Personalized Health Assistance with Intelligent Digital Solutions

Permanent URI for this collectionhttps://hdl.handle.net/10125/112481

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Now showing 1 - 6 of 6

UWB-PostureGuard: A Privacy-Preserving RF Sensing System for Continuous Ergonomic Sitting Posture Monitoring
(2026-01-06) Li, Haotang; Qi, Zhenyu; He, Sen; Peng, Kebin; Tan, Sheng; Ren, Yili; Cerny, Tomas; Zhao, Jiyue; Wang, Zi
Improper sitting posture during prolonged computer use has become a significant public health concern. Traditional posture monitoring solutions face substantial barriers, including privacy concerns with camera-based systems and user discomfort with wearable sensors. This paper presents UWB-PostureGuard, a privacy-preserving ultra-wideband (UWB) sensing system that advances mobile technologies for preventive health management through continuous, contactless monitoring of ergonomic sitting posture. Our system leverages commercial UWB devices, utilizing comprehensive feature engineering to extract multiple ergonomic sitting posture features. We develop PoseGBDT to effectively capture temporal dependencies in posture patterns, addressing limitations of traditional frame-wise classification approaches. Extensive real-world evaluation across 10 participants and 19 distinct postures demonstrates exceptional performance, achieving 99.11% accuracy while maintaining robustness against environmental variables such as clothing thickness, additional devices, and furniture configurations. Our system provides a scalable, privacy-preserving mobile health solution on existing platforms for proactive ergonomic management, improving quality of life at low costs.
Making Genetic Testing Privacy Policies Easier to Read! And Understand? Leveraging LLMs to Improve Readability through Summarization
(2026-01-06) Ness, Nils; Thiebes, Scott; Schmidt-Kraepelin, Manuel; Sunyaev, Ali
Privacy policies in direct-to-consumer genetic testing are often written in complex language, too complex for consumers to read and understand. This study presents a multi-stage summarization pipeline that combines feature extraction and multiple language models to generate readable summaries of such policies. We evaluate the summaries in a between-subject user study with 260 participants, across constructs of cognitive load, readability, trust, and understanding. Although conventional metrics indicated insufficient text quality of the DeepSeek and our model, user evaluations show that summaries can reduce cognitive load and improve perceived readability and trust. However, understanding levels remain consistent across all groups comparing our approach, DeepSeek and the full-text. This approach offers a potentially effective solution to the challenges associated with privacy communication in sensitive domains such as genetic testing, while also enhancing consumer perception and effort.
AI-Supported Digital Health Interventions to Counter Belief Vulnerabilities: Performance-Enhancing Substance Use Intentions Are Predicted by Social Cynicism
(2026-01-06) Nowy, Tobias; Werner, Christian; Werner , Amadeus; Kainz, Florian; Spörrle, Matthias
Performance-enhancing substance (PES) use extends beyond elite sport, yet prevention efforts lack empirically grounded models of the cognitive vulnerabilities underlying substance use decisions. This study examines how social cynicism – a generalized negative social worldview – shapes intentions to use mental and physical performance-enhancing substances (PES) via cognitive appraisals of perceived benefits and harms. In a cross-sectional study of German and Austrian university students (N = 387), social cynicism positively predicted inflated perceptions of PES benefits, but did not reduce perceived unpleasantness of side effects. Mediation analyses revealed indirect effects of social cynicism on PES intentions via increased benefit appeal, whereas unpleasantness played no significant mediating role. These results highlight a benefit-focused cognitive bias that predisposes cynical individuals toward PES use. Building on this pathway, we propose intelligent digital health interventions – such as real-time cognitive reappraisal companion tools – that monitor users’ belief profiles and deliver personalized intervention nudges to reduce risk-prone substance-based enhancement behaviors.
Designing for Engagement in mHealth: A Patient-Centered DSR Approach to Epilepsy Self-Tracking
(2026-01-06) Wolf, Simon; Rahlmeier, Tim; Mazur, Philipp Gabriel; Schoder, Detlef
Mobile health seizure diaries offer superior precision compared to paper logs (85% versus 67% in benchmarking), yet suffer from high dropout rates. The literature identifies poor user engagement as a critical limitation. In response, this study prioritizes engagement-centered design. Guided by Design Science Research, we conducted a systematic literature review and stakeholder interviews, yielding 71 functional and 29 non-functional requirements. Differences between literature and patient perspectives were prioritized by frequency and implemented in a cross-platform prototype. Evaluation using the user version of the Mobile App Rating Scale showed higher scores than the average of 26 benchmarked seizure apps: Engagement (+0.09), Functionality (+0.21), Aesthetics (+0.43), and Information (+0.76). Based on user feedback, we propose actionable design principles to support engagement: progressive disclosure, safety affordance, visual summaries, and cross-platform parity. This study contributes (1) a validated prototype, (2) a stakeholder-grounded requirement set, and (3) a replicable process for engagement-oriented mobile health development.
Expert-Informed Design of an AI-Augmented Preventive Health App for Young Adults
(2026-01-06) Thanthrige, Ayesha; Ulapane, Nalika; Wickramasinghe, Nilmini
Preventive digital health solutions (DHSs) are critical for addressing chronic conditions such as prediabetes, which is a growing concern, particularly among young adults (aged 18–34). Existing wellness apps suffer high dropout rates due to poor usability and inclusivity. MiCARE, an expert-informed, AI-augmented progressive web app (PWA), addresses these gaps through a novel multi-theoretical framework integrating Self-Determination Theory (SDT), CARE (“Compassion”, “Assistance”, “Respect”, “Empathy”), User-Centered Design (UCD) and Inclusive Design. For instance, its empathetic chatbot, using closed-domain natural language processing (NLP) with clinically verified and multilingual responses, promotes culturally adaptive engagement. MiCARE was developed using the Design Science Research Methodology (DSRM) and a WCAG 2.1-compliant prototype was refined through multidisciplinary expert feedback, adhering to La Trobe University ethics approval. A pilot study is planned to evaluate usability, usefulness, and satisfaction using an integrated Task-Technology Fit (TTF)- Unified Theory of Acceptance and Use of Technology (UTAUT) framework. MiCARE offers a replicable, theory-driven blueprint for designing and evaluating inclusive DHSs, validated through expert review.
Introduction to the Minitrack on Personalized Health Assistance with Intelligent Digital Solutions
(2026-01-06) Bodendorf, Freimut; Wickramasinghe, Nilmini; Sloane, Elliot; Kröckel, Pavlina

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