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Article

Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study

by
Paola Pantanetti
1,†,
Giovanni Cangelosi
1,*,†,
Sara Morales Palomares
2,
Gaetano Ferrara
3,
Federico Biondini
4,
Stefano Mancin
5,*,
Gabriele Caggianelli
6,
Mauro Parozzi
7,
Marco Sguanci
8,‡ and
Fabio Petrelli
9,‡
1
Unit of Diabetology, Asur Marche—Area Vasta 4 Fermo, 63900 Fermo, Italy
2
Department of Pharmacy, Health and Nutritional Sciences (DFSSN), University of Calabria, 87036 Rende, Italy
3
Nephrology and Dialysis Unit, Ramazzini Hospital, 41012 Carpi, Italy
4
Units of Psychiatry, Ast Fermo, 63900 Fermo, Italy
5
IRCCS Humanitas Research Hospital, Manzoni 56, 20089 Rozzano, Italy
6
Azienda Ospedaliera San Giovanni Addolorata, 00184 Rome, Italy
7
ASST Santi Paolo e Carlo, 20142 Milano, Italy
8
A.O. Polyclinic San Martino Hospital, Largo R. Benzi 10, 16132 Genova, Italy
9
School of Pharmacy, Polo Medicina Sperimentale e Sanità Pubblica “Stefania Scuri”, 62032 Camerino, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors contributed equally to this work.
Diabetology 2025, 6(1), 7; https://doi.org/10.3390/diabetology6010007
Submission received: 30 November 2024 / Revised: 31 December 2024 / Accepted: 10 January 2025 / Published: 16 January 2025
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Versions Notes

Abstract

:
Background: Diabetes affects over 460 million people worldwide and represents a growing public health challenge driven largely by dietary and lifestyle factors. While Type 2 diabetes (T2D) is more prevalent, Type 1 diabetes (T1D) presents unique therapeutic challenges, particularly in younger individuals. Advances in diabetes management, such as continuous glucose monitoring (CGM), insulin pumps (IP), and, more recently, smart multiple dose injection (MDI) pens, have significantly enhanced glycemic control and improved patients’ quality of life. Aim: This study aims to evaluate the baseline characteristics of patients switching from MDI therapy to the Medtronic Smart MDI system [composed of a smart insulin pen (InPenTM) and a connected CGM Medtronic SimpleraTM sensor] and to assess its impact on glycemic outcomes over different time periods (14, 30, and 90 days). Methods: A retrospective observational study was conducted among adults with T1D who initiated Medtronic Smart MDI therapy. Participants were enrolled voluntarily at the Diabetes and Nutrition Clinic in Ast Fermo, Marche Region, Italy. Glycemic parameters were monitored using CGM data and analyzed with descriptive statistics, including mean, standard deviation (SD), and interquartile range (IQR). Comparisons across time periods were performed using the Wilcoxon signed-rank test, with statistical significance set at p < 0.05. Results: This study included 21 participants with a mean age of 51.5 years, a mean BMI of 24.7, and a mean duration of T1D of 21.9 years. The transition from a traditional MDI system to the Smart MDI system resulted in significant improvements in key glycemic parameters: mean Sensor Glucose (SG) decreased from 171.0 mg/dL to 153.5 mg/dL (p = 0.035), Time In Range (TIR) increased from 58.0% to 64.4% (p = 0.005), and time above range (TAR; >180 mg/dL) decreased from 39.0% to 34.2% (p = 0.015). No significant differences were observed in the time below range (TBR). Conclusions: The transition to the Medtronic Smart MDI system significantly enhanced glycemic control by lowering mean glucose levels and increasing TIR. These findings highlight its efficacy in improving hyperglycemia management while maintaining a stable risk of hypoglycemia.

