Understanding College Students’ Phone Call Behaviors Towards a Sustainable Mobile Health and Well-Being Solution

During the transition from high school to on-campus college life, students leave home and start facing enormous life changes, including meeting new people, taking on more responsibilities, being away from the family, and dealing with academic challenges. These changes lead to an elevation of stress and anxiety, affecting students’ health and well-being. With the help of smartphones and their rich collection of sensors, we can continuously moni tor various factors that affect students’ behavioral patterns, such as communication behaviors associated with their health, well-being, and academic success. In this work, we try to assess college students’ communication patterns (in terms of phone call duration and frequency) that vary across various geographical contexts (e.g., dormitories, class buildings, dining halls) during different times (e.g., epochs of a day, days of a week) using visualization techniques. The findings from this work will help foster the design and delivery of smartphone-based health interventions, thus helping the students adapt to the changes in life.Durante la transición de la escuela secundaria a la vida universitaria en el campus, un estudiante deja su casa y empieza a enfrentarse a enormes cambios en su vida, como cono cer gente nueva, mayores responsabilidades, estar lejos de la familia y retos académicos. Estos cambios provocan un aumento del estrés y la ansiedad, lo que afecta a la salud y el bienestar del estudiante. Con la ayuda de los smartphones y su enriquecida colección de sensores, podemos monitorizar continuamente varios factores que afectan a los patrones de comportamiento de los estudiantes, como las conductas de comunicación asociadas a su salud, bienestar y éxito académico. En este trabajo tratamos de evaluar los patrones de comunicación de los estudian tes universitarios (en términos de duración y frecuencia de las llamadas telefónicas) que varían a través de varios contextos geográficos (por ejemplo, dormitorios, clases, comedores) durante diferentes momentos (por ejemplo, épocas de un día, días de una semana) utilizando técnicas de visualización. Los resultados de este trabajo ayudarán a fomentar el diseño y la realización de intervenciones sanitarias basadas en los teléfonos inteligentes; de este modo, se ayudará a los estudiantes a adaptarse a los distintos cambios en sus vidas

Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models

Millions of people have died worldwide from COVID-19. In addition to its high death toll, COVID-19 has led to unbearable suffering for individuals and a huge global burden to the healthcare sector. Therefore, researchers have been trying to develop tools to detect symptoms of this human-transmissible disease remotely to control its rapid spread. Coughing is one of the common symptoms that researchers have been trying to detect objectively from smartphone microphone-sensing. While most of the approaches to detect and track cough symptoms rely on machine learning models developed from a large amount of patient data, this is not possible at the early stage of an outbreak. In this work, we present an incremental transfer learning approach that leverages the relationship between healthy peoples' coughs and COVID-19 patients' coughs to detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy cough detection model and a relatively small set of patient coughs, reducing the need for large patient dataset to train the model. This type of model can be a game changer in detecting the onset of a novel respiratory virus.Comment: This paper has been accepted to publish at EAI International Conference on Wireless Mobile Communication and Healthcare (MobiHealth'23

Designing for Mixed Reality Urban Exploration

This paper introduces a design framework for mixed reality urban exploration (MRUE), based on a concrete implementation in a historical city. The framework integrates different modalities, such as virtual reality (VR), augmented reality (AR), and haptics-audio interfaces, as well as advanced features such as personalized recommendations, social exploration, and itinerary management. It permits to address a number of concerns regarding information overload, safety, and quality of the experience, which are not sufficiently tackled in traditional non-integrated approaches. This study presents an integrated mobile platform built on top of this framework and reflects on the lessons learned

Designing for Mixed Reality Urban Exploration

This paper introduces a design framework for mixed reality urban exploration (MRUE), based on a concrete implementation in a historical city. The framework integrates different modalities, such as virtual reality (VR), augmented reality (AR), and haptics-audio interfaces, as well as advanced features such as personalized recommendations, social exploration, and itinerary management. It permits to address a number of concerns regarding information overload, safety, and quality of the experience, which are not sufficiently tackled in traditional non-integrated approaches. This study presents an integrated mobile platform built on top of this framework and reflects on the lessons learned.Peer reviewe

Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings

Goal: Millions of people are dying due to res- piratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symp- toms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches consid- ering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Con- clusions: Though guided models outperform other models, they require a better understanding of the environment

