University of Oulu

Low CA, Li M, Vega J, Durica KC, Ferreira D, Tam V, Hogg M, Zeh III H, Doryab A, Dey AK, Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study, JMIR Cancer 2021;7(2):e27975, doi: 10.2196/27975

Digital biomarkers of symptom burden self-reported by perioperative patients undergoing pancreatic surgery : prospective longitudinal study

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Author: Low, Carissa A.1; Li, Meng1; Vega, Julio1;
Organizations: 1Mobile Sensing + Health Institute, Center for Behavioral Health, Media, and Technology, University of Pittsburgh, Pittsburgh, PA, United States
2Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
3Department of Surgery, New York-Presbyterian Hospital & Weill Cornell Medical College, New York, NY, United States
4NorthShore University HealthSystem, Evanston, IL, United States
5Department of Surgery, UT Southwestern Medical Center, Dallas, TX, United States
6Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States
7Information School, University of Washington, Seattle, WA, United States
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link:
Language: English
Published: JMIR Publications, 2021
Publish Date: 2021-06-28


Background: Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms.

Objective: The aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery.

Methods: A total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual’s typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically.

Results: Models using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly.

Conclusions: Results suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions.

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Series: JMIR cancer
ISSN: 2369-1999
ISSN-E: 2369-1999
ISSN-L: 2369-1999
Volume: 7
Issue: 2
Article number: e27975
DOI: 10.2196/27975
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
Funding: This work was supported in part by the Center for Machine Learning and Health at Carnegie Mellon University through the Pittsburgh Health Data Alliance, the National Cancer Institute (K07CA204380 and R37CA242545), the Hillman Fellows for Innovative Cancer Research Program funded by the Henry L. Hillman Foundation, and the Robotic Surgery Research Grant from the Society of American Gastrointestinal and Endoscopic Surgeons. We gratefully acknowledge Lillian Smith for her assistance with data collection and management.
Copyright information: © Carissa A Low, Meng Li, Julio Vega, Krina C Durica, Denzil Ferreira, Vernissia Tam, Melissa Hogg, Herbert Zeh III, Afsaneh Doryab, Anind K Dey. Originally published in JMIR Cancer (, 27.04.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.