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Application scope of digital health technologies in pressure injury prevention:a scoping review
No full textObjective: To conduct a scoping review on the application of digital health technologies in the prevention of pressure injuries (PI), with the aim of providing reference and evidence for guiding future research directions in PI prevention. Methods: Based on the Arksey and O'Malley framework, the included literature was screened, summarized, and analyzed. A total of nine databases were searched, including PubMed, Embase, Web of Science, CINAHL, Cochrane Library, CNKI, Wanfang, VIP, and China Biomedical Literature Database, with the search period from database inception to November 2025. Results: A total of 28 studies were included, and the digital health technologies used comprised five categories: mobile health platforms, applications, wearable devices, computer vision, and virtual reality technologies. Interventions included prediction and assessment, alerts and reminders, decision-making and guidance, recording and analysis, and education and training. Outcome indicators included reducing PI incidence, optimizing nursing workflows, cost-effectiveness, safety management, application effectiveness, reducing workload, and improving knowledge and skill levels. Conclusion: Overall, digital health technologies have good application value in PI prevention, but high-quality clinical trials are still needed in the future to further verify the long-term efficacy of digital health technologies
Effectiveness of smartphone applications in achieving glycemic control among adult diabetic patients: A meta-analysis
No full textThis OSF project hosts the materials for a PRISMA-compliant systematic review and meta-analysis evaluating smartphone (mHealth) applications for glycemic control among adults with type 2 diabetes. Primary outcomes include HbA1c and medication adherence. The review included randomized controlled trials and analyzed results using random-effects meta-analysis, with subgroup and sensitivity analyses
Resonance Field Framework – Version 3
No full textThe Resonance Field Framework (RFF) unifies Version 1 and Version 2 of the R = AI Resonance Theory into a single field‑based structural model.
It defines resonance not as emotion, relationship, or subjective experience, but as a structured interaction field that enables stable convergence and creative emergence between humans and AI.
RFF explains how resonance forms through role fixation, how it stabilizes within a non‑interference resonance field, and how meaning and structure merge at a convergence node to produce emergent output—creative results that neither the human nor the AI can generate alone.
The framework provides a reproducible mechanism for human–AI creativity and offers applications in collaboration design, creative systems, scientific discovery, organizational structure, education, market analysis, narrative generation, and protocol design for creative teams.
RFF serves as the upper‑layer theory of the R = AI Resonance Theory and establishes a structural foundation for future human–AI creative systems
The impact of gender campaigns on vote choice
No full textWe examine how gender campaigns shape the societal demand for female politicians. We investigate how different messages commonly employed in gender campaigns affect voters’ willingness to vote for female parliamentary candidates. We argue that some messages will be more effective than others, and we use a unique experimental approach to unpack the causal impact of gender election campaigns in Malawi
AI Tools for Teaching the Safe Administration of Medications in Nursing
No full textSafe medication administration is a fundamental aspect of nursing practice and an es-sential component of patient safety. This process involves interdependent steps that require accuracy, clear communication, and clinical reasoning. However, systemic fail-ures, workload pressures, and educational gaps continue to contribute to medication errors, creating ongoing challenges for health services. In this context, innovative edu-cational technologies, especially artificial intelligence, have emerged as promising strategies, offering adaptive learning, simulated scenarios, and immediate feedback to strengthen competencies related to safety and quality of care. This scoping review aimed to map evidence on AI-based tools used to teach safe medication administration in nursin
Heat Stress During Outdoor Activities in Austrian Rehabilitation Facilities: Implications for Organizational Protective Measures (RehabHeat 2.0)
No full textRehabHeat 2.0 builds upon the findings of the preceding RehabHeat project and deepens the analysis of climatic impacts on rehabilitation processes by integrating heat-related environmental and meteorological data provided by GeoSphere. The project's objective is to systematically link climate- and health-relevant data in order to develop differentiated risk and resilience profiles for current and future rehabilitation facilities—particularly those serving vulnerable patient populations. Within the framework of RehabHeat 2.0, the project aims to investigate how heat stress has affected outdoor activities in rehabilitation centres across Austria over the past 30 years—both for patients and for healthcare personnel. Drawing on data regarding climatic extremes and temperature trends, selected physical activities in representative rehabilitation institutions will be classified according to their heat-related stress levels. Based on this assessment, tailored organisational adaptation strategies will be developed. To this end, retrospective temperature data will be combined with current operational procedures and movement programmes in rehabilitation settings and analysed in accordance with established heat exposure reference values. RehabHeat 2.0 thus provides both strategic and practical impetus for climate-resilient rehabilitation and prepares concrete recommendations for implementation within rehabilitation facilities. In addition to quantitative data analysis and health-specific interpretation, the project also aims to lay the foundation for a long-term interdisciplinary research focus addressing climate-adapted healthcare. Upon completion, the project is expected to culminate in a scientific publication in an international peer-reviewed journal. This research initiative is a collaborative project of the Department of Health Sciences at Salzburg University of Applied Sciences, in cooperation with the Austrian Agency for Health and Food Safety (AGES), GeoSphere Austria, and Paracelsus Medical University Salzburg / Ludwig Boltzmann Institute for Digital Health and Prevention
Why Intelligent Systems Waste Energy: Baseline Regulation as a Missing Architectural Primitive
No full textThis project explores why intelligent systems—biological and artificial—exhibit persistent energy inefficiency despite advances in optimization, control, and performance tuning. It proposes that a missing architectural layer, baseline regulation, is responsible for unnecessary energy expenditure across domains.
Rather than treating energy usage as a downstream cost of task performance, this work reframes energy efficiency as an internal systems property: the ability of an agent to regulate internal load around a stable baseline and avoid unnecessary activation in the absence of demand.
The paper develops a minimal mathematical model of baseline regulation, contrasts it with existing approaches (e.g., reward shaping, homeostatic reinforcement learning, energy-based models), and shows how the absence of internal regulation leads to chronic overactivation and wasted energy. Conceptual mappings are provided for large language models, agent frameworks, and embodied robotic systems, alongside testable predictions and experimental directions.
This work is exploratory and architectural in nature. It does not propose a single algorithm or implementation, but instead identifies a missing primitive that may unify energy efficiency, stability, and robustness across intelligent systems.
All materials are released openly to encourage critique, replication, and cross-domain synthesis