Bern University of Applied Sciences

Background For general hospital settings, effective restraint reduction strategies are lacking. Patient involvement is proven to be useful in restraint reduction in mental healthcare and in long-term care settings. Since such an approach has never been investigated in a general hospital setting, we investigated whether and how patient involvement regarding restraint reduction is feasible in such a setting. Methods A pilot study following a participatory action research design was conducted. Qualitative and quantitative approaches were applied to develop and pilot an intervention to reduce restraint by preventive involvement of patients (aged 65+) in Switzerland. The intervention entailed reflecting on the potential risk of restraint use together with the patient within 24 h of admission and jointly defining possible prevention measures. The intervention was piloted for one month on one ward. Data collection for the qualitative evaluation included interviews with eight patients, five nurses, two ward managers and one clinical nurse specialist. These data were analysed by means of content analysis. Data collection for the quantitative evaluation consisted of a survey of nurses and an extraction of data from the electronic patient files. These data were descriptively analysed. Results The evaluation comprised the files of 177 patients (pre to post pilot). It was found that that prevalence of restraint was lower during the pilot phase than before (4.8% vs. 10.2%), although a similar number of patients were found to be at a potential risk of restraint use (51.6% vs. 53.3%). In addition, considerably more patients with a potential restraint risk had restraint prevention measures documented (53.1% vs. 10.2%). From the perspective of the nursing staff, feasibility and acceptability of the intervention was not provided. The intervention was considered to be too time-consuming and the target group too unspecific. Conclusions The proactive and structured involvement of patients (aged 65+) in the prevention of restraint use might be an approach to reduce restraint use in a general hospital. Patients were positive about being addressed on the topic during the nursing admission interview. However, the effort was regarded to be high. Limiting the intervention to electively admitted patients should be considered to lower the burden.

Background A severe spinal cord injury (SCI) can profoundly affect an individual’s physical abilities and social independence. For individuals living with tetraplegia, an assistive robotic arm offers the potential to restore some autonomy and reduce the need for constant assistance. However, current assistive technologies are often costly, impractical, and fail to meet the needs of those affected. This leads to high rates of abandonment and user frustration with the technology. The aim of this study was to identify the needs and expectations of both individuals with tetraplegia and their caregivers regarding an assistive robotic arm in performing everyday activities. Methods A mixed-method approach was used, beginning with a focus group interview and followed by two online surveys; one aimed at individuals with tetraplegia and the other at caregivers. Qualitative analysis of the focus groups was performed using Focus Group Illustration Mapping. The online surveys were analyzed descriptively and qualitatively using structured content analysis. Results A total of seven participants (individuals with tetraplegia, caregivers, physiotherapists, and an engineer) took part in the focus group interview. The online surveys were completed by 49 individuals with tetraplegia and nine caregivers. The results showed that the participants were open to using a robotic arm but none used one at the time of reporting. The participants reported that a robotic arm should assist in unilateral activities such as reaching, grasping, handling objects and body manipulation. The greatest need was reported for functions related to object manipulation and for contact with the person’s body. The participants reported wanting control over the robotic arm via voice commands or with a joystick. Concerns were reported regarding costs, the weight and the space required for the robotic arm. Conclusions In our study, individuals with tetraplegia reported that they would use assistive robotic arms for activities related to reaching, grasping, and object manipulation. Concerns regarding costs, weight and space requirements were reported. Our findings provide insights from a user perspective, informing future technical developments relevant to the tetraplegic population. However, generalizability might be reduced.

Background and Objectives To examine whether changes in the Mediterranean Diet (MD) or any of its MD food groups modulate the genetic susceptibility to obesity in European youth, both in cross‐sectional and longitudinal analyses. Methods For cross‐sectional analysis, 1982 participants at baseline, 1649 in follow‐up 1 (FU1) and 1907 in follow‐up 2 (FU2), aged 2–16 years of the IDEFICS/I.Family studies were considered. For the longitudinal design, 1254 participants were included. Adherence to MD was assessed using the Mediterranean Diet Score (MDS), and genetic susceptibility to high BMI was assessed with a polygenic risk score (BMI‐PRS). Multiple linear regression models were fitted to estimate gene × MD effects on markers of obesity. Results In cross‐sectional analyses, at baseline, higher MDS was associated with higher BMI in children with high genetic susceptibility (β = 0.12; 95% CI = [0.01, 0.24]). However, 6 years later, at FU2, higher MDS was associated with lower BMI (β = −0.19; 95% CI = [−0.38, −0.01]) in children with high genetic susceptibility, showing an attenuating MDS effect. Also in FU2, vegetables and legumes (V&L) showed inverse associations with BMI (β = −0.01; CI = [−0.02, −0.00]) and WC (β = −0.02; CI = [−0.03, −0.00]) regardless of the obesity genetic risk, although the effect sizes were small. In the longitudinal analyses, no MDS‐obesity associations or gene × diet interaction effects were observed. Conclusions In cross‐sectional analysis (baseline and FU2), the MD modulated the association between obesity susceptibility and adiposity indicators in European youth, having an exacerbating effect in children measured during infancy years and an attenuating effect in early adolescent years.

