bluuebunny/crossref_metadata_2025_split huggingface dataset:
533-P: Geospatial Analysis of a Diabetes Management Program—Program Participation and Social Determinants of Health
Authors: KANNY SAMANTHA , CUMMINGS WILLIAM H. , QUAYE ISAAC , CARBAJALES-DALE PATRICIA , SHERRILL WINDSOR W. , STANCIL MICHELLE D. , EVATT JANET H. 
Published: June 2024

Introduction & Objective: The American Diabetes Association has recognized Health Extension for Diabetes (HED) as a practice-tested diabetes support program. HED is a community-based program that offers a standardized education and individualized support to those with diabetes. HED improves the health status of individuals living with diabetes, specifically targeting vulnerable populations.
Purpose: The purpose of this study is to utilize Geospatial Analysis to evaluate the social determinants of health (SDoH) of HED program participants.
Methods: Using demographic data collected from individuals enrolled in HED, the research team analyzed the geographic distribution of participants and SDoH variables. Using geospatial analysis, other variables were calculated, such as distance to grocery stores, pharmacies, and hospitals. Key SDoH variables include income, educational attainment, distance to grocery stores, pharmacies and hospitals. These were used to create a Diabetes Burden Index, which in turn characterized the cumulative burden program participants face in managing their diabetes.
Results: The analysis comprised 980 individuals, with an average Diabetes Index Burden score of 0.51. This indicated the typical individual enrolled in HED has a moderate to high SDOH burden when managing their diabetes. Additional geospatial analysis will be presented on drive time to pharmacy and food sources.
Conclusion: Geospatial analysis provides a clear visual representation of geographic distribution of individuals enrolled in HED as well insights into specific SDoH characteristics of those who enrolled in the program. The utilization of GIS in diabetes management programs enhances the ability of both researchers and program facilitators to understand populations served and more accurately target services, particularly for at risk and underserve groups.

Disclosure
S. Kanny: None. W.H. Cummings: None. I. Quaye: None. P. Carbajales-Dale: None. W.W. Sherrill: None. M.D. Stancil: None. J.H. Evatt: None.
539-P: How Do Aging Adults with Diabetes Engage with Technology for Behavior Change?
Authors: BLANCHETTE JULIA E. , HART ALLISON A. , LEE KIPUM , HATIPOGLU BETUL 
Published: June 2024

Introduction: Ensuring diabetes technology and education remain patient-centered, equitable, and relevant is critical. Therefore, we explored how aging adults with diabetes desire to engage with self-management technology for behavior change.
Methods: The transtheoretical model of health behavior change was applied to guide this analysis. A semi-structured interview guide was utilized for 3, 60-minute focus groups. De-identified transcripts were cross-referenced for deductive thematic analysis with a priori codes. Codes were cross-checked independently, and discrepancies were resolved.
Results: Focus Group A comprised 6 individuals with pre-diabetes/recent T2D (66.6% White, non-Hispanic, 66.6% female, mean age 52.83 SD 8.64, preparation/action phase of behavior change). Group B comprised 4 individuals with T2D (75% White, non-Hispanic, 75% female, mean age 64.5 SD 9.11, maintenance phase). Group C comprised 3 individuals with T1D (66.6% White, non-Hispanic, 100% female, mean age 58.33 SD 6.66, termination phase). Key themes across groups were desire for accurate, individualized real-time data, yet data type and interaction level differed by behavior change phase (Table 1).
Conclusion: Aging adults have unique self-management technology and engagement preferences in the stages of the behavior change cycle. Clinicians should empower individuals to choose appropriate technology for behavior change.

Disclosure
J.E. Blanchette: Speaker's Bureau; Insulet Corporation. Board Member; Juvenile Diabetes Research Foundation (JDRF). Research Support; American Heart Association, Association of Diabetes Care & Education Specialists, National Institute of Diabetes and Digestive and Kidney Diseases, Leona M. and Harry B. Helmsley Charitable Trust. Advisory Panel; LifeScan Diabetes Institute. Consultant; WellDoc. Advisory Panel; Cardinal Health/Edgepark. A.A. Hart: None. K. Lee: None. B. Hatipoglu: Research Support; Tandem Diabetes Care, Inc.

