Serial submaximal treadmill tests are often used to evaluate the efficacy of therapy in patients with atrial fibrillation. Since the response to serial tests can be influenced by a ‘learning phenomenon’, we performed maximal exercise tests on 9 patients (mean age 63 ± 4 years) with chronic atrial fibrillation. Points of analysis for the initial and follow-up treadmill exercise tests were 3 mph/0% grade, the gas exchange anaerobic threshold, and maximal exertion. Significant (p < 0.05) reductions in ventilation (l/min) and oxygen uptake (ml/kg/min) were observed on follow-up at a standard submaximal work load of 3.0 mph/0% grade and at the gas exchange anaerobic threshold. There was no significant alteration in these variables at maximal exertion. A reduction in heart rate was observed throughout exercise during the follow-up test with the most marked reduction (21 beats/min) occurring at 3.0 mph/0% grade. There were no differences in respiratory exchange ratio or systolic blood pressure at any point. The reduction in submaximal heart rate and gas exchange variables without a significant change in these variables at maximal exertion is consistent with a learning effect. Therefore, studies comparing consecutive submaximal exercise test responses in patients with atrial fibrillation can be misleading.
Sexual and gender minority (SGM) older adults experience greater health disparities compared to non-SGM older adults. The SGM older adult population is growing rapidly. To address this disparity and gain a better understanding of their unique challenges in healthcare relies on accurate data collection. We conducted a secondary data analysis of 2018–2022 electronic health record data for older adults aged ≥50 years, in 1 large academic health system to determine the source, magnitude, and correlates of missing sexual orientation and gender identity (SOGI) data among hospitalized older adults. Among 153 827 older adults discharged from the hospital, SOGI data missingness was 67.6% for sexual orientation and 63.0% for gender identity. SOGI data are underreported, leading to bias findings when studying health disparities. Without complete SOGI data, healthcare systems will not fully understand the unique needs of SGM individuals and develop tailored interventions and programs to reduce health disparities among these populations.
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