In de loop van het leven wordt muziek voor de meeste mensen een drager van herinneringen en emoties. In de jeugd en vroege volwassenheid ontwikkelen we onze persoonlijke muzieksmaak. De muziek uit die tijd blijft ons het beste bij. Voor mensen met dementie, meestal 80 plussers, is dat ruim zestig jaar geleden. Daarom maakten de lectoraten Psychogeriatrie en Informatie, Technologie en Samenleving, in samenwerking met muziektherapeut Machgiel Bakker, een online muziekstation dat muziek uit die periode draait: Radio Remember. Een station om naar te luisteren, maar ook om in te zetten als psychosociale interventie om de kwaliteit van leven van mensen met dementie te verbeteren. In dit artikel wordt verslag gedaan van de eerste ervaringen en worden de volgende stappen van het project beschreven.
The development of intercultural competences has become a prominent goal for many study programs in higher education. A widely used frame to measure intercultural competence is Cultural Intelligence (CQ). While empirical research has focused extensively on the development of CQ by means of (student) mobility and long-term training, the effects of short-format trainings – a more cost-effective intervention that can be provided to a large number of participants – remain understudied. Existing findings are inconclusive, and it remains unclear under which conditions, and for whom, short-format interventions are effective in improving participants’ CQ. We propose that CQ development is contingent upon individual differences in multicultural personality traits (operationalized through the Multicultural Personality Questionnaire, MPQ). More specifically, in this study we investigate (1) whether a short-format (6-hour) training improves CQ among higher education students (n = 108), and (2) whether the development of CQ is moderated by students’ social-perceptual and stress-related MPQ trait scores prior to the training. Using a pre and post-test design we found that across the whole sample, all four facets of the CQ increased after the training. We also found that some social-perceptual traits of the MPQ moderated the development of CQ: Social initiative on Metacognitive CQ, Openmindedness on Cognitive CQ, and Social initiative and Openmindedness on Motivational CQ. Additionally, we did not find a moderator effect of stress-related MPQ traits on the development of Behavioral CQ. Based on our findings, we conclude that multicultural personality influences individuals’ susceptibility to intercultural education, underscoring the importance of individualized approaches in intercultural education.
Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
