The Data Infrastructure Support Core (DISC) aims to provide a central coordinating hub for data collection, data management, and data delivery processes within Stress in Action. In phase 1, the DISC will catalogue the existing datasets and provide meta-data annotation. In phase 2, we will create the Data Governance
Framework and the Standard Operational Procedures for the new data collection.
In 2024, we released the SiA Cohorts Data Access Protocol, containing information about access procedures for the cohort studies within Stress in Action. We identified vendors of EMA smartphone platforms and negotiated package deals with them for pilot studies in Stress in Action. The vendor mPath was selected, and the DISC provided SiA members with the application forms and protocols to request mPath access for their phase 1 experimental studies. A manual was created on using (elements of) the EMA part of the Stress in Action toolkit. We also provided NWO with the 2024 update of the Data Management Plan, adding the ongoing experimental phase I studies. Finally we worked on an exposure inventory in the SiA cohorts. Since March 2024, our team has been strengthened with postdoctoral researcher Hugo Klarenberg.
DISC members played a key role in setting up the Toolkit Task Force that works in small sub-teams towards a first version of the Stress in Action toolkit for ambulatory data collection of emotional, cognitive, behavioural and physiological stress responses. In conjunction, the DISC is piloting the feasibility of using Sports Data Valley to extract data of fitness trackers of our cohort participants during the phase 2 cohort enrichment.
In 2025, we are building a Shiny app for the SiA wearables database, which we will make available as an open source for stress researchers (in- and outside Stress in Action) to aid optimal wearable selection for their research. The exposure inventory will be completed and we’ll continue with the outcomes inventory. Using the data listed in the EMA inventory sheet we will perform a confirmatory factor analysis on the EMA data in the SiA cohorts to establish the resemblance of the between and within factor structure across the items.
