This study demonstrates how playing a well-designed multitasking motion video game in a virtual reality (VR) environment can positively impact the cognitive and physical health of older players. We developed a video game that combines cognitive and physical training in a VR environment. The impact of playing the game was measured through a four-week longitudinal experiment. Twenty healthy older adults were randomly assigned to either an intervention group (i.e., game training) or a control group (i.e., no contact). Participants played three 45-min sessions per week completing cognitive tests for attention, working memory, reasoning and a test for physical balance before and after the intervention. Results showed that compared to the control group, the game group showed significant improvements in working memory and a potential for enhancing reasoning and balance ability. Furthermore, while the older adults enjoyed playing the video game, ability enhancements were associated with their intrinsic motivation to play. Overall, cognitive training with multitasking VR motion video games has positive impacts on the cognitive and physical health of older adults.

Over the past decade, wearable activity trackers (WATs) have become increasingly popular. However, despite many research studies in different fields (e.g. psychology, health, and design), few have sought to jointly examine the critical aspects of utility (i.e., benefits brought by these devices), privacy, and security (i.e., risks and vulnerabilities associated with them). To fill this gap, we reviewed 236 studies that researched the benefits of using WATs, the implications for the privacy of users of WATs, and the security vulnerabilities of these devices. Our survey revealed that these devices expose users to several threats. For example, WAT data can be mined to infer private information, such as the personality traits of the user. Whereas many works propose empirical findings about users’ privacy perceptions and their behaviors in relation to privacy, we found relatively few studies researching technologies to better protect users’ privacy with these devices. This survey contributes to systematizing knowledge on the utility, privacy, and security of WATs, shedding light on the state-of-the-art approaches with these devices, and discussing open research opportunities.

Sharing multimedia content, without obtaining consent from the people involved causes multiparty privacy conflicts (MPCs). However, social-media platforms do not proactively protect users from the occurrence of MPCs. Hence, users resort to out-of-band, informal communication channels, attempting to mitigate such conflicts. So far, previous works have focused on hard interventions that do not adequately consider the contextual factors (e.g., social norms, cognitive priming) or are employed too late (i.e., the content has already been seen). In this work, we investigate the potential of conversational agents as a medium for negotiating and mitigating MPCs. We designed MediationBot, a mediator chatbot that encourages consent collection, enables users to explain their points of view, and proposes solutions to finding a middle ground. We evaluated our design using a Wizard-of-Oz experiment with N = 32 participants, where we found that MediationBot can effectively help participants to reach an agreement and to prevent MPCs. It produced a structured conversation where participants had well-clarified speaking turns. Overall, our participants found MediationBot to be supportive as it proposes useful middle-ground solutions. Our work informs the future design of mediator agents to support social-media users against MPCs.

The number of users of wearable activity trackers (WATs) has rapidly increased over the last decade. Although these devices enable their users to monitor their activities and health, they also raise new security and privacy concerns, given the sensitive data (e.g., steps, heart rate) they collect and the information that can be inferred from this data (e.g., diseases). In addition to sharing with the service providers (e.g., Fitbit), WAT users can share their fitness data with third-party applications (TPAs) and individuals. Understanding how and with whom users share their fitness data and what kind of approaches they take to preserve their privacy are key to assessing the underlying privacy risks and to further designing appropriate privacy-enhancing techniques. In this work, we perform, through a large-scale survey of N=628 WAT users, the first quantitative and qualitative analysis of users' awareness, understanding, attitudes, and behaviors toward fitness-data sharing with TPAs and individuals. By asking these users to draw their thoughts, we explore, in particular, users' practices and actual behaviors toward fitness-data sharing and their mental models. Our empirical results show that about half of WAT users underestimate the number of TPAs to which they have granted access to their data, and 63% share data with at least one TPA that they do not actively use (anymore). Furthermore, 29% of the users do not revoke TPA access to their data because they forget they gave access to it in the first place, and 8% were not even aware they could revoke access to their data. Finally, their mental models, as well as some of their answers, demonstrate substantial gaps in their understanding of the data-sharing process. Importantly, 67% of the respondents think that TPAs cannot access the fitness data that was collected before they granted access to it, whereas TPAs actually can do this.