Abstract
Background:
Data on culturally tailored diabetes education with and without real-time continuous glucose monitoring (RT-CGM) in Latinos with type 2 diabetes, who are not on intensive insulin management, is lacking.
Research Design and Methods:
This is an open-label randomized control trial of Latinos with uncontrolled (HbA1c > 8.0%) type 2 diabetes conducted in a Federally Qualified Health Center (FQHC). All participants received 12 one-hour culturally tailored education sessions. Patients were randomized (1:1) to education sessions only (blinded CGM) or cyclic (50 days wear: 10 days on, 7 days off) RT-CGM. The primary outcome was a change in HbA1c from baseline to 12 weeks in those with or without CGM. Secondary outcomes included 24-week HbA1c, CGM, and metabolic parameters.
Results:
Participants (n = 120) were 46 years old on average, 44% female, 98% preferred Spanish language, 30% with income <$25,000, 68% uninsured and 26% using basal insulin only. Mean 1-hour session attendance and RT-CGM wear was 7.0 (±4.4) and 27.9 (±20.5) days, respectively. Mean baseline HbA1c was 10.5% (±1.8). HbA1c reduced by 1.9% (95% confidence interval [CI]: 1.5-2.3) overall (P < .001). Participants in the RT-CGM group reduced HbA1c at 12 weeks by 2.3% (95% CI: 1.5-3.2) compared to 1.5% (95% CI: 0.6-2.3) in the blinded CGM group (P =.04). At 24 weeks, overall HbA1c reduction was maintained but between-group differences attenuated.
Conclusions:
In a Latino type 2 diabetes population that was primarily noninsulin-requiring, virtually delivered, culturally tailored education improved HbA1c, with RT-CGM conferring greater improvement. RT-CGM should be an adjunctive therapy to diabetes education, irrespective of insulin use but continued cyclic CGM use may be needed for sustained effect.
Keywords: continuous glucose monitoring, diabetes education, type 2 diabetes, health equity, lifestyle changes
Introduction
In the United States, one in 10 adults live with diabetes 1 and Latinos are disproportionately affected. 2 Latinos are more likely to develop diabetes complications3,4 and face more barriers to accessing care. 5 Despite the growing availability of diabetes medications and technology, many are not reaching glycemic goals 6 while complications rise. 7 Diabetes Self-Management Education and Support (DSMES) is paramount to diabetes management and reducing complications. Many existing programs are not designed for Latino patients with specific cultural preferences and data on effective diabetes education tailored for the Latino population are limited.8,9
The DSMES program Compañeros en Salud (Partners in Health) is a bilingual, multicultural curriculum that uses storytelling to engage diverse populations living with type 2 diabetes.10-12 It consists of 12 sessions including topics such as nutrition, physical activity, medication adherence, stress management, and coping skills. Two earlier studies assessed this program, showing its acceptability and improvements in glycemic outcomes.11,13 Despite the recent rise in the use of telemedicine, 14 which provides a way to address difficulty accessing diabetes education; it is often underutilized among vulnerable groups like the Latino population.14,15 Issues with digital literacy contribute significantly to health disparities.5,16 Support of digital literacy may be a way to increase access to DSMES.
Education alone may not suffice to change behavior and sustain improvements in diabetes outcomes. Real-time continuous glucose monitoring (RT-CGM) is another tool that improves glucose control and reduces hypoglycemia in people with type 1 and type 2 diabetes on intensive insulin management.17,18 Medicare has increased coverage for CGM including those with any insulin requirement or those with a history of severe hypoglycemia; 19 however, CGM remains largely unavailable for noninsulin-requiring patients. 20 In addition, Latinos are less likely to use CGM, even those on intensive insulin therapy. 21 The original study on RT-CGM for type 2 diabetes patients, mostly noninsulin-requiring, showed that three months of RT-CGM lowered glucose levels, with effects lasting beyond the period of use. 22 Similar results were found in studies with insulin-requiring individuals using RT-CGM23,24 or intermittent scan (IS-CGM), 25 though glucose control worsened when CGM use was stopped. 24 RT-CGM provides real-time insights into glycemic excursions and the effects of food, activity, and medication.26,27 More research is needed to integrate RT-CGM with virtual DSMES programs and benefit Latino populations, especially noninsulin users. Our pilot study of 15 participants using RT-CGM with Compañeros en Salud found RT-CGM acceptable, 28 with improved understanding and diabetes management among Latino participants. A larger, more rigorous study is needed to confirm these findings and assess effectiveness.
The primary goal of the study is to analyze the impact on glycemic indices of a 12 week culturally tailored DSMES program delivered virtually in Spanish to Latino patients. Further investigation was performed to show the effect of integrating cyclic RT-CGM during the educational program and assess whether any benefits persisted after the discontinuation of CGM.
