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• W2893594082 abstract "The growing burden of diabetes is fueled by obesity-inducing lifestyle behaviors including high-calorie diets and lack of physical activity. Challenges in access to diabetes specialists and educators, low adherence to medications, and inadequate motivational support for proper disease self-management contribute to poor glycemic control in patients with diabetes. Simultaneously, high patient volumes and low reimbursement rates limit physicians' time spent on lifestyle behavior counseling. These barriers to efficient diabetes care lead to high rates of diabetes-related complications, driving healthcare costs up and reducing the quality of patients' lives. Considering recent advancements in healthcare delivery technologies such as smartphone applications, telemedicine, m-health, device connectivity, machine-learning technology, and artificial intelligence, there is significant opportunity to achieve better efficiency in diabetes care and increase patient involvement in diabetes self-management, which ultimately may put an end to soaring diabetes-related healthcare expenditures. This review explores the patient-driven diabetes care of the future in the technology era. The growing burden of diabetes is fueled by obesity-inducing lifestyle behaviors including high-calorie diets and lack of physical activity. Challenges in access to diabetes specialists and educators, low adherence to medications, and inadequate motivational support for proper disease self-management contribute to poor glycemic control in patients with diabetes. Simultaneously, high patient volumes and low reimbursement rates limit physicians' time spent on lifestyle behavior counseling. These barriers to efficient diabetes care lead to high rates of diabetes-related complications, driving healthcare costs up and reducing the quality of patients' lives. Considering recent advancements in healthcare delivery technologies such as smartphone applications, telemedicine, m-health, device connectivity, machine-learning technology, and artificial intelligence, there is significant opportunity to achieve better efficiency in diabetes care and increase patient involvement in diabetes self-management, which ultimately may put an end to soaring diabetes-related healthcare expenditures. This review explores the patient-driven diabetes care of the future in the technology era. Non-communicable diseases (NCDs), including obesity, hypertension, hyperlipidemia, and diabetes, account for 88% of deaths in the US (World Health Organization, 2014World Health Organization Noncommunicable Diseases (NCD) Country Profiles, United States of America.http://www.who.int/nmh/countries/usa_en.pdfDate: 2014Google Scholar, World Health Organization, 2017World Health Organization Noncommunicable Diseases Fact Sheet.http://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseasesDate: 2017Google Scholar). Over the past several decades, NCDs have become the leading cause of preventable illness and disability in the country, as well as an extraordinary financial strain on the US healthcare system. As disease burden shifted from communicable diseases to NCDs, the percentage of the nation's gross domestic product spent on healthcare climbed from 4% in 1972 to 17.9% in 2016 (Fries et al., 1993Fries J.F. Koop C.E. Beadle C.E. Cooper P.P. England M.J. Greaves R.F. Sokolov J.J. Wright D. Reducing health care costs by reducing the need and demand for medical services. The Health Project Consortium.N. Engl. J. Med. 1993; 329: 321-325Crossref PubMed Scopus (323) Google Scholar, Hartman et al., 2018Hartman M. Martin A.B. Espinosa N. Catlin A. The National Health Expenditure Accounts TeamNational health care spending in 2016: spending and enrollment growth slow after initial coverage expansions.Health Aff. (Millwood). 2018; 37: 150-160Crossref PubMed Scopus (118) Google Scholar). So far, preventative efforts in the US have not been able to alleviate this complex epidemic, the growth of which is catalyzed by the population's consumption of nutrient-poor and calorie-rich foods and its increasingly sedentary lifestyle (Hruby and Hu, 2015Hruby A. Hu F.B. The epidemiology of obesity: a big picture.PharmacoEconomics. 