Proliferation of misinformation is a globally acknowledged problem. Cognitive Inoculation helps build resistance to different forms of persuasion, such as misinformation. We investigate Conversational Inoculation, a method to help people build resistance to misinformation through dynamic conversations with a chatbot. We built a Web-based system to implement the method, and conducted a within-subject user experiment to compare it with two traditional inoculation methods. Our results validate Conversational Inoculation as a viable novel method, and show how it was able to enhance participants’ resistance to misinformation. A qualitative analysis of the conversations between participants and the chatbot highlighted adaptability, independence, trust and friction as the main factors affecting Conversational Inoculation. We discuss the opportunities and challenges of using Conversational Inoculation to combat misinformation. Our work contributes a timely investigation and a promising research direction in scalable ways to combat misinformation.

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 Despite the widely acknowledged importance of well-being, our well-being can regularly be under pressure from external sources. Work is often attributed as a source of stress and dissatisfaction, so, unsurprisingly, extensive efforts are made to measure and improve our well-being in this context. This paper examines opportunities to better design supportive digital solutions through two complementary studies. In the first study, we present a longitudinal assessment of a well-being-focused self-report application deployed in two organizations. Through an analysis of one year of application usage across 219 users, we find both established and novel patterns of application usage and well-being evaluation. While prior work has highlighted substantial dropout rates and daily well-being fluctuations that peak in the morning and early evening, our results highlight that substantial breaks in usage are common, suggesting that users choose to engage with well-being applications mainly when they need them. In the second study, we expand on the topic of well-being reflection at work and the use of technology for this purpose. Through a survey involving 100 participants, we identify current practices in increasing well-being at work, obstacles to sharing and discussing mental well-being states, opportunities for digital well-being solutions and reflections on transparency and communication. Our combined results highlight opportunities for HCI research and practice to address the ongoing challenges of maintaining well-being in today’s work environments.

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.

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.

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.

Abstract Smartphones have become an integral part of people’s everyday lives. Smartphones are used across all household locations, including in the bed at night. Smartphone screens and other displays emit blue light, and exposure to blue light can affect one’s sleep quality. Thus, smartphone use prior to bedtime could disrupt the quality of one’s sleep, but research lacks quantitative studies on how smartphone use can influence sleep. This study combines smartphone application use data from 75 participants with sleep data collected by a wearable ring. On average, the participants used their smartphones in bed for 322.8 s (5 min and 22.8 s), with an IQR of 43.7–456. Participants spent an average of 42% of their time in bed using their smartphones (IQR of 5.87–55.5%). Our findings indicate that smartphone use in bed has significant adverse effects on sleep latency, awake time, average heart rate, and HR variability. We also find that smartphone use does not decrease sleep quality when used outside of bed. Our results indicate that intense smartphone use alone does not negatively affect well-being. Since all smartphone users do not use their phones in the same way, extending the investigation to different smartphone use types might yield more information than general smartphone use. In conclusion, this paper presents the first investigation of the association between smartphone application use logs and detailed sleep metrics. Our work also validates previous research results and highlights emerging future work.

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.

A new generation of wearable devices now enable end-users to keep track of their sleep patterns. This paper reports on a longitudinal study of 82 participants who used a state-of-the-art sleep tracking ring for an average of 65 days. We conducted interviews and questionnaires to understand changes to their lifestyle, their perceptions of the tracked information and sleep, and the overall experience of using an unobtrusive sleep tracking device. Our results indicate that such a device is suitable for long-term sleep tracking and helpful in identifying detrimental lifestyle elements that hinder sleep quality. However, tracking one's sleep can also introduce stress or physical discomfort, potentially leading to adverse outcomes. We discuss these findings in light of related work and highlight the near-term research directions that the rapid commoditisation of sleep tracking technology enables.

