Low-Income Parents’ Values Involving the Use of Technology for Accessing Health Information

  • David MuñozEmail author
  • Rosa I. Arriaga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9298)


Technology is increasingly available to end users of low socioeconomic status (SES), yet little is known about how these users’ values affect the interfaces they prefer when seeking information related to their child’s health. We investigate low-SES parents’ preferences when it comes to technology to track and learn about their child’s developmental milestones using both qualitative and quantitative analyses. We follow the methods outlined by Value Sensitive Design (VSD) and found that the three most relevant values for information seeking are Convenience, Learning/Bonding and Trust. We also discuss how these values drive their technology preferences in tracking their child’s developmental milestones. We also present a series of design principles for information communication technology for low-SES user groups that were derived directly from our qualitative research with 51 participants. We note that although working in this unique problem space necessitated following an abridged VSD paradigm our results align with the core set of values suggested by VSD.


Value sensitive design Public sector Qualitative methods 



We thank Todd Stormant and Gregory Abowd for their advice on the project, Rushil Khurana and Naveena Karusala for help with interview transcription and analysis, and Barbara Stahnke and Marsha Canning for facilitating our research at WIC. This work was supported by NSF Award No. 1029679 and an NSF Graduate Research Fellowship under Grant No. DGE-1148903. This publication was also supported by the Disability Research and Dissemination Center (DRDC) through its Cooperative Agreement Number 5U01DD001007 from the Centers for Disease Control (CDC) and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the DRDC or the CDC.


