Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype
Research output: Contribution to journal › Article › Scientific › peer-review
- University of Helsinki
Objective: The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features.
Methods: We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data.
Results: The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies.
Conclusions: The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry.
|Number of pages||11|
|Journal||JMIR Research Protocols|
|Publication status||Published - 9 Jun 2017|
|MoE publication type||A1 Journal article-refereed|
- data collection framework, mental health, digital phenotyping, big data