A Randomized Comparison of Postprandial Glucose Excursion Using Inhaled Insulin Versus Rapid-Acting Analog Insulin in Adults With Type 1 Diabetes Using Multiple Daily Injections of Insulin or Automated Insulin Delivery.

OBJECTIVE: To compare postprandial glucose excursions following a bolus with inhaled technosphere insulin (TI) or subcutaneous rapid-acting analog (RAA) insulin. RESEARCH DESIGN AND METHODS: A meal challenge was completed by 122 adults with type 1 diabetes who were using multiple daily injections (MDI), a nonautomated pump, or automated insulin delivery (AID) and who were randomized to bolus with their usual RAA insulin (n = 61) or TI (n = 61). RESULTS: The primary outcome, the treatment group difference in area under the curve for glucose >180 mg/dL over 2 h, was less with TI versus RAA (adjusted difference -12 mg/dL, 95% CI -22 to -2, P = 0.02). With TI, the glucose excursion was smaller (P = 0.01), peak glucose lower (P = 0.01), and time to peak glucose shorter (P = 0.006). Blood glucose <70 mg/dL occurred in one participant in each group. CONCLUSIONS: Postmeal glucose excursion was smaller with TI than with RAA insulin in a cohort that included both AID and MDI users.

Reducing Inequity in the Use of Automated Insulin Delivery Systems by Adults With Type 1 Diabetes: Key Learnings From a Safety Net Diabetes Clinic Program.

BACKGROUND: Recent advancements in diabetes technology have significantly improved Type 1 diabetes (T1D) management, but disparities persist, particularly in the adoption of automated insulin delivery (AID) systems within minoritized communities. We aimed to improve patient access to AID system training and overcome clinical inertia to referral. METHODS: We report on a transformative program implemented at Boston Medical Center, the largest safety-net hospital in New England, aimed at reducing disparities in AID system utilization. We employed a multidisciplinary team and quality improvement principles to identify barriers and develop solutions. Strategies included increasing access to diabetes educators, creating a referral system, and developing telemedicine education classes. We also made efforts to raise clinician awareness and confidence in recommending AID therapy. RESULTS: At baseline, 13.5% of our clinic T1D population was using an insulin pump. The population referred included 97 people with T1D (49% female, mean A1c 8.7%, 68% public insurance beneficiaries, 25% Hispanic and 25% non-Hispanic Black). Results from the first year showed a 166% increase in AID system use rates, with 64% of referred patients starting on AID. Notably, 78% of patients with A1c >8.5% adopted AID systems, addressing a gap in representation observed in clinical efficacy trials. The initiative successfully narrowed disparities in AID use among minoritized populations. CONCLUSIONS: The program's success among minoritized patients underscores the significance of tailored, collaborative, team-based care and targeted educational initiatives. Our experience provides a foundation for future efforts to ensure equitable access to diabetes technologies, emphasizing the potential of local quality improvement interventions.

Evaluation of hybrid closed-loop insulin delivery system in type 1 diabetes in real-world clinical practice: One-year observational study.

BACKGROUND: In 2016, the Food and Drug Administration approved the first hybrid closed-loop (HCL) insulin delivery system for adults with type 1 diabetes (T1D). There is limited information on the impact of using HCL systems on patient-reported outcomes (PROs) in patients with T1D in real-world clinical practice. In this independent study, we evaluated glycemic parameters and PROs over one year of continuous use of Medtronic's 670G HCL in real-world clinical practice. AIM: To assess the effects of hybrid closed loop system on glycemic control and quality of life in adults with T1D. METHODS: We evaluated 71 patients with T1D (mean age: 45.5 ± 12.1 years; 59% females; body weight: 83.8 ± 18.7 kg, body mass index: 28.7 ± 5.6 kg/m2, A1C: 7.6% ± 0.8%) who were treated with HCL at Joslin Clinic from 2017 to 2019. We measured A1C and percent of glucose time-in-range (%TIR) at baseline and 12 months. We measured percent time in auto mode (%TiAM) for the last two weeks preceding the final visit and assessed PROs through several validated quality-of-life surveys related to general health and diabetes management. RESULTS: At 12 mo, A1C decreased by 0.3% ± 0.1% (P = 0.001) and %TIR increased by 8.1% ± 2.5% (P = 0.002). The average %TiAM was only 64.3% ± 32.8% and was not associated with A1C, %TIR or PROs. PROs, provided at baseline and at the end of the study, showed that the physical functioning submodule of 36Item Short-Form Health Survey increased significantly by 22.9% (P < 0.001). Hypoglycemia fear survey/worry scale decreased significantly by 24.9% (P < 0.000); Problem Areas In Diabetes reduced significantly by -17.2% (P = 0.002). The emotional burden submodules of dietary diversity score reduced significantly by -44.7% (P = 0.001). Furthermore, analysis of Clarke questionnaire showed no increase in awareness of hypoglycemic episodes. WHO-5 showed no improvements in subject's wellbeing among participants after starting the 670G HCL system. Finally, analysis of Pittsburgh Sleep Quality Index showed no difference in sleep quality, sleep latency, or duration of sleep from baseline to 12 mo. CONCLUSION: The use of HCL in real-world clinical practice for one year was associated with significant improvements in A1C, %TIR, physical functioning, hypoglycemia fear, emotional distress, and emotional burden related to diabetes management. However, these changes were not associated with time in auto mode.