1. Background

Diabetes is one of the most significant public health challenges worldwide [1,2,3]. The World Health Organization (WHO) estimates that over 460 million people are affected by this condition, with predictions of exponential growth in the coming decades, largely driven by changes in dietary habits and lifestyle [4,5,6,7]. Although Type 1 diabetes (T1D) is less prevalent than Type 2 diabetes (T2D), it remains a significant concern, particularly among younger populations, due to its chronic nature and the therapeutic challenges it entails [8,9,10,11]. The epidemiology of diabetes, both nationally and globally, highlights an increasing burden on healthcare systems, exacerbated by factors such as aging populations, rising obesity rates, and the growing prevalence of sedentary lifestyles [12,13,14]. Although T2D affects a larger number of people globally, T1D poses unique challenges that demand specific and innovative therapeutic approaches, especially given its increasingly complex and technologically advanced management [15,16,17].
The management of diabetes, particularly T2D, has made significant progress in recent decades, with a decisive shift toward personalized therapeutic approaches supported by increasingly sophisticated devices [18,19]. Among these innovations, the introduction of continuous glucose monitoring (CGM) systems and insulin pumps (IP) has marked a major step forward toward more precise glycemic control and improved quality of life for patients [20,21]. For individuals with T1D, who must manage daily insulin doses to maintain stable blood sugar levels, the introduction of technologies such as CGM, IP, and “smart pens” has been a crucial innovation [22,23,24]. These devices not only enhance glycemic control but also help reduce the emotional and psychological burden associated with diabetes management—a key aspect that should never be underestimated in the overall and holistic management of T1D patients [25,26].
Living with T1D presents daily challenges that go beyond clinical management: the constant need for blood glucose monitoring and self-management can result in anxiety, stress, and frustration, which may impact both quality of life and treatment adherence, especially among younger patients [27,28,29]. Although technological advancements such as CGM and “smart” MDI systems have the potential to alleviate some of this psychological burden and enhance treatment adherence [30,31], our study focuses specifically on the “wearability” of these technologies rather than directly evaluating their impact on quality of life.
Furthermore, these technologies have demonstrated improvements in treatment compliance, both qualitatively and quantitatively, in clinical practice [32,33]. They have also shown potential benefits in management and economic terms, contributing to a notable reduction in healthcare costs [34,35]. Given the significant challenges in managing T1D, particularly in younger populations, and the promising advancements in diabetes technology, it is crucial to better understand the real-world impact of these innovations. This study represents a pioneering contribution, offering a novel comparative analysis of glycemic management before and after the introduction of the Medtronic Smart MDI device for T1D. It highlights the transformative impact of this technology on clinical outcomes and practice improvements. Specifically, exploring the effectiveness of systems like the Medtronic Smart MDI in improving glycemic outcomes and quality of life is essential to inform clinical practice and guide future research.

1.1. Aim

The aim of this study was to evaluate the impact of transitioning to the Medtronic Smart MDI system in adult patients with T1D.

1.1.1. Primary Objective

To describe the baseline characteristics of patients transitioning from MDI therapy (with or without CGM) to the Medtronic Smart MDI system (Figure 1), consisting of a smart insulin pen (InPen™, Medtronic, Dublin, Ireland) and a connected CGM (Medtronic Simplera™ sensor, Medtronic, Dublin, Ireland; https://www.medtronic-diabetes.com/en-gb/SmartMDI, accessed on 1 November 2024).

1.1.2. Secondary Objectives

To compare glycemic outcomes between the last 14 days of prior therapy and the first 14 days following one month of Medtronic Smart MDI system use. To assess glycemic outcomes over the first 30 and 90 days of treatment with the Medtronic Smart MDI system.

2. Methods

2.1. Study Design

A single-center retrospective observational clinical study was conducted at the Diabetes and Nutrition Clinic of the tertiary hospital in Ast Fermo (Italy). The STROBE checklist was adopted for study reporting (Supplementary File S1) [36,37].

2.2. Ethical Considerations

The study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki and in accordance with institutional and national research ethics guidelines. Ethical approval was granted by the Institutional Review Board of Ast Fermo (approval code: INF04/2024). All participants provided informed consent for the collection and use of their data.

2.3. Sample and Criteria

All patients on Medtronic Smart MDI therapy who authorized the use of their data were included. The sample was selected conveniently at the recruiting center, with all subjects meeting the inclusion criteria outlined below being enrolled in the observation. To minimize potential confounding factors affecting glycemic response, the exclusion criteria were carefully expanded and structured to ensure an accurate interpretation of the collected data. Additionally, the chosen statistical methodologies, as detailed below, were applied to optimize the generalizability of the results. The inclusion criteria were: T1D diagnosis, male or female, age ≥ 14 years, therapy requirement determined by the physician’s judgment, and provision of signed informed consent.
The exclusion criteria were: refusal to consent to data use, presence of concomitant or suspected malignant diseases, pregnancy or breastfeeding, recent (within 3 months of enrollment) acute illnesses (excluding viral illnesses), renal impairment (eGFR < 60 mL/min), severe liver failure, congestive heart failure (NYHA class IV), proliferative diabetic retinopathy, presence of cholelithiasis, chronic pancreatitis or ongoing acute pancreatitis, and adherence to a ketogenic diet or other nutritional intervention that may be considered supplementary therapeutic interventions.

