Utilization of a Cloud-Based Diabetes Management Program for Insulin Initiation and Titration Enables Collaborative Decision Making Between Healthcare Providers and Patients

Abstract

Background: Overseeing proper insulin initiation and titration remains a challenging task in diabetes care. Recent advances in mobile technology have enabled new models of collaborative care between patients and healthcare providers (HCPs). We hypothesized that the adoption of such technology could help individuals starting basal insulin achieve better glycemic control compared with standard clinical practice.

Materials and methods: This was a 12 ± 2-week randomized controlled study with 40 individuals with type 2 diabetes who were starting basal insulin due to poor glycemic control. The control group (n = 20) received standard face-to-face care and phone follow-up as needed in a tertiary center, whereas the intervention group (n = 20) received care through the cloud-based diabetes management program where regular communications about glycemic control and insulin doses were conducted via patient self-tracking tools, shared decision-making interfaces, secure text messages, and virtual visits (audio, video, and shared screen control) instead of office visits.

Results: By intention-to-treat analysis, the intervention group achieved a greater hemoglobin A1c decline compared with the control group (3.2 ± 1.5% vs. 2.0% ± 2.0%; P = 0.048). The Diabetes Treatment Satisfaction Questionnaire showed a significant improvement in the intervention group compared with the control group (an increase of 10.1 ± 11.7 vs. 2.1 ± 6.5 points; P = 0.01). HCPs spent less time with patients in the intervention group compared with those in the control group (65.9 min per subject vs. 81.6 min per subject). However, the intervention group required additional training time to use the mobile device.

Conclusions: Mobile health technology could be an effective tool in sharing data, enhancing communication, and improving glycemic control while enabling collaborative decision making in diabetes care.

FIG. 1.

Self-tracking visualization. The 24-h clock shows all of the subject's scheduled health actions. In this case the subject has three health actions scheduled between 6 a.m. and 10 a.m. (two pills and a blood glucose measurement) and one health action scheduled between 7 p.m. and 11 p.m. (an injection of 13 units of insulin). He can click on any of these health actions to see more information and to report adherence. Subjects can see and report their health actions even before they are due, which allows for proactive planning in their busy lives. The three buttons along the right side of the view are shortcuts to charts, messaging, and frequently asked questions. (The name and photograph used in this example do not belong to any study subject.)

FIG. 2.

Insulin titration decision support (PREDICTIVE 303 protocol). On the left side of the screenshot, the charts of the subject's health actions are displayed with each medication adherence and blood glucose adherence event indicated by a check. Pharmacokinetic curves are drawn for medications to highlight subtherapeutic levels from nonadherence, and individual blood glucose readings are plotted. On the right side of the screenshot, personalized decision support for the PREDICTIVE 303 protocol for insulin titration is visualized. Note that the language of the decision support appreciates the likelihood that a healthcare provider considers much more information in making an informed decision than can be accounted for in such a simple algorithm. (The name and photograph used in this example do not belong to any study subject.)

FIG. 3.

Changes in (top panel) hemoglobin A1c (HbA1c) and (bottom panel) Diabetes Treatment Satisfaction Questionnaire (DTSQ) score in the intervention group versus the control group over a 3-month period.