Interdisciplinary multimodal pain therapy (IMPT) is a biopsychosocial treatment approach for patients with chronic pain that comprises at least psychological and physiotherapeutic interventions. Core outcome sets (COSs) are currently developed in different medical fields to standardize and improve the selection of outcome domains, and measurement instruments in clinical trials, to make trial results meaningful, to pool trial results, and to allow indirect comparison between interventions. The objective of this study was to develop a COS of patient-relevant outcome domains for chronic pain in IMPT clinical trials. An international, multiprofessional panel (patient representatives [n = 5], physicians specialized in pain medicine [n = 5], physiotherapists [n = 5], clinical psychologists [n = 5], and methodological researchers [n = 5]) was recruited for a 3-stage consensus study, which consisted of a mixed-method approach comprising an exploratory systematic review, a preparing online survey to identify important outcome domains, a face-to-face consensus meeting to agree on COS domains, and a second online survey (Delphi) establishing agreement on definitions for the domains included. The panel agreed on the following 8 domains to be included into the COS for IMPT: pain intensity, pain frequency, physical activity, emotional wellbeing, satisfaction with social roles and activities, productivity (paid and unpaid, at home and at work, inclusive presentism and absenteeism), health-related quality of life, and patient's perception of treatment goal achievement. The complexity of chronic pain in a biopsychosocial context is reflected in the current recommendation and includes physical, mental, and social outcomes. In a subsequent step, measurement instruments will be identified via systematic reviews.
Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
People with a visual impairment (PVI) often experience difficulties with wayfinding. Current navigation applications have limited communication channels and do not provide detailed enough information to support PVI. By transmitting wayfinding information via multimodal channels and combining these with wearables, we can provide tailored information for wayfinding and reduce the cognitive load. This study presents a framework for multimodal wayfinding communication via smartwatch. The framework consists of four modalities: audio, voice, tactile and visual. Audio and voice messages are transmitted using a bone conduction headphone, keeping the ears free to focus on the environment. With a smartwatch vibrations are directed to a sensitive part of the body (i.e., the wrist), making it easier to sense the vibrations. Icons and short textual feedback are viewed on the display of the watch, allowing for hands-free navigation.
Teachers have a crucial role in bringing about the extensive social changes that are needed in the building of a sustainable future. In the EduSTA project, we focus on sustainability competences of teachers. We strengthen the European dimension of teacher education via Digital Open Badges as means of performing, acknowledging, documenting, and transferring the competencies as micro-credentials. EduSTA starts by mapping the contextual possibilities and restrictions for transformative learning on sustainability and by operationalising skills. The development of competence-based learning modules and open digital badge-driven pathways will proceed hand in hand and will be realised as learning modules in the partnering Higher Education Institutes and badge applications open for all teachers in Europe.Societal Issue: Teachers’ capabilities to act as active facilitators of change in the ecological transition and to educate citizens and workforce to meet the future challenges is key to a profound transformation in the green transition.Teachers’ sustainability competences have been researched widely, but a gap remains between research and the teachers’ practise. There is a need to operationalise sustainability competences: to describe direct links with everyday tasks, such as curriculum development, pedagogical design, and assessment. This need calls for an urgent operationalisation of educators’ sustainability competences – to support the goals with sustainability actions and to transfer this understanding to their students.Benefit to society: EduSTA builds a community, “Academy of Educators for Sustainable Future”, and creates open digital badge-driven learning pathways for teachers’ sustainability competences supported by multimodal learning modules. The aim is to achieve close cooperation with training schools to actively engage in-service teachers.Our consortium is a catalyst for leading and empowering profound change in the present and for the future to educate teachers ready to meet the challenges and act as active change agents for sustainable future. Emphasizing teachers’ essential role as a part of the green transition also adds to the attractiveness of teachers’ work.
ATAL: Automated Transport and Logistics Automatisering van transportmodaliteiten is overal ter wereld gaande. Met een Duurzaam Living Lab kunnen multimodale geautomatiseerde transportoperaties verder in de praktijk duurzaam en opschaalbaar worden ontwikkeld. Hierbij worden beleidsmakers en organisaties ondersteund in deze transitie. De maatschappelijke voordelen van grootschalige uitrol van Automated Trucks en Platooning, Automated Train Operations en Autonomous Sailing zijn onder andere minder energieverbruik en emissies, betere doorstroming en betere verkeersveiligheid. De Duurzame Living Lab heeft betrekking op het haven-achterland vervoer van Rotterdam richting Duitsland en België. Het wegvervoer maakt gebruik van de TULIP-Corridor, water en spoor modaliteit volgen de MIRT goederencorridors tot in het Ruhrgebied.
De opkomst van Mobility as a Service (MaaS) is een gevolg van verschillende maatschappelijke en technologische ontwikkelingen. MaaS is het aanbod van multimodale, vraag-gestuurde mobiliteitsdiensten, waarbij op maat gemaakte reismogelijkheden via een digitaal platform (bijvoorbeeld een mobiele app) met real-time informatie aan klanten worden aangeboden, inclusief betaling en afhandeling van transacties. Meerdere regio’s en steden zijn momenteel op zoek hoe de ontwikkeling van MaaS succesvol te faciliteren. Ter ondersteuning van deze regio’s en steden ontwikkelen de HAN en Movares in dit onderzoek een “MaaS-ladder”. Deze MaaS-ladder geeft voor elke stad of regio een integraal overzicht hoe zij scoort op verschillende factoren die voor het succes van MaaS belangrijk zijn. Door middel van indicatoren wordt een sectoraal overzicht gegenereerd hoe gereed een stad of regio is en wordt inzichtelijk gemaakt welke ontwikkelingen en beleidsbeslissingen genomen kunnen worden om de implementatie van nieuwe mobiliteitsdiensten, zoals MaaS, te faciliteren. Ook kan de ambitie op de bepalende factoren worden gemeten, zodat inzichtelijk wordt in hoeverre de ambitie behaald wordt. Onderdeel van het onderzoek is een proof-of-concept door het toepassen van de MaaS-ladder in pilots samen met de gemeenten Amsterdam, Nijmegen, Apeldoorn, Den Haag en Doetinchem. Met de uitkomsten van de pilots kan de MaaS-ladder aangescherpt worden en kunnen steden en regio’s van elkaar leren door best practices uit te wisselen.
