Exploring Parental Experiences of Using a Do-It-Yourself Solution for Continuous Glucose Monitoring Among Children and Adolescents With Type 1 Diabetes: A Qualitative Study

Background:MiaoMiao (MM) is a Bluetooth transmitter, which when paired with a smart phone/device, converts the Abbott FreeStyle Libre flash glucose monitoring system into a Do-It-Yourself (DIY) continuous glucose monitor (CGM). Families are increasingly adopting DIY CGM solutions, but little is known about parent and child experiences with these add-on technologies. We aimed to explore experiences of families using MM-CGM including challenges faced and their advice to others who may choose to use the technology.Methods:Between May and July 2019, we conducted 12 semistructured interviews (in person or via video conference) with parents of children (aged ≤16 years) with type 1 diabetes using MM-CGM. Interviews were audio recorded; professionally transcribed and key themes were identified through thematic analysis.Results:Overall, parents used MM-CGM to proactively manage their child’s blood glucose. In all participants, this led to a perceived decrease in frequency of hypoglycemia. Participants reported that the visibility and easy access to blood glucose readings, glucose trends, and customized alarms on parent’s phones decreased their disease burden and improved their sleep quality. Common barriers to using MM-CGM included difficulty of the setting up process, connectivity issues, and lack of support from medical teams.Conclusion:This study highlights the potential feasibility of using a DIY CGM system like MM-CGM, which could be an empowering and cost-effective tool for enabling remote monitoring of blood glucose in real time.     

Healthcare Utilization,Costs, and Adverse Events of Real-Time Continuous Glucose Monitoring versus Traditional Blood Glucose Monitoring Among US Adults with Type 1 Diabetes

Objective:To compare healthcare utilization, costs, and incidence of diabetes-specific adverse events (ie, hyperglycemia, diabetic ketoacidosis, and hypoglycemia) in type 1 diabetes adult patients using real-time continuous glucose monitoring (rtCGM) versus traditional blood glucose monitoring (BG).Methods:Adult patients (≥18 years old) with type 1 diabetes in a large national administrative claims database between 2013 and 2015 were identified. rtCGM patients with 6-month continuous health plan enrollment and ≥1 pharmacy claim for insulin during pre-index and post-index periods were propensity-score matched with BG patients. Healthcare utilization associated with diabetic adverse events were examined. A difference-in-difference (DID) method was used to compare the change in costs between rtCGM and BG cohorts.Results:Six-month medical costs for rtCGM patients (N = 153) increased from pre- to post-index period, while they decreased for matched BG patients (N = 153). DID analysis indicated a $2,807 (P = .062) higher post-index difference in total medical costs for rtCGM patients. Pharmacy costs for both cohorts increased. DID analysis indicated a $1,775 (P < .001) higher post-index difference in pharmacy costs for rtCGM patients. The incidence of hyperglycemia for both cohorts increased minimally from pre- to post-index period. The incidence of hypoglycemia for rtCGM patients decreased, while it increased marginally for BG patients. Inpatient hospitalizations for rtCGM and BG patients increased and decreased marginally, respectively.Conclusions:rtCGM users had non-significantly higher pre-post differences in medical costs but significantly higher pre-post differences in pharmacy costs (mostly due to the rtCGM costs themselves) compared to BG users. Changes in adverse events were minimal.     

Observed Characteristics Associated with Diabetes Device Use Among Teens with Type 1 Diabetes

Background:Despite advancements in diabetes technologies, disparities remain with respect to diabetes device use in youth with type 1 diabetes (T1D). We compared sociodemographic, diabetes, and psychosocial characteristics associated with device (pump and continuous glucose monitor [CGM]) use in 13- to 17-year-old teens with T1D.Materials/Methods:Data were derived from a multicenter clinical trial to optimize self-care and glycemic control in teens with T1D. We categorized teens as pump users versus non-users and CGM users versus non-users based on their diabetes device usage. Chi-square and t-tests compared characteristics according to device use.Results:The sample comprised 301 teens (50% female) with baseline mean ± SD age 15.0 ± 1.3 years, T1D duration 6.5 ± 3.7 years, and HbA1c 8.5 ± 1.1% (69 ± 12 mmol/mol). Two-thirds (65%) were pump users, and 27% were CGM users. Pump users and CGM users (vs. non-users) were more likely to have a family annual household income ≥$150,000, private health insurance, and a parent with a college education (all P < .001). Pump users and CGM users (vs. non-users) also performed more frequent daily blood glucose (BG) checks (both P < .001) and reported more diabetes self-care behaviors (both P < .05). Pump users were less likely to have baseline HbA1c ≥9% (75 mmol/mol) (P = .005) and to report fewer depressive symptoms (P = .02) than pump non-users. Parents of both CGM and pump users reported a higher quality of life in their youth (P < .05).Conclusion:There were many sociodemographic, diabetes-specific, and psychosocial factors associated with device use. Modifiable factors can serve as the target for clinical interventions; youth with non-modifiable factors can receive extra support to overcome potential barriers to device use.     

