ArticleFrontiers in medicine2019
Personally Collected Health Data for Precision Medicine and Longitudinal Research.
Article in Frontiers in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed, 14 citations in OpenAlex.
- Initial Implementation of E-Prescribing: Evidence from Eastern Taiwan.Healthcare (Basel, Switzerland) · 2026Article
- Self-monitoring of health - user viewpoints on gathering data using consumer health technologies during leisure time.Frontiers in digital health · 2025Article
- Blockchain technology and raft consensus for secure physician prescriptions and improved diagnoses in electronic healthcare systems.Scientific reports · 2024Article
- Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology.Sensors (Basel, Switzerland) · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health data autonomously collected by users are presently considered as largely beneficial for wellness, prevention, disease management, as well as clinical research, especially when longitudinal, chronic, home-based monitoring is needed. However, data quality and reliability are the main barriers to overcome, in order to exploit such potential. To this end, we designed, implemented, and tested a system to integrate patient-generated personally collected health data into the clinical research data workflow, using a standards-based architecture that ensures the fulfillment of the major requirements for digital data in clinical studies. The system was tested in a clinical investigation for the optimization of deep brain stimulation (DBS) therapy in patients with Parkinson's disease that required both the collection of patient-generated data and of clinical and neurophysiological data. The validation showed that the implemented system was able to provide a reliable solution for including the patient as direct digital data source, ensuring reliability, integrity, security, attributability, and auditability of data. These results suggest that personally collected health data can be used as a reliable data source in longitudinal clinical research, thus improving holistic patient's personal assessment and monitoring.
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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.