Funding
University Hospitals JobsOhio grant for the Cleveland Innovation District
540-P: Qualitative Findings from the Deaf Diabetes Can Together Pilot
Authors: MIRUS KARISSA A. , ALLEN NANCY A. , LEE SHINDUK , LITCHMAN MICHELLE L. 
Published: June 2024

Introduction & Objective: Deaf Diabetes Can Together (DDCT) is a 10-week intervention committed to providing accessible diabetes education for the deaf and hard of hearing community, using multiple modes of culturally-appropriate communication, such as American Sign Language (ASL), closed captioning, and graphics. The purpose of this study was to pilot test DDCT.
Methods: Eight participants, including those with diabetes (Type 1 or 2) and a care partner, were interviewed to gather feedback on their experience with DCCT. They received DDCT virtually through Zoom and a learning platform (i.e., Canvas). After receiving the 10-week intervention, a semi-structured interview guide was used to explore participant experiences with DCCT. Data were qualitatively coded, line-by-line, using principles of qualitative thematic analysis.
Results: Feedback was overwhelmingly positive and all participants would recommend DDCT to others. Four key themes emerged regarding DDCT were: 1) helpful in managing diabetes by increasing motivation and providing education, practical tips to apply in their daily lives, and community support; 2) highly accessible for deaf individuals due to direct communication, data collection, and resources in ASL; 3) supportive of deaf culture by being deaf-led with an expert clinician fluent in ASL and the ability to learn from deaf peers living with diabetes; and 4) desire for additional content (e.g., lengthening DCCT beyond 10 weeks to cover additional topics like cultural food, stress management, and kidney health) and opportunities for more individualized education.
Conclusion: This is the first deaf-led and group-based diabetes education intervention and pilot results indicate high acceptability and satisfaction in persons with DHH and diabetes and their care partner. This pilot study shows the proof of concept, supporting future clinical trial to test effectiveness and feasibility of the intervention in a diverse community and clinical settings.

Disclosure
K.A. Mirus: None. N.A. Allen: Research Support; Dexcom, Inc. Consultant; Diathrive Health. S. Lee: Research Support; Olera, Clairvoyant Network, Inc. M.L. Litchman: Research Support; American Diabetes Association, Association of Diabetes Care & Education Specialists, Dexcom, Inc. Other Relationship; Association of Diabetes Care & Education Specialists. Research Support; National Institutes of Health.

Funding
This work was funded by the Gordon and Betty Moore Foundation through grant GBMF9048.
554-P: Long-Term Glycemic Improvement after the Korean Government's Home and Self-Care Program (HELP) for Patients with Type 1 Diabetes Mellitus—A Prospective Cohort Study
Authors: KOO DAE-JEONG , DAYEON LEE , KO JIHEE , JOON MOON SUN , PARK CHEOL-YOUNG 
Published: June 2024

Aim: After periodic implementation of the Home and sELf care Program (HELP, a pilot project supported by the ministry of health and welfare of the Korean government) in patients with T1DM, we evaluated the long-term effects on glycemic control.
Methods: We included 120 T1DM patients aged 15 years or older. Structured educations, including physician consultation as well as nursing and nutrition education, were provided to the subjects up to four times a year, and remote support using digital apps and phones was provided between educations. Patients were followed up at average intervals of 2 to 4 months, up to a maximum of 24 months. The primary endpoint was mean HbA1c of subjects at f/u visit.
Results: Patients who received at least one structured education had a mean HbA1c reduction of more than 1.0% from baseline, even after adjusting for confounders [%, from 8.52 ± 2.11(SD) to 7.31 ± 1.20 at 1st f/u visit (p &lt; 0.001); 7.42 (after adjustment, 7.29-7.54, 95% CI), from 8.50 ± 2.12 to 7.37 ± 1.32 at 7th f/u visit (p &lt; 0.001); 7.42 (after adjustment,7.31-7.53, 95% CI)]. TIR was maintained at least 55% or more on average for 24 months, and TBR (70 & 54) also gradually improved to the recommended range (&lt;5% & &lt;1%, 18 - 24 moths).
Conclusion: The effect of HELP on glycemic control in patients with T1DM was found to be a significant improvement, as well as an improvement in CGM-based indices.

