Dataset Information

Effect of Digital-Tool-Supported Basal Insulin Titration Algorithm in Reaching Glycemic Control in Patients with Type 2 Diabetes in Mexico.

ABSTRACT:

Background

My Dose Coach (MDC) is a mobile application combined with a web portal that can suggest optimized basal insulin (BI) injection doses using Self-Measured Plasma Glucose (SMPG) and hypoglycemia data. This study aimed to evaluate its efficacy on patients reaching SMPG and Fasting blood glucose (FBG) target range 90-130 mg/dl (5-7.2 mmol/L) goals without severe hypoglycemic episodes. We also addressed the mean reduction in glycated hemoglobin (A1C), FBG, and SMPG and the improvement in the WHO's Five Well Being Index (WBI).

Methods

This prospective pilot study involved the use of MDC in outpatients with type 2 diabetes (T2DM) from a Hospital in Northern Mexico. Patients on treatment with any BI were included in the study. The follow-up was of 16 weeks. Student t-tests or McNemar test were used for effect comparisons.

Results

We included 158 patients (46.8% women), mean (SD) age 51 (10.3) years. We achieved SMPG target range in 58.9% [mean (95CI) reduction of 30.9 mg/dl (22.5-37.7; P < .001)] of the patients [66(28) days], with no severe hypoglycemia events. FBG goal was reached in 55.7% [mean (95CI) reduction of 63.4 mg/dl (49.6-77.2; P < .001)]. The mean (95CI) reduction of A1C was 1.78% (1.47-2, P < .01) with the last observation carried forward. There was a mean (95CI) increase of 2.23 (-3, -1.4, P < .01) points in WBI scale.

Conclusions

MDC successfully helped to achieve FBG and SMPG goals, reduced A1C, and increased WBI with no severe hypoglycemia events.

SUBMITTER: Tamez-Perez HE

PROVIDER: S-EPMC9631525 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effect of Digital-Tool-Supported Basal Insulin Titration Algorithm in Reaching Glycemic Control in Patients with Type 2 Diabetes in Mexico.

Tamez-Pérez Hector Eloy HE Cantú-Santos Oscar Manuel OM Gutierrez-González Dalia D González-Facio Rosalinda R Romero-Ibarguengoitia Maria Elena ME

Journal of diabetes science and technology 20210729 6

<h4>Background</h4>My Dose Coach (MDC) is a mobile application combined with a web portal that can suggest optimized basal insulin (BI) injection doses using Self-Measured Plasma Glucose (SMPG) and hypoglycemia data. This study aimed to evaluate its efficacy on patients reaching SMPG and Fasting blood glucose (FBG) target range 90-130 mg/dl (5-7.2 mmol/L) goals without severe hypoglycemic episodes. We also addressed the mean reduction in <i>glycated hemoglobin</i> (A1C), FBG, and SMPG and the im ...[more]

PMID: 34323110

Similar Datasets

Project description:IntroductionThe shortage of general practitioners (GPs) and the increasing prevalence of type 2 diabetes create significant pressure on primary healthcare services. To enable that medical services are available to all that need it, innovative solutions are needed. One of those, a Short Message Service (SMS)-supported basal insulin titration service is investigated in this study. The primary objective was to determine the percentage of subjects who achieved stable fasting blood glucose (FBG) within their individual target range with this service after week 16.MethodsThis single-arm, 16-week study aimed to enroll 111 adults diagnosed with type 2 diabetes that needed insulin. The study subjects measured their FBG 4 consecutive days to establish a baseline, then received SMS prompts for daily FBG measurements and evening insulin injections until their FBG stabilized within the target range. Adjusting the insulin based on the FBG. Once stabilization was achieved, subjects continued with their optimal insulin dose for the remainder of the study. Sixteen weeks after the baseline, subjects measured FBG for 4 days before visit 4, where these values were read by the healthcare provider.ResultsOut of the planned 111 subjects, only 30 were enrolled, with one withdrawal prior to service activation. Challenges in subject recruitment were attributed to the COVID-19 outbreak, limited eligibility, competing studies, and new medications delaying insulin initiation. Subjects were on average 59.97 years old, had an HbA1c of 9.29% a FBG of 205.64 mg/dl, and had diabetes for 10 years. Among the 29 subjects who started the service, 72% achieved successful titration at visit 4, with a median time of 49 days. Notable improvements were observed in HbA1c levels (decreased by 1.58%) and FBG levels (decreased by 64 mg/dl) over the 16-week study period. No adverse events or device-related issues were reported.ConclusionsDespite recruitment challenges, guided basal insulin titration holds promise for insulin therapy initiation in individuals. The findings emphasize the potential of tele-medical approaches, specifically through remote messaging, in managing diabetes and improving therapy adherence.

