Grand Challenges in SportsHCI
Don Samitha Elvitigala

Monash University
Melbourne, Australia
don.elvitigala@monash.edu

Laia Turmo Vidal

KTH Royal Institute of Technology
Stockholm, Sweden
laiatv@kth.se

Maria F. Montoya

Monash University
Melbourne, Australia
maria@exertiongameslab.org

Florian DaiberArmağan Karahanoğlu
University of Twente
Enschede, the Netherlands
a.karahanoglu@utwente.nl

Dees Postma

University of Twente
Enschede, the Netherlands
d.b.w.postma@utwente.nl

Daniel Harrison

Andrii Matviienko

KTH Royal Institute of Technology
Stockholm, Sweden
andriim@kth.se

Michael Jones

Brigham Young University
Utah, USAjones@cs.byu.edu

Lars Elbæk

Northumbria University
Newcastle, UK
daniel.b.p.harrison@northumbria.ac.uk

University of Southern Denmark
Odense, Denmark
lelbaek@health.sdu.dk

Lisa Anneke Burr

Rakesh Patibanda

German Research Center for Artificial
Intelligence (DFKI)
Saarbrücken, Germany
florian.daiber@dfki.de

University of Salzburg
Salzburg, Austria
lisaanneke.burr@plus.ac.at

Monash University
Melbourne, Australia
rakesh.patibanda@monash.edu

Paolo Buono

Perttu Hämäläinen

Robby van Delden

University of Bari
Bari, Italy
paolo.buono@uniba.it

Regina Bernhaupt

Eindhoven University of Technology
Eindhoven, the Netherlands
r.bernhaupt@tue.nl

Fabio Zambetta

RMIT University
Melbourne, Australia
fabio.zambetta@rmit.edu.au

Aalto University
Espoo, Finland
perttu.hamalainen@aalto.fi

University of Twente
Enschede, the Netherlands
r.w.vandelden@utwente.nl

Xipei Ren

Vincent van Rheden

Beijing Institute of Technology
Beijing, Chinax.ren@bit.edu.cn

Elise van den Hoven

University of Technology Sydney,
Australia and Eindhoven Universityof Technology, the Netherlands
elise.vandenhoven@uts.edu.au

Dennis Reidsma

University of Twente
Enschede, the Netherlands
d.reidsma@utwente.nl

University of Salzburg
Salzburg, Austria
vincent.vanrheden@plus.ac.at

Carine Lallemand

Eindhoven University of Technology
and University of Luxembourg
Eindhoven, the Netherlands
carine.lallemand@uni.lu

Florian ‘Floyd’ Mueller

Monash University
Melbourne, Australia
floyd@exertiongameslab.org

Figure 1: The image shows examples of SportsHCI systems a). BikeAR, AR bike training system to enhance safety [90] b).
GymSoles, feedback system to improve posture in full-body exercises [37] c). Interactive tag playground, steering behaviour interms of proxemics and movement [170] d). Laina, physicalizes running routes to motivate runners. [104] e). An interactive
wall to gamify climbing. [66] f). SMA based feedback system to train cricket strikes [117] g). Interactive LED floor for volleyballtraining h). Marbowl, an intelligently moving bowl for target training [41] i). Haptic, visual and auditory feedback forskateboarding tricks [128] j). Eye-tracker integrated in ski goggles to capture snow experiences [86, 112] k). Footstrike [28]provide EMS feedback about heel strike during running l). CricketCoach, interactive bat and gloves to improve awarenessof gripping forces [118] m). BodyLights, an open-ended visual feedback wearable to support personalized performancetraining [162] n). Virtual ski-jump training to learn skills in a safer environment. [148] o). Feedback system on rowing
technique in VR [169] p). Grace: physicalisation of social support for exercising motivation [101].

ABSTRACTThe field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active humanbeing. 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 ofgenerating fundamental knowledge for SportsHCI more broadly toadvance 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
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CHI ’24, May 11–16, 2024, Honolulu, HI, USA© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 979-8-4007-0330-0/24/05
https://doi.org/10.1145/3613904.3642050

performance, athlete-coach relationships, spectator engagement,
but also immersive experiences for recreational sports or exercise
motivation, and ultimately, improve human well-being.

CCS CONCEPTS• Human-centered computing → Interaction paradigms.

KEYWORDS
Sports technology, Physical Activity, grand challenges
ACM Reference Format:
Don Samitha Elvitigala, Armağan Karahanoğlu, Andrii Matviienko, Laia
Turmo Vidal, Dees Postma, Michael Jones, Maria F. Montoya, Daniel Harrison, Lars Elbæk, Florian Daiber, Lisa Anneke Burr, Rakesh Patibanda, Paolo
Buono, Perttu Hämäläinen, Robby van Delden, Regina Bernhaupt, Xipei
Ren, Vincent van Rheden, Fabio Zambetta, Elise van den Hoven, Carine
Lallemand, Dennis Reidsma, and Florian ‘Floyd’ Mueller. 2024. Grand Challenges in SportsHCI. In Proceedings of the CHI Conference on Human Factors
in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. ACM,
New York, NY, USA, 22 pages. https://doi.org/10.1145/3613904.3642050

1

INTRODUCTION

Sports Human-Computer Interaction (SportsHCI) is a dynamic,
multi-disciplinary field that merges interactive technologies andGrand Challenges in SportsHCI

human-centric design principles to optimise and enhance the sports
experience of athletes at all levels, from amateurs to elite athletes,
including recreational sportspeople. As sports have evolved into
highly competitive and technologically driven domains, the demand for innovative solutions that empower athletes, coaches, andspectators has become increasingly evident. Furthermore, the last
few years have seen tremendous growth in the use of technology
in the sports industry, often building on human-computer interaction (HCI) research. However, we note that there seem to be
many similar projects and products emerging (mainly in the formof smartphone apps that allow for athletic performance comparison), suggesting that innovation in SportsHCI might have come toa point where it has stagnated. We believe this stagnation might
be occurring because SportsHCI lacks a structured and coherent
research agenda, as is often the case in emerging research fields[21]. And we might, in turn, lack a structured and coherent research
agenda because there has not yet been a systematic articulation of
the field’s grand challenges [131].
In addition, articulating grand challenges for SportsHCI will
broadly impact the rapidly growing technology-driven sports industry, with the sports technology market projected to reach significant
turnover in the coming years [136]. SportsHCI designs interactive
technology that enhances users’ experiences, performance, anddata-driven decision-making, contributing to this market growth.
In this context, identifying grand challenges and articulating a
research agenda will ensure the responsible and sustainable development of SportsHCI in the activity-based sports industry. And,in this regard, the sustainable development of next-generation interactive technologies supports humans’ physically activity, which
has broader positive impacts on our mental and physical well-being
and, consequently, our overall quality of life [87].
In this paper, we articulate 16 challenges, categorised into fivethemes: 1) Athletic performance optimisation analysis; 2) The
athlete as a multifaceted individual; 3) Human-centered designand sports engagement; 4) Technological considerations in thereal world; and 5) Strategic vision on what to strive for throughSportsHCI. By addressing these grand challenges, we aspire to nurture the future of sports, employing technology to augment athletic
prowess and enrich the holistic sports experience for all. We formulated our grand challenges using a community-centric methodology,
comprising a structured five-day workshop involving 22 renowned
SportsHCI researchers and practitioners, during which we drew
upon the insights arising from their broad spectrum of disciplines
and areas of expertise. We drew inspiration from prior works that articulated grand challenges in diverse fields, such as shape-changing
interfaces [2], immersive analytics [40], and human-computer integration [114]. Like these works, we also believe that by articulating
grand challenges, we might be able to help develop the field further, in particular:help contribute towards a structured research
agenda for the growing SportsHCI community; offer guidance tonewcomers to SportsHCI; present a consolidated perspective toexternal stakeholders; foster potential collaboration avenues withindustry partners and funding bodies; and elevate the world ofsports through human-computer interaction. The contributions of
this work are:
• A set of grand challenges that researchers and practitioners in SportsHCI can use to position their future research

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

which might pave the way toward a structured and coherent
research agenda for the growing SportsHCI community. Furthermore, we provide an overview and the interconnectionsof these challenges using figures.
• An overview of state-of-the-art related to grand challenges,
allowing researchers to understand how prior work can begrouped to identify future collaborators.