Pediatric Medicaid Patients With Type 1 Diabetes Benefit From Continuous Glucose Monitor Technology

Background:We determined the uptake rate of continuous glucose monitors (CGMs) and examined associations of clinical and demographic characteristics with CGM use among patients with type 1 diabetes covered by Colorado Medicaid during the first two years of CGM coverage with no out-of-pocket cost.Method:We retrospectively reviewed data from 892 patients with type 1 diabetes insured by Colorado Medicaid (Colorado Health Program [CHP] and CHP+, Colorado Medicaid expansion). Demographics, insulin pump usage, CGM usage, and hemoglobin A1c (A1c) were extracted from the medical record. Data downloaded into CGM software at clinic appointments were reviewed to determine 30-day use prior to appointments. Subjects with some exposure to CGM were compared to subjects never exposed to CGM, and we examined the effect of CGM use on glycemic control.Results:Twenty percent of subjects had some exposure to CGM with a median of 22 [interquartile range 8, 29] days wear. Sixty one percent of CGM users had >85% sensor wear. Subjects using CGM were more likely to be younger (P < .001), have shorter diabetes duration (P < .001), and be non-Hispanic White (P < .001) than nonusers. After adjusting for age and diabetes duration, combined pump and CGM users had a lower A1c than those using neither technology (P = .006). Lower A1c was associated with greater CGM use (P = .002) and increased percent time in range (P < .001).Conclusion:Pediatric Medicaid patients successfully utilized CGM. Expansion of Medicaid coverage for CGM may help improve glycemic control and lessen disparities in clinical outcomes within this population.     

Hypoglycemia and Glycemic Control in Older Adults With Type 1 Diabetes: Baseline Results From the WISDM Study

Background:Knowledge regarding the burden and predictors of hypoglycemia among older adults with type 1 diabetes (T1D) is limited.Methods:We analyzed baseline data from the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, which enrolled participants at 22 sites in the United States. Eligibility included clinical diagnosis of T1D, age ≥60 years, no real-time continuous glucose monitoring (CGM) use in prior three months, and HbA1c <10.0%. Blinded CGM data from 203 participants with at least 240 hours were included in the analyses.Results:Median age of the cohort was 68 years (52% female, 93% non-Hispanic white, and 53% used insulin pumps). Mean HbA1c was 7.5%. Median time spent in the glucose range <70 mg/dL was 5.0% (72 min/day) and <54 mg/dL was 1.6% (24 min/day). Among all factors analyzed, only reduced hypoglycemia awareness was associated with greater time spent <54 mg/dL (median time of 2.7% vs 1.3% [39 vs 19 minutes per day] for reduced awareness vs aware/uncertain, respectively, P = .03). Participants spent a mean 56% of total time in target glucose range of 70-180 mg/dL and 37% of time above 180 mg/dL.Conclusions:Over half of older T1D participants spent at least an hour a day with glucose levels <70 mg/dL. Those with reduced hypoglycemia awareness spent over twice as much time than those without in a serious hypoglycemia range (glucose levels <54 mg/dL). Interventions to reduce exposure to clinically significant hypoglycemia and increase time in range are urgently needed in this age group.     