2.4. Endpoints

Description of the patients in terms of age, gender, Body Mass Index (BMI), duration of diabetes, smoking status, and type of T1D therapy used before switching to Medtronic Smart MDI. Statistical comparison between the last 14 days of the previous therapy and the first 14 days after completing one month on Medtronic Smart MDI in terms of: mean Sensor Glucose (SG), Standard Deviation (SD), and Coefficient of Variation (CV), Time Below Range level 2 (TBR2): time in <54 mg/dL; Time Below Range level 1 (TBR1): time in 54–69 mg/dL; Time Below Range (TBR): time in <70 mg/dL; Time In Range (TIR): time in 70–180 mg/dL; Time Above Range level 1 (TAR1): time in 181–250 mg/dL; Time Above Range level 2 (TAR2): time in >250 mg/dL; Time Above Range (TAR): time in >180 mg/dL. Description of the glycemic outcomes listed above during the first 30 and 90 days of treatment with Medtronic Smart MDI.

2.5. Statistical Analysis

2.5.1. General Methodology

Descriptive statistics were employed to summarize the results. These included the mean and standard deviation (SD), minimum, maximum, and median with interquartile range (IQR) for continuous variables, as well as counts and percentages for categorical variables. Summary statistics were reported with a maximum of two decimal places, as appropriate. Boxplots were created to visually represent the distribution of glycemic outcomes. Comparisons of glycemic outcomes across periods were performed using the Wilcoxon signed-rank test. All statistical tests were two-sided, with p-values < 0.05 considered statistically significant. All analyses were conducted using SAS software, version 9.4 (SAS Institute Inc., Cary, NC, USA).

2.5.2. Data Analysis

All data were collected by researchers from device data files, Medtronic CareLink™ reports [38], clinical records, and other relevant sources. Raw CGM data from Medtronic devices were used to calculate glycemic outcomes, as outlined in the subsequent section. For the last 14 days of the previous T1D therapy, if a subject had used a non-Medtronic CGM system, glycemic outcomes were derived directly from the available CGM data sources using validated methodologies.

2.5.3. Derived Variables

The criteria used to derive glycemic outcomes from CGM data are summarized in Table 1.

2.5.4. Handling of Missing Data and Outliers and Validation Requirements

Potential outliers were retained in the analysis, and no imputation methods were applied to handle missing data. To ensure accuracy and reliability, all analyses were independently reviewed and validated by a second statistician.

3. Results

3.1. Baseline Characteristics

The study included 21 subjects with an average age of 51.5 years. Females represented 38.1% of the cohort. The mean Body Mass Index (BMI) was 24.7 ± 4.1, ranging from 14.6 to 31.1, while the mean duration of T1D was 21.9 ± 12.2 years, ranging from 4 to 52 years. Among the 20 participants with available smoking data, 55% were current non smokers (11/20). Regarding the type of standard MDI therapy used prior to transitioning to the Medtronic Smart MDI system, four patients (20.0%) utilized the Medtronic Guardian™ Connect (GC) CGM system, thirteen (65.0%) used other CGM systems, and three (15.0%) relied solely on a Blood Glucose Meter (BGM) (Table 2).

3.2. Time Study

A total of twenty-one T1D patients were included in this analysis. Prior to switching to the Medtronic Smart MDI, three patients did not use any CGM systems, and for one patient, information on CGM usage was unavailable. Consequently, glycemic outcomes were available only for the remaining seventeen patients when considering the last 14 days of the old therapy. In addition, one patient (ID = 46) was excluded from the analysis of the first 90 days of the new therapy due to a lack of device data beyond what was already included in the other periods (Figure 2).

