bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Metaverse and Accounting: A Paradigm Shift in Emerging Technologies and Their Implications on Accounting Research
Authors: Alawadhi Abdullah S. , Alrefai Abdulrahman A. 
Published: June 2024

ABSTRACT
The advent of the metaverse presents novel opportunities and challenges for the accounting field. As the metaverse integrates state-of-the-art technologies, it signifies a paradigm shift in internet applications and social phenomena. This study examines the future of accounting within the metaverse, concentrating on the potential advantages and hurdles arising from this dynamic environment. Utilizing a systematic literature review methodology, the research analyzes 49 articles published between 2018 and 2022 on the metaverse and associated technologies. The findings reveal that the metaverse affords many accounting opportunities, while concurrently posing unique challenges. Moreover, the emergence of the metaverse is expected to influence various accounting research domains, including financial reporting, auditing, taxation, management accounting, and accounting education. This research contributes to and expands the limited body of literature on accounting and metaverse technologies, offering insights into emerging opportunities, risks, and avenues for future research in this rapidly developing field.
JEL Classifications: M15; M40; M41; M42.
1007-P: Improvements in Glycemic Outcomes among Level2 Members Are Sustained after 24 Months
Authors: BACON SAM , FERRELL DAVID 
Published: June 2024

Background: Level2’s virtual specialty clinic, offered across the United States to people with type 2 diabetes on participating insurance plans, combines providers, coaches, continuous glucose monitors (CGM), and fitness trackers to help members gain insight, and focus on long-term behavior change. We retrospectively assessed differences in glycemic outcomes following one and two years of participation in Level2.
Methods: People joining Level2 between 01/01/21 and 07/31/21 who wore a CGM and had enrollment in a participating commercial health plan 24 months after and for at least 6 months prior to joining were included in the analysis. We used CGM-derived GMI (with ≥ 90% data sufficiency over 14 days) and A1c values available from a database of de-identified administrative claims to determine baseline, 12-month, and 24-month follow-up glycemic outcomes. Student t-tests were used to test for pair-wise differences in glycemic measures between baseline and follow-up periods overall and stratified by baseline A1c.
Results: Of the 2,035 people identified for the 2-year analysis, a subset (N = 161) had baseline A1c &gt; 7.0% and valid GMIs during both follow-up periods. This subset was 48% (N = 78) male with mean age of 54.0 (SD = 8.4) years and had mean baseline A1c of 8.77% (SD = 1.38). The mean differences from baseline A1c to 12- and 24-month GMI were -1.39% (p &lt; 0.001) and -1.36% (p &lt; 0.001), respectively. The mean change from 12- to 24-month GMI (0.02%, p = 0.70) was not statistically significant. After 12 months, 38.5% achieved a target GMI ≤ 7% and 60% of these people continued to achieve this target after 24 months. In those with baseline A1c ≤ 9.0%, 58% had improved by more than 0.5% at 12 months and 80% maintained this reduction at 24 months. In those with A1c &gt; 9.0%, 97% improved by more than 0.5% at 12 months and 95% maintained this reduction at 24 months.
Conclusion: Level2 members with baseline A1c and follow-up GMIs saw significant and sustained improvements in glycemic control after one and two years of engagement.

Disclosure
S. Bacon: Employee; UnitedHealth Group. Research Support; Level2. Stock/Shareholder; UnitedHealth Group. Employee; Optum Labs. Consultant; Level2. D. Ferrell: None.
1009-P: Assessing Patterns of CGM Use and Metrics of Glycemic Control in Type 1 Diabetes and Type 2 Diabetes Patients in the Veterans Healthcare System—Integrating CGM Device Data with EHR Data
Authors: OKUNO TOMOKI , MACWAN SHARON , MILLER DONALD R. , NORMAN GREGORY J. , REAVEN PETER , ZHOU JIN 
Published: June 2024

