LESSONS LEARNED FROM USING A REMOTE STUDY-MANAGEMENT PLATFORM: USE IN AN MHEALTH DIABETES STUDY

Session Name
INFORMATICS IN THE SERVICE OF MEDICINE; TELEMEDICINE, SOFTWARE AND OTHER TECHNOLOGIES
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:35 - 09:36
Presenter
  • Pietro Randine, Norway
Authors
  • Meghan Bradway, Norway
  • Pietro Randine, Norway
  • Eirik Ă…rsand, Norway

Abstract

Background and Aims

The use of an online study-management system can help to ease the burden of both participation in, and administration of, mHealth interventions. We describe the benefits and challenges of using such a platform to manage an intervention (FullFlow Project) involving both patients and their providers in the testing of an mHealth data-sharing system.

Methods

Our remote study-management platform consists of: a website used to monitor status and message the participants, a local server for automatic data-collection and analysis (Piwik, now Matomo) and an open source survey tool (LimeSurvey). Patient recruitment was initiated through health providers and continued through the platform. Two researchers and one developer administrated the study.

Results

The benefits of this platform included security and efficiency in distributing study-information and messages, as well as supporting participants from a single platform, based on open-source systems. For example, if a participant was not actively engaged in the intervention, we could then send messages specific to their situation. In the platforms’ current implementation, we have experienced three main challenges: 1-Participant follow-up requires manual tracking and initiation of messaging; 2-Data-collection requires manual review of data and interaction logs, from separate sources; and 3-Data-analysis requires specific programming to combine the differently structured output from each data source.

Conclusions

Future improvements to the system can include automation of tasks and additional software that can facilitate the organization of these data for analysis. For example, automatic merging of data-sources and generation of simple reports would make the system more efficient, which is especially important for mHealth interventions.

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