Methods
Study Design
The culturally tailored (CUT)-DM with CGM study enrolled 120 adults with uncontrolled type 2 diabetes in a 6 month, investigator-initiated study evaluating the impact of a virtually delivered, culturally tailored diabetes education program on glycemic indices. The full protocol 29 is reported elsewhere and the culturally tailored educational curriculum 30 is available. Baseline social determinants of health were assessed. The study secondly aimed to evaluate behavioral shifts in nutrition and activity between participants randomized to RT-CGM versus blinded CGM. Lifestyle changes from participants will be reported elsewhere.
Participants
Participants were identified through a Federally Qualified Health Center (FQHC) system in the greater Seattle region by reviewing electronic medical records for HbA1c >8.0%, new appointments and follow-up visits for diabetes, and direct referrals from primary-care providers. Included were individuals with type 2 diabetes who self-identified as Latino over the age of 18 with a HbA1c >8.0%. Major exclusion criteria were intensive insulin therapy and CGM use. Full inclusion and exclusion criteria were reported previously. 29 Oral and written information about the study was provided before participant consent was obtained.
The recruitment period was June 2022 to December 2023. Final follow-up occurred July 2024.
Randomization
Diabetes education was a core component for all participants. All participants received DSMES and were randomized (1:1) to either RT-CGM or blinded CGM using a centralized computer-generated randomization process and stratification by baseline HbA1c (<9.0 or ≥9.0%) using a REDCap database (Research Electronic Data Capture, Vanderbilt University). Permuted block randomization was used.
Procedures
After screening and obtaining consent, participants were randomized and their baseline demographics, social determinants of health and lifestyle questionnaires were assessed before undergoing initial laboratory assessment. All participants began with a 10-day blinded CGM and received digital health support. Real-time continuous glucose monitoring participants received education on CGM use and interpretation and were advised to use the device for 50 days over 12 weeks. At 24 weeks, they wore blinded CGM again. Data from both RT-CGM and control groups were gathered at 12 and 24 weeks using the Dexcom G6 system (Dexcom, San Diego, CA), with smartphone provided if necessary.
Measurements
The primary outcome is pre-post education change in HbA1c, from baseline to 12 and 24 weeks, adjusting for CGM use. Secondary outcomes included CGM measures of time below range (TBR < 70 mg/dl), time in range (TIR 70-180 mg/dl), time above range (TAR > 180-250 mg/dl), time very high range (TAR > 250 mg/dl), average glucose, and coefficient of variation (CV). Demographics, baseline food insecurity, and neighborhood safety were collected. Anthropometric measures of weight, body mass index (BMI), systolic and diastolic blood pressure (SBP, DBP) were collected.
Statistical Analysis
Sample size was chosen to assess HbA1c changes from baseline to 12 weeks and compare HbA1c at 12 weeks between RT-CGM and blinded CGM groups. Assuming a standard deviation of 1.2 at baseline and 1.0 at 12 weeks, with a 0.5 correlation between measurements, a sample of 85 participants was needed to detect a 0.4% reduction with over 90% power. The study has 93% power to detect a 0.6% difference at 12 weeks between groups. Initially, a 15% dropout rate was anticipated, aiming for 100 participants, but the target was revised to 120 due to lower follow-up rates.
Baseline characteristics are described by CGM group, with means (±SD) for continuous outcomes and frequencies (%) for categorical outcomes. Differences in proportions were tested with two-sample proportion tests. A random-intercept linear mixed-effects regression model was used to evaluate the curriculum’s effectiveness on changes from baseline to 12 and 24 weeks for the entire cohort and CGM status. A similar model with an interaction term between CGM use and time assessed RT-CGM’s effect on outcomes.
Missing data analysis characterized participants without follow-up data. Sensitivity analysis used inverse probability weighting, multiple imputation, and last value carried forward. Analyses were performed using R version 4.4.1 (The R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org). A two-sided P value <.05 was considered significant. Secondary analyses were exploratory and not adjusted for multiple comparisons.
The study was approved by the University of Washington IRB Committee (IRB ID: STUDY00014396, date 1/7/2022), registered at ClinicalTrials.gov (identifier: NCT05394844) and funded by the American Diabetes Association Disparities of Health Care grant (11-21-ICTSHD-51). Dexcom provided CGM supplies at reduced cost.
Results
Participant Follow-Up
A total of 257 Latino patients with Type 2 diabetes were assessed (Figure 1) with 120 participants enrolled and randomized to RT-CGM (n = 61) or blinded CGM (n = 59). At baseline, 54 (89%) RT-CGM participants and 52 (88%) blinded CGM participants had usable CGM data. Follow-up at 12 weeks was 40 (68%) and 41 (67%) for blinded and RT-CGM, respectively, with 38 and 45 participants with CGM data in each group. At 24 weeks, follow-up was 34 (58%) for blinded and 37 (60%) for RT-CGM.
Figure 1.
Consort diagram.
Baseline Characteristics and Social Determinates of Health
Table 1 details baseline characteristics. The 120 participants had a mean age of 46 (±8) years, 98% preferred Spanish, and 60% had less than high school education. Thirty percent of participants had incomes less than $25,000, 68% did not have medical insurance, and 88% had compatible smartphones.
Table 1.