2015; 33: 673-689Crossref PubMed Scopus (1409) Google Scholar). As healthcare providers attempt to treat a growing number of chronically ill patients, the current outpatient care model should be re-evaluated to identify ways to empower patients in their disease self-management while simultaneously providing them with more efficient and cost-effective healthcare. Given its dependence on diet and exercise, its wide array of complications across the body's physiological systems, and its need for daily monitoring, diabetes is perhaps the most prominent example of a highly prevalent chronic disease that demands a patient's active continuous role in its management. The Centers for Disease Control and Prevention (CDC) estimate that 30.3 million people in the US (9.4% of the population) have diabetes, of which 90%–95% have type 2 diabetes (T2D) (CDC, 2017CDCNational Diabetes Statistics Report.https://www.cdc.gov/diabetes/data/statistics/statistics-report.htmlDate: 2017Google Scholar). An additional 84.1 million people in the US are estimated to have prediabetes, defined as having a fasting blood glucose between 100 and 125 mg/dL or a hemoglobin A1c (HbA1c) of 5.7%–6.4%, putting these individuals at high risk of T2D in the future (CDC, 2017CDCNational Diabetes Statistics Report.https://www.cdc.gov/diabetes/data/statistics/statistics-report.htmlDate: 2017Google Scholar, Tabák et al., 2012Tabák A.G. Herder C. Rathmann W. Brunner E.J. Kivimäki M. Prediabetes: a high-risk state for developing diabetes.Lancet. 2012; 379: 2279-2290Abstract Full Text Full Text PDF PubMed Scopus (1499) Google Scholar). Self-management of this metabolic condition requires patients to commit to significant behavioral and lifestyle changes, making treatment a challenge from the healthcare providers' perspective. Moving forward, patient-driven care––characterized by increased communication of information, transparency, customization of care, and collaboration with patients (Swan, 2009Swan M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking.Int. J. Environ. Res. Public Health. 2009; 6: 492-525Crossref PubMed Scopus (502) Google Scholar)––will be key to improving health outcomes and preventing diabetes complications. As diabetes medical care has focused more on monitoring patients' risk factors and screening for complications in their early stages, some improvements have been made to date in patient health outcomes. Between 1999 and 2010, more patients had achieved target glycemic level (HbA1c < 7%), target low-density lipoprotein cholesterol (LDL-C) level (<100 mg/dL), and target blood pressure (130/80 mmHg) (Ali et al., 2013Ali M.K. Bullard K.M. Saaddine J.B. Cowie C.C. Imperatore G. Gregg E.W. Achievement of goals in U.S. diabetes care, 1999–2010.N. Engl. J. Med. 2013; 368: 1613-1624Crossref PubMed Scopus (731) Google Scholar). Despite these improvements, it was estimated that 33%–49% of diagnosed T2D patients in 2010 still had not met one or more of these targets (Ali et al., 2013Ali M.K. Bullard K.M. Saaddine J.B. Cowie C.C. Imperatore G. Gregg E.W. Achievement of goals in U.S. diabetes care, 1999–2010.N. Engl. J. Med. 2013; 368: 1613-1624Crossref PubMed Scopus (731) Google Scholar). Alarmingly, younger adults with T2D became less likely to meet these targets during the same period (Ali et al., 2013Ali M.K. Bullard K.M. Saaddine J.B. Cowie C.C. Imperatore G. Gregg E.W. Achievement of goals in U.S. diabetes care, 1999–2010.N. Engl. J. Med. 2013; 368: 1613-1624Crossref PubMed Scopus (731) Google Scholar). A different study identified a similar trend; between 2005 and 2014, the rate of diabetes-related preventable hospitalizations due to acute complications increased among patients who were 18–44 years old despite decreasing in older patients (Rubens et al., 2018Rubens M. Saxena A. Ramamoorthy V. Khera R. Hong J. Veledar E. Nasir K. Trends in diabetes-related preventable hospitalizations in the U.S., 2005–2014.Diabetes Care. 2018; 41: e72-e73Crossref PubMed Scopus (13) Google Scholar). These data reveal that there is still great potential for improvement in the way that we treat diabetes, especially among younger generations. In the context of a growing diabetic population, time-crunched clinicians are turning their focus to prescribing more drugs to treat their patients' health issues rather than spending time to discuss long-term disease self-management strategies that promote patient engagement in their healthcare (Wilson and Childs, 2002Wilson A. Childs S. The relationship between consultation length, process and outcomes in general practice: a systematic review.Br. J. Gen. Pract. 