Unlabelled: Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems.
Background: Type 1 diabetes is a demanding chronic condition that requires diligent blood glucose monitoring and timely insulin administration by patients who must integrate self-management into their daily lives.
Objective: This study aimed to better understand what outcome measures are important to individuals living with type 1 diabetes (T1D) in Ontario, Canada, to help inform the development of type 1 diabetes virtual self-management Education and support (T1ME) trial.
Methods: A qualitative approach was used, in which we conducted 6 focus groups with a total of 24 adult participants living with T1D (from age 18 to >65 years) in Ontario. Each focus group was semistructured in nature; participants were encouraged to talk openly about their experiences with T1D self-management and provide their perspectives on more focused topics such as technology and relationships with health care providers.
Results: An interpretive analysis helped us devise a framework for our results that centered around 6 main discussion themes: (1) adapting self-management to meet evolving needs, (2) looking "beyond A1c" toward more personalized indicators of glycemic management, (3) the benefits and challenges of adopting new T1D technology, (4) establishing trusting relationships with diabetes care providers, (5) perceived benefits of peer support, and (6) pre- and post-COVID-19 perspectives on virtual care.
Conclusions: Our goal is for these findings to help facilitate the development of patient-oriented outcome measures that are in line with the unique needs and preferences of T1D patients in this new, more virtual landscape of clinical care, education, and self-management support.
Background: In-home remote foot temperature monitoring (RTM) holds promise as a method to reduce foot ulceration in high-risk patients with diabetes. Few studies have evaluated adherence to this method or evaluated the factors associated with noncompliance.
Objective: The aims of this study were to estimate noncompliance in patients who were enrolled in RTM nationwide across Department of Veterans Affairs (VA) and to evaluate characteristics associated with noncompliance.
Methods: We conducted an observational study including 1137 patients in the VA who were enrolled in RTM between January 2019 and June 2021, with follow-up through October 2021. Patient information was obtained from the VA's electronic health record and RTM use was obtained from the company. Noncompliance was defined as using the mat <2 days per week for ≥4 of the 12 months of follow-up. Using a multivariable model, we calculated odds ratios (ORs) and 95% CIs for associations between various factors and noncompliance and compared using Akaike information criterion statistics, a measure of model fit.
Results: The sample was predominantly male (n=1125, 98.94%) ; 21.1% (n=230) were Black and 75.7% (n=825) were White. Overall, 37.6% (428/1137) of patients were classified as noncompliant. In the multivariable model, an intermediate area deprivation index was statistically significantly and inversely associated with noncompliance (area deprivation index 50-74 vs 1-24; OR 0.56, 95% CI 0.35-0.89); factors significantly and positively associated with noncompliance included recent history of osteomyelitis (OR 1.44, 95% CI 1.06-1.97), Gagne comorbidity index score ≥4 (vs ≤0; OR 1.81, 95% CI 1.15-2.83), telehealth encounters (28+ vs <6; OR 1.70, 95% CI 1.02-2.84), hemoglobin A1c≥10 (vs <5.7; OR 2.67, 95% CI 1.27-5.58), and current smoking (OR 2.06, 95% CI 1.32-3.20). Based on Akaike information criterion differences, the strongest factors associated with noncompliance were behavioral factors (poor glucose control [as measured by hemoglobin A1c] and smoking), and to a lesser extent, factors such as a recent history of osteomyelitis and an elevated Gagne comorbidity index, indicating a high comorbidity burden.
Conclusions: To reduce the risk of ulcer recurrence and amputation, proactively providing additional support for self-monitoring to patients with characteristics identified in this study (poor glucose control, current smoking, high comorbidity burden) may be helpful. Furthermore, research is needed to better understand barriers to use, and whether the addition of design features, reminders, or incentives may reduce noncompliance and the risk of foot ulcers.
Background: Type 2 diabetes mellitus (T2DM) is a common health issue, with heart failure (HF) being a common and lethal long-term complication. Although insulin is widely used for the treatment of T2DM, evidence regarding the efficacy of insulin compared to noninsulin therapies on incident HF risk is missing among randomized controlled trials. Real-world evidence on insulin's effect on long-term HF risk may supplement existing guidelines on the management of T2DM.
Objective: This study aimed to compare insulin therapy against other medications on HF risk among patients with T2DM using real-world data extracted from insurance claims.
Methods: A retrospective, observational study was conducted based on insurance claims data from a single health care network. The study period was from January 1, 2016, to August 11, 2021. The cohort was defined as patients having a T2DM diagnosis code. The inclusion criteria were patients who had at least 1 record of a glycated hemoglobin laboratory test result; full insurance for at least 1 year (either commercial or Medicare Part D); and received glucose-lowering therapy belonging to 1 of the following groups: insulin, glucagon-like peptide 1 receptor agonists (GLP-1 RAs), dipeptidyl peptidase-4 inhibitors (DPP-4Is), or sodium-glucose cotransporter-2 inhibitors (SGLT2Is). The main outcome was the 5-year incident HF rate. Baseline covariates, including demographic characteristics, comorbidities, and laboratory test results, were adjusted to correct for confounding.
