Number of study places for Open University students: 5

Open University

Tampere

Behavioral Health Informatics, Lectures

Contact information Email: open.studies.tau [at] tuni.fi Phone: 0294 520 200More information concerning Open University studies <a href="https://www.tuni.fi/open-university">tuni.fi/open-university</a>

Advanced studies

Faculty of Medicine and Health Technology

Technology and Natural Sciences

Apply for Open University studies by using the following links:<ul><li><a href="https://tau-avoin.ilmoittaudu.fi/lobby/?realization=J-otm-3444214f-6d7d-49f5-917f-7486a60717de_2023-tampere-BBTHTI511" target="_blank">BBT.HTI.511 Behavioral Health Informatics, lectures, Tampere</a></li></ul>You can find a link to the Open University Admissions service and application instructions on the <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a> page.Some of the studies require prior studies. For example, studying intermediate studies may require some completed basic studies. These are described on the Information about studies tab of the study unit under prior requirements.When you have been admitted to the studies, you must enrol separately via the Sisu information system for the implementation of the unit of study and, for example if set, for a practise group of your choice. You will receive instructions on how to do this by e-mail after you have confirmed your acceptance of the study place by paying the required fee.

English

Hervanta Campus

Contact teaching

Look information at <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a>.

<h2>Common</h2><h3>Exercise: </h3>06.03.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 13.03.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 20.03.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 27.03.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 03.04.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 10.04.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 17.04.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) 24.04.2024 12:00 - 14:00, TAU Sähkötalo SG312 opetustila (48) <h3>Lecture: </h3>04.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 07.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 11.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 14.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 18.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 21.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 25.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 28.03.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 01.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 04.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 08.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 11.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 15.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 18.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 22.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33) 25.04.2024 14:00 - 16:00, TAU Sähkötalo SE201 opetustila (33)

When passing this course, the student:<ul><li>Understands the differences in practical requirements for data analysis in preventive care vs. specialised healthcare.</li><li>Understands and can objectively evaluate the merits of different devices for measuring behaviour during daily living.</li><li>Is familiar with the main methods to quantize: sleep quality, cognitive load and stress, food intake, and physical activity.</li><li>Can assess performance of low-cost measurements in different scenarios (risk assessment, lifestyle coaching, chronic disease management)</li><li>Understands the concept of adherence to interventions and is able to apply methods to assess it in practice.</li><li>Has basic knowledge of behaviour change technologies and motivational tools.</li><li>Understand the possibilities and limitations of using AI, ML and advanced data analysis methods for data-driven lifestyle change recommendations </li><li>Has knowledge of the technical considerations to take into account for integrating wellness/behaviour data with traditional healthcare databases.   </li><li>The student has gained knowledge about common methods to model human behavioural patterns and to create personalised profiles.</li></ul>

Traditional lectures &amp;#43; Exercises gaining bonus points. Final exam at the end of the course.

Behavioral Health Informatics, Lectures

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Behavioral Health Informatics, 5 cr

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Behavioral Health Informatics, Lectures