Disclosure
D. Koo: None. L. Dayeon: None. J. Ko: None. S. Moon: None. C. Park: None.
564-P: Insights into How Health Technologies Can Support Patient-Centered Diabetes Self-Management—A Mixed-Methods Approach
Authors: BOBER TIMOTHY M. , KRALL JODI S. , GARVIN SOPHIA , ZUPA MARGARET , WHITE CHRISTINA , SOTAA BHAVINI , LOW CARISSA , ROSLAND ANN-MARIE 
Published: June 2024

Background & Objective: New technologies like mobile applications (apps), activity trackers, and continuous glucose monitors (CGM) are available to support people with diabetes (PWD), but it is not clear how these tools are currently used together for diabetes self-management (DSM). This mixed methods study assessed how PWD use technology to relate diabetes self-care behaviors to DSM goals.
Methods: Sixty-one adults aged 18-75 with type I or II diabetes (DM) who used ≥1 diabetes medication were surveyed about health app and monitor use (glucose, food, exercise/activity, weight, sleep, and stress trackers) as well as digital and general health literacy. 18 survey respondents completed semi-structured interviews examining how technologies were and could be used in daily DSM.
Results: Survey respondents were 34% African American and 38% female, 48% were &gt;45 years old. Regarding DM type and treatment, 72% had Type II diabetes, 59% used insulin, and 56% had used CGMs. Participants had high levels of digital and general health literacy. For tech use, 87% of respondents used ≥1 health app; 59% had used an activity tracker; and 47% had used apps to track ≥3 separate health behaviors. While glucose-tracking apps were more common among CGM (92%) and insulin users (100%), survey respondents used other health apps at similar rates regardless of CGM (57% vs. 60%) or insulin status (61% vs. 60%). Interviewees described using glucose-tracking apps to personalize dietary choices, but less often reported using data from other health apps or monitors for DSM goals. For data to be helpful for DSM, interviewees desired data that was passively collected, easily integrated across monitors, easily visualized, and tailorable to personal health goals.
Conclusion: PWD reported using apps to monitor multiple behaviors that impact DSM. Findings suggest that there are untapped opportunities to integrate diabetes apps with trackable lifestyle data to support patient-centered DSM.

Disclosure
T.M. Bober: None. J.S. Krall: Research Support; Becton, Dickinson and Company, Sanofi. S. Garvin: None. M. Zupa: None. C. White: None. B. Sotaa: None. C. Low: None. A. Rosland: None.

Funding
The Pittsburgh Foundation (MR2022-128717)
566-P: Satisfaction with Information Regarding Pharmaceutic Therapy in Men and Women with Type 2 Diabetes Undergoing Coronary Angiography
Authors: VOGEL JOHANNES , RATZ MAGDALENA , ELSNER PASCAL , PLATTNER THOMAS , VONBANK ALEXANDER , MADER ARTHUR , LARCHER BARBARA , LEIHERER ANDREAS , MUENDLEIN AXEL , FRICK MATTHIAS , DREXEL HEINZ , SAELY CHRISTOPH H. 
Published: June 2024

