The objective of this study was to assess relationships between children's physical environment and afterschool leisure time physical activity (PA) and active transport. Methods: Children aged 10-12 years participated in a 7-day accelerometer and Global Positioning Systems (GPS) protocol. Afterschool leisure time PA and active transport were identified based on locationand speed-algorithms based on accelerometer, GPS and Geospatial Information Systems (GIS) data. We operationalized children's exposure to the environment by combining home, school and the daily transport environment in individualized daily activity-spaces. Results: In total, 255 children from 20 Dutch primary schools from suburban areas provided valid data. This study showed that greenspaces and smaller distances from the children's home to school were associated with afterschool leisure time PA and walking. Greater distances between home and school, as well as pedestrian infrastructure were associated with increased cycling. Conclusion: We demonstrated associations between environments and afterschool PA within several behavioral contexts. Future studies are encouraged to target specific behavioral domains and to develop natural experiments based on interactions between several types of the environment, child characteristics and potential socio-cognitive processes. LinkedIn: https://www.linkedin.com/in/sanned/
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Part 3 Programming and Activating Cyberparks deals with the variety of ways in which urban public spaces can be reinvigorated through the use of digital media technologies. As is outlined in the introduction to this volume, digital media technologies profoundly shape the use and perception of urban public spaces. Critical observers have noted that digital media may threaten the public nature of our cities and civic spaces. For instance, elsewhere we have described these threats in terms of three Cs: commercialisation, control, and capsularisation (de Lange and de Waal 2013). First, the combination of digital media technologies and consumer culture overlays everyday urban life with a market logic of pervasive customer tracing, quantification, and a vying for attention. Datafication and personalized recommendation services capitalise on our habitual everyday movements in the city, turning them into an ever-expanding string of (potential) customer ‘touchpoints’. This affects the spatial, social and cultural dimensions of almost every realm of urban life, from work to meeting to leisure to travel to home. Visible illustrations include the rapidly changing appearance of high streets in most cities, or the nature and quality of inner-city neighbourhoods coinciding with the popularity of platforms like Airbnb (for more on platforms, see van Dijck et al. 2018). As a result, our polyvocal and frictional public open spaces are being transformed into silent and seamless marketplaces, where public interactions are reduced to commercial transactions.
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.