Baseline Characteristics.
| Characteristic | Overall | Blinded CGM | RT-CGM |
|---|---|---|---|
| N = 120 | N = 59 | N = 61 | |
| Age | 46 (8) | 47 (7) | 46 (9) |
| Female | 53 (44%) | 24 (41%) | 29 (48%) |
| Education | |||
| Never attended/Kindergarten only | 4 (3.4%) | 3 (5.1%) | 1 (1.7%) |
| 1st–6th grade | 32 (27%) | 16 (27%) | 16 (27%) |
| 7th–12th grade (no diploma) | 35 (29%) | 20 (34%) | 15 (25%) |
| High school/GED graduate | 37 (31%) | 15 (25%) | 22 (37%) |
| Associates degree or higher | 11 (9.2%) | 5 (8.5%) | 6 (10%) |
| Unknown | 1 | 0 | 1 |
| Language preference Spanish | 117 (98%) | 58 (98%) | 59 (97%) |
| Household income | |||
| <$25 000 | 36 (30%) | 22 (37%) | 14 (23%) |
| $25 000 to $49 999 | 38 (32%) | 17 (29%) | 21 (34%) |
| $50 000 to $99 999 | 14 (12%) | 7 (12%) | 7 (11%) |
| $100 000 or more | 2 (1.7%) | 0 (0%) | 2 (3.3%) |
| Unknown/declined to answer | 30 (25%) | 13 (22%) | 17 (28%) |
| Insurance type | |||
| Commercial | 29 (25%) | 19 (32%) | 10 (17%) |
| Self-pay | 80 (68%) | 36 (61%) | 44 (75%) |
| Medicaid | 9 (7.6%) | 4 (6.8%) | 5 (8.5%) |
| Unknown | 2 | 0 | 2 |
| Use government assistance program | 14 (12%) | 8 (14%) | 6 (9.8%) |
| Complications from diabetes | 6 (5.0%) | 2 (3.4%) | 4 (6.6%) |
| History of disease | |||
| Kidney disease | 5 (4.2%) | 1 (1.7%) | 4 (6.6%) |
| Heart disease | 3 (2.5%) | 0 (0%) | 3 (4.9%) |
| Eye disease | 8 (6.7%) | 3 (5.1%) | 5 (8.2%) |
| Peripheral neuropathy | 30 (25%) | 13 (22%) | 17 (28%) |
| Medication | |||
| No basal insulin nor insulin sparing | 4 (3.3%) | 2 (3.4%) | 2 (3.3%) |
| Only basal insulin | 4 (3.3%) | 3 (5.1%) | 1 (11.6%) |
| Only insulin sparing | 86 (72%) | 41 (70%) | 45 (74%) |
| Both basal insulin and insulin sparing | 26 (22%) | 13 (22%) | 13 (21%) |
| Number of diabetes meds | |||
| 0 | 4 (3.3%) | 2 (3.4%) | 2 (3.3%) |
| 1 | 53 (44%) | 30 (51%) | 23 (38%) |
| 2 or more | 63 (52%) | 27 (45%) | 36 (59%) |
| Has compatible smartphone | 106 (88 %) | 52 (88 %) | 54 (88%) |
Fifty percent of participants felt that the food they bought did not last, and 54% of participants felt that they could not afford a balanced meal (Supplemental Table 1, Appendix 1). Overall, 56% showed food security. At baseline, 75.3% of participants were only on insulin-sparing medications and 26% were on basal insulin. Fifty-three percent of participants were taking 2 or more medications. The average HbA1c at baseline for the blinded and RT-CGM groups was 10.6% (±1.8) and 10.4% (±1.8), respectively (Table 2).
Table 2.
Outcomes Descriptive and Model results.
