Active Design – How the built environment matters to mobile games for health

Mobile games for health aim to provide both for an attractive gaming experience and for a positive effect on their users’ wellbeing. Most of these games are context-sensitive, as they take note of the state of the player’s environment and use this information to adapt the game experience. This article points to the limited research available that validates either the physiological effects of playing context-sensitive games for health regularly, or research that focuses on the complex relationship between mobile games, a players’ health and wellbeing, and the (urban) environment in which many of these games are being played. It reviews aspects of health-oriented urban design that has been shown to influence people’s everyday activity patterns including running and cycling. It speculates how “active design” context can also have an impact on how we play mobile games for health and explains how this knowledge can be used to improve such games

A phase 1b open-label dose-finding study of ustekinumab in young adults with type 1 diabetes

Aim We assessed the safety of ustekinumab (a monoclonal antibody used in psoriasis to target the IL-12 and IL-23 pathways) in a small cohort of recent-onset (<100 days of diagnosis) adults with type 1 diabetes (T1D) by conducting a pilot open-label dose-finding and mechanistic study (NCT02117765) at the University of British Columbia. Methods We sequentially enrolled 20 participants into four subcutaneous dosing cohorts: i) 45mg loading-weeks 0/4/16, ii) 45mg maintenance-weeks 0/4/16/28/40, iii) 90mg loading-weeks 0/4/16 and iv) 90mg maintenance-weeks 0/4/16/28/40. The primary endpoint was safety as assessed by an independent data and safety monitoring board (DSMB) but we also measured mixed meal tolerance test C-peptide, insulin use/kg, and HbA1c. Immunophenotyping was performed to assess immune cell subsets and islet antigen-specific T cell responses. Results Although several adverse events were reported, only two (bacterial vaginosis and hallucinations) were thought to be possibly related to drug administration by the study investigators. At 1 year, the 90mg maintenance dosing cohort had the smallest mean decline in C-peptide AUC (0.1pmol/mL). Immunophenotyping showed that ustekinumab reduced the percentage of circulating Th17, Th1 and Th17.1 cells and proinsulin-specific T cells that secreted IFN-γ and IL-17A. Conclusion Ustekinumab was deemed safe to progress to efficacy studies by the DSMB at doses used to treat psoriasis in adults with T1D. A 90mg maintenance dosing schedule reduced proinsulin-specific IFN-γ and IL-17A-producing T cells. Further studies are warranted to determine if ustekinumab can prevent C-peptide AUC decline and induce a clinical response

Pervasive Behavior Interventions - Using Mobile Devices for Overcoming Barriers for Physical Activity

Extensive cohort studies show that physical inactivity is likely to have negative consequences for one’s health. The World Health Organization thus recommends a minimum of thirty minutes of medium-intensity physical activity per day, an amount that can easily be reached by doing some brisk walking or leisure cycling. Recently, a Taiwanese-American team of scientists was able to prove that even less effort is required for positive health effects and that as little as fifteen minutes of physical activity per day will increase one’s life expectancy by up to three years on the average. However, simply spreading this knowledge is not sufficient. Roughly one in three Europeans and US-Americans does not even meet the minimum recommendations for physical activity, although the majority of these people is aware of the damage that their behavior may do to their health. And this ‘willful wrongdoing’ does not only concern individuals: Due to the large number of inactive people, the problem of sedentary behavior affects societies as a whole, not the least by increasing public health costs. But if it is not a lack of knowledge that causes this problem, what is? And what can be done to stimulate leisure-time physical activity? The Fogg Behavior Model (FBM), developed by psychologist and Stanford-lecturer B.J. Fogg, explains the factors that determine whether or not a given person will show a desired behavior. The core components of the FBM include a trigger that can be perceived by the target person and that she associates with the desired behavior, as well as her ability and motivation for this behavior at the time when the trigger reaches her. If the combined amount of ability and motivation exceeds a lower limit, the so-called activation threshold, then the triggered person will behave in the desired way; otherwise, she will not. Based on the understanding of human behavior that the FBM conveys, this thesis focuses on the question of how mobile devices can assist people in reaching the minimum amount of daily physical activity that is required for health benefits. An in-depth analysis of the problem reveals that of the three possible strategies – trying to increase a user’s ability for leisure-time physical activity, trying to increase her motivation for the same, and trying to increase her short-term awareness for its necessity and feasibility through triggers – the creation of adaptive triggers is the most promising approach. This task in turn consists of several sub-problems, such as the problem of how to recognize the user’s current contextual situation, the problem of how to decide, whether or not the recognized situation is suited for an activation attempt, and the problem of interacting with the user in those cases in which an activation attempt seems worthwhile. Learning from the user’s behavior and understanding her preferences and constraints is the key factor in the creation of accurate and reliable intervention mechanisms. To this end, smartphone sensors, wearables, and Web services are utilized for collecting information about the state of the user and her environment. This data is then analyzed by a supervised learning machine which, based on prior experience, estimates the probability for a successful activation attempt in the current situation. Ideally, the learner will identify a kairotic moment: A situation, in which a trigger is bound to initiate the desired behavior. If it does, it reaches out to the user. Multiple types of such triggering mechanisms were embedded into the mobile exergame ‘Twostone’, an application that requires brisk walking or easy running from its users. During a field study with thirty participants, the performances of these different approaches were compared against one another. The study revealed a surprising result: Not the most-knowledgeable intervention mechanism emerged as a winner, but it was rather the triggering variant that relied on a reduced number of contextual information to achieve both the highest triggering success rates and the best user acceptance. The study also showed that intervention mechanisms can indeed increase the prevalence of a desired behavior, but only if the user has a positive attitude towards the respective activity. As such, both the conceptual model for technology-based interventive measures and the evaluation results that are presented in this thesis offer valuable insights for developers of devices and applications that aim to foster desired behaviors in general and increased levels of daily physical activity in particular

