UAB's virtual fair for Master's Degrees, Graduate Courses and PhDs
Informative sessions with directors and coordinators. From the 6th to the 10th of October: Registration is open!
You can also send the form on this page to request personalized attention.
- UAB Microcredential
- Code: 5235/101
- 1st edition
- Modality: Blended
- Credits: 3 ECTS
- Start date: 04/11/2025
- Finish date: 27/02/2026
- Places: 25
- Orientation: Professional
- Price: 600 €
-
Special price 180 €
Group of application: Amount with the NextGenerationUE Fund discount applied
- Teaching language: Catalan (90%), Spanish (10%)
- Location: Hospital Universitari Parc Taulí
In the theoretical part of this microcredential, the concept and use of multivariable biomarkers in mental health will be introduced and justified. The aim is to provide students with a methodology that allows for non-invasive, quantitative, continuous, easily applicable mental health assessment, aligned with standard clinical practices. This section will also cover the principles of measurement of various types of biosensors, as well as their characteristics and limitations. Additionally, knowledge of digital therapies will be introduced, with a validated example for the treatment of mild and moderate depression.
As an introduction to the practical part, some already proven "success cases" of the methodology will be explained. These include, for example, measurements of induced and chronic stress, depression assessments, and results obtained in the study of ADHD. A portion of the practical sessions will be dedicated to working with wearables to explore their possibilities and limitations. Students will work with medical-grade electronic devices to record physiological signals such as ECG, PPG, EDA, etc., as well as with various wearable devices that, in principle, can record the same signals. At this stage, the goal is for students to learn to observe and evaluate the differences between devices in order to determine the suitability and/or limitations of each one.
Based on the recorded signals, students will use ad-hoc algorithms to extract variables of interest for the intended measurement. In this section, students will practice extracting various variables of interest (HRV could be an example), using different parameters in the algorithms to observe how results may vary depending on, for example, segmentation, time interval width, interpolation method, etc. Students will also be asked to practice parameter extraction in real patients using an app linked to a wearable device, which tracks progress between visits, and to investigate and report on the evolution during that period, on circumstances and/or environments that are not friendly, and on the suitability of one therapy or another. To support this, a platform for capturing and storing medical data will be introduced, used, and practiced with.
Finally, practical sessions will also include an introduction to the REDCap platform for research data management and online surveys, as well as a new platform called LaMevaSalutMental, created for self-screening and self-management of mental health issues in young people and adults. LaMevaSalutMental was developed by the "e-Salut Mental Catalunya" project for the prevention and early detection of mental disorders, and has been designed to complement and improve the current care process by facilitating anonymous access to various services for the general population.
Class schedule: Tuesdays and Thursdays from 2 p.m. to 4 p.m., for 6 weeks until 12/19
As an introduction to the practical part, some already proven "success cases" of the methodology will be explained. These include, for example, measurements of induced and chronic stress, depression assessments, and results obtained in the study of ADHD. A portion of the practical sessions will be dedicated to working with wearables to explore their possibilities and limitations. Students will work with medical-grade electronic devices to record physiological signals such as ECG, PPG, EDA, etc., as well as with various wearable devices that, in principle, can record the same signals. At this stage, the goal is for students to learn to observe and evaluate the differences between devices in order to determine the suitability and/or limitations of each one.
Based on the recorded signals, students will use ad-hoc algorithms to extract variables of interest for the intended measurement. In this section, students will practice extracting various variables of interest (HRV could be an example), using different parameters in the algorithms to observe how results may vary depending on, for example, segmentation, time interval width, interpolation method, etc. Students will also be asked to practice parameter extraction in real patients using an app linked to a wearable device, which tracks progress between visits, and to investigate and report on the evolution during that period, on circumstances and/or environments that are not friendly, and on the suitability of one therapy or another. To support this, a platform for capturing and storing medical data will be introduced, used, and practiced with.
Finally, practical sessions will also include an introduction to the REDCap platform for research data management and online surveys, as well as a new platform called LaMevaSalutMental, created for self-screening and self-management of mental health issues in young people and adults. LaMevaSalutMental was developed by the "e-Salut Mental Catalunya" project for the prevention and early detection of mental disorders, and has been designed to complement and improve the current care process by facilitating anonymous access to various services for the general population.
Class schedule: Tuesdays and Thursdays from 2 p.m. to 4 p.m., for 6 weeks until 12/19
Scholarships and financial aid
Chek all the information on the possibilities for grants and scholarships in the page for UAB financial aids, grants and calls.
Coordinating centres
Escuela de Formación Permanente
Collaborating centres
Instituto Universitario Fundación Parc Taulí
Contact
Jordi Aguilo Llobet
Phone: 638292992
More information
With the funding of