| Outcome | Visit | Blinded | RT-CGM | Average change from baseline | Group change from baseline | Change in changes (RT-blinded) | P value | ||
|---|---|---|---|---|---|---|---|---|---|
| Mean (SD) | Mean (SD) | Estimate (CI) | P value | Blinded | RT-CGM | ||||
| Primary outcome | |||||||||
| HbA1c (%) | Baseline | 10.6 (1.8) | 10.4 (1.8) | − | − | − | − | − | − |
| 12 weeks | 9.0 (2.2) | 8.0 (1.7) | −1.9 (−2.3, −1.5) | <.01 | −1.5 (−2.3, −0.6) | −2.3 (−3.1, −1.5) | −0.9 (−1.7, −0.1) | .04 | |
| 24 weeks | 8.2 (2.0) | 8.4 (1.9) | −2.0 (−2.5, −1.6) | <.01 | −2.2 (−3.1, −1.2) | −1.9 (−2.8, −1.1) | 0.2 (−0.6, 1.1) | .61 | |
| HbA1c (mmol/mol) | Baseline | 92.2 (20.2) | 90.1 (19.3) | − | − | − | − | − | − |
| 12 weeks | 74.5 (24.1) | 64.1 (18.3) | −20.7 (−25.2, −16.3) | <.01 | −15.9 (−25.1, −6.8) | −25.4 (−34.4, −16.3) | −9.4 (−18.2, −0.6) | .04 | |
| 24 weeks | 66.2 (22.0) | 68.1 (21.2) | −22.4 (−27.1, −17.6) | <.01 | −23.8 (−33.9, −13.7) | −21.3 (−30.7, −11.9) | 2.4 (−7.0, 11.9) | .61 | |
| Secondary outcomes | |||||||||
| Time in range (%) | Baseline | 25.7 (28.4) | 30.6 (33.0) | − | − | − | − | − | − |
| 12 weeks | 34.2 (34.9) | 42.6 (32.7) | 10.0 (4.0, 16.0) | <.01 | 6.2 (−6.7, 19.1) | 13.2 (1.3, 25.2) | 7.0 (−5.0, 19.0) | .25 | |
| 24 weeks | 39.5 (31.9) | 44.6 (38.1) | 9.8 (3.2, 16.4) | <.01 | 10.8 (−3.0, 24.7) | 8.6 (−4.9, 22.0) | −2.3 (−15.5, 10.9) | .73 | |
| Average glucose (mg/dL) | Baseline | 247.1 (73.7) | 244.2 (79.0) | − | − | − | − | − | − |
| 12 weeks | 239.6 (84.4) | 208.4 (62.5) | −20.6 (−34.6, −6.6) | <.01 | −1.0 (−30.4, 28.4) | −37.4 (−64.7, −10.1) | −36.5 (−63.9, −9.0) | .01 | |
| 24 weeks | 224.4 (69.8) | 215.6 (79.4) | −17.6 (−33.0, −2.2) | .03 | −13.9 (−45.5, 17.6) | −19.6 (−50.4, 11.2) | −5.7 (−35.8, 24.5) | .71 | |
| Time in very high (%) | Baseline | 45.9 (35.8) | 44.9 (35.8) | − | − | − | − | − | − |
| 12 weeks | 43.3 (38.9) | 26.4 (27.9) | −10.3 (−17.4, −3.3) | <.01 | −0.5 (−15.2, 14.2) | −18.8 (−32.5, −5.1) | −18.3 (−32.0, −4.5) | .01 | |
| 24 weeks | 34.3 (35.9) | 33.4 (36.9) | −8.6 (−16.4, −0.9) | .03 | −8.5 (−24.3, 7.3) | −8.1 (−23.5, 7.3) | 0.4 (−14.7, 15.5) | .96 | |
| Time above range (%) | Baseline | 27.2 (18.5) | 30.4 (28.2) | − | − | − | − | − | |
| 12 weeks | 21.8 (17.3) | 31.3 (18.4) | −2.2 (−7.0, 2.6) | .36 | −5.5 (−15.7, 4.7) | 0.7 (−8.8, 10.2) | 6.2 (−3.4, 15.7) | .20 | |
| 24 weeks | 26.1 (16.4) | 21.4 (16.5) | −4.0 (−9.3, 1.2) | .13 | −1.5 (−12.5, 9.4) | −6.6 (−17.3, 4.1) | −5.0 (−15.5, 5.4) | .34 | |
| Time below range (%) | Baseline | 0.8 (2.9) | 0.4 (1.3) | − | − | − | − | − | − |
| 12 weeks | 0.7 (2.0) | 0.3 (0.5) | −0.1 (−0.5, 0.3) | .68 | −0.0 (−0.9, 0.8) | −0.1 (−0.9, 0.7) | −0.1 (−0.9, 0.7) | .84 | |
| 24 weeks | 0.2 (0.4) | 0.5 (1.6) | −0.3 (−0.7, 0.1) | .17 | −0.6 (−1.5, 0.3) | −0.0 (−0.9, 0.9) | 0.6 (−0.3, 1.4) | .19 | |
| Time in very low (%) | Baseline | 0.4 (1.6) | 0.2 (0.6) | − | − | − | − | − | − |
| 12 weeks | 0.2 (0.5) | 0.1 (0.3) | −0.1 (−0.3, 0.1) | .31 | −0.2 (−0.6, 0.2) | −0.0 (−0.5, 0.4) | 0.1 (−0.3, 0.6) | .48 | |
| 24 weeks | 0.1 (0.3) | 0.2 (0.4) | −0.1 (−0.4, 0.1) | .19 | −0.3 (−0.8, 0.2) | −0.0 (−0.5, 0.4) | 0.2 (−0.2, 0.7) | .28 | |
| Coefficient of variation | Baseline | 23.0 (11.3) | 22.0 (8.1) | − | − | − | − | − | − |
| 12 weeks | 22.4 (7.7) | 22.8 (6.2) | 0.4 (−1.6, 2.3) | .71 | −0.3 (−4.5, 3.8) | 0.9 (−2.9, 4.8) | 1.2 (−2.6, 5.1) | .53 | |
| 24 weeks | 23.0 (7.6) | 21.5 (6.7) | −0.2 (−2.3, 1.9) | .86 | 0.1 (−4.3, 4.6) | −0.5 (−5.0, 3.9) | −0.7 (−5.0, 3.6) | .76 | |
| Weight (lbs) | Baseline | 182.0 (37.4) | 184.1 (39.7) | − | − | − | − | − | − |
| 12 weeks | 188.2 (38.8) | 186.2 (47.4) | 0.6 (−3.8, 5.0) | .79 | 3.5 (−5.7, 12.7) | −2.2 (−11.2, 6.7) | −5.7 (−14.5, 3.1) | .20 | |