3.3. Sensor Use

The sensor usage percentage (when applicable) during the four time periods considered in the analyses was reported anonymously for each subject included in the study (Table 3). As shown in the table, the “wearability” of the product and the percentage of sensor usage increased significantly after switching from the previous device (also Medtronic) to the Medtronic Smart MDI system. This improvement may indirectly reflect an enhanced QoL perceived by patients when using the Smart MDI system. For example, Patient 5 and Patient 6 demonstrated notable increases in sensor usage: Patient 5 transitioned from 74% in the last 14 days with the previous device to 94.22% during the first 90 days of Medtronic Smart MDI use. Similarly, Patient 6 moved from 77% in the last 14 days with the previous device to 98.66% during the first 14 days after one month of using the Medtronic Smart MDI. The only subject (Patient 2) who did not have prior experience with similar devices exhibited fluctuating compliance, possibly due to adjusting to managing T1D with technology. Patient 2’s sensor usage started at 89.66% in the first 14 days after one month of using the Medtronic Smart MDI, dropped to 60.72% in the first 30 days, and then increased to 80.83% in the first 90 days of using the system.

3.4. Glycemic Outcomes with Medtronic Smart MDI

Table 4 summarizes the glycemic outcomes of patients following the switch to Medtronic Smart MDI. Data are presented for three time periods: the first 14 days after one month of the new therapy, the first 30 days of the new therapy, and the first 90 days of the new treatment. The median TIR remained consistent across all three periods, ranging between 64% and 65%. The Sg showed a fluctuating trend during the observed period, although the differences were not statistically significant. Specifically, SG values were 157.8 mg/dL during the first 14 days after one month of using the Medtronic Smart MDI system, 161.6 mg/dL during the first 30 days of the new therapy, and 156.6 mg/dL after the first 90 days of Medtronic Smart MDI use.

3.5. Comparison Between Standard MDI and Medtronic Smart MDI

Statistical comparison between the glycemic outcomes observed during the last 14 days with standard MDI and those observed during the first 14 days after one month of Medtronic Smart MDI is reported in Table 5 for the 17 subjects with available CGM data in both periods. All p-values lower than 0.05 are highlighted in bold. In the period observed, the results demonstrate a significant improvement in glycemic control, with a reduction in SG mean from 171.0 to 153.5 mg/dL (p = 0.035). While no significant changes were observed in SG SD, the SG CV (calculated as (SG SD/SG mean) × 100) increased from 28.1% to 33.3% (p = 0.009). Furthermore, TIR increased from 58.0% to 64.4% (p = 0.005), and TAR decreased from 39.0% to 34.2% (p = 0.015), indicating an overall enhancement in glycemic management. No significant differences were detected in terms of TBR. These results highlight the potential of the Smart MDI system in the modern and comprehensive management of T1D and how it could have significant long-term potential in reducing complications and improving patient well-being.

3.6. Boxplot Analysis

A visual representation of glycemic outcomes for the two periods is illustrated in Figure 3 using boxplots. These graphs highlight the positive impact of transitioning from standard MDI to Medtronic Smart MDI therapy. Specifically, Figure 3a demonstrates a reduction in SG mean, while Figure 3b shows an increase in TIR, both indicating improved glycemic management. The dotted lines in the plots connect the mean values for each period, emphasizing the changes achieved with the new therapy.

4. Discussion

Our study observed improvements in glycemic parameters, particularly the reduction in mean SG levels and the increase in TIR, highlighting the system’s effectiveness in achieving tighter glycemic control. Specifically, the significant reduction in SG mean from 171.0 mg/dL to 153.5 mg/dL (p = 0.035) provides robust evidence of the Smart MDI system’s capability to address hyperglycemia effectively. These findings align with earlier studies that have demonstrated the advantages of advanced insulin delivery technologies, such as CGM-integrated systems, in optimizing glucose management by enhancing precision and personalization of therapy [39,40].
The increase in TIR from 58.0% to 64.4% (p = 0.005) and the corresponding reduction in Time Above Range (TAR) from 39.0% to 34.2% (p = 0.015) further validate the Smart MDI system’s ability to reduce hyperglycemia while maintaining a stable hypoglycemia risk, as suggested by the non-significant changes in TBR. These changes are particularly meaningful as TIR is increasingly recognized as a critical indicator of glycemic control, with direct correlations to better patient outcomes and reduced risks of long-term diabetes-related complications [41,42]. By shifting the glycemic profile toward greater time spent in the optimal range, the Smart MDI system may also help mitigate patient anxiety associated with glucose variability and frequent hyperglycemic episodes [43,44].
An increase in TIR, and consequently a better glycemic control and HbA1c in T2D and T1D, has been shown to correlate strongly with a reduction in the risk of microvascular and macrovascular complications, including diabetic retinopathy, nephropathy, and cardiovascular disease [45,46].
Interestingly, as recent studies demonstrated [47,48,49,50], the study also revealed an increase in the coefficient of CV of SG from 28.1% to 33.3% (p = 0.009), suggesting slightly higher glucose variability. While this finding may initially appear counterintuitive, it is essential to consider it alongside the improvements in SG mean and TIR. A potential explanation for this increase in CV could be the initial adjustment period to the new therapy or more frequent corrections of hyperglycemia, which might result in temporary fluctuations. Nonetheless, the overall reduction in hyperglycemia and improvement in TIR suggest that the benefits of the Smart MDI system outweigh any potential concerns related to glucose variability [51,52].
This innovation is particularly valuable for T1D patients who face the dual challenge of maintaining optimal glycemic levels and managing the psychological burden of their condition [53,54,55].
The findings also have broader implications for public health and healthcare systems in general social and chronic care management [56,57,58,59,60], like the relevant direct and indirect costs saved through this modern model of care [34,35]. Improved glycemic control and reduced hyperglycemia may not only enhance patient outcomes by reducing the need for emergency interventions, hospitalizations, and long-term management of diabetes-related complications. These economic benefits further support the adoption of advanced insulin delivery systems like the Medtronic Smart MDI in routine clinical practice [61,62,63,64].

