Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Patient-Reported Outcomes Improve with a Virtual Diabetes Care Model that Includes Continuous Glucose Monitoring.

Background: The objective was to examine patient-reported outcomes (PROs) associated with access to a virtual clinic model for diabetes care. Methods: Adults with diabetes (N = 234) received virtual care, including support for continuous glucose monitoring (CGM) over a 6-month study period. Care was led by a Certified Diabetes Care and Education Specialist and focused on optimizing self-management skills and response to glucose values observed on CGM. After 6 months of CGM use and access to diabetes education, participants could opt in to another 6 months of follow-up with access to the virtual care team. Participants completed PRO surveys and had health and glycemic measures collected at baseline, 3, 6, and 12 months. Results: Participants with type 1 diabetes (N = 160) were 44 ± 14 years and had mean baseline HbA1c of 61 mmol/mol (7.7%). Participants with type 2 diabetes (N = 74) were 52 ± 12 years and had mean baseline HbA1c of 66 mmol/mol (8.2%). Compared with baseline levels, at 6 months participants experienced less depression, diabetes distress, and hypoglycemic fears while also experiencing greater satisfaction with glucose monitoring, diabetes technology and specifically with CGM, and confidence for managing hypoglycemic (p < 0.05). For participants with type 1 diabetes, more time in the target range for glucose levels (70-180 mg/dL) was associated with less depression, diabetes distress, and hypoglycemic fears. Conclusions: PROs improved for adults with diabetes utilizing virtual diabetes care, including support for CGM use. Paired with the glycemic improvements observed in this virtual clinic study, there were robust benefits on the quality of life of adults with diabetes. ClinicalTrials.gov Identifier: NCT04765358.

Youth with type 2 diabetes have a high rate of treatment failure after discontinuation of insulin: A Pediatric Diabetes Consortium study.

Insulin is commonly used to reverse gluco-toxicity in youth with newly diagnosed type 2 diabetes (T2D), but many are subsequently weaned off insulin. We analyzed Pediatric Diabetes Consortium (PDC) data to determine how long glycemic control is maintained after termination of initial insulin treatment. Youth with T2D who had previously been on insulin but were on either an intensive lifestyle intervention alone or metformin alone upon enrollment in the PDC T2D Registry were studied (N = 183). The primary outcome was time to treatment failure, defined by need to restart insulin or metformin or another diabetes medication. Data were analyzed using logistic regression to assess risk factors for treatment failure. Of the 183 participants studied (mean age 15 years, diabetes duration 1.7 years), 54% experienced treatment failure (median follow-up time 1.7 years). In the subgroup on metformin monotherapy (N = 140), 45% subsequently required restart of insulin. Moreover, of participants in the subgroup treated with an intensive lifestyle intervention alone (N = 43), 81% restarted insulin or were treated with metformin or other diabetes medication. In both groups, median time to treatment failure was 1.2 years. Higher HbA1c at enrollment was significantly associated with treatment failure (p < 0.001). Youth with T2D who are initially treated with insulin have a high rate of treatment failure when switched to intensive lifestyle alone or metformin alone. Our data highlight the severe and progressive nature of youth onset T2D, hence patients should be monitored closely for deteriorating glycemic control after being weaned off insulin.

Adolescent type 2 diabetes: Comparing the Pediatric Diabetes Consortium and Germany/Austria/Luxemburg Pediatric Diabetes Prospective registries.

