Eirik Arsand

• PhD
• Professor (Full) at UiT The Arctic University of Norway

Introduction Type 2 diabetes (T2D) prevalence is rising, which imposes a significant burden on individuals, healthcare systems, and economies worldwide. Lifestyle factors contribute significantly to the escalating incidence of T2D. Consequently, there is an increasing need for interventions that not only target at-risk populations for prevention bu...

Background As type 2 diabetes (T2D) is expected to increase, self-management becomes more crucial. Mobile apps are increasingly supporting self-management with tasks like blood glucose monitoring and medication management. Understanding the behavioral intervention functions used by diabetes apps today, is essential for improving future apps and sys...

Continuous glucose monitoring (CGM) represents a significant advancement in diabetes management, playing an important role in glycemic control for patients with type 1 diabetes (T1D). Despite their benefits, their performance is affected by numerous factors such as the carbohydrate intake, alcohol consumption, and physical activity (PA). Among thes...

Background Most studies do not produce their intended outcomes on time or within budget. However, it is challenging to identify the facilitators and barriers to successful study management when the “behind the scenes action” of especially digitally enabled health research studies are akin to a black box. Therefore, it is necessary to explore first-...

This paper presents the design, implementation and early tests of an app that collects a comprehensive set of health-related data, as part of the EU-project WARIFA. To achieve the main aim of the project – using AI to prevent chronic conditions – a wide range of data needs to be collected and stored at a backend server for processing. The methods a...

Diabetes mellitus (DM) is a chronic condition defined by increased blood glucose, which is suffered by more than 500 million adults. Type 1 diabetes (T1D), predominantly onset in childhood, needs to be treated with insulin. Keeping glucose within the desired range is a challenge. Despite the advances in the mHealth field, the successful appearance...

Short presentation of findings from our qualitative study on end-users' needs and preferences for a new e-health program aiming to improve self-management, metabolic control, and remission of type 2 diabetes.

Background Regular physical activity helps to reduce weight and improve the general well-being of individuals living with obesity. Chatbots have shown the potential to increase physical activity among their users. We aimed to explore the preferences of individuals living with obesity for the features and functionalities of a modern chatbot based on...

Background: Most studies do not produce their intended outcomes on time or within budget. However, it is challenging to identify the facilitators and barriers to successful study management when the “behind the scenes action” of especially digitally enabled health research studies are akin to a black box. Therefore, it is necessary to explore first...

Universities are facing many challenges as they are expected to prepare their students in the best possible way to contribute to sustainable societal- and industrial development. Students will become researchers, innovators, entrepreneurs, and role models, and should be able to contribute in the transition to a greener and smarter future. The task...

Background Individuals with diabetes rely on medical equipment (eg, continuous glucose monitoring (CGM), hybrid closed-loop systems) and mobile applications to manage their condition, providing valuable data to health care providers. Data sharing from this equipment is regulated via Terms of Service (ToS) and Privacy Policy documents. The introduct...

Patient-gathered self-management data and shared decision-making are touted as the answer to improving an individual’s health situation as well as collaboration between patients and their providers leading to more effective treatment plans. However, there is a gap between this ideal and reality – a lack of data-sharing technology. Here, we present...

We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications and wearables to share their collected health related data. The questionnaire was distributed online in English, French, and Norwegian. In addition, a flyer with information of where to locate the online questionnair...

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis...

Social media chatbots could help increase obese adults' physical activity behaviour. The study aims to explore obese adults' preferences for a physical activity chatbot. Individual- and focus group interviews will be conducted in 2023. Identified preferences will inform the development of a chatbot that motivates obese adults to increase their phys...

Background: Today's diabetes-oriented telemedicine systems can gather and analyze many parameters like blood glucose levels, carbohydrate intake, insulin doses, and physical activity levels (steps). Information collected can be presented to patients in a variety of graphical outputs. Despite the availability of several technical means, a large per...

Development of a new e-health program for improved self-management and remission of type 2 diabetes, including preliminary results from a qualitative pre-study investigating user's preferences, needs, and prototypes for an e-health program.

This dataset contains responses from a questionnaire about what motivates people to collect and share their health data for research and public health benefits. The online questionnaire was open for data collection between November 2018 and March 2020. The questionnaire was published in a Norwegian, English and French version, and published online....

