New sensors, miniaturization, the ubiquity of smart phones, networking and the internet of things, to name just a few, have given us a plethora of new applications and systems that promise to support and improve personal health, wellbeing and fitness. New devices are emerging regularly, addressing physical activity, endurance sports and resistance training, sleep monitoring, mindfulness practice, posture monitoring, weight management, breathing techniques, cardiac health status and numerous more. There has been substantial work also in the CHI community demonstrating benefits of short term tracking (e.g. [1, 2]), often in the context of behavior change, with Consolvo et al’s UbiFit Garden [3] maybe the best-known early example.

Now it is more and more understood that there are considerable potential opportunities from long term tracking covering periods of not just weeks or months, but years or decades, and ultimately lifelong use [4, 5, 6]. There are already numerous ways of tracking large amounts of long term data, using dedicated tracking and logging tools, deploying the sensors in our smart phones and smart watches, or analyzing the digital traces that everybody leaves behind in social networks and online systems.  Such long and very long term data should be able to facilitate uses cases beyond behavior change, e.g. discovering long term trends in behavior, monitoring progress against a long term target, reflecting on long term trends and patterns, supporting decision making, giving a lifelong health support, or discovering the impact of N-of-1 experiments.

However, the current state of the art leaves people facing barriers that many people find insurmountable for making such uses of self tracking data. It has become clear that considerable work is needed to turn tracking from a toy to a tool.  Based on previous discussions [7] we suggest three research themes and one cross-topic issue.

The user’s role in long term tracking

The user has a double role in long term tracking: first, acting as the ultimate consumer of all services relating to the tracked data; second, as the producer of data, interacting with tracking devices to collect data in real life over many years. Different needs arise from these two roles, and there may well be conflicting requirements, such as the wish for complete data as a consumer, but with the risk of low adherence or abandonment due to high effort in tracking, as a producer. Understanding these needs and the potential conflicts is crucial for successful self tracking. This includes questions of long term use, adherence and abandonment, interaction with multiple devices, new concepts for tracking devices. It must, moreover, be understood how users wish to make sense of the data, e.g. by visualizing large amounts of temporal and heterogeneous data, or using it for goal setting and verifying personal hypotheses. Questions also relate to secondary data use and data ownership, e.g. provenance management and control, how data ownership is handed over in times of changes, such as from the parent caring for the child to the adolescent, or after the death of a user to her or his digital heir.

Making sense of long term data

Data is the raw material of tracking. Understanding the quality, properties, and limitations of data is therefore crucial for designing successful applications. There is also, but not just, a question of better measurement technology to improve precision. Particularly, the user’s role as a producer has a significant impact on the data. Lapses, breaks, and abandonment of tracker use result in gaps or completely missing data [8]. This compromises the meaningfulness of the tracker data, reducing the user’s trust in the data and the utilization in applications (e.g. [9, 10]). Reducing gaps as well as recognizing and accounting for incomplete data are necessary to facilitate the data. Applications may also face restrictions arising from data vaults, with data being hidden in different producers’ storage systems, with limited APIs and limited access opportunities.

Application needs, challenges and opportunities

Applications are the tools for deploying the tracking data and fulfilling the user’s requirements. There is an emerging need to make the case for if, how and when self tracking can be useful to users [11, 12]. Health is a personal issue, and many applications will relate to personal use, such as behavior support, understanding relations, identifying trends, and improving health literacy. There is also an overlap between self tracking and medicine; this raises questions such as provision of medical information from personal data, regulatory issues, or opportunities arising towards personalized medicine. Lastly, the broad utilization of self tracking also provides new opportunities for social health care. Big data analyses of large amounts of long term self tracking data from large populations may provide highly interesting insights into community health, as has been demonstrated by Althoff et al [13].

Challenges for research in HCI

Long term self tracking also imposes challenges for HCI research. Research on self tracking is inherently interdisciplinary, involving, amongst others, computer science, engineering, design, but also medicine, psychology, and sociology. Conducting studies is a key tool for research; however, there are huge differences in understanding the set-up of studies in, e.g., HCI and medicine. Long term studies, covering years and decades, would, in principle, be necessary to rigorously evaluate long term tracking applications; however, this is implausible, not just because the effort is far too high for most HCI projects, but also it is impossible to design such study to account for the fast-changing technological world. Lastly, collecting long term tracking data is a high effort; however, sharing such data among different researchers is hampered by issues such as lack of data interchange formats, the difficulties of ensuring anonymity e.g. when location data comes into play, or restrictive data protection requirements.

References

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  2. Kim, Y.-H., Jeon, J. H., Lee, B., Choe, E. K., & Seo, J. (2017). OmniTrack: A Flexible Self-Tracking Approach Leveraging Semi-Automated Tracking. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 1(3). https://doi.org/cc6z
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  7. J. Meyer and P. Eslambolchilar. Research Challenges of Emerging Technologies Supporting Life-Long Health and Wellbeing. 2nd Workshop on Multimedia and Health at ACM Multimedia 2017. Mountain View. 2017.
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  13. Tim Althoff, Rok Sosič, Jennifer L Hicks, Abby C King, Scott L Delp, and Jure Leskovec. 2017. Large-scale physical activity data reveal worldwide activity inequality. Nature.