bluuebunny/crossref_metadata_2025_split huggingface dataset:
881-P: Patient and Clinician Usability Rating of Commercially Available Diabetes Apps
Authors: FU HELEN N. , ADAM TERRENCE , WYMAN JEAN F. 
Published: June 2019

Background: Despite the significant clinical benefit in hemoglobin A1c reduction, diabetes applications (apps) use is low. The goal of this study was to assess app usability based on the heuristics of intuitive designs and the Theory of Self-Determination on motivation. Diabetes apps were evaluated by clinicians and patients for motivational properties that target patient needs to learn blood glucose trends and patterns (competence), to personalize choices to change (autonomy) and to connect with healthcare providers (feel related).
Method: Two top-rated Android apps, mySugr and OnTrack, underwent evaluation by four clinicians (three physicians and one diabetes nurse educator) and 92 adults with type 1 or 2 diabetes on insulin. Each app was tested for seven diabetes-related tasks including data entry, analysis, report view options (by the day of the week and by meals), and report email. Clinicians evaluated if the app violated Nielsen's Ten Heuristics on user-friendly designs. Both clinicians and patients completed a questionnaire on the ease of use - System Usability Scale (SUS).
Results: OnTrack and mySugr target two behavior needs important for motivation in diabetes self-management. App usability was suboptimal to low. Clinicians rated an “F” for mySugr (15 ± 5) scoring &lt; 60 and a “D” for OnTrack (61 ± 28) scoring between 60-69. Patients rated an “F” for mySugr (55 ±18) and a “D” for OnTrack (68 ±15). Clinicians identified many heuristic violations (n=169): mySugr (90) and OnTrack (77). Heuristic principles violated the most frequently were “Help and Documentation” (38), followed by “Aesthetic and Minimalist Design” (43) and “Error Prevention” (24).
Conclusions: Commercially available diabetes app usability was marginally acceptable to unacceptable according to clinicians and patients. Future diabetes app design needs to target patient motivation and incorporate key heuristic principles to provide self-tutorials, enhance graphical or screen views, and eliminate error-prone conditions.

Disclosure
H.N. Fu: None. T. Adam: Stock/Shareholder; Self; GlaxoSmithKline plc. J.F. Wyman: None.

Funding
Robert Wood Johnson Foundation; Sigma Theta Tau International-Zeta Chapter
888-P: High Engagement over 12 Months with a Text Messaging Intervention for Type 2 Diabetes
Authors: NELSON LYNDSAY A. , GREEVY ROBERT A. , SPIEKER ANDREW J. , WALLSTON KENNETH , MAYBERRY LINDSAY S. 
Published: June 2019

The extent to which users engage with an intervention can reveal why and when it is effective. However, studies reporting on engagement with text messaging interventions are often short-term and frequency/dose varies widely. Further, limited research has examined engagement in diabetes text-based interventions with racially and socioeconomically diverse patients. We examined engagement with a 12-month text messaging intervention for T2D as part of a larger RCT. We recruited adults with T2D from community health centers and primary care clinics. At baseline, patients completed a survey and an A1c test. Patients randomized to intervention received daily one-way and interactive texts for the first 6 months, and then had the option to receive fewer texts (i.e., low-dose) for the remaining 6 months. We examined associations between patient characteristics and low-dose choice, and the association between choice and subsequent engagement, as measured by odds of response to interactive texts. The sample (N=250) had a mean age of 55.9±9.8 years, was 55% female, and 52% non-white; 41% had ≤ a high school degree, 41% had incomes &lt;$25K, and mean A1c was 8.6±1.8%. On average, patients responded to 81% (IQR: 75-96%) of texts over 12 months. At 6 months, 44% chose to continue receiving daily texts, whereas 56% chose low-dose for the remaining 6 months. No patient characteristics were associated with choice, and choice was not associated with later engagement (b=-0.20, p=0.52). Odds of responding was lower over time in both groups (p&lt;.001) but did not differ across groups. Average response rate was 73% (61-97%) in months 8-12. Among diverse patients with T2D, engagement with text messages remained high over 12 months. Almost half the sample elected to continue receiving daily texts for the entire 12 months. Engagement was similar in both groups in the final 6 months. Our findings support using texts to engage diverse patients in mobile health interventions. Providing an option for text frequency may help sustain engagement.

