Longitudinal Mobile, Wearable, and Ubiquitous Data Collection from Human Subject Studies
The Workshop on Longitudinal Data Collection welcomes contributions and discussions focused on the methods, tools, and frameworks for collection, analysis, and interpretation of human subjects’ mobile, wearable, and ubiquitous data obtained over long periods.
Individuals increasingly use mobile, wearable, and ubiquitous devices capable of unobtrusive collection of vast amounts of scientifically-rich human subject personal data over long periods (months to years), and in the context of their daily life. However, numerous human and technological factors challenge longitudinal data collection, often limiting research studies to very short data collection periods (days to weeks), spawning recruitment biases and affecting participant retention over time. This workshop is designed to bring together researchers involved in longitudinal data collection studies to foster an insightful exchange of ideas, experiences, and discoveries to improve the studies’ reliability, validity, and perceived meaning of longitudinal mobile, wearable, and ubiquitous data collection for the participants.
Workshop at a Glance
- Workshop on Longitudinal Data Collection (LDC 2019)
- Co-located with the ACM UbiComp conference (UbiComp 2019)
- Time: September 9, 2019, half-day workshop
- Place: Queen Elizabeth II Centre, London, United Kingdom
The workshop offers a professional space for researchers to share ideas, approaches, methods, tools, frameworks, and other insights that enable the collection of reliable and valid longitudinal mobile, wearable, and ubiquitous data. We aim:
- To present and discuss state of the art methods for longitudinal mobile, wearable, and ubiquitous data collection in human subject studies.
- To discuss ideas to minimize participants’ burden while maximizing their retention in studies contributing relevant data to support study results that will create value for researchers and participants alike.
- To map the challenges into the implications for the design of human subject studies that will drive this line of research in the coming years.
- To foster collaboration among researchers working in this area.
Call for Papers
The workshop welcomed contributions and discussions focused on the methods, tools, and frameworks for collection, analysis, and interpretation of human subjects’ mobile, wearable, and ubiquitous data obtained over long periods:
- Elaboration on human, technological, and other factors influencing the design and execution of human longitudinal data collection.
- Approaches that increase the quality of mobile, wearable, and ubiquitous data collected as part of scientific studies or identify participant groups likely to exhibit compliance.
- Methodologies to assess and improve retention for a representative sample of participants and specific metrics, e.g., engagement, interruptions, consistency, or time to abandonment.
- Techniques or methods for the analyses of the representatives and quality of collected longitudinal mobile, wearable, and ubiquitous data.
- Novel findings and lessons learned from past or existing longitudinal data collection studies conducted in the user’s context, implying qualitative, quantitative, or mixed analyses.
The workshop organizers have accepted papers by peer review. The authors of the accepted papers will present their work for 10-20 minutes, answer questions from the audience, and participate in the joint discussions. We welcome all interested conference attendees to the workshop.
Update: ACM has recently instructed all submissions for all tracks of UbiComp/ISWC, including the adjunct proceedings, to use the new ACM SIGCHI portrait format.
Camera-ready papers (July 12) in the new portrait template: Minimum 4 and maximum 7 pages in the new ACM SIGCHI portrait format (Word | LaTeX | Overleaf), where Word users should use the interim template downloadable from the ACM link above; LaTeX users should use the sigchi template style.
The deadlines dates are:
Original: June 21 AoE Notification: June 28 AoE Revision: July 5 AoE Notification: July 7 AoE
- Camera-ready: July 17 AoE
The paper should be submitted as a PDF file on Precision, after selecting:
- Society: SIGCHI
- Conference/Journal: UbiComp 2019
- Track: UbiComp 2019 Workshop – LDC
Peer-review assessing the submission’s relevance, significance, originality, clarity, and overall quality.
Accepted workshop papers will be added to the ACM Digital Library and the UbiComp/ISWC adjunct proceedings.
Challenges and lessons learned from implementing longitudinal studies for self-care technology assessment. Ana Vasconcelos, Inês Lopes, Jorge Ribeiro, Ana Correia de Barros.
Whilst literature is rich in lessons learned from recruitment and retention of participants in longitudinal studies, papers sharing practical experience of implementing such studies with or about ICT are lacking. We discuss the challenges and lessons learned in four longitudinal studies with older adults and chronic disease patients for the assessment of self-care technology. Despite apparently prosaic, everyday challenges and potential threats to studies with non-mainstream audiences may be hard to anticipate. A reflection by the researchers leading these studies led to three main themes associated to studies’ timelines, which are described with practical examples.
