HbA1c and telemedicine during COVID-19

Aims/Introduction: To investigate whether the COVID-19 pandemic affected behavioral changes and glycemic control in patients with diabetes and to conduct a survey of telemedicine during the pandemic. Materials and Methods: In this retrospective study, a total of 2,348 patients were included from 15 medical facilities. Patients were surveyed about their lifestyle changes and attitudes toward telemedicine. Hemoglobin A1c (HbA1c) levels were compared among before (from June 1 to August 31, 2019) and in the first (from June 1 to August 31, 2020) and in the second (from June 1 to August 31, 2021) year of the pandemic. A survey of physician attitudes toward telemedicine was also conducted. Results: The HbA1c levels were comparable between 2019 (7.27 ± 0.97%), 2020 (7.28 ± 0.92%), and 2021 (7.25 ± 0.94%) without statistical difference between each of those 3 years. Prescriptions for diabetes medications increased during the period. The frequency of eating out was drastically reduced (51.7% in 2019; 30.1% in 2020), and physical activity decreased during the pandemic (48.1% in 2019; 41.4% in 2020; 43.3% in 2021). Both patients and physicians cited increased convenience and reduced risk of infection as their expectations for telemedicine, while the lack of physician–patient interaction and the impossibility of consultation and examination were cited as sources of concern. Conclusions: Our data suggest that glycemic control did not deteriorate during the COVID-19 pandemic with appropriate intensification of diabetes treatment in patients with diabetes who continued to attend specialized diabetes care facilities, and that patients and physicians shared the same expectations and concerns about telemedicine

MOESM1 of Sarcopenic obesity assessed using dual energy X-ray absorptiometry (DXA) can predict cardiovascular disease in patients with type 2 diabetes: a retrospective observational study

Additional file 1. Clinical characteristics and medications at baseline in the four categories of body composition (normal, sarcopenia, obesity, and sarcopenic obesity) classified using android fat mass, percent of body fat and body mass index. The baseline characteristics and medications in the four categories of body composition classified according to each indicator of obesity other than A/G ratio are shown in Tables S1–S6. Tables S1, S3, S5. The baseline characteristics when using android fat mass, percent of body fat, and body mass index for the classification of obesity, respectively. Tables S2, S4, S6. The medication when using android fat mass, percent of body fat, and body mass index for the classification of obesity, respectively

Behavioral change stage might moderate the impact of multifaceted interventions on non‐attendance from medical care among patients with type 2 diabetes: The Japan Diabetes Outcome Intervention Trial‐2 Large‐Scale Trial 007 (J‐DOIT2‐LT007)

Abstract Aims/Introduction Non‐attendance from regular medical care is a major problem in diabetes patients. This study aimed to examine the impact of a multifaceted lifestyle intervention by face‐to‐face approach (FFA) on non‐attendance from regular medical care in comparison with that by telephone from the technical support center (TSC). Materials and Methods This was secondary analysis from a 1‐year, prospective, cluster randomized, intervention study. Patients with type 2 diabetes, who were regularly visiting primary care physicians cluster‐randomized into the control or intervention (TSC or FFA according to resource availability of the district medical associations) groups, were consecutively recruited. The primary end‐point was non‐attendance from regular medical care. The interaction between the type of intervention (TSC vs FFA) and behavioral change stage (pre‐ vs post‐action stage) in diet and exercise for the dropout rate was assessed. Results Among the 1,915 participants (mean age 56 ± 6 years; 36% women) enrolled, 828, 564 and 264 patients belonged to the control, TSC and FFA groups, respectively. We found evidence suggestive of an interaction between the intervention type and behavioral change stage in diet (P = 0.042) and exercise (P = 0.038) after adjusting for covariates. The hazard ratios (95% confidence interval) of FFA to TSC were 0.21 (0.05–0.93) and 7.69 (0.50–117.78) in the pre‐action and post‐action stages for diet, respectively, whereas they were 0.20 (0.05–0.92) and 4.75 (0.29–73.70) in the pre‐action and post‐action stages for exercise. Conclusions Among diabetes patients, the impact of multifaceted intervention on non‐attendance from medical care might differ by the behavioral change stage