2

RELATED WORKThis section briefly presents what we learned from previous workshops and review papers that aimed to advance the SportsHCI field.
We also highlight how these prior works inspired the seminar. Priorwork related to each grand challenge is described later in the paper.
Over recent years, the prospect of designing interactive systems
to support sports and exercise activities has engaged the HCI community.This engagement has occurred in workshops on “HCI andSports" by Mueller et al. [109] and Nylander et al. [122, 176], andin the Sport-HCI SIG meetings presented at CHI conferences [115].
There has also been a surge in research on embodied interaction
for sports [23, 106, 142]. In this regard, Mencarini et al. [94, 96]
identified emerging trends in SportsHCI, highlighting the sports
experience’s digital evolution over the past 15 years, and Postma
et al. [131] developed a taxonomy for SportsHCI systems basedon related work in HCI and sports science. While these works inspired us to delve deeper, they also identified that future work isneeded [94, 96, 131] because the participants in this field do not
yet appear to have systematically articulated its challenges. Consequently, our work begins to respond to this imperative.
Several studies, including Jensen et al. [62, 63] and Tholander
et al. [157], have tackled challenges tied to specific sports and therole of technology in amplifying the sports experience. However, although they have brought the field forward, due to their case-study
nature, they fall short of comprehensively advancing the field as awhole (evident by, for example, missing key facets of sports suchas audience interaction, long-term practices, and non-competitive
physical activities). We also learned from sports science which,as a discipline, has investigated the incorporation of wearable
and interactive devices [123], instrumentation concerns [44], gear
optimisation [34, 93, 153], and injury prevention technologies [10].
Furthermore, prior literature has reviewed particular SportsHCI
technologies, such as sports wearables [163]. However, these prior
works do not tell us what HCI researchers should do next.
As such, we believe it is worthwhile for the community to articulate the grand challenges in SportsHCI. Therefore, this paper
aims to fill the identified gap by providing a set of grand challenges
formulated systematically and collaboratively.

3

METHOD

We elicited grand challenges in SportsHCI during a 5-days collaborative seminar in July 2023, uniting 22 international experts on
the topic, representing various perspectives and disciplinary backgrounds. Our approach is motivated by previous efforts in HCI to
pinpoint key challenges through extensive multi-day workshops
and discussion sessions. Examples include immersive analytics [40]and topics on food and sustainability [120], as well as broader HCI
challenges [151].

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Elvitigala, et al.

Figure 2: Disciplinary backgrounds of the workshop participants and their specific expertise in the SportsHCI domain.

3.1

Participants

The recruitment process began by inviting experts in several rounds,
with a concern for (a) international and institutional diversity, (b) abalance between experts on the different facets of the research topic,and (c) a mix of senior and junior researchers. Based on these criteria, we approached 55 people other than the organisers. Twenty-six
responded positively, four dropping out due to personal reasons.
Thus, the seminar involved 22 international experts (15 men, seven
women, no non-binary or self-described, aged 28 to 62 years old),
including four co-organisers. All participants signed an informed
consent form. Our pool included 3 PhD candidates, three postdoctoral researchers and lecturers, 11 assistant and associate professors,and five full professors. Participants had 13.7 years of experience in
HCI on average (Min= 3, Max = 25) and 7.3 years of experience inSportsHCI (Min = 1, Max = 25) on average. Participants represented
the following disciplinary backgrounds (some having more thanone): HCI (n=18), computer science (n=10), design (n=6), sports andmovement studies (n=3), psychology/social sciences (n=3).
Our participants’ expertise in SportsHCI spans multiple areas(Figure 2), primarily fun, engagement and immersive experiences,training and skill development, motivation and engagement, andperformance enhancement. Similarly, our experts represent a mix ofmethodological expertise (qualitative methods, quantitative methods, design research, experimental research, lab studies, field studies,
first-person perspective, and embodied methods) and technological expertise (e.g., XR, tangible UIs, wearables, sensors/actuators).
Their research work focuses on a diversity of users: recreational
sportspeople (n=20), sports experts (n=10), individuals with disabilities (n=7), teenagers (n=6), older adults (n=5), children/parents(n=7), professional athletes (n=4) and others (sedentary workers,
larger populations).
Most of the experts in the seminar are practising sports at different levels, from amateur to semi-professional, and more thanhalf are actively involved in sports communities or organisations
beyond their research activities.

3.2

Procedure

The seminar organisers began the first day with a short introduction to the grand challenges activity, including examples from past
grand challenges papers in other areas of HCI. The organisers presented the number and type of grand challenges in these papers,

emphasising that one could find patterns in the main overarchingcategories under which these prior publications clustered the grandchallenges: users, technology, design, and society.
Identification of grand challenges by each presenter. Each seminar
participant gave a presentation introducing their research. Each
presentation also articulated the challenges facing their investigations, which each participant had individually prepared before
the seminar. We added these challenges to four flip-over sheets
during each presentation, initially clustering them under the four
overarching categories (as a starting point).
Collective listing of challenges with all participants. The organisers
encouraged participants to go to the flip-over sheets at any point
during the sessions and add challenges and opportunities for designing SportsHCI systems. Through the presentations, we collated acomprehensive list of challenges, consisting of challenges identified
by the authors in their preparation for the seminar and challenges
identified by participants while listening to other presentations.
These outputs provided a foundation for steering discussions during later activities.
Initial clustering. Based on the challenges gathered by the participants, one of the authors completed an initial clustering (Figure 3).
Extending on the four clusters derived from previous exemplary
grand challenges papers, the author grouped the challenges across“users”, “technology”, “design”, “society”, “research/transversal”,
and "policy, politics and industry". They added these extra clusters
because research issues and the topics of policy, politics, and industry seemed to resonate with people during the previous step. All
participants discussed this clustering at the start of the next day
to reach a consensus before deriving grand challenges from theresulting collection of materials.
Definition of a grand challenge. After the initial clustering, we discussed the following inclusion criteria to omit common challenges
not specific to SportsHCI.
(1) Is the challenge specific to SportsHCI, or at least moresalient? If not, does it at least play out differently inSportsHCI than in other fields?
(2) Is the challenge important for the field and not easily solved?
(3) Is the challenge not addressed yet in the current work?

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Figure 3: Digital whiteboard used by participants for the initial clustering of the Grand Challenges.
(4) Is the challenge feasible, i.e., solvable in, say, the next ten
years?
Group discussion on each grand challenge. Based on the challenges
collectively gathered, participants discussed a list of potential grandchallenges according to the defined criteria. This list included reconciling performance and experience, the feeling of data (objective
vs. subjective data), the temporal aspect in SportsHCI, sports data
in a wider context (home, nature, city, work) from both a technology and an activity perspective, designing for political futures,
athletic performance from the experiential perspective, the role of
the audience, promoting physical literacy, addressing inequality byreaching people at a disadvantage, developing a strategic vision
for the field, and engineering challenges for SportsHCI. Those topics were all identified in the group discussion as potentially being“grand” challenges.
The participants split themselves into four breakout groups. We
tasked each group with discussing and elaborating on one of the
proposed grand challenges. We then conducted a second round,
for which the groups were ‘shuffled’ to work a different challenge.
After each round, the researchers conducted a plenary presentationof highlights (a brief description of each possibly revised challenge
and its list of sub-facets).
Final grouping and selection of grand challenges and sub-challenges.
We took the various sub-facets of the proposed Grand Challenges
already discussed and the detailed and extended list of underlying
challenges identified in earlier steps and incrementally iterated
it into a final grouping. We conducted this stage of analysis in
an AI-assisted manner, using ChatGPT1 to obtain the following
outcomes: (a) additional suggestions for challenges that we might
have missed in the initial discussions; (b) additional input regarding
the possible interrelated structure between ideas that we alreadyhad; and (c) align our work with the kind of grouping that existing
grand challenges papers typically use, so we could follow a common
best practice for narrative structures of grand challenges. As input,
we used the challenges identified in the first steps, examples of theearlier grand challenges papers mentioned in the introduction [2,40, 114], and specific instructions for what we wanted to achieve
at the end of this step, such as “what additional challenges are
1 https://chat.openai.com

missing?”. By iterating over the ChatGPT output, we gathered about
20 additional fragments and ideas, which we manually verified
to represent the underlying data more accurately. Subsequently,
within a breakout group, we discussed and fit them with the existingideas and groupings. This re-fitting continued asynchronously after
the seminar and as part of writing this paper. Finally, the authors
wrote this paper without using ChatGPT to generate any text orundertake any copy-editing.
Selecting related work to elaborate on the grand challenges. Figure 3
shows some intermediate results, including a fragment of the Miroboard used for the initial clustering. This way, we arrived at thebroader grand challenges that can inform priorities for the field’s
research over the next ten years. After identifying grand challenges,
we reviewed the literature to identify the state-of-the-art related toeach grand challenge.

