A Simple Meal Bolus Strategy is Effective in Adolescents and Young Adults with Type 1 Diabetes on Multiple Automated Insulin Delivery Systems

Abstract

Background:

Carbohydrate counting (CC) is challenging and may be a barrier to automated insulin delivery (AID) use. Given the ability of AID to modulate insulin delivery, a simpler meal bolus strategy may be adequate for glycemic management.

Methods:

Participants aged 14–26 using AID were enrolled in a randomized crossover trial comparing glycemic management using simple boluses versus CC for 4 weeks each. Before the simple period, participants were educated to enter a set carbohydrate amount for a small (30 g), medium (60 g), or large (90 g) meal, and carbohydrate ratios were standardized. Glycemic outcomes were compared between study periods. We hypothesized that time in range (TIR) 70–180 mg/dL would not be inferior by more than 5% during the simple period compared with the CC period. Other measures of glycemia were compared using paired t-tests.

Results:

Among 31 participants (17.4 years; 51.6% female; type 1 diabetes duration [T1D_Dur.] 8.3 years; baseline TIR 64.1%), TIR with simple meal boluses was not inferior to TIR with CC (simple TIR 64.2%, CC TIR 66.0%, difference −1.8%, lower limit of 95% confidence interval: −3.9, P = 0.008 indicating noninferiority at a margin of Δ = 5). There were no differences in % time >250 mg/dL (difference 1.2%, P = 0.3) and % time <70 mg/dL (difference −0.02%, P = 0.9). After the study, 31.3% of participants preferred CC, 40.6% preferred simple boluses, and 28.1% preferred their prestudy method.

Conclusion:

Precise CC may be an unnecessary burden for T1D adolescents using AID, and requiring it could impact success with or access to these systems. Using a simple meal bolus strategy is one option to reduce burden without significant impact on glycemic outcomes (Clinical Trials Registration number- NCT06575790).

Keywords

simple meal boluses hybrid closed loop automated insulin delivery carbohydrate counting

Introduction

Type 1 diabetes (T1D) affects over 300,000 children and adolescents and 1.7 million adults in the United States.¹ The American Diabetes Association recommends a goal HbA1c of <7% and time in range (TIR) 70–180 mg/dL target of >70% for most children.² Reaching goal targets through intensive therapy is important to prevent or delay complications including nephropathy, retinopathy, neuropathy, and cardiovascular disease.^3–6 Diabetes management is also uniquely important in children given the risk of negative neurocognitive impacts from chronic hyperglycemia on the developing brain.⁷ In addition, management habits established early in life may predict glycemia later in life.⁷

Automated insulin delivery (AID) systems combine an insulin pump, continuous glucose monitor (CGM), and automated control algorithm to automatically adjust basal insulin and give correction insulin to improve glucose management.⁸ Due to delays in insulin absorption and action, current AID systems are termed hybrid closed loop as they still require user-initiated boluses before meals, and most require users to enter the number of carbohydrates they are going to consume. Adults with T1D and parents of children with T1D understand the importance of mealtime bolus insulin, but the vast majority report challenges surrounding mealtime boluses and only a third report feeling very confident in accurate bolus size estimation.⁹ Carbohydrate counting (CC) accuracy is a documented challenge for adolescents with prior reports showing that only 23% of adolescents are able to demonstrate accurate carbohydrate estimation for common meals.^10,11 In addition, those with low numeracy skills may have even more difficulty with CC, and low numeracy has been associated with poorer glycemic control.^12,13

The ability to count carbohydrates may also impact patients’ access to these systems, which is important because AID systems are now standard of care for T1D management as they significantly improve HbA1c in both pediatric and adult populations; patients who use these systems are more likely to reach the goal HbA1c of <7%.^14,15 In a survey, 80% of endocrinologists reported that the ability to carbohydrate count had an impact on their decision to initiate an insulin pump.¹⁶

Given the clear benefit of AID systems on glycemic management, the ability to use an alternative meal bolus strategy may make AID systems more accessible. Although AID systems cannot yet provide automatic meal bolus insulin, such systems can minimize hyperglycemia through basal modulation and automated correction dosing and should be able to mitigate some of the impact of non-precise carbohydrate coverage. The objective of this prospective randomized crossover trial in adolescents and young adults on commercially available AID systems was to evaluate if simple meal boluses are safe, feasible, and provide adequate glycemic management in comparison with precise CC.

