Using Digital Health Technology to Prevent and Treat Diabetes.

Abstract

Diabetes Technology & TherapeuticsVol. 19, No. S1 Original ArticlesFree AccessUsing Digital Health Technology to Prevent and Treat DiabetesNeal Kaufman and Ayten SalahiNeal KaufmanFielding School of Public Health, Geffen School of Medicine, UCLA, Los Angeles, CA.Canary Health, Los Angeles, CA.Search for more papers by this author and Ayten SalahiIndependent Consultant to Canary Health, Los Angeles, CA.Search for more papers by this authorPublished Online:1 Feb 2017https://doi.org/10.1089/dia.2017.2506AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionAs anyone reading this yearbook knows, diabetes mellitus is a chronic disease that requires long-term medical attention both to limit the development of severe complications and to manage them when they do occur. It is one of the leading causes of morbidity and mortality worldwide because of its role in the development of cardiovascular, renal, neuropathic, and retinal disease.The prognosis in patients with diabetes mellitus is strongly influenced by the degree of control of their disease. Patients with diabetes have a lifelong challenge to achieve and maintain blood glucose levels as close to the targeted range as possible. Attention must also be paid to achieving normal blood pressure and lipids. With appropriate control, the risk of microvascular and neuropathic complications is decreased markedly.People with diabetes face a myriad of barriers to better health. Managing diabetes, often along with other comorbidities, can be a daunting task. Depressive symptoms and major depressive disorders are common in people with diabetes and pose a major challenge for clinical practice. People need to be able to confidently interact with their physicians and an increasingly complex medical system, administer the right medicines and therapies at the right time, eat properly, exercise appropriately, and (often) lose weight. While most people know what they should do, they often find themselves unable to actually take the steps necessary to achieve better health. Many complications of diabetes are preventable if patients take greater responsibility for the management of their condition.Self-management support is one approach to help people attain the knowledge, attitudes, skills, and behaviors they need to better manage chronic disease symptoms and improve their health outcomes. It addresses how to recognize and act on symptoms, use medication appropriately, maintain nutrition and exercise programs, interact effectively with health-care providers, and manage psychological responses to illness.Self-management support, coupled with diabetes education, leads to the best outcomes over time. Diabetes education has traditionally been, and in many places still is, primarily taught by health professionals in clinical settings with professionals assessing patients' needs and instructing them how to control blood glucose and other key parameters and avoid diabetes complications. Patients and professionals work collaboratively toward an individualized self-management plan. Unfortunately, the combination of the large number of people living with diabetes, cultural and socio-economic factors, as well as a limited number of skilled diabetes educators, means that most patients receive little or no diabetes education or self-management support.The articles reviewed in this article are all related to the central theme that activated and empowered patients, when given the information, wisdom, and tools they need to self-manage their conditions and their lives, can improve their disease trajectories and lower the rate of getting complications and adding additional comorbidities. The papers address a variety of ways that information technology is leading the way in transforming health-care delivery to put the patient back in the center of his or her health journey.Articles are divided into 4 themes: 1. Intervention design.2. Prevention of type 2 diabetes.3. Interventions to impact overall diabetes self-management.4. Useful tools/interventions addressing one element of diabetes self-management.Key Articles Reviewed for the ArticleUser-centered design for psychosocial intervention development and implementationAaron R. Lyon, Kelly KoernerClin Psychol Sci Pract 2016; 23: 180–200The systematic design of a behavioural mobile health application for the self-management of type 2 diabetesGoyal S, Morita P, Lewis GF, Yu C, Seto E, Cafazzo JACan J Diabetes 2016; 40: 95–104Evidence-based mHealth chronic disease mobile app intervention design: development of a frameworkWilhide Iii CC, Peeples MM, Anthony Kouyaté RCJMIR Res Protoc 2016; 5: e25Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: a scoping reviewMatthew-Maich N, Harris L, Ploeg J, Markle-Reid M, Valaitis R, Ibrahim S, Gafni A, Isaacs SJMIR Mhealth Uhealth 2016; 4: e29A