4

GRAND CHALLENGES

Table 1 presents the final 16 grand challenges. By organising the
grand challenges into five main categories, researchers and practitioners can better focus their efforts in each respective area, fostering advancements that collectively contribute to the broader goal ofelevating the world of sports through human-computer interaction.
In the following sections, we (1) explain the challenges aroundperformance optimisation in Sports HCI that current research efforts can and should tackle and provide the big picture of the challenge space; (2) delve into the human-centred challenges of Sports
HCI from the perspective of the three main stakeholders (i.e., athletes, coaches and spectators); (3) zoom into the athlete-centred
challenges and articulate the complexity of designing for multifaceted athlete needs; (4) and explicate the challenge of designing forthe real world. Finally, we will explain the challenges relating to (5)the strategic research vision in Sports HCI.

4.1

Challenge 1: Lack of knowledge of how
interactive technology can support
performance optimization

Imagine that a national governing body for track and field asks us toenhance the performance of a 100m runner. However, we do not yet

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Elvitigala, et al.

know how interactive technology can support performance optimisation.
Most contemporary SportsHCI systems are concerned with performance analysis and optimisation [131]. Sensor technologies suchas motion capture systems and wearable devices are used to measureathletes’ performance. Various real-time and non-real-time performance analysis techniques are used to model and interpret this data.
The data science life cycle runs from gathering and analysing tocommunicating and using insights [183]. There are two main ways
in which SportsHCI builds upon this life cycle: first, dashboard
and retrieval systems communicate the insights directly to athletes
and coaches; and, second, interactive training systems where adigital-physical training exercise can be adapted to the athlete’s
specific performance. For both of these applications, the challenges
are closely tied to the nature of the data and data processing ofunderlying performance analysis and optimisation.

delivering feedback across multiple modalities in quick successive
repetition.
Suppose we wish to address the challenge of providing the rightfeedback based on real-time performance analysis. In that case, it
is not enough to address specific feedback for specific measurements. We must take into account the complex interconnectivity
between many facets, including the athlete’s physiological data,
body characteristics, and biomechanical data; training routinesand the mechanisms underlying injury risk and prevention; and a
deep understanding of athletes’ sensemaking of their performanceand the corresponding data (analysis), and how feedback leads tochanges in an athlete’s behaviour and thus their performance anddevelopment. We cannot disentangle and address these individually.
Instead, we need a holistic understanding of performance optimisation that can underlie our designs that use data effectively andbeneficially for the athlete’s performance.

4.1.1 Lack of knowledge of how to design real-time bodily performance analysis systems. The insights and advice regarding performance optimization need to be communicated to athletes andcoaches using technology. Dashboard and retrieval-like approaches
allow coaches and athletes to gain more insight into performanceand thus make better decisions about modifying their strategies
and exercise regimes. Such SportsHCI systems may involve, for example, augmented retrieval and browsing of video recordings [77],or specialised overviews and visualisations that provide concise
insights to support the athlete’s and coach’s sensemaking of thedata [25, 126, 129, 144]. Interactions with such systems often occur after training and during post-game analysis [82, 167, 168].
During a real-time training activity, an athlete can use real-time
performance visualisation to steer their execution of an exercise.
Real-time visualisation represents the summarised measurements
directly and in comparison with ideal schedules or past self or
peer performance [155, 171]. This situation raises the question of
how exactly to provide performance-related feedback to athletes
and coaches. For example, the right time to provide feedback mayvary from case to case. Sometimes, the right time may be immediately during competition or training. In other cases, the righttime may be after several repetitions of a skill in practice or after
the conclusion of a competition. Immediate feedback may allow
adaptive tuning, while delayed feedback may allow reflection. For
example, figure 1K shows prior work that delivered prompt feedback during sports activities. In this system, Hassan et al. used an
EMS system to provide feedback about footstrike in running [54].
The system gives feedback mid-stride while the foot is in the air
by directly actuating calf muscles to drop the forefoot and avoid
heel strikes. Many researchers have instead used vibrotactile orauditory modalities to provide feedback to enhance a specific skill
in a sports activity [141, 175]. In all such cases, researchers must
undertake more work to identify effective content, the best modality to provide feedback, and when to deliver the feedback. While
sports science knowledge might help with this work [131, 145],
it is primarily focused on individual feedback on a particular aspect as captured by a coach that is then delivered verbally, thereby
missing the opportunities that interactive technology can provide,
such as capturing more fine-grained data at a faster pace and then

4.1.2 Lack of knowledge in designing interactive technologies forthe longitudinal nature of athletic performance. Sports performance
has many longitudinal characteristics. Acquisition of skills takes along time. Lasting behaviour change regarding sports and physicalactivity is a long-term endeavour. Our bodies change over longer
timeframes (sometimes months and years), not hours or days.
Longitudinal performance tracking has been primarily investigated through data-driven approaches, including machine learning algorithms, to identify trends and patterns in an athlete’s
progress. These patterns are used for sports analytics, recruitment
and management of teams, betting, and gathering long-term statistics to better understand the nature of sports performance. However, in SportsHCI, we seldom focus on the longitudinal aspects ofsports. We must address longitudinal issues because understanding
SportsHCI in a longitudinal setting is essential to generalising theimpact of interactive technologies in athlete performance optimisation in the long run and understanding the life cycle of performance
optimisation.
However, carrying out long-term studies in SportsHCI has various challenges. Technologically, designing longitudinal studies withinteractive technologies requires prototypes to be robust enough towithstand complex, repetitive biomechanical activities over long periods. If a technology uses novel sensing and feedback, confirmingthat the sensing and actuation response remains the same over timeis important. Finally, for long-term data, the technology may getoutdated more quickly than the temporal scope of the effects we areinterested in. Regarding the role of participants in long-term studies, it is a challenge to develop suitable injury mitigation protocols
and monitoring mechanisms for the athlete. Furthermore, having
regular SportsHCI interactions with a participant over a long period poses unique challenges to keeping participants engaged andon board. Regarding the organisation of longitudinal studies, thecomplex logistics, higher dropout rates, and more complex research
ethics challenges make such studies harder to organise and carryout than one-shot studies.
On top of that, there is a major challenge related to the fact that
longitudinal studies in HCI are not a commonly established tradition compared to fields such as health and medicine. Long-term
studies are complex and require substantial time to plan and execute [59, 69]. They commonly have a larger scope than PhD projects,

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CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Challenges Related to Performance Optimization
SportsHCI
Real-time feedback

Responsive sensory
technology
Reliability of data
providing systems

HumanSportsResearch

Sensemaking of data

Athletic performance

Longitudinal studies

Unique characteristics
of athletes

Biomechanics integration

Data science
approaches and results

Experience Enhancement
Figure 4: summarises how performance optimisation and analysis challenges arise from the interplay between SportsHCI andthe human athlete doing their sports activities.
typically requiring a large, sustainable, collaborative research infrastructure to maintain a study over a longer time. In contrast, inthe field of HCI, the tradition is more for a PhD thesis to explore
multiple variations of a system or intervention, possibly even in
multiple related usage domains. While one paper about a long-term
study of a single usage domain and population would require morework, it might not yield as much acknowledged scientific output.
Consequently, planning and carrying out long-term studies may
constitute a risky career move, placing yet more obstacles in theway of successful long-term SportsHCI explorations of interaction
technology. However, with the field of SportsHCI maturing, weshould gain the necessary confidence in our systems, interventions,and hypotheses to design and execute longitudinal studies.

4.1.3 Lack of knowledge on how to integrate biomechanics into
sportsHCI. Integration of biomechanics principles in interactive
technologies is vital for performance optimisation. Biomechanics
principles suggest what to measure and what feedback to give. Developing ways of integrating complex biomechanics into SportsHCI
is still a challenge. This integration includes measuring biomechanical systems in the body, such as muscles or bones. For example,
certain tasks (including localising the activity of a particular muscle group or tracking the interconnectivity of muscle groups andskeletons) require highly responsive sensing technologies withcapabilities beyond those available in a wearable IMU (Inertial
Measurement Unit) and EMG (Electromyography), which are commonly used to measure muscle reactions in response to a nerve’s
stimulation of a muscle.
Integrating biomechanics into interactive technology will require extensive collaboration between the biomechanics and HCI
research communities. However, creating these collaborations ischallenging because biomechanics reduces the body to a mechanicalsystem without considering lived experience. In contrast, HCI focuses on creating an experience without understanding the body’s
biomechanics. The two fields use data differently and place different demands on accuracy and precision. The two fields might evensee the importance of one type of measurement or classification
error over another differently. When using measurement systems
as inputs into interactive systems, the nature and purpose of that interactive system may substantially determine which types of errors,
level of noise, or accuracy the system requires to function well fora specific user in a specific setting. These requirements may differ
from what is needed to draw significant generalised conclusions
from the data regarding biomechanics and human performance [8].
4.1.4 Lack of knowledge on how to utilise real-time sensemakingof bodily performance analysis in novel digital-physical exercises.
Dashboards and retrieval systems offer the athlete or coach quick
access to sports data and, through the data, often also to salient
recordings of past training situations— to base training programs,
match strategies, and other decisions, often to optimise the athlete’s
performance. Stein et al. [150], who published extensively on visual
sports data analytics, discussed how this step is about making
sense of the data, from analysing the data to re-representing it anddisclosing it in a way that contributes real insight. Importantly,
this approach focuses not only on finding out what situations andevents have occurred but also on gaining insights into when andwhy they occurred [150].
However, SportsHCI systems can also be used in real-time during
training to adapt the training session, that is, to modify the training
or steer the player’s behaviour to enhance the training experience.
These interactive systems use specialised hardware [113] whereby
the moving body and the data derived from it form the interface
through which athletes interact with digital-physical exercise systems by providing the input triggers to which the system should

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respond in interaction. This approach provides rich learning environments for better motor learning in controlled circumstances.
Athletes and coaches can use the data-based insights regarding
performance optimisation and analysis generated by these systems
to customise training experiences to specific goals.
We see several challenges associated with the relationship between performance optimisation and these types of interactive
digital-physical SportsHCI systems. First, we consider that where
the data science technology used in performance optimisation andanalysis is quite advanced, SportsHCI systems typically do not remotely leverage the full power of advanced data processing. Usually,
these systems focus on innovative interaction technology but use
only quite basic forms of sensor measurements and data processing
compared to the state of the art. Thus, the challenge lies in developing SportsHCI systems that effectively utilise state-of-the-art data
science approaches and results.
Furthermore, various detection and modelling solutions employ
different approaches, which may vary due to the facts, such as
online or offline, real-time or requiring more computational power.
In the specific parametrisations of a method, it may be possible
to impact the typical mistakes that an algorithm makes, for example, favouring false negatives over false positives, accepting single
errors as long as the cumulative statistics of certain events in thedata remain correct, putting more focus on discriminating between
certain subsets of categories than on others, etc. As mentioned
earlier, the choices to be made in that respect heavily depend on
the application of the algorithms [8].
Finally, while substantial knowledge exists regarding performance optimisation and analysis for athletes in their sports, wehave much less understanding of how to integrate an athlete’s physiology and biomechanics in a technology-enhanced novel training
setting. Furthermore, the methods and feedback modalities of skill
training in a technological setting differ from those in the realworld. Consequently, we still need a deeper understanding of the
dynamics of different methods and modalities that will work intechnology-enhanced settings. Moreover, our understanding oftransfer and retention from (not fully representative) technological exercise settings to the real world remains underdeveloped.
Therefore, it is imperative not only to create SportsHCI systems
that align with contemporary data science but also to potentiallycustomise the data science techniques to suit the specific demandsof the SportsHCI applications. This imperative arises where thenature of the SportsHCI interactive application closely interacts
with the characteristics of the technology behind the performance
analysis.
Figure 4 summarises Challenge 1 and its sub-challenges. Next,
we turn to challenges related to various parties involved in sports
engagement.

4.2

Challenge 2: Lack of understanding of how
to design interactive technologies forvarious parties involved in Sports
Engagement

Suppose we were asked to design a system to manage a beginner
athlete’s engagements with a new coach. We do not yet know how to

Elvitigala, et al.

design an interactive system to support the various parties involvedin sports engagement.
Various approaches in SportsHCI foster sports engagement
through interactive technologies, such as augmented reality (e.g.,[140]), virtual reality (e.g., [48]), and gamification techniques (e.g.,[75]). Recently, a different human-centred approach emerged, whichbrings forward “movement” as a core creative resource. As a result, a
growing body of literature describes these creative methods and thefacilitation of movement-based design sessions [88, 108, 135, 177].
Human-centred design has been a prominent starting point forthese endeavours, with studies emphasising the importance of involving three main stakeholders (i.e., athletes, coaches, and spectators) in the design process to create intuitive and user-friendly
interfaces. For example, context-aware coaching research has investigated adaptive coaching feedback based on the specific context
and situation during training and competitions [186, 187]. Gamification and motivation techniques have been examined to encourage
athletes to adhere to training regimes and continuously improvetheir performance [75, 166]. Still, many human-centred challenges
remain unaddressed. We discuss them with specific reference tothe three stakeholders’ needs, goals, and experiences.
4.2.1 Lack of understanding of how to support the coach-athlete
relationship using interactive technology: Coaches also face the challenge of tailoring coaching methods to each athlete’s unique attributes [14, 32]. Balancing personalized guidance with broader
training strategies is a hurdle they navigate [58, 181]. Coaches,similar to athletes, need to balance data-driven performance enhancement with nurturing athletes’ holistic experiences. They playa crucial role in recognising and addressing athlete motivation andtheir emotional and psychological aspects [64, 85].
Furthermore, coaches are central to translating objective and subjective data into actionable insights. They use their expertise to contribute to the interpretation of quantitative metrics and an athlete’s
subjective experiences [119]. On the other hand, poor coaching canresult in an athlete doubting their own skills and performance [45].
The challenge is aligning coaching methods with an athlete’s aspirations and goals, which involves finding a balance between personalised guidance and broader training strategies. This challenge
also extends to merging data-driven performance enhancementwith improving athletes’ overall sporting experiences [14]. Collaborating with coaches to integrate their expertise into SportsHCI
design is crucial for creating solutions that combine performance
objectives and athletes’ well-being (e.g., [14]. This integration can
be challenging because coaching usually focuses on performance
while SportsHCI focuses on experience and performance. However,involving coaches in technology development ensures that the insights provided are relevant, practical, and aligned with coaches’
philosophies.
4.2.2 Lack of understanding of how to support the intricate relationship between athletes and spectators using interactive technology:
Spectatorship in sports entails an active process whereby spectators engage with athletes, teams, and other participants [11].
SportsHCI research on spectatorship has predominantly revolved
around the dynamics among the spectators themselves [11]. This

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CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Figure 5: summarises the challenges related to the three main stakeholders and their interactions with SportsHCI, with eachchallenge having its own focus but cannot be seen fully independently from each other.
work includes designs to allow the creation, sharing, and crowdsourcing among spectators of multimedia content related to sporting events [42, 61, 72]. Other studies have delved into the intricate relationship between athletes and spectators [47]. Within thissubset of works, some have primarily focused on one-way interactions from the athlete directed toward the spectator. For instance,
TickTockRun facilitates sharing runners’ performances and daily
training updates with interested spectators in their homes [74].
Other solutions have aspired to establish synchronous and bidirectional interactions between athletes and spectators. For example,
the HeartLink platform shares long-distance runners’ heart rate
information with spectators, and the spectators can reciprocate bysending supportive cheering vibrations back to the runners [26].
Furthermore, SportsHCI projects have started to create closer
relationships between spectators and athletes by allowing distancetracking of individuals and direct contact with athletes [26, 27]. Applications to encourage and keep tracking friends during a race [27]and social media channels to connect with famous athletes havegained use [22, 154]. Few studies have addressed the direct lossof analogue social relations using computer-supported interaction
during sports, although direct relationship building during sports
for various athletes is highly valued [36]. Additionally, only a few
studies have investigated the spectator-athlete dynamics from theexperiential aspects [84].