| 24 weeks | 180.1 (46.3) | 191.8 (45.1) | 0.1 (−4.6, 4.9) | .95 | −2.3 (−12.3, 7.7) | 2.1 (−7.2, 11.5) | 4.4 (−4.9, 13.8) | .35 | |
| BMI (kg/m²) | Baseline | 35.0 (27.5) | 31.7 (5.5) | − | − | − | − | − | − |
| 12 weeks | 33.1 (5.5) | 32.8 (5.7) | −0.7 (−4.4, 3.0) | .72 | −2.4 (−10.1, 5.3) | 1.0 (−6.6, 8.6) | 3.4 (−4.0, 10.8) | .37 | |
| 24 weeks | 32.6 (5.8) | 32.5 (6.0) | −1.0 (−4.9, 2.9) | .62 | −3.0 (−11.3, 5.4) | 0.7 (−7.0, 8.5) | 3.7 (−4.1, 11.5) | .35 | |
| SBP (mm Hg) | Baseline | 126.5 (15.4) | 125.5 (17.0) | − | − | − | − | − | − |
| 12 weeks | 125.3 (17.0) | 123.7 (17.1) | −1.4 (−4.7, 1.9) | .41 | −0.3 (−7.2, 6.7) | −2.5 (−9.3, 4.4) | −2.2 (−8.9, 4.5) | .52 | |
| 24 weeks | 125.1 (14.0) | 123.9 (14.5) | −0.7 (−4.2, 2.8) | .68 | 0.1 (−7.3, 7.6) | −1.6 (−8.7, 5.5) | −1.7 (−8.8, 5.4) | .64 | |
| DBP (mm Hg) | Baseline | 83.4 (10.4) | 81.9 (10.9) | − | − | − | − | − | − |
| 12 weeks | 86.6 (9.1) | 82.6 (11.2) | 1.8 (−0.7, 4.2) | .16 | 3.3 (−1.8, 8.4) | 0.3 (−4.8, 5.3) | −3.0 (−8.0, 1.9) | .22 | |
| 24 weeks | 82.6 (8.9) | 79.8 (8.3) | −1.4 (−4.0, 1.2) | .28 | −0.3 (−5.8, 5.2) | −2.5 (−7.7, 2.8) | −2.2 (−7.3, 3.0) | .41 | |
Education Session Attendance
Participants attended on average 7.0 (±4.4) sessions (Supplemental Table 2, Appendix 1), with 37 (63%) participants of the blinded and 38 (62%) of the RT-CGM group attending more than 50% of sessions (Figure 2a). Make-up recorded sessions versus live virtual sessions were not differentiated for the first cycle, but in later cycles of education participants attended on average 5.6 (±4.3) sessions live online and watched recorded sessions (1.6 (±2.3). Values were comparable between social determinants of health, with no significant difference in attendance across education level, insurance type or income (Supplemental Table 3, Appendix 1). When comparing the session attendance with the difference in HbA1c between baseline and 12 weeks, the RT-CGM group average difference is similar between those who attended 3 or 12 sessions (Figure 2b). Among participants randomized to RT-CGM, 23% did not use the device and 50% used the device for 31 to 50 days (Figure 2c). Participants who wore the RT-CGM for fewer than 20 days demonstrated less change in HbA1c between visits compared to those who wore the RT-CGM more than 30 days (Figure 2d). Days of RT-CGM use was highly correlated with the number of sessions attended (Figure 2e).
Figure 2.
Data after 12 weeks. Frequency of sessions attendance (a). Association between difference in HbA1c by session attendance (b). Days of RT-CGM use (c). Association of days of RT-CGM use and difference in HbA1c (d). Association between session attendance and days of RT-CGM use (e).
Primary Outcome (HbA1c)
Among all participants, HbA1c reduced by 1.9% (95% confidence interval [CI]: 1.5, 2.3) from 10.5% (±1.8) at baseline to 8.5% (±2.0) at 12 weeks (P < .001) and 2.0% (95% CI: 1.6, 2.5; P < .001) lower at 24 weeks (Table 2). Participants in the RT-CGM group had a statistically significant greater reduction in HbA1c of 2.3% (95% CI: 1.5, 3.2) compared to a reduction of 1.5% (95% CI: 0.6, 2.3) in the blinded CGM group (P = .04) at 12 weeks (Figure 3a).
Figure 3.
(a) Primary outcomes at baseline, 12 and 24 weeks. Error bars indicate model-based confidence intervals around point estimates. Colors indicate CGM group. (b) Percentage of participants of each group with HbA1c less than 7%, 7% to less than 8%, 8% to less than 9 and 9% or more at baseline, 12 and 24 weeks. (c) Average glucose, TIR, TAB (180-250 mg/dl) and TAB(>250 mg/dl) at baseline, 12 and 24 weeks.