Introduction & Objective: To integrate long-term daily continuous glucose monitoring (CGM) device data with electronic health records (EHR) for type 1 and type 2 diabetes patients in the Veterans Affairs Healthcare System to assess real-world patterns of CGM use and the reliability of EHR-based CGM information.
Methods: This observational study used Dexcom CGM device data linked with EHR (from 2015-2020) for a large national cohort of diabetes patients. We tracked the initiation and consistency of CGM use, assessed the concordance of CGM use and measures of glucose control between CGM device data and EHR records, and examined results by age, ethnicity, and diabetes type.
Results: The time from pharmacy release of CGM to patients to initiation of uploading CGM data to Dexcom servers averaged three weeks but demonstrated wide variation among individuals; importantly, this delay decreased markedly over the later years. The average daily wear time of CGM exceeded 22 hours over nearly three years of follow-up. Patterns of CGM use were generally consistent across age, race/ethnicity groups and diabetes type. There was strong concordance between EHR-based estimates of CGM use and Dexcom CGM wear time and between estimates of glucose control from both sources.
Conclusions: The study demonstrates our ability to reliably integrate CGM devices and EHR data to provide valuable insights into CGM use patterns. The results indicate in the real-world environment that CGM is worn consistently over many years for both type 1 and type 2 patients and is similar across major race/ethnicity and age groups.

Disclosure
T. Okuno: None. S. Macwan: None. D.R. Miller: None. G.J. Norman: Employee; Dexcom, Inc. P. Reaven: Research Support; Dexcom, Inc. J. Zhou: None.

Funding
This work was supported by the Veterans Affairs Cooperative Studies Program, Department of Veterans Affairs Office of Research and Development. Additional support was received from the National Institutes of Health R01-067690 and 5R01-094775 to P.D.R., and the American Diabetes Association to P.D.R.; J.J.Z was supported by NIH (K01DK106116, R21HL150374, R01HG006139), and National Science Foundation (DMS-2054253, IIS-2205441).
1010-P: In Vivo Evaluation of Novel Long-Term Intravascular Implantable Continuous Blood Glucose Monitor in a Chronic Ovine Model—A Glucotrack Inc. Investigation
Authors: TAPSAK MARK A. , GOODE PAUL , THROWER JP , TALCOTT MICHAEL R. , GARCIA JOSE , ROUTH TIMOTHY , TAPSAK STEPHEN 
Published: June 2024

Introduction & Objective: This study provides the first in vivo assessment of Glucotrack's long-term implantable Continuous Blood Glucose Monitor (CBGM) in an ovine model. The primary objectives were to evaluate device performance, assess and refine implantation techniques, and examine chronic gross biocompatibility through explant and necropsy.
Methods: Two adult male sheep were selected for the study, each receiving 2 intravascular device implants—one each in the left and right jugular vein. MARD (CBGM-BGM) scores were calculated at 30 and 60 days post-implantation. Regular blood glucose measurements were performed twice weekly. Glucose excursions were conducted twice during the study and compared against conventional blood glucose monitoring values measured in triplicate. Comprehensive blood analyses were performed at the beginning and end of the study.
Results: The results demonstrated successful intravascular implantation (~20 minutes) without sophisticated techniques or tools. During the study, no serious adverse events were observed. Notably, MARD improved over time, indicating sustained accuracy of the Glucotrack CBGM. MARD average across the 4 devices was 8.1% at Day 30 and 4.5% at Day 60.
Conclusion: Glucotrack’s CBGM may be a reliable and accurate long-term solution for humans managing diabetes mellitus.

