Episodic use: Practices of care in self-tracking

The development of self-tracking technologies has resulted in a burst of research considering how self-tracking practices manifest themselves in everyday life. Based on a 5-month-long photo elicitation study of Danish self-trackers, we argue that no matter how committed people might be to tracking their activities, their use of self-tracking technologies can be best described as episodic rather than continuous. Using Annemarie Mol’s theoretical framework for understanding care practices as a lens, we show how episodic use can be interpreted through the logic of care. By using self-tracking devices episodically, users employ strategies of care in a way that can be productive and useful. These strategies often come in conflict with the logics of choice that underlie the design of many self-tracking technologies. We argue that this has consequences for the way self-tracking devices need to be imagined, designed, and introduced as part of workplace and insurance-type tracking programs

Delivering real-world ubiquitous location systems

Location-enhanced applications are poised to become the first real-world example of ubiquitous computing. In this paper, we emphasize the practical aspects of getting location-enhanced applications deployed on existing devices, such as laptops, tablets, PDAs, and cell phones, without the need to purchase additional sensors or install special infrastructure. Our goal is to provide readers with an overview of the practical considerations that are currently being faced, and the research challenges that lie ahead. We ground the article with a summary of initial work on two deployments of location- enhanced computing: multi-player location-based games and a guide for the Edinburgh Festival

Innovation in practice: mobile phone technology in patient care

Mobile phones are becoming increasingly important in everyday life and now in healthcare. There has been a steady growth of information and communication technologies in health communication and technology is used progressively in telemedicine, wireless monitoring of health outcomes in disease and in the delivery of health interventions. Mobile phones are becoming an important method of encouraging better nurse-patient communication and will undoubtedly increase in application over coming years. This article presents recent developments and applications of mobile technology for health promotion and patient-monitoring in chronic disease

Location tracking: views from the older adult population

Background: there has been a rise in the use of social media applications that allow people to see where friends, family and nearby services are located. Yet while uptake has been high for younger people, adoption by older adults is relatively slow, despite the potential health and social benefits. In this paper, we explore the barriers to acceptance of location-based services (LBS) in a community of older adults. Objective: to understand attitudes to LBS technologies in older adults. Methods: eighty-six older adults used LBS for 1-week and completed pre- and post-use questionnaires. Twenty available volunteers from the first study also completed in-depth interviews after their experience using the LBS technology. Results: the pre-use questionnaire identified perceptions of usefulness, individual privacy and visibility as predictive of intentions to use a location-tracking service. Post-use, perceived risk was the only factor to predict intention to use LBS. Interviews with participants revealed that LBS was primarily seen as an assistive technology and that issues of trust and privacy were important. Conclusion: the findings from this study suggest older adults struggle to see the benefits of LBS and have a number of privacy concerns likely to inhibit future uptake of location-tracking services and devices

Effectiveness of a smartphone app in increasing physical activity amongst male adults: a randomised controlled trial.

BACKGROUND: Smartphones are ideal for promoting physical activity in those with little intrinsic motivation for exercise. This study tested three hypotheses: H1 - receipt of social feedback generates higher step-counts than receipt of no feedback; H2 - receipt of social feedback generates higher step-counts than only receiving feedback on one's own walking; H3 - receipt of feedback on one's own walking generates higher step-counts than no feedback (H3). METHODS: A parallel group randomised controlled trial measured the impact of feedback on steps-counts. Healthy male participants (n = 165) aged 18-40 were given phones pre-installed with an app that recorded steps continuously, without the need for user activation. Participants carried these with them as their main phones for a two-week run-in and six-week trial. Randomisation was to three groups: no feedback (control); personal feedback on step-counts; group feedback comparing step-counts against those taken by others in their group. The primary outcome measure, steps per day, was assessed using longitudinal multilevel regression analysis. Control variables included attitude to physical activity and perceived barriers to physical activity. RESULTS: Fifty-five participants were allocated to each group; 152 completed the study and were included in the analysis: n = 49, no feedback; n = 53, individual feedback; n = 50, individual and social feedback. The study provided support for H1 and H3 but not H2. Receipt of either form of feedback explained 7.7 % of between-subject variability in step-count (F = 6.626, p < 0.0005). Compared to the control, the expected step-count for the individual feedback group was 60 % higher (effect on log step-count = 0.474, 95 % CI = 0.166-0.782) and that for the social feedback group, 69 % higher (effect on log step-count = 0.526, 95 % CI = 0.212-0.840). The difference between the two feedback groups (individual vs social feedback) was not statistically significant. CONCLUSIONS: Always-on smartphone apps that provide step-counts can increase physical activity in young to early-middle-aged men but the provision of social feedback has no apparent incremental impact. This approach may be particularly suitable for inactive people with low levels of physical activity; it should now be tested with this population

Interacting with eHealth - Towards grand challenges for HCI

While health records are increasingly stored electronically, we, as citizens, have little access to this data about ourselves. We are not used to thinking of these official records either as ours or as useful to us. We increasingly turn to the Web, however, to query any ache, pain or health goal we may have before consulting with health care professionals. Likewise, for proactive health care such as nutrition or fitness, or to find fellow-sufferers for post diagnosis support, we turn to online resources. There is a potential disconnect between points at which professional and lay eHealth data and resources intersect for preventative or proactive health care. Such gaps in information sharing may have direct impact on practices we decide to take up, the care we seek, or the support professionals offer. In this panel, we consider several places within proactive, preventative health care in particular HCI has a role towards enhancing health knowledge discovery and health support interaction. Our goal is to demonstrate how now is the time for eHealth to come to the forefront of the HCI research agenda

Self-configuring data mining for ubiquitous computing

Ubiquitous computing software needs to be autonomous so that essential decisions such as how to configure its particular execution are self-determined. Moreover, data mining serves an important role for ubiquitous computing by providing intelligence to several types of ubiquitous computing applications. Thus, automating ubiquitous data mining is also crucial. We focus on the problem of automatically configuring the execution of a ubiquitous data mining algorithm. In our solution, we generate configuration decisions in a resource aware and context aware manner since the algorithm executes in an environment in which the context often changes and computing resources are often severely limited. We propose to analyze the execution behavior of the data mining algorithm by mining its past executions. By doing so, we discover the effects of resource and context states as well as parameter settings on the data mining quality. We argue that a classification model is appropriate for predicting the behavior of an algorithm?s execution and we concentrate on decision tree classifier. We also define taxonomy on data mining quality so that tradeoff between prediction accuracy and classification specificity of each behavior model that classifies by a different abstraction of quality, is scored for model selection. Behavior model constituents and class label transformations are formally defined and experimental validation of the proposed approach is also performed

Night optimised care technology for users needing assisted lifestyles

There is growing interest in the development of ambient assisted living services to increase the quality of life of the increasing proportion of the older population. We report on the Night Optimised Care Technology for UseRs Needing Assisted Lifestyles project, which provides specialised night time support to people at early stages of dementia. This article explains the technical infrastructure, the intelligent software behind the decision-making driving the system, the software development process followed, the interfaces used to interact with the user, and the findings and lessons of our user-centred approach