Ecological Momentary Assessment of Positive and Negative Affect and Associations with Blood Glucose in Teens with Type 1 Diabetes

Background:Affect (i.e., emotions) can be associated with diabetes self-care and ambient glucose in teens with type 1 diabetes (T1D). We used momentary sampling to examine associations of daily affectwithblood glucose (BG) monitoring,BG levels,and BG variability in teens with T1D.Method:Over 2 weeks, 32 teens reported positive and negative affect (Positive and Negative Affect Scale) and BG levels on handheld computers 4x/day, coordinated with planned daily BG checks. BG values were classified as: in-range (70-180 mg/dL); low (<70 mg/dL); severe low (<54 mg/dL); high (>180 mg/dL); severe high (>250 mg/dL). Daily BG variability was derived from BG coefficient of variation (BGCV). To determine associations of positive and negative affect with BG checks, BG levels, and BGCV, separate generalized estimating equations were performed, adjusting for demographic and diabetes-related variables, for the overall sample and stratified by HbA1c (≤8%, >8%).Results:Teens (44% male, ages 14-18, 63% pump-treated, HbA1c 8.8 ± 1.4%) reported 51% in-range, 6% low (2% severe low), and 44% high (19% severe high) BG. In teens with HbA1c ≤8%, positive affect was associated with in-range BG (OR = 1.08, 95% CI = 1.04-1.13, P = .0002), reduced odds of very low glucose (OR = 0.35, 95% CI = 0.16-0.74, P = .006), and less daily BGCV (β = −0.9; 95% CI = −1.6, −0.2; P = .01). In teens with HbA1c >8%, negative affect was associated with less likelihood of checking BG (OR = 0.75, 95% CI = 0.64-0.87, P = .0003).Conclusions:Our findings shed light on individual differences in metabolic reactivity based on glycemic levels and the importance of incorporating affect into automated insulin delivery systems.     

Treatment Recommendations Following 3-Day Masked Continuous Glucose Monitoring (CGM) in Youth With Type 1 Diabetes

Glycemic control remains suboptimal in youth with type 1 diabetes. Retrospective continuous glucose monitoring (CGM) has demonstrated utility in fine-tuning diabetes management by detecting postprandial hyperglycemia and hypoglycemia. In this study, we explored the process of 3-day masked CGM use, subsequent treatment recommendations, and impact on A1c in a clinic-based sample of youth with type 1 diabetes. Over 2 years, 122 youth were referred for masked CGM. Patients/families completed a diary of blood glucose levels, insulin doses, food intake, and exercise during CGM use. A1c was assessed pre- and 2-3 months post-CGM. Treatment recommendations were formulated using data from CGM reports and diaries. Mean age was 14.3 ± 3.9 years, diabetes duration was 7.5 ± 4.7 years, and A1c was 8.5 ± 1.1% (69 ± 12 mmol/mol); 61% were pump-treated. Patients received an average of 3.1 ± 1.1 treatment recommendations following review of the CGM report. Most (80%) received reinforcement of the importance of preprandial bolusing; 37% received a recommendation regarding advanced insulin management (use of combination boluses/attend to active insulin). Receipt of the latter recommendation was related to A1c improvement ≥0.5% (OR: 4.0, P < .001). Masked CGM offers opportunities to guide advanced insulin management (by injection or pump), which may yield A1c improvements in youth with type 1 diabetes.     

Continuous Glucose Monitoring Decreases Hypoglycemia Avoidance Behaviors,but Not Worry in Parents of Youth With New Onset Type 1 Diabetes

Background:Existing research shows that hypoglycemia fear (HF) is common in parents of children with established type 1 diabetes (T1D). We examined parental HF in the T1D recent-onset period and evaluated whether continuous glucose monitoring (CGM) adoption relates to improved outcomes of parental HF.Methods:In TACKLE-T1D, a prospective study of five- to nine-year olds with recent-onset T1D, parents completed the Hypoglycemia Fear Survey-Parents (HFS-P) at baseline (T1) and 6 (T2) and 12 (T3) months post-baseline. The HFS-P measures worry about hypoglycemia (HFS-Worry score) as well as hypoglycemia avoidance behaviors (HFS-Behavior score). We recorded CGM start dates for youth during the same time period through medical record review.Results:Between T1 and T2, 31 youth (32.3%) initiated CGM therapy, and between T2 and T3, an additional 17 youth (17.7%) began using CGM, leaving 48 youth who never initiated CGM therapy (50%) in the recent-onset period. Parents reported moderate HFS-Worry scores at T1 (32.9 ± 11.9), which increased between T1 and T2 (37.6 ± 11.4, P < .001) and plateaued between T2 and T3 (37.7 ± 12.4, P = .89). In contrast, parental HFS-Behavior scores decreased between T1 (33.1 ± 5.8) and T2 (32.2 ± 6.0, P = .005) and plateaued between T2 and T3 (32.2 ± 6.0, P = .95). Baseline HFS-Behavior and Worry scores were associated with increased adoption of CGM between T1-T2 and T2-T3, respectively. Parents of children initiating CGM therapy between T1 and T2 showed the largest decrease in HFS-Behavior (P = .03).Conclusions:Initiating CGM therapy within the first 12 months of T1D may help reduce parents’ use of hypoglycemia avoidance behaviors, but has little effect on parents’ hypoglycemia worry.     