OBJECTIVE: To examine and compare the clinical characteristics and treatment of youth with type 2 diabetes (T2D) in two registries: one in Europe and one in the United States. METHODS: Youth with onset of T2D at 10 to 18 years of age with current age <20 years and an office visit after diabetes duration >1 year were identified in the European (Prospective Diabetes Follow-up, DPV) and the United States (Pediatric Diabetes Consortium, PDC) databases. Demographic, physical and clinical characteristics and treatment at diagnosis as well as physical characteristics, treatment, laboratory data, and diabetes adverse events at most recent visit were analyzed from both registries. RESULTS: At diagnosis, the majority were female and obese; 70% of DPV vs 34% of PDC youth were diagnosed by targeted diabetes testing. PDC youth were younger, 12 vs 13 years (P < 0.001), had a greater body mass index-SDS, 3.07 vs 2.74 (P < 0.001), a higher hemoglobin A1c (HbA1c), 9.9% vs 7.1% (P < 0.001), were more likely to present in DKA, 7.5% vs 1.3% (P < 0.001) and more likely to be treated with insulin, 62% vs 32% (P < 0.001); insulin treatment difference was not significant when adjusted for HbA1c. At follow-up, DPV youth had shorter diabetes duration, 2.1 vs 3.2 years (P < 0.001), lower HbA1c, 6.5% vs 7.8% (P < 0.001), were less likely to be treated with insulin, 36% vs 56%, (P < 0.001), and were more likely to have dyslipidemia and hypertension than PDC youth. PDC youth had a higher rate of microalbuminuria. CONCLUSIONS: Both DPV and PDC youth have multiple risks for diabetes complications. Understanding reasons for persistently higher HbA1c in PDC youth requires further study.

Eligibility for clinical trials is limited for youth with type 2 diabetes: Insights from the Pediatric Diabetes Consortium T2D Clinic Registry.

BACKGROUND/OBJECTIVE: Restrictive eligibility criteria have hampered enrollment into trials for new drugs for youth with type 2 diabetes (T2D). We utilized Pediatric Diabetes Consortium (PDC) T2D Registry enrollment data to estimate the percentage of patients who would be excluded from current T2D trials based on out-of-range HbA1c levels. We also examined whether well-controlled patients could be included because baseline HbA1c would rise during a 6 to 12-month study if assigned to control group. METHODS: Clinical characteristics and HbA1c levels were collected from 956 T2D patients aged 10 to <18 years upon Registry enrollment. HbA1c levels were also analyzed in 6-month intervals during the first 30 months of T2D duration. RESULTS: There was an approximately 2:1 ratio of females to males; the majority were obese and from economically disadvantaged minority families. On enrollment in the Registry, 53% of patients would be excluded from current trials because HbA1c levels were either <6.5% (<48 mmol/mol) (37%) or >10.5% (>91 mmol/mol) (16%). Furthermore, in patients with HbA1c levels <6.5% (<48 mmol/mol) and T2D duration between 6 and 30 months, mean HbA1c levels increased by 0.6% (6 mmol/mol) and 0.9% (10 mmol/mol) over the subsequent 6 and 12 months, respectively. CONCLUSIONS: Eligibility criteria for current clinical trials still exclude a large proportion of pediatric T2D patients because of HbA1c levels. Including patients with HbA1c <6.5% (<48 mmol/mol) would enhance recruitment and allow comparisons of the investigational treatment with placebo-assigned subjects in whom HbA1c levels would on average increase during the 6 to 12 months of the trial.

Racial Differences in the Relationship of Glucose Concentrations and Hemoglobin A1c Levels.

Background: Debate exists as to whether the higher hemoglobin A1c (HbA1c) levels observed in black persons than in white persons are due to worse glycemic control or racial differences in the glycation of hemoglobin. Objective: To determine whether a racial difference exists in the relationship of mean glucose and HbA1c. Design: Prospective, 12-week observational study. Setting: 10 diabetes centers in the United States. Participants: 104 black persons and 104 white persons aged 8 years or older who had had type 1 diabetes for at least 2 years and had an HbA1c level of 6.0% to 12.0%. Measurements: Mean glucose concentration, measured by using continuous glucose monitoring and compared by race with HbA1c, glycated albumin, and fructosamine values. Results: The mean HbA1c level was 9.1% in black persons and 8.3% in white persons. For a given HbA1c level, the mean glucose concentration was significantly lower in black persons than in white persons (P = 0.013), which was reflected in mean HbA1c values in black persons being 0.4 percentage points (95% CI, 0.2 to 0.6 percentage points) higher than those in white persons for a given mean glucose concentration. In contrast, no significant racial differences were found in the relationship of glycated albumin and fructosamine levels with the mean glucose concentration (P > 0.20 for both comparisons). Limitation: There were too few participants with HbA1c levels less than 6.5% to generalize the results to such individuals. Conclusion: On average, HbA1c levels overestimate the mean glucose concentration in black persons compared with white persons, possibly owing to racial differences in the glycation of hemoglobin. However, because race only partially explains the observed HbA1c differences between black persons and white persons, future research should focus on identifying and modifying barriers impeding improved glycemic control in black persons with diabetes. Primary Funding Source: Helmsley Charitable Trust.