Disclosure
L.A. Nelson: None. R.A. Greevy: None. A.J. Spieker: None. K. Wallston: None. L.S. Mayberry: None.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100694); Center for Diabetes Translational Research (P30DK92986)
89-LB: Low Level of Agreement in Evaluation of Continuous Glucose Monitoring Profiles
Authors: HACHMANN-NIELSEN ELISE , KVIST KAJSA , BERGENSTAL RICHARD M. 
Published: June 2019

Nearly 20 years after the introduction of Continuous Glucose Monitoring (CGM) it is clear that CGM reports can effectively guide clinical decision making but the large amount of information contained in the CGM profile may also complicate the evaluation of the profiles.
We hypothesized that there would be a relatively low level of agreement in evaluations of CGM-profiles performed by a diverse group of HCPs and patients, with higher level of agreement between diabetes experts. We investigated the interobserver variation in the two groups when asked to subjectively prioritize 9 Ambulatory Glucose Profiles from ‘best’ to ‘worst’ through an internet-based portal (www.glucoseprofile.com).
A total of 88 responses were collected. The groups were defined as non-experts (N = 73) and self-evaluated experts (&gt;10 years of experience and &gt; 1 CGM evaluation per week) (N = 15). The interobserver variation between the groups were calculated using Interclass Correlation Coefficients (ICC) showing moderate agreement among the non-experts group (ICC = 0.60) and a good degree of agreement among the diabetes experts (ICC=0.75) [Figure].
Despite standardized CGM metrics and reports there is still a relatively low level of agreement in interpretation and ranking of CGM-profiles even among diabetes experts. This may allow for undesirable variation in diabetes management. More tools are needed to optimize the use of CGM for clinical decision making.

Disclosure
E. Hachmann-Nielsen: Employee; Self; Novo Nordisk A/S. K. Kvist: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. R.M. Bergenstal: Research Support; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Glooko, Inc., Hygieia, JDRF, Johnson & Johnson Medical Devices Companies, Lilly Diabetes, Medtronic MiniMed, Inc., Merck & Co., Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk Inc., Roche Diabetes Care, Sanofi. Other Relationship; Self; Abbott, Helmsley Charitable Trust, Hygieia, Lilly Diabetes, Novo Nordisk Inc., Onduo LLC, Sanofi, UnitedHealth Group Inc.

Funding
Novo Nordisk
896-P: Standard Breakfast as a More Physiological Test of Glucose Tolerance than OGTT in Obese Patients—Relationship with 24-h Glycemic Variability and Insulin Resistance
Authors: REZKI AMEL , FYSEKIDIS MARINOS , COSSON EMMANUEL , VALENSI PAUL 
Published: June 2019