Satisfaction with information regarding pharmaceutic therapy is important because it may strongly affect the appropriate use of drug therapy including medication adherence. Data on this parameter in the clinically important population of patients with type 2 diabetes (T2DM) undergoing coronary angiography is scarce and is therefore addressed in the present study. We consecutively enrolled a cohort of 515 patients (383 men and 132 women) who underwent coronary angiography for the evaluation of suspected or established stable coronary artery disease. Satisfaction with information regarding pharmaceutic therapy was measured using the validated Satisfaction with Information about Medicines Scale (SIMS-D) which consists of two sub-scales covering action and usage (questions 1-9) or potential problems (questions 10-17), respectively. Further, health literacy, which includes the ability to understand and follow instructions for treatment was determined using the EU-HLS-Q16 questionnaire. For SIMS-D a response rate of 68.4% (n=353, 280 men and 73 women) was achieved. From our patients, 124 (36.4% of males and 30.1% of females) had T2DM according to ADA criteria. SIMS-D scores did not differ significantly between T2DM patients and those who did not have diabetes among men 11.5 [IQR=6-16] vs. 9 [IQR=6-15] p=0.121, nor among women 10 [IQR=5.5-12.25] vs. 10 [IQR=5-17] p=0.537. Independent of gender and diabetes status patients scored significantly lower on items targeting satisfaction with information about potential problems of medication than the in sub-scale for action and usage (p&lt;0.001). In analysis of covariance, health literacy independently of T2DM, gender and age predicted SIMS-D (F=41.3; p&lt;0.001). From our findings we conclude that satisfaction with information about medicines in angiographied coronary patients does not depend on the presence of T2DM or gender but is significantly impacted by health literacy.

Disclosure
J. Vogel: None. M. Ratz: None. P. Elsner: None. T. Plattner: None. A. Vonbank: None. A. Mader: None. B. Larcher: None. A. Leiherer: None. A. Muendlein: None. M. Frick: None. H. Drexel: None. C.H. Saely: None.
567-P: Health Literacy in Men and in Women with Type 2 Diabetes Undergoing Coronary Angiography
Authors: RATZ MAGDALENA , VOGEL JOHANNES , ELSNER PASCAL , PLATTNER THOMAS , VONBANK ALEXANDER , MADER ARTHUR , LARCHER BARBARA , LEIHERER ANDREAS , MUENDLEIN AXEL , FRICK MATTHIAS , DREXEL HEINZ , SAELY CHRISTOPH H. 
Published: June 2024

Health literacy reflects the ability to make appropriate health decisions and affects health outcomes. It therefore is an important parameter in patient care and for health care providers. Health literacy in patients undergoing coronary angiography, the standard procedure for the definite evaluation of coronary artery disease (CAD) is unclear and is addressed in the present study. We recruited 515 consecutive patients (383 men and 132 women) undergoing coronary angiography for the evaluation of established or suspected stable CAD in a tertiary care setting in central Europe. Health literacy was measured using the validated HLS-EU-Q16 questionnaire. A response rate of 80.4% was achieved. Overall, 177 patients (i.e. 34.3% of the cohort) had type 2 diabetes (T2DM); the prevalence of T2DM was 36.8% in men and in 27.3% in women (p=0.047). Comparing T2DM patients to those who did not have diabetes, overall median health literacy scores (HLS) were 13 [IQR=11-15] vs. 13 [IQR=10-15] among men (p=0.424) and 12 [IQR=10-15] vs. 13 [IQR=10.25-15] among women (p=0.517). Prevalence rates of adequate (HLS 13-16), problematic (HLS 9-12) and inadequate (HLS 0-8) health literacy did not differ significantly between patients with T2DM vs. subjects without T2DM (53.0% vs. 55.4%, 34.8% vs. 29.9% and 12.2% vs. 14.7% among men and 37.5% vs. 55.6%, 45.8% vs. 31.9% and 16.7% vs. 12.5% among women, respectively. We conclude that among patients undergoing coronary angiography for the evaluation of established or suspected stable CAD, health literacy regardless of sex is suboptimal both in patients with T2DM and in nondiabetic subjects.