While the use of smartphones in extreme temperatures does not necessarily occur every day nor in all parts of the world, numerous use cases can be highlighted where the use of smartphones in cold temperatures is mandatory. Modern smartphones are designed to function in a wide range of temperatures, but when exposed to extreme cold temperatures the performance and reliability can significantly suffer. This paper presents a controlled laboratory experiment, using a clinical cold chamber to expose seven smartphone models to both medium cold (0 °C to −20 °C) and extreme cold (−30 °C) environments. The results showcase the smartphones’ sensing software’s lack of awareness of the cold environment, as well as reliability issues in the form of device crashes across the whole range of tested devices. We present a strategy for implementing monitoring application designs to both appropriately sense the effect of cold environments, as well as predicting device shutdowns in extreme cold.

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.

Abstract Despite large investments in smartwatch development, the market growth remains smaller than forecasted. The purpose of smartwatch use remains unclear, indicated by the lack of large-scale adoption. Thus, we aim to better understand the early adoption and everyday smartwatch use. We investigate a diverse usage data of smartwatches logged over a period of up to 14 months from 79 individuals between December 2015 and March 2017, one of the largest wearable datasets collected. First, we identify both explorative and accepted behaviours that users exhibit and further investigate how the individual usage traits and features differ between the two categories. Our analysis offers an insightful perspective on how smartwatch use evolves organically. Our results improve our shared understanding of smartwatch use and users adapting their use of smartwatch over time to match the capabilities of the technology by validating numerous findings from previous literature.

Spiral drawing has been utilized for years as a clinical tool to observe tremors and other abnormal movements in the assessment of different movement disorders. Specifically, in Parkinson's Disease (PD), patients' motor functionalities are measured by various tests, and spiral drawing is one of the proven techniques for assessing the severity of PD motor symptoms. Traditionally, this test is performed on pen and paper, and visually assessed by a clinician. There have been successful efforts for digitizing this test on tablets. Here, we describe a smartphone-based digitized version of the spiral drawing test. Moreover, we introduce a square-shaped drawing to solve an identified challenge of a smaller screen estate: finger occlusion while drawing. Both approaches are evaluated with 8 Parkinson's Disease patients and 6 age-matching control participants. Based on earlier studies and our data, we select suitable motion parameters for quantifying the task. Our results show an observable, statistically difference in performance between users with Parkinson's Disease and the control group in drawing accuracy.

Crowdsourcing platforms are increasingly being harnessed for creative work. The platforms' potential for creative work is clearly identified, but the workers' perspectives on such work have not been extensively documented. In this paper, we uncover what the workers have to say about creative work on paid crowdsourcing platforms. Through a quantitative and qualitative analysis of a questionnaire launched on two different crowdsourcing platforms, our results revealed clear differences between the workers on the platforms in both preferences and prior experience with creative work. We identify common pitfalls with creative work on crowdsourcing platforms, provide recommendations for requesters of creative work, and discuss the meaning of our findings within the broader scope of creativity-oriented research. To the best of our knowledge, we contribute the first extensive worker-oriented study of creative work on paid crowdsourcing platforms.

As smartphones are increasingly an integral part of daily life, recent literature suggests a deeper relationship between personality traits and smartphone usage. However, this relationship depends on many complex factors such as geographic location, demographics, or cultural influence, just to name a few. These factors provide crucial knowledge for e.g. usage support, recommendations, marketing, general usage improvements. We use six months of application usage data from 739 Android smartphone user together with the IPIP 50-item Big Five personality traits questionnaire. As our main contribution, we show that even category-level aggregated application usage can predict Big Five traits at up to 86%–96% prediction fit in our sample. Our results show the effect of personality traits on application usage (mean error improvement on random guess 17.0%). We also identify which application usage data best describe the Big Five personality traits. Our work enables future personality-driven research, and shows that when studying personality, application categories can provide sufficient predictions in general traits.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP for tracking hand's motor symptoms, and a medication journal for recording medication intake. We followed 13 PD patients from two countries for a 1-month long real-world deployment. We found that PD patients are willing to use digital tools, such as STOP, to track their medication intake and symptoms, and are also willing to share such data with their caregivers and medical personnel to improve their own care.

