About this Research Topic
With the advent of low-cost networked sensors and devices (e.g. smartphones & wearables) in recent years, it has become possible for the non-technical and medical layperson to track, log, and collect data about one's health, behavior, and life in a way that could barely be imagined a decade ago. The focus of research and development related to the use of these devices has so far been on short-term use, typically for a couple of weeks or months, e.g. for behavior change interventions. With the increasing availability of more and more of these tracking devices, with mobile phones and smart watches becoming widespread, and as a by-product of our digital life style, we can now collect data over years and decades, providing totally new opportunities for the management for personal health and wellbeing. It is now more and more apparent that this long-term self-tracking is distinct from short-term tracking, providing new opportunities as well as challenges.
Long-term tracking over many years has distinct characteristics that need to be addressed:
o The user is of particular importance, with their tracking behavior determining the type, amount, and quality of data.
o Tracking occurs on a continuum from incidental to purposeful, and reasons for tracking may change over time ; this means that the data quality and quantity may therefore vary over time and its very nature makes it challenging to interpret meaningfully, especially for new questions that were never envisaged at the time of some of the data collection.
o Data is usually incomplete and its quality and reliability may be inconsistent.
o Data may be unstructured or media-rich such as text, images, videos, or calendar entries.
o Data may be heterogeneous between two different users, making analyses and comparisons difficult, even though this may be important for making meaningful interpretations of it.
o Systems for tracking may change over time, some becoming unavailable, others abandoned by the user.
o Ethical, legal and social implications play an important role, e.g. data protection, access rights, security, safety, or data ownership.
o Research about long-term tracking is challenging as the naïve collection of data and conduction of prospective studies would take many years, making it particularly challenging to design meaningful studies that are plausible to conduct in the quickly evolving technical world.
Research is going on in various disciplines, tackling, among others, multimedia retrieval, personal visual analytics, and user-interaction design. This research, however, is distributed over multiple communities , making exchange between the different disciplines difficult.
With this Research Topic, we aim to bring together research from technical and non-technical disciplines tackling these and other challenges and identifying new opportunities of long-term self-tracking.
We welcome contributions dealing with all topics relevant to tracking and logging personal data over periods of years and more, including, but not limited to:
o Analyzing such long-term self-tracking data to extract novel insights
o Understanding and dealing with incomplete, unreliable, imprecise self-tracking data
o Unlocking secondary personal data sources such as social networks, calendars, diaries
o Visualizing and understanding long-term self-tracking data
o Systems and infrastructures for long-term self-tracking
o New applications and long-term interventions arising from use of long-term data
o Ethical, legal, and social implications
Contributions may come from technical disciplines such as human-machine interaction, multimedia retrieval, or data analytics, but are also welcome from broader disciplines, such as psychology, medicine, health, sport and ethics.
We welcome the following article types: Original research, Systematic review, Methods, Review, Perspective, Data report, Opinion
Dr. Cathal Gurrin is the Founder of SeekLater Ltd. and has received a grant from Microsoft Research (2020-2021). All other Topic Editors declare no conflicts of interest.
Long-term tracking over many years has distinct characteristics that need to be addressed:
o The user is of particular importance, with their tracking behavior determining the type, amount, and quality of data.
o Tracking occurs on a continuum from incidental to purposeful, and reasons for tracking may change over time ; this means that the data quality and quantity may therefore vary over time and its very nature makes it challenging to interpret meaningfully, especially for new questions that were never envisaged at the time of some of the data collection.
o Data is usually incomplete and its quality and reliability may be inconsistent.
o Data may be unstructured or media-rich such as text, images, videos, or calendar entries.
o Data may be heterogeneous between two different users, making analyses and comparisons difficult, even though this may be important for making meaningful interpretations of it.
o Systems for tracking may change over time, some becoming unavailable, others abandoned by the user.
o Ethical, legal and social implications play an important role, e.g. data protection, access rights, security, safety, or data ownership.
o Research about long-term tracking is challenging as the naïve collection of data and conduction of prospective studies would take many years, making it particularly challenging to design meaningful studies that are plausible to conduct in the quickly evolving technical world.
Research is going on in various disciplines, tackling, among others, multimedia retrieval, personal visual analytics, and user-interaction design. This research, however, is distributed over multiple communities , making exchange between the different disciplines difficult.
With this Research Topic, we aim to bring together research from technical and non-technical disciplines tackling these and other challenges and identifying new opportunities of long-term self-tracking.
We welcome contributions dealing with all topics relevant to tracking and logging personal data over periods of years and more, including, but not limited to:
o Analyzing such long-term self-tracking data to extract novel insights
o Understanding and dealing with incomplete, unreliable, imprecise self-tracking data
o Unlocking secondary personal data sources such as social networks, calendars, diaries
o Visualizing and understanding long-term self-tracking data
o Systems and infrastructures for long-term self-tracking
o New applications and long-term interventions arising from use of long-term data
o Ethical, legal, and social implications
Contributions may come from technical disciplines such as human-machine interaction, multimedia retrieval, or data analytics, but are also welcome from broader disciplines, such as psychology, medicine, health, sport and ethics.
We welcome the following article types: Original research, Systematic review, Methods, Review, Perspective, Data report, Opinion
Dr. Cathal Gurrin is the Founder of SeekLater Ltd. and has received a grant from Microsoft Research (2020-2021). All other Topic Editors declare no conflicts of interest.
Keywords: long-term tracking, self-tracking, quantified self, life-logging, health informatics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
