Mental disorders deteriorate well-being in many ways. Clinical mental health care is insufficient and struggles to keep up with the growing demand for services in many parts of the world. Human–Computer Interaction researchers have focused on building different interactive systems that support mental well-being. In our work, we focus on collecting, peer-assessing, and assisting in the discovery of mental health self-care techniques through an online tool. Our target demographic is the higher education community. The implemented tool can offer new self-care techniques to its users, and our user studies validate its usefulness in capturing and offering valuable mental health care information. Further, we study how source disclosure affects the perception of the discovered techniques, finding no major differences in the measured variables. Finally, we discuss how user-generated self-care suggestions in this context warrants caution and the implications of our study.

Abstract Human cognitive performance affects a wide range of aspects of our daily lives. Numerous factors influence our cognitive performance, and cognitive performance in turn impacts our capabilities. Partial sleep deprivation in particular negatively affects vigilance, a key factor in many work tasks. Sleep in general plays a large role in physiological recovery and our capability to perform mental tasks. In this work, we focus on two research questions. First, we investigate how fluctuations in sleep quality influence cognitive vigilance. Second, we study how smartphone typing can be leveraged as a continuous measurement for cognitive vigilance and can thus be an indicator of decline in cognitive capabilities and sleep quality. We report on a 2-month field study in which we collected cognitive performance data using the Psychomotor Vigilance Task (PVT), mobile keyboard typing metrics from participants’ personal smartphones, and sleep quality metrics through a wearable sleep-tracking ring. Our findings highlight that individual sleep metrics such as night-time heart rate, sleep latency, sleep timing, sleep restfulness, and overall sleep quantity significantly influence vigilance. Long sleep latencies can reduce reaction times up to 30 ms, abnormal sleep durations up to 20 ms, and night-time awake time up to 10 ms. Heart rate is a well-known indicator of recovery quality, and improvements in both heart rate and heart rate variability (HRV) show positive variations of 15–20 ms in reaction test performance. To expand the current research on cognitive computing, we introduce smartphone typing metrics as a proxy or a complementary method for continuous passive measurement of cognitive vigilance and report on statistically significant correlations in PVT performance and typing speed and error rates. Together, our findings contribute to ubiquitous computing via a longitudinal case study with a novel wearable device, the resulting findings on the association between sleep and cognitive function, and the introduction of smartphone keyboard typing as a proxy of cognitive function.

The value of personal health data continues to be a debated topic in HCI and society more broadly. We investigate the monetary value people attach to their health data. Using a custom mobile app for 14 days with 55 participants, we collected health data (sleep duration, sleep quality, pain intensity, wake-up times) and a daily monetary data valuation using a reverse second-price auction. Participants bid to sell their data to a for-profit company, the government, or academia. Our findings indicate that people value their data differently based on who is buying. We also show that people are interested in monetizing their personal health data despite privacy and data protection concerns. The presented study helps us understand the data value landscape and paves way to a healthier data-driven future where people may benefit more from their own contributions, either in monetary or other forms.

Artificial Intelligence is seen as humanity’s current best bet to solve the looming crisis in healthcare. Conversational Agents, or chatbots, rely on advances in AI and are increasingly investigated in the context of digital mental health care. Given how they are end-user-facing and interactive communication tools, the user engagement felt when interacting with the bots is a critical consideration. In this work, we examine the effects of chatbot personalities on the experienced user engagement with the bot. We employed personalities that rely on the Big-5 Personality Theory. Among other findings, our quantitative results indicate that a highly conscientious chatbot is likely to foster the highest user engagement. Our qualitative and content analysis also reveals desired and undesired personality features for future mental health chatbots. We discuss our findings in light of digital mental health and propose novel research directions.

Abstract Interactive public displays are versatile two-way interfaces between the digital world and passersby. They can convey information and harvest purposeful data from their users. Surprisingly little work has exploited public displays for collecting tagged data that might be useful beyond a single application. In this work, we set to fill this gap and present two studies: (1) a field study where we investigated collecting biometrically tagged video-selfies using public kiosk-sized screens, and (2) an online narrative transportation study that further elicited rich qualitative insights on key emerging aspects from the first study. In the first study, a 61-day deployment resulted in 199 video-selfies with consent to leverage the videos in any non-profit research. The field study indicates that people are willing to donate even highly sensitive data about themselves in public. The subsequent online narrative transportation study provides a deeper understanding of a variety of issues arising from the first study that can be leveraged in the future design of such systems. The two studies combined in this article pave the way forward towards a vision where volunteers can, should they so choose, ethically and serendipitously help unleash advances in data-driven areas such as computer vision and machine learning in health care.

Public controversies around the unethical use of personal data are increasing, spotlighting data ethics as an increasingly important field of study. MyData is a related emerging vision that emphasizes individuals’ control of their personal data. In this paper, we investigate people’s perceptions of various data management scenarios by measuring the perceived ethicality and level of felt concern concerning the scenarios. We deployed a set of 96 unique scenarios to an online crowdsourcing platform for assessment and invited a representative sample of the participants to a second-stage questionnaire about the MyData vision and its potential in the field of healthcare. Our results provide a timely investigation into how topical data-related practices affect the perceived ethicality and the felt concern. The questionnaire analysis reveals great potential in the MyData vision. Through the combined quantitative and qualitative results, we contribute to the field of data ethics.