Self-care barriers and facilitators in older adults with T1D during a time of sudden isolation.

Older adults with type 1 diabetes (T1D) have unique challenges and needs. In this mixed-methods study, we explored the impact of isolation during a pandemic on diabetes management and overall quality of life in this population. Older adults (age ≥ 65 years) with T1D receiving care at a tertiary care diabetes center participated in semi-structured interviews during COVID-19 pandemic isolation between June and August 2020. A multi-disciplinary team coded transcripts and conducted thematic analysis. Thirty-four older adults (age 71 ± 5 years, 97% non-Hispanic white, diabetes duration of 38 ± 7 years, A1C of 7.4 ± 0.9% (57.3 ± 10.1 mmol/mol) were recruited. Three themes related to diabetes self-care emerged regarding impact of isolation on: (1) diabetes management and self-care behaviors (how isolation prompted changes in physical activity and dietary habits); (2) emotional stress and anxiety (related to isolation and lack of support system, economic concerns); and (3) concerns regarding the COVID-19 pandemic (impact on timely medical care and access to information). Our findings identify modifiable barriers and challenges faced by older adults with T1D during isolation. As this population has a higher risk of decline in physical and psychosocial support even during non-pandemic times, clinicians will benefit from understanding these issues to improve care of this population.

Use of Telemedicine in Adults with Type 1 Diabetes: Do Age and Use of Diabetes-Related Technology Matter?

Objective: Older adults are generally less proficient in technology use compared with younger adults. Data on telemedicine use during the COVID-19 pandemic in older persons with type 1 diabetes (T1D) and the association of telemedicine with the use of diabetes-related technology are limited. We evaluated care delivery to older adults compared with younger adults with T1D in a prepandemic and pandemic period. Methods: Data from electronic health records were evaluated for visit types (in-person, phone, and video) from two sequential 12-month intervals: prepandemic (April 2019-March 2020) and pandemic (April 2020-March 2021). Results: Data from 2,832 unique adults with T1D were evaluated in two age cohorts: younger (40-64 years) and older (≥65 years). Half of each group used continuous glucose monitoring (CGM), whereas 54% of the younger and 37% of the older cohort used pump therapy (p < 0.001). During the pandemic compared with the prepandemic period, visit frequency increased in both the younger (0.65 vs. 0.76 visits/patient/quarter; p < 0.01) and older (0.72 vs. 0.80 visits/patient/quarter; p < 0.01) cohorts. During the pandemic, older adults used more phone visits compared with younger adults (48% vs. 32%; p = 0.001). Patients using either pump therapy or CGM were more likely to use video visits compared with phone visits in both younger (41% vs. 24%; p < 0.001) and older cohorts (53% vs. 42%; p < 0.001). Conclusions: Adults using diabetes-related technologies, independent of age, accessed more video visits than those not using devices. Telemedicine visits appeared to maintain continuity of care for younger and older adults with T1D, supporting the future of a hybrid-care model.

Use of Telecommunication and Diabetes-Related Technologies in Older Adults With Type 1 Diabetes During a Time of Sudden Isolation: Mixed Methods Study.