Introduction: Standardized meals have been suggested as a potential tool to diagnose glycemic disorders. In obese patients with different glycemic status (normal glucose tolerance (NGT) or intermediate hyperglycemia (IH: IFG and/or IGT), or type 2 diabetes (T2D), we aimed to examine the relationship between the glucose response to 75g OGTT and a “French” standard breakfast test including 75g carbohydrates, and between the glucose response to both tests with insulin resistance (IR) and glucose variability (GV) derived from continuous glucose monitoring (CGM).
Patients and Methods: We included 80 obese patients without known dysglycemia, separated according to OGTT in 24 NGTs, 37 with IH and 19 T2Ds. Several IR indexes were calculated. GV was evaluated by calculating SD-glucose, CONGA, J index and MAGE from 24h CGM including the OGTT on day 1 and the standard breakfast test on day 2.
Results: Using CGM data, the 120-minute area under curve (AUC-glucose) and glucose peak were similar after OGTT and breakfast, the time to the interstitial glucose peak was longer (p&lt;0.02) and glucose 2 hours levels higher after OGTT than after breakfast (p&lt;0.001). AUC-glucose after OGTT and breakfast correlated with 24h GV indexes respectively on days 1 and 2 (p&lt;0.001), with IR indexes including HOMA-IR and FIRI, and negatively with QUICKI, ISI0min, ISI120min, Gutt and Matsuda indexes (p&lt;0.02 to 0.0001). Most of 24h GV indexes correlated with several IR indexes (p&lt;0.01 to 0.0001). All T2Ds, 35 out of the 37 patients with IH and 3 of the 24 NGTs had either glucose 60 or 120 minutes after breakfast ≥7.8 mmol/l, giving a 96% positive predictive value to predict abnormal results during OGTT.
Conclusion: In obese patients without known dysglycemia, the magnitude of glucose excursion after the standard breakfast correlates with the glucose response to OGTT, 24h GV and IR. This more physiological test offers a good performance to predict abnormal response to OGTT.

Disclosure
A. Rezki: None. M. Fysekidis: None. E. Cosson: Board Member; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., LifeScan, Inc., Lilly Diabetes, Medtronic, Merck Sharp & Dohme Corp., Novartis France, Novo Nordisk A/S, Roche Diagnostics France, Sanofi. Research Support; Self; Air Liquide, Lilly Diabetes, Novo Nordisk A/S, Roche Diagnostics France, Roche Foundation, Sanofi. P. Valensi: None.
899-P: Barriers and Facilitators to Implementation of the Group-Based Diabetes Prevention Program within a Large Health-Care System
Authors: HALLEY MEGHAN , NASRALLAH CATHERINE , SZWERINSKI NINA K. , PETERSEN JOHN P. , ROMANELLI ROBERT J. , AZAR KRISTEN 
Published: June 2019

Background: Lifestyle change programs based on the National Diabetes Prevention Program (DPP) are associated with clinically significant weight loss and decreases in cardiometabolic risk factors. However, these benefits depend on successful implementation in the real world. Studies have examined implementation in community settings, but less is known about integration in healthcare systems. We examined the barriers and facilitators to successful DPP implementation in a large multi-site healthcare system.
Methods: Using purposive sampling, we conducted semi-structured interviews with past and present DPP lifestyle coaches in the healthcare system. Qualitative data were analyzed using deductive and inductive techniques to identify themes, which were organized into a conceptual framework using an iterative consensus process.
Results: We conducted 33 interviews across 19 clinics serving 10 counties. Six interrelated themes and subthemes were identified: 1) Broader context, including the surrounding physical and demographic context; 2) Institutional setting, including finances, infrastructure, and personnel; 3) Program characteristics, including curriculum, administration, cost, goals, and visibility; 4) Participant recruitment, including screening and referrals; 5) Lifestyle coaches’ role, including their characteristics, behaviors, and morale; and 6) Participants’ characteristics, including attention, retention and group dynamics. Each theme and subtheme describes a key factor that served as a barrier and/or facilitator in the implementation process depending on the site-specific context and on each lifestyle coach’s approach to implementation.
Conclusion: The study highlights the complexities associated with implementing a group-based DPP in a large healthcare system and emphasizes the key role of lifestyle coaches in overcoming site-specific barriers to implementation and adapting the program to the local context.

