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      Usability of a Novel Mobile Health iPad App by Vulnerable Populations

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          Abstract

          Background

          Recent advances in mobile technologies have created new opportunities to reach broadly into populations that are vulnerable to health disparities. However, mobile health (mHealth) strategies could paradoxically increase health disparities, if low socioeconomic status individuals lack the technical or literacy skills needed to navigate mHealth programs.

          Objective

          The aim of this study was to determine whether patients from vulnerable populations could successfully navigate and complete an mHealth patient decision aid.

          Methods

          We analyzed usability data from a randomized controlled trial of an iPad program designed to promote colorectal cancer (CRC) screening. The trial was conducted in six primary care practices and enrolled 450 patients, aged 50-74 years, who were due for CRC screening. The iPad program included a self-survey and randomly displayed either a screening decision aid or a video about diet and exercise. We measured participant ability to complete the program without assistance and participant-rated program usability.

          Results

          Two-thirds of the participants (305/450) were members of a vulnerable population (limited health literacy, annual income < US $20,000, or black race). Over 92% (417/450) of the participants rated the program highly on all three usability items (90.8% for vulnerable participants vs 96.6% for nonvulnerable participants, P=.006). Only 6.9% (31/450) of the participants needed some assistance to complete the program. In multivariable logistic regression, being a member of a vulnerable population was not associated with needing assistance. Only older age, less use of text messaging (short message service, SMS), and lack of Internet use predicted needing assistance.

          Conclusions

          Individuals who are vulnerable to health disparities can successfully use well-designed mHealth programs. Future research should investigate whether mHealth interventions can reduce health disparities.

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          Most cited references33

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          The social determinants of health: coming of age.

          In the United States, awareness is increasing that medical care alone cannot adequately improve health overall or reduce health disparities without also addressing where and how people live. A critical mass of relevant knowledge has accumulated, documenting associations, exploring pathways and biological mechanisms, and providing a previously unavailable scientific foundation for appreciating the role of social factors in health. We review current knowledge about health effects of social (including economic) factors, knowledge gaps, and research priorities, focusing on upstream social determinants-including economic resources, education, and racial discrimination-that fundamentally shape the downstream determinants, such as behaviors, targeted by most interventions. Research priorities include measuring social factors better, monitoring social factors and health relative to policies, examining health effects of social factors across lifetimes and generations, incrementally elucidating pathways through knowledge linkage, testing multidimensional interventions, and addressing political will as a key barrier to translating knowledge into action.
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            Social determinants of health inequalities.

            The gross inequalities in health that we see within and between countries present a challenge to the world. That there should be a spread of life expectancy of 48 years among countries and 20 years or more within countries is not inevitable. A burgeoning volume of research identifies social factors at the root of much of these inequalities in health. Social determinants are relevant to communicable and non-communicable disease alike. Health status, therefore, should be of concern to policy makers in every sector, not solely those involved in health policy. As a response to this global challenge, WHO is launching a Commission on Social Determinants of Health, which will review the evidence, raise societal debate, and recommend policies with the goal of improving health of the world's most vulnerable people. A major thrust of the Commission is turning public-health knowledge into political action.
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              Socioeconomic disparities in health in the United States: what the patterns tell us.

              We aimed to describe socioeconomic disparities in the United States across multiple health indicators and socioeconomic groups. Using recent national data on 5 child (infant mortality, health status, activity limitation, healthy eating, sedentary adolescents) and 6 adult (life expectancy, health status, activity limitation, heart disease, diabetes, obesity) health indicators, we examined indicator rates across multiple income or education categories, overall and within racial/ethnic groups. Those with the lowest income and who were least educated were consistently least healthy, but for most indicators, even groups with intermediate income and education levels were less healthy than the wealthiest and most educated. Gradient patterns were seen often among non-Hispanic Blacks and Whites but less consistently among Hispanics. Health in the United States is often, though not invariably, patterned strongly along both socioeconomic and racial/ethnic lines, suggesting links between hierarchies of social advantage and health. Worse health among the most socially disadvantaged argues for policies prioritizing those groups, but pervasive gradient patterns also indicate a need to address a wider socioeconomic spectrum-which may help garner political support. Routine health reporting should examine socioeconomic and racial/ethnic disparity patterns, jointly and separately.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                April 2017
                11 April 2017
                : 5
                : 4
                : e43
                Affiliations
                [1] 1Wake Forest School of Medicine Department of Internal Medicine Winston-Salem, NCUnited States
                [2] 2Wake Forest School of Medicine Department of Social Sciences & Health Policy Winston-Salem, NCUnited States
                [3] 3Wake Forest School of Medicine Department of Biostatistical Sciences Winston-Salem, NCUnited States
                [4] 4Wake Forest Health Sciences Enterprise Information Management Winston-Salem, NCUnited States
                [5] 5University of Texas Dell Medical School Department of Internal Medicine Austin, TXUnited States
                [6] 6Wake Forest School of Medicine Department of Family & Community Medicine Winston-Salem, NCUnited States
                Author notes
                Corresponding Author: David P Miller Jr dmiller@ 123456wakehealth.edu
                Author information
                http://orcid.org/0000-0001-7879-4427
                http://orcid.org/0000-0001-9006-4064
                http://orcid.org/0000-0002-5706-4377
                http://orcid.org/0000-0003-1899-5731
                http://orcid.org/0000-0001-6737-9818
                http://orcid.org/0000-0002-8498-0792
                http://orcid.org/0000-0002-6657-7342
                http://orcid.org/0000-0002-7949-7050
                Article
                v5i4e43
                10.2196/mhealth.7268
                5405290
                28400354
                8994c56e-675a-4264-a8e5-39d1e51c950f
                ©David P Miller Jr, Kathryn E Weaver, L Doug Case, Donald Babcock, Donna Lawler, Nancy Denizard-Thompson, Michael P Pignone, John G Spangler. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.04.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 5 January 2017
                : 28 January 2017
                : 7 February 2017
                : 9 March 2017
                Categories
                Original Paper
                Original Paper

                decision support techniques,technology assessment,primary care,health literacy,vulnerable populations

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