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20182023

Research activity per year

Personal profile

Personal profile

Judith Austin is a researcher working at the intersection of health psychology, positive psychology and eHealth & design. She currently works as an assistant professor at the department of Psychology, Health & Technology.

In her doctoral research (defended sept 2023), Austin co-designed and evaluated a mobile self-compassion intervention (Compas-Y) for people with newly diagnosed cancer. This Compassionate Mind Training application is uniquely tailored to people with cancer and to mobile technology. The co-design process was based on systematically integrating theory and evidence with user wishes and needs. Using mixed methods and integrative analysis, Austin evaluated the use, appreciation and mental health impact of the application.

Building on her work on co-design and eHealth, Austin continues to examine creative ways to include people’s wishes and needs regarding the scope and acceptance of a technology. As part of the 4TU RECENTRE project, wishes and needs of people with cancer or obesity regarding lifestyle-related sensor technologies will be investigated with adaptive virtual reality and sensor related future-self perceptions.

Austin most aligns with interdisciplinary and mixed methods research. Both of these facilitate original thinking by challenging established conventions, and embrace knowledge emerging from divergence and complexity. Austin is involved in student supervision and guest lectures. Her overall research agenda is focused on promoting health, compassion and well-being in a high tech society.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Keywords

  • BF Psychology
  • eHealth
  • compassion
  • co-design
  • well-being
  • health
  • interdisciplinary research
  • mixed methods

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