Methods

Study design

We conducted a prospective randomized crossover trial in adolescents and young adults with T1D to evaluate glycemic outcomes while using a simple meal bolus strategy compared with precise CC (Fig. 1). The primary objective was to compare TIR during the simple meal bolus period with the CC period. The secondary objective was to compare the participants’ survey-reported burden and related self-management outcomes between study periods.

The study was conducted at the Barbara Davis Center at the University of Colorado (Aurora, Colorado). The study was approved by the Colorado Multi-Institutional Review Board in July 2024 (COMIRB #24-1159). Participants were enrolled between November 2024 and September 2025. All subjects provided written informed consent before enrollment in the study.

After enrollment, there was a 2-week monitoring period of usual care. Study participants were randomized via REDCap 1:1 to Group A or Group B. Neither the participants nor the investigators were blinded to group assignments. Group A completed 4 weeks of simple meal boluses (simple) followed by 1 week of usual care washout and then 4 weeks of CC. Group B completed 4 weeks of CC first, then 1 week of usual care washout, and finally, 4 weeks of simple. Participants were instructed to use their AID systems as they typically would, except for the assigned strategy for carbohydrate entry for meal boluses.

Before the CC period, participants were given a brief education on CC and label reading and then instructed to enter precise carbohydrate counts to the best of their ability during the 4-week CC study period. Before the simple period, participants were instructed to enter 30 (for large snacks/small meals), 60 (for medium meals), or 90 g (for large meals) as their carbohydrate count and were given a brief presentation of examples of small, medium, and large meals. All participants were given the same instructions.

Before the simple period, participants had their insulin-to-carbohydrate ratios set based on the standard formula of 450/total daily dose (TDD) for all meals. In select cases, if there was concern that this change would put the patient at risk for hypoglycemia, these settings were adjusted for safety. No correction factors or other settings were modified.

Participants

The inclusion criteria were as follows: (1) aged 14–26 years; (2) clinical diagnosis of T1D of at least 1-year duration; (3) current use of a commercial AID system except for iLet; (4) willingness to not start any new noninsulin glucose-lowering agent during trial; (5) willingness to participate in all study procedures; and (6) investigator has the confidence that the participant can successfully operate all study devices and adhere to the protocol. The exclusion criteria included the following: (1) History of >1 severe hypoglycemic event with seizure or loss of consciousness in the last 6 months or >1 diabetic ketoacidosis (DKA) event in the last 6 months not related to illness or infusion set failure; (2) history of chronic renal disease or currently on hemodialysis, adrenal insufficiency, hypothyroidism that is not adequately treated, use of oral or injectable steroids within the last 8 weeks, or known ongoing adhesive intolerance; (3) condition, which in the opinion of the investigator or designee, would put the participant or study at risk; and (4) participation in another interventional trial at the time of enrollment.

Outcomes

Demographic and baseline characteristics were obtained at the time of enrollment and included participant age, sex, weight, height, body mass index (BMI), race, ethnicity, duration of T1D, and current technology use. CGM and pump/device use metrics were obtained at the end of the 2-week baseline usual care period and both 4-week study periods. CGM metrics included mean sensor glucose (SG), glucose management index derived from mean SG, TIR 70–180 mg/dL, SG standard deviation, coefficient of variation (CV), SG % <54 mg/dL, SG % <70 mg/dL, SG % >180 mg/dL, and SG % >250 mg/dL. Device use metrics included average total daily insulin dose, average daily basal insulin, average daily bolus insulin, average daily autocorrection insulin (only available for MiniMed 780G and Control-IQ+ users), number of user-initiated boluses/day, number of automated correction boluses/day, number of announced meals, number of announced carbohydrates, and time in automation. Episodes of DKA and severe hypoglycemia were self-reported via survey at each study visit. Participants completed a survey at enrollment and at the end of each study period with items evaluating self-reported meal bolus behaviors, worry, burden, compensatory behaviors, and quality of life (Supplementary Data). Some of the survey questions were adapted from Lane et al.⁹