Clinical outcomes in youth beyond the first year of type 1 diabetes: Results of the Pediatric Diabetes Consortium (PDC) type 1 diabetes new onset (NeOn) study.

OBJECTIVE: Current data are limited on the course of type 1 diabetes (T1D) in children and adolescents through the first few years of diabetes. The Pediatric Diabetes Consortium T1D new onset (NeOn) Study was undertaken to prospectively assess natural history and clinical outcomes in children treated at 7 US diabetes centers from the time of diagnosis. This paper describes clinical outcomes in the T1D NeOn cohort during the first 3 years postdiagnosis. RESULTS: A total of 1048 participants (mean age 9.2 years, 49% female, 65% non-Hispanic White) were enrolled between July 2009 and April 2011. Mean glycated hemoglobin (HbA1c) (±SD) was 7.2% (55 mmol/mol) at 3 months, followed by a progressive rise to 8.4% (68 mmol/mol) at 36 months postdiagnosis, with only 30% of participants achieving target HbA1c<7.5% (58 mmol/mol). The percentage of participants in partial remission estimated by insulin dose adjusted HbA1c [HbA1c % + (4×insulin dose unit/kg/24 h)] ≤9 sharply declined from 23% at 12 months to 7% at 36 months. The percentage of participants developing diabetic ketoacidosis (DKA) was 1% in the first year after diagnosis, increasing to 6% in years 2 and 3. CONCLUSIONS: These results demonstrate the gradual decline in glycemic control due to waning residual endogenous insulin secretion with increasing duration of T1D in children and adolescents. These data indicate the need to translate recent advances in automated insulin delivery, new insulin analogs, and adjunctive pharmacologic agents into novel treatment strategies to maintain optimal glycemic control even early in the course of T1D.

The effect of donor diabetes history on graft failure and endothelial cell density 10 years after penetrating keratoplasty.

OBJECTIVE: To examine the long-term effect of donor diabetes history on graft failure and endothelial cell density (ECD) after penetrating keratoplasty (PK) in the Cornea Donor Study. DESIGN: Multicenter, prospective, double-masked, controlled clinical trial. PARTICIPANTS: One thousand ninety subjects undergoing PK for a moderate risk condition, principally Fuchs' dystrophy or pseudophakic or aphakic corneal edema, were enrolled by 105 surgeons from 80 clinical sites in the United States. METHODS: Corneas from donors 12 to 75 years of age were assigned by 43 eye banks to participants without respect to recipient factors. Donor and recipient diabetes status was determined from existing medical records. Images of the central endothelium were obtained before surgery (baseline) and at intervals for 10 years after surgery and were analyzed by a central image analysis reading center to determine ECD. MAIN OUTCOME MEASURES: Time to graft failure (regraft or cloudy cornea for 3 consecutive months) and ECD. RESULTS: There was no statistically significant association of donor diabetes history with 10-year graft failure, baseline ECD, 10-year ECD, or ECD values longitudinally over time in unadjusted analyses, nor after adjusting for donor age and other significant covariates. The 10-year graft failure rate was 23% in the 199 patients receiving a cornea from a donor with diabetes versus 26% in the 891 patients receiving a cornea from a donor without diabetes (95% confidence interval for the difference, -10% to 6%; unadjusted P=0.60). Baseline ECD (P=0.71), 10-year ECD (P>0.99), and changes in ECD over 10 years (P=0.86) were similar comparing donor groups with and without diabetes. CONCLUSIONS: The study results do not suggest an association between donor diabetes and PK outcome. However, the assessment of donor diabetes was imprecise and based on historical data only. The increasing frequency of diabetes in the aging population in the United States affects the donor pool. Thus, the impact of donor diabetes on long-term endothelial health after PK or endothelial keratoplasty, or both, warrants further study with more precise measures of diabetes and its complications.

Corticosteroids for treating optic neuritis.