Cohort analyses of in-person interactions in temporally evolving student social groups. Rahul Majethia, Gurleen Kaur, Sidra Effendi, Vadlamudi Pratiksha Sharma.
In social interaction systems, the formation and testing of theories is significantly difficult because social interaction systems cannot be easily manipulated and controlled. It is also not possible to reproduce large-scale systems in a lab setting or in a short fixed time duration. Detecting short-term non-recurrent interactions between individuals is very different from studying an individual’s long term social group(s). However, over the last decade the rate of digital data availability using smartphones and wearables has increased consistently at a high pace which allows social scientists gain a comprehensive understanding of how groups form and evolve over time using recurrent in-person interaction networks. In this paper, we design a long term data-driven study on a finite student population of a residential university campus. Our aim is to study a student’s recurrent in-person interactions, or long-term social groups, between the time that one enters into a cohort, e.g., Class of 2022, until that cohort graduates. In this sensor-data driven study using state-of-the-art interaction-detection algorithms, we monitor parameters such as social group size, formation-time and longevity. We also conduct a retrospective cohort analysis of self-reported social group parameters, e.g. social group size, time spent with each group type and associated satisfaction. Preliminary results from the same make an extremely strong case for a longitudinal study, especially indicated by the evolution of one’s social circles over a long period of time.
Capturing contextual morality: applying game theory on smartphones. Niels van Berkel, Simo Hosio, Benjamin Tag, Jorge Goncalves.
In order to build more fair Artificial Intelligence applications, a thorough understanding of human morality is required. Given the variable nature of human moral values, AI algorithms will have to adjust their behaviour based on the moral values of its users in order to align with end user expectations. Quantifying human moral values is, however, a challenging task which cannot easily be completed using e.g. surveys. In order to address this problem, we propose the use of game theory in longitudinal mobile sensing deployments. Game theory has long been used in disciplines such as Economics to quantify human preferences by asking participants to choose between a set of hypothetical options and outcomes. The behaviour observed in these games, combined with the use of mobile sensors, enables researchers to obtain unique insights into the effect of context on participant convictions.
The workshop is planned to cover a half day. The workshop will start with a round of introduction by all attendees and a keynote speech. The organizers will then introduce the papers, after which the respective authors will present their work for 10-20 minutes. Following the presentations, all attendees will contribute to a group discussion on the work to identify the implications for the broader research agenda. One organizer will summarize and present the lessons learned from the ongoing discussions. We expect these findings to fuel dialogue over a joint dinner. A tentative schedule can be seen in the table below.
|15 minutes||Opening||Opening notes and round of introduction by all attendees|
|30 minutes||Keynote||Keynote speech on experiences with longitudinal human subject studies|
|60 minutes||Presentations||Author presentations of their work for 10-20 minutes, and questions|
|15 minutes||Coffee Break|
|60 minutes||Brainstorm Sessions||Group discussion on the work to identify the implications for broader research|
|15 minutes||Lessons Learned||Presentation of the lessons learned from the ongoing discussions|
|15 minutes||Closing||Best paper & presentation vote and closing remarks|
PhD Student, University of Copenhagen
Research interests: mobile and wearable computing, mobile health, machine learning
PhD Student, University of Geneva
Research interests: human-computer interaction, mobile and wearable computing, mobile health, human stress
PhD Student, University of Geneva
Research interests: mobile and wearable computing, quality of experience, context awareness, machine learning
Naja Holten Møller
Assistant Professor, University of Copenhagen
Research interests: computer-supported cooperative work, human-computer interaction, science and technology studies, ethnography, workplace studies
Associate Professor, University of Geneva
Associate Professor, University of Copenhagen
Research interests: pervasive and mobile computing, behavior modeling, digital health, quality of experience, quality of life
Research Associate Professor, New York University
Chief Scientific Officer, Datacubed Research
Research interests: decision making, human conditions, big data, urban studies
Professor, Technical University of Denmark
Research interests: complex networks, social networks, social data
Professor, Stanford University
Research interests: genomics, precision medicine, personalized medicine, inherited cardiovascular disease, cardiomyopathy
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