arXiv subjects

Jan David Smeddinck

Publications and source records attributed to Jan David Smeddinck.

14 recordsLinked to original sources

arXiv2026

Verify, Offload, Extend & Recommend: Selective Complementarity in AI Support for Physical Activity Planning with Longitudinal Patient Data

Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical decision support system for physical-activity planning in cardiovascular disease. In a counterbalanced within-subjects study, 26 exercise physiologists developed plans for four real cardiovascular cases with and without AI support, followed by evaluation of an AI exercise-plan generator. AI did not significantly improve workload, usability, confidence, or plan quality overall; however, its effect on plan quality increased as visualization literacy decreased and its effect on workload increased as visualisation literacy increased. Interviews and 152 chatbot queries revealed three recurring uses: verifying, offloading, extend and generate. Our findings position AI support as a selective complement to professional expertise while highlighting validation challenges when clinicians seek support precisely where their own knowledge is limited.

cs.HC↗

arXiv2026

Designing for the Moment: How One-Minute Interventions Fit or Falter Across Domains

This paper explores the design space for one-minute digital interventions that prompt immediate action without onboarding or sensing. By embracing Fogg's Behavior Model and four design principles informed by literature, the goal of these interventions was to provide triggers that encourage actions so simple that even people with low motivation would be willing to complete them. We examined the utility of these prompts by conducting a 14-day study with 22 participants interested in making small lifestyle improvements in at least one of three domains: physical activity, healthy eating, and mental well-being. When combined with insights drawn from participants' rewrites of our prompts, our findings suggest that intentional personalization through co-authorship could be a lightweight personalization mechanism that balances relevance with low friction.

cs.HC↗

arXiv2026

AktivTalk: Digitizing the Talk Test for Voice-Based Exercise Intensity Self-Assessment and Exploring Automated Classification from Speech

Monitoring exercise intensity is critical for safe and effective physical activity, particularly for individuals with cardiovascular disease, where overexertion can pose serious risks. Although physiological measures such as heart rate are widely used for avoiding overexertion, they can be unreliable in certain cases, such as when affected by medication or when wearables are worn too loosely. We introduce AktivTalk, a mobile prototype that digitizes the clinically validated Talk Test to support voice-based, in-the-moment self-assessment of exertion. In a within-subject study with 20 participants, we collected exertion-labeled voice samples and found that AktivTalk was rated as highly usable and preferred over conductor-guided assessment. We further explored automated exertion classification from Talk Test speech. Using MFCC-based features with class balancing and cross-validation, a lightweight neural classifier achieved up to 90% accuracy for detecting high vs.non-high exertion from Talk Test recordings. This work highlights the potential of structured voice interactions for accessible exertion assessment and motivates future passive exertion monitoring from speech.

cs.HC↗

arXiv2026

Exploring Self-Tracking Practices of Older Adults with CVD to Inform the Design of LLM-Enabled Health Data Sensemaking

Wearables and mobile health applications are increasingly adopted for self-management of chronic illnesses; yet the data feels overwhelming for older adults with cardiovascular disease (CVD). This study explores how they make sense of self-tracked data and identifies design opportunities for Large Language Model (LLM)-enabled support. We conducted a seven-day diary study and follow-up interviews with eight CVD patients aged 64-82. We identified six themes: navigating emotional complexity, owning health narratives, prioritizing bodily sensations, selective engagement with health metrics, negotiating socio-technical dynamics of sharing, and cautious optimism toward AI. Findings highlight that self-tracking is affective, interpretive, and socially situated. We outline design directions for LLM-enabled data sensemaking systems: supporting emotional engagement, reinforcing patient agency, acknowledging embodied experiences, and prompting dialogue in clinical and social contexts. To support safety, expert-in-the-loop mechanisms are essential. These directions articulate how LLMs can help translate data into narratives and carry implications for human-data interaction and behavior-change support.

