Georgios I. Gogolos, Eleni I. Georga, E. Rizos, D. Fotiadis
{"title":"评估1型糖尿病血糖变异性的移动健康平台","authors":"Georgios I. Gogolos, Eleni I. Georga, E. Rizos, D. Fotiadis","doi":"10.1109/BIBE.2017.00-42","DOIUrl":null,"url":null,"abstract":"We present a short-term observational study of the fluctuation of glycemic variability with or without the presence of physical exercise based on the ambulatory glucose profile recommendations and measures using an mHealth platform for patient monitoring and data collection. The correlation of physical exercise to diabetes type 1 is presented using the Pearson’s and Spearman’s correlation coefficients. The multi–parametric dataset comes from the continuous monitoring of seven type 1 diabetic individuals under free living conditions for 14 consecutive days. The results enable the clinician to develop individualized treatment plans for each patient and they can potentially be used in prediction models.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An mhealth Platform to Evaluate Glycaemic Variability in Type 1 Diabetes\",\"authors\":\"Georgios I. Gogolos, Eleni I. Georga, E. Rizos, D. Fotiadis\",\"doi\":\"10.1109/BIBE.2017.00-42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a short-term observational study of the fluctuation of glycemic variability with or without the presence of physical exercise based on the ambulatory glucose profile recommendations and measures using an mHealth platform for patient monitoring and data collection. The correlation of physical exercise to diabetes type 1 is presented using the Pearson’s and Spearman’s correlation coefficients. The multi–parametric dataset comes from the continuous monitoring of seven type 1 diabetic individuals under free living conditions for 14 consecutive days. The results enable the clinician to develop individualized treatment plans for each patient and they can potentially be used in prediction models.\",\"PeriodicalId\":262603,\"journal\":{\"name\":\"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2017.00-42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2017.00-42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An mhealth Platform to Evaluate Glycaemic Variability in Type 1 Diabetes
We present a short-term observational study of the fluctuation of glycemic variability with or without the presence of physical exercise based on the ambulatory glucose profile recommendations and measures using an mHealth platform for patient monitoring and data collection. The correlation of physical exercise to diabetes type 1 is presented using the Pearson’s and Spearman’s correlation coefficients. The multi–parametric dataset comes from the continuous monitoring of seven type 1 diabetic individuals under free living conditions for 14 consecutive days. The results enable the clinician to develop individualized treatment plans for each patient and they can potentially be used in prediction models.