Pervasive Behavior Interventions - Using Mobile Devices for Overcoming Barriers for Physical Activity

Extensive cohort studies show that physical inactivity is likely to have negative consequences for one’s health. The World Health Organization thus recommends a minimum of thirty minutes of medium-intensity physical activity per day, an amount that can easily be reached by doing some brisk walking or leisure cycling. Recently, a Taiwanese-American team of scientists was able to prove that even less effort is required for positive health effects and that as little as fifteen minutes of physical activity per day will increase one’s life expectancy by up to three years on the average. However, simply spreading this knowledge is not sufficient. Roughly one in three Europeans and US-Americans does not even meet the minimum recommendations for physical activity, although the majority of these people is aware of the damage that their behavior may do to their health. And this ‘willful wrongdoing’ does not only concern individuals: Due to the large number of inactive people, the problem of sedentary behavior affects societies as a whole, not the least by increasing public health costs. But if it is not a lack of knowledge that causes this problem, what is? And what can be done to stimulate leisure-time physical activity? The Fogg Behavior Model (FBM), developed by psychologist and Stanford-lecturer B.J. Fogg, explains the factors that determine whether or not a given person will show a desired behavior. The core components of the FBM include a trigger that can be perceived by the target person and that she associates with the desired behavior, as well as her ability and motivation for this behavior at the time when the trigger reaches her. If the combined amount of ability and motivation exceeds a lower limit, the so-called activation threshold, then the triggered person will behave in the desired way; otherwise, she will not. Based on the understanding of human behavior that the FBM conveys, this thesis focuses on the question of how mobile devices can assist people in reaching the minimum amount of daily physical activity that is required for health benefits. An in-depth analysis of the problem reveals that of the three possible strategies – trying to increase a user’s ability for leisure-time physical activity, trying to increase her motivation for the same, and trying to increase her short-term awareness for its necessity and feasibility through triggers – the creation of adaptive triggers is the most promising approach. This task in turn consists of several sub-problems, such as the problem of how to recognize the user’s current contextual situation, the problem of how to decide, whether or not the recognized situation is suited for an activation attempt, and the problem of interacting with the user in those cases in which an activation attempt seems worthwhile. Learning from the user’s behavior and understanding her preferences and constraints is the key factor in the creation of accurate and reliable intervention mechanisms. To this end, smartphone sensors, wearables, and Web services are utilized for collecting information about the state of the user and her environment. This data is then analyzed by a supervised learning machine which, based on prior experience, estimates the probability for a successful activation attempt in the current situation. Ideally, the learner will identify a kairotic moment: A situation, in which a trigger is bound to initiate the desired behavior. If it does, it reaches out to the user. Multiple types of such triggering mechanisms were embedded into the mobile exergame ‘Twostone’, an application that requires brisk walking or easy running from its users. During a field study with thirty participants, the performances of these different approaches were compared against one another. The study revealed a surprising result: Not the most-knowledgeable intervention mechanism emerged as a winner, but it was rather the triggering variant that relied on a reduced number of contextual information to achieve both the highest triggering success rates and the best user acceptance. The study also showed that intervention mechanisms can indeed increase the prevalence of a desired behavior, but only if the user has a positive attitude towards the respective activity. As such, both the conceptual model for technology-based interventive measures and the evaluation results that are presented in this thesis offer valuable insights for developers of devices and applications that aim to foster desired behaviors in general and increased levels of daily physical activity in particular

Low-cost indoor localization using cameras: Evaluating AmbiTrack and its applications in Ambient Assisted Living

Many systems have been proposed in recent years that provide for the tracking and localization of users in indoor environments, often with a specific focus on pervasive computing settings. Our solution AmbiTrack, as presented here, allows for a marker-free localization and tracking of multiple persons, meaning that users are not required to carry special items or tags with them in order for the system to work. This approach allows for an application of AmbiTrack in circumstances where wearing a tag is not viable, e.g., in typical Ambient Assisted Living scenarios where the users of the provided technological systems are usually not technologically well-versed. In this contribution, we explain AmbiTrack and also introduce the adaptations we made for the 3rd EvAAL competition of 2013 in order to make the system more reliable in tracking multiple persons, using context information for improving the recognition rate, and for simplifying the set up and configuration process

An Optical Guiding System for Gesture Based Interactions in Smart Environments

Using gestures to control Ambient Intelligence environments can result in mismatches between the user's intention and the perception of the gesture by the system. One way to cope with this problem is to provide the user with an instant feedback on what the system has perceived. In this work, we present an approach for providing visual feedback to users of Ambient Intelligence systems that rely on gestures to control individual devices within their environments. This paper extends our previous work on this topic 1 and introduces several enhancements to the system

An Optical Guiding System for Gesture Based Interactions in Smart Environments

Using gestures to control Ambient Intelligence environments can result in mismatches between the user's intention and the perception of the gesture by the system. One way to cope with this problem is to provide the user with an instant feedback on what the system has perceived. In this work, we present an approach for providing visual feedback to users of Ambient Intelligence systems that rely on gestures to control individual devices within their environments. This paper extends our previous work on this topic 1 and introduces several enhancements to the system