Trial Outcomes & Findings for The Effect and Safety of a Novel CGM-Based Titration Algorithm for Basal Insulin in T2DM Participants. (NCT NCT06111508)
NCT ID: NCT06111508
Last Updated: 2026-03-04
Results Overview
Change in CGM-measured time in range (TIR) 3.9-10.0 mmol/L (70-180 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TIR = TIR (weeks 14-16) - TIR (baseline). Change in TIR is measured with percentage points as TIR is measured with the percentage time spent within the range 3.9-10.0 mmol/L (70-180 mg/dL).
Recruitment status
COMPLETED
Study phase
NA
Target enrollment
30 participants
Primary outcome timeframe
From baseline (-2 to 0 weeks) to weeks 14-16 (2 weeks)
Results posted on
2026-03-04
Participant Flow
39 participants signed consent at two clinical sites between Nov 2023 - Sept 2024. Nine did not pass screening or withdrew prior to randomization; two participants dropped after randomization.
Enrollment was defined when the ICF was signed by participant \& study team. Once screening \& training were completed, participants began a 2-wk at home use of a blinded CGM. Participants were asked to follow their UC without changes in their insulin parameters. CGM equipment was returned to the study team to facilitate downloading the data from the CGM, which must have been used 10 out of 14 days \& a day must contain 70% measurements to be counted.
Participant milestones
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Overall Study
STARTED
|
20
|
10
|
|
Overall Study
COMPLETED
|
19
|
9
|
|
Overall Study
NOT COMPLETED
|
1
|
1
|
Reasons for withdrawal
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Overall Study
Incomplete CGM dataset
|
0
|
1
|
|
Overall Study
Lost to Follow-up
|
1
|
0
|
Baseline Characteristics
The Effect and Safety of a Novel CGM-Based Titration Algorithm for Basal Insulin in T2DM Participants.
Baseline characteristics by cohort
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=20 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=10 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
Total
n=30 Participants
Total of all reporting groups
|
|---|---|---|---|
|
Age, Continuous
|
64.1 years
STANDARD_DEVIATION 10.8 • n=41 Participants
|
59.7 years
STANDARD_DEVIATION 8.4 • n=35 Participants
|
62.6 years
STANDARD_DEVIATION 10.1 • n=76 Participants
|
|
Sex: Female, Male
Female
|
7 Participants
n=41 Participants
|
3 Participants
n=35 Participants
|
10 Participants
n=76 Participants
|
|
Sex: Female, Male
Male
|
13 Participants
n=41 Participants
|
7 Participants
n=35 Participants
|
20 Participants
n=76 Participants
|
|
Race (NIH/OMB)
American Indian or Alaska Native
|
0 Participants
n=41 Participants
|
0 Participants
n=35 Participants
|
0 Participants
n=76 Participants
|
|
Race (NIH/OMB)
Asian
|
1 Participants
n=41 Participants
|
0 Participants
n=35 Participants
|
1 Participants
n=76 Participants
|
|
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
|
0 Participants
n=41 Participants
|
0 Participants
n=35 Participants
|
0 Participants
n=76 Participants
|
|
Race (NIH/OMB)
Black or African American
|
2 Participants
n=41 Participants
|
3 Participants
n=35 Participants
|
5 Participants
n=76 Participants
|
|
Race (NIH/OMB)
White
|
14 Participants
n=41 Participants
|
6 Participants
n=35 Participants
|
20 Participants
n=76 Participants
|
|
Race (NIH/OMB)
More than one race
|
0 Participants
n=41 Participants
|
0 Participants
n=35 Participants
|
0 Participants
n=76 Participants
|
|
Race (NIH/OMB)
Unknown or Not Reported
|
3 Participants
n=41 Participants
|
1 Participants
n=35 Participants
|
4 Participants
n=76 Participants
|
|
Ethnicity (NIH/OMB)
Hispanic or Latino
|
7 Participants
n=41 Participants
|
3 Participants
n=35 Participants
|
10 Participants
n=76 Participants
|
|
Ethnicity (NIH/OMB)
Not Hispanic or Latino
|
13 Participants
n=41 Participants
|
7 Participants
n=35 Participants
|
20 Participants
n=76 Participants
|
|
Ethnicity (NIH/OMB)
Unknown or Not Reported
|
0 Participants
n=41 Participants
|
0 Participants
n=35 Participants
|
0 Participants
n=76 Participants
|
|
BMI
|
34.3 kg/m2
STANDARD_DEVIATION 8.9 • n=41 Participants
|
30.6 kg/m2
STANDARD_DEVIATION 7.4 • n=35 Participants
|
33.1 kg/m2
STANDARD_DEVIATION 8.5 • n=76 Participants
|
PRIMARY outcome
Timeframe: From baseline (-2 to 0 weeks) to weeks 14-16 (2 weeks)Change in CGM-measured time in range (TIR) 3.9-10.0 mmol/L (70-180 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TIR = TIR (weeks 14-16) - TIR (baseline). Change in TIR is measured with percentage points as TIR is measured with the percentage time spent within the range 3.9-10.0 mmol/L (70-180 mg/dL).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Time in Range 3.9-10.0 mmol/L (70-180 mg/dL)
|
20.3 percentage points
Standard Deviation 18.1
|
8.3 percentage points
Standard Deviation 20.0
|
SECONDARY outcome
Timeframe: From week 0 to week 16Percent change in HbA1c measured as percentage
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in HbA1c