cs.HC↗

arXiv2026

Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction

Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such data could transform preventative care, its integration into clinical practice is hindered by its scale, heterogeneity and the time pressure and data literacy of healthcare professionals (HCPs). We explore how large language models (LLMs) can support sensemaking of patient-generated health data (PGHD) with automated summaries and natural language data exploration. Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed multimodal PGHD in a mixed-methods study with a prototype that integrated common charts, LLM-generated summaries, and a conversational interface. Findings show that AI summaries provided quick overviews that anchored exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance. We contribute empirical insights and sociotechnical design implications for integrating AI-driven summarization and conversation into clinical workflows to support PGHD sensemaking.

cs.HC↗

arXiv2025

Towards Data-Enabled Physical Activity Planning: An Exploratory Study of HCP Perspectives On The Integration Of Patient-Generated Health Data

Physical activity planning is an essential part of cardiovascular rehabilitation. Through a two-part formative design exploration, we investigated integrating patient-generated health data (PGHD) into clinical workflows supporting shared decision-making (SDM) in physical activity planning. In part one, during a two-week situated study, to reduce risk of working with cardiovascular disease patients, we recruited healthy participants who self-tracked health and physical activity data and attended a physical activity planning session with a healthcare professional (HCP). Subsequently both HCPs and participants were interviewed. In part two, findings from part one were presented to HCPs in a card-sorting workshop to corroborate findings and identify information needs of HCPs alongside patient journeys and clinical workflows. Our outcomes highlight HCP information needs around patient risk factors, vital signs, and adherence to physical activity. Enablers for PGHD integration include adaptive data sense-making, standardization and organizational support for integration. Barriers include lack of time, data quality, trust and liability concerns. Our research highlights implications for designing digital health technologies that support PGHD in physical activity planning during cardiac rehabilitation.

cs.HC↗

arXiv2025

Exploring Human-AI Interaction with Patient-Generated Health Data Sensemaking for Cardiac Risk Reduction

Patient-generated health data (PGHD) allows healthcare professionals to have a holistic and objective view of their patients. However, its integration in cardiac risk reduction remains unexplored. Through co-design with experienced healthcare professionals (n=5) in cardiac rehabilitation, we designed a dashboard, INSIGHT (INvestigating the potentialS of PatIent Generated Health data for CVD Prevention and ReHabiliTation), integrating multi-modal PGHD to support healthcare professionals in physical activity planning in cardiac risk reduction. To further augment healthcare professionals' (HCPs') data sensemaking and exploration capabilities, we integrate large language models (LLMs) for generating summaries and insights and for using natural language interaction to perform personalized data analysis. The aim of this integration is to explore the potential of AI in augmenting HCPs' data sensemaking and analysis capabilities.

cs.HC↗

arXiv2025

Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices

Wearable devices with photoplethysmography (PPG) sensors are widely used to monitor heart rate (HR), yet often suffer from accuracy issues. However, users typically do not receive an indication of potential measurement errors. We present a real-time warning system that detects and communicates inaccuracies in PPG-derived HR, aiming to enhance transparency and trust. Using data from Polar and Garmin devices, we trained a deep learning model to classify HR accuracy using only the derived HR signal. The system detected over 80% of inaccurate readings. By providing interpretable, real-time feedback directly to users, our work contributes to HCI by promoting user awareness, informed decision-making, and trust in wearable health technology.

cs.HC↗

arXiv2025

The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual's current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.

cs.HC↗

arXiv2022

Human-GDPR Interaction: Practical Experiences of Accessing Personal Data

In our data-centric world, most services rely on collecting and using personal data. The EU's General Data Protection Regulation (GDPR) aims to enhance individuals' control over their data, but its practical impact is not well understood. We present a 10-participant study, where each participant filed 4-5 data access requests. Through interviews accompanying these requests and discussions scrutinising returned data, it appears that GDPR falls short of its goals due to non-compliance and low-quality responses. Participants found their hopes to understand providers' data practices or harness their own data unmet. This causes increased distrust without any subjective improvement in power, although more transparent providers do earn greater trust. We propose designing more effective, data-inclusive and open policies and data access systems to improve both customer relations and individual agency, and also that wider public use of GDPR rights could help with delivering accountability and motivating providers to improve data practices.

cs.HC↗

arXiv2021

Fragments of the Past: Curating Peer Support with Perpetrators of Domestic Violence