BACKGROUND: Optic neuritis is an inflammatory disease of the optic nerve. It usually presents with an abrupt loss of vision and recovery of vision is almost never complete. It occurs more commonly in women than in men. Closely linked in pathogenesis, optic neuritis may be the initial manifestation for multiple sclerosis. In some people, no underlying cause can be found. OBJECTIVES: The objective of this review was to assess the effects of corticosteroids on visual recovery in eyes with acute optic neuritis. SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2015, Issue 4), MEDLINE (January 1950 to April 2015), EMBASE (January 1980 to April 2015), Latin American and Caribbean Health Sciences Literature (LILACS) (January 1982 to April 2015), PubMed (January 1946 to April 2015), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrials.gov), and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). There were no date or language restrictions in the electronic searches for trials. The metaRegister of Controlled Trials (mRCT) was last searched on 6 March 2014. The electronic databases were last searched on 7 April 2015. We also searched reference lists of identified trial reports for additional trials. SELECTION CRITERIA: We included randomized controlled trials (RCTs) that evaluated systemic corticosteroids, in any form, dose or route of administration, in people with acute optic neuritis. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures expected by Cochrane. MAIN RESULTS: We included six RCTs with a total of 750 participants. Each trial was conducted in a different country: Denmark, Germany, India, Japan, UK, and United States. Additionally, we identified two ongoing trials not due to be completed until 2016. Among the six trials included in this review, we judged one to be at high risk of bias. The remaining five trials were judged to be at either low or uncertain risk of biases.Five trials compared only two intervention groups and one trial had a three-arm comparison of oral corticosteroids or intravenous corticosteroids with placebo. Of the five trials with only two intervention groups, two trials compared oral corticosteroids versus placebo, two trials compared intravenous corticosteroids with placebo, and one trial compared intravenous dexamethasone with intravenous methylprednisolone plus oral prednisolone.Three trials evaluating oral corticosteroids used varying doses of corticosteroids versus placebo. In the meta-analyses to assess visual acuity, the risk ratio (RR) was 1.00 (95% confidence interval (CI) 0.82 to 1.23; participants = 398) at one month; 0.92 (95% CI 0.77 to 1.11; participants = 355) at six months; and 0.93 (95% CI 0.70 to 1.24; participants = 368) at one year. In the meta-analyses of two trials evaluating corticosteroids with total dose greater than 3000 mg administered intravenously, the RR of normal visual acuity (defined as 20/20 Snellen fraction or equivalent) in the intravenous corticosteroids group compared with the placebo group was 1.05 (95% CI 0.88 to 1.26; participants = 346) at six months. The RR of contrast sensitivity in the normal range for the same comparison was 1.11 (95% CI 0.92 to 1.33; participants = 346) at six months follow-up. The RR of normal visual field for this comparison was 1.08 (95% CI 0.96 to 1.21; 346 participants) at six months; and 1.01 (95% CI 0.86 to 1.19; participants = 316) at one year. Four trials reported adverse events primarily related to gastrointestinal symptoms and sleep disturbance; one trial reported minor adverse event of acne. AUTHORS' CONCLUSIONS: There is no conclusive evidence of benefit in terms of recovery to normal visual acuity, visual field or contrast sensitivity six months after initiation with either intravenous or oral corticosteroids at the doses evaluated in trials included in this review.

Changing Glucose Levels During the Menstrual Cycle as Observed in Adults in the Type 1 Diabetes Exercise Initiative Study.