BACKGROUND: The COVID-19 lockdown imposed a sudden change in lifestyle with self-isolation and a rapid shift to the use of technology to maintain clinical care and social connections. OBJECTIVE: In this mixed methods study, we explored the impact of isolation during the lockdown on the use of technology in older adults with type 1 diabetes (T1D). METHODS: Older adults (aged ≥65 years) with T1D using continuous glucose monitoring (CGM) participated in semistructured interviews during the COVID-19 lockdown. A multidisciplinary team coded the interviews. In addition, CGM metrics from a subgroup of participants were collected before and during the lockdown. RESULTS: We evaluated 34 participants (mean age 71, SD 5 years). Three themes related to technology use emerged from the thematic analysis regarding the impact of isolation on (1) insulin pump and CGM use to manage diabetes, including timely access to supplies, and changing Medicare eligibility regulations; (2) technology use for social interaction; and (3) telehealth use to maintain medical care. The CGM data from a subgroup (19/34, 56%; mean age 74, SD 5 years) showed an increase in time in range (mean 57%, SD 17% vs mean 63%, SD 15%; P=.001), a decrease in hyperglycemia (>180 mg/dL; mean 41%, SD 19% vs mean 35%, SD 17%; P<.001), and no change in hypoglycemia (<70 mg/dL; median 0.7%, IQR 0%-2% vs median 1.1%, IQR 0%-4%; P=.40) during the lockdown compared to before the lockdown. CONCLUSIONS: These findings show that our cohort of older adults successfully used technology during isolation. Participants provided the positive and negative perceptions of technology use. Clinicians can benefit from our findings by identifying barriers to technology use during times of isolation and developing strategies to overcome these barriers.

Impact of Diabetes Duration on Functional and Clinical Status in Older Adults With Type 1 Diabetes.

OBJECTIVE: Adults with type 1 diabetes (T1D) are aging successfully. The impact of diabetes duration on clinical and functional status as people age with T1D is not well known. RESEARCH DESIGN AND METHODS: We performed a cross-sectional study of older adults (age ≥65 years) with T1D. RESULTS: We evaluated 165 older adults, mean age 70 ± 10 years. After adjustment for age, sex, and A1C, longer duration of T1D, ≥50 years, was associated with a higher likelihood of depression (odds ratio [OR] 2.8; P = 0.008), hypoglycemia unawareness (OR 2.6; P = 0.01), lower scores on 6-Minute Walk Test (OR 0.99; P = 0.01) and the Physical Component Summary (PCS) of Short Form-36 (SF-36) (OR 0.96; P = 0.02), and greater daily medication use (OR 1.1; P = 0.004) compared with those with duration <50 years. CONCLUSIONS: In older adults with T1D, duration of diabetes impacts clinical and functional status, independent of age and glycemic control, and should be considered in development of management strategies for safety and success.

Closed-Loop Insulin Therapy in Older Adults with Type 1 Diabetes: Real-World Data.

Objective: To assess the impact of initiation of closed-loop control (CLC) on glycemic metrics in older adults with type 1 diabetes (T1D) in the real world. Methods: Retrospective analysis of electronic health records from a single tertiary diabetes center of older adults prescribed CLC between January and December 2020. Results: Forty-eight patients (mean age 70 ± 4 years, T1D duration 42 ± 14 years) were prescribed CLC and 39/48 started on the CLC. Among the CLC starters, 97.5% and 95% were prior pump and continuous glucose monitoring (CGM) users, respectively. CGM metrics showed an increase in time-in-range (62% ± 13% to 76% ± 9%; P < 0.001), a reduction in both time spent <70 mg/dL [2% (1%-3%) to 1% (1%-2%); P = 0.03] and >180 mg/dL (30% ± 11% to 20% ± 9%; P < 0.001) at 3 months. Conclusion: In this real-world data most of the older patients with T1D initiating CLC were prior pump and CGM users. Initiation of CLC improved glycemic control and reduced time spent in hypoglycemia compared with prior therapy.

Use of Technology in Older Adults with Type 1 Diabetes: Clinical Characteristics and Glycemic Metrics.