While several isolated attempts have been made to support
spectator-athlete engagement, the intricate relationship between
athletes and spectators still needs to be better understood. This
understanding will help to find answers for pre- and post-gamespectator engagement and real-time interactions to enhance thespectatorship experience. For instance, in sports where the spectators do not see the athletes for a relatively long time (e.g., longdistance running or cycling), the interaction between the athlete
and the spectator is yet to be explored [11].

4.2.3 Lack of understanding of how to design for the athlete-oriented
challenges. Despite the progress made in SportsHCI, creating personalised solutions for individual athletes remains a significant
challenge due to the many intricate factors that impact an athlete’s
performance [179]. Elements such as kinematics, biomechanics, andtraining responses are unique, making it hard to develop solutions
that individually fit different athletes [149]. SportsHCI can take on
this challenge via personalised and gamified technology development. However, it takes time and effort to find the right balance
between offering Personalised sports experiences and scaling theapproach [113] because creating tailored solutions for each athlete
can be resource-heavy and technically complex [30]. The challenge
is encouraging athletes to actively contribute to the technology

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

development studies that suit their diverse needs and create a collaborative atmosphere. Technology enhances their performancein such contexts without disregarding their distinct physical andcognitive attributes.
While SportsHCI has already benefited from human-centred
design methods involving athletes [83, 116, 177], an important challenge remains: addressing athlete’s subjective sports experiences
along with their performance-related ambitions and aspirations.
We need to grasp how athletes feel and translate that understanding into helpful advice that complements performance values instead of solely quantifying and summarising the sports experiences.
This endeavour requires a thoughtful understanding of how athletes’ sports (dis)engagement can impact athletes mentally (for an
overview, see [156]). While we aim for athletes to perform well, we
also want them to enjoy the experience. Sports HCI should strikethe right balance between experience and performance.
Designing for sports motivation is another athlete-oriented challenge because failures, injuries, and physical and mental fatigue
can impact an athlete’s motivation [67, 100, 137]. These factors
can result in maladaptive behaviours or irrational beliefs [165],
including disbelief in self and “awfulising” (i.e., it will be awful
if I do not succeed). SportsHCI work could contribute to healthy
motivation by carefully designing technologies to handle failures,investigating technology and methods of post-injury management,
and identifying the means of physical and mental pain. However,designing for motivation is not trivial, as it needs collaborations
with experts in sports psychology, coaches, trainers, and athletes.
Last, motivation, experience, and performance relate not only tothe athlete’s sports practice but also to other aspects of their lives.
Thus, the challenge for a proper athlete-centric design approachto SportsHCI is to also consider non-sports factors, like the development of personal values, life goals, and other facets beyond thesports itself.
Figure 5 summarises Challenge 2 and its sub-challenges. In thenext challenge, we will zoom in to Athlete and discuss the challenges we face when designing interactive technologies for individual athletes, who are multifaceted.

4.3

Challenge 3: Lack of knowledge of how todesign interactive technologies for theathlete being a multifaceted individual

Imagine the National Cricket Board asking us to design a system
to manage the injury recovery of a cricket player. At the moment,
we do not have an understanding of how we can provide a solution
through interaction technologies for the athlete, who is a multifaceted
individual.
Prior research in sports science and psychology has emphasised
the importance of supporting athletes in transitions, such as injury recovery and maintaining their health and well-being [33].
Researchers have also emphasised the importance of technology
and the collaborative synthesis of knowledge with the domains ofsports science, psychology, and data science to support the athlete’s
health and well-being during these transitions [31]. The healthand well-being of an athlete’s body, including resilience, also haveextensive mental facets, both cognitive and emotional in addition
to bodily facets. Furthermore, these bodily and mental facets vary

Elvitigala, et al.

from individual to individual, presenting challenges for modelingan athlete, determining what kind of support to offer them, anddeciding how to tailor SportsHCI to them.
4.3.1 Lack of knowledge of how to model the athlete to design interactive technologies. Understanding an athlete’s physical, emotionaland cognitive states is essential to supporting them during transition periods, such as during injury recovery, a competitive game, ora tournament. Developing an interactive system that can take into
account the athlete in all their complexity is a challenging task as
the athlete’s behaviour depends on multiple internal factors, suchas the athlete’s level of experience, physical and emotional state,
and mental resilience, as well as external factors, such as training
and family environment, food intake, etc. Hence, it is importantto take a multimodal approach that covers internal and external
factors to understand an athlete’s physical and mental state.
Previous research has emphasised the importance of analysing
athlete behaviour on and off the field to support their wellbeing [33]. Developing interactive technologies to track this data
across all facets is challenging, as some of these modalities cancontain very private information. Furthermore, identifying the interconnectivity of those elements of an athlete’s state is even hardersince such interconnected models require much data. And yet, when
one needs to model specific athletes in their individual characteristics, by definition, less data is available. Finally, this latter problem
may be exacerbated when looking at amateur and recreational
athletes, rather than elite athletes who may habitually track large
amounts of varied data about their performance. In such cases, thechallenge relates to the system’s ability to model the athlete as
an individual, including their mental and emotional states, using
only limited amounts of individualised data. In this instance, mostof the available reference data is drawn from a more generalised
population.
4.3.2 Lack of knowledge about developing interaction technologies
to support the athlete beyond bodily performance advice. The nextchallenging task is to develop technologies to support athletes emotionally, and not just with their physical performance. While priorwork [38, 91] explored self-emotional awareness systems to build
intervention mechanisms that support stress management techniques, these intervention mechanisms have not been tested withathletes, whose emotional dynamics can differ from non-athletes.
Moreover, the dynamics of emotional support for athletes are likely
to be different for athletes who are children or adolescents and often
have different emotional needs to adults. Also, long-term evaluations should be conducted in collaboration with coaches, trainers,
and sports psychologists to better understand feedback methods
for emotional support from an athlete’s point of view. Interactive
technologies in understanding behaviour and technologies to provide support should be developed together. Hence, any interactive
technology developed to provide emotional support should also
use the best modalities to understand behaviour.
4.3.3 Lack of knowledge of how to consider individual non-athletic
performance facets when designing SportsHCI technologies. Even
more than when providing support for physical athletic performance, support for the mental and emotional side of the athlete

Grand Challenges in SportsHCI

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Figure 6: summarises the challenges related to athletes as multifaceted individuals. Individualised support needs to consider
this wider perspective on athletes; modelling approaches must explicitly address the relationship between limited individual
data and larger-scale generalised data on larger populations.
must take into account the individual nature of each athlete, requiring personalisation of the support provided. Certainly, technologies
for sports have provided athletes and coaches with tools to improvetheir outcomes and achieve personalised performance goals [68].
For example, mobile apps to guide personalised nutrition [143],
personalised monitoring from coaches [19], guidance from digitalcoaches [71] and virtual training [160], tangible feedback in the environment [173, 174], and personalised wearable feedback [138, 161].
Although these initiatives try to tackle the individual and contextual requirements of athletes by adopting a personalised approachto technology development, the recognition of individual humanfactors, such as individual sensations of the living body [134, 138],
personal enjoyment of the physical activity [121, 158], sports goals
and life goals balance [92, 133], and coaches growth and aligningathletes interests [185], need to be considered explicitly in thispersonalisation.
Figure 6 summarises Challenge 3 and its sub-challenges. Given
that the interactions between SportsHCI and its users do not happen
in isolation, the next challenge we discuss looks at the long-term,
real-life perspective, considering a wider view of how SportsHCI ispracticed in a wider context.