Disclosure
M. Halley: None. C. Nasrallah: None. N.K. Szwerinski: None. J.P. Petersen: None. R.J. Romanelli: None. K. Azar: None.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases
90-LB: Treatments and Trends in Glycemic Control for Patients with Type 2 Diabetes Mellitus Treatment following Metformin
Authors: BAJPAI SANJAY K. , NAIR RADHIKA , CHANGAMIRE TICH , SHEER RICHARD , WANG QIANQIAN , PATEL HIREN 
Published: June 2019

Objectives: To compare characteristics of patients with type 2 diabetes mellitus (T2DM) who did/did not achieve short-term (3-12 months) and/or long-term (24-36 months) glycemic control (HbA1c &lt; 7%) following treatment with metformin (MET) monotherapy and determine factors associated with glycemic control.
Methods: This was a retrospective study using administrative claims from a national health plan. Patients with T2DM on MET monotherapy for ≥1 year, HbA1c ≥7% and initiating a new glucose lowering medication between January 2010-December 2014 were eligible for this study with date of the first prescription set as index date. Patients were required to have ≥1 HbA1c between 24-36 months post-index; a subset of patients with HbA1c values between 3-12 months was identified. Descriptive analyses were used to compare patients by achievement of glycemic control. Multivariable analyses were used to evaluate factors associated with glycemic control.
Results: A total of 7,416 patients were eligible of which 6,561 had data for short-term assessment. Mean A1c (%) during pre-index was 8.2+/-1.3 which decreased to 7.3+/- 1.1 in the short-term and 7.5+/-1.3 in long term, with 39.8% achieving long-term glycemic control. Among patients achieving glycemic control in the short-term (44.4%), 58.2% maintained glycemic control in long-term. For every 1.0% higher HbA1c at baseline, odds of attaining glycemic control decreased by 16% in the short-term and 12% in the long-term. Treatments associated with greater odds ratio (OR) (95% CI) of achieving HbA1c &lt;7% in long-term were SGLT2 inhibitors [OR= 2.45 (1.93-3.12)], GLP-1 RA [OR= 1.95 (1.60-2.36)] and Thiazolidinediones [OR= 1.73 (1.52-1.96)].
Conclusion: Although the majority of patients intensifying treatment after MET monotherapy did not achieve optimal glycemic goals in the long term, understanding how to facilitate treatments that encourage better glycemic control can aid in treatment selection.

Disclosure
S.K. Bajpai: Employee; Self; Eli Lilly and Company. R. Nair: Stock/Shareholder; Self; Abbott Laboratories, AbbVie Inc., GlaxoSmithKline plc. T. Changamire: Employee; Self; Humana. R. Sheer: None. Q. Wang: None. H. Patel: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company.
916-P: Comparison of Personal vs. Professional Continuous Glucose Monitoring Related to Calibration
Authors: TAKEISHI SOICHI , TSUBOI HIROKI 
Published: June 2019

Sensor glucose levels (SGs) after the capillary blood glucose level used to calibrate (BGc) are not influenced by the future BGc in personal continuous glucose monitoring (CGM) (psCGM), but by the future BGc in professional CGM (pfCGM). This leads to a stronger influence of the BGc on SG just after the BGc (SGja) in psCGM than that in pfCGM. Inaccurate SGs or BGc causes dissociation. We investigated the accuracy of psCGM and pfCGM. We cross-sectionally investigated 192 sets including the BGc and SGs before and after the BGc in 12 patients (pts) with type 2 diabetes (96 sets in 6 pts with psCGM (GUARDIAN CONNECT) and pfCGM each). Preprandial and bedtime glucose levels on days 2-5 (of wearing CGM devices) were evaluated in all pts. We calculated the degree of dissociation between the BGc and nearest SGs (DBS) using the following formula: |[(SG just before BGc (SGjb) - BGc) + (SGja - BGc)]/2| = calibration index (CaI) A. We calculated the effect of the BGc on post-SGs (EBpS) using the following formulae: |(SGja - SGjb) - [(SGjb - SG 15 min before SGjb)/3]| = a, |(SGjb - BGc)|/|(SGja - BGc) (if 0, 1)| = b, √(a × b) = CaI B. CaI C was defined as CaI A + CaI B. We defined cases wherein the BGc had dissociation cause potential (CBGc) as both I and II in psCGM group (psCGMg) (I: CaI B &gt; mean in each of two consecutive sets, II: magnitude relation between SGjb and BGc was different between two consecutive sets). CaI B was significantly higher in psCGMg than in pfCGM group (pfCGMg) (p &lt; 0.001), but CaI A and CaI C were not different. CaI A and CaI B were significantly correlated in psCGMg (r = 0.54, p &lt; 0.001), but did not correlate in pfCGMg. High CaI B was significantly associated with high CBGc (p = 0.002). The optimal cutoff value for CBGc in CaI B was 5.5 (sensitivity: 88%, specificity: 85%, area under the curve: 0.87 p &lt; 0.001) in psCGMg. The EBpS was stronger in psCGMg than in pfCGMg, while the DBS was not different. The result of CaI C shows equal accuracy of psCGM to pfCGM in terms of retrospective data. At CaI B &gt; 5.5, recalibration is necessary.

