China
Africa
Explore chapters and articles related to this topic
Laughter Is the Best Therapy for Happiness and Healthy Life Expectancy
Published in Goh Cheng Soon, Gerard Bodeker, Kishan Kariippanon, Healthy Ageing in Asia, 2022
Tetsuya Ohira, Masahiko Ichiki
We conducted a randomized interventional study to investigate the effect of the laughter program on blood glucose levels. Twenty-seven community residents aged 60 years or older were randomly divided into an intervention group and a control group, and the intervention group received 120 minutes of laughter therapy (a program that combines watching rakugo, comedy, etc., with mild exercise) for 10 weeks. The results showed that hemoglobin A1c, an indicator of diabetes control, improved significantly in the intervention group compared with that in the control group (Hirosaki et al., 2013). There was also an improvement in bone density and subjective health perception. Hence, it was suggested that laughter may improve the control of diabetes in the long term. Conversely, a study of 17 healthy subjects on the possibility that laughter may improve vascular endothelial function, an indicator of early atherosclerosis, reported a significant improvement in vascular endothelial function after viewing a comedy video for 60 minutes compared with viewing a documentary video (Sugawara, Tarumi and Tanaka, 2010). However, few studies, including this one, have been conducted on the long-term effects of other lifestyle diseases, such as hypertension, dyslipidemia, and atherosclerosis, and therefore, future reports are expected.
Measurement Models for Patient-Reported Outcomes and Other Health-Related Outcomes
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
In Figure 7.4, we identify the formative construct for metabolic risk (consisting of continuous indictors of five risk factors commonly used to define metabolic syndrome). We achieve this by constraining the disturbance term to zero and the indicator for waist circumference to one and regressing observed hemoglobin A1c and cardiovascular event outcomes on the formative construct. In the model, we hypothesize that metabolic risk has a direct effect on both observed indicators for hemoglobin A1c (higher levels above 6.5% serving as a proxy for type 2 diabetes) and time to first cardiovascular event (i.e. myocardial infarction, stroke or cardiovascular-related mortality). Also, there is an indirect path from metabolic risk to cardiovascular event via hemoglobin A1c.
How to revise a posterior lumbar fusion that has developed adjacent-level stenosis with or without instability
Published in Gregory D. Schroeder, Ali A. Baaj, Alexander R. Vaccaro, Revision Spine Surgery, 2019
Preoperative planning occurs in several areas. The patient's functional status and comorbidities should be optimized. Patients who smoke should be required to quit and be nicotine free for 4–6 weeks prior to surgery. Nutritional parameters should be evaluated and supplemented if deficient. Many patients will have low or borderline vitamin D levels. This should be supplemented with 1,000–2,000 IU vitamin D3 for patients with mild deficiency. For patients with severe deficiency, vitamin D2 50,000 IU weekly for 3 months is also an option. Many elderly patients will have poor protein intake. This should be addressed with supplemental nutrition. Hemoglobin A1C is a good indicator of average blood glucose, and if it is elevated, the risk of perioperative complications, particularly postoperative wound infection, increases.
Piloting a Telehealth Interprofessional Diabetes Clinic During Covid 19: Continuing patient care and student learning
Published in Social Work in Health Care, 2023
Joan Pittman, Heather Brennan Congdon, Gina C. Rowe, Barbara Nathanson, Phyllis McShane, Rhonique Shields
During the Fall 2020 and Spring 2021 academic semesters, 38 patients with prediabetes or diabetes were seen for a total of 51 IPTCC telehealth visits. Of these 38 patients, 19 had both pre- and post-hemoglobin A1C data results within the targeted time range. In the 19 matched pairs, the average hemoglobin A1C decreased from an average of 9.3% ± 2.9% to an average of 8.0% ± 2.6% (p = .0462), a statistically significant improvement. Six patients seen had either pre-diabetes or controlled diabetes with hemoglobin A1C under 7%. A secondary data analysis was performed with those patients removed, therefore, only evaluating the patients with uncontrolled diabetes. In those 13 patients, hemoglobin A1C decreased from an average of 10.8% ± 2.2% to an average of 9.1% ± 2.5% (p = .0671), which although not statistically significant, is clinically significant.
Evaluation of interference from 16 hemoglobin variants on hemoglobin A1c measurement by five methods
Published in Scandinavian Journal of Clinical and Laboratory Investigation, 2023
Xiaoling Yang, Xianwei Zeng, Yonggang Zhang, Wenbin Kuang, Dabao He
Hemoglobin A1c (HbA1c) is widely accepted as an important biomarker in the management of diabetes. HbA1c provides valuable information for long-term glycemic control and predicts the risk of chronic complications in patients with diabetes, and it has been recommended as a diagnostic test for diabetes for more than 10 years [1–3]. However, its measurement is challenging in the presence of Hb variants, which remain a common cause of erroneous HbA1c results. The interference from Hb variants on HbA1c measurements depends on testing methods and the type of Hb variant [4,5]. Although over 1000 Hb variants have been described to date [6,7], most studies on the effects of Hb variants on HbA1c assays have been limited to the four most common Hb variants worldwide, i.e. HbS, HbC, HbD, and HbE [8,9].
Stepwise approach to continuous glucose monitoring interpretation for internists and family physicians
Published in Postgraduate Medicine, 2022
Emily D. Szmuilowicz, Grazia Aleppo
Continuous glucose monitoring (CGM) use has expanded rapidly in recent years [1] and imparts well-established benefits in terms of glycemia and well-being among people with both type 1 and type 2 diabetes [2–7]. Hemoglobin A1c (HbA1c), which reflects average glycemia over the preceding 8–12 weeks, remains a cornerstone of diabetes diagnosis and treatment monitoring, enables cross-sectional comparison of glycemia across populations, and correlates with risk for microvascular diabetes complications in clinical studies [8]. However, this measure of average glycemia does not provide information about individual glycemic patterns, glucose trends, the presence or absence of hypoglycemia, or glycemic variability. Thus, while an elevated HbA1c does indicate that therapeutic action is needed, it does not provide actionable information that can be harnessed to guide therapeutic adjustments. Furthermore, HbA1c measurements are commonly discordant from average glucose measurements due to non-glycemic factors such as hemoglobinopathies or commonly encountered conditions in which red-cell turnover is altered, such as iron-deficiency anemia, chronic hemolysis, and chronic kidney disease [9].