Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform
Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient&...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/1/128 |
| _version_ | 1798041247737184256 |
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| author | Elisa Salvi Pietro Bosoni Valentina Tibollo Lisanne Kruijver Valeria Calcaterra Lucia Sacchi Riccardo Bellazzi Cristiana Larizza |
| author_facet | Elisa Salvi Pietro Bosoni Valentina Tibollo Lisanne Kruijver Valeria Calcaterra Lucia Sacchi Riccardo Bellazzi Cristiana Larizza |
| author_sort | Elisa Salvi |
| collection | DOAJ |
| description | Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy. |
| first_indexed | 2024-04-11T22:18:49Z |
| format | Article |
| id | doaj.art-1b49f62ce3414954a53f42be16239d1d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:18:49Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1b49f62ce3414954a53f42be16239d1d2022-12-22T04:00:17ZengMDPI AGSensors1424-82202019-12-0120112810.3390/s20010128s20010128Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM PlatformElisa Salvi0Pietro Bosoni1Valentina Tibollo2Lisanne Kruijver3Valeria Calcaterra4Lucia Sacchi5Riccardo Bellazzi6Cristiana Larizza7Department of Electrical, Computer and Biomedical Engineering University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering University of Pavia, 27100 Pavia, ItalyIRCCS Istituti Clinici Scientifici Maugeri, 27100 Pavia, ItalyAcademic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The NetherlandsPediatric and Adolescent Unit, Department of Internal Medicine, University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering University of Pavia, 27100 Pavia, ItalyDiabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.https://www.mdpi.com/1424-8220/20/1/128flash glucose monitoringtemporal data analysistemporal abstractionpatient-generated health datatelemedicineactivity tracker |
| spellingShingle | Elisa Salvi Pietro Bosoni Valentina Tibollo Lisanne Kruijver Valeria Calcaterra Lucia Sacchi Riccardo Bellazzi Cristiana Larizza Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform Sensors flash glucose monitoring temporal data analysis temporal abstraction patient-generated health data telemedicine activity tracker |
| title | Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform |
| title_full | Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform |
| title_fullStr | Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform |
| title_full_unstemmed | Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform |
| title_short | Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform |
| title_sort | patient generated health data integration and advanced analytics for diabetes management the aid gm platform |
| topic | flash glucose monitoring temporal data analysis temporal abstraction patient-generated health data telemedicine activity tracker |
| url | https://www.mdpi.com/1424-8220/20/1/128 |
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