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abstract

LDC '19: international workshop on longitudinal data collection in human subject studies

Published:09 September 2019Publication History

ABSTRACT

Individuals increasingly use mobile, wearable, and ubiquitous devices capable of unobtrusive collection of vast amounts of scientifically rich 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.

References

  1. O Azmak et al. 2015. Using big data to understand the human condition: the Kavli HUMAN project. Big Data 3, 3 (2015), 173--188.Google ScholarGoogle ScholarCross RefCross Ref
  2. FW Booth et al. 2011. Lack of exercise is a major cause of chronic diseases. Comprehensive Physiology 2, 2 (2011), 1143--1211.Google ScholarGoogle Scholar
  3. K Breckenridge et al. 2015. How to routinely collect data on patient-reported outcome and experience measures in renal registries in Europe: an expert consensus meeting. Nephrology Dialysis Transplantation 30, 10 (2015), 1605--1614.Google ScholarGoogle ScholarCross RefCross Ref
  4. AI Canhoto et al. 2017. Exploring the factors that support adoption and sustained use of health and fitness wearables. Journal of Marketing Management 33, 1--2 (2017), 32--60.Google ScholarGoogle ScholarCross RefCross Ref
  5. GA Colditz and SE Hankinson. 2005. The Nurses' Health Study: lifestyle and health among women. Nature Reviews Cancer 5, 5 (2005), 388.Google ScholarGoogle ScholarCross RefCross Ref
  6. PN Dawadi et al. 2013. Automated assessment of cognitive health using smart home technologies. Technology and Health Care 21, 4 (2013), 323--343. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A De Masi et al. 2016. mQoL smart lab: quality of life living lab for interdisciplinary experiments. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Association for Computing Machinery, 635--640. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A de Thurah et al. 2010. Compliance with methotrexate treatment in patients with rheumatoid arthritis: influence of patients' beliefs about the medicine. A prospective cohort study. Rheumatology International 30, 11 (2010), 1441--1448.Google ScholarGoogle ScholarCross RefCross Ref
  9. A Doherty et al. 2017. Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study. PloS one 12, 2 (2017), e0169649.Google ScholarGoogle ScholarCross RefCross Ref
  10. KR Evenson and F Wen. 2015. Performance of the ActiGraph accelerometer using a national population-based sample of youth and adults. BMC Research Notes 8, 1 (2015), 7.Google ScholarGoogle ScholarCross RefCross Ref
  11. EJ Heckman et al. 2017. Wearable sleep epidemiology in the Framingham Heart Study. Journal of Sleep and Sleep Disorders Research 40, suppl_1 (2017), A289--A289.Google ScholarGoogle Scholar
  12. NL Holten Møller et al. 2017. Data tracking in search of workflows. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery, 2153--2165. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S Ickin et al. 2012. Factors influencing quality of experience of commonly used mobile applications. IEEE Communications Magazine 50, 4 (2012), 48--56.Google ScholarGoogle ScholarCross RefCross Ref
  14. H Jeong et al. 2017. Smartwatch wearing behavior analysis: a longitudinal study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J Kim et al. 2018. Enchanted life space: adding value to smart health by integrating human desires. J Healthc Inform Res 24, 1 (2018), 3--11.Google ScholarGoogle ScholarCross RefCross Ref
  16. R Majethia et al. 2019. Cohort analyses of in-person interactions in temporally evolving student social groups. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Association for Computing Machinery. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. MV McConnell et al. 2017. Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart Counts Cardiovascular Health Study. JAMA Cardiology 2, 1 (2017), 67--76.Google ScholarGoogle ScholarCross RefCross Ref
  18. DD McManus et al. 2019. Design and Preliminary Findings From a New Electronic Cohort Embedded in the Framingham Heart Study. Journal of Medical Internet Research 21, 3 (2019), e12143.Google ScholarGoogle ScholarCross RefCross Ref
  19. P Phongsavan et al. 2004. Estimating physical activity level: the role of domestic activities. Journal of Epidemiology & Community Health 58, 6 (2004), 466--467.Google ScholarGoogle ScholarCross RefCross Ref
  20. GA Roth. 2018. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (2018), 1736--1788.Google ScholarGoogle ScholarCross RefCross Ref
  21. A Stopczynski et al. 2014. Measuring large-scale social networks with high resolution. PloS one 9, 4 (2014), e95978.Google ScholarGoogle ScholarCross RefCross Ref
  22. EJ Topol. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 25, 1 (2019), 44.Google ScholarGoogle ScholarCross RefCross Ref
  23. N Van Berkel et al. 2019. Capturing contextual morality: applying game theory on smartphones. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Association for Computing Machinery. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A Vasconcelos et al. 2019. Challenges and lessons learned from implementing longitudinal studies for self-care technology assessment. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Association for Computing Machinery. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
          September 2019
          1234 pages
          ISBN:9781450368698
          DOI:10.1145/3341162

          Copyright © 2019 Owner/Author

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 9 September 2019

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