Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)最新文献

Advances in user interfaces, pattern recognition, and ubiquitous computing continue to pave the way for better navigation towards our health goals. Quantitative methods which can guide us towards our personal health goals will help us optimize our daily life actions, and environmental exposures. Ubiquitous computing is essential for monitoring these factors and actuating timely interventions in all relevant circumstances. We need to combine the events recognized by different ubiquitous systems and derive actionable causal relationships from an event ledger. Understanding of user habits and health should be teleported between applications rather than these systems working in silos, allowing systems to find the optimal guidance medium for required interventions. We propose a method through which applications and devices can enhance the user experience by leveraging event relationships, leading the way to more relevant, useful, and, most importantly, pleasurable health guidance experience.

Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed Emu, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the Emu API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.

We report the design and deployment of a mobile health system for patients receiving primary care-based mental health services (Collaborative Care) for post-traumatic stress disorder and/or bipolar disorder in rural health centers. Here we describe the clinical model, our participatory approach to designing and deploying the mobile system, and describe the final system. We focus on the integration of the system into providers' clinical workflow and patient registry system. We present lessons learned about the technical and training requirements for integration into practice that can inform future efforts to incorporate health technologies to improve care for patients with psychiatric conditions.

The advancement of digital technologies particularly in the domain of mobile health (mHealth) holds great promise in the promotion of health behavior. However, keeping users engaged remains a central challenge. This paper proposes a novel approach to address this issue by supplementing existing and future mHealth applications with an engagement wrapper - a collection of engagement strategies integrated into a single, coherent model. The engagement wrapper is operationalized within the format of an ambient display on the lock screen of mobile devices.

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do - in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.

Despite the recent progress in sensor technologies, many relevant health data can be only captured with manual input (e.g., food intake, stress appraisal, subjective emotion, substance use). A common problem of manual logging is that users often disengage within a short time because of high burden. In this work, we propose SARA, a novel app to engage users with ongoing tracking using timely rewards thereby reinforcing users for data input. SARA is developed for adolescents and emerging adults at risk for substance abuse. The rewards in SARA are designed to be developmentally and culturally appropriate to the target demographic and are theoretically grounded in the behavioral science literature. In this paper, we describe SARA and its rewards to increase data collection. We also briefly discuss future plans to evaluate SARA and develop just in time adaptive interventions for engagement and behavior change.

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) trigger. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant's activity, conversation status, audio, and location, we can predict if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.

People stop using personal tracking tools over time, referred to as the lapsing stage of their tool use. We explore how designs can support people when they lapse in tracking, considering how to design data representations for a person who lapses in Fitbit use. Through a survey of 141 people who had lapsed in using Fitbit, we identified three use patterns and four perspectives on tracking. Participants then viewed seven visual representations of their Fitbit data and seven approaches to framing this data. Participant Fitbit use and perspective on tracking influenced their preference, which we surface in a series of contrasts. Specifically, our findings guide selecting appropriate aggregations from Fitbit use (e.g., aggregate more when someone has less data), choosing an appropriate framing technique from tracking perspective (e.g., ensure framing aligns with how the person feels about tracking), and creating appropriate social comparisons (e.g., portray the person positively compared to peers). We conclude by discussing how these contrasts suggest new designs and opportunities in other tracking domains.

Ecological Momentary Assessment (EMA) is a method of in situ data collection for assessment of behaviors, states, and contexts. Questions are prompted during everyday life using an individual's mobile device, thereby reducing recall bias and increasing validity over other self-report methods such as retrospective recall. We describe a microinteraction-based EMA method ("micro" EMA, or μEMA) using smartwatches, where all EMA questions can be answered with a quick glance and a tap - nearly as quickly as checking the time on a watch. A between-subjects, 4-week pilot study was conducted where μEMA on a smartwatch (n=19) was compared with EMA on a phone (n=14). Despite an ≈8 times increase in the number of interruptions, μEMA had a significantly higher compliance rate, completion rate, and first prompt response rate, and μEMA was perceived as less distracting. The temporal density of data collection possible with μEMA could prove useful in ubiquitous computing studies.

Persuasive technologies aim to influence user's behaviors. In order to be effective, many of the persuasive technologies de-veloped so far relies on user's motivation and ability, which is highly variable and often the reason behind the failure of such technology. In this paper, we present the concept of Mindless Computing, which is a new approach to persuasive technology design. Mindless Computing leverages theories and concepts from psychology and behavioral economics into the design of technologies for behavior change. We show through a systematic review that most of the current persuasive technologies do not utilize the fast and automatic mental processes for behavioral change and there is an opportunity for persuasive technology designers to develop systems that are less reliant on user's motivation and ability. We describe two examples of mindless technologies and present pilot studies with encouraging results. Finally, we discuss design guidelines and considerations for developing this type of persuasive technology.