indirect measures of cancer progression and its
symptoms. Increased out-of-clinic patient and clinician engagement via these tools will
allow more precise delivery of cancer care after as well as during cancer remission.
Ultimately, these passive sensing platforms’ data for digital biomarkers will afford
clinicians: 1) more objective metrics of response to therapeutics; 2) control and autoreporting
of symptoms and their fluctuations; 3) monitoring of side-effects of
experimental or standard of care therapies; and 4) more ecologically valid clinical
endpoints, all decreasing assessment burden via increased continuity of physiological
measurement sampling and patient context in the ambulatory setting. Furthermore,
such data present an opportunity to measure population-based statistics from large
cohorts of cancer patients by way of the myriad of devices currently available or being
developed.
Unfortunately, despite the availability of a myriad of COTS wearables capable of
measuring physiological variables, their use for remote cancer patient monitoring or for
out-of-clinic cancer research is yet to become mainstream. There is a considerable need
for scalable informatics tools that allow automated data aggregation, integration and
machine learning/artificial intelligence (AI)/predictive analytics that can pull from
disparate data sets across COTS device vendors and have the flexibility to add new
measures as they are developed. Furthermore, a central software platform is needed that
could obtain wearable or external device data and uniformly compare/contrast/couple
data streams to understand physiology versus patient context with respect to time: such a
capability will substantially advance this unique approach to aid cancer patients, clinician
assessment and clinical trial design.
This work seeks to overcome these bottlenecks and provide a workflow and an
infrastructure for out-of-clinic remote patient monitoring and online research
collaboration for advancing population-based research. By developing a software
system, comprised of a smartphone app, database, and a Web portal, which can a)
collect and standardize raw sensor data from multitude of wearables, b) perform
intelligent multi-sensor data analytics to provide clinically relevant outcomes in real
time, c) store these data in a common repository, and d) provide online interfaces to view
and analyze data, the proposed effort will significantly advance out-of-clinic cancer
research and patient monitoring.

## Key facts

- **NIH application ID:** 10265744
- **Project number:** 75N91020C00057-0-9999-1
- **Recipient organization:** INTELLIGENT AUTOMATION, INC.
- **Principal Investigator:** Sridhar Ramakrishnan
- **Activity code:** N43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $399,980
- **Award type:** —
- **Project period:** 2020-09-16 → 2021-06-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10265744

## Citation

> US National Institutes of Health, RePORTER application 10265744, Topic 412: CHAMP-WARE: Continuous Health Monitoring and Predictions using a Wearables-Agnostics Platform for Cancer Patients, Phase I (75N91020C00057-0-9999-1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10265744. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*