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Diversity and Inclusion in Unregulated mHealth Research: Addressing the Risks

Published online by Cambridge University Press:  01 January 2021

Shawneequa Callier  and
Stephanie M. Fullerton
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Abstract

mHealth devices and applications, with their wide accessibility and ease of use, have the potential to address persistent inequities in biomedical research participation. Yet, while mHealth technologies may facilitate more inclusive research participation, negative features of some unregulated use in research — misleading enrollment practices, the promotion of secondary mHealth applications, discriminatory profiling, and poorer quality feedback due to dependencies on biased data and algorithms — may threaten the trust and engagement of underrepresented individuals and communities. To maximize the participation of currently disenfranchised groups, those involved in unregulated mHealth research must become aware of potential risks, adopt targeted education policies, audit algorithms for hidden biases, and engage citizen scientists and other community members to identify and forestall possible harms.

Type
Symposium Articles
Information
Journal of Law, Medicine & Ethics , Volume 48 , Issue S1: Unregulated Health Research Using Mobile Devices , Spring 2020 , pp. 115 - 121
Copyright
Copyright © American Society of Law, Medicine and Ethics 2020

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References

Konkel, L., “Racial and Ethnic Disparities in Research Studies: The Challenge of Creating More Diverse Cohorts,” Environmental Health Perspectives 123, no. 12: (2015): A297-302; Society for Women’s Health Research, Office of Women’s Health, Food and Drug Administration (FDA), “Successful Strategies for Engaging Women and Minorities in Clinical Trials,” available at <https://www.fda.gov/media/84982/download> (last visited February 10, 2020).CrossRefGoogle Scholar
See United States Census Bureau, “Quick Facts,” available at <https://www.census.gov/quickfacts/fact/table/US/PST045218> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
James, D.C. and Harville, C., “Barriers and Motivators to Participating in mHealth Research among African American Men,” American Journal of Men’s Health 11, no. 6 (2017): 1605-1613; Anderson-Lewis, C. et al., “mHealth Technology Use and Implications in Historically Underserved and Minority Populations in the United States: Systematic Literature Review,” JMIR mHealth and uHealth 6, no. 6 (2018): e128.CrossRefGoogle Scholar
Montgomery, K., Chester, J., and Kopp, K., “Health Wearable Devices in the Big Data Era: Ensuring Privacy, Security, and Consumer Protection,” Center for Digital Democracy Report (2016), available at <https://www.democraticmedia.org/sites/default/files/field/public/2016/aucdd_wearablesre-port_final121516.pdf> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
See James and Harville, supra note 3; Anderson-Lewis et al., supra note 3; Nebeker, C. et al., “Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists,” JMIR mHealth and uHealth 5, no. 6 (2017): e87.CrossRefGoogle Scholar
Andrews, L. et al., “Virtual Clinical Trials: One Step Forward, Two Steps Back,” Journal of Health Care Law & Policy 19, no. 2 (2017): 189-245; see Montgomery, Chester, and Kopp, supra note 4.Google Scholar
Zou, J. and Schiebinger, L., “AI Can Be Sexist and Racist - It’s Time to Make It Fair,” Nature 559, no. 7714 (2018): 324-326.CrossRefGoogle Scholar
Fullerton, S.M., “The Input-Output Problem: Whose DNA Do We Study, and Why Does It Matter?” in Achieving Justice in Genomic Translation: Re-thinking the Pathway to Benefit, Burke, W., Edwards, K.A., and Goering, S., eds. (New York: Oxford University Press, 2011): at 40-55.Google Scholar
See All of Us Research Program Investigators, et al., “The ‘All of Us’ Research Program,” New England Journal of Medicine 381, no. 7 (2019): 668-676.CrossRefGoogle Scholar
See National Human Genome Research Institute, “The Population Architecture Using Genomics and Epidemiology (PAGE) Consortium,” available at <https://www.genome.gov/Funded-Programs-Projects/Population-Architecture-Using-Genomics-and-Epidemiology> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
See Human Heredity and Health in Africa, available at <https://h3africa.org/> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
Taliun, D. et al., “Sequencing of 53,831 Diverse Genomes from the NHLBI TOPMed Program,” BioRxiv (2019): 563866; see National Heart, Lung and Blood Institute (NHLBI), “The Trans-Omics for Precision Medicine (TOPMed) program,” available at https://www.nhlbiwgs.org/ (last visited October 22, 2019).Google Scholar
