Conclusions: The present study revealed clinically meaningful improvements in glycemic control among participants enrolled in a digital diabetes management intervention.

Higher program usage was associated with greater improvements in HbA 1c. The findings of the present study suggest that a digital health intervention may represent an accessible, scalable, and effective solution to diabetes management and improved HbA 1c.

The study was limited by a nonrandomized, observational design and limited postenrollment follow-up data. Diabetes continues to plague the United States and the rest of the globe [ 1 ].

An estimated Amid the seemingly inexorable rise, it can be easy to forget what a truly modern phenomenon this is. In The Principles and Practice of Medicine of , William Osler estimated a diabetes prevalence of just 2.

This modernity would seem to suggest the tide can be rolled back if only its causes were understood, but, alas, the disease marches on [ 4 ]. Among those with diabetes, disease control is clearly a major challenge. Although clinical guidelines broadly agree on hemoglobin A 1c HbA 1c targets of 7.

This serves only to highlight the challenges people with diabetes face. It is a disease that requires daily attention to and navigation of myriad decisions—choosing foods, taking medication, monitoring blood glucose, and accessing preventive and acute care [ 7 ].

Although diabetes self-care behaviors have been found to be positively correlated with improved glycemic control and quality of life, clearly many people with diabetes struggle to adopt such behaviors [ 8 ].

With great prevalence and barriers to control comes great cost. A safe, effective, efficient, and scalable intervention would be welcome.

Many drug trials have shown disappointing results notably with no improvement in macrovascular outcomes in the UK Prospective Diabetes Study UKPDS 33 trial and increased mortality despite lower HbA 1c achieved in the Action to Control Cardiovascular Risk in Diabetes ACCORD trial [ 10 , 11 ].

Lifestyle interventions have similarly seen prominent disappointments in the Look AHEAD and MOVE! projects [ 12 , 13 ]. Some interventions, such as the Diabetes Remission Clinical Trial DiRECT , have shown promise, but it remains unclear whether strategies that include such intensive interventions as meal replacement can be scaled up to the millions of people living with diabetes in highly varied social, economic, and cultural settings [ 14 - 16 ].

Digital health may offer some solutions. Traditional outpatient interventions, however extensive, are limited by their sporadic nature and thus leave a substantial burden on the patient to internalize behaviors.

A digital solution has the potential to deliver guidance and support anywhere and anytime it may be needed. Benefits may include increased access to care and health improvements.

In addition to removing traditional barriers to face-to-face interactions, such as transportation and daytime office hours, digital platforms are linked to mental and metabolic outcomes.

Small randomized controlled trials of these programs have found improvements in diabetes self-care behaviors and self-efficacy along with glycemic and mental health measures [ 22 , 23 ]. A common theme in qualitative analyses of these interventions is the perception of feeling connected at all times to a human who cares [ 23 ].

As Markert et al [ 24 ] note in a literature review of telehealth coaching for seniors, it can be challenging and time consuming to foster a therapeutic relationship and tailor the intervention to the individual.

Furthermore, there is little standardization of digital intervention components in both the literature and products in the market. Greenwood et al [ 25 ] conducted a systematic review of technology-enabled diabetes management interventions.

Of these interventions, 18 reported significant reductions in HbA 1c albeit with heterogeneity in intervention components and methodologies. They did identify 4 key intervention elements present with HbA 1c reduction: two-way communication, patient-generated health data tracking or analysis, education, and feedback.

These elements are cornerstones of the Vida Health program. Vida Health is an app-based digital health platform for chronic disease prevention and management. Vida Health is available as an employee benefit through select health plans and direct to consumers across the United States.

Type 2 diabetes management is one of the core offerings on the Vida Health platform. App content covers a wide spectrum of lifestyle priorities including nutrition, blood glucose self-monitoring, and medication management. From a standard initial sequence, content is rapidly tailored to patient needs using both machine-learning recommendation algorithms and provider input.

Our hypothesis was that this continuously available, highly personalized combination of provider guidance and content would drive improvements in diabetes control as assessed by changes in HbA 1c. We further hypothesized that app-based usage would be positively correlated with HbA 1c improvements.

The study was approved by an independent institutional review board Western Institutional Review Board, Inc , which waived informed consent because the study was identified as having minimal risk and because the data were fully anonymized before use in the analysis. The study included adults 18 years or older from 2 major insurance carriers that were clients of Vida Health, and so participants received the Vida Health Program free of charge.

HbA 1c data were obtained directly from these insurance carriers via their data sharing arrangements with outpatient laboratory networks. Participants were eligible for the study if they had a baseline HbA 1c value of at least 7. Vida has made the Program available in both English and Spanish through professional translation and employs bilingual providers.

Eligible participants were recruited through a combination of brochures, outbound calling campaigns, and email announcements with general information provided about the Program and how to enroll.

They were directed to download the Vida Health app from the Apple App Store Apple Inc or Google Play Store Google and to enter an invitation code to confirm insurance coverage. After installing the app and prior to enrolling in the Vida Health Program, participants were presented with a series of brief in-app intake forms through which they provided contact information, basic demographic information self-reported weight, height, age, and gender , and existing health conditions.

Informed consent for digital nutrition therapy was a standard part of the initial app content. Exclusion criteria were type 1 diabetes, chronic kidney disease stages 4 or 5, congestive heart failure classes III or IV, pregnancy, and breastfeeding. The Program is a digital diabetes intervention program with remote coaching sessions encouraged up to weekly for the first 12 weeks and monthly thereafter.

Participants are paired with a Vida provider—certified health coach, registered dietitian, or certified diabetes care and education specialist—who specializes in diabetes self-management.

Vida providers receive intensive evidence-based training on motivational interviewing techniques that promote self-efficacy and autonomy for behavior change [ 26 ]. The Program combined one-to-one support, educational content, biomarker tracking, and data analysis to address self-care behaviors.

Provider support was delivered through live in-app audio-video sessions audio-only also available and text messaging. The initial encounter included a detailed health assessment. The Vida provider used motivational interviewing to guide the participant in defining the initial area of focus for lifestyle change and identifying any associated barriers.

Subsequent sessions followed up on these goals and worked to resolve ambivalence to change. Each session concluded with an individualized wellness plan including specific goals. Between counseling sessions, participants were encouraged to text message their Vida provider for further support.

The Vida provider used text messaging to offer feedback on data tracking and motivational interviewing to overcome barriers to change.

App content was the primary emphasis to support scalability. It included structured lessons and multimedia content see Figure 1 with evidence-based approaches to health behavior change, such as blood glucose self-monitoring, medication adherence, and nutrition [ 27 ].

Participants could review and interact with the lessons by responding to question prompts therein. The Vida provider reviewed completed lessons to help members apply their learnings to their goals and diabetes self-management behaviors.

For those participants who reported having been recommended self-monitoring of blood glucose, logging was encouraged. The Vida app supports connections to a variety of commercially available cellular connected blood glucose meters and also allows for manual logging of data. Structured logging capabilities for food intake and physical activity are also available.

The primary outcome measure for this study was HbA 1c. Baseline HbA 1c was defined as the laboratory test closest to Program start, measured between 6 months before to within 21 days after enrollment. The follow-up measure was defined as a HbA 1c test completed a minimum of 90 days post Program start.

In order to evaluate possible systematic baseline differences between participants with a valid follow-up measure and those with no follow-up, we performed a 2-tailed chi-square test to assess gender-based differences.

Additionally, a set of 2-tailed t tests were employed to evaluate differences between groups based on age and baseline HbA 1c. A paired t test was used to assess change in HbA 1c from baseline. A repeated measures analysis of variance ANOVA with the measurement period as a within-subject factor was used to analyze changes in HbA 1c from the pre-enrollment measure to baseline and from baseline to follow-up.

Pre-enrollment was defined as a HbA 1c measure obtained at least 90 days prior to the baseline. A Mauchly test was used to confirm that assumptions of sphericity had not been violated.

We conducted a series of post hoc pairwise comparisons of means to evaluate HbA 1c changes between each measurement window. Program usage was a secondary focus of this study.

User engagement, on the other hand, includes the subjective experience of the digital intervention with a focus on the quality of the experience [ 28 , 29 ]. Although the behavioral aspect of engagement usage and the subjective or experiential aspect eg, satisfaction, interest, perceived relevance can no doubt influence one another, their independent or interactive effect on clinical outcomes in the context of digital health remains unclear [ 29 ].

Measures of the experiential dimension of engagement were not assessed in this study. Program usage was conceptualized using 3 in-app behaviors. First, we computed a cumulative sum for each of the following factors: number of counseling sessions, number of messages sent to the provider by the participant, and the number of lessons completed within the first 6 weeks of Program start.

We then created a binary program usage variable where high usage was defined as participants with greater-than-or-equal-to-median coach interaction and greater-than-or-equal-to-median content interaction.

A cluster-robust multiple regression analysis was used to evaluate the association between the extent of usage and HbA 1c change.

Data preparation and analyses were performed using Python Version 3. The data sets analyzed for this study are available from the corresponding author upon request.

In all, participants enrolled in the Vida Health Diabetes Management Program. Thus, u-healthcare service has many other beneficial effects pertaining to healthy lifestyle changes encouraged by automated messages.

Several studies have used a different telehealth system in different settings. A previous study from our group confirmed the use of the glucometer with a mobile system and Zigbee communication protocol and showed improved self-care in diabetes management in elderly diabetic patients 8.

