Developing IoT-Based Rehabilitation Frameworks : A Mixed-Methods Study with Healthcare Professionals, Stroke Survivors, and Design Experts

Namnaqani, Fayez and Tsekleves, Emmanuel and Eccles, Fiona (2026) Developing IoT-Based Rehabilitation Frameworks : A Mixed-Methods Study with Healthcare Professionals, Stroke Survivors, and Design Experts. PhD thesis, Lancaster University.

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Abstract

This thesis investigates the potential role of Internet of Things (IoT) technologies in post-stroke rehabilitation through a design research lens, focusing on perceived benefits, challenges, and resulting design implications. Stroke rehabilitation requires continuous, personalised care, which traditional models often struggle to deliver due to limited accessibility, reliance on in-person sessions, and the absence of real-time feedback. Reflecting my dual positionality as a design researcher with a clinical background in physiotherapy, the study adopts a human-centred approach to bridging the gap between clinical needs and design implementation. A mixed-methods design was employed, structured around five sequential phases: (1) an international online survey with healthcare professionals (n=67); (2) a primary participatory workshop with stroke survivors exploring foundational usability (n=18); (3) a secondary participatory workshop in the same location investigating advanced personalisation (n=18); (4) a validation study with healthcare practitioners (n=18); and (5) expert interviews with design researchers (n=4) to critique and refine the findings from a disciplinary perspective. The qualitative data were analysed using Reflexive Thematic Analysis to ensure a rigorous, iterative interpretation of stakeholder and expert discourses. Findings indicate that IoT is perceived as offering considerable potential to support rehabilitation by enabling remote monitoring, personalised treatment, and improved access for underserved populations. The primary contribution of this work to the field of Design is the articulation of a Four-Principle Framework: Adaptive Simplicity, the Human-in-the-Loop Service Model, Configurable Data Transparency, and Contextual Motivation. These principles informed the development of 11 design-informed recommendations—translated from stakeholder insights into actionable design guidance. While the study does not provide empirical evidence of clinical effectiveness, it makes a novel contribution by synthesising the lived experiences of stakeholders with the disciplinary insights of design experts. This integration is employed specifically to generate a robust set of design-informed recommendations and a structured framework. By combining these diverse perspectives, the research bridges the gap between the theoretical potential of IoT and the practical requirements for its implementation in stroke rehabilitation. This research addresses a critical gap in design and digital health literature and provides actionable guidance for developing inclusive, scalable, and clinically relevant IoT solutions.