Background: The use of diabetes-related technology, both for insulin administration and glucose monitoring, has shown benefits in older adults with type 1 diabetes (T1D). However, the characteristics of older adults with T1D and their use of technology in real-world situations are not well documented. Methods: Older adults (age ≥65 years) with T1D, using insulin pump or multiple daily injections (MDI) for insulin administration, and continuous glucose monitoring (CGM) or glucometer (blood glucose monitoring [BGM]) for glucose monitoring were evaluated. Participants wore CGM for 2 weeks, completed surveys, and underwent laboratory evaluation. Results: We evaluated 165 older adults with T1D; mean age 70 ± 10 years, diabetes duration 40 ± 17 years, and A1C 7.4% ± 0.9% (57 ± 10 mmol/mol). For insulin administration, 63 (38%) were using MDI, while 102 (62%) were using pump. Compared to MDI, pump users were less likely to have cognitive dysfunction (49% vs. 65%, P = 0.04) and had lower scores on the hypoglycemia fear survey (P = 0.03). For glucose monitoring, 95 (58%) used CGM, while 70 (42%) used BGM. Compared to BGM, CGM users were more likely to report impaired awareness of hypoglycemia (IAH) (P = 0.01), and had lower A1C (P = 0.02). Participants who used any technology (pump or CGM) had lower A1C (P = 0.04, 0.006), less hypoglycemia ≤54 mg/dL (P = 0.0006, <0.0001) and <70 mg/dL (P = 0.0002, 0.0001), and fewer glycemic excursions (coefficient of variation %) (P = 0.0001, <0.0001), while reporting more IAH (P = 0.04, P = 0.006) and diabetes distress (P = 0.02, 0.004). Conclusion: Older adults with T1D who use newer diabetes-related technology had better glycemic control, lower hypoglycemia risk, and fewer glycemic excursions. However, they were more likely to report IAH and diabetes-related distress. Clinical trials.gov NCT03078491.

Usefulness of CGM-Derived Metric, the Glucose Management Indicator, to Assess Glycemic Control in Non-White Individuals With Diabetes.

OBJECTIVE: To assess the relationship between the glucose management indicator (GMI) and HbA1c in non-White individuals with diabetes. RESEARCH DESIGN AND METHODS: We performed a retrospective analysis of continuous glucose monitoring metrics in individuals with diabetes divided by race into non-White and White cohorts. RESULTS: We evaluated 316 individuals (non-White n = 68; White n = 248). Although GMI was not different (7.6 vs. 7.7; P = not significant) between the cohorts, HbA1c was higher in the non-White cohort (8.7% vs. 8.1%; P = 0.004). HbA1c higher than GMI by ≥0.5% was more frequently observed in the non-White cohort (90% vs. 75%; P = 0.02). In the non-White cohort only, duration of hypoglycemia was longer among those with HbA1c higher than GMI by ≥0.5% compared with those with HbA1c and GMI within 0.5%. CONCLUSIONS: A differential relationship between HbA1c and GMI in non-White versus White individuals with diabetes was observed. In non-White individuals, a greater difference between HbA1c and GMI was associated with higher risk of hypoglycemia.

Continuous Glucose Monitoring and Glycemic Control in Young Adults with Type 1 Diabetes: Benefit for Even the Simplest Insulin Administration Methods.

The impact of continuous glucose monitoring (CGM) on glycemic control in young adults with type 1 diabetes (T1D) is controversial. Data from 888 young adults with T1D were reviewed (ages 18-30 years, 52% female, glycated hemoglobin [HbA1c] 8.1% ± 1.5%). Prescription of CGM was recorded for 54% of young adults; 66% were pump users, 46% on insulin injections and carbohydrate (carb) counting, and 32% on insulin doses without carb counting (P ≤ 0.001). HbA1c was lower in young adults with CGM versus no CGM (7.7% ± 1.3% vs. 8.2% ± 1.7%, P ≤ 0.001). Difference in HbA1c between CGM versus no CGM was greater in young adults noncarb counting (7.9% ± 1.4% vs. 8.9% ± 2.3%, P = 0.002) than carb counting (7.7% ± 1.5% vs. 8.2% ± 1.7%, P = 0.0008), or pump users (7.6 ± 1.2 vs. 7.9 ± 1.1, P = 0.01). Prescription of CGM was higher with increasing complexity of insulin regimens; however, impact of CGM was greatest with simpler insulin administration methods. Further studies are needed to clarify this association.