An Internet-based glucose control system used in the middle-aged type 2 diabetic patients recently showed a significant reduction of the A1C level More recently, another group from South Korea reported that the combined application of a mobile device and a Web-based monitoring system for 12 weeks improved various metabolic parameters in obese patients with diabetes and hypertension Thus, various applications of advanced information technology in different settings and populations would be helpful in diabetes management, and as a result, a great number of studies are ongoing in this field 13 , 23 — Patients in this study were allowed to change their therapeutic regimen according to the text messages generated by the CDSS rule engine.

Generally, every change in the drug regimen in Korea must be certified by a doctor. This study, however, was conducted under controlled and special circumstances where the participants were educated intensively and the dose of self-adjustment was limited to a very narrow range.

In addition, the investigators frequently monitored the text messages, and an active alert system also sent timely notification of adverse events. Nevertheless, the issue of self-changing therapy by patients is an area of uncertainty and concern, and more clinical studies are required to support its validity.

This study has some limitations. First, the study population size was relatively small. Second, the overall follow-up period was only 6 months. In addition, study participants were limited to elderly individuals, and only blood glucose levels were involved.

Thus, a large-scale, long-term clinical trial using the advanced CDSS rule engine for type 2 diabetic patients is required in the near future.

Despite more frequent testing, This could be attributed to education, reminder messages generated by the glucometer, and patient satisfaction with the glucose results associated with frequent testing.

The high proportion of elderly patients who completed the study is important for several reasons. The successful completion serves as an indication that even technologically challenged elderly individuals can adopt a new and advanced system.

It is critical, however, to provide sufficient education and training before such system implementation, as was done in this study. Another significance of this adapted population is its implication on the use of a new system in the general public.

Successful adoption of new technology by the general public when the u-healthcare system is widely implemented is anticipated from the results of our study. In conclusion, we have demonstrated that the 6-month application of the u-healthcare system, which is characterized by a proactive automated communication system using the CDSS rule engine, helped diabetic patients achieve target glycemic control with less hypoglycemia.

In the near future, we hope that the individualized u-healthcare system will contribute to diabetes management by reducing complications and improving quality of life and self-care in patients with diabetes.

and S. wrote the manuscript, researched data, and contributed to the discussion; H. contributed to the discussion; and H. researched data, contributed to the discussion, and reviewed and edited the manuscript.

Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care. Advanced Search.

User Tools Dropdown. Sign In. Skip Nav Destination Close navigation menu Article navigation. Volume 34, Issue 2. Previous Article Next Article. RESEARCH DESIGN AND METHODS. Article Navigation. Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System Soo Lim, MD ; Soo Lim, MD.

This Site. Google Scholar. Seon Mee Kang, MD ; Seon Mee Kang, MD. Hayley Shin, BS ; Hayley Shin, BS. Hak Jong Lee, MD ; Hak Jong Lee, MD. Ji Won Yoon, MD ; Ji Won Yoon, MD. Sung Hoon Yu, MD ; Sung Hoon Yu, MD. So-Youn Kim, RN ; So-Youn Kim, RN.

Soo Young Yoo, PHD ; Soo Young Yoo, PHD. Hye Seung Jung, MD ; Hye Seung Jung, MD. Kyong Soo Park, MD ; Kyong Soo Park, MD. Jun Oh Ryu, MD ; Jun Oh Ryu, MD. Hak C. Jang, MD Hak C. Jang, MD. Corresponding author: Hak C.

Jang, janghak snu. contributed equally to this work. Diabetes Care ;34 2 — Article history Received:. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. The CDSS-generated messages were patient-specific, for example:.

Table 1 Baseline characteristics of participants by group. Age, years View Large. Table 2 Changes of anthropometrics, biochemical parameters, and frequency of self-monitoring blood glucose by the groups after 6 months.

Affordable Care Act ACA Find Health Care Frequently Asked Questions Health Care Health Topics Improve Your Health Patient Forms. LISTSERV Email Groups National Combined Councils Patient Education Primary Care Provider Risk Management Veteran Resources.

Behavioral Health Community Health Representatives CHR Contacts Environmental Health Environmental Sustainability. Health Communications Health Promotion Injury Prevention Nutrition School Health Visualizing Data. Career Paths Health Professions Indian Health Careers.

Loan Repayment Student Opportunities Contact a Recruiter. Announcements Congressional Testimony Contact Us Director's Speeches Fact Sheets IHS Blog. IHS Updates Press Releases Reports to Congress Tribal Leader Letters Urban Leader Letters.

About IHS Special Diabetes Program for Indians SDPI SDPI Grant Resources Best Practices Glycemic Control. Importance Good glycemic control, as measured by A1C, reduces the risk of diabetes complications.

IMPORTANT NOTICE. However, it is important to individualize A1C goals when needed. For example, tighter goals for younger and healthier people may be appropriate.

Improvement: Increasing the number and percent of individuals in your Target Group who achieve this measure shows improvement. Timeframe: The timeframe for collecting data on the Required Key Measure will be January 1 st to December 31 st.

Data Collection: For more information on data collection and reporting, see the SDPI Outcomes System SOS.