Disclosure
M.A. Tapsak: Employee; Glucotrack Inc. P. Goode: Employee; Glucotrack Inc. J. Thrower: Employee; Glucotrack Inc. M.R. Talcott: Employee; AbbVie Inc. Consultant; Stereotaxis, GlucoTrack, Medibeacon. J. Garcia: Consultant; Glucotrack. T. Routh: Employee; GlySens Incorporated, Biolinq. Consultant; GlucoTrack Incorporated. S. Tapsak: Employee; Glucotrack, Inc.
1024-P: Remote Continuous Glucose Monitoring following Hospital Discharge
Authors: FOLK SARA , SOBOL TREVOR , GATTI THAINA , HARRIS CARA , FAULDS EILEEN R. , BUSCHUR ELIZABETH , WYNE KATHLEEN , DUNCAN PHILICIA , DUNGAN KATHLEEN M. 
Published: June 2024

Introduction & Objective: Hospital discharge is a vulnerable period for persons with type 2 diabetes (T2D). We assessed the feasibility and efficacy of remote (RM) vs. manual (MM) monitoring using a continuous glucose monitor (CGM).
Methods: In this prospective cohort study, persons with T2D, A1c &gt;8%, and basal insulin use received a Dexcom G6 following discharge. This analysis includes 14-day CGM reports at 4 weeks after discharge for the first 100 participants. Key outcomes were stratified by either RM vs MM of CGM data and include % time in range (70-180 mg/dl, TIR).
Results: Among the 100 participants, 52 were RM and 48 were MM. Barriers in the MM group included incompatible cell phone (48%), no smartphone (46%), or other (4.2%). The RM group was younger, more likely to be employed, and had higher numeracy compared to the MM group. CGM data were available at 4 weeks in 62% of RM vs 56% of MM, p=0.69, and time in use was &gt;95% in both groups. Median glucose was 176 (IQR 143, 215) vs 203 (IQR 183, 248, p=0.02) mg/dL in the RM vs MM group. Median TIR was 53% (IQR 33, 82) vs 40% (IQR 20, 49, p=0.04) in the RM vs. MM group and % time &lt;70 mg/dl was &lt;1% for both groups.
Conclusion: Early findings suggest that CGM with RM is associated with higher TIR compared to CGM with manual downloads following hospitalization. Overcoming technological barriers to RM may improve glycemic outcomes post-discharge.

Disclosure
S. Folk: None. T. Sobol: None. T. Gatti: None. C. Harris: None. E.R. Faulds: Advisory Panel; Dexcom, Inc. Research Support; Dexcom, Inc., Insulet Corporation. Other Relationship; A1Control. E. Buschur: Research Support; Dexcom, Inc. K. Wyne: None. P. Duncan: None. K.M. Dungan: Advisory Panel; Eli Lilly and Company, Dexcom, Inc. Research Support; Dexcom, Inc. Advisory Panel; Elsevier. Research Support; Abbott, ViaCyte, Inc., Sanofi, Omnipod. Advisory Panel; Omnipod. Other Relationship; Up-To-Date. Speaker's Bureau; Med Learning Group, Medscape, Cardiometabolic Health Congress. Consultant; Oppenheimer & Co. Speaker's Bureau; Integritas.

Funding
Dexcom
1030-P: Feature Selection of Gait Measures Discriminating Prediabetes from Healthy Controls Using Machine Learning
Authors: SHAH VRUTANGKUMAR V. , MUZYKA DANIEL F. , CARLSON-KUHTA PATRICIA , MANCINI MARTINA , SOWALSKY KRISTEN , HORAK FAY B. 
Published: June 2024

Introduction & Objective: Key challenges in using body-worn inertial sensors to monitor gait characteristics are an excessive number of measures. This study aimed to determine the best combination of gait measures to discriminate prediabetes from healthy control (HC) subjects.
Methods: In this study, 108 individuals with prediabetes (age: 71.20 ± 5.11 years) and 63 healthy controls (age: 70.40 ± 6.25 years) undertook a 400-meter fast walk test. Wearing Opals inertial sensors from APDM Wearable Technologies, participants had six sensors attached to their feet, wrists, sternum, and lumbar regions. Employing the best subset selection method with forward stepwise regression, 70% of the training dataset was used for feature selection, validated with the remaining 30% for testing the model's performance, assessed by the area under the curve (AUC), accuracy, and F1 score on the validation dataset.
Results: From a pool of 37 gait measures, the best subset identified three measures from the training dataset: Cadence, total number of turns, and transverse range of motion of the trunk. A logistic regression model was built with these three gait measures using the training dataset and tested on the validation dataset. The logistic regression model on validation dataset showed AUC=0.77 (sensitivity=0.65 and specificity=0.85), accuracy=0.78 and F1 score=0.68. The performance of the proposed model with these 3 gait measures is similar to performance of model train and tested using all 37 gait measures.
Conclusion: The optimal combination of gait measures distinguishing prediabetes from HC gait involves three measures from Feet and Lumbar locations. These insights deepen our understanding of prediabetes-related gait deficits, aiding informed clinical decisions and intervention assessments.

