The field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.

Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers’ experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing tools that support self-trackers’ sensemaking practices. To address this, we carried out a systematic literature review by synthesizing the findings of 91 articles published before 2021 in HCI literature. We identified four data sensemaking modes that self-trackers go through (i.e., self-calibration, data augmentation, data handling, and realization). We also identified four design implications for designing self-tracking tools that support self-trackers’ data sensemaking practices (i.e., customized tracking experience, guided sensemaking, collaborative sensemaking, and learning sensemaking through self-experimentation). We provide a research agenda with nine directions for advancing HCI studies on data sensemaking practices. With these contributions, we created an analytical information source that could guide designers and researchers in understanding, studying, and designing for self-trackers’ data sensemaking practices.

Research has extensively explored how personal informatics tools can support people's health goal setting practices. To understand the current state and reflect on the future of goal setting in personal informatics, we report the results of a scoping review of 51 papers that use and provide design implications for implementing goal setting. Our review highlights six implications for using goal setting in personal informatics tools (clarity, transparency, flexibility, framing and reframing, personalization, and reflection). We find that goal setting is becoming increasingly complex as the number of goals and their characteristics increase. We discuss these insights and point towards the importance of supporting self-efficacy during goal setting, showing adaptive goal evolution over time, reducing burden during goal setting, and framing goals to understand the complexity of health goals and support a holistic view on goal setting.

The purpose of this research is to explore the roles that sports trackers and running-related data play in runners’ personal goal achievement. A two-week diary study and semi-structured interviews were conducted with 22 runners to explore how runners engage with their running-related data to set and achieve their running goals. We found that participants pursued and transitioned between different running goals as their needs, abilities, and surrounding environment changed. We also found multiple motivations that shaped the use of sports trackers. We identified two main categories in runners’ motivations for using trackers and data to achieve their goals. These categories were (i) documenting and tracking in running, and (ii) supporting goal-oriented reflections and actions, with various reasons for use while preparing for and during running. This study provides insights into the psychological effects of running-related data and signals practical implications for runners and developers of tracking technology.

Training Load Management (TLM) is crucial for achieving optimal athletic performance and preventing chronic sports injuries. Current sports trackers provide runners with data to manage their training load. However, little is known about the extent and the way sports trackers are used for TLM. We conducted a survey (N=249) and interviews (N=24) with runners to understand sports tracker use in TLM practices. We found that runners possess some understanding of training load and generally trust their trackers to provide accurate training load-related data. Still, they hesitate to strictly follow trackers’ suggestions in managing their training load, often relying on their intuitions and body signals to determine and adapt training plans. Our findings contribute to SportsHCI research by shedding light on how sports trackers are incorporated into TLM practices and providing implications for developing trackers that better support runners in managing their training load.