ReviewCA: a cancer journal for clinicians2020
Innovations in research and clinical care using patient-generated health data.
Review in CA: a cancer journal for clinicians, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
84 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review.Journal of medical Internet research · 2024Pooled it
- The acceptability of using wearable electronic devices to monitor physical activity of patients with Multiple Myeloma undergoing treatment: a systematic review.Clinical hematology international · 2024Pooled it
- A Systematic Review on Visualizations for Self-Generated Health Data for Daily Activities.International journal of environmental research and public health · 2022Pooled it
- The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Trial
- Research Study Attrition and Participant-Informed Retention Strategies Among Cancer Survivors.JAMA network open · 2026Article
- Physicians' Perceptions and Demand Regarding Clinical and Secondary Use of Patient-Generated Health Data: Cross-Sectional Survey.Journal of medical Internet research · 2026Article
- Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation.Journal of medical Internet research · 2026Observational
- Patients' and healthcare providers' perceptions in outpatient palliative cancer care services at a tertiary care hospital in Sri Lanka: prospects for adoption of a digital health intervention.BMC health services research · 2026Article
- Exploring approaches to computational representation and classification of user-generated meal logs.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- International Consensus-Driven Recommendations for Patient-Reported Outcome Research Objectives in Early Phase Dose-Finding Oncology Trials: OPTIMISE-ROR.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- ATTITUDE - Addressing attrition in longitudinal cancer cohorts: an in-depth qualitative analysis of experiences and perspectives on participation in longitudinal studies among breast cancer survivors.Breast cancer research and treatment · 2026Observational
- The effectiveness of telemonitoring, either self-administered or professionally assisted, in cancer care management: a descriptive, retrospective, observational cohort study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Observational
- Patterns and predictors of variability in patient-generated daily pain severity collected via a mobile health smartphone app.PloS one · 2026Article
- Artificial intelligence in thoracic surgery: Perspectives and challenges.Surgery in practice and science · 2025Review
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
- American Life in Realtime: Benchmark, publicly available person-generated health data for equity in precision health.PNAS nexus · 2025Article
- The digital intelligent precise nursing framework: theory development in health recommender system.BMC nursing · 2025Article
- Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer.JCO clinical cancer informatics · 2025Article
- Exploring Clinician Experiences With a Digital Platform Supporting Orthopedic Care That Integrates Patient-Generated Health Data: Qualitative Study of Early Users.JMIR human factors · 2025Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.