2002; 52: 1012-1020PubMed Google Scholar). Technological advancements in digital health designed to improve healthcare efficiency hold extraordinary promise for a better future in diabetes care. Telemedicine, defined as medical care provided remotely through audiovisual technology, is increasingly being used to treat patients who are unable to attend physician appointments in person. Furthermore, mobile health (m-health), the use of mobile technology to deliver real-time health and metabolic information, has the capacity to engage patients in their diabetes management to improve their diet, help them to lose weight, and achieve better health outcomes. By providing diabetes education, self-tracking, and physician appointments from the convenience of the patient's location, telemedicine and m-health empower patients to take greater control over their disease. Moreover, these technologies save patients time and money by reducing travel to in-person appointments and may lead to decreased overhead and administrative healthcare costs. We are currently at the forefront of healthcare delivery technology; researchers and developers are actively innovating tools to help patients self-manage their diabetes and facilitate virtual patient-physician interactions. Here, we review recent advancements in healthcare technologies, along with current findings on the cost-effectiveness of their use in patient care. Through a literature search of meta-analyses and systematic reviews published within the past 5 years, we sought examples of unique strategies through which telemedicine and m-health interventions have been delivered to patients with diabetes to replace or supplement their medical care. Original research studies on digital health conducted within the last two decades were included in our review. Furthermore, we explore the possibility of combining and expanding upon existing technologies to develop a “virtual diabetes clinic”––a comprehensive digital healthcare ecosystem that defies the boundaries of location and time––as a promising vision for the future of diabetes care. Patient-physician interactions are essential for improving health outcomes and preventing long-term complications in patients with diabetes. Studies have found that patients who had a greater frequency of patient-physician encounters, defined as a note in the patient's medical record, or had better adherence to scheduled appointments, achieved HbA1c, blood pressure, and LDL-C level targets faster and with higher success rates than patients who had less frequent interactions with their primary care physicians (Karter et al., 2004Karter A.J. Parker M.M. Moffet H.H. Ahmed A.T. Ferrara A. Liu J.Y. Selby J.V. Missed appointments and poor glycemic control: an opportunity to identify high-risk diabetic patients.Med. Care. 2004; 42: 110-115Crossref PubMed Scopus (199) Google Scholar, Morrison et al., 2011Morrison F. Shubina M. Turchin A. Encounter frequency and serum glucose level, blood pressure, and cholesterol level control in patients with diabetes mellitus.Arch. Intern. Med. 2011; 171: 1542-1550Crossref PubMed Scopus (80) Google Scholar). However, frequently traveling to clinic appointments is inconvenient for patients with busy schedules and particularly burdensome for patients living in rural or underserved areas, the elderly, and people with disabilities, leading to reduced adherence to clinic appointments (American Diabetes Association, 2018aAmerican Diabetes Association1. improving care and promoting health in populations: standards of medical care in diabetes—2018.Diabetes Care. 2018; 41: S7-S12Crossref PubMed Scopus (83) Google Scholar, Kahn, 2015Kahn J.M. Virtual visits—confronting the challenges of telemedicine.N. Engl. J. Med. 2015; 18: 1684-1685Crossref Scopus (95) Google Scholar). Virtual telemedicine appointments are becoming increasingly common to enable patients to interact with physicians and educators without the barriers of distance and commute time. The use of telemedicine visits has been well-studied in populations with limited access to specialized clinicians, registered dietitians, and diabetes educators. Telemedicine programs with visits that match usual care models for diabetes treatment have already demonstrated success in helping patients maintain or improve their health (Lee et al., 2017Lee S.W.H. Chan C.K.Y. Chua S.S. Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis.Sci. Rep. 2017; 7: 12680Crossref PubMed Scopus (87) Google Scholar). For example, a proof-of-concept randomized controlled trial on telemedicine for diabetes care that randomly assigned patients into either a telemedicine intervention group (two in-person visits and two telemedicine interactions over 12 months) or a control group (four in-person visits over 12 months) found no significant differences in HbA1c, blood pressure, blood lipids, or BMI between the two groups at 12 months (Leichter et al., 2013Leichter S.B. Bowman K. Adkins R.A. Jelsovsky Z. Impact of remote management of diabetes via computer: the 360 study–a proof-of-concept randomized trial.Diabetes Technol. Ther. 2013; 15: 434-438Crossref PubMed Scopus (23) Google Scholar). The telemedicine intervention group displayed a significantly greater reduction in mean body weight compared with the control group (Leichter et al., 2013Leichter S.B. Bowman K. Adkins R.A. Jelsovsky Z. Impact of remote management of diabetes via computer: the 360 study–a proof-of-concept randomized trial.Diabetes Technol. Ther. 2013; 15: 434-438Crossref PubMed Scopus (23) Google Scholar). A 3-year study in rural Montana also found that patients with diabetes who used telemedicine visits versus face-to-face visits had similar, positive health outcomes (Ciemins et al., 2011Ciemins E. Coon P. Peck R. Holloway B. Min S.-J. Using telehealth to provide diabetes care to patients in rural Montana: findings from the promoting realistic individual self-management program.Telemed. J. E Health. 2011; 17: 596-602Crossref PubMed Scopus (49) Google Scholar). More recently, several meta-analyses examining published diabetes telemedicine programs determined that patients participating in telemedicine interventions in fact had greater reduction in HbA1c on average when compared with patients in non-telemedicine groups (Faruque et al., 2017Faruque L.I. Wiebe N. Ehteshami-Afshar A. Liu Y. Dianati-Maleki N. Hemmelgarn B.R. Manns B.J. Tonelli M. Effect of telemedicine on glycated hemoglobin in diabetes: a systematic review and meta-analysis of randomized trials.CMAJ. 2017; 189: E341-E364Crossref PubMed Scopus (149) Google Scholar, Heitkemper et al., 2017Heitkemper E.M. Mamykina L. Travers J. Smaldone A. Do health information technology self-management interventions improve glycemic control in medically underserved adults with diabetes? a systematic review and meta-analysis.J. Am. Med. Inform. Assoc. 2017; 24: 1024-1035Crossref PubMed Scopus (63) Google Scholar, Lee et al., 2017Lee S.W.H. Chan C.K.Y. Chua S.S. Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis.Sci. Rep. 2017; 7: 12680Crossref PubMed Scopus (87) Google Scholar, Marcolino et al., 2013Marcolino M.S. Maia J.X. Alkmim M.B.M. Boersma E. Ribeiro A.L. Telemedicine application in the care of diabetes patients: systematic review and meta-analysis.PLoS One. 2013; 8: e79246Crossref PubMed Scopus (125) Google Scholar, Su et al., 2016aSu D. Zhou J. Kelley M.S. Michaud T.L. Siahpush M. Kim J. Wilson F. Stimpson J.P. Pagán J.A. Does telemedicine improve treatment outcomes for diabetes? a meta-analysis of results from 55 randomized controlled trials.Diabetes Res. Clin. Pract. 2016; 116: 136-148Abstract Full Text Full Text PDF PubMed Scopus (94) Google Scholar). Alongside these encouraging results, patients reported travel time savings and high satisfaction rates with telemedicine programs, and total clinician time spent was reduced by as much as 40% (Xu, 2018Xu T. Telemedicine in the management of type 1 diabetes.Prev. Chronic. Dis. 2018; 15: E13Crossref PubMed Scopus (59) Google Scholar, Izquierdo et al., 2003Izquierdo R.E. Knudson P.E. Meyer S. Kearns J. Ploutz-Snyder R. Weinstock R.S. A comparison of diabetes education administered through telemedicine versus in person.Diabetes Care. 2003; 26: 1002-1007Crossref PubMed Scopus (157) Google Scholar, Bashshur et al., 2015Bashshur R.L. Shannon G.W. Smith B.R. Woodward M.A. The empirical evidence for the telemedicine intervention in diabetes management.Telemed. J. E Health. 