We collected, reviewed and edited articles submitted to Sustainability on the topic of communicative and behavioral interventions to increase sustainability.In this Special Issue, we would like to highlight research on interventions. How we can find interventions that help in encouraging (sustainable or less consumption) the use of fossil-free methods of transport, the implementation of renewable energy, etc. We invite you to submit your work to this Special Issue on “Communicative and Behavioral Interventions to Increase Sustainability” that contribute to the establishment of a sustainable future. We would like to collect a Special Issue that highlight evidence-based interventions to change behavior to establish the sustainable society.This Special Issue is positioned to bring together the best work on communicative and behavioral interventions. It can include (but is not limited to) dialogue, stakeholder engagement, educational programs, nudging, community building, corporate social responsibility (CSR), social innovations, etc. We would like you to focus on evidence-based interventions and encourage practical insights or ideas for applications of findings
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Financially vulnerable consumers are often associated with suboptimal financial behaviors. Evaluated financial education programs so far show difficulties to effectively reach this target population. In our attempt to solve this problem, we built a behaviorally informed financial education program incorporating insights from both motivational and behavioral change theories. In a quasi-experimental field study among Dutch financially vulnerable people, we compared this program with both a control group and a traditional program group. In comparison with the control group, we found robust positive effects of the behaviorally informed program on financial skills and knowledge and self-reported financial behavior, but not on other outcomes. Additionally, we did not find evidence that the behaviorally informed program performed better than the traditional program. Finally, we discuss the findings and limitations of this study in light of the financial education literature and provide implications for policymaking and directions for future research.
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Design, Design Thinking, and Co-design have gained global recognition as powerful approaches for innovation and transformation. These methodologies foster stakeholder engagement, empathy, and collective sense-making, and are increasingly applied to tackle complex societal and institutional challenges. However, despite their collaborative potential, many initiatives encounter resistance, participation fatigue, or only result in superficial change. A key reason lies in the overlooked undercurrent—the hidden systemic dynamics that shape transitions. This one-year exploratory research project, initiated by the Expertise Network Systemic Co-design (ESC), aims to make systemic work accessible to creative professionals and companies working in social and transition design. It focuses on the development of a Toolkit for Systemic Work, enabling professionals to recognize underlying patterns, power structures, and behavioral dynamics that can block or accelerate innovation. The research builds on the shared learning agenda of the ESC network, which brings together universities of applied sciences, design practitioners, and organizations such as the Design Thinkers Group, Mindpact, and Vonken van Vernieuwing. By integrating systemic insights—drawing from fields like systemic therapy, constellation work, and behavioral sciences—into co-design practices, the project strengthens the capacity to not only design solutions but also navigate the forces that shape sustainable change. The central research question is: How can we make systemic work accessible to creative professionals, to support its application in social and transition design? Through the development and testing of practical tools and methods, this project bridges the gap between academic insights and the concrete needs of practitioners. It contributes to the professionalization of design for social innovation by embedding systemic awareness and collective learning into design processes, offering a foundation for deeper impact in societal transitions.
E-cycling intelligence is a research project directly connected to the PhD-research of Joost de Kruijf at the Utrecht University. Within the program the effects of the introduction of e-bikes in daily commuting are being investigated. Using a large-scale incentive program targeting on behavioral change among car-oriented commuters the next four specific components are being :- Modal shift to e-cycling- Well-being and travel satisfaction of e-bikes vs. car- Weather circumstances and e-cycling- Behavioral intention to e-bike vs. actual behavior Using a combination of three surveys (baseline, one month and half a year) and continuous GPS-measurement on the behavior of more than 800 participants makes this research unique. In collaboration with the TU/e the GPS-dataset is being translated into relevant information on modal shift on different trip purposes offering a new range of possibilities to analyses behavioral change. Knowledge on every of the four topics in the project is translated scientific paper. The expected end of the project is July 2021.With the research not new insights are being gained, the Breda University of Applied Sciences also develops a scientific network of cycling related researchers together with a network of cycling engaged road authorities.