The Relationship Between CGM-Derived Metrics, A1C, and Risk of Hypoglycemia in Older Adults With Type 1 Diabetes.

OBJECTIVE: Continuous glucose monitoring (CGM) is now commonly used in the management of type 1 diabetes (T1D). The CGM-derived coefficient of variation (CV) measures glucose variability, and the glucose management indicator (GMI) measures mean glycemia (previously called estimated A1C). However, their relationship with laboratory-measured A1C and the risk of hypoglycemia in older adults with T1D is not well studied. RESEARCH DESIGN AND METHODS: In a single-center study, older adults (age ≥65 years) with T1D wore a CGM device for 14 days. The CV (%) and GMI were calculated, and A1C and clinical and demographic information were collected. RESULTS: We evaluated 130 older adults (age 71 ± 5 years), of whom 55% were women, 97% were White, diabetes duration was 39 ± 17 years, and A1C was 7.3 ± 0.6% (56 ± 15 mmol/mol). Participants were stratified by high CV (>36%; n = 77) and low CV (≤36%; n = 53). Although there was no difference in A1C levels between the groups with high and low CV (7.3% [56 mmol/mol] vs. 7.3% [53 mmol/mol], P = 0.4), the high CV group spent more time in hypoglycemia (<70 mg/dL and ≤54 mg/dL) compared with the group with low CV (median 31 vs. 84 min/day, P < 0.0001; 8 vs. 46 min/day, P < 0.001, respectively). An absolute difference between A1C and GMI of ≥0.5% was observed in 46% of the cohort. When the A1C was higher than the GMI by ≥0.5%, a higher duration of hypoglycemia was observed (P = 0.02). CONCLUSIONS: In older adults with T1D, the use of CGM-derived CV and GMI can better identify individuals at higher risk for hypoglycemia compared with A1C alone. These measures should be combined with A1C for better diabetes management in older adults with T1D.

Examining the Relationship Between Pre- and Postprandial Glucose Levels and Insulin Bolus Timing Using Bluetooth-Enabled Insulin Pen Cap Technology and Continuous Glucose Monitoring.

Background: Timing of insulin administration in persons using multiple daily injections (MDI) is self-reported. New technology enabling tracking and logging of insulin doses, combined with continuous glucose monitoring (CGM), may provide insight into the relationship between insulin administration and glucose levels. Methods: We performed a prospective observational study using Bluetooth-enabled pen caps, along with CGM, in participants with type 1 diabetes (T1D) on MDI to objectively assess the relationship between the timing of insulin administration and its impact on pre- and postprandial glucose levels for a 2-week period. Results: We evaluated 50 participants (age 40.3 ± 19 years; A1c 8.2% ± 1.5%, duration of T1D 20 ± 15 years). Thirty-seven percent of total boluses resulted in persistent hyperglycemia (glucose >180 mg/dL 3 h postprandially), while 10% resulted in clinically significant hypoglycemia (glucose <55 mg/dL 3 h postprandially) on CGM. Preprandial glucose levels at the time of the bolus did not correlate with postprandial glucose levels. Late boluses, defined as a rise in glucose of ≥50 mg/dL before a bolus, were seen two times/patient/week. Missed boluses, defined as a rise in glucose of ≥50 mg/dL without a bolus within 2 h, occurred 17 times/patient/week. Late and missed boluses were associated with worse glycemic control (A1c; R2 = 0.1, P = 0.02; R2 = 0.1, P = 0.02). Conclusions: The use of Bluetooth-enabled pen caps, with CGM, in persons with T1D on MDI can illustrate the relationship between insulin bolus timing and postprandial glucose. These data may help clinicians and patients understand the impact of timing of insulin doses on glucose levels and glycemic control.

Experiences of Adults With Type 1 Diabetes Using Glucose Sensor-Based Mobile Technology for Glycemic Variability: Qualitative Study.