systematic review on incentive-driven mobile health technology: as used in diabetes managementde Ridder M, Kim J, Jing Y, Khadra M Nanan RJ Telemed Telecare 2017; 23:26-35 [epub 2016 Jul9]Do weight management interventions delivered by online social networks effectively improve body weight, body composition, and chronic disease risk factors? A systematic reviewWillis EA, Szabo-Reed AN, Ptomey LT, Steger FL, Honas JJ, Washburn RA, Donnelly JEJ Telemed Telecare 2016. [Epub ahead of print] pii: 1357633X16630846Diabetes prevention and weight loss with a fully automated behavioral intervention by email, web, and mobile phone: a randomized controlled trial among persons with prediabetesBlock G, Azar KMJ, Romanelli RJ, Block TJ, Hopkins Ds, Carpenter HA, Dolginsky MS, Hudes ML, Palaniappan LP, Block CHJ Med Internet Res 2015; 17: e240Use of m-Health technology for preventive interventions to tackle cardiometabolic conditions and other non-communicable diseases in Latin America – challenges and opportunitiesBeratarrechea A, Diez-Canseco F, Irazola V, Miranda J, Ramirez-Zea M, Rubinstein AProgress in Cardiovascular Diseases 2016; 58: 661–673Return on investment for digital behavioral counseling in patients with prediabetes and cardiovascular diseaseSu W, Chen F, Dall TM, Iacobucci W, Perreault LPrev Chronic Dis 2016; 13: 150357.Cost effectiveness of an internet-delivered lifestyle intervention in primary care patients with high cardiovascular riskSmith KJ, Kuo S, Zgibor JC, McTigue KM, Hess R, Bhargava T, Bryce CLPreventive Medicine 2016; 87: 103–109Mobile technology and the digitization of healthcareBhavnani SP, Narula J, Sengupta PPEuropean Heart Journal 2016; 37: 1428–1438Personalized telehealth in the future: a global research agendaDinesen B, Nonnecke B, Lindeman D, Toft E, Kidholm K, Jethwani K, Young HM, Spindler H, Oestergaard CU, Southard JA, Gutierrez M, Anderson N, Albert NM, Han JJ, Nesbitt TJ Med Internet Res 2016; 18: e53A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countriesStephani V, Opoku D, Quentin WBMC Public Health 2016; 16: 572The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic reviewWhitehead L, Philippa Seaton PJ Med Internet Res 2016; 18: e97The use of videogames, gamification, and virtual environments in the self-management of diabetes: a systematic review of evidenceTheng YL, Lee JW, Patinadan PV, Foo SSGames Health J 2015; 4: 352–361A review of nutritional tracking mobile applications for diabetes patient useDarby A, Strum MW, Holmes E, Gatwood JDiabetes Technology and Therapeutics 2016; 18: 200–212The effectiveness of prompts to promote engagement with digital interventions: a systematic reviewAlkhaldi G, Hamilton FL, Lau R, Webster R, Michie S, Murray EJ Med Internet Res 2016; 18: e6.User-centered design for psychosocial intervention development and implementationLyon Aaron R.1, Koerner Kelly21Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA2Kelly Koerner, Evidence-Based Practice Institute, LLC, University of Washington. Seattle, WAClin Psychol Sci Pract 2016; 23: 180–200BackgroundUser-centered design (UCD) principles can be leveraged to improve the implementation and scalability of various evidence-based treatments (EBTs) in psychosocial intervention. This paper presents particularly salient points for improvement in the research, design, implementation, and scaling-up of behavioral EBTs by describing UCD as a cornerstone for effective innovation.MethodsA description of studies pertaining to EBT design and implementation protocols was completed, alongside a discussion of studies related to psychosocial findings with potential to improve the usability, packaging, and dissemination of such therapies. The authors synthesized this information into a UCD-focused design agenda composed of four primary design concepts.ResultsEBT success is inherently diminished by design and development weaknesses related to flexibility, complexity, and sustainability of treatment. These factors are exacerbated in the growing field of digital EBTs. There are previously described design goals for the development of EBTs in behavioral health that aim to address these. The authors present 4 salient design methods therein: (1) deliberate identification of both end user populations and their needs; (2) design of clear protocols for rapid replication; (3) streamlining pre-existing procedures; and (4) leveraging natural design constraints of the product or service's destination context.ConclusionsThere are gaps to EBT design that undermine its potential for effectiveness, scalability, and replicability in psychosocial interventions. EBT researchers and developers must adopt a user-centered design approach to maximize the potential for EBTs in behavioral therapies of any kind.CommentThe hallmarks of user-centered design put the participant in a digital intervention in the center of his or her own health