Statistical analysis

The primary outcome, difference in TIR between the simple and CC periods, was assessed via a one-sided noninferiority analysis comparing mean TIR with a 95% confidence interval (CI) and a noninferiority margin of 5%. This value was chosen based on international consensus for a clinically significant difference in TIR.¹⁷ Mixed models were used to evaluate for period and sequence effects. Other numeric CGM and device use metrics were compared between the simple and CC study periods using paired t-tests with a two-sided significance level of α = 0.05. Linear regression was done to examine the correlation between weight or BMI and change in TIR. Results between device manufacturers were not compared as the prespecified analysis plan aimed to investigate AID as a class and was not designed or powered to correct for potential bias in device selection. Survey results were summarized as means where higher scores indicated worse outcomes (frequency, burden, worry, difficulty, or negative impact). Survey items were compared across the baseline, simple, and CC time periods using a Friedman test. For the survey item with statistical differences, Conover’s post hoc test was used to complete standard pairwise comparisons, and P values were adjusted using the Benjamini–Hochberg procedure. Statistics were performed using R version 4.5.1 or SAS OnDemand for Academics.

Results

Participants and baseline characteristics

A total of 31 participants were recruited and consented to enroll in the study. All participants completed the study. Characteristics of the study population are in Table 1. Participants (51.6% female) had a mean age of 17.4 ± 3.0 years (14.0, 23.0), a diabetes duration of 8.3 ± 3.4 years (2.4, 14.5), and a baseline TIR of 64.1 ± 10.4% (43.0, 89.0). AID system use included Tandem Control-IQ+ system (67.7%), Omnipod 5 (19.4%), and MiniMed 780G (12.9%).

Table 1.

Demographics

Metric	Value
N	31
Age (years ± SD)	17.4 ± 3.0 [14.0, 23.0]
Race/Ethnicity—n (%)
Non-Hispanic White	28 (90.3%)
Hispanic	1 (3.2%)
Black or African American	1 (3.2%)
Asian	1 (3.2%)
Sex—n (%)
Female	16 (51.6%)
Male	15 (48.4%)
Weight (kg ± SD)	70.7 ± 15.1 [41.8, 103.9]
BMI (kg/m² ± SD)	24.2 ± 4.2 [17.1, 33.9]
Insurance—n (%)
Public	5 (16.1%)
Private	24 (77.4%)
Other	2 (6.5%)
Diabetes duration (years ± SD)	8.3 ± 3.4 [2.4, 14.5]
AID system—n (%)
Tandem Control-IQ+	21 (67.7%)
Omnipod 5	6 (19.4%)
Medtronic 780G	4 (12.9%)
Baseline TIR (mean % ± SD)	64.1 ± 10.6 [43.0, 89.0]
Baseline no. of user boluses/day (mean % ± SD)	4.6 ± 2.6 [1.0, 12.0]

AID, automated insulin delivery; BMI, body mass index; SD, standard deviation; TIR, time in range.

At baseline, participants had a mean number of user-initiated boluses of 4.6 per day. Self-reported methods to determine what to enter at meals at baseline included educated guessing (90.3%), entering similar values each meal (6.5%), or using labels and resources to carbohydrate count (3.23%). No participants reported, “do not usually bolus at meals.” Most participants (30 of 31) reported that the patient alone is usually responsible for CC or deciding how much insulin to give at a mealtime, and one participant reported that both the patient and the guardian are usually responsible for CC.