2015; 21: 321-354Crossref PubMed Scopus (57) Google Scholar, Leichter et al., 2013Leichter S.B. Bowman K. Adkins R.A. Jelsovsky Z. Impact of remote management of diabetes via computer: the 360 study–a proof-of-concept randomized trial.Diabetes Technol. Ther. 2013; 15: 434-438Crossref PubMed Scopus (23) Google Scholar). Collectively, these findings affirm that telemedicine diabetes care models may produce similar or better health outcomes than in-person care models with greater time- and cost-efficiency. Due to the capacity of telemedicine programs to be tailored to the needs of specific communities, telemedicine design can vary greatly from site to site. One study in patients from an underserved, rural South Carolina community compared patients participating in a 12-month, 13-session telemedicine intervention (involving mostly group videoconferencing sessions with a self-management education team) with control group participants who received usual care at their primary care clinic found that patients who participated in the telemedicine program had significantly lower HbA1c and LDL-C levels after 12 months compared with the control group (Davis et al., 2010Davis R.M. Hitch A.D. Salaam M.M. Herman W.H. Zimmer-Galler I.E. Mayer-Davis E.J. TeleHealth improves diabetes self-management in an underserved community.Diabetes Care. 2010; 33: 1712-1717Crossref PubMed Scopus (132) Google Scholar). Given the high frequency of visits in the telemedicine group, perhaps these positive health outcomes were to be expected. However, the study highlighted a benefit of telemedicine: the increased flexibility provided to patients to receive comprehensive diabetes education that may otherwise not be accessible within their geographic location. The authors also conducted group telemedicine visits to increase efficiency by hosting videoconferencing education sessions at a local primary care clinic and having multiple patients participate at each session. This group virtual interaction can be particularly useful for educating patients on self-management skills such as diet, exercise, and glucose monitoring in communities that lack such health services. In our opinion, telemedicine use for diabetes care will become more common as evidence shows that health outcomes are as effective as standard, in-person care, if not more effective. While heterogeneity can make comparison of existing telemedicine programs challenging, evidence demonstrates that telemedicine presents an opportunity to both patients and providers for improved access to care as well as more convenient interactions, which is particularly important in the context of clinicians' time and resource limitations, and patients' already extensive diabetes self-management responsibilities. Given the promising findings thus far, virtual appointments will undoubtedly continue to grow in popularity as they become available to patients across the globe. The diabetes clinic of the future may have no walls. Telemedicine programs are also beneficial when focused on specific complications of diabetes, such as diabetic retinopathy (DR), the leading cause of legal blindness among US adults. Several major risk factors for DR progression can be managed, including poor glycemic control, hypertension, and dyslipidemia (Yau et al., 2012Yau J.W.Y. Rogers S.L. Kawasaki R. Lamoureux E.L. Kowalski J.W. Bek T. Chen S.-J. Dekker J.M. Fletcher A. Grauslund J. et al.Global prevalence and major risk factors of diabetic retinopathy.Diabetes Care. 2012; 35: 556-564Crossref PubMed Scopus (2745) Google Scholar). Despite an estimated 86% of type 1 diabetes (T1D) patients and 40% of T2D patients having DR (Kempen et al., 2004Kempen J.H. O’Colmain B.J. Leske M.C. Haffner S.M. Klein R. Moss S.E. Taylor H.R. Hamman R.F. Eye Diseases Prevalence Research GroupThe prevalence of diabetic retinopathy among adults in the United States.Arch. Ophthalmol. 2004; 122: 552-563Crossref PubMed Scopus (923) Google Scholar, Roy et al., 2004Roy M.S. Klein R. O’Colmain B.J. Klein B.E.K. Moss S.E. Kempen J.H. The prevalence of diabetic retinopathy among adult type 1 diabetic persons in the United States.Arch. Ophthalmol. 