Background Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population’s health. They can present us with a picture of our metabol...

Background: Smart scales with wireless data transfer have existed for more than a decade. These smart body composition scales can, together with smart watches and trackers, observe changes in the population's health. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a...

Type 2 diabetes mellitus (T2D) and prediabetes prevalence rates are high. Consequences are serious, but current treatment is often not efficient for achieving remission. Remission may be achieved through lifestyle intervention. Frequent follow-up is necessary, and health care personnel (HCP) lack resources, time, and often adequate knowledge. Self-...

Patient self-management is vital to improved health outcomes for patients with chronic diseases. The objective of this study was to understand the role of wearable sensors in patients’ self-management. A survey encompassing factors related to motivation in mHealth was conducted. Ease of use and sensory accuracy was found most important when choosin...

Medical consultations for chronic diseases form an arena to provide information from health personnel to patients. This information is necessary for patients to understand how to deal with the possible lifelong symptoms and needed self-management activities. The amount of patient-generated health data is increasing. Today’s patients gather an incre...

Performing regular physical activity can be challenging. Integrating chatbots with social media platforms and physical activity sensors can potentially increase physical activity. The objective of this study was to identify design preferences for integrating an activity tracker supported chatbot in a social media platform. Norwegian adults (n=120)...

The increased development and use of ubiquitous digital services reinforce the trend where health-related data is generated everywhere. Data usage in different areas introduces different terms for the same or similar concepts. This adds to the confusion of what these terms represent. We aim to provide an overview of concepts and terms used in conne...

Clinical trials need to adapt to the rapid development of today’s digital health technologies. The fast phase these technologies are changing today, make the clinical study administration demanding. To meet this challenge, new and more efficient platforms for performing clinical trials in this domain need to be designed. Since the process of follow...

Recruitment is a bottleneck for research – especially digital health studies. Studies often focus on those who are easy to reach or already engaged in their health, leaving those who are uninterested or un-engaged, as “un-reached”. This contributes to the “digital divide”. COVID-19 restrictions made recruitment more difficult. During a virtual work...

Background Medical consultations are often critical meetings between patients and health personnel to provide treatment, health-management advice, and exchange of information, especially for people living with chronic diseases. The adoption of patient-operated Information and Communication Technologies (ICTs) allows the patients to actively partici...

Background For people with Type 2 diabetes (T2D), lifestyle changes may be the most effective intervention. Online groups for people with diabetes holds a great potential to support such changes. However, little is known about the association between participation in online groups and lifestyle changes based on internet information in people with T...

Intervention research is often highly controlled and does not reflect real-world situations. More pragmatic approaches, albeit less controllable and more challenging, offer the opportunity of identifying unexpected factors and connections. As the introduction of mHealth into formal diabetes care settings is relatively new and less often explored fr...

Health-dedicated groups on social media provide different contents and social support to their peers. Our objective is to analyze users’ engagement with health education and physical activity promotion posts according to the expressed social support and social media. All health education and physical activity promotion posts on Facebook, Twitter, a...

Diabetes self-management, an integral part of diabetes care, can be improved with the help of digital self-management tools such as apps, sensors, websites, and social media. The study objective was to reach a consensus on the criteria required to assess and recommend digital diabetes self-management tools targeting those with diabetes in Norway. H...

The health and well-being of informal caregivers often take a backseat to those that they care for. While systems, technologies, and services that provide care and support for those with chronic illnesses are established and continuously improved, those that support informal caregivers are less explored. An international survey about motivations to...

In this study, an application was developed for Android-based smartwatches which has the capacity of monitoring the state of diabetes mellitus and indicating the data concerning the physical activities and cardiac rhythm. Android Studio was used to develop and design the application. The application consists of five pages (glucose, insulin, carbohy...

BACKGROUND A consultation is a meeting between patients and health personnel, conducted physically or in any form of non-face-to-face interaction. Consultations are critical for providing treatment and health-management advice, and for the exchange of information especially for people living with chronic diseases. Consultations can be supported by...

Background Individuals with diabetes are using mobile health (mHealth) to track their self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users’ needs and exp...