Glycemic analysis

Glycemic outcomes during the study periods are presented in Table 2 and Figure 2. TIR was not inferior during the simple period compared with CC using a noninferiority margin of 5% (simple TIR 64.2%, CC TIR 66.0%, difference −1.8%, lower limit of 95% CI: −3.9, P = 0.008). In a mixed-effects analysis, there was no evidence of a period effect (P = 0.57) or a sequence effect (P = 0.41).

Table 2.

Continuous Glucose Monitor and Pump Metrics During Simple Compared with Carbohydrate Counting

Metric	Simple meal bolus strategy	Precise carbohydrate counting	Difference	P value
Time in range, 70–180 mg/dL, %	64.2 (11.0)	66.0 (9.4)	−1.8	0.008*
Time less than 70 mg/dL, %	1.5 (1.2)	1.5 (1.0)	−0.2	0.57^**
Time less than 54 mg/dL, %	0.5 (0.6)	0.6 (0.7)	−0.3	0.31^**
Time over 250 mg/dL, %	11.7 (7.9)	10.6 (6.1)	1.2	0.26^**
Time over 180 mg/dL, %	21.9 (5.4)	21.5 (5.2)	0.4	0.56^**
Mean sensor glucose, mg/dL	167.1 (23.3)	161.7 (17.7)	5.3	0.11^**
Coefficient of variation of sensor glucose, %	39.2 (3.7)	38.3 (4.6)	0.7	0.24^**
Mean frequency of daily user-initiated boluses	4.5 (2.4)	5.1 (2.4)	−0.7	0.04**
Mean total daily dose (TDD) of insulin, units	68.6 (68.6)	69.3 (23.8)	−0.7	0.71^**
Mean automatic correction units	12.1 (8.2)	11.6 (6.9)	0.5	0.28^**
Percent of TDD given as autocorrection, %	17.3 (9.3)	16.9 (8.4)	0.4	0.5^**
Bolus daily insulin, %	50.2 (9.4)	50.7 (8.3)	−0.5	0.70^**
Basal daily insulin, %	49.8 (9.4)	50.8 (11.3)	−1.0	0.46^**
Average daily carbs, g	139.2 (72.2)	144.3 (75.1)	−5.0	0.67^**

One-sided, noninferiority test concluding noninferiority at a prespecified margin of Δ = 5.

Paired t-test.

There was no difference in percent time spent with BG less than 70 mg/dL, time spent above 250 mg/dL, mean SG, CV of SG, total daily insulin dose, basal/bolus distribution, amount of insulin given as autocorrection, or average daily carbohydrates entered. There was no significant relationship with the change in TIR and weight or BMI. Participants had a significantly higher number of user-initiated boluses during the CC study period compared with the simple period (5.1 vs. 4.5, difference of 0.7, P = 0.04). There were no reported episodes of DKA or severe hypoglycemia. During the simple meal bolus period, participants adhered to only entering 30, 60, or 90 g for 94.1% of the time.

There were five total instances where carbohydrate ratios were set weaker than 450/TDD at the start of the simple meal bolus period due to concern for hypoglycemia based on baseline ratios. Carbohydrate ratios were left at prestudy ratios for usual care, CC study period, and the washout period. There were two instances where carbohydrate ratios were set using 450/TDD before the CC study period due to the participant not using the bolus calculator before the study. In addition, one participant requested to change to the profile they use when running lower BG during their CC study period. No other setting changes were reported for any participants during the study.

Survey analysis

Survey results regarding compensatory behaviors, burden, and quality of life at baseline, at the end of the CC study period, and at the end of the simple study period are presented in Table 3. There was no significant difference between study periods in self-reported compensatory behaviors including eating extra food to avoid a low BG from too much insulin at a meal or taking extra insulin due to a high BG after a meal. There was no significant difference in how often participants reported forgetting to give insulin before a meal, how difficult it was to determine how much insulin to give at a meal, or how often they reported worrying about a postmeal BG. There was a significant difference in their response to how burdensome bolusing for meals felt (P = 0.0007) with a mean of 2.4 during CC and 1.6 during simple (adj. P = 0.03) on a Likert scale, with a 4 representing very burdensome and a 1 representing never burdensome (Fig. 3).

