ArticleJMIR cancer2021
Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study.
Article in JMIR cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Clinical Outcomes of Passive Sensors in Remote Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- The Use of Wearable Devices in Oncology Patients: A Systematic Review.The oncologist · 2024Pooled it
- Mobile applications in gastrointestinal surgery: a systematic review.Surgical endoscopy · 2023Pooled it
- Wearables research for continuous monitoring of patient outcomes: A scoping review.PLOS digital health · 2025Article
- Advancing perioperative care with digital applications and wearables.NPJ digital medicine · 2025Article
- Article
- Application of Patient-Generated Health Data Among Older Adults With Cancer: Scoping Review.Journal of medical Internet research · 2025Article
- Adolescents' Self-Regulation of Social Media Use During the Beginning of the COVID-19 Pandemic: An Idiographic Approach.Journal of technology in behavioral science · 2024Article
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Review
- Review
- [Application possibilities of digital tools in postoperative pain therapy].Schmerz (Berlin, Germany) · 2023Review
- Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2022Review
- Article
- Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer.Journal of thoracic disease · 2021Article
- Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices.Frontiers in digital health · 2021Article
- Holistic Needs Assessment of Cancer Survivors-Supporting the Process Through Digital Monitoring of Circadian Physiology.Integrative cancer therapiesReview
- Application of wearables for remote monitoring of oncology patients: A scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundCancer 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.
objectiveThe 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.
methodsA 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.
resultsModels 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.
conclusionsResults 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.