To improve people’s lives, human-computer interaction researchers are increasingly designing technological solutions based on behavior change theory, such as social comparison theory (SCT). However, how researchers operationalize such a theory as a design remains largely unclear. One way to clarify this methodological step is to clearly state which functional elements of a design are aimed at operationalizing a specific behavior change theory construct to evaluate if such aims were successful. In this article, we investigate how the operationalization of functional elements of theories and designs can be more easily conveyed. First, we present a scoping review of the literature to determine the state of operationalizations of SCT as behavior change designs. Second, we introduce a new tool to facilitate the operationalization process. We term the tool blueprints. A blueprint explicates essential functional elements of a behavior change theory by describing it in relation to necessary and sufficient building blocks incorporated in a design. We describe the process of developing a blueprint for SCT. Last, we illustrate how the blueprint can be used during the design refinement and reflection process.
Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.
Objective: To describe the development of a goal-directed movement intervention in two medical wards, including recommendations for implementation and evaluation. Design: Implementation Research. Setting: Pulmonology and nephrology/gastroenterology wards of the University Medical Centre Utrecht, The Netherlands. Participants: Seven focus groups were executed including 28 nurses, 7 physical therapists and 15 medical specialists. Patients' perceptions were repeatedly assessed during the iterative steps of the intervention development. Intervention: Interventions were targeted to each ward's specific character, following an Intervention Mapping approach using literature and research meetings. Main measures: Intervention components were linked to Behavior Change Techniques and implementation strategies will be selected using the Expert Recommendation Implementing Change tool. Evaluation outcomes like number of patients using the movement intervention will be measured, based on the taxonomy of Proctor. Results: The developed intervention consists of: insight in patients movement behavior (monitoring & feedback), goal setting (goals & planning) and adjustments to the environment (associations & antecedents). The following implementation strategies are recommended: to conduct educational meetings, prepare & identify champions and audit & provide feedback. To measure service and client outcomes, the mean level of physical activity per ward can be evaluated and the Net Promoter Score can be used. Conclusion(s): This study shows the development of a goal-directed movement intervention aligned with the needs of healthcare professionals. This resulted in an intervention consisting of feedback & monitoring of movement behavior, goal setting and adjustments in the environment. Using a step-by-step iterative implementation model to guide development and implementation is recommended.
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).
A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
Socio-economic pressures on coastal zones are on the rise worldwide, leaving increasingly less room for natural coastal change without affecting humans. The challenge is to find ways for social and natural systems to co-exist, co-develop and create synergies. The recent implementation of multi-functional, nature-based solutions (NBS) on the sandy Dutch coast seem to offer great potential in that respect. Surprisingly, the studies evaluating these innovative solutions paid little attention to how the social and natural systems interact in the NBS-modified coastal landscapes and if these interactions strengthen or weaken the primary functions of the NBS. It is not clear whether the objectives to improve coastal resilience and spatial quality will be met throughout the lifetime of the intervention. In the proposed project we will investigate the socio-bio-physical dynamics of anthropogenic sandy shores applying a Living Lab approach, documenting and analyzing interactions between evolving anthropogenic shores (Sand Motor and Hondsbossche Duinen, Fig.1) and people that use and manage these NBS-modified landscapes. Socio-bio-physical interactions will be investigated at various scales, and consequences for the long-term functionality of the NBS will be assessed, by coupling an agent-based social model and a cellular automata landscape model. By studying the behavior of the coupled system we aim to identify limits to, and optima in, multi-functionality of the NBS design, and will study how various stakeholders can influence the development of the NBS in desired directions with respect to primary NBS functions, including social and ecological goals. Together with consortium partners from public and private sectors we will co-create guidelines for management and maintenance of multifunctional NBS and design procedures and visualization tools for intervention design.
