MD
M. Dierikx
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To reduce morbidity and mortality caused by multiple chronic conditions, the number of steps people take each day should be gradually increased. For this, a recommended step goal can be created that is based on an individual's previous walking behaviour. However, for a person, the achievability of this recommended goal can change daily because of that person's state, such as their mood or self-motivation. It could be, for example, that if someone's self-motivation is low, proposing a lower goal than the recommended one, increases their self-motivation and allows them to achieve the recommended goal the next day. Therefore, we investigated the use of a person's state to personalize daily step goal proposals. To do so, we designed and implemented a virtual coach, named Steph, to propose daily step goals to people during an observational study. We used people's states collected in that study to train a reinforcement learning model to optimally personalize the step goal proposals. Based on simulations of our model, we found that people in high states (e.g. who were very motivated and had a positive mood) were more likely to achieve their recommended goals, while people in low states (e.g. who were not motivated and had a negative mood) were less likely to achieve their goals. We also found that proposing higher goals to people in certain states was better than for people in other states. This was because, for some people, a higher goal improved their state while for others, it worsened it. This suggests that personalizing people's step goal proposals optimally could change people's states to where they are more likely to achieve their recommended step goals. So, this thesis provides a model for personalizing daily step goal proposals which can be used as part of behaviour change support systems. It can also serve as a basis for different approaches to predict and change people's walking behaviour to make them more active and less susceptible to chronic diseases.
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To reduce morbidity and mortality caused by multiple chronic conditions, the number of steps people take each day should be gradually increased. For this, a recommended step goal can be created that is based on an individual's previous walking behaviour. However, for a person, the achievability of this recommended goal can change daily because of that person's state, such as their mood or self-motivation. It could be, for example, that if someone's self-motivation is low, proposing a lower goal than the recommended one, increases their self-motivation and allows them to achieve the recommended goal the next day. Therefore, we investigated the use of a person's state to personalize daily step goal proposals. To do so, we designed and implemented a virtual coach, named Steph, to propose daily step goals to people during an observational study. We used people's states collected in that study to train a reinforcement learning model to optimally personalize the step goal proposals. Based on simulations of our model, we found that people in high states (e.g. who were very motivated and had a positive mood) were more likely to achieve their recommended goals, while people in low states (e.g. who were not motivated and had a negative mood) were less likely to achieve their goals. We also found that proposing higher goals to people in certain states was better than for people in other states. This was because, for some people, a higher goal improved their state while for others, it worsened it. This suggests that personalizing people's step goal proposals optimally could change people's states to where they are more likely to achieve their recommended step goals. So, this thesis provides a model for personalizing daily step goal proposals which can be used as part of behaviour change support systems. It can also serve as a basis for different approaches to predict and change people's walking behaviour to make them more active and less susceptible to chronic diseases.
Research showed that the number of students that use their mobile devices and apps for learning purposes doubled between 2012 and 2016. This growth enables new forms of learning to be used in higher education settings, like mobile learning. To make sure mobile learning can be successful in higher education, it is necessary to understand what the factors are that influence the acceptance and adoption of mobile learning apps in higher education. Different studies mention different factors. This systematic review lists the most emerging ones of the last decade like Self-Efficacy and Perceived Enjoyment by looking at the influences on both students and teachers. This study also proposes an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, called the Acceptance and Adoption of Mobile Learning Applications (AAMLA) model, using these found factors. Based on the research model, twenty hypotheses were defined for other studies to be able to validate and use the model. This model could provide app developers, higher education institutes and other researchers useful insights to explain the acceptance and adoption of mobile learning apps in higher education.
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Research showed that the number of students that use their mobile devices and apps for learning purposes doubled between 2012 and 2016. This growth enables new forms of learning to be used in higher education settings, like mobile learning. To make sure mobile learning can be successful in higher education, it is necessary to understand what the factors are that influence the acceptance and adoption of mobile learning apps in higher education. Different studies mention different factors. This systematic review lists the most emerging ones of the last decade like Self-Efficacy and Perceived Enjoyment by looking at the influences on both students and teachers. This study also proposes an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, called the Acceptance and Adoption of Mobile Learning Applications (AAMLA) model, using these found factors. Based on the research model, twenty hypotheses were defined for other studies to be able to validate and use the model. This model could provide app developers, higher education institutes and other researchers useful insights to explain the acceptance and adoption of mobile learning apps in higher education.