A New Taxonomy for Technology-Enabled Diabetes Self-Management Interventions: Results of an Umbrella Review.

BACKGROUND: A 2017 umbrella review defined the technology-enabled self-management (TES) feedback loop associated with a significant reduction in A1C. The purpose of this 2021 review was to develop a taxonomy of intervention attributes in technology-enabled interventions; review recent, high-quality systematic reviews and meta-analyses to determine if the TES framework was described and if elements contribute to improved diabetes outcomes; and to identify gaps in the literature. METHODS: We identified key technology attributes needed to describe the active ingredients of TES interventions. We searched multiple databases for English language reviews published between April 2017 and April 2020, focused on PwD (population) receiving diabetes care and education (intervention) using technology-enabled self-management (comparator) in a randomized controlled trial, that impact glycemic, behavioral/psychosocial, and other diabetes self-management outcomes. AMSTAR-2 guidelines were used to assess 50 studies for methodological quality including risk of bias. RESULTS: The TES Taxonomy was developed to standardize the description of technology-enabled interventions; and ensure research uses the taxonomy for replication and evaluation. Of the 26 included reviews, most evaluated smartphones, mobile applications, texting, internet, and telehealth. Twenty-one meta-analyses with the TES feedback loop significantly lowered A1C. CONCLUSIONS: Technology-enabled diabetes self-management interventions continue to be associated with improved clinical outcomes. The ongoing rapid adoption and engagement of technology makes it important to focus on uniform measures for behavioral/psychosocial outcomes to highlight healthy coping. Using the TES Taxonomy as a standard approach to describe technology-enabled interventions will support understanding of the impact technology has on diabetes outcomes.

Complementarity of Digital Health and Peer Support: "This Is What's Coming".

Purpose: This study examined integration of peer support and a Food and Drug Administration-cleared, diabetes management app (DMA) in diabetes self-management support as a scalable model for those with type 2 diabetes mellitus (T2DM). Methods: Two lay health Coaches delivered telephone-based self-management support to adults (N = 43) with T2DM recruited through a primary group practice. Those eligible were offered no-cost access to DMA for the entire 6-month study. Coaches introduced DMA and contacted individuals by phone and text with frequency dependent on participant needs/preferences. DMA supported monitoring of blood glucose, carbohydrate intake, and medication use, as well as messaging personalized to participants' medication regimens. Clinical data were extracted from DMA, electronic medical records, and Coaches' records. Structured interviews of 12 participants, 2 Coaches, and 5 project staff were analyzed using deductive pre-identified codes (regarding adoptability, patterns of use, value added, complementarity, and sustainability) utilizing standard procedures for qualitative analysis. Results: Of the 43 participants, 38 (88.4%) enrolled in DMA. In general, participants used both DMA and lay health coaches, averaging 144.14 DMA entries (structured, e.g., medications, and free form, e.g., "ate at a restaurant" and "stressed") and 5.86 coach contacts over the 6-month intervention. Correlation between DMA entries and coach contacts (r = .613, p < 0.001) was consistent with complementarity as were participants' and coaches' observations that (a) DMA facilitated recognition of patterns and provided reminders and suggestions to achieve self-management plans, whereas (b) coaching provided motivation and addressed challenges that emerged. Mean hemoglobin A1c (A1c) declined from 9.93% to 8.86% (p < 0.001), with no pattern of coaching or DMA use significantly related to reductions. Staff identified resources to coordinate coach/DMA interventions as a major sustainability challenge. Conclusions: DMA and peer support for diabetes management are compatible and complementary. Additional practice integration research is needed for adoption and scale-up.

A Framework for Optimizing Technology-Enabled Diabetes and Cardiometabolic Care and Education: The Role of the Diabetes Care and Education Specialist.

PURPOSE: The purpose of this article is to present a framework for optimizing technology-enabled diabetes and cardiometabolic care and education using a standardized approach. This approach leverages the expertise of the diabetes care and education specialist, the multiplicity of technologies, and integration with the care team. Technology can offer increased opportunity to improve health outcomes while also offering conveniences for people with diabetes and cardiometabolic conditions. The adoption and acceptance of technology is crucial to recognize the full potential for improving care. Understanding and incorporating the perceptions and behaviors associated with technology use can prevent a fragmented health care experience. CONCLUSION: Diabetes care and education specialists (DCES) have a history of utilizing technology and data to deliver care and education when managing chronic conditions. With this unique skill set, DCES are strategically positioned to provide leadership to develop and deliver technology-enabled diabetes and cardiometabolic health services in the rapidly changing healthcare environment.