Equitable measures for nature conservation require an in‐depth understanding of human‐nature relations. Using qualitative and quantitative data from semi‐structured household surveys, we investigated people's perception of nature's contribution to their perceived well‐being along an elevational gradient in eastern Nepal. We used linear and ordinal regressions to identify the factors influencing these perceptions and qualitative analyses to identify nature's contributions to people (NCP) likely contributing to this well‐being. We found nuanced and context‐specific relationships between people and nature in Nepal, emphasising how geographic location, formal education, socio‐economic factors and gender shape perceptions of how nature contributes to well‐being. Participants provided examples of a variety of material, non‐material and regulating NCP that are crucial for multiple aspects of their well‐being, underscoring the need for integrated conservation approaches that extend beyond prioritising habitat maintenance to also encompass enhancing material and non‐material NCP. While conservation interventions may be informed by global conceptual frameworks and policy agreements such as the IPBES Conceptual Framework, the Kunming‐Montreal Global Biodiversity Framework and the Sustainable Development Agenda, they must be rooted in the collective perspectives and experiences of the local context in which conservation actually happens. Read the free Plain Language Summary for this article on the Journal blog.

This study examines the intention–behaviour dynamics of intrapreneurship at the individual level, an area that remains largely underexplored compared with the widely studied domain of firm‐level intrapreneurship. Based upon the theory of planned behaviour, we investigate the relationships between individual entrepreneurial orientation (intention), job fit (context) and employee intrapreneurship (behaviour) in small‐ and medium‐sized enterprises (SMEs). We, therefore, conducted moderated regression analyses using a sample of 649 employees from SMEs. Our results indicate that individual entrepreneurial orientation has a positive effect on employee intrapreneurship, whereas surprisingly, job fit has no significant effect. However, job fit positively moderates the relationship between individual entrepreneurial orientation and employee intrapreneurship, especially its subdimensions person–organization fit and demands–abilities fit. Our findings enhance the theoretical comprehension of intrapreneurship, particularly the connection between intention and behaviour, while also offering valuable practical insights for the contextual employment settings within SMEs.

Despite widespread adoption of electronic medical records (EMRs), concerns persist regarding their usability and implications for patient safety. This national cross-sectional survey assessed physicians’ perceptions of EMR usability across safety-relevant domains. Among 1933 respondents from diverse care settings, 56% reported that their EMR did not enhance patient safety, and 50% perceived their system as inefficient. Usability ratings averaged 52% of the maximum score. Statistically significant differences were observed between EMRs in outpatient (η² = 0.13) and hospital (η² = 0.37) settings. Multilevel modeling attributed 38% of the variance in usability ratings to differences between EMRs, 51% to hospital-level variation within EMRs, and 11% to physician-level differences. Canonical discriminant analysis identified key differentiating usability features, including system response times, excessive alerts, prevention of data entry errors, and support for collaboration. These findings underscore substantial limitations in current EMR systems and reinforce the value of comparative usability assessments to inform targeted improvements in digital health infrastructure.

Infertility, affecting one in six individuals worldwide, poses substantial emotional and physical challenges. Its impact on quality of life (QoL), mental health and social relationships is well documented. However, qualitative insights into the lived experiences of those affected remain limited, as do the perspectives of health professionals involved in their care. This study presents qualitative findings from a mixed-method approach. Data were collected through 23 semi-structured interviews with 26 affected individuals and three focus group discussions with 20 healthcare professionals. The results underscore the multifaceted burden of infertility, including emotional distress, relationship strain and a pervasive sense of social exclusion. Participants expressed a clear need for more empathetic and individualized care, while healthcare professionals pointed to structural barriers. The study also identified a lack of societal recognition for infertility and the complex challenges. In some narratives, spirituality emerged as a personal coping resource – though one that is often overlooked or insufficiently addressed in clinical practice. Key recommendations include the expansion of peer support networks, public awareness raising, flexible treatment models and interprofessional collaboration. Future research should evaluate the effectiveness of peer support systems and interdisciplinary care models in addressing the complex and diverse needs of individuals affected by infertility.

