The increased demand for Computer Science (CS) skills across most professions has necessitated providing CS training for post-secondary learners regardless of their fields of study. This is especially true in developing countries with high unemployment rates, where workers need to update their skills continuously to remain employable. Understanding the motivations of students who enroll in CS courses with limited prior training is critical for designing learning environments that help them achieve their desired goals. This paper studied the motivations of 2376 African students and working professionals enrolled in a smartphone-based programming MOOC. We found that the non-CS major participants wanted to learn programming to develop business applications, keep up with the demand for computing skills in their careers, and produce tools that positively impact their community. We also found that factors such as employment status and participants' motivations influenced students' persistence through the course. Our research contributes valuable insights for accessible and effective programming MOOC design for African professionals, especially for those without CS backgrounds.

Researchers are interested in understanding the emotions of couples as it relates to relationship quality and dyadic management of chronic diseases. Currently, the process of assessing emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, there exists no ubiquitous system that recognizes the emotions of couples in everyday life while addressing the complexity of dyadic interactions such as turn-taking in couples? conversations. In this work, we seek to develop a smartwatch-based system that leverages multimodal sensor data to recognize each partner's emotions in daily life. We are collecting data from couples in the lab and in the field and we plan to use the data to develop multimodal machine learning models for emotion recognition. Then, we plan to implement the best models in a smartwatch app and evaluate its performance in real-time and everyday life through another field study. Such a system could enable research both in the lab (e.g. couple therapy) or in daily life (assessment of chronic disease management or relationship quality) and enable interventions to improve the emotional well-being, relationship quality, and chronic disease management of couples.

Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet? an open-hardware, open-software wrist-worn computing device? and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.

Married adults share illness management with spouses and it involves social support and common dyadic coping (CDC). Social support and CDC have an impact on health behavior and well-being or emotions in couples' dyadic management of diabetes in daily life. Hence, understanding dyadic interactions in-situ in chronic disease management could inform behavioral interventions to help the dyadic management of chronic diseases. It is however not clear how well social support and CDC can be assessed in daily life among couples who are managing chronic diseases. In this ongoing work, we describe the development of DyMand, a novel open-source mobile and wearable system for ambulatory assessment of couples' dyadic management of chronic diseases. Our first prototype is used in the context of diabetes mellitus Type II. Additionally, we briefly describe our experience deploying the prototype in two pre-pilot tests with five subjects and our plans for future deployments.

The ability to monitor a person's level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults' daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n=29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.