Evidence-Based mHealth Chronic Disease Mobile App Intervention Design: Development of a Framework.

BACKGROUND: Mobile technology offers new capabilities that can help to drive important aspects of chronic disease management at both an individual and population level, including the ability to deliver real-time interventions that can be connected to a health care team. A framework that supports both development and evaluation is needed to understand the aspects of mHealth that work for specific diseases, populations, and in the achievement of specific outcomes in real-world settings. This framework should incorporate design structure and process, which are important to translate clinical and behavioral evidence, user interface, experience design and technical capabilities into scalable, replicable, and evidence-based mobile health (mHealth) solutions to drive outcomes. OBJECTIVE: The purpose of this paper is to discuss the identification and development of an app intervention design framework, and its subsequent refinement through development of various types of mHealth apps for chronic disease. METHODS: The process of developing the framework was conducted between June 2012 and June 2014. Informed by clinical guidelines, standards of care, clinical practice recommendations, evidence-based research, best practices, and translated by subject matter experts, a framework for mobile app design was developed and the refinement of the framework across seven chronic disease states and three different product types is described. RESULTS: The result was the development of the Chronic Disease mHealth App Intervention Design Framework. This framework allowed for the integration of clinical and behavioral evidence for intervention and feature design. The application to different diseases and implementation models guided the design of mHealth solutions for varying levels of chronic disease management. CONCLUSIONS: The framework and its design elements enable replicable product development for mHealth apps and may provide a foundation for the digital health industry to systematically expand mobile health interventions and validate their effectiveness across multiple implementation settings and chronic diseases.

Integration of a mobile-integrated therapy with electronic health records: lessons learned.

BACKGROUND: Responses to the chronic disease epidemic have predominantly been standardized in their approach to date. Barriers to better health outcomes remain, and effective management requires patient-specific data and disease state knowledge be presented in methods that foster clinical decision-making and patient self-management. Mobile technology provides a new platform for data collection and patient-provider communication. The mobile device represents a personalized platform that is available to the patient on a 24/7 basis. Mobile-integrated therapy (MIT) is the convergence of mobile technology, clinical and behavioral science, and scientifically validated clinical outcomes. In this article, we highlight the lessons learned from functional integration of a Food and Drug Administration-cleared type 2 diabetes MIT into the electronic health record (EHR) of a multiphysician practice within a large, urban, academic medical center. METHODS: In-depth interviews were conducted with integration stakeholder groups: mobile and EHR software and information technology teams, clinical end users, project managers, and business analysts. Interviews were summarized and categorized into lessons learned using the Architecture for Integrated Mobility® framework. RESULTS: Findings from the diverse stakeholder group of a MIT-EHR integration project indicate that user workflow, software system persistence, environment configuration, device connectivity and security, organizational processes, and data exchange heuristics are key issues that must be addressed. CONCLUSIONS: Mobile-integrated therapy that integrates patient self-management data with medical record data provides the opportunity to understand the potential benefits of bidirectional data sharing and reporting that are most valuable in advancing better health and better care in a cost-effective way that is scalable for all chronic diseases.

Computerized learning technologies for diabetes: a systematic review.

BACKGROUND: The objective of this study was to evaluate computerized learning technology interventions that can empower patients in the self-management of diabetes and support diabetes education over a distance. METHODS: We searched Medline (1966-2006), CINAHL (1982-2006), and the Cochrane Central Register of Controlled Trials (first quarter 2007) databases. We also reviewed reference lists from included studies to identify additional studies. We included 25 articles representing 21 randomized controlled trials that evaluated a computerized learning technology and measured the outcome of patient care. We extracted patient sample, intervention, educational content topics, outcome measures, and statistical significance. RESULTS: Of 21 eligible trials, 18 trials (85.7%) reported significant positive outcomes. Almost 44% (43.8%) of the outcomes demonstrated significant improvements (49 of 112 outcomes). CONCLUSIONS: Patient self-management behaviors are important in chronic disease management, and initial evidence suggests that computerized learning technology interventions can play a significant role in the future.