Projects

Prehab2Rehab

An integrated and digitally supported care project connecting patient pathways from prehabilitation to rehabilitation for improved surgical outcomes.

KlimaFIT

A research project on active mobility, climate change, and rehabilitation - digital pathways to heat resilience.

HERO - the Heart Rehab App

The ‘Heart Rehab’ Information Tool - A digital prototype to support patient pathways to cardiac rehabilitation.

Digi:Green

Digital & Green Prevention and Rehabilitation - Combining nature-based health resources with digital health applications.

Human-AI Interaction

Exploring the impact of working with systems that are adaptive, learning, or produce output that is difficult to predict, as traditional interaction design paradigms are challenged.

Modular Open Research Platform

Modular Open Research Platform for Digital Health: An infrastructure to foster data-driven innovation.

AktivPlan

A digital health application supporting healthcare professionals and patients in planning and monitoring physical activities.

Virtual Reality Exposure Therapy

A project on engagement and empowerment using playful user-generated treatment in virtual reality exposure therapy.

Centre for Digital Citizens

The Centre for Digital Citizens (CDC) will address emerging challenges of digital citizenship, taking an inclusive, participatory approach to the design and evaluation of new technologies and services that support smart, data-rich living in urban and rural communities.

IDEA-FAST

A project on identifying digital endpoints to assess fatigue, sleep and activities of daily living in neurodegenerative disorders and immune-mediated inflammatory diseases building on data from sensing devices an apps.

Deep Player Behavior Models

A project on developing deep player behavior models for dynamic and generative non-player character interactions, game testing, player substitution and fraud detection.

Understanding Pathways to Sustainable Diets

A N8 priming project to review the literature and scope research on transitioning to sustainable diets.

Questionnaires in Virtual Reality

A project on potential biases and pathways to operationalisation of questionnaire-based research in virtual reality.

Mooqita

A project that aims to help students gain certifications, incomce, and employment through embedding real work tasks in online learning courses.

Science Jam

48 hours to go from zero to poster. The idea of the Science Jam is to apply the principle of game jams and hackathons to the rapid conceptualization, execution, and analysis of small-scale experiments, studies, or other pieces of research that can provide exploratory evidence or function as a pilots for larger follow-ups.

Space Project Y

A project that investigated the potential of motion-based exergames in virtual reality, focusing on locomotion techniques and feedback.

Adaptify

A project on supporting individually adapted therapy with digital games. It focused on harnessing the potential of motion-based games for health to support motivation, guidance, and feedback.

sPortal

A project that aims to help students gain certifications, incomce, and employment through embedding real work tasks in online learning courses.

Spiel Dich Fit

A project on motion-based games for health for the support of physiotherapy and rehabilitation for older adults.

WuppDi!

An early project exploring the applicability of exergames for supporting physiotherapy for people living with Parkinson’s.

Live SDI

The ‘live space-display-interaction’ project worked with the Troja pixel room to generate unique whole-room display interactions that investigate immersive displays.

Selected Publications

More Publications

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual’s current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025
DOI

Background: There has been a surge in the development of applications that aim to improve health, physical activity (PA), and well-being through behavior change. These apps often focus on creating a long-term and sustainable impact on the user. Just-in-time adaptive interventions (JITAIs) based on passive sensing of the current user context (e.g., from smartphones and wearables) have been devised to enhance the effectiveness of these apps and foster PA. JITAIs aim to provide personalized support and interventions, such as encouraging messages, in a context-aware manner. However, based on a limited range of passive sensing capabilities, getting the timing and context right for delivering well accepted and effective interventions is often challenging. Ecological Momentary Assessment (EMA) can provide personal context by directly capturing user assessments (e.g., moods and emotion). Thus, EMA might be a useful complement to passive sensing in determining when JITAIs are triggered. Yet, extensive EMA schedules need to be scrutinized as they can increase user burden. Objective: Use machine learning (ML) to balance feature set size of EMA questions with prediction accuracy regarding likelihood of enacting PA. Methods: A total of 43 healthy participants (ages 19-67) completed four EMA surveys daily for three weeks. These surveys prospectively assessed different states including both motivational and volitional variables of PA preparation (e.g., intrinsic motivation, self-efficacy, perceived barriers) alongside stress and mood/emotions. PA enactment was assessed retrospectively via EMA and served as the outcome variable. Results: The best performing ML models predicted PA engagement with an AUC score of 0.87 ± 0.02 SD in 5-fold cross validation and 0.87 on test set. Particularly strong predictors included self-efficacy, stress, planning, and perceived barriers, indicating that a small set of EMA predictors can yield accurate PA prediction for these participants. Conclusions: A small set of EMA based features like self-efficacy, stress, planning and perceived barriers can be enough to predict PA reasonably well and can thus be used to meaningfully tailor JITAIs such as sending well-timed and context aware support messages.
JMIR mHealth and uHealth, 2025
DOI