Background Diabetes patient associations and diabetes-specific patient groups around the world are present on social media. Although active participation and engagement in these diabetes social media groups has been mostly linked to positive effects, very little is known about the content that is shared on these channels or the post features that e...

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 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. Infe...

Background: 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...

Background: There is growing evidence that apps and digital interventions have a positive impact on diabetes self-management. Standard self-management for patients with diabetes could therefore be supplemented by apps and digital interventions to increase patients’ skills. Several initiatives, models, and frameworks suggest how health apps and digi...

Mobile and wearable technologies offer patients with diabetes mellitus new possibilities for data collection and their more effective analysis. The Diabesdagboga smartphone application and the Diani web portal enable to collect and analyze glycaemia values, carbohydrates intake, insulin doses and the level of physical activity. The data are not onl...

Background and objective: The number of publications on the use of chatbots for health is recently increasing, however to our knowledge, there are no publications summarizing what is known about using chatbots for public health yet. The objective of this work is to provide an overview of the existing scientific literature on the use of chatbots fo...

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 fo...

This dataset contains questions, answer options, and responses from a questionnare about what motivates people to collect and share that health data for research and publich health benefits.

Psycho-social factors are often addressed in behavioral health studies. While the purpose of many mHealth interventions is to facilitate behavior change, the focus is more prominently on the functionality and usability of the technology and less on the psycho-social factors that contribute to behavior change. Here we aim to identify the extent to w...

Patients with diabetes are often worried about having low blood glucose because of the unpleasant feeling and possible dangerous situations this can lead to. This can make patients consume more carbohydrates than necessary. Ad-hoc carbohydrate estimation and dosing by the patients can be unreliable and may produce unwanted periods of high blood glu...

BACKGROUND Diabetes patient associations and diabetes-specific patient groups around the world are present on social media. Although active participation and engagement in these diabetes social media groups has been mostly linked to positive effects, very little is known about the content that is shared on these channels or the post features that e...

Background: Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or h...

BACKGROUND Infections incidence in people with type 1 diabetes often makes self-management problematic, i.e. difficulties in controlling blood glucose (BG) levels. During the course of infections, the body demands more energy in order to supply the active tissues in the immune response. Thus, alteration in carbohydrate metabolism is expected to kee...

Background: 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 th...

BACKGROUND 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...

BACKGROUND There is growing evidence that apps and digital interventions have a positive impact on diabetes self-management. Standard self-management for patients with diabetes could therefore be supplemented by apps and digital interventions to increase patients’ skills. Several initiatives, models, and frameworks suggest how health apps and digit...

Background and Aims: Physical activities have a significant impact on blood glucose homeostasis of patients with type 1 diabetes. The risk of hypoglycemia (low blood glucose) is significantly higher during and after physical activities, especially for individuals who experience hypoglycemia unawareness. Our research aims to reduce the risk of hypog...

Background: There is rising demand for health care's limited resources. Mobile health (mHealth) could be a solution, especially for those with chronic illnesses such as diabetes. mHealth can increases patients' options to self-manage their health, improving their health knowledge, engagement, and capacity to contribute to their own care decisions....

We performed a search to identify available wearable sensors systems that can collect patient health data and have data sharing capabilities. Findings available in “Wearable sensors with possibilities for data exchange: Analyzing status and needs of different actors in mobile health monitoring systems” [1]. We performed an initial search of the Van...

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...

Research often presents patient needs from perceptions of healthcare professionals and researchers. Today, patients can formulate tailored questions and seek solutions for what they need to self-manage in many ways. We aimed to compare reported outcomes of mHealth and online intervention studies for diabetes self-management to patient-reported need...

Research often presents patient needs from perceptions of healthcareprofessionals and researchers. Today, patients can formulate tailored questions and seek solutions for what they need to self-managein many ways. We aimed to compare reported outcomes of mHealth and online intervention studies for diabetes self-management to patient-reported needs,...

Background: Wearable devices with an ability to collect various type of physiological data are increasingly becoming seamlessly integrated into everyday life of people. In the area of electronic health (eHealth), many of these devices provide remote transfer of health data, as a result of the increasing need for ambulatory monitoring of patients. T...