Table 3.

Comparison of Mean Values Between Survey Responses^a

Question	Baseline	Precise carbohydrate counting	Simple meal bolus strategy	P value^b
How often did you need to eat extra food to avoid low blood sugar due to too much insulin at a meal	2.52 (0.93)	2.55 (0.93)	2.81 (1.01)	0.3310
How often did you need to take extra insulin because blood sugar was high after eating	3.43 (0.82)	3.67 (0.61)	3.70 (0.99)	0.1625
How often did you forget to give insulin before a meal	2.74 (1.03)	2.80 (0.96)	2.57 (0.90)	0.4169
How often did you decide not to eat out at a restaurant/with friends/at school due to being unsure of how much insulin to take	1.23 (0.6)	1.23 (0.50)	1.10 (0.40)	0.2276
How often are you worried about postmeal blood glucose	1.94 (0.51)	2.07 (0.52)	2.17 (0.79)	0.2319
How did this meal bolus strategy impact quality of life	—	2.90 (0.92)	2.50 (1.04)	0.1249
How burdensome did bolusing for meals feel	2.03 (0.89)	2.43 (0.82)	1.60 (0.67)	0.0007
How difficult was it to determine how much insulin to give at meals	1.58 (0.62)	1.83 (0.75)	1.63 (0.61)	0.3321

Bold values are statistically significant with p-values <0.05.

Higher scores indicated worse outcomes (frequency, burden, worry, difficulty, or negative impact).

The P value is based on a Friedman test (nonparametric repeated measures analysis of variance).

At the end of the trial, 40.6% of participants preferred the simple method, 31.3% preferred CC, and 28.1% preferred their baseline habits. Among those who preferred their baseline habits, they were asked to describe what their baseline habits were, which was generally described as an estimation based on experience.

Participants were also asked what they liked and disliked about the simple period and the CC period. General positive themes reported about CC included feeling confident in the count, having a better understanding of their carbohydrate intake, and less fluctuation in BG. Although some users reported self-perceived less fluctuation in BG during the CC period, the overall CV of SG was not different between the study periods. Negatives about CC reported included the extra time and energy it required and specifically being more difficult when going out to eat. General positive themes reported about the simple period included less worry about what was in the meal, having to think less, feeling easy, and taking less time. The most common negatives reported about the simple method included the options feeling too restrictive/increments too large, desiring the option to enter more in between numbers, and desiring a smaller option for snacks (e.g., 15 g).

Discussion

In this prospective randomized crossover trial in adolescents and young adults with T1D using AID systems, we found that a simple meal bolus strategy achieved a noninferior TIR when compared with precise CC. In addition, there was no difference in time spent above or below the range. Glycemic outcomes were similar between methods despite a higher number of mean user boluses during the CC study period. In addition, participants reported that bolusing for meals felt less burdensome while using the simple method.

The concept of using a simple meal bolus approach has been described as early as 1986, when Pernick et al. described using a simple code of 0–5 for different meals.¹⁸ Prior studies have shown that simplified meal bolus approaches can be feasible in AID, but the existing literature has largely been limited to single-system evaluations and more individualized implementation. Petrovski et al. demonstrated that a simplified meal announcement could be successfully used with the MiniMed 780G in adolescents (ages 12–18) and achieved a TIR >70% during the simple approach although TIR during their CC study period was significantly higher. Their approach relied on three personalized fixed carbohydrate amounts selected using a review of food diaries.¹⁹ Their 12-month follow-up similarly remained restricted to the MiniMed 780G and continued to show a lower TIR with the fixed carbohydrate approach, but no difference in A1c.²⁰ The SMASH trial using CamAPS FX demonstrated a noninferior TIR with 3 months of simple meal announcements compared with 3 months of CC in people ages 12–20.²¹ Similarly to Petrovski’s study, the meal sizes for the simple intervention were customized to the individual using a 3-day diet history.