Disclosure
M. Ratz: None. J. Vogel: None. P. Elsner: None. T. Plattner: None. A. Vonbank: None. A. Mader: None. B. Larcher: None. A. Leiherer: None. A. Muendlein: None. M. Frick: None. H. Drexel: None. C.H. Saely: None.
582-P: Enhanced Patient Education—Use of Body Composition and Muscle Strength Data to Improve HbA1c Control in Early Diabetes
Authors: KIMURA AKIRA 
Published: June 2024

The study addresses the gap in motivation for lifestyle modifications in early diabetes patients without obesity, a common scenario in Asian populations. Japanese healthcare systems offer frequent diet and exercise guidance with minimal economic burden, yet there is a lack of short-term, high-confidence physical activity information promoting continued exercise. This study focuses on body composition and exercise-specific muscle strength in early-stage diabetic patients.
Subjects and Methods:1200 patients attending a suburban Japanese diabetic clinic were observed from April 1, 2022, to March 31, 2023. Inclusion criteria were recent onset of diabetes and consent to participate alongside standard treatment. Bi-monthly body composition assessments were performed using Inbody (USA), including measurements of muscle mass, fat mass, and grip strength. Statistical analyses involved ROC analysis with HbA1c levels as dependent variables.
Results:65 of 221 first-time patients participated. The average participant age was 65 years, with 66% male and an average BMI of 24.4. No dropouts occurred. ROC analysis identified significant influences of right upper arm and trunk muscle mass on achieving an HbA1c of less than 6% after 6 months. A significant change in body fat mass between the second and third assessments was correlated with a prediction of HbA1c &gt;8%.
Considerations:This study suggests that changes in muscle strength and fat mass in non-obese early-stage diabetic patients can influence HbA1c levels. Detailed body composition information could offer new insights into data analysis and improve self-management practices for both patients and healthcare providers, particularly through the use of cloud-based technologies.

Disclosure
A. Kimura: None.
59-PUB: Leveraging Machine Learning for Precise Prediction of Type 2 Diabetes
Authors: BARAKEH RAGHAD 
Published: June 2024

Introduction: Diabetes, a global chronic ailment, necessitates advanced predictive models.
Methods: This study leverages the Pima Indian dataset, implementing Logistic Regression (LR), Naïve Bayes, Random Forest, and Decision Tree algorithms. Algorithmic performance is assessed using accuracy, precision, recall, and F1-score.
Results: Table 1 data reveal commendable performance. LR led with 83% accuracy, Decision Tree at 82%, Random Forest at 81%, and Naïve Bayes at 80%. Glucose, BMI, age, insulin, skin fold thickness were identified by the Correlation Heatmap (Fig. 1).
Conclusion: This study affirms the effectiveness of four machine learning algorithms, achieving approximately 80% accuracy in predicting type II diabetes. Findings underscore the relevance and applicability in healthcare scenarios, aligning with real-world considerations.

Disclosure
R. Barakeh: None.
596-P: Real-World Evaluation of Exercise Consensus Guidelines in Adults with Type 1 Diabetes—New Findings from the T1DEXI study
Authors: JACOBS PETER G. , MARAK CHASE , CALHOUN PETER , GAL ROBIN L. , RIDDELL MICHAEL C. , CASTLE JESSICA 
Published: June 2024

Objective: Consensus guidelines may help people with type 1 diabetes (T1D) improve exercise glucose outcomes. Glucose outcomes were evaluated in real-world exercise when participants did or did not follow guidelines.
Methods: In a 4-week observational study, continuous glucose, insulin and nutrient data were collected from 561 adults with T1D. Participants completed study and personal exercise. Glucose outcomes were compared when participants followed versus did not follow exercise guidelines for open-loop (OL) and automated insulin delivery (AID).
Results: Guidelines requiring behavior modification (e.g. reduce meal insulin before exercise) were rarely followed while guidelines not requiring modification were often followed (Fig). The guideline recommending reduced meal insulin before exercise was associated with lower % time &lt;70 mg/dL (T&lt;70) during exercise (-2.2%, P=0.02) for OL but was not significant for AID (-1.5%, P=0.22). Compared to exercise with low glucose (&lt;70 mg/dL) in prior 24 hours, sessions without recent low glucose had lower T&lt;70 during exercise (-1.2%, P&lt;0.001). The AID guideline for no carbs before exercise if CGM is flat and increasing was not associated with improved outcomes.
Conclusions: Real-world evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 hours had less time below range.