Participatory design (PD), i.e. the systematic involvement of stakeholders into the design process, is increasingly used for the design of digital health interventions. In this work, we assess to what extent research is reporting aspects relevant for understanding the PD process with examples from mental health.

Advances in Artificial Intelligence (AI) and other technologies together with increasing economic and care pressures have popularized the use and development of digital health solutions and interventions, in particular in mental health. Associated with this is an increase of demonstrated potential to harm patients. Using case studies showing harms to patients using AI-based mental health tools, we highlight the need to strengthen surveillance, via digitalovigilance. By this, we raise awareness on six competencies for citizens and users of digital health interventions that are necessary to reduce risks and improve patient safety.

Introduction: Participatory Health (PH) emerges as a consequence of the rise of the internet, which has led to a patient-centered approach. Participatory Health Informatics (PHI) uses information technologies and evaluates the use of tools. The emergence of new Artificial Intelligence (AI) techniques represents a great advance for PH. The objective of this article is to study the facilitators and opportunities that AI offers to PH, but also the challenges and barriers it faces. Methods: A literature review on barriers and facilitators of AI in PH was conducted, including articles published in the last 10 years. Results: 38 articles were eventually selected for review. Several aspects and applications of AI in PH were identified, including health domains and types of participation; types of AI used; reported barriers and challenges; facilitators and opportunities; impact on participatory health; and ethical, legal and patient safety considerations. Discussion and conclusion: 6 main thematic areas of interaction between AI and PHI were identified. There is a wide variety of applications, with special impact on predictive analysis, the management of healthcare data and conversational agents. Legal and privacy issues are seen as the main barriers for the use of AI in PHI, whereas improving diagnostic accuracy, optimizing patient flow, and patient empowerment are considered the main opportunities.

The increasing adoption of deep learning methods has intensified the demand for explanations regarding how AI systems generate their results. This necessity originated primarily in the domain of image processing and has expanded to encompass the complexities of large language models (LLMs), particularly in medical contexts. For example, when LLM-based chatbots provide medical advice, the challenge lies in articulating the rationale behind their recommendations, especially when specific features may not be identifiable. This paper explores the distinction between explanation, interpretation, and understanding within AI-driven decision support systems. By adopting Daniel Dennett’s intentional stance, we propose a methodology for analyzing how AI explanations can facilitate deeper user engagement and comprehension. Furthermore, we examine the implications of this methodology for the development and regulation of medical chatbots.

Participatory design (PD) is an essential method in the development of digital health solutions since it promises to increase acceptance, usability, and trust in the developed solution. Although careful planning and preparation is crucial for the success of PD workshops, a framework of key components to consider is still missing. The objective of this work is to develop such framework enriched with examples for aspects to be considered when planning and conducting PD workshops for designing and developing digital interventions in healthcare. We applied the nominal group technique with four participants with backgrounds in computer science, health informatics, psychology, and social anthropology to identify key components of PD workshops. The resulting framework was applied by an expert in PD to a case of a digital health solution for fatigue self-management for multiple sclerosis. The feasibility and applicability of the framework and its shortcomings were assessed. As a result, a framework consisting of five main categories and a total of 36 factors were assigned and defined in relation to the categories. The categories are participatory process, involved persons and their roles, workshop definition, setting, privacy and ethics, including regulations. The application of the framework to the test case demonstrated the feasibility and applicability of the suggested framework as well as the shortcomings of the analyzed PD process. This framework provides practical guidance while highlighting the complexity of PD workshops, encouraging their broad adoption, critical reflection, and continuous refinement. It has potential to improve the conduct of PD workshops and, in this way, potential to improve usability, acceptance, and usefulness of digital health solutions. In future work, the user perspective could be used to extend the framework.

This chapter explores the intersection of real-time de-identification, trust, creativity, and innovation in virtual groups. It provides an in-depth analysis of good practices, methods, and tools for using real-time de-identification to increase creativity and innovation in organizations. The chapter highlights the importance of de-identification in protecting individual privacy, examines the role of psychological safety in fostering trust, and explores the unique dynamics of virtual teams. The findings are based on a comprehensive review of research.

