Supporting Self-Regulation of Children with ADHD Using Wearables: Tensions and Design Challenges

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch in various contexts. These results indicate key considerations—from both the child and caregiver viewpoints—for designing technological interventions supporting children with ADHD

Towards Multi-Device Ecosystems for Personal and Family Health Tracking

The increasing ubiquity of personal devices and technologies, such as home displays, smart-speakers, and wearables, has created new opportunities for individuals and families to monitor and manage their health and wellbeing. However, personal and family informatics systems have not kept up with such increased ubiquity, leading to a missed opportunity in making health informatics more convenient, connected, and meaningful. Many continue to struggle to effectively incorporate tracking into their daily lives and leverage the multiple technologies they have contact with. In my dissertation, I investigate how the design of multi-device health informatics systems can better support individuals in their self-tracking practices and facilitate collaboration within families for collective wellbeing. By leveraging the diverse capabilities of smartphones, smartwatches, smart-speakers, and more, I demonstrate how informatics tools can be designed to accommodate the varied needs and contexts of everyday life, both for individual use and for family collaboration.Towards self-tracking, I demonstrate how multi-device, multimodal systems can provide flexibility and redundancy of options for rich data capture and help individuals overcome situational barriers to tracking. Through the design and evaluation of the ModEat system, I show how supporting food journaling across smartphones, computers, and voice assistants with various input modalities can accommodate people's goals, preferences, and contexts.Examining how to help families collaborate towards health and wellbeing management, I explore the design of multi-device systems to facilitate family co-regulation practices. My evaluation of the CoolTaco deployment demonstrates that a smartwatch-based system can mediate shared awareness and remote collaboration between parents and children with ADHD around behavioral regulation. Through a co-design study, I reveal opportunities for in-home displays to integrate family data and guide productive discussions around each member's regulation needs relating to moods, exercise, and goals. Building on these insights, my design and evaluation of FamilyBloom showcases how integrating personal and shared devices can support diverse co-regulation practices, enabling reflection and mutual care in the face of varied routines and individual preferences within families.Through these studies, my dissertation demonstrates that multi-device, multimodal health informatics can support the needs of individuals and families in their everyday practices surrounding tracking, reflecting, and acting for self and co-regulation of health and wellbeing needs. I suggest that leveraging device ecosystems and designing for varied levels of engagement and collaboration can result in more available and useful tools. Through empirical findings and novel systems, my dissertation contributes to understanding of how to create effective tools for personal and family health tracking in everyday life

Children with ADHD and their Care Ecosystem: Designing Beyond Symptoms

Designing for children with ADHD has been of increasing interest to the HCI community. However, current approaches do not adequately involve all relevant stakeholders, and primarily focus on addressing symptoms, following a medical model of disability that is extrinsic to neurodivergent interests. To address this, we employed a multi-step, multi-stakeholder approach (N=31). First, we conducted 1) interviews with children with ADHD and their care ecosystem followed by 2) a co-design pilot with one child with ADHD and his therapists and an interview with a UX designer and an occupational therapist. We then employed 3) co-design sessions with neurotypical children and children with ADHD, and 4) a focus group with their therapists. We identified communication and reflection as key concepts for empowering and promoting the well-being of children with ADHD and their care ecosystem. We contribute design implications for future systems aiming to promote the overall well-being of this population

From hospital to home. The application of e-health solutions for monitoring and management of people with epilepsy

Background. In the last 10 years, there has been an explosion in the development of mobile and wearable technologies. Recent events such as Covid 19 emergency, showed the world how clinicians need to focus more on the application of these technologies to monitor and manage their patients. Despite this, the use of innovative technologies is not now a common practice in epilepsy. This thesis aims to demonstrate how people with epilepsy (PWE) are ready to use these mobile and wearable technologies and how data collected from these solutions can have a direct impact on PWE’s life. Methods. A systematic literature search was performed to provide an accurate overview of new non- invasive EEGs and their applications in epilepsy health care and an online survey was performed to fill the literature gap on this topic. To accurately study the PWE’s experience using wearable sensors, and the value of physiological and non-physiological data collected from wearable sensors, we used EEG data collected from the hospital (RADAR-CNS), and we collected original data from an at-home study (EEG@HOME). The data can be divided into two main categories: qualitative data (online survey, semi-structured interviews), and quantitative data analysis (questionnaires, EEG, and additional non-invasive physiological variables). Results. The systematic review showed us how non-invasive portable EEGs could provide valuable data for clinical purposes in epilepsy and become useful tools in different settings (i.e., rural areas, Hospitals, and homes). These are well accepted and tolerated by PWE and health care providers, especially for the easy application, cost, and comfort. The information obtained on the acceptability of repeated long-term non-invasive measures at home (EEG@HOME) showed that the use of the portable EEG cap was in general well tolerated over the 6 months but, the use of a smartwatch and the e-seizure diary was usually preferred. The level of compliance was good in most of the individuals and any barriers or issues which affected their experience or quality of the data were highlighted (i.e., life events, issues with equipment, and hairstyle of patients). Semi-structured interviews showed that participants found the combination of the three solutions very well-integrated and easy to use. The support received and the possibility to be trained and monitored remotely were well accepted and no privacy issues were reported by any of the participants. Most of the participants also suggested how they will be happy to have a mobile solution in the future to help to monitor their condition. The graph theory measures extracted from short and/or repeated EEG segments recorded from hospitals (RADAR-CNS) allowed us to explore the temporal evolution of brain activity prior to a seizure. Finally, physiological data and non-physiological data (EEG@HOME) were combined to understand and develop a model for each participant which explained a higher or lower risk of seizure over time. We also evaluated the value of repeated unsupervised resting state EEG recorded at home for seizure detection. Conclusion. The use of new technologies is well accepted by PWE in different settings. This thesis gives a detailed overview of two main points. First: PWE can be monitored in the hospital or at home using new wearable sensors or smartphone apps, and they are ready to use them after a short training and minimal supervision. Second: repeated data collection could provide a new way of a monitor, managing, and diagnosing people with epilepsy. Future studies should focus on balancing the acceptability of the solutions and the quality of the data collected. We also suggest that more studies focusing on seizure forecasting and detection using data collected from long-term monitoring need to be conducted. Digital health is the future of clinical practice and will increase PWE safety, independency, treatment, and monitoring