Disclosure
V.V. Shah: None. D.F. Muzyka: None. P. Carlson-Kuhta: None. M. Mancini: Consultant; Clario. K. Sowalsky: None. F.B. Horak: Employee; Clario.

Funding
This study was supported by VA Clinical Sciences Research & Development (NCT01804049).
1038-P: National Trends in Antidiabetes Drug Spending for U.S. Adults with Diabetes—2000–2021
Authors: ZHANG PING , WANG YU , ZHOU XILIN , TANG SHICHAO 
Published: June 2024

Introduction & Objective: The number of drugs for diabetes has been increasing and spending on prescriptions has become the largest part of total medical spending in adults with diabetes. We assessed national trends on anti-diabetes drugs spending in adults with diabetes.
Methods: Data were from 2000-2021 U.S. Medical Expenditure Panel Surveys. Medications were grouped as insulin and noninsulin. The noninsulin medications included metformin, other conventional [sulfonylurea, thiazolidinedione, alpha-glucosidase inhibitor, meglitinide], newer [dipeptidyl peptidase-4 inhibitor, glucagon-like peptide-1 agonist, sodium-glucose cotransporter-2 inhibitor, amylin analog], and combination drugs. Costs were in 2021 US dollars. We used the join-point regression to identify trend changes (Figure).
Results: Total per person drug spending increased 400% from $677 in 2000 to $3,448 in 2021 or an average annual increase of $138.5. By group, the average annual spending increased $56.5 for insulin, $108 for the newer drugs (since 2006), and decreased $8.5 for the older drugs. Over time, we observed different trends by drug group. Spending on the newer drugs exhibited an upward trend from 2006 to 2013 and the trend was more accelerated after 2014.
Conclusion: Trends in spending of different anti-diabetes drug groups have changed in the last two decades. Future studies could examine reasons associated with these changes.

Disclosure
P. Zhang: None. Y. Wang: None. X. Zhou: None. S. Tang: None.
1048-P: A Financially Sustainable "Pharmacist Plus" Care Model Shows Superior Diabetes Outcomes
Authors: MALKANI SAMIR , HAAS RICHARD A. , SHAH VRUSHALI , MCELNEA WILLIAM , PIZ LILLIAN , BARRY CHERYL , SHARMA HINAL , PUGLIESE GABRIELLA , LEBEL (SHNAYDERMAN) STELLA 
Published: June 2024

Introduction: Individuals with persistently uncontrolled diabetes (Hemoglobin A1C ≥9) frequently face socioeconomic, behavioral, or other barriers to treatment plans despite optimal medical and education interventions. Despite demonstrated efficacy of one-on-one coaching, it is often not reimbursed by health insurance.
Objective: At the UMass Memorial Diabetes Center of Excellence, we evaluated the financial sustainability and efficacy of a “Care Coach” program to provide additional services to these patients.
Methods: Pharmacists from the UMass Specialty Pharmacy, trained in diabetes care, delivered high-frequency, individualized comprehensive coaching covering medication management, nutrition and lifestyle counseling, refill coordination, SDOH screening/referrals and mental health referrals.
Results: Notable outcomes are A1C reduction averaging 2% (sustained at month 15) and over 50% lower rates of diabetes-related ER visits and hospitalizations (Table). The medical center investment of $2,666 per coach patient annually is offset by a combination of patients filling prescriptions through the Specialty Pharmacy and the 340B drug pricing program.
Conclusion: With a third of Coach patients on Medicaid, the program showcases the potential of leveraging the 340B program to enhance care for the economically vulnerable, while reducing diabetes-related ER visits and hospitalizations.