Results: After adjusting for a broad list of confounders, patients receiving insulin were found to be associated with an 11.8% (95% CI 11.0%-12.7%), 12.0% (95% CI 11.5%-12.4%), and 15.1% (95% CI 14.3%-16.0%) higher 5-year HF rate compared to those using GLP-1 RAs, DPP-4Is, and SGLT2Is, respectively. Subgroup analysis showed that insulin's effect of a higher HF rate was significant in the subgroup with high HF risk but not significant in the subgroup with low HF risk.
Conclusions: This study generated real-world evidence on the association of insulin therapy with a higher 5-year HF rate compared to GLP-1 RAs, DPP-4Is, and SGLT2Is based on insurance claims data. These findings also demonstrated the value of real-world data for comparative effectiveness studies to complement established guidelines. On the other hand, the study shares the common limitations of observational studies. Even though high-dimensional confounders are adjusted, remaining confounding may exist and induce bias in the analysis.
Background: Despite the existence of an increasing array of digital technologies and tools for diabetes management, there are disparities in access to and uptake and use of continuous glucose monitoring (CGM) devices, particularly for those most at risk of poor diabetes outcomes.
Objective: This study aims to assess communication technology and CGM access, literacy, and use among patients receiving treatment for diabetes at an inner-city safety-net hospital.
Methods: A survey on digital technology ownership and use was self-administered by 75 adults with type 1 and type 2 diabetes at the diabetes clinic of Grady Memorial Hospital in Atlanta, Georgia. In-depth interviews were conducted with 16% (12/75) of these patient participants and 6 health care providers (HCPs) to obtain additional insights into the use of communication technology and CGM to support diabetes self-management.
Results: Most participants were African American (66/75, 88%), over half (39/75, 52%) were unemployed or working part time, and 29% (22/75) had no health insurance coverage, while 61% (46/75) had federal coverage. Smartphone ownership and use were near universal; texting and email use were common (63/75, 84% in both cases). Ownership and use of tablets and computers and use and daily use of various forms of media were more prevalent among younger participants and those with type 1 diabetes, who also rated them as easier to use. Technology use specifically for diabetes and health management was low. Participants were supportive of a potential smartphone app for diabetes management, with a high interest in such an app helping them track blood sugar levels and communicate with their care teams. Younger participants showed higher levels of interest, perceived value, and self-efficacy for using an app with these capabilities. History of CGM use was reported by 56% (42/75) of the participants, although half (20/42, 48%) had discontinued use, above all due to the cost of the device and issues with its adhesive. Nonuse was primarily due to not being offered CGM by their HCP. Reasons given for continued use included convenience, improved blood glucose control, and better tracking of blood glucose. The in-depth interviews (n=18) revealed high levels of satisfaction with CGM by users and supported the survey findings regarding reasons for continued use. They also highlighted the value of CGM data to enhance communication between patients and HCPs.
Conclusions: Smartphone ownership was near universal among patients receiving care at an inner-city hospital. Alongside the need to address barriers to CGM access and continued use, there is an opportunity to leverage increased access to communication technology in combination with CGM to improve diabetes outcomes among underresourced populations.
Background: Children and adolescents with type 1 diabetes require frequent outpatient evaluation to assess glucose trends, modify insulin doses, and screen for comorbidities. Continuous glucose monitoring (CGM) provides a detailed glycemic control assessment. Telemedicine has been increasingly used since the COVID-19 pandemic.
Objective: To investigate CGM profile parameter improvement immediately following pediatric outpatient diabetes visits and determine if visit modality impacted these metrics, completion of screening laboratory tests, or diabetic emergency occurrence.
Methods: A dual-center retrospective review of medical records assessed the CGM metrics time in range and glucose management indicator for pediatric outpatient diabetes visits during 2021. Baseline values were compared with those at 2 and 4 weeks post visit. Rates of completion of screening laboratory tests and diabetic emergencies following visits were determined.
Results: A total of 269 outpatient visits (41.2% telemedicine) were included. Mean time in range increased by 1.63% and 1.35% at 2 and 4 weeks post visit (P=.003 and .01, respectively). Mean glucose management indicator decreased by 0.07% and 0.06% at 2 and 4 weeks post visit (P=.003 and .02, respectively). These improvements in time in range and glucose management indicator were seen across both telemedicine visits and in-person visits without a significant difference. However, patients seen in person were 2.69 times more likely to complete screening laboratory tests (P=.03). Diabetic emergencies occurred too infrequently to analyze.
Conclusions: Our findings demonstrate an immediate improvement in CGM metrics following outpatient visits, regardless of modality. While statistically significant, the magnitude of these changes was small; hence, multiple visits over time would be required to achieve clinically relevant improvement. However, completion of screening laboratory tests was found to be more likely after visits occurring in person. Therefore, we suggest a hybrid approach that allows patient convenience with telemedicine but also incorporates periodic in-person assessment.
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