arXiv subjects

Jan Smeddinck

Publications and source records attributed to Jan Smeddinck.

7 recordsLinked to original sources

arXiv2026

Conceptualising an Initial Design Space for Guidance in Digital Physical Activity Support

Providing guidance is frequently referenced as a key capability of digital health interventions targeting physical activity, yet the term remains poorly defined and inconsistently applied. Existing work often conflates guidance with related constructs such as personalisation, feedback, or persuasion, limiting both theoretical clarity and design progress. This paper conceptualises an initial design space of guidance in the context of digital physical activity support. We define guidance for physical activity as situated, action-oriented support that scaffolds users' embodied engagement in physical activity. Drawing on literature from behaviour change, human-computer interaction, embodied cognition, and digital health, we outline a design space that characterises guidance along multiple dimensions: scope, purpose, timing, context, modality, embodiment, adaptivity, autonomy, and affective quality. By offering a structured vocabulary and conceptual foundation, this work aims to support more coherent research, comparisons, and responsible design of digital health interventions featuring guidance for physical activity support.

cs.HC↗

arXiv2026

Designing and Evaluating Granular Consent for Data Sharing in Cardiac Disease Prevention

Dynamic consent can promise end users with greater control, but little is known about how older adults with chronic conditions navigate the tradeoff between control and burden in granular consent mechanisms in health data life-cycles. Using a two-stage design process we evaluated this tradeoff. An expert workshop (n=5) informed the design requirements for granular dynamic consent prototype. We evaluated single step vs multi-step granularity in dynamic consent using prototypes with cardiac patients (n=7) using a mixed-methods study. Quantitative measures showed no significant differences between low- and high-granularity consent screens in usability, workload, perceived information control or willingness to share data. However, qualitative findings revealed a control-burden paradox and trust-dependent engagement with granularity. Participants sought greater transparency and control over AI-mediated data processing. We contribute implications for designing granular consent in health data life-cycles.

cs.HC↗

arXiv2026

Tailored Behavior-Change Messaging for Physical Activity: Integrating Contextual Bandits and Large Language Models

Contextual multi-armed bandit (cMAB) algorithms offer a promising framework for adapting behavioral interventions to individuals over time. However, cMABs often require large samples to learn effectively and typically rely on a finite pre-set of fixed message templates. In this paper, we present a hybrid cMABxLLM approach in which the cMAB selects an intervention type, and a large language model (LLM) which personalizes the message content within the selected type. We deployed this approach in a 30-day physical-activity intervention, comparing four behavioral change intervention types: behavioral self-monitoring, gain-framing, loss-framing, and social comparison, delivered as daily motivational messages to support motivation and achieve a daily step count. Message content is personalized using dynamic contextual factors, including daily fluctuations in self-efficacy, social influence, and regulatory focus. Over the trial, participants received daily messages assigned by one of five models: equal randomization (RCT), cMAB only, LLM only, LLM with interaction history, or cMABxLLM. Outcomes include motivation towards physical activity and message usefulness, assessed via ecological momentary assessments (EMAs). We evaluate and compare the five delivery models using pre-specified statistical analyses that account for repeated measures and time trends. We find that the cMABxLLM approach retains the perceived acceptance of LLM-generated messages, while reducing token usage and providing an explicit, reproducible decision rule for intervention selection. This hybrid approach also avoids the skew in intervention delivery by improving support for under-delivered intervention types. More broadly, our approach provides a deployable template for combining Bayesian adaptive experimentation with generative models in a way that supports both personalization and interpretability.

cs.LG↗

arXiv2025

Micro-Health Interventions: Exploring Design Strategies for 1-Minute Interventions as a Gateway to Healthy Habits

One-minute behavior change interventions might seem too brief to matter. Could something so short really help people build healthier routines? This work explores this question through two studies examining how ultra-brief prompts might encourage meaningful actions in daily life. In a formative study, we explored how participants engaged with one-minute prompts across four domains: physical activity, eating, screen use, and mental well-being. This revealed two common design approaches: Immediate Action prompts (simple, directive tasks) and Reflection-First prompts (self-awareness before action). We then conducted a 14-day, within-subjects study comparing these two flows with 28 participants. Surprisingly, most participants did not notice differences in structure -- but responded positively when prompts felt timely, relevant, or emotionally supportive. Engagement was not shaped by flow type, but by content fit, tone, and momentary readiness. Participants also co-designed messages, favoring those with step-by-step guidance, personal meaning, or sensory detail. These results suggest that one-minute interventions, while easily dismissed, may serve as meaningful gateways into healthier routines -- if designed to feel helpful in the moment.

cs.HC↗

arXiv2025

Study Protocol: Shared Achievements: Exploring the Design of Gameful Collaborative Elements and Fostering Social Relatedness through Team Effort Contributions in a Social Physical Activity App

This study protocol outlines the design and methodology of a research study investigating collaborative game elements to promote physical activity within digital health interventions. The study aims to examine how social relatedness influences motivation and adherence to step-count goals. Participants will use Shared Achievements, a minimalistic multiplayer step counter game, over two weeks, one week contributing absolute step counts and one week sharing step counts as a relative percentage of a team goal. Data will be collected through usage metrics and participant feedback to evaluate engagement, motivation, and perceived challenges. Findings will inform the design of digital health tools that balance competition and collaboration, optimising social and behavioural support mechanisms.

cs.HC↗

arXiv2020

Mobile Game User Research: The World as Your Lab?

With the advent of mobile games and the according growing and competitive market, game user research can provide valuable insights and a competitive edge if methods and procedures are employed that match the distinct challenges that mobile devices, games and usage scenarios induce. We present a summary of parameters that frame the research setup and procedure, focusing on the trade-offs between lab and field studies and the related decision whether to pursue large-scale and quantitative or small-scale focused research accompanied by qualitative methods. We then illustrate the implications of these considerations on real world projects along the lines of two evaluations of different input methods for the action-puzzle mobile game Somyeol: a local study with 37 participants and a mixed design of qualitative and quantitative methods, and the strictly quantitative analysis of game-play data from 117,118 users. The findings underline the importance of small-scale evaluations prior to release.

cs.HC↗

arXiv2020

Towards Generating Virtual Movement from Textual Instructions A Case Study in Quality Assessment

Many application areas ranging from serious games for health to learning by demonstration in robotics, could benefit from large body movement datasets extracted from textual instructions accompanied by images. The interpretation of instructions for the automatic generation of the corresponding motions (e.g. exercises) and the validation of these movements are difficult tasks. In this article we describe a first step towards achieving automated extraction. We have recorded five different exercises in random order with the help of seven amateur performers using a Kinect. During the recording, we found that the same exercise was interpreted differently by each human performer even though they were given identical textual instructions. We performed a quality assessment study based on that data using a crowdsourcing approach and tested the inter-rater agreement for different types of visualizations, where the RGBbased visualization showed the best agreement among the annotators.

cs.HC↗