OBJECTIVES: Evidence suggests that glucose levels in menstruating females with type 1 diabetes change throughout the menstrual cycle, reaching a peak during the luteal phase. The Type 1 Diabetes Exercise Initiative (T1DEXI) study provided the opportunity to assess glycemic metrics between early and late phases of the menstrual cycle, and whether differences could be explained by exercise, insulin, and carbohydrate intake. METHODS: One hundred seventy-nine women were included in our analysis. Glycemic metrics, carbohydrate intake, insulin requirements, and exercise habits during the early vs late phases of their menstrual cycles (i.e. 2 to 4 days after vs 2 to 4 days before reported menstruation start date) were compared. RESULTS: Mean glucose increased from 8.2±1.5 mmol/L (148±27 mg/dL) during the early follicular phase to 8.6±1.6 mmol/L (155±29 mg/dL) during the late luteal phase (p<0.001). Mean percent time-in-range (3.9 to 10.0 mmol/L [70 to 180 mg/dL]) decreased from 73±17% to 70±18% (p=0.002), and median percent time >10.0 mmol/L (>180 mg/dL) increased from 21% to 23% (p<0.001). Median total daily insulin requirements increased from 37.4 units during the early follicular phase to 38.5 units during the late luteal phase (p=0.02) and mean daily carbohydrate consumption increased slightly from 127±47 g to 133±47 g (p=0.05); however, the difference in mean glucose during early follicular vs late luteal phase was not explained by differences in exercise duration, total daily insulin units, or reported carbohydrate intake. CONCLUSIONS: Glucose levels during the late luteal phase were higher than those of the early follicular phase of the menstrual cycle. These glycemic changes suggest that glucose management for women with type 1 diabetes may need to be fine-tuned within the context of their menstrual cycles.

The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents With Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes. RESEARCH DESIGN AND METHODS: In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads. RESULTS: Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001). CONCLUSIONS: Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.

Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study.

Background: Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D. Objective: We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study. Methods: During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available). Results: The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min). Conclusions: Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.

Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia. RESEARCH DESIGN AND METHODS: This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected. RESULTS: A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3,319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h. CONCLUSIONS: In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes.

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes.

Objective: To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. Methods: Adolescents with T1D (n = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.3%; T1D duration = 5 ± 4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGMs), logged 3738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within 2 h after starting exercise. Results: RMRF achieved high precision predicting composite risk and was more accurate than RMLR Area under the receiver operating characteristic curve (AUROC 0.737 vs. 0.661; P < 0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 h, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk. Conclusions: Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.

Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes.

Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, -16.2 (39.0) mg/dL for sessions classified as interval, and -11.6 (38.8) mg/dL for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes.

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

Effect of Impaired Awareness of Hypoglycemia on Glucose Decline During and After Exercise in the T1DEXI Study.

CONTEXT: Adults with type 1 diabetes (T1D) face the necessity of balancing the benefits of exercise with the potential hazards of hypoglycemia. OBJECTIVE: This work aimed to assess whether impaired awareness of hypoglycemia (IAH) affects exercise-associated hypoglycemia in adults with T1D. METHODS: We compared continuous glucose monitoring (CGM)-measured glucose during exercise and for 24 hours following exercise from 95 adults with T1D and IAH (Clarke score ≥4 or ≥1 severe hypoglycemic event within the past year) to 95 "aware" adults (Clarke score ≤2 and no severe hypoglycemic event within the past year) matched on sex, age, insulin delivery modality, and glycated hemoglobin A1c. A total of 4236 exercise sessions, and 1794 exercise days and 839 sedentary days, defined as 24 hours following exercise or a day without exercise, respectively, were available for analysis. RESULTS: Participants with IAH exhibited a nonsignificant trend toward greater decline in glucose during exercise compared to "aware" (-21 ± 44 vs -19 ± 43 mg/dL [-1.17 ± 2.44 vs -1.05 ± 2.39 mmol/L], adjusted group difference of -4.2 [95% CI, -8.4 to 0.05] mg/dL [-0.23 95% CI, -.47 to 0.003 mmol/L]; P = .051). Individuals with IAH had a higher proportion of days with hypoglycemic events below 70 mg/dL [3.89 mmol/L] (≥15 minutes <70 mg/dL [<3.89 mmol/L]) both on exercise days (51% vs 43%; P = .006) and sedentary days (48% vs 30%; P = .001). The increased odds of experiencing a hypoglycemic event below 70 mg/dL (<3.89 mmol/L) for individuals with IAH compared to "aware" did not differ significantly between exercise and sedentary days (interaction P = .36). CONCLUSION: Individuals with IAH have a higher underlying risk of hypoglycemia than "aware" individuals. Exercise does not appear to differentially increase risk for hypoglycemia during the activity, or in the subsequent 24 hours for IAH compared to aware individuals with T1D.

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative.

AIMS: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. METHODS: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. RESULTS: Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. CONCLUSIONS: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.