Project description:BackgroundSelf-monitoring of blood glucose (SMBG) using web-based diabetes management platforms has demonstrated promise in managing type 2 diabetes (T2D). However, the effectiveness of such systems incorporating algorithm-guided insulin titration has not been extensively studied in Asian populations.ObjectiveThis study evaluates the efficacy and safety of the ALRT telehealth solution-a US Food and Drug Administration-cleared, web-based platform that integrates SMBG with algorithm-driven insulin dose adjustments-in improving glycemia in insulin-treated T2D.MethodsThis 24-week, pre-post intervention study enrolled 25 adults with T2D (mean age 58.9, SD 7.0 y; n=14, 56% male) on twice-daily premixed insulin. Inclusion criteria included a baseline hemoglobin A1c (HbA1c) level between 7.5% to 9.9% (58-86 mmol/mol), a BMI ≤40 kg/m², and experience with SMBG. Participants uploaded twice-daily SMBG data weekly via a mobile app, which generated insulin titration recommendations based on a predefined algorithm. Physicians reviewed and approved the recommendations, which were then communicated back to participants via the app. The primary outcome was the change in HbA1c level from baseline to 24 weeks. Secondary outcomes included changes in fasting plasma glucose, insulin dose, hypoglycemia incidence, and SMBG adherence.ResultsParticipants achieved significant reductions in HbA1c level from 8.6% (70 mmol/mol) at baseline to 7.4% (57 mmol/mol) at 24 weeks (P<.001), with reductions of 0.8% and 0.4% in the first and second 12 weeks, respectively. Fasting plasma glucose decreased from 8.7 (SD 2.0) mmol/L to 7.1 (SD 1.4) mmol/L (P<.001). Mean total daily insulin dose increased modestly from 0.73 (SD 0.31) units/kg/day to 0.79 (SD 0.34) units/kg/day (P=.007). Participants demonstrated high adherence, completing 97.3% (327/336) of prescribed SMBG measurements. During the study, 48% (12/25) of participants experienced at least 1 hypoglycemia episode, predominantly mild hypoglycemia (85/96, 88.5%; glucose 3.0-3.9 mmol/L). Hypoglycemia episodes increased from 24 during weeks 0-12 to 72 during weeks 13-24. There were no episodes of severe hypoglycemia requiring external assistance. BMI increased slightly from 29.0 (SD 3.6) kg/m² to 29.5 (SD 3.6) kg/m² (P=.03), reflecting a modest weight gain associated with improved glycemia.ConclusionsIn conclusion, patients with insulin-treated T2D initiated on a web-based glucose monitoring system with algorithm-guided dosing recommendations showed significant improvement in glycemic control compared to baseline. High adherence rates underscore the feasibility of integrating algorithm-guided insulin titration into routine care. While hypoglycemia incidence rose slightly, episodes were predominantly mild, and no severe events occurred. This intervention shows promise for broader adoption in T2D management, particularly in resource-constrained settings.

Project description:Most patients who use insulin do not achieve optimal glycemic control and become susceptible to complications. Numerous clinical trials have shown that frequent insulin dosage titration is imperative to achieve glycemic control. Unfortunately, implementation of such a paradigm is often impractical. We hypothesized that the Diabetes Insulin Guidance System (DIGS™) (Hygieia, Inc., Ann Arbor, MI) software, which automatically advises patients on adjustment of insulin dosage, would provide safe and effective weekly insulin dosage adjustments.In a feasibility study we enrolled patients with type 1 and type 2 diabetes, treated with a variety of insulin regimens and having suboptimal glycemic control. The 12-week intervention period followed a 4-week baseline run-in period. During the intervention, DIGS processed patients' glucose readings and provided insulin dosage adjustments on a weekly basis. If approved by the study team, the adjusted insulin dosage was communicated to the patients. Insulin formulations were not changed during the study. The primary outcome was the fraction of DIGS dosage adjustments approved by the study team, and the secondary outcome was improved glycemic control.Forty-six patients were recruited, and eight withdrew. The DIGS software recommended 1,734 insulin dosage adjustments, of which 1,731 (99.83%) were approved. During the run-in period the weekly average glucose was stable at 174.2±36.7?mg/dL (9.7±2.0?mmol/L). During the following 12 weeks, DIGS dosage adjustments resulted in progressive improvement in average glucose to 163.3±35.1?mg/dL (9.1±1.9?mmol/L) (P<0.03). Mean glycosylated hemoglobin decreased from 8.4±0.8% to 7.9±0.9% (P<0.05). Concomitantly, the frequency of hypoglycemia decreased by 25.2%.The DIGS software provided patients with safe and effective weekly insulin dosage adjustments. Widespread implementation of DIGS may improve the outcome and reduce the cost of implementing effective insulin therapy.

Dataset Information

Effect of Digital-Tool-Supported Basal Insulin Titration Algorithm in Reaching Glycemic Control in Patients with Type 2 Diabetes in Mexico.

Background

Methods

Results

Conclusions

Publications

Effect of Digital-Tool-Supported Basal Insulin Titration Algorithm in Reaching Glycemic Control in Patients with Type 2 Diabetes in Mexico.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets