Advertisement

Using Participatory Design to Inform the Connected and Open Research Ethics (CORE) Commons

  • John Harlow
  • Nadir Weibel
  • Rasheed Al Kotob
  • Vincent Chan
  • Cinnamon Bloss
  • Rubi Linares-Orozco
  • Michelle Takemoto
  • Camille NebekerEmail author
Original Research/Scholarship
First Online:

Abstract

Mobile health (mHealth) research involving pervasive sensors, mobile apps and other novel data collection tools and methods present new ethical, legal, and social challenges specific to informed consent, data management and bystander rights. To address these challenges, a participatory design approach was deployed whereby stakeholders contributed to the development of a web-based commons to support the mHealth research community including researchers and ethics board members. The CORE (Connected and Open Research Ethics) platform now features a community forum, a resource library and a network of nearly 600 global members. The utility of the participatory design process was evaluated by analyzing activities carried out over an 8-month design phase consisting of 86 distinct events including iterative design deliberations and social media engagement. This article describes how participatory design yielded 55 new features directly mapped to community needs and discusses relationships to user engagement as demonstrated by a steady increase in CORE member activity and followers on Twitter.

Keywords

Participatory design Research ethics mHealth Pervasive technology Digital medicine IRB 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This research was supported by the Robert Wood Johnson Foundation (Title: Connected and Open Research Ethics (CORE), Principal Investigator: Nebeker, #72876, 2015-2017) and the UC San Diego Chancellor’s Interdisciplinary Collaboratory (Co-PIs: Nebeker, Weibel and Bloss). We thank the IRB community (members, analysts) and researchers who participated in interviews and working groups to guide the design of the CORE platform features. Human subjects protection statement: The research study was certified as Exempt (45 CFR 46.101) by the UC San Diego Human Research Protections Program.

