Coronary heart disease leads to approximately 14% of deaths in the US; an acute myocardial infarction (AMI) occurs every 34 seconds and an AMI-related death occurs every 84 seconds.¹ Approximately 635 000 individuals within the US have their first AMI each year, and almost half experience a recurrent AMI the same year.¹ One in 5 patients with AMI is rehospitalized within 30 days of discharge.^1-3

Context Little evidence exists regarding the positive and negative impacts of continuous glucose monitor system (CGM) alarm settings for diabetes control in patients with type 1 diabetes (T1D). Objective Evaluate the associations between CGM alarm settings and glucose outcomes. Design and Setting A cross-sectional observational study in a single academic institution. Patients and Main Outcome Measures CGM alarm settings and 2-week CGM glucose information were collected from 95 T1D patients with > 3 months of CGM use and ≥ 86% active usage time. The associations between CGM alarm settings and glucose outcomes were analyzed. Results Higher glucose thresholds for hypoglycemia alarms (ie, ≥ 73 mg/dL vs < 73 mg/dL) were related to 51% and 65% less time with glucose < 70 and < 54 mg/dL, respectively (P = 0.005; P = 0.016), higher average glucose levels (P = 0.002) and less time-in-range (P = 0.005), but not more hypoglycemia alarms. The optimal alarm threshold for < 1% of time in hypoglycemia was 75 mg/dL. Lower glucose thresholds for hyperglycemia alarms (ie, ≤ 205 mg/dL vs > 205 mg/dL) were related to lower average glucose levels and 42% and 61% less time with glucose > 250 and > 320 mg/dL (P = 0.020, P = 0.016, P = 0.007, respectively), without more hypoglycemia. Lower alarm thresholds were also associated with more alarms (P < 0.0001). The optimal alarm threshold for < 5% of time in hyperglycemia and hemoglobin A1c ≤ 7% was 170 mg/dL. Conclusions Different CGM glucose thresholds for hypo/hyperglycemia alarms are associated with various hypo/hyperglycemic outcomes. Configurations to the hypo/hyperglycemia alarm thresholds could be considered as an intervention to achieve therapeutic goals.

Objective: Our objective was to determine how social vulnerabilities, broadband access, and rurality relate to telemedicine use across the United States through large-scale analysis of real-world telemedicine data. Materials and Methods: We conducted a retrospective, observational study of dyadic U.S. telemedicine sessions that occurred January 1, 2022 to December 31, 2022, linked to the 2020 Centers for Disease Control and Prevention Social Vulnerability Index (SVI) and the National Center for Health Statistics Urban-Rural Classification Scheme for Counties. We examined county-level telemedicine use rates (sessions per 1000 population) in relation to SVI indexes, broadband internet access, and rurality classifications using polynomial regression and data visualization. Results: We found a negative, nonlinear association between overall social and socioeconomic status vulnerabilities and telemedicine use. Telemedicine rates in urban counties exceeded that of rural counties. There was more variability in telemedicine use for the urban counties according to social vulnerability and broadband access. Discussion: Rurality and broadband access demonstrated a greater effect on telemedicine use than social vulnerability, and the relationship between social vulnerability, broadband access, and telemedicine use differed for rural versus urban areas. Conclusion: This observational study of nearly 8 million U.S. telemedicine sessions showed that rurality and broadband access are key drivers of telemedicine use and may be more important than many social vulnerabilities in determining community-level telemedicine use. We also found nuanced differences in the relationship between social vulnerability and telemedicine use between rural and urban counties, and at different levels of broadband access.

Background: Digital health and telemedicine are potentially important strategies to decrease health care’s environmental impact and contribution to climate change by reducing transportation-related air pollution and greenhouse gas emissions. However, we currently lack robust national estimates of emissions savings attributable to telemedicine. Objective: This study aimed to (1) determine the travel distance between participants in US telemedicine sessions and (2) estimate the net reduction in carbon dioxide (CO2) emissions attributable to telemedicine in the United States, based on national observational data describing the geographical characteristics of telemedicine session participants. Methods: We conducted a retrospective observational study of telemedicine sessions in the United States between January 1, 2022, and February 21, 2023, on the doxy.me platform. Using Google Distance Matrix, we determined the median travel distance between participating providers and patients for a proportional sample of sessions. Further, based on the best available public data, we estimated the total annual emissions costs and savings attributable to telemedicine in the United States. Results: The median round trip travel distance between patients and providers was 49 (IQR 21-145) miles. The median CO2 emissions savings per telemedicine session was 20 (IQR 8-59) kg CO2). Accounting for the energy costs of telemedicine and US transportation patterns, among other factors, we estimate that the use of telemedicine in the United States during the years 2021-2022 resulted in approximate annual CO2 emissions savings of 1,443,800 metric tons. Conclusions: These estimates of travel distance and telemedicine-associated CO2 emissions costs and savings, based on national data, indicate that telemedicine may be an important strategy in reducing the health care sector’s carbon footprint.

Background: Continuous glucose monitoring (CGM) systems help reduce hypoglycemia in patients with type 1 diabetes (T1D). It remains unclear whether T1D patients with impaired awareness of hypoglycemia (IAH) continue to develop more hypoglycemia than those with normal hypoglycemia awareness (NA) despite CGM use. Materials and Methods: For this cross-sectional observational study, 99 T1D patients using real-time CGMs for ≥86% of time were recruited. Fifty and 49 patients were found to have NA and IAH (based on the Clarke questionnaire), respectively. Two-week CGM hypoglycemia data were collected. Results: IAH was associated with greater percentages of CGM values <70 and <54 mg/dL ( P = 0.012, P = 0.004) compared to NA. Clarke scores correlated positively with the percentage of CGM values <70 and <54 mg/dL ( P = 0.013, P = 0.004). IAH was also related to more events with glucose <70 and <54 mg/dL determined either with at ≥1 time point ( P = 0.048, P = 0.003) or lasting ≥20 min ( P = 0.016, P = 0.004). IAH patients presented with more day-time events with glucose <54 mg/dL ( P = 0.015), nocturnal events with glucose levels <70 and <54 mg/dL ( P = 0.009, P = 0.007) and longer day-time event duration with glucose levels <70 and <54 mg/dL ( P < 0.001, P = 0.006), respectively. Conclusions: T1D patients with IAH continue to experience more hypoglycemia despite dedicated CGM use.