Many online games suffer when players drop off due to lost connections or quitting prematurely, which leads to match terminations or game-play imbalances. While rule-based outcome evaluations or substitutions with bots are frequently used to mitigate such disruptions, these techniques are often perceived as unsatisfactory. Deep learning methods have successfully been used in deep player behavior modelling (DPBM) to produce non-player characters or bots which show more complex behavior patterns than those modelled using traditional AI techniques. Motivated by these findings, we present an investigation of the player-perceived awareness, believability and representativeness, when substituting disconnected players with DPBM agents in an online-multiplayer action game. Both quantitative and qualitative outcomes indicate that DPBM agent substitutes perform similarly to human players and that players were unable to detect substitutions. Notably, players were in fact able to detect substitution with agents driven by more traditional heuristics.
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020
Preprint PDF Project DOI

Questionnaires are among the most common research tools in virtual reality (VR) evaluations and user studies. However, transitioning from virtual worlds to the physical world to respond to VR experience questionnaires can potentially lead to systematic biases. Administering questionnaires in VR (inVRQs) is becoming more common in contemporary research. This is based on the intuitive notion that inVRQs may ease participation, reduce the Break in Presence (BIP) and avoid biases. In this paper, we perform a systematic investigation into the effects of interrupting the VR experience through questionnaires using physiological data as a continuous and objective measure of presence. In a user study (n=50), we evaluated question-asking procedures using a VR shooter with two different levels of immersion. The users rated their player experience with a questionnaire either inside or outside of VR. Our results indicate a reduced BIP for the employed inVRQ without affecting the self-reported player experience.
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020
Preprint PDF Project DOI

This paper provides a critical examination of how digital systems within a charitable organisation in the North of England are being used to both support and challenge male perpetrators of domestic violence. While there exists a range of digital tools to support the victim-survivors of domestic violence, no tools are available to challenge the abusive and harmful behaviours of perpetrators. Through this work, we uncovered the compelling moral responsibilities intrinsic within interactions with technological systems between perpetrators and support workers. As such, we highlight four spaces of negotiation concerning a person’s responsibility in changing their abusive behaviour, which we have coined as mechanisms to represent their fundamental and interconnected nature. These mechanisms include self-awareness, acknowledging the extent of harms, providing peer support and respecting authorities. These insights are the basis for offering some practical considerations for HCI scholars, policymakers and intervention designers in their work with perpetrators of violence.
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020
Preprint PDF DOI

Recent Publications

More Publications

The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Prospective Cardiac Rehabilitation Setting

DOI

Active Waiting: Facilitating Short Bouts of Exercise During Idle Times for Promoting Physical Activity in Daily Living

DOI

Contextual and affective precursors of physical activity intention and enactment examined through ecological momentary assessment

DOI

Contextual and affective precursors of physical activity intention and enactment examined through ecological momentary assessment

DOI

Evaluation of the “ActiveWaiting App”: A Waitlist-Control Pilot Study

DOI

Teaching

  • Apr. 2025 - Aug. 2025 [exp.]: Proseminar Media Informatics Modul P12. Topic lead: Human-Centered Health Technologies. LMU Munich.
  • Nov. 2023: Applied Games (Multi-Media Technologies Master Programme). FH Salzburg. Guest lecturer on “Serious Games and Plafulness in Digital Health”.
  • Mar. 2023: IK (Interdisciplinary College) 2023 special topic course: SC14 – Human-Technology Interaction: Considering Minds, Bodies & Things (B.Sc. / M.Sc. / PhD / Postdoc). Module leader.
  • Mar. 2022 - Jul. 2022: Usability Engineering and Interaction Design VU2;703509. University of Innsbruck. Module leader.
  • 2022: Flexible Human-AI Interaction (IK Interdisciplinary College 2022 practical online course; levels: Bachelor / Master / PhD). Invited lecturer.
  • 2022: Usability Engineering and Interaction Design (VU2;703509; Systems Engineering). University of Innsbruck. Module leader.
  • 2020 - 2021: Degree Programme Director for the MSc Human-Computer Interaction, School of Computing, Newcastle University.
  • 2020 - 2021: CSC8611 Human-Artificial Intelligence (AI) Interaction & Futures, School of Computing, Newcastle University. Module leader.
  • 2020 - 2021: CSC8607 Research Methods in HCI, School of Computing, Newcastle University. Module leader.
  • 2019 - 2020: CSC8008 Information Systems, School of Computing, Newcastle University. Module leader (Interaction Design).
  • 2018 - 2019: CSC8602 Research Methods for Digital Civics, School of Computing, Newcastle University. Module leader.
  • Apr. 2017 - Jul. 2017: Entertainment Computing (B.Sc. / M.Sc. in Computer Science / Digital Media). University of Bremen. Co-Lecturer.
  • Mar. 2017: IK (Interdisciplinary College) 2017 practical course: Scientific Methods: Hands-on Research from Conceptualizing to Data Analysis (B.Sc. / M.Sc. / PhD). Main lecturer.
  • Oct. 2016 - Mar. 2017: Graduate seminar for the project Motion-based Exergames in VR (M.Sc. / MA in Computer Science / Digital Media). University of Bremen. Main lecturer.
  • Mar. 2016: IK (Interdisciplinary College) 2016 lecture: A Crash Course in Human Subject Research (B.Sc. / M.Sc. / PhD). Main lecturer.

Tags

Contact