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In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project “Zukunftslabor Gesundheit” (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.

Continuous health monitoring in private spaces such as the car is not yet fully exploited to detect diseases in an early stage. Therefore, we develop a redundant health monitoring sensor system and signal fusion approaches to determine the respiratory rate during driving. To recognise the breathing movements, we use a piezoelectric sensor, two accelerometers attached to the seat and the seat belt, and a camera behind the windscreen. We record data from 15 subjects during three driving scenarios (15 min each) city, highway, and countryside. An additional chest belt provides the ground truth. We compare the four convolutional neural network (CNN)‑based fusion approaches: early, sensor‑based late, signal‑based late, and hybrid fusion. We evaluate the performance of fusing for all four signals to determine the portion of driving time and the signal combination. The hybrid algorithm fusing all four signals is most effective in detecting respiratory rates in the city ( P = 62.42), highway (P = 62.67 ), and countryside (P = 60.94). In summary, 60% of the total driving time can be used to measure the respiratory rate. The number of signals used in the multi‑signal fusion improves reliability and enables continuous health monitoring in a driving vehicle

Umweltfaktoren wie die Raumtemperatur oder Luftqualität können gesundheitsschädigend wirken. Pflegende benötigen eine an die Pflegeprozesse angepasste Darstellung der Messwerte und Risiken um präventiv handeln zu können. In dieser Arbeit wird die Gestaltung einer entsprechenden Benutzungsschnittstelle anhand wissenschaftlicher Erkenntnisse zur Risikokommunikation thematisiert. Das Resultat soll als initialer Anhaltspunkt für eine weiterführende Anforderungsanalyse unter Einbeziehung von Pflegenden dienen, um Erfordernisse für die Integration des Monitorings von Umweltfaktoren im Versorgungskontext zu identifizieren.

Die Digitalisierung beeinflusst neben den Arbeitsprozessen zunehmend auch die Rollen und Qualifikationsanforderungen von Pflegefachpersonen. Die Kompetenzen der unterschiedlichen Abschlüsse sind noch unzureichend aufeinander abgestimmt und spezifische Aufgabenfelder im Bereich der Digitalisierung und Technik fehlen. Eine stärkende Rolle im Entwicklungsprozess von Technologien könnte ein Tätigkeitsbereich sein, um zukünftig neue Lösungen nutzer*innenorientiert entwickeln und in der Pflegepraxis implementieren zu können. Anhand mehrerer Schritte wurde interdisziplinär im Projekt Data-Driven- Health ein partizipatives Konzept entwickelt, dass unterschiedliche Beteiligungsformen ermöglicht. Ein zentrales Element hierbei ist die aktive Einbindung von Pflegestudierenden als Co-Forschende. Neben beteiligten Kliniken und Pflegefachpersonen aus unterschiedlichen Einrichtungen, sollen Studierende nach theoretischen Inhalten anwendungsbezogene Kompetenzen in einem Forschungsprojekt erlernen.

Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories "governance", "modeling" and "standards", the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.