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Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-03-22)
Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected ...
Telemedicine Services for the Arctic: A systematic review
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-06-28)
Background: Telemedicine services have been successfully used in areas where there are adequate infrastructures such as reliable power and communication lines. However, despite the increasing number of merchants and seafarers, maritime and Arctic telemedicine have had limited success. This might be linked with various factors such as lack of good infrastructure, lack of trained onboard personnel, ...
A systematic review of cluster detection mechanisms in syndromic surveillance: Towards developing a framework of cluster detection mechanisms for EDMON system
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018)
Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level and other supporting parameters from people with type 1 diabetes, as indicator variables for outbreak detection. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing ...
Appendix E Telemedisinske løsninger i maritime operasjoner og redningstjeneste
(Research report; Forskningsrapport, 2016)
Folk som jobber i maritime miljø har ikke enkel tilgang til sentrale helsetjenester, og det gjelder spesielt for sjøfolk som arbeider i arktiske områder. Selv om telemedisin har vært en suksess på land, har telemedisin kun i begrenset grad blitt tatt i bruk til havs. Dette skyldes blant annet fravær av gode kommunikasjonsløsninger, dårlige værforhold, store avstander og lange perioder utenfor ...
K-CUSUM: Cluster Detection Mechanism in EDMON
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11)
The main goal of the EDMON (Electronic Disease Monitoring Network) project is to detect the spread of contagious diseases at the earliest possible moment, and potentially before people know that they have been infected. The results shall be visualized on real-time maps as well as presented in digital communication. In this paper, a hybrid of K-nearness Neighbor (KNN) and cumulative sum (CUSUM), known ...
EDMON - a system architecture for real-time infection monitoring and outbreak detection based on self-recorded data from people with type 1 diabetes: system design and prototype implementation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11)
Infection incidences in people with diabetes can create sever health complications mainly due to the effect of stress hormones, such as cortisol and adrenaline, which increases glucose production and insulin resistance in the body. The proposed electronic disease surveillance monitoring network (EDMON) relies on self-recorded data from people with Type 1 diabetes and dedicated algorithms to detect ...
A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-12)
<i>Background</i>: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged ...
Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-12)
<i>Background</i>: Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key ...
Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in Type 1 diabetes
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-05-01)
<p><i>Background - </i>Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading ...
User Expectations and Willingness to Share Self-Collected Health Data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
The rapid improvement in mobile health technologies revolutionized what and how people can self-record and manage data. This massive amount of information accumulated by these technologies has potentially many applications beyond personal need, i.e. for public health. A challenge with collecting this data is to motivate people to share this data for the benefit of all. The purpose of this study is ...