A5016441840- Diabetes Management and Research
- Mobile Health and mHealth Applications
- Artificial Intelligence in Healthcare
- Machine Learning in Healthcare
- Anomaly Detection Techniques and Applications
- Diabetes and associated disorders
- Physical Activity and Health
- Data-Driven Disease Surveillance
- Non-Invasive Vital Sign Monitoring
- Diabetes Management and Education
- COVID-19 epidemiological studies
- Digital Mental Health Interventions
- Technology Use by Older Adults
- Time Series Analysis and Forecasting
- Zoonotic diseases and public health
- Electronic Health Records Systems
- Artificial Intelligence in Healthcare and Education
- Telemedicine and Telehealth Implementation
- Innovative Human-Technology Interaction
- ICT in Developing Communities
- Healthcare Operations and Scheduling Optimization
- Health Literacy and Information Accessibility
- Mental Health via Writing
- Hemoglobinopathies and Related Disorders
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
University Hospital of North Norway
2022-2024
UiT The Arctic University of Norway
2016-2023
Centre for Advanced Study
2023
Charles University
2018
Addis Ababa Science and Technology University
2017
Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices equipped with different sensors, algorithms, accompanying mobile apps. With recent advances in sensor technology, privately collected physical activity data can be used as an addition existing methods for health collection research. Furthermore, from these have possible applications patient diagnostics treatment. increasing number of diverse brands, there is a need overview device...
The widespread adoption of smartphones creates an enormous potential to improve healthcare services. Numerous apps, sensors, and devices are developed for health self-management purposes. However, rates remain low long-term user engagement is a major issue. goal this study identify motivational factors that can facilitate prolonged use mobile systems. To end, we conducted 16 interviews with representatives various cultural backgrounds, disease history, age, gender. Participants' experiences...
Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both and clinicians. Such an initiative allow to more proactive in their disease management clinicians provide tailored medical services. Optimally, electronic record systems (EHRs) should able receive a standard representation of such as Fast Healthcare Interoperability Resources (FHIR), like mobile apps display the directly users-the However, although Norwegian EHRs are working on...
Sickle cell disease (SCD) is a hematological genetic affecting over 25 million people worldwide. The main clinical manifestations of SCD, hemolytic anemia and vaso-occlusion, lead to chronic pain organ damages. With recent advances in childhood care, high-income countries have seen SCD drift from early mortality neglected adulthood. In particular, coordinated, preventive, comprehensive care for adults with largely underresourced. Consequently, patients are left self-manage. Mobile health...
The rapid improvement in mobile health technologies revolutionized what and how people can self-record manage data. This massive amount of information accumulated by these has potentially many applications beyond personal need, i.e. for public health. A challenge with collecting this data is to motivate share the benefit all. purpose study survey examine factors that may sharing We asked 447 participants four questions related motivation. Participants a chronic disease were concerned about...
The use of mobile diabetes self-management applications (apps) is rising. However, current reviews mainly focus on clinically relevant features, and very little those that affect a user's perception. This review highlights recent developments these systems, coupled with user perceptions, public presence availability. After including novel criteria, we found most apps have comprehensive range features received good scores from reviewers. the visibility in social media or press scarce, few...
Background Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these are popular the medical domain because of their suitability for where there is lack sufficient set other classes. Infection incidence often brings prolonged hyperglycemia frequent insulin injections people with type 1 diabetes, which significant anomalies. Despite potentials, very few studies that focused on...
Background Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by lack insulin secretion from pancreas cells. In people with type diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings uncover effect incidences on key parameters dynamics to support effort toward developing digital infectious disease detection system. Objective The study aims...
We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications wearables to share their collected related data. The was distributed online in English, French, Norwegian. In addition, flyer with information where locate the during Swiss conference. used snowball sampling encouraged participants forward questionnaires friends, family, others. Data were between October 2018 March 2020. 58.1 % (n = 473) responded English...
Purpose: The objective of this review is to asses and analyse the current status tele-medicine services in context offshore, Arctic extreme weather its use within search rescue scenarios. Moreover, it presents state art systems for implementing successful telemedicine (extreme remote) regions also indicates extent challenge imposed by existing geographical problems.Method: Literature was conducted, between September 7 October 28, 2015, through various databases, such as Google Scholar,...
Abstract Objectives Accelerometer-based wrist-worn fitness trackers and smartwatches (wearables) appeared on the consumer market in 2011. Many wearable devices have been released since. The objective of this data paper is to describe a dataset 423 wearables before July 2017. Data description We identified extracted information from six online offline databases. also visited websites for all companies/brands identify additional wearables, as well obtained each device. Twelve attributes were...
People with diabetes experience elevated blood glucose (BG) levels at the time of an infection. We propose to utilize patient-gathered information in Electronic Disease Surveillance Monitoring Network (EDMON), which may support identification a cluster infected people BG on spatiotemporal basis. The system incorporates data gathered from apps, continuous monitoring (CGM) devices, and other appropriate physiological indicators type 1 diabetes. This paper presents novel approach towards...