Grant Details
Grant Number: |
4R37CA242545-06 Interpret this number |
Primary Investigator: |
Low, Carissa |
Organization: |
University Of Pittsburgh At Pittsburgh |
Project Title: |
A Mobile Sensing System to Monitor Symptoms During Chemotherapy |
Fiscal Year: |
2024 |
Abstract
Abstract
The aims of the parent R37 grant were (1) to develop and refine a mobile sensing system that
applies machine learning to smartphone and wearable sensor data to passively detect symptom
burden during chemotherapy and (2) to evaluate the feasibility and acceptability of using this
system in an academic oncology clinic. Since the start of the R37 award on July 1, 2019, we have
enrolled 154 (77% of our target n = 200) patients into a 90-day study assessing daily and weekly
symptoms and continuous behavioral sensing via wearable device and smartphone during
chemotherapy, conducted interim analyses demonstrating that next-day overall symptom
burden can be predicted from passive sensor data, and developed infrastructure to deploy
symptom prediction models in real-time.
The next logical step for the original R37 research is to integrate sensor- and machine learning-
based predictions into a patient-centered digital health intervention to personalize supportive
cancer care. The two-year extension will enable us to design and develop a web application that
shares real-time symptom predictions with patients and uses them to trigger personalized
symptom management instructions. To provide useful recommendations, we also need to
develop prediction models that can predict specific, clinically significant and actionable
symptoms rather than overall symptom burden. In addition, we need to determine the best way
to share machine learning-based predictions and recommendations in a way that fosters trust
and that supports patient autonomy as well as patient-provider communication. The R37
extension will enable us to address two new aims: (1) develop additional machine learning
models to predict specific clinically meaningful and actionable symptoms and (2) design and
develop a web application that integrates these predictions and uses them to inform real-time
symptom management.
This work falls well within the scope of the parent grant and represents continued refinement of
both the clinical utility of our machine learning models as well as our integration of these
models into supportive cancer care within an academic oncology clinic.
Publications
Associations between performance-based and patient-reported physical functioning and real-world mobile sensor metrics in older cancer survivors: A pilot study.
Authors: Low C.A.
, Bartel C.
, Fedor J.
, Durica K.C.
, Marchetti G.
, Rosso A.L.
, Campbell G.
.
Source: Journal Of Geriatric Oncology, 2024 Mar; 15(2), p. 101708.
EPub date: 2024-01-26 00:00:00.0.
PMID: 38277879
Related Citations
Digital health and telehealth in cancer care: a scoping review of reviews.
Authors: Shaffer K.M.
, Turner K.L.
, Siwik C.
, Gonzalez B.D.
, Upasani R.
, Glazer J.V.
, Ferguson R.J.
, Joshua C.
, Low C.A.
.
Source: The Lancet. Digital Health, 2023 May; 5(5), p. e316-e327.
PMID: 37100545
Related Citations
Patient and Provider Perspectives on Symptom Monitoring During Outpatient Chemotherapy: Interview Study.
Authors: Chen L.
, Bartel C.
, Cai X.
, Cheng Y.
, Perer A.
, McClaine S.
, Kairis E.
, Durica K.
, Huang W.
, Low C.A.
.
Source: Jmir Formative Research, 2023-04-17 00:00:00.0; 7, p. e46001.
EPub date: 2023-04-17 00:00:00.0.
PMID: 37067857
Related Citations
Another step (count) towards leveraging mobile health data for clinical prediction.
Authors: Murphy A.R.
, Low C.A.
.
Source: The Lancet. Digital Health, 2021 11; 3(11), p. e687-e688.
PMID: 34711373
Related Citations
Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study.
Authors: Low C.A.
, Li M.
, Vega J.
, Durica K.C.
, Ferreira D.
, Tam V.
, Hogg M.
, Zeh Iii H.
, Doryab A.
, Dey A.K.
.
Source: Jmir Cancer, 2021-04-27 00:00:00.0; 7(2), p. e27975.
EPub date: 2021-04-27 00:00:00.0.
PMID: 33904822
Related Citations
Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices.
Authors: Vega J.
, Li M.
, Aguillera K.
, Goel N.
, Joshi E.
, Khandekar K.
, Durica K.C.
, Kunta A.R.
, Low C.A.
.
Source: Frontiers In Digital Health, 2021; 3, p. 769823.
EPub date: 2021-11-18 00:00:00.0.
PMID: 34870271
Related Citations
Harnessing consumer smartphone and wearable sensors for clinical cancer research.
Authors: Low C.A.
.
Source: Npj Digital Medicine, 2020; 3, p. 140.
EPub date: 2020-10-27 00:00:00.0.
PMID: 33134557
Related Citations