IMPROVE will use Patient Generated Health Data (PGHD) gathered via m-health and e-health technologies to gain improved insights into the real-life behavior of, and challenges faced by, patients of all ages with complex, chronic diseases and comorbidities. Already today, a wealth of patient and citizen information is available, but fragmented, and therefore not coming to its full utility and value. These personal data will complement and improve existing approaches for Patient-Centered Outcome Measures beyond those currently available in state-of-the-art platforms. The IMPROVE platform that the consortium will build will enable the smart use of patient input and patient generated evidence to 1) advance the role of patient preference and patient experience in the context of treatment selection, 2) improve medical device design based on patient preferences and experiences, and 3) facilitate faster market entry of patient-centric and cost-effective advanced integrated care solutions. Improved clinical adoption of Value Based Health Care, and enhanced return on research and innovation investments will be demonstrated in different care settings across the EU, for 10 use cases in at least 5 different disease areas (e.g., ophthalmology, oncology, cardiovascular disease, chronic inflammation, and neurology). The use cases will be conducted using a large variety of implementation strategies, building on a design thinking approach, to optimally test the innovative framework of data gathering and translation into controlled change and action. In addition, a significant contribution from implementation science is planned to reach out to all stakeholders that are relevant for this initiative and maximise the impact to IMPROVE healthcare provision.

The L2TOR project capitalises on recent developments in human-robot interaction in which the use of social robots is explored in the context of teaching and tutoring. Social robots have been shown to have marked benefits over screen-based tutoring technologies, and have demonstrable positive impacts on motivation in learners and their learning outcomes. L2TOR focuses on the domain of second language learning in early childhood: due to increased mobility of European citizens and increasing internationalisation, most children in Europe will be required to fluently use two or more languages. As language acquisition benefits from early, personalised and interactive tutoring, current language tutoring delivery is often ill-equipped to deal with this. As resources are insufficient to offer one-to-one tutoring with (near) native speakers in educational and home contexts, L2TOR will further the science and technology of language tutoring robots, with a strong focus on multimodal interactive tutoring for young children (4 years of age). L2TOR will focus on native speaking Dutch, German and Turkish children learning English. In addition, Turkish immigrant children in the Netherlands and Germany will be supported by a robot in acquiring Dutch and German. To realise this ambition L2TOR needs to address both technical aspects -such as multimodal interaction, human-robot interaction management and social signal processing-, pedagogical aspects -such as exploring the pedagogy of social robots and the use of social robot to assist in language tutoring- and developmental psychology aspects -such as understanding how children learn a first and second language from others and how this can be transposed to learning from robots.

Time allocation among household members, across various paid and unpaid activities throughout the day, gets negotiated through individual preferences, gender norms, and the bargaining power of respective individuals. The time allocation across activities by gender, age, and location is associated with Sustainable Development Goal 5 on Gender Equality (Indicator 5.4.1), and the differences in time allocation by gender are linked to the process of economic development of a country. Despite the societal and economic significance, scholarly discourse on time use is until now mainly restricted to developed countries, with a lack of data hampering the analysis of intrahousehold time allocations in developing-country contexts. In this project, I will use Time Use Survey 1998-99 and 2019 data from India, together with the National Family Health Survey II (1998-99) and V (2019-21) data from India to examine the intrahousehold allocation of time across paid and unpaid activities. I will use econometric specifications to examine the variation in paid work, unpaid work (domestic work, care, other unpaid work), leisure, and necessary activities by considering the intersectionality of gender with socio-economic identities, location, and their change over 1998-99 and 2019. In addition, I will explore how various measures of married women’s power – women’s education level relative to their husbands and mothers-in-law, their decision-making autonomy, and freedom of movement outside the house - influence intra-household time allocations across activities. This project moves beyond the narrative of power previously explored in developed countries and examines the forms of power that influences women’s time allocations in a developing-country context. The approach of this project is likely to be relevant for other countries that are governed by similar gender norms and the findings will add to the evidence base for monitoring the global progress toward SDG 5 and specifically for India.