journey. The accomplishment this goal requires a multidisciplinary team in which developers work with clinicians and end-users to iterate approaches which are most likely to get a sustained impact. The more the team understands the unique characteristics of the target population, the more appropriate and impactful the intervention will be. Modern website and intervention design takes into account these principles and when coupled with foundational work demonstrating what approaches work for what behaviors with which individuals, effective and efficient interventions can be created. These interventions need to be able to be modified over time as information on outcomes and user input is obtained.The systematic design of a behavioural mobile health application for the self-management of type 2 diabetesGoyal S1,2, Morita P1, Lewis GF4, Yu C5,6, Seto E1,3, Cafazzo JA1,2,31Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada2Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada3Institute of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Toronto, ON, Canada4Departments of Medicine and Physiology, Division of Endocrinology and the Banting and Best Diabetes Centre, University of Toronto, Toronto, ON, Canada5Division of Endocrinology & Metabolism and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada6Faculty of Medicine and Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaCan J Diabetes 2016; 40: 95–104BackgroundThe significance of self-monitoring of blood glucose (SMBG) has been unclear for noninsulin requiring patients with type 2 diabetes (T2). By effectively improving self-management skills like clinical decision-making and self-awareness, structured SMBG has now been shown to demonstrate significant benefit for this subset of patients. The authors conjecture that successful self-management for noninsulin requiring patients with T2 requires behavioral intervention that elucidates the relationship between glycemic control and lifestyle. Few mHealth apps attempt to bring about positive behavioral adjustments for the purpose of cultivating self-care skills. The purpose of this paper is to describe a model for the design and development of a diabetes self-management mHealth app that elicits behavioral change through social cognitive theory and patient incentives.MethodsA comprehensive literature review examining the state of mHealth for diabetes self-management was completed. The authors described a theoretical model for the development and validation of diabetes self-management applications through user-centered design following the Knowledge to Action (KTA) and Medical Research Council's (MRC) blended framework for complex interventions. This framework incorporates elements of social cognitive theory (SCT) to incite and sustain positive behavioral changes known to improve patient compliance and clinical outcomes.ResultsThe result was KTA-MRC Phase 1 development of bant II, a user-centered mHealth app for self-management of diabetes with four primary functions: tracking SMBG and wellness-related lifestyle choices (e.g., diet, exercise), highlighting patterns between glycemic control and lifestyle choices, providing remedial direction to improve clinical decision-making, and guiding eventual mastery of diabetes self-management through incentivized, gamified positive behavioral change.ConclusionsBant II shows promise in guiding the development of sustainable self-management, improved quality of life, and prevention of diabetes-related complications through incentivized positive behavioral change for patients with T2. Phase 2 and 3 of app development will involve a patient usability evaluation and a randomized control trial to measure the effectiveness of the app, with a primary clinical outcome of HbA1c.Evidence-based mHealth chronic disease mobile app intervention design: development of a frameworkWilhide CCIII, Peeples MM, Anthony Kouyaté RCWellDoc Inc, Baltimore, MDJMIR Res Protoc 2016; 5: e25BackgroundChronic disease management through mHealth innovation offers promising new possibilities for real-time, remote health-care interventions. Such innovations require a framework for development and evaluation that is evidence-based, replicable, scalable, and adaptable across chronic disease states, and effective in the achievement of specific health outcomes. The purpose of this review is to describe the process required to identify, develop, and enhance such an app-based intervention design framework in the context of chronic disease management.MethodsA three-phased approach was used over the span of two years (June 2012 to June 2014) to develop, apply, refine, and finalize a framework for the evolution of an evidence-based mHealth app design. The framework was developed iteratively across three chronic disease management app types and seven chronic disease states. The preliminary model was applied to type 2 