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.
Highlights
Personal mHealth Apps combined with the Internet of Health Things (IoHT) technologies have the potential to help patients managing medical conditions, monitor lifestyles, and provide medical advice [1]
Every System step was fulfilled, to test this scenario we used UPDRSIII and Unified Dyskinesia Rating Scale (UDysRS) forms; 2a. the system was tested with Google Chrome and Internet Explorer 11 as browsers, no information were visualized on all the other steps except 2,3; 2b
The system integrates personally collected health data into the data acquisition process for a clinical trial, using a standards-based architecture that ensured the fulfillment of the major requirements [4]
Summary
Personal mHealth Apps combined with the Internet of Health Things (IoHT) technologies have the potential to help patients managing medical conditions, monitor lifestyles, and provide medical advice [1]. Clinical research may benefit from such technologies as well: personally collected data may enable capturing the personal perspectives on new therapies compliance or tolerability, patient’s conditions or symptoms. These new types of data, directly collected by patients in their ecologic environment, can be used in clinical trials to provide a more realistic view on the target of the clinical investigation [2]. The possibility to access and monitor data related to disease progression improves patient’s awareness and compliance to therapy [3].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