Tracking in the wild: exploring the everyday use of physical activity trackers

As the rates of chronical diseases, such as obesity, cardiovascular disease and diabetes continue to increase, the development of tools that support people in achieving healthier habits is becoming ever more important. Personal tracking systems, such as activity trackers, have emerged as a promising class of tools to support people in managing their everyday health. However, for this promise to be fulfilled, these systems need to be well designed, not only in terms of how they implement specific behavior change techniques, but also in how they integrate into people’s daily lives and address their daily needs. My dissertations provides evidence that accounting for people’s daily practices and needs can help to design activity tracking systems that help people get more value from their tracking practices. To understand how people derive value from their activity tracking practices, I have conducted two inquiries into people’s daily uses of activity tracking systems. In a fist attempt, I led a 10-month study of the adoption of Habito, our own activity tracking mobile app. Habito logged not only users’ physical activity, but also their interactions with the app. This data was used to acquire an estimate of the adoption rate of Habito, and understanding of how adoption is affected by users’ ‘readiness’, i.e., their attitude towards behavior change. In a follow-up study, I turned to the use of video methods and direct, in-situ observations of users’ interactions to understand what motivates people to engage with these tools in their everyday life, and how the surrounding environment shapes their use. These studies revealed some of the complexities of tracking, while extending some of the underlying ideas of behavior change. Among key results: (1) people’s use of activity trackers was found to be predominantly impulsive, where they simultaneously reflect, learn and change their behaviors as they collect data; (2) people’s use of trackers is deeply entangled with their daily routines and practices, and; (3) people use of trackers often is not in line with the traditional vision of these tools as mediators of change – trackers are also commonly used to simply learn about behaviors and engage in moments of self-discovery. Examining how to design activity tracking interfaces that best support people’s different needs , my dissertation further describes an inquiry into the design space of behavioral feedback interfaces. Through a iterative process of synthesis and analysis of research on activity tracking, I devise six design qualities for creating feedback that supports people in their interactions with physical activity data. Through the development and field deployment of four concepts in a field study, I show the potential of these displays for highlighting opportunities for action and learning.À medida que a prevalência de doenças crónicas como a obesidade, doenças cardiovasculares e diabetes continua a aumentar, o desenvolvimento de ferramentas que suportam pessoas a atingir mudanças de comportamento tem-se tornado essencial. Ferramentas de monitorização de comportamentos, tais como monitores de atividade física, têm surgido com a promessa de encorajar um dia a dia mais saudável. Contudo, para que essa promessa seja cumprida, torna-se essencial que estas ferramentas sejam bem concebidas, não só na forma como implementam determinadas estratégias de mudança de comportamento, mas também na forma como são integradas no dia-a-dia das pessoas. A minha dissertação demonstra a importância de considerar as necessidades e práticas diárias dos utilizadores destas ferramentas, de forma a ajudá-las a tirar melhor proveito da sua monitorização de atividade física. De modo a entender como é que os utilizadores destas ferramentas derivam valor das suas práticas de monitorização, a minha dissertação começa por explorar as práticas diárias associadas ao uso de monitores de atividade física. A minha dissertação contribui com duas investigações ao uso diário destas ferramentas. Primeiro, é apresentada uma investigação da adoção de Habito, uma aplicação para monitorização de atividade física. Habito não só registou as instâncias de atividade física dos seus utilizadores, mas também as suas interações com a própria aplicação. Estes dados foram utilizados para adquirir uma taxa de adopção de Habito e entender como é que essa adopção é afetada pela “prontidão” dos utilizadores, i.e., a sua atitude em relação à mudança de comportamento. Num segundo estudo, recorrendo a métodos de vídeo e observações diretas e in-situ da utilização de monitores de atividade física, explorei as motivações associadas ao uso diário destas ferramentas. Estes estudos expandiram algumas das ideias subjacentes ao uso das ferramentas para mudanças de comportamento. Entre resultados principais: (1) o uso de monitores de atividade física é predominantemente impulsivo, onde pessoas refletem, aprendem e alteram os seus comportamentos à medida que recolhem dados sobe estes mesmos comportamentos; (2) o uso de monitores de atividade física está profundamente interligado com as rotinas e práticas dos seus utilizadores, e; (3) o uso de monitores de atividade física nem sempre está ligado a mudanças de comportamento – estas ferramentas também são utilizadas para divertimento e aprendizagem. A minha dissertação contribui ainda com uma exploração do design de interfaces para a monitorização de atividade física. Através de um processo iterativo de síntese e análise de literatura, seis qualidades para a criação de interfaces são derivadas. Através de um estudo de campo, a minha dissertação demonstro o potencial dessas interfaces para ajudar pessoas a aprender e gerir a sua saúde diária