BACKGROUND: Adults with type 1 diabetes (PWDs) face challenging self-management regimens including monitoring their glucose values multiple times a day to assist with achieving glycemic targets and reduce the risk of long-term diabetes complications. Recent advances in diabetes technology have reportedly improved glycemia, but little is known about how PWDs utilize mobile technology to make positive changes in their diabetes self-management. OBJECTIVE: The aim of this qualitative study was to explore PWDs' experiences using Sugar Sleuth, a glucose sensor-based mobile app and Web-based reporting system, integrated with the FreeStyle Libre glucose monitor that provides feedback about glycemic variability. METHODS: We used a qualitative descriptive research design and conducted semistructured interviews with 10 PWDs (baseline mean glycated hemoglobin, HbA1c) 8.0%, (SD 0.45); 6 males and 4 females, aged 52 years (SD 15), type 1 diabetes (T1D) duration 31 years (SD 13), 40% (4/10, insulin pump) following a 14-week intervention during which they received clinical support and used Sugar Sleuth to evaluate and understand their glucose data. Audio-recorded interviews were transcribed, coded, and analyzed using thematic analysis and NVivo 11 (QSR International Pty Ltd). RESULTS: A total of 4 main themes emerged from the data. Participants perceived Sugar Sleuth as an Empowering Tool that served to inform lifestyle choices and diabetes self-management tasks, promoted preemptive self-care actions, and improved discussions with clinicians. They also described Sugar Sleuth as providing a Source of Psychosocial Support and offering relief from worry, reducing glycemic uncertainty, and supporting positive feelings about everyday life with diabetes. Participants varied in their Approaches to Glycemic Data: 40% (4/10) described using Sugar Sleuth to review data, understand glycemic cause and effect, and plan for future self-care. On the contrary, 60% (6/10) were reluctant to review past data; they described receiving benefits from the immediate numbers and trend arrows, but the app still prompted them to enter in the suspected causes of glucose excursions within hours of their occurrence. Finally, only 2 participants voiced Concerns About Use of Sugar Sleuth; they perceived the app as sometimes too demanding of information or as not attuned to the socioeconomic backgrounds of PWDs from diverse populations. CONCLUSIONS: Results suggest that Sugar Sleuth can be an effective educational tool to enhance both patient-clinician collaboration and diabetes self-management. Findings also highlight the importance of exploring psychosocial and socioeconomic factors that may advance the understanding of PWDs' individual differences when using glycemic technology and may promote the development of customized mobile tools to improve diabetes self-management.

Nonadherence to Insulin Therapy Detected by Bluetooth-Enabled Pen Cap Is Associated With Poor Glycemic Control.

OBJECTIVE: To objectively evaluate adherence to timing and dosing of insulin by using Bluetooth pen caps and examine factors related to adherence. RESEARCH DESIGN AND METHODS: Bluetooth-enabled insulin pen caps were used in younger (ages 18-35 years) and older (ages ≥65 years) adults on two or more insulin injections per day. RESULTS: We evaluated 75 participants with diabetes, 42 younger (29 ± 4 years) and 33 older (73 ± 7 years). Nonadherence was found in 24% of bolus (Apidra) doses and 36% of basal (Lantus) doses. We divided participants into tertiles on the basis of overall adherence, with the most adherent tertile having 85% dose adherence compared with 49% in the least adherent tertile (P < 0.001). Participants in the most adherent tertile had better glycemic control than those in the least adherent tertile (7.7 ± 1.1% [61 ± 12 mmol/mol] vs. 8.6 ± 1.5% [70 ± 16.4 mmol/mol], P < 0.03). CONCLUSIONS: Nonadherence to insulin dosing and timing can be objectively assessed by Bluetooth pen caps and is associated with poor glycemic control.

Experiences of health care transition voiced by young adults with type 1 diabetes: a qualitative study.