diabetes (T2) and incorporated data from clinical and behavioral evidence-based research, clinical practice subject matter experts, standards of care, clinical procedures, and best practices.ResultsThe final outcome was the development of the Chronic Disease mHealth App Intervention Design Framework. This iterative design model sequentially uses seven key decision-making and content domains to guide the development of mHealth app features, and is based on both clinical and behavioral evidence known to improve health outcomes in chronic disease. The model concurrently evaluates the effectiveness of particular design features towards a specific outcome, and feeds the output of a preceding iteration into the subsequent domain in a process that is referred to as a waterfall process. The effect of the waterfall process across multiple chronic disease states and app types was to evolve each iteration of the mHealth app design framework across multivariate disease management strata.ConclusionsThe Chronic Disease mHealth App Intervention Design Framework offers a scalable, replicable, and iterative model upon which mHealth innovation developers can expand to enhance the usability, applicability, and evidence-based effectiveness of mHealth technologies across chronic disease states and real-life settings.CommentThe authors have done a terrific job in having summarized a very complex software development process, providing a coherent approach to building a technology platform which can be used to power a variety of chronic disease self-management approaches. This paper is made all the more interesting and important since it comes from a for-profit company willing to share some of its “secret sauce” serving as the foundation of its business model. They have also highlighted some of the challenges inherent in using evidence-based practices proven effective in-person and transforming them for digital delivery. There is a need to balance often competing principles of behavior change, application and user-centered design, and condition or behavior specific protocols and curriculum. This paper demonstrates one successful approach.Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: a scoping reviewMatthew-Maich N1,2, Harris L3, Ploeg J2, Markle-Reid M2, Valaitis R2, Ibrahim S4, Gafni A5, Isaacs S21Aging, Community & Health Research Unit, McMaster University, Mohawk College/McMaster University School of Nursing, Hamilton, ON, Canada2Aging, Community & Health Research Unit, School of Nursing, McMaster University, Hamilton, ON, Canada3SickKids Hospital, mHealth Innovation, Toronto, ON, Canada4Arthur Labatt Family School of Nursing, Department of Nursing, Western University, London, ON, Canada5Aging, Community & Health Research Unit, Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, CanadaJMIR Mhealth Uhealth 2016; 4: e29BackgroundAging patients managing multiple chronic conditions present a particularly complex challenge for health-care providers and caregivers. While mHealth technologies have demonstrated the potential to support this demographic, insufficient evidence-based research has been conducted to inform the development of such technologies. This review describes the current landscape of mHealth support in the management of chronic conditions in aging, community-living adults, and provides recommendations for enhanced usability, feasibility, and user acceptance.MethodsThe authors completed a comprehensive, five-stage scoping literature review, ranging from January 2005 through March 2015, to identify mHealth innovations designed for use by aging, community-living adults managing a minimum of one chronic condition. They also included information on both formal and informal health-care services offered in the home and community setting for this population.ResultsIn total, the authors identified 42 papers that met the abovementioned search criteria. Seventeen of the 42 articles (40.4%) specifically described mHealth technologies developed for aging adults with a particular chronic condition or set of conditions, while 6 (14.3%) described such innovations for chronic conditions in general and 18 (42.9%) did so for those receiving home and/or community care. They identified three prominent themes among these publications: evaluation methods of mHealth innovation; factors that detract from mHealth usability, feasibility, and user acceptance; and considerations specific to the development of mHealth technologies.ConclusionsUser acceptance for mHealth technologies in aging adults is limited but increasing. To improve user acceptance, feasibility, and usability of mHealth home health-care in older adults, developers must adopt an integrative, user-centric, and collaborative approach to design and development of mHealth innovations.CommentThis study emphasizes a fundamental tenant of all patient education and support. To maximize