There is growing evidence that digital peer-support networks can have a positive influence on behaviour change and wellbeing outcomes for people who harm themselves and others. However, making and sustaining such networks are subject to ethical and pragmatic challenges, particularly for perpetrators of domestic violence whom pose unique risks when brought together. In this work we report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past; a socio-material system that connects audio messages with tangible artefacts. We share how crafting digitally-augmented artefacts - 'fragments' - of experiences of desisting from violence can translate messages for motivation and rapport between peers, without subjecting the process to risks inherent with direct inter-personal communication. These insights provide the basis for practical considerations for future network design with challenging populations.

cs.HC↗

arXiv2021

From Personal Data to Digital Legacy: Exploring Conflicts in the Sharing, Security and Privacy of Post-mortem Data

As digital technologies become more prevalent there is a growing awareness of the importance of good security and privacy practices. The tools and techniques used to achieve this are typically designed with the living user in mind, with little consideration of how they should or will perform after the user has died. We report on two workshops carried out with users of password managers to explore their views on the post-mortem sharing, security and privacy of a range of common digital assets. We discuss a post-mortem privacy paradox where users recognise value in planning for their digital legacy, yet avoid actively doing so. Importantly, our findings highlight a tension between the use of recommended security tools during life and facilitating appropriate post-mortem access to chosen assets. We offer design recommendations to facilitate and encourage digital legacy planning while promoting good security habits during life.

cs.CR↗

arXiv2021

Evaluating User Experiences in Mixed Reality

Measure user experience in MR (i.e., AR/VR) user studies is essential. Researchers apply a wide range of measuring methods using objective (e.g., biosignals, time logging), behavioral (e.g., gaze direction, movement amplitude), and subjective (e.g., standardized questionnaires) metrics. Many of these measurement instruments were adapted from use-cases outside of MR but have not been validated for usage in MR experiments. However, researchers are faced with various challenges and design alternatives when measuring immersive experiences. These challenges become even more diverse when running out-of-the lab studies. Measurement methods of VR experience recently received much attention. For example, research has started embedding questionnaires in the VE for various applications, allowing users to stay closer to the ongoing experience while filling out the survey. However, there is a diversity in the interaction methods and practices on how the assessment procedure is conducted. This diversity in methods underlines a missing shared agreement of standardized measurement tools for VR experiences. AR research strongly orients on the research methods from VR, e.g., using the same type of subjective questionnaires. However, some crucial technical differences require careful considerations during the evaluation. This workshop at CHI 2021 provides a foundation to exchange expertise and address challenges and opportunities of research methods in MR user studies. By this, our workshop launches a discussion of research methods that should lead to standardizing assessment methods in MR user studies. The outcomes of the workshop will be aggregated into a collective special issue journal article.

cs.HC↗

arXiv2020

Human-Computer Interaction with Adaptable & Adaptive Motion-based Games for Health

Physical activity plays a major role both in prevention and in the treatment of afflictions linked to a modern sedentary lifestyle and improvements on life expectancy, for example though the application area of physiotherapy. Motion-based games for health (MGH) are being discussed in research and industry for their ability to play a supportive role in health, by offering motivation to engage in treatments, objective insights on status and development, and guidance regarding treatment activities. Difficulty settings in games are typically limited to few discrete tiers. For most serious applications in health, more fine-grained and far-reaching adjustments are required. The need for applying adjustments on complex sets of parameters can be overwhelming for patient-players and even trained professionals. Automatic adaptivity and efficient manual adaptability are thus major concerns for the design and development of MGH. Despite a growing amount of research on specific methods for adaptivity, general considerations on human-computer interaction with adaptable and adaptive MGH are rare. This thesis therefore focuses on establishing and augmenting theory for adaptability and adaptivity in human-computer interaction in the context of MGH. Working with older adults and people with Parkinson's disease as frequent target groups that can benefit from tailored activities, explorations and comparative studies that investigate the design, acceptance, and effectiveness of MGH are presented. The outcomes encourage the application of adaptivity for MGH following iterative human-centred design that considers the respective interests of stakeholders, provided that the users receive adequate information and are empowered to exert control over the automated system when desired or required, and if adaptivity is embedded in such a way that it does not interfere with the users' sense of competence or autonomy.

cs.HC↗