At baseline, 78% and 75% of participants in the blinded and RT-CGM groups had a HbA1c of ≥9% (Figure 3b). After 12-weeks of educational intervention, 20% of the blinded group reduced their HbA1c to <7% compared to 37% of participants in the RT-CGM group (P = .16). At 24 weeks, a similar but large percentage of participants in the blinded (39%) and RT-CGM (32%) arms had a meaningful decrease in HbA1c of <7%.
Secondary Outcomes Including, Medications, CGM Indices and Other Metabolic Health Indices
After the 12-week educational intervention, four participants in the blinded group and four participants in RT initiated or increased at least one diabetes medication, while three and two participants stopped or lowered at least one diabetes medication, respectively (Supplemental Table 4, Appendix 1).
For the secondary outcomes of CGM indices, there were significant changes in TIR, TAR (>250 mg dl) and average glucose (Table 2 and Figure 3c). In both groups, TIR increased by 10.0% (95% CI: 4.0%, 16.0%; P = .001) after 12 weeks and this change was maintained at 24 weeks (9.8%; 95% CI: 3.2%, 16.4%; P = .004). Differences in TIR were not statistically different between blinded and RT-CGM groups at either time point. TAR (>250 mg/dL) in both groups decreased by 10.3% (95% CI: 3.3%, 17.4%; P = .004), and at 24 weeks was lower by 8.6% (95% CI: 0.9%, 16.4%; P = .03) points. The change was significantly different between groups, with time >250 mg/dL improving by 18.3% (95% CI: 4.5%, 32.0%) in the RT-CGM group compared to the blinded group (P = .01). Finally, among all participants, the average glucose was 20.6 mg/dL (95% CI: 6.6, 34.6; P = .004) lower after 12 weeks of educational intervention, and 17.6 mg/dL (95% CI: 2.2, 33.0; P = .03) lower at 24 weeks. The change was significantly different between CGM groups after 12 weeks (P = .01), with an average glucose 37.4 mg/dL lower after 12 weeks for the RT-CGM, compared to 1.0 mg/dL for the blinded CGM group. There were no statistically significant changes from baseline TBR (<70 mg/dL or <54 mg/dL), TAR (180-250 mg/dL), CV, weight, BMI, SBP and DBP at either 12 or 24-weeks or by CGM group.
Sensitivity analysis on missing data showed no substantial difference in demographics between patients with missing outcomes (Supplemental Table 5, Appendix 2) but reduced glycemic control in patients with missing follow-ups (Supplemental Table 6, Appendix 2). However, a significant reduction in HbA1c of 1.2% (95% CI: 0.9, 1.5; P value < .01) at 12 and 24 weeks was maintained when conservatively imputing no change in glycemic outcomes for those with missing data or with IPW (Supplemental Table 7, Appendix 2). Multiple imputation preserved overall differences in HbA1c, TIR, TAR (>250 mg/dl), and average glucose, but differences between blinded and RT-CGM were no longer statistically significant.
Discussion
Education is a core component of diabetes management and standard of care. This study demonstrated that participation in a culturally tailored educational intervention resulted in a significant improvement in glycemic indices among a primarily Spanish speaking population with uncontrolled type 2 diabetes on basal insulin and/or insulin-sparing medications. Participants using RT-CGM had an even greater reduction in HbA1c at 12 weeks and a larger decrease in TAR compared to the education-only group. Despite a higher dropout rate, the educational intervention and RT-CGM effect was consistent in sensitivity analyses accounting for missing data.
The virtually delivered, culturally tailored DSMES proved effective, with 62.5% of participants completing over half of the sessions. Our study aligns with The American Diabetes Association’s recommendation to encourage virtual education. 31 Some participants utilized recorded sessions and preferred YouTube videos, highlighting the need for hybrid learning models. In this study, culturally adapted educational materials improved glycemic indices. This study also demonstrates the feasibility of virtual education for the Latino community. Further research is needed to better understand diverse populations in healthcare education. However, we should consider that each community, given its racial and ethnic diversity, may need to have education approaches tailored to account for their unique experiences.
Diabetes self-management education and support is fundamental to diabetes management, and our study identified additional requirements for delivering effective education to the Latino population. Moreover, our study combined education with RT-CGM, yielding four distinct insights in RT-CGM research: (1) a Latino population facing inequities in health care, (2) a primary noninsulin and/or basal insulin only requiring population, (3) the impact of coupling RT-CGM with DSMES, and (4) employing cyclical rather than continuous RT-CGM use.
The MOBILE study was the first large randomized controlled trial (RCT) with long-term follow-up to include a diverse population, such as Latino participants. 32 However, our patient characteristics were different: 50% of the MOBILE’s participants had commercial insurance and only 29% were Latino. Our study uniquely focuses on the Latino population, who often fall short of HbA1c goals and have more diabetes complications. 4
Our population had lower income and health literacy than the MOBILE study. The greater reduction in HbA1c among RT-CGM participants, even those attending fewer DSMES sessions, suggests that RT-CGM helps people comprehend glucose changes regardless of their educational background, language barriers, or ability to attend educational sessions.