4.4

Challenge 4: Lack of knowledge of how totake SportsHCI research and design into thereal sporting world

Imagine we have an interactive technology that works in the lab totrack the heart rate of a rugby player, and we have been asked if it
can be used in a match. We do not yet know how to take interactive
technology from the research lab into the sporting arena.
Here, we discuss challenges related to this move from research topractice, including real-world validation, the experience of sports

and technology as part of daily life, data integration and interoperability, and social impact.
4.4.1 Lack of knowledge of how to validate SportsHCI technology
in complex and dynamic real-world sports environments. SportsHCI
technologies are often evaluated in controlled settings or short-term
experiments [95, 163]. An opportunity in SportsHCI for researchers
conducting field studies and experiments is to deploy their technologies in actual sports environments to validate the effectivenessof their solutions in the real world. This opportunity brings its
own challenges because sports are complex, dynamic, and evolving
practices, and sports technology needs to be robust, dependable,
and accurate. Furthermore, there is a need for longitudinal studies
to tell us how a deployed technology changes a sporting practice,as technology is never neutral and will shape actions, perceptions,emotions, and behaviour [178]. Video assistant referee technology(VAR) for professional soccer provides a real-world example of asystem that has fundamentally altered how soccer is played [1].
4.4.2 Lack of understanding of how to design interactive technology
to support the experiential side of sports in a real-world environment.
In addition to the athlete’s experiential perspectives on sports, there
is also the experience of the technology as a potential intrusion
into daily life. For many athletes, the experience of being in nature,“away from it all”, is a crucial part of the experience in real-world
environments. Mueller and Young [111] describe the many virtues
people may ascribe to sports; not all of these virtues are equallyeasy to align with interactions with a piece of technology. Thus,
we raise the challenges of making SportsHCI unobtrusive withadded urgency. We need a deeper understanding of the interrelation
between the sense of wilderness and the interaction with ubiquitousSportsHCI systems [12].

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Elvitigala, et al.

Challenges Related to Real-World Sports Environments
Current state
Real world Controlled setting,validation short-term experiments

Experiencing
Small intersection between
sports intechnology and sports
daily life

Data integration Complexity & size of data;& interpolation Use of AI & big data analysis

Challenge space

Prospective state

Practices that are:• Complex• Dynamic• Evolving

Longitudinal and in-context
SportsHCI experiments

Objective measures;
Subjective experiences.

Improve performance, decision
making & understanding;Interaction across an ecosystemof services & devices

Embedded in daily life

Interaction possibilities formassive, multifaceted data

Integration of interactive technology into daily life

Unobtrusive SportsHCI
Figure 7: summarises the current and prospective states of three challenges relating to the lack of knowledge of how to takeSportsHCI research and design into the real sporting world.
Furthermore, the two (experience of sports and experience oftechnology) are not unrelated. The measures, metrics, and feedback that SportsHCI offers regarding performance will change
how people train for the sport and what they value and pay attention to. For example, many current SportsHCI systems, especiallysmartphone apps, appear to invest heavily in sharing numericalperformance metrics, which may lead to athletes developing an
unhealthy obsession with athletic performance and “being emotionally invested” in achieving specific numbers [105]. Alternative
approaches emerged in recent years ( [104, 137], see [80] for many
examples in SportsHCI), arguing that “much of our experience isqualitative rather than quantitative”.
A move into the real world might bring the fundamental purpose of SportsHCI into question. Sports technologies are part of theinnovative wave in sports [159]. Various exertion artefacts [108]build on the preconception that movement games support people’s change from a sedentary lifestyle to an active one. However,
Cooper [24] and Kent [70] point out that the Nintendo Wii, of
which the exertion interface character was one of the main innovative characteristics, stopped sale2 . In this context, what else
do we require to make lab-proven concepts work in the market,
2 https://www.statista.com/statistics/349078/nintendo-wii-and-wii-u-console-sales/

and what are they really good for? A dilemma arises regardingwhether movement values in sports technology are individualised
and thus aesthetic, as computer use often is. Contrary values ingame and play may have a more self-contained character as one
element and, on the other side, have an ethical dimension of interpersonal obligation and helpfulness [16]. In sports, these values
and virtues may be described as lenses or logics of sport, play,and movement [39, 107, 184]. Brinkmann’s [15] interpretation ofKierkegaard (1813-1855) states that these stances occur in a continuum between ethical and aesthetic values. Brinkmann et al. [15]claim that contemporary people mainly strive for the aesthetic –enjoyment, feel-good, and beautiful experiences – and perhaps too
little as being an ethical dimension of life. The challenging dilemma
may arise from too often arguing for SportsHCI design based on instrumentalised purposes and too little on self-contained and ethicalvalues, striving for sustainable values. SportsHCI scientists should
not be an extension of societal health preachers for a better healtheconomy. They should also focus on building human inter-related
existence and sports as its own end.
4.4.3 There is a lack of understanding of how to design interactions
to deal with enormous volumes of multifaceted sports data. Data provides the basis for understanding and improving performance andGrand Challenges in SportsHCI

making strategic decisions [89]. Data visualisation allows coaches,
athletes, and analysts to develop data models, identify patterns, andmake more informed decisions [127]. The complexity and size ofdata have favoured the use of artificial intelligence (AI) and big
data analytics, enabling the automatic analysis of large amounts ofcomplex information [79]. Integrating data management and analysis with visualisation techniques and HCI aspects may improve
performance, decision-making, and understanding in sports. Even
if there are many approaches to data analysis and management, it
is still challenging to present the data because data and text mustbe transformed into forms of presentation (such as stories) that aresuitable for the intended users.
Moreover, integrating athlete’s data to create a generalised understanding of a particular factor will be challenging, as it needs
a deeper level of data integration using AI and machine learning while considering human factors. Also, different sports technologies have already been developed in the sporting arenas (e.g.,camera-based player trackers, ball tracking in cricket and baseball,heat signature tracking in cricket, speed analysis, and projections).
Hence, integrating data collected from many interactive technologies through standard technologies is important to create holistic
understandings. This integration is challenging because it requires
further development of standardisations with relevant parties suchas sports technology companies and sports managers. Furthermore,this introduces interaction challenges with SportsHCI: how does auser interact with large longitudinal data? How do they interact in
an integrated manner with data that comes from multiple devices
across disparate activities and parts of life? How does a user manageinteraction across the ecosystem of services and devices cohesively
and consistently when some interactions are frequent, others very
infrequent, some momentary, others episodic, some only salientin limited periods in life, and others salient across years or even
decades? We lack understanding of the interaction possibilities formassive, multifaceted, disparate data across the past, present, andfuture, and we do not know well enough how users experience andunderstand such vast data.
Figure 7 summarises Challenge 4 and its sub-challenges. In thenext section, we step away from the challenges within SportsHCI
to consider what challenges can or should be addressed throughSportsHCI, leading to a more strategic perspective on the field.

