连续血糖监测在体育活动和营养管理中的作用:对目前和可能用途的展望。

Physical activity and nutrition Pub Date : 2023-09-01 Epub Date: 2023-09-30 DOI:10.20463/pan.2023.0028
Young-Im Kim, Youngju Choi, Jonghoon Park
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引用次数: 0

摘要

目的:随着肥胖和糖尿病患病率的增加,持续血糖监测(CGM)呈上升趋势。这篇综述旨在探索CGM的用途及其在体育活动和营养管理中的潜在新应用。方法:我们使用关键词“连续血糖监测仪”、“营养”、“体育活动”和“数值建模”搜索PubMed、Web of Science和Wiley Online Library数据库结果:连续血糖测量对肥胖和糖尿病患者是有用的。长期血糖数据允许对营养成分、用餐时间、体育活动类型和强度进行个性化规划,并有助于预防低血糖和高血糖。因此,了解CGM的局限性对其有效使用至关重要。结论:CGM系统正越来越多地用于监测和确定适当的血糖控制干预措施。血糖水平受到各种因素的影响,如营养成分、用餐时间、身体活动、昼夜节律和皮质醇水平。数值模型可以用来分析压力、睡眠、营养和体育活动之间的复杂关系,这些因素会影响血糖水平。未来,血糖、睡眠和压力数据将被整合,以预测血糖管理的适当生活方式水平。这种综合方法改善了血糖控制和整体健康,有可能降低社会成本。
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The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses.
[Purpose] Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management. [Methods] We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords ‘continuous glucose monitor,’ ‘nutrition,’ ‘physical activity,’ and ‘numerical modeling.’ [Results] Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use. [Conclusion] CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.
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