Limitations

This analysis has several limitations that should be taken into account when interpreting the results. First, the study was conducted on a selected cohort of patients, and the retrospective observational nature of the data introduces a potential risk of selection bias. Additionally, the relatively small sample size limits the statistical power of the analysis and restricts the generalizability of the findings to broader and more diverse populations. Consequently, the results should be considered preliminary, providing valuable insights that require confirmation through larger, multicenter studies. Further research involving more diverse and larger patient cohorts is crucial to validate these findings and to evaluate the long-term efficacy and safety of the Medtronic Smart MDI system in routine clinical practice. Moreover, the absence of a control group and the potential influence of confounding factors, such as differences in patient adherence and the presence of comorbid conditions, should be addressed in future studies to offer a more comprehensive assessment of the therapy’s impact.

5. Conclusions

This study demonstrates that transitioning to the Medtronic Smart MDI system significantly improves glycemic control in T1D patients, as evidenced by reductions in SG mean, increases in TIR, and decreases in TAR. These improvements highlight the potential of Smart MDI therapy to enhance overall diabetes management, reduce hyperglycemia, and support better long-term outcomes. The findings underscore the importance of integrating advanced technologies, such as CGM and Smart MDI systems, into routine diabetes care. These systems not only improve clinical outcomes but also have the potential to alleviate the psychological and logistical burden of diabetes management, thereby enhancing treatment adherence and patient quality of life. However, while the results of this study are promising, it is important to acknowledge the need for further research. Longitudinal studies with larger sample sizes and diverse patient populations are necessary to confirm these findings and evaluate the Smart MDI system’s impact on long-term clinical outcomes. Additionally, exploring the psychosocial benefits of this technology, including its effects on patient adherence, satisfaction, and quality of life, could provide a more comprehensive understanding of its role in diabetes care.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diabetology6010007/s1, File S1: Continuous GlucoSe Monitor And smaRT insulin pen systeM: a real worlD lIfe analysis of cohort study; File S2: Continuous GlucoSe Monitor And smaRT insulin pen systeM: a real worlD lIfe analysis of cohort study.

Author Contributions

Conceptualization, P.P., G.C. (Giovanni Cangelosi), S.M. and F.P.; methodology, S.M. and S.M.P.; validation, P.P., M.P., S.M., M.S., S.M.P. and G.C. (Giovanni Cangelosi); formal analysis, M.P.; investigation, G.C. (Giovanni Cangelosi); data curation, G.C. (Giovanni Cangelosi); writing—original draft preparation, G.C. (Giovanni Cangelosi), S.M., M.S., G.F., F.B. and S.M.P.; writing—review and editing, P.P., M.P., F.B., G.F., G.C. (Gabriele Caggianelli), S.M. and F.P.; visualization, G.C. (Giovanni Cangelosi), S.M., M.S. and S.M.P.; supervision, M.S. and F.P.; project administration, G.C. (Giovanni Cangelosi) and P.P.; P.P. and G.C. (Giovanni Cangelosi) made equal contributions as first authors; M.S. and F.P. made equal contributions as last authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study protocol was approved by the Institutional Review Board of Ast Fermo with authorization code INF04/2024, 2 May 2024.

