Six Digital Health Technologies That Will Transform Diabetes

Importance of Cybersecurity/The Relevance of Cybersecurity to Diabetes Devices: An Update from Diabetes Technology Society

As medical devices become more integrated with wireless technologies, the risks of cyberattacks and data breaches increase, making stringent cybersecurity measures essential. The implementation of rigorous cybersecurity standards is essential for enhancing the cybersecurity of devices. This article examines the evolving cyber threats faced by the medical technology industry, the role of IEEE 2621 in providing comprehensive security benchmarks for medical devices, and the need for continuous risk assessments and adherence to regulatory standards to mitigate future cyber risks. Adherence to cybersecurity standards establishes ensures the effective protection of sensitive data and critical infrastructure.

Availability of Open Dynamic Glycemic Data in the Field of Diabetes Research: A Scoping Review

BACKGROUND

Poor data availability and accessibility characterizing some research areas in biomedicine are still limiting potentialities for increasing knowledge and boosting technological advancement. This phenomenon also characterizes the field of diabetes research, in which glycemic data may serve as a basis for different applications. To overcome this limitation, this review aims to provide a comprehensive analysis of the publicly available data sets related to dynamic glycemic data.

METHODS

Search was performed in four different sources, namely scientific journals, Google, a comprehensive registry of clinical trials and two electronic databases. Retrieved data sets were analyzed in terms of their main characteristics and on the typology of data provided.

RESULTS

Twenty-five data sets were identified including data from challenge tests (5 of 25) or data from Continuous Glucose Monitoring (CGM, 20 of 25). As for the data sets including challenge tests, all of them were freely downloadable; most of them (80%) related only to oral glucose tolerance test (OGTT) with standard duration (2 h), but varying for timing and number of collected blood samples, and variables collected in addition to glucose levels (with insulin levels being the most common); the remaining 20% of them also included intravenous glucose tolerance test (IVGTT) data. As for the data sets related to CGM, 7 of 20 were freely downloadable, whereas the remaining 13 were downloadable upon completion of a request form.

CONCLUSIONS

This review provided an overview of the readily usable data sets, thus representing a step forward in fostering data access in diabetes field.

Digital health technologies and innovation patterns in diabetes ecosystems

Background

The global rise in type-2 diabetes (T2D) has prompted the development of new digital technologies for diabetes management. However, despite the proliferation of digital health companies for T2D care, scaling their solutions remains a critical challenge. This study investigates the digital transformation of T2D ecosystems and seeks to identify key innovation patterns. We examine: (1) What are emerging organizations in digital diabetes ecosystems? (2) What are the value streams in digital T2D ecosystems? (3) Which innovation patterns are present in digital T2D ecosystems?

Methods

We conducted a literature review and market analysis to characterize organizations and value streams in T2D ecosystems, pre- and post-digital transformation. We used the e3-value methodology to visualize T2D ecosystems (RQ1 and RQ2) and conducted expert interviews to identify emerging innovation patterns in digital diabetes ecosystems (RQ3).

Results

Our analyses revealed the emergence of eight organization segments in digital diabetes ecosystems: real-world evidence analytics, healthcare management platforms, clinical decision support, diagnostic and monitoring, digital therapeutics, wellness, online community, and online pharmacy (RQ1). Visualizing the value streams among these organizations highlights the crucial importance of individual health data (RQ2). Furthermore, our analysis revealed four major innovation patterns within the digital diabetes ecosystem: open ecosystem strategies, outcome-based payment models, platformization, and user-centric software (RQ3).

Conclusions

Our findings illustrate the transition from traditional value chains in T2D care to platform-based and outcome-oriented models. These innovation patterns can inform strategic decisions for companies and healthcare providers, potentially helping anticipate new digital trends in diabetes care and across other chronic disease ecosystems.

Digital twins and artificial intelligence in metabolic disease research

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

The Need for Data Standards and Implementation Policies to Integrate CGM Data into the Electronic Health Record

The current lack of continuous glucose monitor (CGM) data integration into the electronic health record (EHR) is holding back the use of this wearable technology for patient-generated health data (PGHD). This failure to integrate with other healthcare data inside the EHR disrupts workflows, removes the data from critical patient context, and overall makes the CGM data less useful than it might otherwise be. Many healthcare organizations (HCOs) are either struggling with or delaying designing and implementing CGM data integrations. In this article, the current status of CGM integration is reviewed, goals for integration are proposed, and a consensus plan to engage key stakeholders to facilitate integration is presented.

'Much more convenient, just as effective': Experiences of starting continuous glucose monitoring remotely following Type 1 diabetes diagnosis

AIM

Initiating continuous glucose monitoring (CGM) shortly after Type 1 diabetes diagnosis has glycaemic and quality of life benefits for youth with Type 1 diabetes and their families. The SARS-CoV-2 pandemic led to a rapid shift to virtual delivery of CGM initiation visits. We aimed to understand parents' experiences receiving virtual care to initiate CGM within 30 days of diagnosis.

METHODS

We held focus groups and interviews using a semi-structured interview guide with parents of youth who initiated CGM over telehealth within 30 days of diagnosis during the SARS-CoV-2 pandemic. Questions aimed to explore experiences of starting CGM virtually. Groups and interviews were audio-recorded, transcribed and analysed using thematic analysis.

RESULTS

Participants were 16 English-speaking parents (age 43 ± 6 years; 63% female) of 15 youth (age 9 ± 4 years; 47% female; 47% non-Hispanic White, 20% Hispanic, 13% Asian, 7% Black, 13% other). They described multiple benefits of the virtual visit including convenient access to high-quality care; integrating Type 1 diabetes care into daily life; and being in the comfort of home. A minority experienced challenges with virtual care delivery; most preferred the virtual format. Participants expressed that clinics should offer a choice of virtual or in-person to families initiating CGM in the future.

CONCLUSION

Most parents appreciated receiving CGM initiation education via telehealth and felt it should be an option offered to all families. Further efforts can continue to enhance CGM initiation teaching virtually to address identified barriers.