Disclosure
P.G. Jacobs: Advisory Panel; Eli Lilly and Company. Research Support; Dexcom, Inc. Stock/Shareholder; Pacific Diabetes Technologies. C. Marak: None. P. Calhoun: None. R.L. Gal: None. M.C. Riddell: Consultant; Eli Lilly and Company. Speaker's Bureau; Novo Nordisk. Advisory Panel; Supersapiens. Consultant; Dexcom, Inc. Speaker's Bureau; Sanofi. Advisory Panel; Zealand Pharma A/S. Speaker's Bureau; Dexcom, Inc. Stock/Shareholder; Zucara Therapeutics. J. Castle: Employee; Dexcom, Inc.

Funding
The Leona M. and Harry B. Helmsley Charitable Trust
60-OR: Implementing a Machine-Learning Model to Predict Risk of Chronic Kidney Disease (CKD) Progression
Authors: TANGRI NAVDEEP , SINGH RAKESH , BETTS KEITH A. , DU YUXIAN , GAO SOPHIE , KATTA ARVIND , FARAG YOUSSEF , FATOBA SAMUEL T. , LIU HONGJIAO , CHEN JINGYI , FERGUSON THOMAS , WHITLOCK REID , LEON SILVIA J. , SINGH AJAY K. 
Published: June 2024

Introduction & Objective: Early identification of at-risk CKD can facilitate timely interventions and improve patient (pt) outcomes. This study applied a previously published and validated machine learning model, Klinrisk, to predict CKD progression risk in a US population.
Methods: A retrospective observational study was conducted in 1,050,552 adult pts with CKD from Optum’s electronic health records database (1/1/2007 - 9/30/2022). Klinrisk was applied to predict risk of CKD progression. The predicted risk was used to classify pts into 3 risk groups: low, medium, and high, which were compared to the Kidney Disease Improving Global Outcomes (KDIGO) classification criteria. Kaplan-Meier (KM) analysis of actual disease progression was performed in each risk group defined by 2-year predicted risk.
Results: Pts classified as high-risk were older, with a larger proportion of males, lower eGFR levels, and higher urine albumin-to-creatinine ratio (UACR) levels. Compared to the KDIGO criteria, Klinrisk identified more pts who progressed into medium and high-risk groups. Observed progression rate matched the predicted progression risk with clear separations observed for different risk groups (Figure).
Conclusion: The Klinrisk model accurately identifies pts at high-risk, enabling early detection and intervention.

Disclosure
N. Tangri: Consultant; AstraZeneca. Research Support; Bayer Inc. Consultant; Bayer Inc., Eli Lilly and Company, Boehringer-Ingelheim, Roche Diagnostics, Otsuka America Pharmaceutical, Inc. Other Relationship; Klinrisk. R. Singh: None. K.A. Betts: Consultant; Bayer Inc. Y. Du: Employee; Bayer Inc. Stock/Shareholder; Bayer Inc. S. Gao: Other Relationship; Bayer Inc. A. Katta: None. Y. Farag: Employee; Bayer Inc. S.T. Fatoba: Employee; Bayer Inc. H. Liu: Other Relationship; Bayer Inc. J. Chen: Other Relationship; Bayer Inc. T. Ferguson: Stock/Shareholder; Klinrisk Inc. Consultant; Klinrisk Inc., Strategic Health Resources Ltd., ClinPredict Ltd, AlphaLabs. R. Whitlock: None. S. Leon: Other Relationship; Quanta Dialysis Technologies. A.K. Singh: Consultant; Bayer Inc., GlaxoSmithKline plc, Novartis AG, Novo Nordisk.