Disclosure
S. Takeishi: None. H. Tsuboi: None.
94-LB: Measurement Accuracy of a Newly Developed Prototype System for Noninvasive Glucose Monitoring
Authors: FRECKMANN GUIDO , PLEUS STEFAN , WINTERGERST PETER , WALDENMAIER DELIA , JENDRIKE NINA R. , LINK MANUELA , HAUG CORNELIA 
Published: June 2019

Non-invasive glucose monitoring (NIGM) may be beneficial for people with diabetes in avoiding the need for finger pricking to obtain blood samples. The aim was to assess measurement accuracy of a prototype system for NIGM in a mixed outpatient and in-clinic setting.
A total of 10 subjects with type 1 diabetes participated in the study which lasted for 27 days per subject. Subjects wore a FreeStyle Libre (FL) system on the upper arm for continuous glucose monitoring, and they performed standard blood glucose (BG) monitoring with a Contour® next ONE meter and NIGM at the thenar with the prototype system. At least 6 times per day, measurement sets were performed which consisted of 1st BG meter measurement, 1st FL scan, 1st NIGM measurement, 2nd BG meter measurement, 2nd FL scan, 2nd NIGM measurement. Results from the same measurement set were averaged by device before analysis. In-clinic sessions took place on the 6th day and on the 27th day, during which rapid glucose excursions with high and low glucose values were induced. During these sessions, measurement sets were performed every 15 minutes between 0730 and 1500. Data from 24/19 days were used for calibration of the NIGM system for the first/second group of five subjects. The data from the remaining 3/8 days (including 1 in-clinic day each) were used for independent validation of the calibration. For data from validation days, mean absolute relative differences (MARD) were calculated for FL and the NIGM prototype in comparison with the BG meter values for each subject.
Outpatient MARD ranged from 8.0% to 26.2% for FL (mean: 16.1%) and from 9.1% to 26.9% for the prototype (mean: 20.2%), whereas in-clinic MARD ranged from 13.7% to 45.3% for FL (mean 23.3%) and from 19.8% to 36.0% for the prototype (mean: 25.5%).
Although MARD was somewhat higher for the NIGM prototype than for FL, this proof-of-concept study showed promising results for the newly developed NIGM prototype.

Disclosure
G. Freckmann: Advisory Panel; Self; Abbott, Novo Nordisk Pharma GmbH, Sensile Medical AG. Consultant; Self; Metronom Health Europe, Roche Diabetes Care, Sanofi. Speaker’s Bureau; Self; Ascensia Diabetes Care, Dexcom, Inc., Ypsomed AG. S. Pleus: None. P. Wintergerst: None. D. Waldenmaier: None. N.R. Jendrike: None. M. Link: None. C. Haug: None.

