N. Zholdas, M. Mansurova, Magzhan Sarsembayev, O. Postolache, A. Shomanov, T. Sarsembayeva
{"title":"Application of mHealth Technologies to Improve Self-Control of Children and Adolescents with Type 1 Diabetes","authors":"N. Zholdas, M. Mansurova, Magzhan Sarsembayev, O. Postolache, A. Shomanov, T. Sarsembayeva","doi":"10.1109/MeMeA54994.2022.9856485","DOIUrl":null,"url":null,"abstract":"Our research has shown that the use of mobile technologies (mHealth) to improve self-control of children and adolescents with diabetes, as well as for parental control, gives positive results. A functional implemented in a mobile application has been developed that contains recommendations from endocrinologists based on the analysis of data from sensors, taking into account the individual characteristics of the patient's body. Digital health profiles of patients with diabetes mellitus, containing the values of state indicators obtained from various sensors, mobile phones, medical watches and fitness bracelets, make it possible to develop systems for monitoring and supporting personalized decision-making. By observing a patient's digital profile, clinicians can determine some of the possible causes of deviations in glucose readings in predetermined time segments. During the project, under the close supervision of endocrinologists and a pediatrician, patient data were collected, such as continuous monitoring glucose sensor values, fitness bracelet records, anthropometric data, disease and family history data, eating behavior data, HbAl c (glycated hemoglobin) level data in beginning and end of the study to assess carbohydrate metabolism compensation, FA (fructosamine) level data twice during the study period in order to short-term assess the degree of carbohydrate metabolism compensation, general blood analysis and general urine analysis data in order to additionally assess the reliability of previous tests for data analysis.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MeMeA54994.2022.9856485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Our research has shown that the use of mobile technologies (mHealth) to improve self-control of children and adolescents with diabetes, as well as for parental control, gives positive results. A functional implemented in a mobile application has been developed that contains recommendations from endocrinologists based on the analysis of data from sensors, taking into account the individual characteristics of the patient's body. Digital health profiles of patients with diabetes mellitus, containing the values of state indicators obtained from various sensors, mobile phones, medical watches and fitness bracelets, make it possible to develop systems for monitoring and supporting personalized decision-making. By observing a patient's digital profile, clinicians can determine some of the possible causes of deviations in glucose readings in predetermined time segments. During the project, under the close supervision of endocrinologists and a pediatrician, patient data were collected, such as continuous monitoring glucose sensor values, fitness bracelet records, anthropometric data, disease and family history data, eating behavior data, HbAl c (glycated hemoglobin) level data in beginning and end of the study to assess carbohydrate metabolism compensation, FA (fructosamine) level data twice during the study period in order to short-term assess the degree of carbohydrate metabolism compensation, general blood analysis and general urine analysis data in order to additionally assess the reliability of previous tests for data analysis.