4.5

Challenge 5: Lack of a long-term vision on
the design of SportsHCI for social impact

Let’s assume that we need to convince policymakers to provide moregrant opportunities for SportsHCI work; at the moment, we do nothave a long-term vision of how SportsHCI should be designed forsocial impact.
SportHCI has been designed to support autonomous learning insports [78, 139, 152], to enable geographically distributed athletes
to play sports together [107, 110], to enhance performance [20] or
prevent injuries [137, 138], and to balance gameplay [3, 46, 49]. Thisis just a small selection of the research ambitions that the field ofSportsHCI has pursued so far [43, 55, 56, 65, 162, 164]. These works
show that HCI can make a meaningful contribution to sports. The
next step is to widen the scope of research beyond the boundaries
of singular sporting disciplines. Given the quality and the quantity

CHI ’24, May 11–16, 2024, Honolulu, HI, USAof recent SportsHCI work, we argue that the field has matured tothe point that it can take on more complex societal issues, such as
physical inactivity, physical illiteracy, and inclusivity in sports. We
need a collective and focused effort to address these and other problems. Researchers and designers in the field of SportsHCI should
be working more programmatically to address these societal challenges on a larger scale – this requires close collaborations withthe fields of sports science, human movement science, life science,etc. In broad strokes, this section paints an initial research agenda
addressing the major contemporary societal issues in sports andmovement.
4.5.1 Lack of understanding of how to address the pandemic of physical inactivity through SportsHCI. Physical activity has been defined
as “any bodily movements produced by skeletal muscles that resultin energy expenditure” [18]. Sports, household, and occupational
activities all contribute to an active lifestyle. The benefits of physical activity are widely established. Physical activity is a known
protective factor against non-communicable chronic diseases, suchas cardiovascular disease, diabetes, cancer, and depression [76, 180].
Additionally, it has been shown that physical activity is beneficial
for mental health, the maintenance of a healthy weight, and thedevelopment of cognitive functioning and prosocial behaviour inchildren and adolescents [17, 51, 52]. Despite its benefits, physicalinactivity is reported as being the fourth leading cause of deathworldwide [51, 76]. Worldwide, 27.5% of the population is insufficiently active [17, 51]; among adolescents, this number is as highas 81.0% [52]. Globally, women are more than 8% less physically
active than men [52], and specific challenges have been described
for this population [97, 101, 102]. Furthermore, it was found that theprevalence of physical inactivity increased by 5.9% in high-income
countries between 2001 and 2016 [51]. “Given the prevalence, global
reach, and health effect of physical inactivity, the issue should beappropriately described as a pandemic, with far-reaching health, economic, environmental, and social consequences.” [76].
The pandemic of physical inactivity is not easily remedied. The
issue is truly complex as the correlates and determinants of physicalactivity are multi-dimensional. Individual determinants (psychological and biological), interpersonal determinants, environmental
determinants (social, built, natural), and regional and global determinants all relate to physical activity [7]. The World HealthOrganisation’s Global action plan on physical activity sets out aframework for action to reduce the global prevalence of physicalinactivity in 2030 by 15% [124]. We argue that researchers in thefield of SportsHCI can contribute towards making that happen, andindeed, there is already a lot of work in SportsHCI that pursues
improved physical activity. However, much of the work remains
limited in scope. First, many works in SportsHCI aim to improvephysical activity and sports participation by focusing on the separable outcomes of sports and physical activity, such as step count,energy expenditure, and standing hours. This approach treats thesymptoms, not the disease [81]. SportsHCI should support people intheir ambitions to be physically active, focusing on the inherent factors that make sports and physical activity fun and engaging while
being mindful of the barriers and enabling factors that promote
physical activity. This focus requires us to change how we think
about and design for physical activity [130]. Second, researchers

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Elvitigala, et al.

Figure 8: summarises the three challenges of the lack of vision on the design of SportsHCI for Social Impact and the points foraction for SportsHCI.
in the field of SportsHCI should invest (even) more in collaborations with neighbouring fields (e.g., social sciences, psychology,
sports science, epidemiology, physical education, etc.) to address
the pandemic of physical inactivity. Such collaborations involve
more than just talking to experts. It requires researchers in thefield to set up multidisciplinary consortia – not only for (awarded)
grants but also for teaching. Students in HCI are a valuable asset
to our research infrastructure, yet rarely do they get the chance topeer past the boundaries of their scientific discipline. Researchers in
SportsHCI need to fundamentally rethink how they organise theirresearch and teaching infrastructure to accommodate the development of meaningful interventions for physical activity. Third, toevaluate the long-term effects of our designs and interventions, theSportsHCI field should emphasise longitudinal study designs. There
are too many ideas and too little follow-up. SportsHCI, as a field,
has an obligation to the rest of the scientific community to clearlycommunicate how human-computer interaction may contributeto solving the pandemic of physical inactivity. Longitudinal study
designs will be the ‘proof of the pudding’ – acting to separate thewheat from the chaff. This approach again requires us to rethink
how we organise our research infrastructure – organising studies
that transcend the boundaries of singular (PhD) student projects.

We challenge researchers in the field to work holistically, acrossdisciplines, and longitudinally, on studies that positively impact the
pandemic of physical inactivity.
4.5.2 Lack of evaluation criteria if a SportsHCI intervention improves physical literacy. Physical literacy and motor competence
are among the chief determinants of physical activity [5, 53, 81].
"Failure to consider motor competence as a key antecedent of physicalactivity and positive health and developmental trajectories in children and adolescents likely results in treating the symptoms rather
than the cause of physical inactivity and ill health." [81]. Motor competence concerns a person’s ability to perform a range of motor
tasks (fine and gross) [53, 57] and it is part of the broader conceptof physical literacy [81, 182], which is defined as: "the motivation,
confidence, physical competence, knowledge, and understanding tovalue and take responsibility for engagement in physical activities
for life." [6]. Physical literacy promotes physical health, well-being,
participation in sports, self-esteem, and personal growth [5, 60].
Conversely, children and adolescents with low levels of neuromotor
fitness can experience difficulties in participating in sports; are lesslikely to participate in sports later in life; run an increased risk ofnegative health outcomes at all ages [5]; and are at risk of various
psychological difficulties [53, 146]. Moreover, "physically inactiveGrand Challenges in SportsHCI

parents tend to raise physically inactive children" [81]. Physical literacy and motor competence are in decline among children andadolescents – impacting speed and agility, upper-body strength,and flexibility [5, 60]. This decline is problematic because lower
levels of motor competence cause lower confidence and motivation
to participate in sports, which can become a vicious cycle.
This perspective of physical literacy is increasingly seen as amain objective to pursue in sports and health sciences. However,
we must overcome many challenges for SportsHCI to work productively on physical literacy. First, because of its multidimensional
nature, it is challenging to directly assess the effects of technologicalinterventions on physical literacy. Second, one of the core determinants of physical literacy – motor competence – can be measured
but requires elaborate study designs that involve testing over time(pre-test, intervention-test, post-test, retention-test, transfer-test)
for multiple conditions (test-group, placebo-group, control-group),
rendering investigation both time- and labour-intensive. This complexity is further exacerbated by the fact that design processes areiterative – ideally, one would want to investigate how different design choices impact physical literacy differently. However, it seems
neither practical nor feasible to carry out multiple longitudinal studies within the scope of a single design project. This impracticality
hinders the field of SportsHCI from productively working on issues
in motor learning and physical literacy.
4.5.3 Lack of understanding of how to overcome barriers to sports access. Sports and physical activity are fundamental human activities.
In their Olympic Charter, the International Olympic Committee
posits, “The practice of sport is a human right. Every individual must
be able to practise sport, without discrimination of any kind andin the Olympic spirit, which requires mutual understanding with aspirit of friendship, solidarity and fair play.” Yet, access to sports
and other physical activities is far from equal. Many groups inour society are disadvantaged in the extent to which they are enabled to be physically active. Women are less physically active thanmen [51], and the WHO calls for more “opportunities and safe andaccessible leisure-time activities for women” to close this gender gap.
People with a low socioeconomic background are less physically
active, with time, money, health concerns, lack of physical literacy,
and other factors raising barriers to participation. Neurodiverse
children suffer the same fate, with the barriers to participation in
sports being greater for neurodiverse children than for neurotypicalchildren [73], causing lower levels of physical activity and motor
competence such as in Autism Spectrum Disorder [13, 125]. Many
more such disparities may be added to this list, considering, forinstance, people with a physical disability who also face greater
sports participation barriers.
Most SportsHCI work exclusively targets participants with normative bodies and capabilities, and this conceptualisation is sharedacross much research on body technologies [147]. A few design
studies focus on encouraging physical activity for individuals witha disability (e.g., lower limb disability [4]) or who are recoveringfrom a disease (e.g., breast cancer [103]). Others address barriers
to exercising motivation in various populations of recreational
users [29, 98–100, 102, 104, 172, 174]. Overall, we contend thatthe challenge for SportsHCI is to reduce inequalities in sports participation – considering underrepresented bodies, disabled and

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

non-normative bodies, and individuals of different genders and age
groups as worthy subjects for research in SportsHCI. Such inclusion
in our research endeavours may involve identifying and addressing
political powers and ethical considerations related to participation
and access to sporting activities and technology. Even when notdesigned for underrepresented groups, researchers in SportsHCI
should be aware of the impact that their technology may have on
equity, accessibility, and inclusion in sports. These matters are notmerely separate, additional research topics with standalone groups
working on dedicated SportsHCI for special target groups. On thecontrary, all SportsHCI practitioners should be mindful of whether
the systems under development adequately consider inclusivity. For
one, novel technologies tend to be expensive, offering access only
to those with the (financial) resources to spend time and money on
sports and physical activity [35, 132]. Further, technologies might
not be accessible to all due to logistic, physical, mental, or social
constraints. As such, it is easy for novel systems to widen the gapof inclusive sports participation.