Haidar et al. used a similar approach to our study with four simple meal categories (<30, 30–60, 60–90, and >90 g) compared with CC and found a high TIR during the simple approach of 70.5%, but noninferiority was not confirmed.²² This study was in an adult population using a research device (iPancreas) and so is less generalizable to our population. A study by Minsky et al. in adults on MiniMed 780G also did not rely on diet history but rather used a preset amount based on age and weight. They found no difference in TIR using the preset amount compared with CC and actually found that using three preset amounts achieved a higher TIR than CC.²³ Blervaque et al. found that a semiqualitative method of meal estimation achieved a similar TIR as CC in a large population (n = 1959). This was a retrospective study of adults on the DBLG-1 system and so also less generalizable to our population.²⁴

There has been one study examining simple meal bolus strategies in Control-IQ+ users, but it only included adults with type 2 diabetes. This study found that in new Control-IQ+ users, those who counted carbohydrates, those using preset carbohydrate amounts, and those using fixed insulin dosing all saw improvement in A1c with low hypoglycemia rates. They also observed that over time, more users were switching to simpler bolus strategies.²⁵

Taken together, prior work suggests that simplified meal bolusing is feasible, but much of the published evidence has been in a single system or required individually tailored strategies that may be more time-consuming to implement during routine clinical care. The current study extends this literature in several important ways.

First, rather than evaluating a single AID platform, adolescents and young adults using multiple commercially available systems were included, including Tandem Control-IQ+, Omnipod 5, and MiniMed 780G. While studies have been published using MiniMed 780G, there are limited data available for Omnipod 5 or Tandem Control-IQ+ users regarding simple meal bolus strategies in T1D. These systems all help mitigate hyperglycemia but do so in different ways. The MiniMed 780G adjusts basal rates (which are auto-calculated daily based on TDD) every 5 min and provides autocorrection boluses as frequently as every 5 min. Control-IQ+ adjusts basal rates every 5 min using the programmed basal rates as the baseline and provides autocorrection boluses up to once per hour. Omnipod 5 does not give automatic correction boluses, but adjusts basal rates (auto-calculated at each pod change using TDD) every 5 min to correct hyperglycemia.²⁶

The second major way this study extends the literature on simple meal bolus strategies is the intentionally pragmatic nature of the intervention. Participants were taught a standardized, clinic-ready strategy of entering 30, 60, or 90 g for small, medium, or large meals, without requiring food diaries or individualized preset carbohydrate categories. This strategy was unique from prior studies and suggests that a lower burden, easily deployable simple meal bolus approach may be sufficient to maintain glycemic outcomes in many adolescents using AID.

AID system manufacturers have recognized the practice of using a simple meal bolus method. Omnipod 5 has a custom food feature that could be used to create preset small, medium, and large meals.²⁷ Tandem has created worksheets to fill out for providers who want to communicate a simple meal bolus strategy to their patients with either set carbs or set units.²⁸ MiniMed has created a preset bolus feature to set up a set bolus amount for different meals, but it cannot be used in its automated mode (SmartGuard).²⁹

This study has several strengths. The prospective randomized crossover design allowed each participant to serve as his or her own control, reducing interindividual variability in glycemic outcomes. The inclusion of a washout period further minimized potential carryover effects between interventions. The intervention itself was simple, pragmatic, and easily implementable. In addition, participants used multiple commercially available AID systems, increasing the relevance of the findings across currently used technologies.