Informed Consent Statement

All participants signed an informed consent form.

Data Availability Statement

Data supporting this research are available in the Supplementary Materials.

Acknowledgments

The authors wish to thank the following Medtronic employees for their technical and statistical support of this study. Special thanks to Ivan Merlo and Claudio Carrara.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Smart MDI system model.
Figure 1. Smart MDI system model.
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Figure 2. Chronological Study Flow Chart. Legend. T1D: Type 1 diabetes.
Figure 2. Chronological Study Flow Chart. Legend. T1D: Type 1 diabetes.
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Figure 3. (a) Change in SG mean from standard MDI to Medtronic Smart MDI; (b) Change in TIR from standard MDI to Medtronic Smart MDI. Legend. SG: Sensor Glucose; TIR: Time In Range.
Figure 3. (a) Change in SG mean from standard MDI to Medtronic Smart MDI; (b) Change in TIR from standard MDI to Medtronic Smart MDI. Legend. SG: Sensor Glucose; TIR: Time In Range.
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Table 1. Criteria that were used to derive glycemic outcomes from CGM data.
Table 1. Criteria that were used to derive glycemic outcomes from CGM data.
VariableDerivation for a Given Period
Sensor usage (%)[Number of CGM measurements/(Number of minutes in the period of interest/5)] × 100
SG mean, SD, and CVMean, SD, and CV of CGM measurements
TIR metrics(Number of CGM measurements in the range of interest/Number of CGM measurements) × 100
Legend. SG: Sensor Glucose; SD: Standard Deviation; CV: Coefficient of Variation; TIR: Time In Range; CGM: continuous glucose monitoring.
Table 2. Baseline Characteristics.
Table 2. Baseline Characteristics.
MeasureSummary StatisticTotal
(n = 21)
Age (years)Available Measures (%)21 (100.0%)
Mean ± SD51.5 ± 16.1
Median (IQR)53.0 (40.0–63.0)
Min–Max17.0–76.0
Female% (n/Available Measures)38.1% (8/21)
BMIAvailable Measures (%)20 (95.2%)
Mean ± SD24.7 ± 4.1
Median (IQR)24.7 (23.0–28.6)
Min–Max14.6–31.1
Duration of T1D (years)Available Measures (%)20 (95.2%)
Mean ± SD21.9 ± 12.2
Median (IQR)21.5 (14.5–28.5)
Min–Max4.0–52.0
Smoke
No% (n/Available Measures)55.0% (11/20)
Yes% (n/Available Measures)25.0% (5/20)
Former smoker% (n/Available Measures)20.0% (4/20)
Previous therapy
SMBG% (n/Available Measures)15.0% (3/20)
Other CGM% (n/Available Measures)65.0% (13/20)
MEDTRONIC GC% (n/Available Measures)20.0% (4/20)
Legend. GC: Medtronic Guardian Connect; CGM: continuous glucose monitoring; SMBG: Self-monitoring of blood glucose; SD: Standard Deviation; T1D: Type 1 diabetes; Min–Max: minimum–maximum.
Table 3. Sensor use (%).
Table 3. Sensor use (%).
PatientPrevious
Therapy		Last 14
Days of
Old Therapy		First 14
Days After
One Month of
Medtronic Smart MDI		First 30
Days of
Medtronic Smart MDI		First 90
Days of
Medtronic Smart MDI
1SMBG 95.4493.6894.93
2None 89.6660.7280.83
3Other CGM89.097.8995.5890.99
4SMBG 92.8196.2596.98
5Other CGM74.094.0093.3194.22
6Other CGM77.098.6694.2196.47
7Other CGM89.055.1393.2454.00
8Other CGM100.099.4395.8997.25
9SMBG 97.2095.8896.94
10MEDTRONIC GC92.895.9894.2294.59
11MEDTRONIC GC77.059.2585.8480.76
12Other CGM79.097.2596.6097.94
13Other CGM77.098.2980.3790.40
14MEDTRONIC GC95.295.1994.4695.61
15Other CGM94.099.6899.63
16Other CGM96.097.8793.1896.05
17Other CGM96.098.7496.5397.60
18Other CGM99.093.6391.9381.97
19MEDTRONIC GC94.584.4084.8686.92
20Other CGM100.072.0095.5088.99
21Other CGM98.099.9896.4098.01
Legend. GC: Medtronic Guardian Connect; CGM: continuous glucose monitoring; SMBG: Self-monitoring of blood glucose.
Table 4. Glycemic Outcomes with Medtronic Smart MDI.
Table 4. Glycemic Outcomes with Medtronic Smart MDI.
MeasureSummary
Statistic		First 14
Days After
One Month of
Medtronic Smart MDI
(n = 21)		First 30
Days of
Medtronic Smart MDI
(n = 21)		First 90
Days of
Medtronic Smart MDI
(n = 20)
SG mean (mg/dL)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD157.8 ± 26.5161.6 ± 27.4156.6 ± 18.5
Median (IQR)153.5 (142.2–176.7)156.5 (141.5–171.1)158.2 (140.4–171.3)
Min–Max119.2–222.9123.1–248.3123.9–191.3
