Augmented Reality (AR) gamification shows growing promise as an approach to the design of rehabilitation tools, but evidence on intervention effectiveness remains difficult to synthesise and compare across systems and studies. As part of a broader scoping review, we analyse 21 papers that report on AR gamified rehabilitation and examine how design choices, mechanisms, and measurement practices are connected in those studies. Our analysis reveals a recurring Design – Mechanism – Measurement (D-M-M) misalignment: systems increasingly combine heterogeneous AR delivery and sensing pipelines with gamified feedback, yet intervention mechanisms are rarely specified as testable pathways, and evaluations more often emphasise usability and feasibility of tools than indicators of rehabilitation change. We argue that future work should report AR gamified rehabilitation as an explicit intervention logic and make minimal D-M-M claims linking core design ingredients to hypothesised mechanisms and aligned measures. The next phase of this work will extend the same analytic lens across the wider review corpus to examine whether this pattern is specific to rehabilitation or reflects broader reporting tendencies in AR gamified physical activity research.

Physical inactivity remains a persistent public health challenge, while many digital interventions struggle to sustain engagement beyond initial novelty. This PhD investigates how Augmented Reality (AR) gamified systems can be designed to support sustained motivation and participation in everyday physical activity. The research frames AR gamified interventions as experience-centred interactive systems that can shape how movement is experienced in embodied, situated, and potentially social ways. The research adopts a multi-phase, mixed-methods approach combining an ongoing scoping review, formative inquiry, iterative prototype development, and early-stage mechanism-informed evaluation. It is expected to contribute a clearer conceptual, methodological, and design-oriented account of how AR gamified systems may support daily physical activity in more meaningful and sustainable ways.

This paper contains the research proposal of Deniz Mevlevioglu that was presented at the MMSys 2022 doctoral symposium. The benefits of real-time anxiety prediction in Virtual Reality are vast, including uses from therapy to entertainment. These anxiety predictions can be made using biosensors by tracking physiological measurements such as heart rate, electrical brain activity and skin conductivity. However, there are multiple challenges when trying to achieve accurate predictions. First of all, defining anxiety in a useful context and getting objective measurements to predict it can be difficult due to different interpretations of the word. Secondly, personal differences can make it difficult to fit everyone into a generalisable model. Lastly, Virtual Reality strives for immersion, and many systems that use objective measures such as on-body sensors to detect anxiety can make it hard for the user to immerse themselves into the virtual world. Our research aims to come up with a system that will address these problems and manage to get accurate and objective predictions of anxiety in real-time while still allowing the users to be immersed in the experience. To this end, we aim to use fast-performing classification models with multi-modal on-body sensor data to maximise comfort and minimise noise and inaccuracies.

Stroop Colour-Word Task has been widely used as a cognitive task. There are computerised and Virtual Reality versions of this task that are commonly used. The emotional version of the task, called the Emotional Stroop Colour-Word task is commonly used to induce certain emotions in a person. We are developing an application that brings the Emotional Stroop Colour-Word task into Virtual Reality. The aim of this application is to elicit different stress levels on the user and to record associated brain, heart and skin activity using wearable sensors. It is an immersive application that includes a tutorial, artificial intelligence generated audio instructions and a logging system for the user activity.

As the use of Virtual Reality (VR) expands across fields, new kinds of interaction methods are introduced. This study presents the Visual Heights VR experience that integrates natural breathing as an input method to provide visual respiratory feedback. Incorporating spatial audio, haptic feedback and breath visualisation, the experience aims to be highly immersive. This experience was made to be used as part of a controlled pilot study to see the effect of respiratory feedback on the user’s anxiety levels. The user’s anxiety is assessed by their heart rate, brain electrical activity, skin conductance and respiratory rate. These biosignals are recorded within the experience; captured by external hardware. The pieces of hardware used were Galvanic Skin Response to measure skin conductance, photoplethysmogram to measure heart rate; Electroencephalogram to measure the electrical activity in the brain, and a prototype device that records airflow on an axis from -1 to 1 for respiratory rate. It was found that the aforementioned prototype was not sufficient for calculating the respiratory rate. Results of the controlled study showed that the Visual Heights VR experience delivered the expected positive correlation between skin conductance and perceived height (r=.491, p < .05, N=1543) which suggests it is plausible to be used as a material for further research. As the integration of user’s physiological signals and breathing for visual feedback can contribute to therapeutic uses of VR, research with bigger sample sizes will be conducted to better investigate the relationship between visual respiratory feedback and anxiety using the Visual Heights VR experience.

Anxiety-inducing and assessment methods in Virtual Reality has been a topic of discussion in recent literature. The importance of the topic is related to the difficulty of getting accurate and timely measurements of anxiety without relying on self-report and breaking the immersion. To this end, the current study utilises the emotional version of a well-established cognitive task; the Stroop Color-Word Task and brings it to Virtual Reality. It consists of three levels; congruent which is used as control and corresponds with no anxiety, incongruent, which corresponds with mild anxiety and emotional, which corresponds with severe anxiety. This pilot serves two functions. The first is to validate the effects of the task using biosignal measurements. The second is to use the bio signal information and the labels to train a machine-learning algorithm. The information collected by the pilot will be used to decide what types of signals and devices to use in the final product, as well as what algorithm and time frame will be better suited for the purpose of accurately determining the user’s anxiety level within Virtual Reality without breaking the immersion.