Bi, Tao; (2023) Informing the design of voice-based technology for capturing runners' subjective and affective experience while running. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Despite significant advances in running technologies for objective running metrics (e.g., pace, heart rate, cadence), current technology is unable to fully capture the subjective and affective running experience (SARE) such as perceived exertion, pain, mood and their complex and dynamic interplay. In addition, there has been limited research on how SARE can be effectively collected, considering its complexity and dynamics in naturalistic situations. This thesis aims to address this gap through four contributions.
 Firstly, we present an in-depth understanding of what components of the running experience matter to runners and their supporters, and why. This is addressed through qualitative approaches in the context of long-distance running events. Secondly, we present findings on using ubiquitous ESM based on voice and wearables to gather in-situ self-reports and running data for creating automatic recognition systems for subjective and affective running experience (SARE-ARS). The mixed- method analysis shows the feasibility of such an approach to SARE self-report gathering and suggests design implications for creating a chatbot for interactivity, companionship, and self-reflection while running. Thirdly, we demonstrate the design of a chatbot’s conversation flow to facilitate voice-based SARE self-reports while running. Based on the results from user studies with the chatbot, we propose the novel Modality-Autonomy-Prompting (MAP) framework for designing chatbots for SARE self-reports. Finally, we contribute to the understanding of the dynamic and reflective elements of SARE self-reports in two steps: 1) by evaluating the effect of using a MAP-based chatbot on runners’ reflective self-reporting processes; and 2) by proposing the Description-Appraisal-Management (DAM) framework that goes beyond just simple affect labels. This framework helps unpack the experiential and reflective components of SARE and guide the chatbot design to support runners to continuously self-reflect and self-report their SARE while running. The thesis concludes by discussing some ethical considerations as well as opportunities for the use of such frameworks.

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