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Towards Supporting Data-Driven Practices in Stroke Telerehabilitation Technology

Clara Caldeira, Mayara Costa Figueiredo, Lucy Dodakian, Cleidson R. B. de Souza, Steven C. Cramer, Yunan Chen

2021Proceedings of the ACM on Human-Computer Interaction19 citationsDOI

Abstract

Telerehabilitation technology has the potential to support the work of patients and clinicians by collecting and displaying patients' data to inform, motivate, and support decision-making. However, few studies have investigated data-driven practices in telerehabilitation. In this qualitative study, we conducted interviews and a focus group with the use of data visualization probes to investigate the experience of stroke survivors and healthcare providers with game-based telerehabilitation involving physical and occupational therapy. We find that \hlstudy participants saw potential value in the data to support their work. However, they experienced challenges when interpreting data to arrive at meaningful insights and actionable information. Further, patients' personal relationships with their goals and data stand in contrast with clinicians' more matter-of-fact perspectives. Informed by these results, we discuss implications for telerehabilitation technology design.

Topics & Concepts

TelerehabilitationFocus groupTelemedicineWork (physics)Health careVisualizationQualitative researchQualitative propertyMedical educationComputer sciencePsychologyMedicineArtificial intelligenceEngineeringEconomic growthMarketingMechanical engineeringSociologyBusinessEconomicsMachine learningSocial scienceStroke Rehabilitation and RecoveryVirtual Reality Applications and ImpactsData Visualization and Analytics
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