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants' behavioral patterns or sensing data correlation. In this paper, we perform an extensive literature review of learning-assisted optimization approaches in MCS. Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations, which can inform and guide future research directions.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. It is commonly accepted that improving medication adherence alleviates symptoms and maintains motor capabilities. Not following the medication regimen (e.g., skipping or over-medicating) may worsen side-effects, which mislead clinicians and patients. We developed and evaluated a mobile application, STOP, for screening the PD symptoms and medication intake. It contains a game for tracking the PD symptoms, and a medication journal for recording medical intake and adherence. We conducted a 1-month long real-world deployment with 13 PD patients from two countries. We found that the application medication adherence tracking provides non-bias information, and users are receptive to share such data with their care and medical personnel.

The connection between stress and smartphone usage behavior has been investigated extensively. While the prediction results using machine learning are encouraging, the challenge of how to cope with data loss remains. Addressing this problem, we propose an Intelligent Recommender System for logging stress based on adding a subjective user data-based validation to predictions made by intelligent algorithms. In a user study involving 731 daily stress self-reports from 30 participants we found discrepancies between subjective and smartphone usage data, i.e. battery, call information, or network usage. Despite the good prediction accuracy of 65% using a Random Forest classifier, combining both information would be beneficial for avoiding data and improving prediction accuracy. For realizing such a system (i.e., a mobile application), we propose three design recommendations, based on the capabilities of frequently used machine learning classifiers, enabling users to annotate their daily stress levels with a predict-and-validate methodology.

We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

While the population is aging the role of information and communication technology (ICT) has grown in elderly care. This development has brought versatile ICT-related supportive systems to professionals and laymen working with aging people. The current study analyzed how professionals in elderly care perceived their workflow challenges before new ICT is developed and implemented to support their work. The results of this study are set to inform the design of a novel ICT system for a sheltered care home.

Previous work suggests that Quantified-Self applications can retain long-term usage with motivational methods. These methods often require intermittent attention requests with manual data input. This may cause unnecessary burden to the user, leading to annoyance, frustration and possible application abandonment. We designed a novel method that uses on-screen alert dialogs to transform recurrent smartphone usage sessions into moments of data contributions and evaluate how accurately machine learning can reduce unintended interruptions. We collected sensor data from 48 participants during a 4-week long deployment and analysed how personal device usage can be considered in scheduling data inputs. We show that up to 81.7% of user interactions with the alert dialogs can be accurately predicted using user clusters, and up to 75.5% of unintended interruptions can be prevented and rescheduled. Our approach can be leveraged by applications that require self-reports on a frequent basis and may provide a better longitudinal QS experience.

Digital marketing is increasingly moving from desktop (e.g., browser) to mobile environments (e.g., within mobile applications). The means for delivering ads however, remains largely unchanged: banners and videos. In this work, we explore transforming ad delivery methods to the mobile environment while mitigating issues causing frustration and distractions to the users, evident in both web and mobile marketing. We demonstrate that softly enforcing interaction with the ad - with minimal usable screen space reduction - can improve user's attitude towards mobile advertising. Brand recollection is also influenced via increased interactions with the ad delivery method.

An important facet of smartphone's usage is internet. Everything works flawlessly, as long as you have a good internet connection. A smartphone's functionality is immediately limited by the absence of internet: applications are not up-to-date; instant chat messages are not delivered when intended, or one is unable to get directions. Besides internet performance tuning, research has been scarce in leveraging users' internet access routines to improve smartphone's usage. By understanding smartphone internet availability, one may utilise this information to minimise data costs and improve users' experience while using internet-enabled applications. Our paper provides insight into when is it likely that an individual user is online, based on personal connectivity routines.