SD (mg/dL)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD52.2 ± 13.055.6 ± 10.755.0 ± 10.0
Median (IQR)52.5 (45.2–59.5)54.7 (48.9–60.4)54.0 (49.0–62.0)
Min–Max27.4–75.039.3–76.638.2–75.6
CV (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD33.0 ± 5.634.6 ± 5.135.2 ± 5.3
Median (IQR)33.6 (28.1–38.0)33.3 (30.4–38.7)35.5 (29.7–39.5)
Min–Max22.9–41.327.4–45.327.4–42.6
TBR2 (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD0.5 ± 0.90.5 ± 1.10.6 ± 0.9
Median (IQR)0.0 (0.0–0.4)0.1 (0.1–0.4)0.1 (0.1–0.5)
Min–Max0.0–3.30.0–4.70.0–3.0
TBR1 (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD2.2 ± 2.52.1 ± 2.32.4 ± 2.2
Median (IQR)0.8 (0.6–3.2)1.4 (0.5–2.9)1.4 (0.7–3.6)
Min–Max0.0–9.50.0–8.60.0–8.0
TBR (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD2.7 ± 3.32.7 ± 3.23.0 ± 3.0
Median (IQR)0.8 (0.6–4.1)1.4 (0.6–3.4)1.5 (0.9–4.2)
Min–Max0.0–11.70.0–13.30.0–10.8
TIR (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD66.5 ± 16.363.8 ± 16.166.3 ± 12.5
Median (IQR)64.4 (58.0–77.6)64.9 (57.8–72.5)64.2 (57.8–76.8)
Min–Max33.1–97.918.6–87.343.0–89.5
TAR1 (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD22.9 ± 10.524.4 ± 9.023.6 ± 9.0
Median (IQR)26.2 (16.2–29.1)24.0 (17.9–30.1)22.1 (16.8–29.5)
Min–Max1.5–42.310.6–43.68.9–44.1
TAR2 (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD7.9 ± 9.09.1 ± 10.87.2 ± 4.8
Median (IQR)4.2 (2.2–12.0)6.5 (3.3–9.9)6.9 (2.4–10.9)
Min–Max0.0–38.60.8–49.91.1–16.8
TAR (%)Available Measures (%)21 (100.0%)21 (100.0%)20 (100.0%)
Mean ± SD30.8 ± 16.933.5 ± 16.830.7 ± 13.0
Median (IQR)34.2 (19.8–41.3)33.4 (22.7–40.2)32.7 (20.3–40.0)
Min–Max1.5–66.012.3–81.010.0–56.9
Legend. SG: Sensor Glucose; SD: Standard Deviation; IQR: InterQuartile Range; Min–Max: minimum–maximum; CV: Coefficient of Variation; TBR2: Time Below Range level 2; TBR1: Time Below Range level 1; TBR: Time Below Range; TIR: Time In Range; TAR1: Time Above Range level 1; TAR2: Time Above Range level 2; TAR: Time Above Range.
Table 5. Comparison Between Standard MDI and Medtronic Smart MDI.
Table 5. Comparison Between Standard MDI and Medtronic Smart MDI.
MeasureSummary
Statistics		Last 14
Days of
Old Therapy
(n = 17)		First 14
Days After
One Month of
New Therapy
(n = 17)		p-Value
SG mean (mg/dL)Available Measures (%)17 (100.0%)17 (100.0%)0.035
Mean ± SD172.5 ± 25.3158.5 ± 26.5
Median (IQR)171.0 (158.0–189.0)153.5 (142.2–176.7)
Min–Max116.7–230.0119.2–222.9
SD (mg/dL)Available Measures (%)17 (100.0%)17 (100.0%)0.159
Mean ± SD48.3 ± 12.552.9 ± 12.8
Median (IQR)47.0 (41.1–54.0)52.5 (45.2–59.5)
Min–Max30.0–72.030.0–75.0
CV (%)Available Measures (%)17 (100.0%)17 (100.0%)0.009
Mean ± SD28.1 ± 6.333.3 ± 5.4
Median (IQR)28.1 (25.1–31.7)33.6 (28.5–38.0)
Min–Max16.3–41.624.4–41.3
TBR2 (%)Available Measures (%)17 (100.0%)17 (100.0%)0.320
Mean ± SD1.0 ± 1.50.6 ± 1.0
Median (IQR)0.0 (0.0–2.0)0.0 (0.0–0.9)
Min–Max0.0–5.00.0–3.3
TBR1 (%)Available Measures (%)17 (100.0%)17 (100.0%)0.747
Mean ± SD3.1 ± 3.52.3 ± 2.6
Median (IQR)1.0 (1.0–6.0)0.8 (0.6–3.2)
Min–Max0.0–10.00.2–9.5
TBR (%)Available Measures (%)17 (100.0%)17 (100.0%)0.378
Mean ± SD4.1 ± 4.82.8 ± 3.5
Median (IQR)1.0 (1.0–8.0)0.8 (0.6–4.1)
Min–Max0.0–12.10.3–11.7
TIR (%)Available Measures (%)17 (100.0%)17 (100.0%)0.005
Mean ± SD55.7 ± 13.765.8 ± 15.6
Median (IQR)58.0 (48.0–63.2)64.4 (58.0–77.6)
Min–Max23.0–82.033.1–93.9
TAR1 (%)Available Measures (%)17 (100.0%)17 (100.0%)0.040
Mean ± SD27.6 ± 9.023.3 ± 9.4
Median (IQR)28.0 (25.0–33.0)26.2 (16.8–29.1)
Min–Max5.3–40.05.4–42.3
TAR2 (%)Available Measures (%)17 (100.0%)17 (100.0%)0.023
Mean ± SD12.6 ± 10.38.1 ± 9.8
Median (IQR)12.0 (5.0–14.0)3.1 (2.2–12.0)
Min–Max0.5–43.00.0–38.6
TAR (%)Available Measures (%)17 (100.0%)17 (100.0%)0.015
Mean ± SD40.3 ± 15.031.4 ± 16.3
Median (IQR)39.0 (35.0–46.0)34.2 (19.8–41.3)
Min–Max5.9–76.05.4–66.0
Legend. SG: Sensor Glucose; SD: Standard Deviation; IQR: InterQuartile Range; Min–Max: minimum–maximum; CV: Coefficient of Variation; TBR2: Time Below Range level 2; TBR1: Time Below Range level 1; TBR: Time Below Range; TIR: Time In Range; TAR1: Time Above Range level 1; TAR2: Time Above Range level 2; TAR: Time Above Range.
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MDPI and ACS Style

