A Comparison of Online Self-Training and Standard Bedside Training in Lung Ultrasonography for Medical Students.

PURPOSE: Point-of-care ultrasonography (POCUS) is increasingly integrated into medical education. Traditionally taught at the bedside using a hands-on approach, POCUS is limited by cost, time, faculty availability, and access to POCUS resources. With the recent transition to digitalization in medical education, the authors compare lung POCUS performance and pathology identification among medical students to examine whether using an online, self-learning lung POCUS module is noninferior to traditional bedside, faculty-led lung POCUS training. METHOD: This study assessed the performance of 51 medical students from August to October 2021 on an elearning lung POCUS course with traditional bedside training and no training. POCUS students were scored on use of a simulator to identify pathologies, ability to identify lung ultrasonographic pathological clips, and scanning technique. RESULTS: The elearning group had a significantly higher median (interquartile range [IQR]) total test score (15/18 [10.5-16] vs. 12/18 [9-13]; P = .03) and scanning technique score (5/5 [4-5] vs. 4/5 [3-4]; P = .03) compared with the standard curriculum group. The median (IQR) accuracy in the clip segment of the examination was 7.5 of 10 (4-9) in the self-learning group and 6 of 10 (4-7) in the standard curriculum group ( P = .18). The median (IQR) grade on the simulator segment of the examination was 2 of 3 (2-3) in the self-learning group and 2 of 3 (1-2) in the standard curriculum group ( P = .06). CONCLUSIONS: This study suggests that self-directed elearning of lung POCUS is at least noninferior to bedside teaching and possibly even a superior method of learning lung POCUS. This teaching method POCUS is feasible for medical students to learn lung ultrasonography and has potential to complement or augment the traditional learning process or eliminate or lessen the requirement for bedside teaching by reaching a larger audience while minimizing costs and human resources.

Paradoxical Rise in Hypoglycemia Symptoms With Development of Hyperglycemia During High-Intensity Interval Training in Type 1 Diabetes.

OBJECTIVE: To assess the reliability of self-perception of glycemia during high-intensity interval training (HIIT) in subjects with type 1 diabetes. RESEARCH DESIGN AND METHODS: This randomized crossover study included subjects who completed four fasted HIIT sessions. Subjects answered the Edinburgh Hypoglycemia Scale, estimated their blood glucose (BG), and had plasma glucose (PG) collected throughout exercise and recovery. RESULTS: As PG increased throughout exercise, hypoglycemia scores increased across each category: autonomic (3.1-4.4, P < 0.05), neuroglycopenic (1.5-2.4, P < 0.05), and nonspecific (1.3-1.9, P < 0.05). Subjects' estimated BG showed a negative bias that widened as exercise progressed and peaked at -1.6 ± 3.3 mmol/L (P < 0.001) postinsulin correction. CONCLUSIONS: During HIIT, despite progressing hyperglycemia, subjects experience increased hypoglycemia symptoms and tend to underestimate their BG level.

Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes.

Background: This study investigated the accuracy of real-time continuous glucose monitoring (rtCGM) during high intensity interval training (HIIT) in patients with type 1 diabetes (T1D). Methods: Seventeen participants with T1D, using multiple daily injections (MDI) with basal insulin glargine 300 U/mL (Gla-300), completed four fasted HIIT sessions over 4 weeks while wearing a Dexcom rtCGM G4 Platinum system. Each exercise consisted of high intensity interval cycling and multimodal training over 25 min. Reference venous plasma glucose (PG) was measured at 60- and 10-min before exercise (Stage 1), every 10 min during exercise and then every 15 min until 180 min after the end of exercise (Stage 2: during exercise and 45-min early recovery; Stage 3: 45 min to 3 h after the end of exercise); and at 6-, 10-, and 13-h postexercise (Stage 4). Results: In the 64 HIIT sessions that resulted in hyperglycemia, PG increased 90.0 ± 32.4 mg/dL (mean ± standard deviation), peaking at 68.0 ± 18.4 min from the start of HIIT. Mean absolute relative difference was highest during exercise and early recovery (Stage 2) at 17.8%, versus Stage 1 (10.4%), Stage 3 (10.6%), and Stage 4 (11.5%) (P < 0.001). During Stage 2, rtCGM showed a significant negative bias of 35.3 mg/dL (P < 0.001) compared to reference glucose. Lag time to reach the half-maximal glucose rise was 35 min in rtCGM versus PG. The Surveillance Error Grid found that in Stage 2, only 65.5% of paired values were in the no-risk zone and the %15/15 was 50%, significantly lower than the other stages (P < 0.001). Conclusions: During HIIT and early recovery, there is an increase in lag time and a related decline in accuracy of Dexcom rtCGM G4, compared to pre-exercise and later recovery, in patients with T1D using MDI.