Low CA, Dey AK, Ferreira D, Kamarck T, Sun W, Bae S, Doryab A. Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study. J Med Internet Res 2017;19(12):e420 URL: http://www.jmir.org/2017/12/e420. DOI: 10.2196/jmir.9046. PMID: 29258977. PMCID: 5750420
Estimation of symptom severity during chemotherapy from passively sensed data : exploratory study
|Author:||Low, Carissa A1,2; Dey, Anind K3; Ferreira, Denzil4;|
1Department of Medicine, University of Pittsburgh
2Department of Psychology, University of Pittsburgh
3Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University
4Center for Ubiquitous Computing, University of Oulu
5Division of Medical Oncology, School of Medicine, University of Kansas
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201802123340
|Publish Date:|| 2018-02-12
Background: Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden.
Objective: The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy.
Methods: A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability.
Results: Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants.
Conclusions: Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.
Journal of medical internet research
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research was funded by the University of Pittsburgh University Center for Social and Urban Research Steven D. Manners Faculty Development Award, National Cancer Institute awards K07CA204380 and P30CA047904, and a grant from the Academy of Finland (276786-AWARE).
|Academy of Finland Grant Number:||
276786 (Academy of Finland Funding decision)
©Carissa A Low, Anind K Dey, Denzil Ferreira, Thomas Kamarck, Weijing Sun, Sangwon Bae, Afsaneh Doryab. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.12.2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.