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Article

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

by
Elisa Salvi
1,
Pietro Bosoni
1,
Valentina Tibollo
2,
Lisanne Kruijver
3,
Valeria Calcaterra
4,5,
Lucia Sacchi
1,
Riccardo Bellazzi
1,2 and
Cristiana Larizza
1,*
1
Department of Electrical, Computer and Biomedical Engineering University of Pavia, 27100 Pavia, Italy
2
IRCCS Istituti Clinici Scientifici Maugeri, 27100 Pavia, Italy
3
Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
4
Pediatric and Adolescent Unit, Department of Internal Medicine, University of Pavia, 27100 Pavia, Italy
5
Pediatric Endocrinologic Unit, Department of Maternal and Children’s Health, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 128; https://doi.org/10.3390/s20010128
Submission received: 28 October 2019 / Revised: 17 December 2019 / Accepted: 20 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Abstract

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.
Keywords: flash glucose monitoring; temporal data analysis; temporal abstraction; patient-generated health data; telemedicine; activity tracker flash glucose monitoring; temporal data analysis; temporal abstraction; patient-generated health data; telemedicine; activity tracker

Share and Cite

MDPI and ACS Style

Salvi, E.; Bosoni, P.; Tibollo, V.; Kruijver, L.; Calcaterra, V.; Sacchi, L.; Bellazzi, R.; Larizza, C. Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform. Sensors 2020, 20, 128. https://doi.org/10.3390/s20010128

AMA Style

Salvi E, Bosoni P, Tibollo V, Kruijver L, Calcaterra V, Sacchi L, Bellazzi R, Larizza C. Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform. Sensors. 2020; 20(1):128. https://doi.org/10.3390/s20010128

Chicago/Turabian Style

Salvi, Elisa, Pietro Bosoni, Valentina Tibollo, Lisanne Kruijver, Valeria Calcaterra, Lucia Sacchi, Riccardo Bellazzi, and Cristiana Larizza. 2020. "Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform" Sensors 20, no. 1: 128. https://doi.org/10.3390/s20010128

APA Style

Salvi, E., Bosoni, P., Tibollo, V., Kruijver, L., Calcaterra, V., Sacchi, L., Bellazzi, R., & Larizza, C. (2020). Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform. Sensors, 20(1), 128. https://doi.org/10.3390/s20010128

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