2004; 122: 546-551Crossref PubMed Scopus (121) Google Scholar), most patients are not receiving adequate screening and the monitoring needed to reduce incidence of vision loss (Beaser et al., 2018Beaser R.S. Turell W.A. Howson A. Strategies to improve prevention and management in diabetic retinopathy: qualitative insights from a mixed-methods study.Diabetes Spectr. 2018; 31: 65-74Crossref PubMed Scopus (15) Google Scholar). Telescreening/telemonitoring programs for DR (teleDR) enable patients to receive fundus imaging at mobile clinics or during their primary care appointments. Images are then sent to and remotely interpreted by ophthalmologists (Bashshur et al., 2015Bashshur R.L. Shannon G.W. Smith B.R. Woodward M.A. The empirical evidence for the telemedicine intervention in diabetes management.Telemed. J. E Health. 2015; 21: 321-354Crossref PubMed Scopus (57) Google Scholar). These images can be taken through non-mydriatic (without pupil dilation) digital photography performed by a trained technician. Unlike mydriatic (with pupil dilation) imaging, which requires pharmacological dilation, non-mydriatic cameras rely on physiologic dilation that occurs naturally in a dark room, reducing the cost of the imaging, avoiding the 20-min wait period required for pupil dilation, and resulting in higher patient satisfaction with screening procedures (Bashshur et al., 2015Bashshur R.L. Shannon G.W. Smith B.R. Woodward M.A. The empirical evidence for the telemedicine intervention in diabetes management.Telemed. J. E Health. 2015; 21: 321-354Crossref PubMed Scopus (57) Google Scholar, Cummings et al., 2001Cummings D.M. Morrissey S. Barondes M.J. Rogers L. Gustke S. Screening for diabetic retinopathy in rural areas: the potential of telemedicine.J. Rural Health. 2001; 17: 25-31Crossref PubMed Scopus (33) Google Scholar). Although findings vary among different comparative studies, false-positive and false-negative rates of DR diagnosis through non-mydriatic imaging are typically low (Bruce et al., 2013Bruce B.B. Newman N.J. Pérez M.A. Biousse V. Non-mydriatic ocular fundus photography and telemedicine: past, present, and future.Neuroophthalmology. 2013; 37: 51-57Crossref Scopus (17) Google Scholar), making these images an economical choice for screening use in rural primary care clinics and telemedicine programs. In studies on teleDR, rates of vision loss and blindness due to DR decreased significantly as patients improved adherence to retinopathy screening and monitoring appointments (Zimmer-Galler et al., 2015Zimmer-Galler I.E. Kimura A.E. Gupta S. Diabetic retinopathy screening and the use of telemedicine.Curr. Opin. Ophthalmol. 2015; 26: 167-172Crossref PubMed Scopus (62) Google Scholar, Conlin et al., 2006Conlin P.R. Fisch B.M. Cavallerano A.A. Cavallerano J.D. Bursell S.-E. Aiello L.M. Nonmydriatic teleretinal imaging improves adherence to annual eye examinations in patients with diabetes.J. Rehabil. Res. Dev. 2006; 43: 733-740Crossref PubMed Scopus (48) Google Scholar). In one study, 495 patients with diabetes at an inner-city, nurse-managed primary care clinic were offered either teleDR or a referral to an ophthalmology clinic (Taylor et al., 2007Taylor C.R. Merin L.M. Salunga A.M. Hepworth J.T. Crutcher T.D. O’Day D.M. Pilon B.A. Improving diabetic retinopathy screening ratios using telemedicine-based digital retinal imaging technology: the Vine Hill study.Diabetes Care. 2007; 30: 574-578Crossref PubMed Scopus (87) Google Scholar). Over 40% of patients chose the teleDR option, all of whom received DR screening during their primary care appointment (Taylor et al., 2007Taylor C.R. Merin L.M. Salunga A.M. Hepworth J.T. Crutcher T.D. O’Day D.M. Pilon B.A. Improving diabetic retinopathy screening ratios using telemedicine-based digital retinal imaging technology: the Vine Hill study.Diabetes Care. 2007; 30: 574-578Crossref PubMed Scopus (87) Google Scholar). In contrast, only 31% of patients who selected in-person ophthalmology referral actually followed up with their screening appointments (Taylor et al., 2007Taylor C.R. Merin L.M. Salunga A.M. Hepworth J.T. Crutcher T.D. O’Day D.M. Pilon B.A. Improving diabetic retinopathy screening ratios using telemedicine-based digital retinal imaging technology: the Vine Hill study.Diabetes Care. 2007; 30: 574-578Crossref PubMed Scopus (87) Google Scholar). Telemedicine