5

LIMITATIONS AND FUTURE WORK

Our work has limitations, as does any that aims to steer an entire sub-field of HCI at once. The articulation of these limitations
might inform future work and ultimately lead to a more complete
picture of the SportsHCI field. We especially point out that ourapproach of conducting a week-long seminar is not the only way toarticulate grand challenges for a particular sub-field. Others haveheld one-day workshops [21], worked in smaller teams [151], andeven individually [9]. Therefore, alternative formats could result in
additional grand challenges articulations that could complement
our work.
Furthermore, we acknowledge that the composition of our seminar might have biased our results and that our positionality asorganisers and authors influenced the outcomes to some extent.
First, our seminar participants were all experts and, hence, had afavourable view of the topic and were eager to see the field flourish.
As such, our view on the future of SportsHCI might be overly positive. We might identify additional challenges that could complement
our work if we involve participants with a more critical view on
SportsHCI, including researchers who have left the field (perhaps
because they have encountered too many roadblocks when trying
to work with an industry that is seemingly only interested in elite
athletes, or when trying to work with under-resourced community
organisations) and non-experts.
Our seminar participants exhibited a great range of sports expertise, including conducting various sports at various intensity
levels (up to representing their state, but not country or being aprofessional). This expertise made the seminar experience unique
because participants provided training sessions before and after
each day and during the breaks. Including these activities also informed our discussions, as has "body-storming" in HCI [88]. It has
been argued that "moving" during creative tasks facilitates different(and seemingly better) outcomes. As such, we consider the diversity
in our seminar participants’ sports engagement a strength of ourapproach. However, we acknowledge that participants with otherexperiences (for example, people with an aversion to sports andCHI ’24, May 11–16, 2024, Honolulu, HI, USA

people who have been injured and had to give up sports) could
expand our discussions in future seminars.
We also acknowledge that we have only briefly touched upon
what role HCI could play in addressing the negative consequencesof sports, such as discouraging the use of performance-enhancing
drugs. This could occur in the form of existing technology, such asapps that educate athletes about the associated health risks, and in
future high-tech systems that detect such drugs through implanted
interfaces to immediately report to the governing body (yet raising
serious privacy concerns). Furthermore, we discussed the sayingthat sports can bring out both the best and the worst in people. We
often observe the worst in the rivalries between local clubs thatlead to clashes between fans and violence that goes beyond the(football) pitch. What role does SportsHCI play in these contexts?
We discussed these "dark" sides of sports during the seminar, and, in
this regard, we direct interested readers to prior work using "darkpatterns" to investigate the negative implications of particular subfields of HCI [50].
Taken together, we acknowledge the limitations of our approach,and we have pointed out important issues that we decided not toinvestigate further (yet) but encourage others to explore to see whatrole SportsHCI could play in them. As such, our grand Challenges
are not to be understood as a final list but rather as a starting
point for others to build on, develop further, and critique throughadditional investigations and research. With this, we can paint amore vivid picture of SportsHCI.

6

CONCLUSIONSportsHCI has transformed from an HCI application domain into astandalone interdisciplinary field as a result of significant technological progress, and now comprises a growing body of literatureinvestigating various facets. However, as SportsHCI becomes a fieldin its own right, certain challenges prevent it from blossoming
and thriving. We believe it is valuable to articulate the nature ofthese challenges so that we might work together as a community
to address and ultimately overcome them and advance the whole
SportsHCI field. Through this articulation, researchers and industry
alike will better understand the pressing issues and be better able
to identify what matters to tackle next. Such a structured approach
might be more beneficial than leaving individuals to work in isolation, which would risk the duplication of research efforts and the‘fixing’ of problems that others have already solved.
In this paper, we have used the outcomes from a week-longseminar involving 22 experts to articulate five grand challenges and
17 sub-challenges in SportsHCI. These are our starting points. While
we acknowledge that there might be more or that the challenges
could be differently framed or elaborated upon, we still believethat they can be useful for others interested in starting to work toadvance the whole SportsHCI field.
In conclusion, we believe that the grand challenges in SportsHCI
offer the potential to revolutionise the world of sports, benefitting
recreational sports participants and elite athletes, coaches, and fansalike. By collectively addressing these challenges, researchers andpractitioners can advance the state-of-the-art, foster innovation,and create a positive impact. Embracing a collaborative and multidisciplinary approach will be key to realising the full transformative
potential of SportsHCI in shaping the future sports experience.

Elvitigala, et al.

ACKNOWLEDGMENTS
The authors are grateful to Nathalie Overdevest for her work sketching the paper’s images. Laia Turmo Vidal thanks the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 101002711)
supporting her research. Xipei Ren thanks the Beijing Social Science
Foundation Young Talent Project (grant nr. 23YTC045). Vincent van
Rheden gratefully acknowledges the financial support from theAustrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry
for Digital and Economic Affairs, and the federal state of Salzburg
under the research programme COMET - Competence Centers for
Excellent Technologies - in the project DiMo-NEXT Digital Motion
in Sports, Fitness and Well-being (Project number: FO999904898).
Dennis Reidsma thanks the EU Erasmus+ project Method Cards for
Movement-based Interaction Design (MeCaMInD), grant number
2020-1-DK01-KA203-075164. Florian ‘Floyd’ Mueller and Elise vanden Hoven thank the Australian Research Council for supporting
their research on Muscle Memory: the key to novel interactive
memory support systems (DP190102068). Florian ‘Floyd’ Mueller
thanks the Australian Research Council, especially DP200102612
and LP210200656. This work was initiated by Dagstuhl Seminar
23292 "SportsHCI.” We thank the Leibnitz-Zentrum für Informatik
for hosting us at Schloss Dagstuhl.

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Elvitigala, et al.

Table 1: Five Categories of Grand Challenges in SportsHCI

Challenge 1: Lack of knowledge howinteractive technology can support
performance optimization- Lack of knowledge how to design real-time bodily performance
analysis systems to coach and athlete’s feedback and sense-making- Lack of knowledge in designing interactive technologies
for the longitudinal nature of athletic performance- Lack of knowledge how to integrate biomechanics in toSportsHCI methods- Lack of knowledge how to utilise realtime sensemaking of bodily
performance analysis in novel digital-physical exercises

Challenge 2: Lack of understanding how
to design interactive technologies for various
parties involved in Sports Engagement- Lack of understanding how to support the coach-athlete
relationship using interactive technology-Lack of understanding how to support the intricate relationshipbetween athletes and spectatotrs using interactive technology- Lack of understanding how to design for theathlete-oriented challenges

Challenge 3: Lack of knowledge how todesign interactive technologies for the athlete
being a multifaceted individual- Lack of knowledge how to model the athlete to designinteractive technologies-Lack of knowledge how to develop interaction technologies
to support the athlete beyond bodily performance advice- Lack of knowledge of how take into consideration individual
non-athletic performance facets when designing SportsHCI
technologies

Challenge 4: Lack of knowledge how totake SportsHCI research and design
into the real sporting world- Lack of knowledge how to validate SportsHCI technology
in complex and dynamic Real-World sporting- Lack of understand how to design interactive technology
to supports experiencial side of sports in daily life
- Lack of understanding how to design interactions to deal
with multifaceted enormous sports data

Challenge 5: Lack of a long-term
vision on how SportsHCI should bedesigned to for Social Impact- Lack of understanding how to address the pandemicof physical inactivity through SportsHCI- Lack of evaluation criteria if a SportsHCI intervention improves
physical literacy- Lack of understanding how to overcome barriers
to sports access