Pantanetti, P.; Cangelosi, G.; Morales Palomares, S.; Ferrara, G.; Biondini, F.; Mancin, S.; Caggianelli, G.; Parozzi, M.; Sguanci, M.; Petrelli, F. Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study. Diabetology 2025, 6, 7. https://doi.org/10.3390/diabetology6010007

AMA Style

Pantanetti P, Cangelosi G, Morales Palomares S, Ferrara G, Biondini F, Mancin S, Caggianelli G, Parozzi M, Sguanci M, Petrelli F. Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study. Diabetology. 2025; 6(1):7. https://doi.org/10.3390/diabetology6010007

Chicago/Turabian Style

Pantanetti, Paola, Giovanni Cangelosi, Sara Morales Palomares, Gaetano Ferrara, Federico Biondini, Stefano Mancin, Gabriele Caggianelli, Mauro Parozzi, Marco Sguanci, and Fabio Petrelli. 2025. "Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study" Diabetology 6, no. 1: 7. https://doi.org/10.3390/diabetology6010007

APA Style

Pantanetti, P., Cangelosi, G., Morales Palomares, S., Ferrara, G., Biondini, F., Mancin, S., Caggianelli, G., Parozzi, M., Sguanci, M., & Petrelli, F. (2025). Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study. Diabetology, 6(1), 7. https://doi.org/10.3390/diabetology6010007

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