has the power of integrating retinopathy screening and preventative eye care into primary care visits, enabling identification of retinopathy at its early stages and helping patients in reducing financial and quality of life costs incurred by vision loss. With the average American using their smartphone for over 2 hr each day (The Economist, 2015The Economist Planet of the phones. The Economist.https://www.economist.com/leaders/2015/02/26/planet-of-the-phonesDate: 2015Google Scholar), mobile devices have become convenient and effective instruments to engage patients in their healthcare. Numerous smartphone applications exist that aim at improving patients' glycemic control through diabetes education and blood glucose monitoring. Studies on these interventions point overall to HbA1c reduction by as much as 0.8% and 0.3% in patients with T2D and T1D, respectively, in 12 months or less when compared with patients receiving usual care (Kitsiou et al., 2017Kitsiou S. Paré G. Jaana M. Gerber B. Effectiveness of mHealth interventions for patients with diabetes: an overview of systematic reviews.PLoS One. 2017; 12: e0173160Crossref PubMed Scopus (178) Google Scholar). Through its ability to promote communication between patients and healthcare providers and to customize health monitoring for each patient, smartphones are uniquely positioned to empower patients in their day-to-day diabetes self-management (Wiederhold, 2015Wiederhold B.K. mHealth apps empower individuals.CyberPsychol. Behav. Soc. Netw. 2015; 18: 429-430Crossref PubMed Scopus (16) Google Scholar). m-Health programs for diabetes care are heterogeneous in their scope and design. At the simplest level, m-health interventions can utilize texting to remind patients of glycemic goals set in the clinic, to enable patients to contact providers, or to provide diabetes education. In a comprehensive example, clinicians implemented a 6-month, text message-based diabetes education program that delivered automated diet, exercise, and diabetes monitoring modules along with optional notifications to remind patients of self-care activities (Nundy et al., 2014Nundy S. Dick J.J. Chou C.-H. Nocon R.S. Chin M.H. Peek M.E. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants.Health Aff. (Millwood). 2014; 33: 265-272Crossref PubMed Scopus (106) Google Scholar). HbA1c levels were reduced by 0.7% after 6 months, and patients reported improved dietary practices, better foot care, and more frequent blood glucose monitoring as a result of the program (Nundy et al., 2014Nundy S. Dick J.J. Chou C.-H. Nocon R.S. Chin M.H. Peek M.E. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants.Health Aff. (Millwood). 2014; 33: 265-272Crossref PubMed Scopus (106) Google Scholar). Text message-based systems can also improve medication adherence among patients with diabetes. In T2D patients, an estimated 23% of cases of uncontrolled HbA1c, serum lipid, and blood pressure are due to non-compliant medication use, defined as taking less than 80% of the prescribed medications (American Diabetes Association, 2018aAmerican Diabetes Association1. improving care and promoting health in populations: standards of medical care in diabetes—2018.Diabetes Care. 2018; 41: S7-S12Crossref PubMed Scopus (83) Google Scholar, Raebel et al., 2013Raebel M.A. Schmittdiel J. Karter A.J. Konieczny J.L. Steiner J.F. Standardizing terminology and definitions of medication adherence and persistence in research employing electronic databases.Med. Care. 2013; 51: S11-S21Crossref PubMed Scopus (308) Google Scholar). While compliance is affected by a multitude of factors including prescription costs, low health literacy, and lack of social support, text" @default.
• W2893594082 created "2018-10-05" @default.
• W2893594082 creator A5072681482 @default.
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• W2893594082 date "2019-03-01" @default.
• W2893594082 modified "2023-10-12" @default.
• W2893594082 title "Patient-Driven Diabetes Care of the Future in the Technology Era" @default.
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• W2893594082 doi "https://doi.org/10.1016/j.cmet.2018.09.005" @default.
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• W2893594082 hasPublicationYear "2019" @default.
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