References

  1. Bloss, C., Nebeker, C., Bietz, M., Bae, D., Bigby, B., Devereaux, M., et al. (2016). Reimagining human research protections for 21st century science. Journal of Medical Internet Research, 18(12), e329.  https://doi.org/10.2196/jmir.6634.CrossRefGoogle Scholar
  2. Bort-Roig, J., Gilson, N. D., Puig-Ribera, A., Contreras, R. S., & Trost, S. G. (2014). Measuring and influencing physical activity with smartphone technology: A systematic review. Sports Medicine (Auckland, N.Z.), 44(5), 671–686.  https://doi.org/10.1007/s40279-014-0142-5.CrossRefGoogle Scholar
  3. Dunseath, S., Weibel, N., Bloss, C. S., & Nebeker, C. (2018). NIH support of mobile, imaging, pervasive sensing, social media and location tracking (MISST) research: Laying the foundation to examine research ethics in the digital age. npj Digital Medicine, 1(1), 20171.  https://doi.org/10.1038/s41746-017-0001-5.CrossRefGoogle Scholar
  4. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.  https://doi.org/10.1038/nature21056.CrossRefGoogle Scholar
  5. Galna, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., & Rochester, L. (2014). Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait & Posture, 39(4), 1062–1068.  https://doi.org/10.1016/j.gaitpost.2014.01.008.CrossRefGoogle Scholar
  6. Hashemi, J., Tepper, M., Vallin Spina, T., Esler, A., Morellas, V., Papanikolopoulos, N., et al. (2014). Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants. Autism Research and Treatment, 2014, 1–12.  https://doi.org/10.1155/2014/935686.CrossRefGoogle Scholar
  7. Hodges, S., Williams, L., Berry, E., Izadi, S., Srinivasan, J., Butler, A., & et al. (2006). SenseCam: A retrospective memory aid. In Proceedings of Ubicomp 2006 (pp. 177–193).  https://doi.org/10.1007/11853565_11.
  8. Kelly, P., Marshall, S. J., Badland, H., Kerr, J., Oliver, M., Doherty, A. R., et al. (2013). An ethical framework for automated, wearable cameras in health behavior research. American Journal of Preventive Medicine, 44(3), 314–319.  https://doi.org/10.1016/j.amepre.2012.11.006.CrossRefGoogle Scholar
  9. Kerr, J., Marshall, S. J., Godbole, S., Chen, J., Legge, A., Doherty, A. R., et al. (2013). Using the SenseCam to improve classifications of sedentary behavior in free-living settings. American Journal of Preventive Medicine, 44(3), 290–296.  https://doi.org/10.1016/j.amepre.2012.11.004.CrossRefGoogle Scholar
  10. Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.  https://doi.org/10.1109/MCOM.2010.5560598.CrossRefGoogle Scholar
  11. Lave, J. (1991). Situating learning in communities of practice. In Perspectives on socially shared cognition. (Vol. 2, pp. 63–82). Washington: American Psychological Association.  https://doi.org/10.1037/10096-003.
  12. Lidz, C. W., & Garverich, S. (2013). What the ANPRM missed: Additional needs for IRB reform. The Journal of Law, Medicine & Ethics: A Journal of the American Society of Law, Medicine & Ethics, 41(2), 390–396.  https://doi.org/10.1111/jlme.12050.CrossRefGoogle Scholar
  13. Mariakakis, A., & Patel, S. (2016). Ocular symptom detection using smartphones. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: Adjunct (pp. 435–440). New York, NY, USA: ACM.  https://doi.org/10.1145/2968219.2971354.
  14. Marinac, C., Merchant, G., Godbole, S., Chen, J., Kerr, J., Clark, B., & et al. (2013). The feasibility of using SenseCams to measure the type and context of daily sedentary behaviors. In ACM international conference proceeding series, pp. 42–49.  https://doi.org/10.1145/2526667.2526674.
  15. Martin, R. C. (2003). Agile software development: Principles, patterns, and practices. Upper Saddle River, NJ: Prentice Hall PTR.Google Scholar
  16. McKinley, D. C., Miller-Rushing, A. J., Ballard, H. L., Bonney, R., Brown, H., Cook-Patton, S. C., et al. (2017). Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation, 208, 15–28.  https://doi.org/10.1016/j.biocon.2016.05.015.CrossRefGoogle Scholar
  17. Murnane, E. L., & Counts, S. (2014). Unraveling abstinence and relapse: Smoking cessation reflected in social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1345–1354). New York, NY: ACM.  https://doi.org/10.1145/2556288.2557145.
  18. Nebeker, C. (2017). Getting to the CORE of MISST-related research. Amp&rsand: The PRIM&R blog. https://blog.primr.org/core-misst-research/. Accessed January 16, 2019.
  19. Nebeker, C., Harlow, J., Espinoza Giacinto, R., Orozco-Linares, R., Bloss, C. S., & Weibel, N. (2017a). Ethical and regulatory challenges of research using pervasive sensing and other emerging technologies: IRB perspectives. AJOB Empirical Bioethics, 8(4), 266–276.  https://doi.org/10.1080/23294515.2017.1403980.CrossRefGoogle Scholar