Several limitations should also be considered. The sample size was modest, and participants were recruited from a single center, which may limit generalizability. Participants also had relatively high baseline engagement with meal bolusing and a mean diabetes duration of 8.3 years, suggesting that this cohort may have some self-selection bias and represent a more adherent or experienced population than the broader population of adolescents and young adults with T1D. Therefore, results may differ in individuals who rarely bolus at meals or are closer to the time of diagnosis. It also demonstrates that this intervention could be successful even in an engaged population. The study duration for each intervention period was relatively short, so longer term glycemic outcomes and behavioral adaptation were not assessed. The simplified bolus strategy used fixed meal categories without a dedicated snack option or intermediate carbohydrate amounts, which was intentional, but some participants identified it as restrictive and may have influenced both glycemic outcomes and user preference. Finally, it is important to recognize the potential for bias from investigator and participant interaction.

Although a larger proportion of participants preferred the simple meal bolus strategy over precise CC, nearly one-third still preferred CC. These findings suggest that simplified meal bolusing may be a valuable option, but not a universal replacement for CC. Mealtime bolus strategies should therefore be individualized, as patients differ in their preferences, confidence with carbohydrate estimation, desired level of precision, daily routines, and tolerance for treatment burden. Some individuals may value the flexibility and reduced cognitive load of a simple approach, whereas others may prefer the sense of control and precision that comes with CC. Accordingly, clinicians should use a shared decision-making approach to align mealtime bolus recommendations with each patient’s goals, preferences, and self-management capacity.

In adolescents and young adults with T1D using AID systems, a simplified meal bolus strategy based on preset carbohydrate amounts for small, medium, and large meals resulted in glycemic outcomes that were noninferior to those achieved with precise CC. This approach reduced the perceived burden of mealtime bolusing while maintaining similar TIR and rates of hypoglycemia and hyperglycemia. These findings suggest that strict CC may not be necessary for all individuals using current AID systems and that simpler bolus strategies may help reduce treatment burden without compromising glycemic management. Incorporating simplified meal bolus approaches into clinical practice may help expand access to AID systems for patients who find CC difficult. Larger and longer term studies are needed to evaluate the effectiveness of this strategy in broader populations and in individuals with lower baseline engagement in meal bolusing.

Authors’ Contributions

Alexandra S.: Conceptualization, methodology, formal analysis, investigation, and writing—original draft preparation. L.T.: Investigation, project administration, and writing—review and editing. S.L.: Investigation, project administration, and writing—review and editing. Amy S.: Methodology, formal analysis, and writing—review and editing. C.B.: Conceptualization, methodology, investigation, project administration, and writing—review and editing. G.F.: Conceptualization, methodology, investigation, supervision, and writing—review and editing.

Ethical Considerations

The Colorado Institutional Review Board approved this study on July 26, 2024, COMIRB #24-1159.

Consent to Participate

Written consent was obtained from all participants and their guardians (if participant younger than 18 years) before enrollment in the study.

Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available.

Supplemental Material

sj-docx-1-dtt-10.1177_15209156261467128 — Supplemental material for A Simple Meal Bolus Strategy is Effective in Adolescents and Young Adults with Type 1 Diabetes on Multiple Automated Insulin Delivery Systems

Supplemental material, sj-docx-1-dtt-10.1177_15209156261467128 for A Simple Meal Bolus Strategy is Effective in Adolescents and Young Adults with Type 1 Diabetes on Multiple Automated Insulin Delivery Systems by Alexandra Sawyer, Lindsey Towers, Samantha Lange, Amy Stein, Cari Berget, and Gregory P. Forlenza

Footnotes

Author Disclosure Statement

G.F. conducts research sponsored by Medtronic, Dexcom, Abbott, Tandem, Insulet, Beta Bionics, MannKind, Sequel, and Lilly and has been a speaker/consultant/advisory board member for Medtronic, Dexcom, Abbott, Tandem, Insulet, Beta Bionics, MannKind, Sequel, and Lilly.

Funding Information

This study was supported by funding from the EFF. Alexandra Sawyer was supported on a fellowship training grant (NIDDK T32: 2T32DK063687) during the time of study.

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