This study compared the effects of cold water immersion (CWI) and hot water immersion (HWI) on muscle recovery following a muscle-damaging exercise protocol in women. Thirty healthy women (23.3 ± 2.9 years) were randomly assigned to either the CWI, HWI, or control (CON) groups. Participants completed a standardised exercise protocol (5 x 20 drop-jumps), followed by a 10 min recovery intervention (CWI, HWI, or CON) immediately and 120 min post-exercise. Physiological responses, including muscle oxygen saturation (SmO2), core and skin temperature, and heart rate, were assessed at baseline, immediately post-exercise, after the first recovery intervention (postInt), and during 30 min follow-up. Recovery was evaluated through maximal voluntary isometric contraction, muscle swelling, muscle soreness ratings, and serum creatine kinase at baseline, 24, 48, and 72 h post-exercise. A mixed-effects model was used to account for repeated measures over time. Results showed lower SmO2 values in the CWI compared to the HWI group at 20 min (Δ-6.76%, CI: −0.27 to −13.25, p = 0.038) and 30 min (Δ-9.86%, CI: −3.37 to −16.35, p = 0.001), and compared to CON at 30 min (Δ-7.28%, CI: −13.77 to −0.79, p = 0.022). Core temperature was significantly higher in the HWI than the CWI group (postInt and 30 min), while it was significantly lower in the CWI group than CON (30 min). CWI caused a substantial decrease in skin temperature compared to HWI and CON between postInt and 30 min follow-up (all p < 0.001). Skin temperature was higher in the HWI group compared to CON at postInt and throughout 30 min follow-up (all p < 0.001). No significant differences in recovery markers were observed between CWI and HWI groups, although HWI led to slightly higher creatine kinase levels (24 h and 72 h) and greater muscle swelling (24 h) compared to CON. Despite distinct acute physiological responses to CWI and HWI, neither improved subjective or objective recovery outcomes during the 72 h follow-up compared to CON in women following a muscle-damaging exercise protocol. Trial registration number NCT04902924 (ClinicalTrials.gov), SNCTP000004468 (Swiss National Clinical Trial Portal).

Past and current policies appear ineffective in limiting global warming to below 2 °C, as the share of fossil energy has stayed well above 80% for the last 30 years, and global greenhouse gas emissions still show a rising trend. A small but fundamental change in perspective could have the potential to enable a democratically supported last-minute turn toward a sustainable economy. We define a set of criteria that climate policy instruments should ideally meet in order to be both effective and sustainable. For fulfilling these criteria, we rationally propose a paradigm shift with respect to carbon taxing. Instead of taxing emissions at the chimney, our proposal is to re-consider a consumer-level tax on the carbon footprint of products and services combined with a rigorous lump sum refund policy. This could potentially eliminate relevant obstacles for unilateral application of effective carbon prices. We also mention potential challenges with the proposed option. It is our aim to bring the possibility of a consumer-level carbon footprint tax to the attention of the scientific community and to trigger multidisciplinary research on this topic.

Providing argumentation feedback is considered helpful for students preparing to work in collaborative environments, helping them with writing higher-quality argumentative texts. Domain-independent natural language processing (NLP) methods, such as generative models, can utilize learner errors and fallacies in argumentation learning to help students write better argumentative texts. To test this, we collect design requirements, and then design and implement two different versions of our system called ALure to improve the students’ argumentation skills. We test how ALure helps students learn argumentation in a university lecture with 305 students and compare the learning gains of the two versions of ALure with a control group using video tutoring. We find and discuss the differences of learning gains in argument structure and fallacies in both groups after using ALure, as well as the control group. Our results shed light on the applicability of computer-supported systems using recent advances in NLP to help students in learning argumentation as a necessary skill for collaborative working settings.

This study investigates the transition from analog to digital Huddle-Boards within the operational environment of a hospital. By analyzing the current analog system and implementing a digital prototype, the study aims to measure efficiency, usability, and overall system improvements. A combination of observational analyses, surveys, interviews, and usability studies were conducted to assess the potential benefits of digitization. The results highlight significant time savings, increased accessibility, and enhanced usability of the digital system compared to its analog counterpart.

Challenges such as time constraints, distractions and multi-tasking can compromise patient safety in the demanding environment of surgery. To mitigate these risks, checklists have emerged as simple yet effective tools for ensuring critical aspects of patient care, such as verifying patient identity and planned interventions. However, their consistent and accurate implementation in daily practice remains a challenge. This paper presents an intelligent assistant, VoiceCheck, designed to enhance patient safety during surgical procedures by guiding the use of checklists. Seamlessly integrated into the surgical workflow, VoiceCheck uses advanced speech recognition technology to ensure compliance with safety protocols. Combining speech-to-text and text-to-speech capabilities, the assistant facilitates interactive communication with users and accurately captures approved information. Future work will study user acceptance and usability. An open issue is linking the system to a hospital information for retrieving relevant patient data.