Potentials of Chatbot Technologies for Higher Education: A Systematic Review

Chatbots are used in different areas such as customer service, healthcare and education. The potential for improving outcomes and processes in education is high but differs for different types of chatbots. As universities want to provide excellent teaching, it is important to find the chatbot technologies with the greatest possible benefit. This paper presents a systematic review of chatbot technologies in five application areas. For each application area, the ten most cited publications are analysed and a possible categorisation scheme for chatbot technologies is derived. Furthermore, it is investigated which chatbot technology types are used and their suitability for higher education is analysed. The results show that chatbots can be categorised using five categories derived from the 50 publications. A total of 14 different types of chatbot technologies are found in the five areas. Nine of them are suitable for use in higher education

Prospective Follow-up of Adolescents with and at Risk for Depression::Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) Longitudinal Assessments

Objective: To present the protocol and methods for the prospective longitudinal assessments — including clinical and digital phenotyping approaches — of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study, which comprises Brazilian adolescents stratified at baseline by risk of developing depression or presence of depression. Method: Of 7,720 screened adolescents aged 14-16, we recruited 150 participants (75 boys, 75 girls) based on a composite risk score: 50 with low risk for developing depression (LR), 50 with high risk for developing depression (HR), and 50 with an active untreated major depressive episode (MDD). Three annual follow-up assessments were conducted, involving clinical measures (parent and adolescent-reported questionnaires and psychiatrist assessments), active and passive data sensing via smartphones, and neurobiological measures (neuroimaging and biological material samples). Retention rates were 96% (Wave 1), 94% (Wave 2), and 88% (Wave 3), with no significant differences by sex or group (p &gt; 0.05). Participants highlighted their familiarity with the research team and assessment process as a motivator for sustained engagement.Discussion: This protocol relied on novel aspects, such as the use of a WhatsApp bot, which is particularly pertinent for low-to-middle-income countries, and the collection of information from diverse sources in a longitudinal design, encompassing clinical data, self-reports, parental reports, GPS data, and ecological momentary assessments. The study engaged adolescents over an extensive period and demonstrated the feasibility of conducting a prospective follow-up study with a risk-enriched cohort of adolescents in a middle-income country, integrating mobile technology with traditional methodologies to enhance longitudinal data collection. <br/

Towards Explainable and Safe Conversational Agents for Mental Health: A Survey

Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role of VMHAs is to provide emotional support through information, focusing less on developing a reflective conversation with the patient. A more comprehensive, safe and explainable approach is required to build responsible VMHAs to ask follow-up questions or provide a well-informed response. This survey offers a systematic critical review of the existing conversational agents in mental health, followed by new insights into the improvements of VMHAs with contextual knowledge, datasets, and their emerging role in clinical decision support. We also provide new directions toward enriching the user experience of VMHAs with explainability, safety, and wholesome trustworthiness. Finally, we provide evaluation metrics and practical considerations for VMHAs beyond the current literature to build trust between VMHAs and patients in active communications.Comment: 10 pages, 3 figures, 2 table

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

Using Mobile Apps to Support the Implementation of Coping-relevant Behaviour Change Techniques for Self-management of Stress

Mobile apps have shown potential in early stress self-management interventions, yet they remain less beneficial than face-to-face therapies. One of the most effective ways people can cope with stress is to identify what their stressors are and take action in managing them. Coping-relevant behaviour change techniques (BCTs), such as self-monitoring, goal setting, and action planning, have the potential to support this process. Nevertheless, there is little guidance on how to incorporate such techniques into stress management apps. Drawing on mixed methods research, this thesis provides two contributions. First, it improves our understanding of how existing stress management apps support coping-relevant BCTs and suggests areas for improvements. An app functionality review and follow-up 3-week intervention using Welltory stress monitoring and Coach.me goal setting apps revealed that existing apps do not support users’ efforts with coping-relevant BCTs. Participants reported that Welltory did not yield sufficient data to gain insights into the factors affecting their stress. Relatedly, the way in which these apps implemented coping-relevant BCTs diminished peoples’ sense of autonomy and competence. Drawing on peoples’ experiences with existing apps and principles of positive computing, the second contribution of this thesis is the design and evaluation of Reffy - a chatbot prototype that integrates coping-relevant BCTs in a way that meets people’s stress management needs. Based on findings from a field evaluation study, we identify specific benefits and challenges of using a stress self-management chatbot. We find that chatbot-based reflective questioning helps people identify how factors impact their stress during early stages of self-tracking. Likewise, adding features that promote users’ sense of autonomy and competence improves Welltory’s ability to support coping strategies. This thesis advances our understanding of how behaviour change and stress coping techniques can be incorporated into mobile apps to effectively support stress self-management

Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability

Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee’s own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented, as the basis for the WearMe project. The included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system