Investigating Contextual Cues as Indicators for EMA Delivery

In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) prompt. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant\u27s activity, conversation status, audio, and location, we can predict whether an EMA prompt triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.410. Using this knowledge, the researchers conducting field studies can efficiently schedule EMA prompts and achieve higher response rates

Older and younger adults are influenced differently by dark pattern designs

Considering that prior research has found older users undergo a different privacy decision-making process compared to younger adults, more research is needed to inform the behavioral privacy disclosure effects of these strategies for different age groups. To address this gap, we used an existing dataset of an experiment with a photo-tagging Facebook application. This experiment had a 2x2x5 between-subjects design where the manipulations were common dark pattern design strategies: framing (positive vs. negative), privacy defaults (opt-in vs. opt-out), and justification messages (positive normative, negative normative, positive rationale, negative rationale, none). We compared older (above 65 years old, N=44) and young adults (18 to 25 years old, N=162) privacy concerns and disclosure behaviors (i.e., accepting or refusing automated photo tagging) in the scope of dark pattern design. Overall, we find support for the effectiveness of dark pattern designs in the sense that positive framing and opt-out privacy defaults significantly increased disclosure behavior, while negative justification messages significantly decreased privacy concerns. Regarding older adults, our results show that certain dark patterns do lead to more disclosure than for younger adults, but also to increased privacy concerns for older adults than for younger

Interaction Techniques for In-the-Moment Privacy Control Over Data Generated by Wearable Technologies

Wearable technologies provide users with actionable insights regarding personal health information because of their ability to capture and analyze data continuously and in-the-moment through their rich set of sensors. While these technologies offer the advantages of conveniently capturing personal health data and behaviors outside of a clinical setting, they pose significant privacy challenges. Wearables continuously collect and store sensitive personal information about the wearer. In some instances, personal information amassed by a wearable may be shared without user awareness. In addition to the privacy-invasive risks posed by wearable technologies, executing usable privacy control directly on wearables poses an even greater challenge due to lack of input space and constrained interaction. Most privacy controls options for wearables are separate from the device itself, which prevents the user from having integrated and in-the-moment control over the data they are producing. In light of the privacy risks and challenges for usable privacy-enhanced design for wearables, this dissertation uses a human-centered approach to advance the design space for usable and effective privacy control mechanisms. In particular, this research focuses on understanding how to develop privacy control mechanisms that provide adopters and potential adopters of wearables with integrated, in-the-moment control over personal information collected by wearables. This is accomplished through four user studies. In the first study, I investigate the preferences of adopters and potential adopters of wearable health technologies as they relate to privacy and sharing of extra-clinical health information generated from a wearable. This study also examines whether individual preferences vary based on the recipient, type, and valence (e.g., positive or negative rating) of health information. I found that the recipient and valence of data predicted privacy and sharing preferences for extra-clinical datagenerated by wearables. Participants were more willing to share extra-clinical data with healthcare providers, family, and friends compared to their employer or broader social network. Participants were also less willing to share negatively valenced data. Applying the knowledge that users have granular preferences for sharing data from wearables, the second study evaluates the impact of the location of privacy control and decision timing for privacy control on wearables. I designed four privacy interfaces that provide different combinations of location (e.g., integrated versus decoupled) and decision timing (e.g., in-the-moment/synchronousversus a priori/asynchronous) of privacy control. To evaluate the interfaces, I conducted a 2x2 between-subjects experiment where different groups of participants interacted with each interface and assessed the ease of use, perceived privacy control, and perceived oversharing threat for the assigned interface. The results show that only the location of control significantly influences the overall ease of use of the privacy interface. In further exploratory analyses, I find that intentions to adopt a settings interface are influenced by the timing of when privacy decisions can be executed, if it is easy to manage those decisions, and if the privacy settings interface reduces the threat of oversharing personal information. Adding more detail to understanding user preferences for privacy controls for wearables, the third study is an interaction elicitation study that identifies a set of device-independent interactions that allow integrated and in-the-moment privacy control over data from a wearable. I found differences in the types of interactions people produced for situations requiring more versus less subtlety. In this study, I also establish a taxonomy that organizes interactions based on interaction mapping and physical characteristics of the interaction. In the final study, I extend the findings of the interaction-elicitation study by further exploring the identified interactions in terms of their noticeability. This is done to determine a set of additional interactions wearers could adopt when they need to provide input to their device privately without being noticed. The results from this study produce a set of interactions(e.g., teeth click, single head nod) that are subtle enough to be used with any existing or emerging invisible wearable device. The overall findings of this dissertation offer privacy researchers and designers of wearable technologies insight into the future development of wearables. The findings of this dissertation also present hardware and software considerations to designers as they design interfaces that provide a usable and effective means for adopters and potential adopters to maintain their privacy over data produced by wearables

Design recommendations to improve the user interaction with wrist worn devices

Wrist worn devices (WWDs) including fitness trackers and smart watches have been successfully employed to support various applications, ranging from gesture recognition to authentication. Despite the increasing number of WWDs in the market, their limited dimensions and capabilities make their interaction design challenging. Designers struggle to downsize interactions originally designed for mobile phones, and end users wish for interactions that are easy to use. To better understand the users’ concerns regarding the interaction with WWDs, we collected feedback about existing wearable bands, and analyzed the most critical issues currently faced in the interaction with such devices. The analysis of the findings enabled us to derive 10 design recommendations that can aid to improve the interaction design in novel WWDs

Continuous Detection of Physiological Stress with Commodity Hardware

Timely detection of an individual’s stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer’s stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors