Pass the Ball: Enforced Turn-Taking in Activity Tracking

We have developed a mobile application called Pass The Ball that enables users to track, reflect on, and discuss physical activity with others. We followed an iterative design process, trialling a first version of the app with 20 people and a second version with 31. The trials were conducted in the wild, on users' own devices. The second version of the app enforced a turn-taking system that meant only one member of a group of users could track their activity at any one time. This constrained tracking at the individual level, but more successfully led users to communicate and interact with each other. We discuss the second trial with reference to two concepts: social-relatedness and individual-competence. We discuss six key lessons from the trial, and identify two high-level design implications: attend to "practices" of tracking; and look within and beyond "collaboration" and "competition" in the design of activity trackers

Delivering "Just-In-Time" Smoking Cessation Support Via Mobile Phones: Current Knowledge and Future Directions.

UNLABELLED: Smoking lapses early on during a quit attempt are highly predictive of failing to quit. A large proportion of these lapses are driven by cravings brought about by situational and environmental cues. Use of cognitive-behavioral lapse prevention strategies to combat cue-induced cravings is associated with a reduced risk of lapse, but evidence is lacking in how these strategies can be effectively promoted. Unlike most traditional methods of delivering behavioral support, mobile phones can in principle deliver automated support, including lapse prevention strategy recommendations, Just-In-Time (JIT) for when a smoker is most vulnerable, and prevent early lapse. JIT support can be activated by smokers themselves (user-triggered), by prespecified rules (server-triggered) or through sensors that dynamically monitor a smoker's context and trigger support when a high risk environment is sensed (context-triggered), also known as a Just-In-Time Adaptive Intervention (JITAI). However, research suggests that user-triggered JIT cessation support is seldom used and existing server-triggered JIT support is likely to lack sufficient accuracy to effectively target high-risk situations in real time. Evaluations of mobile phone cessation interventions that include user and/or server-triggered JIT support have yet to adequately assess whether this improves management of high risk situations. While context-triggered systems have the greatest potential to deliver JIT support, there are, as yet, no impact evaluations of such systems. Although it may soon be feasible to learn about and monitor a smoker's context unobtrusively using their smartphone without burdensome data entry, there are several potential advantages to involving the smoker in data collection. IMPLICATIONS: This commentary describes the current knowledge on the potential for mobile phones to deliver automated support to help smokers manage or cope with high risk environments or situations for smoking, known as JIT support. The article categorizes JIT support into three main types: user-triggered, server-triggered, and context-triggered. For each type of JIT support, a description of the evidence and their potential to effectively target specific high risk environments or situations is described. The concept of unobtrusive sensing without user data entry to inform the delivery of JIT support is finally discussed in relation to potential advantages and disadvantages for behavior change.This is the accepted manuscript. The final version is available at http://ntr.oxfordjournals.org/content/early/2016/06/15/ntr.ntw143

My data, my choice?! The difference between fitness and stress data monitoring on employees’ perception of privacy

Besides the vast distribution in the private sector, employers begin to integrate wearables in occupational health management (OHM). Through the implementation of 'stress' and 'fitness monitoring', organizations are able to invest in employees' health and well-being. While employees' consent is mandatory for the implementation, these, in turn, might perceive monitoring as a risk instead of realizing the benefits going along. By conducting an experimental study, we compare employees' perceived privacy risks/costs (PRC) and benefits (PBE) regarding the two monitoring cases. According to our results, employees interpret their stress data as rather sensitive while rating the PBE of fitness monitoring higher. Further, fair communication practices towards employees plays an essential role in the successful implementation of OHM. The research article provides theoretical and practical implications and sheds light on paths for further research regarding actual use behavior, international aspects, and employers' interests

Privacy threats with retail technologies: A consumer perspective

This paper focuses on new retail technologies that acquire information from consumers, advancing that such devices represent privacy management concerns. Specifically, we propose that privacy perceptions in a retail environment are driven by retailer- and technology-related factors as well as consumers’ personality traits. By running a moderated serial mediation analysis, we address the technologies’ fairness and hedonism as antecedents of consumer privacy perceptions, technology acceptance and perceived value, and account for consumers’ trust in the retailer. We find that privacy perceptions are directly affected by distributive fairness, while the technology’s hedonism affects acceptance. Further, the effects extend to patronage intention and word-of- mouth

Systematic review of smartphone-based passive sensing for health and wellbeing

OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts

Informing the Design of Privacy-Empowering Tools for the Connected Home

Connected devices in the home represent a potentially grave new privacy threat due to their unfettered access to the most personal spaces in people's lives. Prior work has shown that despite concerns about such devices, people often lack sufficient awareness, understanding, or means of taking effective action. To explore the potential for new tools that support such needs directly we developed Aretha, a privacy assistant technology probe that combines a network disaggregator, personal tutor, and firewall, to empower end-users with both the knowledge and mechanisms to control disclosures from their homes. We deployed Aretha in three households over six weeks, with the aim of understanding how this combination of capabilities might enable users to gain awareness of data disclosures by their devices, form educated privacy preferences, and to block unwanted data flows. The probe, with its novel affordances-and its limitations-prompted users to co-adapt, finding new control mechanisms and suggesting new approaches to address the challenge of regaining privacy in the connected home.Comment: 10 pages, 2 figures. To appear in the Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20