|
-0.74 percentage
Standard Deviation 0.60
|
-0.28 percentage
Standard Deviation 1.16
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16Percent change in time in tight range (TITR) 3.9-7.8 mmol/L (70-140 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TITR = TITR (weeks 14-16) - TITR (baseline).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Time in Tight Range 3.9-7.8 mmol/L (70-140 mg/dL)
|
21.2 percentage points
Standard Deviation 17.4
|
5.3 percentage points
Standard Deviation 14.4
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16Percent of time spent above 10.0 mmol/L (180 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TAR = TAR (weeks 14-16) - TAR (baseline).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Time Above 10.0 mmol/L (180 mg/dL)
|
-20.2 percentage points
Standard Deviation 18.3
|
-7.9 percentage points
Standard Deviation 20.9
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16Percent of time spent above (TAR2) 13.9 mmol/L (250 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TAR2 = TAR2 (weeks 14-16) - TAR2 (baseline).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Time Above 13.9 mmol/L (250 mg/dL)
|
-8.9 percentage points
Standard Deviation 14.6
|
-4.0 percentage points
Standard Deviation 15.6
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16The average CGM-measured blood glucose level (mg/dL).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Mean Glucose Level
|
-30.3 mg/dL
Standard Deviation 30.4
|
-8.2 mg/dL
Standard Deviation 38.5
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16The statistical measure (%) of the relative dispersion of data points in a data series around the average CGM-measured blood glucose level.
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Continuous Glucose Monitoring Coefficient of Variation (%)
|
0.22 percentage points
Standard Deviation 7.09
|
0.06 percentage points
Standard Deviation 6.05
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16Percent of time spent below (TBR) 3.9 mmol/L (70 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TBR = TBR (weeks 14-16) - TBR (baseline).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Time Below 3.9 mmol/L (70 mg/dL)
|
-0.11 percentage points
Standard Deviation 1.12
|
-0.43 percentage points
Standard Deviation 1.36
|
SECONDARY outcome
Timeframe: From baseline (week -2-0) to week 14-16Percent of time spent below (TBR2) 3.0 mmol/L (54 mg/dL) from baseline to weeks 14-16, compared between control and experimental arm. change in TBR2 = TBR2 (weeks 14-16) - TBR2 (baseline).
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Change in Time Below 3.0 mmol/L (54 mg/dL)
|
-0.04 percentage points
Standard Deviation 0.25
|
-0.17 percentage points
Standard Deviation 0.51
|
SECONDARY outcome
Timeframe: From week 0 to week 16The investigator changes the dose from baseline to week 16
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
n=9 Participants
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Basal Insulin Dose Changes
|
1.23 Units
Geometric Coefficient of Variation 64
|
1.31 Units
Geometric Coefficient of Variation 53
|
SECONDARY outcome
Timeframe: From week 0 to week 16Investigator acceptance rate of weekly dose guidance from Experimental arm only. Measure is calculated for each participant as 100x(number of accepted doses)/(number of recommended doses). Median and IQR is reported.
Outcome measures
| Measure |
Continuous Glucose Monitoring (CGM) Based Titration
n=19 Participants
The CGM-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. For dose computation the algorithm is comprised of three components; titration glucose level, personalized target, and safety hypoglycemia feature.
|
Standard Self-Monitoring Blood Glucose (SMBG) Titration
The SMBG-based titration algorithm will run on the Diabetes Assistant and Amazon Web Services (AWS) platform (DiAs-Cloud). DiAs-Cloud enables the seamless integration of a smart phone application and AWS server architecture to enable data capture, dose computation, review by the clinical team, and communication to study participants. Participants in the standard SMBG based titration group will wear a blinded CGM during the whole study. The total daily basal insulin dose will be converted 1:1 to Degludec. Algorithm informed dose changes will be made once weekly and checked by study physician.
|
|---|---|---|
|
Percent Acceptance Rate
|
92.8 %-point
Interval 80.6 to 93.8
|
—
|
Adverse Events
Continuous Glucose Monitoring (CGM) Based Titration
Serious events: 0 serious events
Other events: 0 other events
Deaths: 0 deaths
Standard Self-Monitoring Blood Glucose (SMBG) Titration
Serious events: 0 serious events
Other events: 0 other events
Deaths: 0 deaths
Serious adverse events
Adverse event data not reported
Other adverse events
Adverse event data not reported
Additional Information
Results disclosure agreements
- Principal investigator is a sponsor employee
- Publication restrictions are in place