Disclosure
S. Malkani: None. R.A. Haas: None. V. Shah: None. W. McElnea: None. L. Piz: None. C. Barry: None. H. Sharma: None. G. Pugliese: None. S. Lebel (Shnayderman): None.
1068-P: Reassessing Insulin Therapy In Diabetes of the Exocrine Pancreas—A Pilot Study
Authors: CICCARELLI GEA , DI GIUSEPPE GIANFRANCO , BRUNETTI MICHELA , SOLDOVIERI LAURA , DI GIORGI NICOLETTA , IACOMINI CHIARA , MASCIOCCHI CARLOTTA , PATARNELLO STEFANO , CAPECE UMBERTO , MORCIANO CASSANDRA , GUGLIANDOLO SHAWN , AVOLIO ADRIANA , MOFFA SIMONA , CINTI FRANCESCA , GIACCARI ANDREA , MEZZA TERESA 
Published: June 2024

Cohort studies have demonstrated that subjects with diabetes of the exocrine pancreas (DEP) are mostly treated with basal bolus insulin. To reassess insulin therapy in subjects affected by DEP, we evaluated the prevalence of insulin therapy in our cohort and investigated the role of other glucose-lowering drugs.We first conducted a retrospective study on DEP subjects referred to our hospital over three years (2021 - 2023). We then conducted an intervention study on 13 subjects with diabetes and acute or chronic pancreatitis, treated with basal bolus insulin. All patients suspended rapid insulin and started a combination of glucose-lowering drugs, chosen according to standard of care. Subjects used a flash glucose monitoring (FGM) system. FGM parameters and quality of life (DQOL) were reported at baseline and after one and three months.We found that 381/490 subjects with DEP were treated with basal bolus insulin. In the intervention study, all subjects had high cardiovascular risk. Basal insulin was titrated during follow-up (10±1 UI vs 16±2 UI; p=0.036) and oral therapy prescribed as follows: metformin 11/13, pioglitazone 5/13, GLP-1RA 3/13, DPP-4i 5/13, SGLT2i 9/13. At one month FGM parameters showed Time In Range (TIR) 64±4 vs 77±7%, Time Above Range (TAR) 35±4 vs 21±8%, Time Below Range (TBR) 1±0.4 vs 1.2±0.7%, Glucose Management Indicator (GMI) 7.2±0.2 vs 6.4±0.2% (p=0.031). At three months we found improved glycemic control (TIR 64±4 vs 86±4%, p=0.009; TAR 35±4 vs 10±8%; p=0.009; TBR 1±0.4 vs 1±0.6% ns; GMI 7.2±0.2 vs 6.1±0.3%; p=0.042) and quality of life (2.5±0.6 vs 1.3±0.6; p=0.041). No DKA or acute pancreatitis recurrence occurred. Combination of basal insulin and oral glucose-lowering drugs significantly improved glycemic control and quality of life compared to basal-bolus insulin therapy. Therapies used in type 2 diabetes may be useful in DEP to improve metabolic control and reduce cardiovascular risk. Larger studies are warranted to assess safety and efficacy of these drugs in DEP.

Disclosure
G. Ciccarelli: None. G. Di Giuseppe: None. M. Brunetti: None. L. Soldovieri: None. N. Di Giorgi: None. C. Iacomini: None. C. Masciocchi: None. S. Patarnello: None. U. Capece: None. C. Morciano: None. S. Gugliandolo: None. A. Avolio: None. S. Moffa: None. F. Cinti: None. A. Giaccari: None. T. Mezza: None.
1075-P: Evaluating the Safety of Basal Insulin for the Management of Type 2 Diabetes in the Emergency Room Setting
Authors: HERRERA AZUCENA M. , TABASSUM HUMERA , RAHMAN SHAZIA , MAITY ALOKE , BARON SARAH , MANAVALAN ANJALI , LEE HANNA 
Published: June 2024