Funding
RSP Systems A/S
946-P: HbA1c Is Already Not Enough for Glucose Control Assessment in Type 1 and Type 2 Diabetes
Authors: TOTOMIROVA TZVETELINA , DASKALOVA IVONA , ARNAUDOVA MILA 
Published: June 2019

Recommendations for control assessment in diabetes include measurement of glycated hemoglobin (HbA1c) and pre- and postprandial blood glucose (BG). Continuous Glucose Monitoring is a novel method that could be used for insulin dose adjustment but also reveal precise 24-hour glycemic profile through followed days. In our study we compare routine used glycemic control assessment tools with results derived from CGM record. We studied a group of 106 patients (60 men, 46 women; mean age 58.23±10.81 years), 21 patients with type 1 diabetes (on intensified regimen) and 85 patients with type 2 diabetes (33 - on oral medications, 31 - on pre-mixed insulin, 21 - on intensified insulin regimen). Continuous glucose monitoring with iProTM was performed for seven days and HbA1c was measured at the end of this period. In oral medication treated and pre-mixed insulin treated group a high positive correlation was found between HbA1c (7.13±0.85% and 7.64±1.40) and average blood glucose level during period of CGM (7.15±1.80mmol/l and 7.64±1.48mmol/l) (p&lt;0.01). Correlation coefficient were (r1=0.742 and r2=0.642) (p&lt;0.05). In group of type 2 patients on intensified regimen correlation was lower (r3=0.507). No correlation was found in type 1 diabetes group (r4=0.083). Only in oral treated type 2 group HbA1c showed correlation with the number of above and below excursion. No correlations were found between HbA1c and number of excursions in other groups. We conclude that HbA1c is appropriate assessment tool only in patients with stable glucose control as in type 2 on oral treatment. In all other cases we need additional method like pre-and postprandial glucose measurement. CGM could be also useful for assessment nevertheless short period of time that it reflects.

Disclosure
T. Totomirova: None. I. Daskalova: None. M. Arnaudova: None.
949-P: Associations between Body Composition and Glycemic Variability in Japanese Type 2 Diabetes
Authors: MIZOGUCHI ASAKO 
Published: June 2019

Objective: This clinical study investigated the association between body composition and glycemic variability in Japanese type 2 diabetes patients.
Research Design and Methods: A total 34 Japanese type 2 diabetes out-patients were enrolled. Glycemic variability were assessed using continuous glucose monitoring (CGM) and we excluded the glycemic data within 24 hours after mounted. Their body composition were measured by Inbody 770 (Inbody Japan, Japan). Statistical analyses were done by SPSS ver.23 and the data were showed by mean±SD.
Results: Baseline characteristics are indicated thus; male/female 22/12, 68.4±10.2 years old, period of CGM measuring 11.1±2.8 days, height 1,61±0.08 m, body weight 64.3±13.3 kg, A1c6.9±0.8%, duration of diabetes 5.4±9.8 years, maximum glucose 256±56 mg/dl, minimum58±17 mg/dl, average 137±31 mg/dl, SD37.8±14.9 mg/dl, body muscle weight 42.2±8.6 kg, body fat weight19.2±7.3kg. The patients with hypoglycemia (less than 50 mg/dl) in CGM were significantly less body height (1.64±0.07 m vs. 1.57±0.08 m, P=0.012), less body weight (70±14.6 kg vs. 57.8±7.9 kg, P=0.005)and less amount of total body muscle (45.8±8.9 kg vs. 38.3±6.4 kg, P=0.009).The patients with glycemic SD more than 40 mg/dl were lighter weight(67.7±13.9 kg vs. 57.4±9.0 kg, P=0.033) and lower BMI (25.6±3.9 vs. 22.2±2.3, P=0.014). There were no significantly difference in body fat and muscle amount in each group. The patients who had more than 5% of duration time with more than 200 mg/dl were lower BMI (25.6±4.0 vs. 22.7±2.6, P=0.035)and less amount of total body muscle (44.6±9.3 vs. 37.9±5.8, P=0.018).In multiple regression analysis after adjustment by age, sex and BMI hyperglycemia was found be significantly associated with body muscle percentage and body fat percentage.(OR0.007 CI;0.000to0.791 P=0.040 OR0.010 CI;0.000to0.852 P=0.042, respectively).
Conclusions: The Japanese type 2 patients with less body muscle amount may tend to show higher glycemic variability.

Disclosure
A. Mizoguchi: None.