Zhang, S., “23andMe Wants Its DNA Data to Be Less White,” The Atlantic, April 23, 2018, available at <https://www.the-atlantic.com/science/archive/2018/04/23andme-diversity-dna/558575/> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
James, D.C. et al., “Participation of African Americans in E-Health and M-Health Studies: A Systematic Review,” Tele-medicine Journal and E-Health 23, no. 5 (2017): 351-364; Krebs, P. and Duncan, D.T., “Health App Use among US Mobile Phone Owners: A National Survey,” JMIR mHealth and uHealth 3, no. 4 (2015): e101.CrossRefGoogle Scholar
Anderson-Lewis et al., supra note 3.Google Scholar
Jones, B.L. et al., “If We Would Only Ask: How Henrietta Lacks Continues to Teach Us about Perceptions of Research and Genetic Research among African Americans Today,” Journal of Racial and Ethnic Health Disparities 4, no. 4 (2017): 735-745.CrossRefGoogle Scholar
See Andrews et al., supra note 6.Google Scholar
See Id.Google Scholar
Gehlert, S. and Mozersky, J., “Seeing Beyond the Margins: Challenges to Informed Inclusion of Vulnerable Populations in Research,” Journal of Law, Medicine & Ethics 46, no. 1 2018: 30-43.CrossRefGoogle Scholar
See James, D.C. et al., “You Have to Approach Us Right: A Qualitative Framework Analysis for Recruiting African Americans Into mHealth Research,” Health Education & Behavior 44, no. 5 (2017): 781-790; James, D.C. and Harville, C., “Smart-phone Usage, Social Media Engagement, and Willingness to Participate in mHealth Weight Management Research among African American Women,” Health Education & Behavior 45, no. 3 (2018): 315-322; James, D.C. et al., “Willingness of African American Women to Participate in E-Health/M-Health Research,” Telemedicine Journal and E-Health 22, no. 3 (2016): 191-197.CrossRefGoogle Scholar
Krebs and Duncan, supra note 14.Google Scholar
Andrews, L., “A New Privacy Paradigm in the Age of Apps,” Wake Forest Law Review 53, no. 3 (2018): 421-478.Google Scholar
Montgomery, Chester, and Kopp, supra note 4; Spring, B. et al., “Healthy Apps: Mobile Devices for Continuous Monitoring and Intervention,” IEEE Pulse 4, no. 6 (2013): 34-40.CrossRefGoogle Scholar
See James et al., supra note 14.Google Scholar
See Andrews et al., supra note 6; see also Montgomery, Chester, and Kopp, supra note 4.Google Scholar
See Andrews et al., supra note 6.Google Scholar
See Montgomery, Chester, and Kopp, supra note 4.Google Scholar
Id.Google Scholar
Id.; see also Andrews et al., supra note 6.Google Scholar
See Montgomery, Chester, and Kopp, supra note 4.Google Scholar
See Andrews et al., supra note 6.Google Scholar
See generally United States Government Accountability Office, “Insurance Markets: Benefits and Challenges Presented by Innovative Uses of Technology,” GAO-19-423, Published: June 7, 2019; see Dongjing, H. et al., “Security Concerns in Android mHealth Apps,” in AMIA Annual Symposium Proceedings, vol. 2014, at 645 (American Medical Informatics Association, 2014); see also Blenner, S.R. et al., “Privacy Policies of Android Diabetes Apps and Sharing of Health Information,” Journal of the American Medical Association 315, no. 10 (2016): 1051-1052.Google Scholar
See Andrews et al., supra note 6.Google Scholar
See Montgomery, Chester, and Kopp, supra note 4.Google Scholar
Id.Google Scholar
Id.Google Scholar
Id.; Hayden, E.C., “Mobile-Phone Health Apps Deliver Data Bounty,” Nature 531, no. 7595 (2016): 422-423.CrossRefGoogle Scholar
Id.; see Andrews et al., supra note 6.Google Scholar
Perrin, A. and Turner, E., “Smartphones Help Blacks, Hispanics Bridge Some – But Not All – Digital Gaps with Whites,” Pew Research Center, August 20, 2019, available at <www.pewre-search.org/fact-tank/2019/08/20/smartphones-help-blacks-hispanics-bridge-some-but-not-all-digital-gaps-with-whites> (last visited February 10, 2020).Google Scholar
See Andrews et al., supra note 6.Google Scholar
See Montgomery, Chester, and Kopp, supra note 4, at 64.Google Scholar
Id.Google Scholar
See Montgomery, Chester, and Kopp, supra note 4.Google Scholar
See Andrews et al., supra note 6.Google Scholar
Krebs and Duncan, supra note 14.Google Scholar
Id.Google Scholar
Smith, A., “Smartphone Ownership 2013,” June 5, 2013, available at <https://www.pewinternet.org/2013/06/05/smart-phone-ownership-2013/> (last visited October 22, 2019).+(last+visited+October+22,+2019).>Google Scholar
See Montgomery, Chester, and Kopp, supra note 4.Google Scholar
See Konkel, supra note 1; FDA supra note 1.Google Scholar
Fullerton, supra note 8.Google Scholar
National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, and Committee on the Return of Individual-Specific Research Results Generated in Research Laboratories (Washington, D.C.: The National Academies Press, 2018).Google Scholar