  1. 1.
    Berger, C., Blauth, R., Boger, D., Bolster, C., Burchill, G., DuMouchel, W., Walden, D.: Kano’s methods for understanding customer-defined quality. Cent. Qual. Manage. J. 2(4), 3–35 (1993)Google Scholar
  2. 2.
    Borning, A., Muller, M.: Next steps for value sensitive design. In: CHI 2012 Conference Proceedings, pp. 1125–113. ACM Press (2012)Google Scholar
  3. 3.
    Boujarwah, F.A., Hong, H., Arriaga, R.I., Abowd, G.D., Isbell, J.: Training social problem skills in adolescents with high-functioning autism. In: Proceedings of PervasiveHealth. ACM Press (2010)Google Scholar
  4. 4.
    Boyle, C.A., Boulet, S., Schieve, L.A., Cohen, R.A., Blumberg, S.J., Yeargin-Allsopp, M., Kogan, M.D.: Trends in the prevalence of developmental disabilities in US children, Pediatrics, pp. 1997–2008 (2011)Google Scholar
  5. 5.
    Carroll, J.M., Stein, C., Byron, M., Dutram, K.: Using interactive multimedia to deliver nutrition education to maine WIC clients. J. Nutr. Educ. 28(1), 19–25 (1996)CrossRefGoogle Scholar
  6. 6.
    Dawson, G., Jones, E.J., Merkle, K., Venema, K., Lowy, R., Faja, S., Webb, S.J.: Early behavioral intervention is associated with normalized brain activity in young children with autism. J. Am. Acad. Child Adolesc. Psychiatry 51(11), 1150–1159 (2012)CrossRefGoogle Scholar
  7. 7.
    Denning, T., Borning, A., Friedman, B., Gill, B.T., Kohno, T., Maisel, W.H.: Patients, pacemakers, and implantable defibrillators: Human values and security for wireless implantable medical devices. In: CHI 2010 Conference Proceedings, pp. 917–926. ACM Press (2010)Google Scholar
  8. 8.
    Draper, V.: Mobile moms, mobile first (2013).
  9. 9.
    Dufau, S., Duñabeitia, J.A., Moret-Tatay, C., McGonigal, A., Peeters, D., Alario, F.X., Grainger, J.: Smart phone, smart science: how the use of smartphones can revolutionize research in cognitive science. PLoS ONE 6(9), e24974 (2011)CrossRefGoogle Scholar
  10. 10.
    Durkin, M.S., Maenner, M.J., Meaney, F.J., Levy, S.E., DiGuiseppi, C., Nicholas, J.S., Schieve, L.A.: Socioeconomic inequality in the prevalence of autism spectrum disorder: evidence from a US cross-sectional study. PLoS ONE 5(7), e11551 (2010)CrossRefGoogle Scholar
  11. 11.
    Fountain, C., King, M.D., Bearman, P.S.: Age of diagnosis for autism: Individual and community factors across 10 birth cohorts. J. Epidemiol. Community Health 65(6), 503–510 (2011)CrossRefGoogle Scholar
  12. 12.
    Friedman, B., Kahn Jr., P.H., Borning, A., Huldtgren, A.: Value sensitive design and information systems. In: Doorn, N., Schuurbiers, D., van de Poel, I., Gorman, M.E. (eds.) Early Engagement and New Technologies: Opening up the Laboratory, vol. 16, pp. 55–95. Springer, Netherlands (2013)Google Scholar
  13. 13.
    Georgia Department of Health. Eligibility Income Guidelines (2012).
  14. 14.
    Glaser, B.G., Strauss, A.L.: Awareness of Dying. Transaction Publishers, New York (1966)Google Scholar
  15. 15.
    Grimes, A., Grinter, R.E.: Designing Persuasion: Health Technology for Low-Income African American Communities. In: de Kort, Y.A.W., IJsselsteijn, W.A., Midden, C., Eggen, B., Fogg, B.J. (eds.) PERSUASIVE 2007. LNCS, vol. 4744, pp. 24–35. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Hong, H., Kim, J.G., Abowd, G.D., Arriaga, R.I.: Designing a social network to support the independence of young adults with autism. In: Proceedings CSCW 2012, pp. 627–636. ACM Press (2012)Google Scholar
  17. 17.
    Janssen, A.P., Tardif, R.R., Landry, S.R., Warner, J.E.: “Why tell me now?” the public and healthcare providers weigh in on pandemic influenza messages. J. Public Health Manage. Pract. 12(4), 388–394 (2006)CrossRefGoogle Scholar
  18. 18.
    Jeong, H.Y., Hayes, G.R., Yun, T.J., Sung, J.Y., Abowd, G.D., Arriaga, R.I.: Act collectively: opportunities for technologies to support low-income children with asthma. In: Proceedings of the 25th BCS Conference on Human-Computer Interaction, pp. 413–420. British Computer Society (2011)Google Scholar
  19. 19.
    Kientz, J.A., Arriaga, R.I., Chetty, M., Hayes, G.R., Richardson, J., Patel, S.N., Abowd, G.D.: Grow and know: understanding record-keeping needs for tracking the development of young children. In: CHI 2007 Conference Proceedings, pp. 1351–1360. ACM Press (2007)Google Scholar
  20. 20.
    Kientz, J.A., Arriaga, R.I., Abowd, G.D.: Baby steps: evaluation of a system to support record-keeping for parents of young children. In: CHI 2009 Conference Proceedings, pp. 1713–1722. ACM Press (2009)Google Scholar
  21. 21.
    Le Dantec, C.A., Edwards, W.K.: Designs on dignity: perceptions of technology among the homeless. In: CHI 2008 Conference Proceedings, pp. 627–636. ACM Press (2008)Google Scholar
  22. 22.
    Liptak, G.S., Benzoni, L.B., Mruzek, D.W., Nolan, K.W., Thingvoll, M.A., Wade, C.M., Fryer, G.: Disparities in diagnosis and access to health services for children with autism: data from the national survey of children’s health. J. Dev. Behav. Pediatr. 29(3), 152–160 (2008)CrossRefGoogle Scholar
  23. 23.
    Liu, L.S., Hirano, S.H., Tentori, M., Cheng, K.G., George, S., Park, S.Y., Hayes, G.R.: Improving communication and social support for caregivers of high-risk infants through mobile technologies. In: CHI 2011 Conference Proceedings, pp. 475–484. ACM Press (2011)Google Scholar
  24. 24.
    Lovaas, O.I.: Behavioral treatment and normal educational and intellectual functioning in young autistic children. J. Consult. Clin. Psychol. 55(1), 3–9 (1987)CrossRefGoogle Scholar
  25. 25.
    Moorman, J.: Leveraging the Kano model for optimal results. UX Magazine (2012).
  26. 26.
    Munteanu, C., Molyneaux, H., Maitland, J., McDonald, D., Leung, R., Fournier, H., Lumsden, J.: Hidden in plain sight: low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools. J. Pers. Ubiquit. Comput. 1–15 (2013)Google Scholar
  27. 27.
    Ngo-Metzger, Q., Hayes, G.R., Chen, Y., Cygan, R., Garfield, C.E.: Improving Communication Between Patients and Providers Using Health Information Technology and Other Quality Improvement Strategies: Focus on Low-Income Children. In: Medical Care Research and Review, p. 67 (2010)Google Scholar
  28. 28.
    Perry, A., Cummings, A., Dunn Geir, J., Freeman, N.L., Hughs, S., LaRose, L., et al.: Effectiveness of intensive behavioral intervention in a large, community-based program. In: Research in Autism Spectrum Disorders, pp. 621–642 (2008)Google Scholar
  29. 29.
    Rosenberg, R.E., Landa, R., Law, J.K., Stuart, E.A., Law, P.A.: Factors affecting age at initial autism spectrum disorder diagnosis in a national survey. In: Autism Research and Treatment (2011)Google Scholar
  30. 30.
    Siek, K.A., LaMarche, J.S., Maitland, J.: Bridging the information gap: collaborative technology design with low-income at-risk families to engender healthy behaviors. In: Proceedings OZCHI 2009, pp. 89–96. ACM Press (2009)Google Scholar
  31. 31.
    Suh, H., Porter, J.R., Hiniker, A., Kientz, J.A.: @ BabySteps: design and evaluation of a system for using twitter for tracking children’s developmental milestones. In: CHI 2014 Conference Proceedings, pp. 2279–2288 ACM Press (2014)Google Scholar
  32. 32.
    Text4baby.: Text4baby research and evaluation (2012).
  33. 33.
    Thomas, P., Zahorodny, W., Peng, B., Kim, S., Jani, N., Halperin, W., Brimacombe, M.: The association of autism diagnosis with socioeconomic status. Autism 16(2), 201–213 (2012)CrossRefGoogle Scholar
  34. 34.
    Trepka, M.J., Newman, F.L., Huffman, F.G., Dixon, Z.: Food safety education using an interactive multimedia kiosk in a WIC setting: correlates of client satisfaction and practical issues. J. Nutr. Educ. Behav. 42(3), 202–207 (2010)CrossRefGoogle Scholar
  35. 35.
    U.S. Department of Education.: Part C Child Count (1997–2006).
  36. 36.
    U.S. Department of Agriculture.: About WIC’s Mission (2013).
  37. 37.
    Volkmar, F.R., Paul, R., Klin, A., Cohen, D.J.: Handbook of Autism and Pervasive Developmental Disorders, Diagnosis, Development, Neurobiology, and Behavior. Wiley, New York (2005)CrossRefGoogle Scholar
  38. 38.
    Warren, Z., Stone, W.L.: Why Is Early Intervention Important in ASC? Autism Spectrum Conditions: FAQs on Autism, Asperger Syndrome, and Atypical Autism Answered by International Experts, p.167 (2011)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.IBM WatsonIBMPittsburghUSA
  2. 2.School of Interactive ComputingGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations