Cancer treatments can cause a variety of poorly-managed symptoms and toxicities that can
impair quality of life and functioning and lead to early discontinuation of life-prolonging
medical treatment. The goals of the proposed study are (1) to develop a novel mobile sensing
system to passively monitor symptom burden during chemotherapy and (2) to evaluate the
feasibility and acceptability of using this system to recommend patient-provider
communication. To accomplish Aim 1, we will enroll 200 oncology patients starting a new
chemotherapy regimen and will collect smartphone and wearable sensor data continuously as
well as daily patient-reported symptoms via smartphones for 90 days. Using machine learning
methods, we will retrospectively analyze data from the first 100 participants and develop a
generalizable and parsimonious population-level model to identify severe symptom days. For
the second 100 participants, we will run these computational models in real-time and
prospectively evaluate the accuracy of our classifications relative to patient-reported symptoms.
For Aim 2, we will enroll 50 patients in a prospective single-arm trial of a system that uses
inferences based on sensing and machine learning to recommend contact with providers and
will evaluate patient accrual, attrition, and compliance as well as information from both patients
and their providers about acceptability and perceived usefulness of the system. The scientific
premise of this proposal is that mobile sensing of subtle fluctuations in behavior coupled with
real-time computational modeling could enable earlier detection and ultimately better
management of severe symptoms during cancer treatment. The proposed project builds on our
prior research and completes the necessary development and feasibility work to support a large
multisite trial comparing the mobile sensing system to standard of care to determine effects on
symptom burden, quality of life, health care utilization, and survival.
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