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      Technology and health inequities in diabetes care: How do we widen access to underserved populations and utilize technology to improve outcomes for all?

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          Abstract

          Digital health technologies are being utilized increasingly in the modern management of diabetes. These include tools such as continuous glucose monitoring systems, connected blood glucose monitoring devices, hybrid closed-loop systems, smart insulin pens, telehealth, and smartphone applications (apps). Although many of these technologies have a solid evidence base, from the perspective of a person living with diabetes, there remain multiple barriers preventing their optimal use, creating a digital divide. In this article, we describe many of the origins of these barriers and offer recommendations on widening access to digital health technologies for underserved populations living with diabetes to improve their health outcomes.

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          Social Determinants of Health and Diabetes: A Scientific Review

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            Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial

            Summary Background Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes. Methods In this multicentre, open-label, randomised controlled trial, we recruited women aged 18–40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527. Findings Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference −0·19%; 95% CI −0·34 to −0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy). Interpretation Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use. Funding Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research.
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              Digital inclusion as a social determinant of health

              The use of digital tools and applications is steadily increasing and can support a range of health information needs 1–3 . As tools such as patient portals, health trackers, and remote monitoring devices see greater use, research suggests that tools such as health apps and patient portals can foster greater patient engagement, better support for patients outside of the clinic visit, and can improve health outcomes 3–9 . However, greater reliance on digital tools has the potential to increase disparities between those who have skills and access to digital tools and those who do not and thereby existing health disparities. According to a recent Brookings Institution report, 15–24% of Americans lack any sort of broadband connection to the Internet with which to use mobile health technology. These differences only increase when examining the issue by income groups: 38% of households earning less than $20,000 lack a broadband subscription 10 . The digital divide by income exists in both rural and urban areas. As practitioners working at the intersection of digital inclusion and health, we would like to highlight some less visible dimensions of the digital divide and offer suggestions to facilitate digital inclusion and ensure equitable and impactful adoption of mobile health technologies. Digital literacies and Internet connectivity have been called the “super social determinants of health” because they address all other social determinants of health (SDOH), as shown in Fig. 1 11 . For example, applications for employment, housing, and other assistance programs, each of which influences an individual’s health, are increasingly, and sometimes exclusively, accessible online. The costs of equipping a person to use the Internet are substantially lower than treating health conditions and the benefits are persistent and significant 12 , making the efforts to improve digital literacy skills and access valuable tools to reduce disparities. Fig. 1 Digital literacies and social determinants of health. Digital literacy and access, including skills, connectivity, devices and training and technical support, relate to all other domains of social determinants of health. With these challenges in mind, we offer the following recommendations. First, healthcare systems should adopt a digital inclusion-informed strategy regarding mobile health that (1) recognizes their community’s level of access to devices and Internet connectivity and (2) supports patients in their initial and sustained technology use. Digital inclusion refers to the activities necessary to ensure equitable access to and use of information and communication technologies, including (1) affordable broadband Internet service, (2) Internet-enabled devices, (3) access to digital literacy training, (4) quality technical support, and (5) applications and online content designed to enable and encourage self-sufficiency, participation, and collaboration 13 . These form the foundation for use of mobile technology in healthcare. While knowing whether an individual’s access is important, it is vital for health systems to understand the larger environment shaping patients’ digital experience. Adoption rates are nearing ubiquity among highly educated individuals with at least moderate income, but important pockets of nonadoption remain. Most mobile health technology requires a data plan and/or home broadband, yet the American Community Survey shows that 40% of low-income households lack a subscription, requiring them to use limited cell plan data or local public wifi hotspots 12 . These options may appear affordable but they contain important limitations. Using prepaid plans, patients may run out of data or need to prioritize data for specific uses. Even with their lower cost, they may still be unaffordable, particularly for families in need of multiple devices. Open wifi access points are another option but may only be available in public locations in which patients may feel uncomfortable accessing their personal health information. Prior to the rapid increase in telehealth use due to COVID-19, patient portals to their electronic health record (EHR) were the most common form of mobile health and a gateway to other mobile health applications. However, studies show that lack of Internet access is a leading factor inhibiting use of patient portals 14 . Smartphones may seem to be a logical and ubiquitous substitute for home Internet, but significant gaps still exist for rural, poor, and older adults. Research shows that nearly one-half of older adults and 30% of those earning less than $30,000 own a smartphone and many low-income households share devices, raising both access and privacy issues 15 . Understanding the nuances of access in the communities they serve can help healthcare systems implement more inclusive strategies. Digitally inclusive strategies of health system adoption also support patients in their use of technology at all levels and should include digital skill training, particularly for recent adopters of technology or those who may have devices with limited features. Patients may also need assistance with setting up email and patient portal accounts. In addition, it is critical to provide ongoing support for patients, reduce medical jargon, and provide interpretive resources, and ensure that technology and training are offered equitably to all patients, not just to those who are confident enough to request help 16 . Second, we recommend systematically assessing individual patients’ access and digital literacies. This became particularly clear since the rapid and pervasive shift to telehealth during the COVID-19 pandemic. Simply asking patients what devices they own and how they access the Internet is not typical in the clinical context, but this information can shape the type of technology a clinician can recommend. The lack of routine assessment prior to COVID-19 meant that some patients fell between the cracks as care shifted to nearly all virtual 17 . Incorporating this and other SDOH into the EHR encourages more consistent documentation and allows assessment of population-level metrics of access 18 . When digital skill and connectivity gaps are assessed systematically and universally, a health system can document overall population-level metrics, examine disparities, and track changes over time. Third, health systems should partner with community organizations with expertise in training in digital literacy skills and facilitating connectivity. Libraries not only offer the Internet but also provide a spectrum of training services from basic digital literacies to skills required for specific devices and applications. Some communities have leveraged community health workers and patient navigators to screen and refer patients for gaps in basic digital literacies and connectivity 19,20 . They can provide hands-on training in the use of mobile health technologies for patients who do have adequate digital access. Allied health professional education programs leverage a “train the trainer” model to prepare the future healthcare workforce to undertake these tasks 21–23 . The National Digital Inclusion Alliance (NDIA) offers a comprehensive list of organizations across the country that provide digital literacy training and national and local resources for free/low-cost Internet and computers 13 . Mobile health technologies hold significant promise to increase the efficiency of care and improve health outcomes. Yet, we must be cognizant of their potential to increase health disparities. National efforts have been undertaken to promote broadband, such as the Federal Communications Commission’s (FCC) Lifeline program that subsidizes the cost of smartphones and Internet service for low-income individuals 24,25 . However, the Lifeline Program’s impact is limited by low consumer awareness, and the qualification process varies by state and by the service provider. In addition, Internet service may still be unaffordable even with the monthly subsidy. Another program, the Federal Broadband Opportunities Program, supported over 4 million people to get online for the first time with a $4 billion program but those one-time dollars are long gone, leaving a gap in the need for adult digital literacy support. BTOP has only two remaining operational programs with no new funding on the horizon 25,26 . In response to the current COVID-19 pandemic, the FCC also introduced a variety of programs to increase Internet access for the use of telehealth, including paying for devices and access. However, the future of these programs after the COVID-19 pandemic is unclear 13 . As clinical care incorporates more technology in more contexts, we suggest the recommendations above to facilitate equitable adoption of mobile health technology.
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                Author and article information