OBJECTIVE: This qualitative study aimed to explore the experience of transition from pediatric to adult diabetes care reported by posttransition emerging adults with type 1 diabetes (T1D), with a focus on preparation for the actual transfer in care. METHODS: Twenty-six T1D emerging adults (mean age 26.2±2.5 years) receiving adult diabetes care at a single center participated in five focus groups stratified by two levels of current glycemic control. A multidisciplinary team coded transcripts and conducted thematic analysis. RESULTS: FOUR KEY THEMES ON THE PROCESS OF TRANSFER TO ADULT CARE EMERGED FROM A THEMATIC ANALYSIS: 1) nonpurposeful transition (patients reported a lack of transition preparation by pediatric providers for the transfer to adult diabetes care); 2) vulnerability in the college years (patients conveyed periods of loss to follow-up during college and described health risks and diabetes management challenges specific to the college years that were inadequately addressed by pediatric or adult providers); 3) unexpected differences between pediatric and adult health care systems (patients were surprised by the different feel of adult diabetes care, especially with regards to an increased focus on diabetes complications); and 4) patients' wish list for improving the transition process (patients recommended enhanced pediatric transition counseling, implementation of adult clinic orientation programs, and peer support for transitioning patients). CONCLUSION: Our findings identify modifiable deficiencies in the T1D transition process and underscore the importance of a planned transition with enhanced preparation by pediatric clinics as well as developmentally tailored patient orientation in the adult clinic setting.

Patient-provider relationships across the transition from pediatric to adult diabetes care: a qualitative study.

PURPOSE: The purpose of this study was to explore perceptions that emerging adults with type 1 diabetes (T1D) have of their patient-provider relationships across the transition from pediatric to adult care. METHODS: Twenty-six emerging adults with T1D (mean age 26.2 ± 2.5 years) participated in 5 focus groups stratified by current level of glycemic control (A1C). Coded audio-recorded data were analyzed using thematic analysis and aided by NVivo software. RESULTS: Three major themes emerged from the analysis: (1) loss and gain in provider relationships across the transition-patients expressed 3 key responses to leaving pediatric providers that differed by A1C levels: sad reluctance and "natural progression" (mean A1C ± SD 7.4% ± 0.6%) and wanting to go (mean A1C ± SD 9.8% ± 1.0%); (2) partners in care versus on one's own-patients valued how adult providers' collaborative conversations promoted their involvement and accountability compared to "parent-centric" interactions with pediatric providers, but they also expressed ambivalence over increased independence in adult care; (3) improving provider approaches to transition-patients recommended that pediatric providers actively promote emerging adults' autonomy while maintaining parental support, communication with adult providers, and follow-up with transitioning patients. CONCLUSIONS: Findings highlight the importance of enhanced provider awareness of T1D emerging adults' complex feelings about the transition in care. Improved integration of individual- and family-centered approaches to developmentally tailored diabetes care is needed to augment patient and provider relationships.

Dietary fat acutely increases glucose concentrations and insulin requirements in patients with type 1 diabetes: implications for carbohydrate-based bolus dose calculation and intensive diabetes management.

OBJECTIVE: Current guidelines for intensive treatment of type 1 diabetes base the mealtime insulin bolus calculation exclusively on carbohydrate counting. There is strong evidence that free fatty acids impair insulin sensitivity. We hypothesized that patients with type 1 diabetes would require more insulin coverage for higher-fat meals than lower-fat meals with identical carbohydrate content. RESEARCH DESIGN AND METHODS: We used a crossover design comparing two 18-h periods of closed-loop glucose control after high-fat (HF) dinner compared with low-fat (LF) dinner. Each dinner had identical carbohydrate and protein content, but different fat content (60 vs. 10 g). RESULTS: Seven patients with type 1 diabetes (age, 55 ± 12 years; A1C 7.2 ± 0.8%) successfully completed the protocol. HF dinner required more insulin than LF dinner (12.6 ± 1.9 units vs. 9.0 ± 1.3 units; P = 0.01) and, despite the additional insulin, caused more hyperglycemia (area under the curve >120 mg/dL = 16,967 ± 2,778 vs. 8,350 ± 1,907 mg/dL⋅min; P < 0001). Carbohydrate-to-insulin ratio for HF dinner was significantly lower (9 ± 2 vs. 13 ± 3 g/unit; P = 0.01). There were marked interindividual differences in the effect of dietary fat on insulin requirements (percent increase significantly correlated with daily insulin requirement; R(2) = 0.64; P = 0.03). CONCLUSIONS: This evidence that dietary fat increases glucose levels and insulin requirements highlights the limitations of the current carbohydrate-based approach to bolus dose calculation. These findings point to the need for alternative insulin dosing algorithms for higher-fat meals and suggest that dietary fat intake is an important nutritional consideration for glycemic control in individuals with type 1 diabetes.