BACKGROUND Despite the prevalence and noted health impacts of mobile health (mHealth) technologies for patients, there is still no clear standard for how to evaluate these tools for patient self-management of chronic conditions. OBJECTIVE This scoping review aims to document the methods and measures used to assess mHealth apps and system intervent...

BACKGROUND Mobile health (mHealth) is publicized as a potential solution for the rising demand of healthcare’s limited resources and poor health outcomes. This is especially true for chronic illnesses such as diabetes, as mHealth increases the options for individuals to self-manage their health. These options can improve patients’ health knowledge,...

Background Individuals with diabetes are using mobile health (mHealth) to make decisions regarding self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users’...

Background: Individuals with diabetes are using mobile health (mHealth) to make and track their decisions regarding self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gath...

Background: Individuals with diabetes are using mobile health (mHealth) to track their self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users’ needs and ex...

Background: Individuals with diabetes are using mobile health (mHealth) to track their self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users’ needs and ex...

Background: Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) shou...

Background: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended range, is cen...

Background: The prevalence of diabetes and the use of electronic health (eHealth) are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be highly valuable. However, little is known about the use of eHealth in different socioeconomic groups among people with diabetes. Objective:...

Background: 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...

Background: Despite the increasing prevalence of diabetes and increasing use of electronic health (eHealth) among people with diabetes, little is known about the association between the use of eHealth and the use of provider-based health services. Objective: The objective of this study was to investigate whether the use of eHealth might change p...

Background Despite the increasing prevalence of diabetes and the increasing use of eHealth, little is known about the association between provider-based health services and eHealth among people with diabetes. This is the second study in a project exploring the associations between the use of eHealth and the use of provider-based health services. O...

BACKGROUND Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Patients can be more proactive in their disease management and clinicians can provide more tailored medical services. In the best situation, EHRs should be able to receive self-collected health data in...

Introduction: Self-management of chronic diseases using mobile health (mHealth) systems and applications is becoming common. Current evaluation methods such as formal usability testing can be very costly and time-consuming; others may be more efficient but lack a user focus. We propose an enhanced cognitive walkthrough (CW) method, the user-center...

BACKGROUND Despite the increasing prevalence of diabetes and the increasing use of eHealth, little is known about the association between provider-based health services and eHealth among people with diabetes. This is the second study in a project exploring the associations between the use of eHealth and the use of provider-based health services. O...

Background:: Use of social media is increasing rapidly, also in health care and diabetes. However, patients, health care personnel, and patient organizations discuss diabetes on social media very differently. This has led to a lack of common ground when these stakeholders communicate about diabetes and a gap in understanding one another's point of...

Background: When developing a mobile health app, users' perception of the technology should preferably be evaluated. However, few standardized and validated questionnaires measuring acceptability are available. Objective: The aim of this study was to assess the validity of the Norwegian version of the Service User Technology Acceptability Questi...

Background Nowadays, rapid and accessible participatory research on diabetes can be carried out using social media platforms. The objective of this study was to identify preferences and interests of diabetic social media users regarding a health-promotion intervention targeting them. Methods Social media followers of the Norwegian Diabetes Associa...

Background: The prevalence of diabetes and the use of electronic health (eHealth) are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be of great value. However, little is known about the association between the use of eHealth and provider-based health care services among peopl...

Background The Introduction of mobile health (mHealth) devices to health intervention studies challenges us as researchers to adapt how we analyse the impact of these technologies. For interventions involving chronic illness self-management, we must consider changes in behaviour in addition to changes in health. Fortunately, these mHealth technolog...

How-to: selection of relevant data from usage logs. (DOCX)

Results of Pearson Correlations run between interactions with the Goals functionalities within the app and In-Range BG measurements per quarter of the year (n = 61). (DOCX)

Percentage distribution of the six FTA interaction types used by clusters 1 (n = 16) and 2 (n = 40) for each quarter. (DOCX)

RENEWING HEALTH study protocol. (PDF)

K-means clustering. (DOCX)

Minutes vs. interactions with goals functionalities. (DOCX)

Patients’ self-registered blood glucose values. (DOCX)

Background: Health authorities recommend educating diabetic patients and their families and initiating measures aimed at improving self-management, promoting a positive behavior change, and reducing the risk of complications. Social media could provide valid channel to intervene in and deliver diabetes education. However, it is not well known wheth...