Our initial 12 month RT-CGM study in a basal or noninsulin-requiring population using older CGM technology showed a 0.8% greater reduction in HbA1c compared to SMBG. 22 In this study, only 26% were on basal insulin, adding to evidence of CGM’s benefit for those not needing insulin. Recent studies in noninsulin or mixed medication populations also show significant HbA1c reductions. Larger RCTs like DIAMOND, MOBILE, and Steno2tech,18,32,33 which included participants on insulin, observed HbA1c improvements of 0.3%, 0.4%, and 0.9% over 6, 8, and 12 months, respectively.
Our study highlights CGM’s potential benefits for all type 2 diabetes patients, noting an almost 0.9% greater HbA1c reduction over education alone, despite dropout and some loss of effect. These studies, differing in duration, baseline HbA1c, TIR, and medication use, support CGM’s efficacy. The MOBILE study sub-analysis, 24 in particular, showed greater benefits with higher initial HbA1c (>10.0%). Our study also emphasizes CGM’s utility in uncontrolled diabetes, especially for noninsulin-requiring individuals with type 2 diabetes.
Published data are sparse in younger populations, with average age in most studies greater than 55 compared with our younger cohort (mean = 47 y). Studies in CGM coupled to education are also limited.34,35 Only two recent studies have coupled DSMES with IS-CGM technology. The IMMEDIATE 36 study investigated DSMES with and without IS-CGM in a noninsulin-requiring population and showed those with higher initial HbA1c levels (>9.0%) demonstrated more glycemic reduction. Conversely, the GLiMPSE study, 37 which provided IS-CGM weekly for 6-weeks then monthly for 24 weeks showed equal benefit from self-monitoring blood glucose and IS-CGM with 0.6% change in both arms, an improvement that persisted through one year. While the IMMEDIATE study results resembled our findings, their analysis did not evaluate posttherapy changes. Our study followed participants for an additional 3 months after cessation of RT-CGM and observed attenuation of CGM effects at 24 weeks. This loss of benefit was similar to the follow-up results from the MOBILE study, which demonstrated worsening glycemic indices after stopping RT-CGM. 38
There is increasing data on CGM for type 2 diabetes for patients without insulin, but cost remains a significant issue. Medicare has expanded coverage to include everyone on insulin and those with severe hypoglycemia. 19 However, only 12% of individuals start using insulin within one year of diagnosis. 1 If CGM coverage recommendations extended to all individuals with diabetes regardless of insulin use, the cost burden would rise. One potential insight from our study that could lead to cost savings is the consideration of cyclical/intermittent use. We recommended 10 days on and 1 week off during a 12-week educational intervention, aligning with our original study. 22 A smaller RCT showed benefits from one to two sessions of CGM, particularly for those testing their blood glucose >1.5 times a day, suggesting that regular glucose monitoring feedback is necessary. 39 The GLiMPSE study highlighted that frequent monitoring from either CGM or SMBG is effective, using 6 weeks of IS-CGM, followed by monthly IS-CGM, with a sustained effect at 1-year. 37 Since frequent SMBG creates a burden for patients, use of cyclical CGM may prevent fatigue 40 and lower costs if CGM indications expand. Data on cyclical use of RT-CGM; however limited, support intermittent use and underscore the importance of continued long-term use in contrast with our initial study. 22
The study has limitations, such as being conducted at a single healthcare center, though participants were from various regional clinics. Continuous glucose monitoring is a tool for patient empowerment, but not everyone chose to use it, with 23% not wearing the device. More education on the device might be needed, or it may not be desired by all. The major limitation was the higher-than-anticipated dropout rate, potentially biasing results despite sensitivity analyses continuing to demonstrate CGM’s benefits.
Conclusions
Overall, virtually delivered DSMES was clinically effective at improving glycemic indices in a Latino population with uncontrolled type 2 diabetes (Hba1c > 10%) in the setting of food insecurity, low health literacy and low income. Real-time CGM coupled to education added additional benefits over 12 weeks, but the effect dissipated by 24 weeks. The use of CGM with education was effective, but may highlight the need for continued CGM use to manage diabetes successfully. Technology is advancing with lower-cost CGM options and early access to virtual DSMES and RT-CGM is needed for all populations.