effectiveness, they need to follow a number of principles one of which is being designed for a specific target population. Ideally the target population is involved with the development, testing, and refinement of the approach during all phases of planning, implementing, and evaluating a program. No surprise, interventions created for “older” adults…don't know what that age is, but I assume always older than I am at the time…need to be different than those designed for younger people.A systematic review on incentive-driven mobile health technology: as used in diabetes managementde Ridder M1,2, Kim J1,2,3, Jing Y1,2, Khadra M4, Nanan R5,61Personal Digital Assistant, Biomedical and Multimedia Information Technology Research Group, The University of Sydney, Australia2Personal Digital Assistant, Institute of Biomedical Engineering and Technology, The University of Sydney, Australia3Personal Digital Assistant, Nepean Telehealth Technology Centre, Nepean Hospital, Australia4Personal Digital Assistant, Sydney Medical School, The University of Sydney, Australia5Personal Digital Assistant, Nepean Clinical School, The University of Sydney, Australia6Personal Digital Assistant, Charles Perkins Centre Nepean, The University of Sydney, AustraliaJ Telemed Telecare 2017; 23:26-35 [epub 2016 Jul9]BackgroundBenefits of mHealth intervention towards the management of chronic diseases are undermined by difficulty in retaining and engaging the patients who use them. These difficulties are commonly addressed by utilizing incentive-driven technology (IDT) design to maintain and empower mHealth app users. This review catalogues various methods of IDT implementation in diabetes-related mHealth technologies, as well as impediments to user engagement with IDTs.MethodsA systematic literature review across seven major databases was completed to analyze mHealth solutions for diabetes management, as well as mHealth solutions for diabetes management using IDTs. Publications ranged from 2008 to 2014. The search rendered a total of 42 papers.ResultsWhile automation and personalization components of IDTs have advanced over the course of the last five years in mHealth design, the predominating IDT categories have remained largely unchanged. Of the 42 search results, 34 described mHealth innovations that leveraged IDTs. More than half of these studies used ≥1 IDT method (n=19). The remaining eight articles were used to qualitatively assess impediments to user engagement with IDTs. Authors characterized IDT methods in the following categories: (1) financial (n=2); (2) reminder (n=11); (3) social (n=8); (4) alert (n=5); (5) gamification (n=3); (6) feedback (n=10); and (7) education (n=21).ConclusionsIDTs are used to improve rates of retention in mobile telehealth technologies. mHealth innovations for diabetes management currently employ seven primary categories of IDTs to incentivize retention and empowerment of end users. There methods are characterized here based upon a review of literature dating to 2014.CommentIncentives are a tried and true approach to increasing engagement and retention. The problems is that they do not necessarily work with many people and under many circumstances. Which incentives work and under what circumstances are they most effective is an important research question. This study helps to catalog the approach to incentives and could help others as they design studies regarding incentives.Do weight management interventions delivered by online social networks effectively improve body weight, body composition, and chronic disease risk factors? A systematic reviewWillis EA, Szabo-Reed AN, Ptomey LT, Steger FL, Honas JJ, Washburn RA, Donnelly JECardiovascular Research Institute, Division of Internal Medicine, Kansas City, MOJ Telemed Telecare 2016. [Epub ahead of print] pii: 1357633X16630846BackgroundThe role of online social networks (OSNs) as a primary intervention delivery method remains largely unexamined. This report constitutes the first systematic review and meta-analysis evaluating the effectiveness of OSN-delivered weight management interventions on weight loss outcomes.MethodsStudies ranging from December 1990 through November 2015 were included within the initial scope of candidate articles. A systematic literature review across five pertinent databases was completed to identify data describing the influence of OSNs on weight loss outcomes. Among other requirements, inclusion criteria required the use of peer-reviewed, primary source publications that collected weight loss data specifically related to the use of OSN-delivered body weight management and lifestyle interventions. A total of five papers met inclusion criteria for final review.ResultsOf the five studies selected for analysis, three reported significant reductions to body weight. In these instances, significant weight loss was observed when OSN-delivered weight