Background: Evidenced based guidelines for hyperglycemia management in the emergency room (ED) are lacking. Basal plus bolus with correction insulins are the preferred treatment in noncritically ill hyperglycemic inpatients. We aimed to study if basal plus correction insulins can be safely and effectively initiated earlier in the hospitalization course in the ED.
Methods: Inclusion criteria: triage glucose &gt;300 mg/dL, age &gt; 18, type 2 diabetes (T2D), boarded for admission. Exclusion criteria: DKA, HHS, critically ill or simultaneous cirrhosis with pancreatitis. 23 patients received basal plus correction insulins according to the intervention. 259 patients received standard therapy. Intervention: The lower of glargine 0.1U/kg or 10 U was administered if the last dose of basal insulin was &gt; 24 hours ago. A lispro correction of 2 U was administered every 6 hours if the last dose of basal was &lt;24 hours ago or if the glucoses remained &gt;300 mg/dL. Outcomes: Time to target glucose was defined as time from triage to two successive glucose values &lt; 300 mg/dl at least 4 hours apart. Hypoglycemia was defined as glucose &lt; 80 mg/dl.
Results: The control group comprised of 53% males, 47% females, 40% black and 42% who identified as Hispanic/Latino with an average age of 61.2, GFR 55.8, A1c 11.2% and mean BMI of 26.9. The intervention group comprised of 42% males, 58% females,48% black and 48% who identified as Hispanic/Latino with an average age of 65.4, GFR 48.2, A1c 11.5% and a mean BMI of 25.62. Time to target glucose was 23.3 hours in the intervention vs 21.6 hours in the control group. No hypoglycemia occurred in the intervention group; however, the incidence of hypoglycemia was 12.7% in the control group.
Conclusion: Basal plus correction insulins can be safely initiated, in the ED with less hypoglycemia, in noncritically ill T2D patients boarded for admission.

Disclosure
A.M. Herrera: None. H. Tabassum: None. S. Rahman: None. A. Maity: None. S. Baron: Stock/Shareholder; Extend Fertility. A. Manavalan: None. H. Lee: None.
1076-P: Online Presence and Accessibility of the National Diabetes Prevention Program in Socially Vulnerable Communities
Authors: MASSEY MCKAYLA , NGUYEN JORDAN , PENG NG BOON 
Published: June 2024

Introduction & Objective: Prediabetes affects an estimated 98 million adults in the United States, yet there exists a relative underutilization of diabetes prevention programs, such as the CDC National Diabetes Prevention Program (NDPP). Given the widespread internet and social media use among US adults, effective online dissemination of information concerning the NDPP is key to heightening awareness and fostering enrollment in such programs. This study sought to assess the content of the NDPP organizations’ websites, evaluating their ability to provide pertinent information that motivates individuals to enroll and actively participate in the program.
Methods: Using data from the NDPP registry and the CDC Social Vulnerability Index, we identified and systematically reviewed 368 organizations delivering in-person NDPP in socially vulnerable communities.
Results: Of these, 21.7% (n=80) listed a web address for their DPP, of which only 59 were functional. Among these functional websites, 39% provided a comprehensive description of the NDPP, with 5% specifying cultural objectives. Additionally, 54% provided information on class schedules, 15% allowed online enrollment, and 39% maintained an active presence on social media platforms.
Conclusion: Approximately 84% of NDPP organizations in socially vulnerable communities are without a functioning website, despite the disproportionate impact of diabetes on these populations. Only 5% of organizations with operational websites reported offering culturally and racially specific programs. These findings underscore a critical gap in the current online presence of NDPP information in socially vulnerable areas for individuals seeking diabetes prevention resources. Targeted efforts should focus on enhancing website accessibility with relevant information and social media engagement to effectively communicate the availability of the NDPP to vulnerable communities.

Disclosure
M. Massey: None. J. Nguyen: None. B. Ng: None.