Manrai, A.K., Patel, C.J., and Ioannidis, J.P.A., “In the Era of Precision Medicine and Big Data, Who Is Normal?” Journal of the American Medical Association 319, no. 19 (2018): 1981-1982; Dobkin, B.H. and Martinez, C., “Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice,” Current Neurology and Neuroscience Reports 18, no. 12 (2018): 87-018-0896-5; Cheung, C., Krahn, A.D., and Andrade, J.G., “The Emerging Role of Wearable Technologies in Detection of Arrhythmia,” Canadian Journal of Cardiology 34, no. 8 (2018): 1083-1087.CrossRefGoogle Scholar
De La Vega, F.M. and Bustamante, C.D., “Polygenic Risk Scores: A Biased Prediction?” Genome Medicine 10, no. 1 (2018):100-018-0610-x; Martin, A.R. et al., “Clinical Use of Current Polygenic Risk Scores May Exacerbate Health Disparities,” Nature Genetics 51, no. 4 (2019): 584-591.CrossRefGoogle Scholar
J. Zou and L. Schiebinger, supra note 12.Google Scholar
See Nebeker, C. et al., “Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists,” Journal of Medical Internet Research mHealth and uHealth 5, no. 6 (2017): e87.CrossRefGoogle Scholar
Pew Research Center, “Internet/Broadband Fact Sheet,” Internet & Technology June 12, 2019, available at <https://www.pewinternet.org/fact-sheet/internet-broadband/> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
Lunn, M.R. et al., “Using Mobile Technology to Engage Sexual and Gender Minorities in Clinical Research,” PloS One 14, no. 5 (2019): e0216282.CrossRefGoogle Scholar
Merriam-Webster Dictionary, “Gordian Knot,” available at <https://www.merriam-webster.com/dictionary/Gordian%20knot> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
Rothstein, M.A. et al., “Unregulated Health Research Using Mobile Devices: Ethical Considerations and Policy Recommendations,” Journal of Law, Medicine & Ethics 48, no. 1, Suppl. 1 (2020): 196-226.CrossRefGoogle Scholar
Mays, V.M., “The Legacy of the U. S. Public Health Services Study of Untreated Syphilis in African American Men at Tuskegee on the Affordable Care Act and Health Care Reform Fifteen Years After President Clinton’s Apology,” Ethics & Behavior 22, no. 6 (2012): 411-418; Garrison, N.A., “Genomic Justice for Native Americans: Impact of the Havasupai Case on Genetic Research,” Science, Technology & Human Values 38, no. 2 (2013): 201-223.CrossRefGoogle Scholar
Smirnoff, M.I. et al., “A Paradigm for Understanding Trust and Mistrust in Medical Research: The Community VOICES Study,” American Journal of Bioethics Empirical Bioethics 9, no. 1 (2018): 39-47.CrossRefGoogle Scholar
See Rothstein et al. supra note 59.Google Scholar
Matthews, A.K. et al., “A Community Engagement Advisory Board as a Strategy to Improve Research Engagement and Build Institutional Capacity for Community-Engaged Research,” Journal of Clinical and Translational Science 2, no. 2 (2018): 66-72; Newman, S.D. et al., “Community Advisory Boards in Community-Based Participatory Research: A Synthesis of Best Processes,” Preventing Chronic Disease 8, no. 3 (2011): A70; Treem, J.W. et al., “Exploring the Potential Role of Community Engagement in Evaluating Clinical and Translational Science Grant Proposals,” Journal of Clinical and Translational Science 2, no. 3 (2018): 139-146.CrossRefGoogle Scholar
Njie-Carr, V.P. et al., “Leveraging Community Engagement to Develop a Mobile Health Application for Older Women with HIV Infection,” Journal of Obstetric, Gynecologic & Neonatal Nursing 47, no. 6 (2018): 833-843; Yingling, L.R. et al., “Community Engagement to Optimize the Use of Web-based and Wearable Technology in a Cardiovascular Health and Needs Assessment Study: A Mixed Methods Approach,” Journal of Medical Internet Research mHealth and uHealth 4, no. 2 (2016): e38.CrossRefGoogle Scholar
See Rothstein et al., supra note 59.Google Scholar
See Fullerton, supra note 8.Google Scholar
See Rothstein et al., supra note 59.Google Scholar
Woolley, J.P. et al., “Citizen Science Or Scientific Citizenship? Disentangling the Uses of Public Engagement Rhetoric in National Research Initiatives,” BMC Medical Ethics 17, no. 1 (2016): 33-016-0117-1; Evans, B.J., “Barbarians at the Gate: Consumer-Driven Health Data Commons and the Transformation of Citizen Science,” American Journal of Law and Medicine 42, no. 4 (2018): 651-685.CrossRefGoogle Scholar
Fussell, S., “How an Attempt at Correcting Bias in Tech Goes Wrong,” available at <https://www.theatlantic.com/technology/archive/2019/10/google-allegedly-used-homeless-train-pixel-phone/599668/> (last visited February 10, 2020).+(last+visited+February+10,+2020).>Google Scholar
Benjamin, R., Race after Technology: Abolitionist Tools for the New Jim Code (Cambridge, UK: Polity Press, 2019): at 5.Google Scholar
Id., at 5-7.Google Scholar