                Journal
                100883645
                22008
                Diabetes Obes Metab
                Diabetes Obes Metab
                Diabetes, obesity & metabolism
                1462-8902
                1463-1326
                17 April 2024
                March 2024
                31 January 2024
                24 April 2024
                : 26
                : Suppl 1
                : 3-13
                Affiliations
                [1 ]T1D Exchange, Boston, Massachusetts, USA
                [2 ]Department of Population Health, University of Mississippi, Jackson, Mississippi, USA
                [3 ]Section of Endocrinology, Diabetes and Nutrition, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
                [4 ]Alta Bates Summit Medical Centre, Sutter East Bay Medical Foundation, Oakland, California, USA
                [5 ]Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA
                [6 ]Centre for Health System Research, Sutter Health, Santa Barbara, California, USA
                Author notes

                AUTHOR CONTRIBUTIONS

                David Kerr conceptualized the manuscript. All authors contributed to different sections of the manuscript, reviewed, edited, and approved the final version.

                Correspondence: David Kerr, MD, Centre for Health System Research, Sutter Health, 2121 N California Blvd., Suite 310, Walnut Creek, CA 94596, USA. david.kerr@ 123456sutterhealth.org
                Author information
                http://orcid.org/0000-0002-8473-249X
                http://orcid.org/0000-0003-1335-1857
                Article
                NIHMS1983648
                10.1111/dom.15470
                11040507
                38291977
                87c14c7e-59c9-4b82-9b17-cd1c65829cb1

                This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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                Article

                Endocrinology & Diabetes
                digital divide,digital health,digital literacy,health equity
                Endocrinology & Diabetes
                digital divide, digital health, digital literacy, health equity

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