Supplemental Material
Supplemental material, sj-docx-1-dst-10.1177_19322968251331526 for A Randomized Clinical Trial of a Culturally Tailored Diabetes Education Curriculum With and Without Real-Time Continuous Glucose Monitoring in a Latino Population With Type 2 Diabetes: The CUT-DM With Continuous Glucose Monitoring Study by Nicole Ehrhardt, Laura Montour, Peter Berberian, Ana Gabriela Vasconcelos, Bryan Comstock and Lorena Alarcon-Casas Wright in Journal of Diabetes Science and Technology
Supplemental material, sj-docx-2-dst-10.1177_19322968251331526 for A Randomized Clinical Trial of a Culturally Tailored Diabetes Education Curriculum With and Without Real-Time Continuous Glucose Monitoring in a Latino Population With Type 2 Diabetes: The CUT-DM With Continuous Glucose Monitoring Study by Nicole Ehrhardt, Laura Montour, Peter Berberian, Ana Gabriela Vasconcelos, Bryan Comstock and Lorena Alarcon-Casas Wright in Journal of Diabetes Science and Technology
Acknowledgments
Ka’imi A. Sinclair PHD, MPH, previously Jaqueline Two Feathers, Associate Professor, Co-Director, Institute for Research and Education to Advance Community Health, College of Nursing, Washington State University. We acknowledge Dr. Sinclair’s lifelong dedication to the empowerment and support of vulnerable populations and her initial work on the culturally tailored diabetes education curriculum. We sadly report her passing during her work on this project. We thank Tamara Swigert, RN, MSN, CDCES at Children’s Hospital Colorado for her work on the CGM education materials. We also thank Sea Mar Health Clinics for the opportunity to work with their wonderful diabetes patients, Alyssa Grant, Justice Kurihara, and all the participating health educators at Sea Mar for their partnership in diabetes education. We extend our heartfelt gratitude to Evelin Jones from the University of Washington for her significant contributions in translating the curriculum into Spanish, alongside Darinka Gil Menchaca, who spearheaded the educational initiatives and authored the curriculum toolkit/manual. We thank Dr. Irl Hirsch of the University of Washington for his thoughtful comments on the manuscript. We thank the American Diabetes Association for their grant support and Dexcom for providing us the CGM devices at a reduced cost.
Footnotes
Abbreviations: Latino, Persons of Latin American cultural or ethnic identity in the United States; RT-CGM, Real-time continuous glucose monitoring; DSMES, Diabetes self-management education and support; FQHC, Federally Qualified Health Center; HbA1c, Hemoglobin A1C; HE, Health Educator; CHW, Community Healthcare Worker; REDCap, Research Electronic Data Capture; CDE, Certified Diabetes Educator; SGLT2I, Sodium glucose co-transporter 2 inhibitor; GLP-1 RA, Glucagon like peptide 1 receptor agonist; SMBG, Self-monitoring blood glucose; BGM, Blood glucose monitoring; IS-CGM, Intermittently Scanned Continuous Glucose Monitoring.
Authors’ Note: A Randomized Trial of Real Time Continuous Glucose Monitoring (RT-CGM) with Education Culturally Tailored for Latino Patients with Type 2 Diabetes. Poster Presentation American Diabetes Association annual meeting 2024.
Author Contributions: NE wrote the manuscript. PB, LM, and LW contributed to the protocol and discussion and reviewed/edited the manuscript. BC wrote the data analysis and sample size estimation and GV and BC completed the data analysis and reviewed/edited the manuscript. NE is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Guarantor: NE takes responsibility and is the guarantor for the content of this article.
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: NE has been on an advisory board for Dexcom, Bayer and Novo Nordisk, Medtronic and received investigator-initiated grants from Dexcom and educational grants from Merck and Novo Nordisk and Boehringer-Ingelheim. LW has been on patient advisory board for Genentech. LM, PB, GV, and BC have nothing to disclose.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the American Diabetes Association Disparities of Health Care grant (grant no: 11-21-ICTSHD-51).
ORCID iDs: Nicole Ehrhardt 
https://orcid.org/0000-0003-1813-0789
Peter Berberian
https://orcid.org/0009-0000-4113-4695
Ana Gabriela Vasconcelos
https://orcid.org/0009-0002-1069-2429
Bryan Comstock
https://orcid.org/0000-0003-2599-1608
Lorena Alarcon-Casas Wright
https://orcid.org/0000-0002-0092-361X
Supplemental Material: Supplemental material for this article is available online.
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Supplementary Materials
Supplemental material, sj-docx-1-dst-10.1177_19322968251331526 for A Randomized Clinical Trial of a Culturally Tailored Diabetes Education Curriculum With and Without Real-Time Continuous Glucose Monitoring in a Latino Population With Type 2 Diabetes: The CUT-DM With Continuous Glucose Monitoring Study by Nicole Ehrhardt, Laura Montour, Peter Berberian, Ana Gabriela Vasconcelos, Bryan Comstock and Lorena Alarcon-Casas Wright in Journal of Diabetes Science and Technology
Supplemental material, sj-docx-2-dst-10.1177_19322968251331526 for A Randomized Clinical Trial of a Culturally Tailored Diabetes Education Curriculum With and Without Real-Time Continuous Glucose Monitoring in a Latino Population With Type 2 Diabetes: The CUT-DM With Continuous Glucose Monitoring Study by Nicole Ehrhardt, Laura Montour, Peter Berberian, Ana Gabriela Vasconcelos, Bryan Comstock and Lorena Alarcon-Casas Wright in Journal of Diabetes Science and Technology