We have recently finished the curation process of the mobile big dataset obtained in our first study (around 18 million records) so that it can be used both by the POSTCOVID-AI platform and by any other interested researcher or entity. As we have already indicated in previous posts, this is a one-month study where, by using a mobile application developed for the project, heterogeneous data have been collected from 110 strategically selected individuals. This dataset is available at osf.io/6wrv3 where you can find 10 files with cleaned and preprocessed data as well as a detailed description of them. Specifically, in this first curated dataset generated by the POSTCOVID-AI project one can find:
- Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).
- Data corresponding to an initial questionnaire, with information related to demographic data, symptoms and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised scales (PANAS, PHQ, GAD, BRS and AAQ).
- Data corresponding to daily questionnaires, where variables related to mood (valence, activation, energy and emotional events) are measured.
- Data corresponding to weekly questionnaires, where information on work situation, symptoms and COVID vaccination, hours of physical activity per week, questions related to physical and mental health, etc. are requested.
We are currently working on obtaining new higher-level or statistical indicators from the curated dataset that can be directly interpreted by POSTCOVID-AI stakeholders.