An Examination of the Glucose Management Indicator in Young Children with Type 1 Diabetes

Background:Previous studies utilizing glucose data from continuous glucose monitors (CGM) to estimate the Glucose Management Indicator (GMI) have not included young children or determined appropriate GMI formulas for young children with type 1 diabetes (T1D).Methods:We extracted CGM data for 215 children with T1D (0-6 years) from a repository. We defined sampling periods ranging from the 3-27 days prior to an HbA1c measurement and compared a previously established GMI formula to a young child-specific GMI equation based on the sample’s CGM data. We examined associations between HbA1c, GMI values, and other CGM metrics for each sampling period.Results:The young child-specific GMI formula and the published GMI formula did not evidence significant differences when using 21-27 days of CGM data. The young child-specific GMI formula demonstrated higher correlations to laboratory HbA1c when using 18 or fewer days of CGM data. Overall, the GMI estimate and HbA1c values demonstrate a strong relationship in young children with T1D.Conclusions:Future research studies may consider utilizing the young child-specific GMI formula if the data collection period for CGM values is under 18 days. Further, researchers and clinicians may consider changing the default number of days of data used to calculate glycemic metrics in order to maximize validity of CGM-derived metrics.     

Analysis of the ADA’s Standard of Diabetes Care Recommendation for Continuous Glucose Monitoring System

Transparency is a key factor to understand how recommendations were reached and to decide whether to adopt them or not. Currently, the American Diabetes Association (ADA) recommends the usage of real-time continuous glucose monitors in certain subgroups of people living with type 1 diabetes mellitus. In this commentary we sought to critically appraise this recommendation, mainly regarding the outcomes assessed, the evidence used for each outcome, and how the balance of benefits and harms was made. We found that the decision-making process followed to reach the ADA recommendation was not clear, and that the final recommendation does not seem to be supported in the assessed evidence.     

Time Lag of Glucose From Intravascular to Interstitial Compartment in Type 1 Diabetes

Method:Four microdialysis catheters were inserted into the abdominal subcutaneous space in 6 T1D subjects under overnight fasted conditions. Plasma glucose was maintained at 113.7 ± 6.3 mg/dl using a continuous intravenous insulin infusion. After sequential intravenous bolus administrations of glucose isotopes, timed plasma and interstitial fluid samples were collected chronologically and analyzed for tracer enrichments.Results:We observed a median (range) time lag of tracer appearance (time to detection) into the interstitial space after intravenous bolus of 6.8 (4.8-9.8) minutes, with all participants having detectable values by 9.8 minutes.Conclusions:We conclude that in the overnight fasted state in T1D adults, the delay of glucose appearance from the vascular to the interstitial space is less than 10 minutes, thereby implying that this minimal physiological time lag should not be a major impediment to the development of an effective closed-loop control system for T1D.     

Practical Approach to Using Trend Arrows on Real-Time Continuous Glucose Monitoring System in Type 1 Diabetes Adolescents Living Camp Setting Treated With Multiple Daily Injection or Continuous Subcutaneous Insulin Infusion Insulin Therapy

Background:The aim of the study was to determine the effect of an educational intervention on the use of trend arrows of a real-time continuous glucose monitoring (rt-CGM) to manage daily therapy decisions in a group of adolescents with type 1 diabetes attending a camp. The secondary aim was to evaluate the variations in total daily dose (TDD) of insulin requirement.Methods:Twenty patients (15-17 years) on multiple insulin injections (n = 8) or continuous subcutaneous insulin infusion (n = 12) attended a training session at the beginning of the camp to learn our algorithm for the management of therapy depending on trend arrows. TDD, time in range (TIR), time above range (TAR), and time below range (TBR) (in the 24 hours and in the three hours after breakfast) before the training session (run-in) and at the end of the camp (T1) were analyzed.Results:Data showed a reduction of TAR (run-in 42.6%, T1 32.05%, P = .036) and an increase in TIR (run-in 52.9%, T1 62.4%, P = .013). Reduction of TBR (run-in 42.5%, T1 37.5%, P = .05) and improvement in TIR (run-in 49.0%, T1 57.0%, P = .02) were also observed in the post-breakfast period. Data showed a significant reduction in the TDD (run-in 52.02 ± 17.44 U/die, T1 46.49 ± 12.39 U/die, P = .024).Conclusions:Statistically significant improvement of glycemic control and reduction of TTD were observed in all patients regardless of therapy type. The improvement between run-in and T1 demonstrates the importance of patients’ education on the correct use of rt-CGM with simple algorithms for the management of therapy.     

Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care

Background:Little data exists regarding the impact of continuous glucose monitoring (CGM) in the primary care management of type 2 diabetes (T2D). We initiated a quality improvement (QI) project in a large healthcare system to determine the effect of professional CGM (pCGM) on glucose management. We evaluated both an MD and RN/Certified Diabetes Care and Education Specialist (CDCES) Care Model.Methods:Participants with T2D for >1 yr., A1C ≥7.0% to <11.0%, managed with any T2D regimen and willing to use pCGM were included. Baseline A1C was collected and participants wore a pCGM (Libre Pro) for up to 2 weeks, followed by a visit with an MD or RN/CDCES to review CGM data including Ambulatory Glucose Profile (AGP) Report. Shared-decision making was used to modify lifestyle and medications. Clinic follow-up in 3 to 6 months included an A1C and, in a subset, a repeat pCGM.Results:Sixty-eight participants average age 61.6 years, average duration of T2D 15 years, mean A1C 8.8%, were identified. Pre to post pCGM lowered A1C from 8.8% ± 1.2% to 8.2% ± 1.3% (n=68, P=0.006). The time in range (TIR) and time in hyperglycemia improved along with more hypoglycemia in the subset of 37 participants who wore a second pCGM. Glycemic improvement was due to lifestyle counseling (68% of participants) and intensification of therapy (65% of participants), rather than addition of medications.Conclusions:Using pCGM in primary care, with an MD or RN/CDCES Care Model, is effective at lowering A1C, increasing TIR and reducing time in hyperglycemia without necessarily requiring additional medications.     

HbA1c Is Disproportionately Higher in Women and Older People With Type 1 Diabetes Compared With Flash Glucose Monitoring Metrics of Glycemic Control

Aims:Discrepancy between HbA1c and glucose exposure may have significant clinical implications. We sought to assess predictors of disparity between HbA1c and flash monitoring metrics and how these relate to microvascular complications.Methods:We conducted a cross-sectional study of adults with type 1 diabetes (n = 518). We assessed the relationship between clinic HbA1c and flash monitoring metrics, predictors of discrepancy between these measurements, and whether discrepancy was associated with microvascular complications.Results:Actual HbA1c and estimated HbA1c were strongly correlated (r = .779, P < .001). The likelihood of having a higher actual HbA1c than estimated HbA1c was greater with increasing age (OR = 1.055 per year, P < .001) and lower in men (OR = .208, P < .001). HbA1c was significantly lower in men (58 mmol/mol [51-67]) (7.5% [6.8-8.3]) compared to women (61 mmol/mol [54-70], P = .021) (7.7% [7.1-8.6]), despite no significant differences in any flash monitoring metrics. Whereas HbA1c was not different between younger (≤39 years) and older individuals (>39 years) despite significantly higher glucose exposure, in younger people, based on multiple flash monitoring metrics. Having a lower estimated than actual HbA1c was independently associated with a lower prevalence of retinopathy (OR = .55, P = .004).Conclusions:HbA1c appears to overestimate glucose exposure in women and older people with type 1 diabetes. This has potentially important clinical implications, as is hinted at by the independent relationship with retinopathy prevalence. It may also be of relevance when considering the use of HbA1c for the diagnosis of diabetes.     

The Benefits of Continuous Glucose Monitoring and a Glucose Monitoring Schedule in Individuals with Type 1 Diabetes during Recreational Diving

Background

Our objective is to evaluate the Medtronic CGMS^® continuous glucose monitoring system and plasma glucose (PG) measurement performed in a monitoring schedule as tools to identify individuals with type 1 diabetes at risk when diving.

Methods

We studied 24 adults, 12 type 1 diabetes subjects and 12 controls, during 5 recreational scuba dives performed on 3 consecutive days. The CGMS was used by all participants on all the days and all the dives. Comparisons were made between PG performed in a monitoring schedule during the days of diving, self-monitored blood glucose (SMBG) performed 2 weeks prior to diving, and the CGMS during the study.

