TOPIC 412: CHAMP-WARE: CONTINUOUS HEALTH MONITORING AND PREDICTIONS USING A WEARABLES-AGNOSTICS PLATFORM FOR CANCER PATIENTS, PHASE I

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.