  20. Nebeker, C., Lagare, T., Takemoto, M., Lewars, B., Crist, K., Bloss, C. S., et al. (2016). Engaging research participants to inform the ethical conduct of mobile imaging, pervasive sensing, and location tracking research. Translational Behavioral Medicine, 6(4), 577–586.  https://doi.org/10.1007/s13142-016-0426-4.CrossRefGoogle Scholar
  21. Nebeker, C., Murray, K., Holub, C., Haughton, J., & Arredondo, E. M. (2017b). Acceptance of mobile health in communities underrepresented in biomedical research: Barriers and ethical considerations for scientists. JMIR mHealth and uHealth, 5(6), e87.  https://doi.org/10.2196/mhealth.6494.CrossRefGoogle Scholar
  22. Nebeker, C., Orozco-Linares, R., & Crist, K. (2015). A multi-case study of research using mobile imaging, sensing and tracking technologies to objectively measure behavior: Ethical issues and insights to guide responsible research practice. Journal of Research Administration, 46(1), 118–134.Google Scholar
  23. Norman, D., & Draper, S. (1986). User centered system design: New perspectives on human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.CrossRefGoogle Scholar
  24. Ramesh, V., Rick, S., Agrawal, K., Meyers, B., Cauwenberghs, G., & Weibel, N. (2016). A neurobehavioral evaluation system using 3d depth tracking and computer vision: The case of stroke-kinect. In Society for Neuroscience, Posters.Google Scholar
  25. Schuler, D., & Namioka, A. (1993). Participatory design: Principles and practices. Hillsdale, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
  26. Thangarajan, N., Green, N., Gupta, A., Little, S., & Weibel, N. (2015). Analyzing social media to characterize local HIV at-risk populations. In Proceedings of the conference on Wireless Health—WH’15 (pp. 1–8). New York, NY: ACM.  https://doi.org/10.1145/2811780.2811923.
  27. Triantafyllidis, A. K., Velardo, C., Salvi, D., Shah, S. A., Koutkias, V. G., & Tarassenko, L. (2017). A survey of mobile phone sensing, self-reporting, and social sharing for pervasive healthcare. IEEE Journal of Biomedical and Health Informatics, 21(1), 218–227.  https://doi.org/10.1109/JBHI.2015.2483902.CrossRefGoogle Scholar
  28. Vayena, E., & Blasimme, A. (2018). Health research with big data: Time for systemic oversight. The Journal of Law, Medicine & Ethics, 46(1), 119–129.  https://doi.org/10.1177/1073110518766026.CrossRefGoogle Scholar
  29. Vitak, J., Proferes, N., Shilton, K., & Ashktorab, Z. (2017). Ethics regulation in social computing research: Examining the role of institutional review boards. Journal of Empirical Research on Human Research Ethics.  https://doi.org/10.1177/1556264617725200.Google Scholar
  30. Vitak, J., Shilton, K., & Ashktorab, Z. (2016). Beyond the Belmont Principles: Ethical challenges, practices, and beliefs in the online data research community. In Proceedings of the 19th ACM conference on computer-supported cooperative work and social computing—CSCW’16 (pp. 939–951). New York, NY: ACM Press.  https://doi.org/10.1145/2818048.2820078.
  31. Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., & et al. (2014). StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In Ubicomp’14, 3–14.  https://doi.org/10.1145/2632048.2632054.
  32. Wenger, E. (2001). Communities of practice: Learning, meaning, and identity. New York, NY: Cambridge University Press.Google Scholar
  33. Wenger-Trayner, E., & Wenger-Trayner, B. (2015). Communities of practice: A brief introduction. http://wenger-trayner.com/wp-content/uploads/2015/04/07-Brief-introduction-to-communities-of-practice.pdf. Accessed January 18, 2019.
  34. Young, S. D., Rivers, C., & Lewis, B. (2014). Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Preventive Medicine, 63, 112–115.  https://doi.org/10.1016/j.ypmed.2014.01.024.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • John Harlow
    • 1
  • Nadir Weibel
    • 2
  • Rasheed Al Kotob
    • 3
  • Vincent Chan
    • 2
  • Cinnamon Bloss
    • 4
  • Rubi Linares-Orozco
    • 5
  • Michelle Takemoto
    • 4
  • Camille Nebeker
    • 4
    Email author
  1. 1.School for the Future of Innovation in SocietyArizona State UniversityTempeUSA
  2. 2.Department of Computer Science and EngineeringUniversity of California San DiegoLa JollaUSA
  3. 3.Department of Nano EngineeringUniversity of California San DiegoLa JollaUSA
  4. 4.Department of Family Medicine and Public HealthUniversity of California San DiegoLa JollaUSA
  5. 5.Office of Regulatory ComplianceUniversity of California San DiegoLa JollaUSA

Personalised recommendations

Cite article

Buy options