Results

One hundred seventeen dives were performed. Hypoglycemia (<70 mg/dl) was found in six individuals and on nine occasions. However, no symptoms of hypoglycemia were present during or immediately postdiving. In one case, repetitive hypoglycemia prediving gave rise to a decision not to dive. None of the dives were aborted. The number of hypoglycemic episodes, 10 min prediving or immediately postdiving, were related to the duration of diabetes, r = 0.83 and p =0.01, and the percentage of SMBG values below target (<72 mg/dl), r = 0.65 and p =0.02. Moreover, the number of hypoglycemic episodes was also related to the total duration below low limit (<70 mg/dl), measured by the CGMS, r =0.74 and p =0.006.

Conclusion

Safe dives are possible to achieve by well-informed, well-controlled individuals with type 1 diabetes. Using downloaded SMBG, CGMS, and repetitive PG in a monitoring schedule, it is possible to identify those subjects who are suitable for diving.     

Patient Controlled,Off-label Use of Continuous Glucose Monitoring: Real-World Medical Costs and Effects of Patient Controlled Sensor Augmented Pump Therapy in Adult Patients Type 1 Diabetes

Background:Continuous glucose monitoring (CGM) has shown promise to reduce glycated hemoglobin (HbA1c) levels, but its cost-effectiveness is seen as uncertain by reimbursement agencies. The aim of this study was to explore the impact of real-world, off-label, patient controlled CGM use in combination with continuous subcutaneous insulin infusion (CSII) on costs and effects in patients with type 1 diabetes in a Swedish clinic.Methods:A real-world, retrospective study with questionnaire on CGM use by adult patients with type 1 diabetes on CSII (Animas Vibe) were offered sensor augmented pump therapy (SAPT) (Dexcom G4) as part of hospital innovation funding program. Direct medical costs, HbA1c, and complications following switch from CSII with self-monitoring of blood glucose (SMBG) to SAPT were calculated.Results:Questionnaire data showed that CGM sensors were on average used 92% of the time for 22 days. One hundred and thirty-nine (95%) of 146 respondents used each sensor for longer than one week. Data analysis showed a statistically significant HbA1c decrease of 0.56% (6.1 mmol/mol) after change to SAPT. In patients using the sensor 100%, the decrease was 0.89% (9.8 mmol/mol). The analysis showed that SAPT led to higher costs (5500 USD/year) than CSII + SMBG (3680 USD/year), with incremental costs being 1815 USD per year to achieve an HbA1c decrease of 0.56% (6.1 mmol/mol). The incidence of all complications declined after switch to SAPT.Conclusion:The primary data analysis showed a decrease in HbA1c values following switch to SAPT, corresponding to previous cost-effectiveness studies, but at substantially lower costs due to longer sensor off-label use.     

Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

Background:Providing real-time magnitude and direction of glucose rate-of-change (ROC) via trend arrows represents one of the major strengths of continuous glucose monitoring (CGM) sensors in managing type 1 diabetes (T1D). Several literature methods were proposed to adjust the standard formula (SF) used for insulin bolus calculation by accounting for glucose ROC, but each of them provides different suggestions, making it difficult to understand which should be applied in practice. This work aims at performing an extensive in-silico assessment of their performance and safety.Methods:The methods of Buckingham (BU), Scheiner (SC), Pettus/Edelman (PE), Klonoff/Kerr (KL), Aleppo/Laffel (AL), Ziegler (ZI), and Bruttomesso (BR) were evaluated using the UVa/Padova T1D simulator, in single-meal scenarios, where ROC and glucose at mealtime varied between [-2,+2] mg/dL/min and [80,200] mg/dL, respectively. Efficacy of postprandial glucose control was quantitatively assessed by time in, above and below range (TIR, TAR, and TBR, respectively).Results:For negative ROCs, all methods proved to increase TIR and decrease TAR and TBR vs SF, with KL, PE, and BR being the most effective. For positive ROCs, a general worsening of the performances is present, only BR improved the glycemic control when mealtime glucose was close to hypoglycemia, while SC resulted the safest in the other conditions.Conclusions:Insulin bolus adjustment methods are effective for negative ROCs, but they generally appear to overdose for positive ROCs, calling for safer strategies in such a scenario. These results can be useful in outlining guidelines to identify which adjustment to apply based on the mealtime condition.