management interventions were utilized alongside individualized health educator support for program participants. One of the five studies indicated clinically significant total weight loss of ≥5%.ConclusionsThe findings described here are promising and suggest potential for OSN-delivered interventions to be effective for weight management; however, further research is needed to recapitulate and build upon these findings, in order to clarify which components of OSN-delivered interventions are most critical to weight management or other health benefits.CommentOnline social networks certainly should be effective as a way to decrease social isolation and improve health outcomes. This review, with only five studies, did not demonstrate very significant results but that is not surprising. Social networks by themselves, without specific protocols and content to help participants overcome barriers, will probably not be robust enough to help patients change often lifelong behaviors.Diabetes prevention and weight loss with a fully automated behavioral intervention by email, web, and mobile phone: a randomized controlled trial among persons with prediabetesBlock G1,2, Azar KMJ3, Romanelli RJ3, Block TJ1, Hopkins Ds1, Carpenter HA1, Dolginsky MS3, Hudes ML4, Palaniappan LP3, Block CH11Turnaround Health, a division of NutritionQuest, Berkeley, CA2Division of Community Health and Human Development, School of Public Health, University of California, Berkeley, CA3Palo Alto Medical Foundation Research Institute, Palo Alto, CA4Center for Weight and Health, University of California, Berkeley, CAJ Med Internet Res 2015; 17: e240BackgroundOne in three U.S. adults has prediabetes. About 70% of those living with prediabetes are expected to advance to type 2 (T2), and about one in 10 U.S. health-care dollars were spent in the associated costs of prediabetes and diabetes in 2012. Lifestyle adjustments like increased physical activity, weight loss, and dietary modifications can reduce risk of progression to diabetes by 40% to 70%. This study assesses the efficacy of Alive-PD, a fully-automated, remote behavioral intervention for diabetes prevention that uses a combined mobile phone, e-mail, and the Internet delivery methods to reduce the biomarkers that constitute the benchmarks for diabetes (e.g., HbA1c, fasting blood glucose(BG)).MethodsThis study was a wait-list controlled RCT, with primary outcome measures of HbA1c and fasting BG at baseline and at six months. Secondary outcome measures included body mass index (BMI), weight, waist circumference, metabolic syndrome, Framingham diabetes score, and triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio. Analysis was completed by intention-to-treat. Participants were required to meet inclusion criteria for clinical evidence of prediabetes. Patients were randomized to either the Alive-PD intervention group (n=339) or the wait-list usual-care control group (n=176). Subjects in the Alive-PD group received personalized behavioral support for lifestyle modifications, including dietary habits, exercise, sleep, stress, and weight loss, in addition to a mobile phone app, and automated phone calls. Subjects also received weekly e-mails outlining suggestions for incremental goals and providing access to a separate website that could be used for data tracking, social support, competitions, coaching, and health material.ResultsParticipants in the Alive-PD group had reduced their Framingham eight year diabetes risk significantly more than the control group (P<0.001), from 16% to 11%. When compared to controls, the Alive-PD group also showed significantly greater reductions in HbA1c (mean −0.26%, 95% confidence interval (CI) −0.27 to −0.24 vs. mean −0.18%, 95% CI −0.19 to −0.16, P<0.001), fasting glucose (mean −7.36 mg/dL, 95% CI −7.85 to −6.87 vs. mean −2.19, 95% CI −2.64 to −1.73, P<0.001), body weight, waist circumference, TG/HDL, and BMI. Of the total subjects in the Alive-PD group who had metabolic syndrome at baseline, 46.5% (40/86) no longer had metabolic syndrome at six months, compared to 20.0% (20/110) in the control group. Participant engagement with the program began and remained high through the course of the study, with 70.6% (115/163) of the intervention group still engaging with the program in the final month of the study.ConclusionsAlive-PD was proven effective in reducing the biomarkers that constitute diabetes, as well as risk of progressing from prediabetes to diabetes. Further research is required to elucidate which features of the intervention were salient to its success. Fully automated, evidence-based behavioral interventions such as Alive-PD have potential for large-scale implementation as a preventative option for at-risk populations and people with prediabetes.CommentThis study is promising because not only did it get positive results (at six months), it did so using technology w