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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 workshop of our peers, we discussed recruitment of un-reached groups for digital health studies, especially during COVID-times. All agreed; we need to go where the un-reached are by collaborating with community-based services and organizations.

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 only accessible in the corresponding smartphone but also automatically transferred to an Internet portal, where they may be completed by the records from an electronic pedometer and continuous glucose monitor. All these data may then be displayed in various types of graphical outputs and are available to both the patient and the physician. The case report of a patient who has used the system for almost two years shows a significant improvement in metabolic compensation (a decrease in the mean HbA1c value by 18.6 mmol/mol as compared with the previous period).

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 T2D. The aim of this study was to investigate the association between self-reported lifestyle changes and participation in online groups in people with T2D. Methods We used e-mail survey data from 1,250 members of The Norwegian Diabetes Association, collected in 2018. Eligible for analyses were the 540 respondents who reported to have T2D. By logistic regressions we studied the association between self-reported lifestyle changes and participation in online groups. Analyses were adjusted for gender, age, education, and time since diagnosis. Results We found that 41.9 % of the participants reported lifestyle changes based on information from the internet. Only 6 % had participated in online groups during the previous year. Among those with a disease duration of less than 10 years, 56.0 % reported lifestyle changes, whereas 33.4 % with a disease duration of 10 years or more did so. The odds for lifestyle changes were more than doubled for those who participated in online groups. People who had been diagnosed with diabetes for less than 10 years were significantly more likely to change their lifestyle compared to those with a longer disease duration. Conclusions Lifestyle changes based on information from the internet among people with T2D are associated with participation in online groups. Lifestyle changes are also associated with time since diagnosis, making the first years after a T2D diagnosis particularly important for lifestyle interventions. People with T2D, web site developers, online group moderators, health care services, and patient organisations should be aware of this important window for lifestyle change, and encourage participation in online groups.

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, and Instagram during 2017-2019 by a diabetes association were extracted. We identified the type of social support within these posts; and analysed the users' engagement with these posts according to the type of social support and social media channel. A total of 260 posts dealing with health education (n=200) and physical activity promotion (n=60) were published. Posts promoting physical activity received 54% more likes than posts on health education (p0.05), but they were 69% less likely to receive comments and be shared (both p0.01). Posts expressing tangible assistance received 6 times more likes (p0.001), and the ones indicating network support almost 11 times more shares (p0.05). Posts expressing two or more types of social support were the most engaging (3 times more likes, 2 times more comments, and over 6 times more shares, all p0.001). Health-dedicated social media groups can be effective channels for providing health education and for promoting physical activity among individuals with diabetes. Our findings suggest that engagement with health education and physical activity promotion posts can be increased by providing tangible assistance, network support, or expressing two or more types of social support; and by posting on Facebook and Instagram.

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. Healthcare professionals working with diabetes care from all health regions in Norway were recruited to participate in a three-round Delphi study. In all rounds, the panellists rated criteria identified in a systematic review and interviews on a scale from 0-10, with the option to provide comments. On a scale of 0:not important to 10:extremely important, the highest rated criteria for assessing and recommending digital diabetes self-management tools were "Usability" and "Information quality", respectively. For assessing apps, "Security and privacy" was one of the lowest rated criteria. Having access to a list of criteria for assessing and recommending digital self-management tools can help diabetes care stakeholders to make informed choices in recommending and choosing suitable apps, websites, and social media for self-management. Future work on quality assessment of digital health tools should place emphasis on security and privacy compliance, to enable diabetes care stakeholders focus on other relevant criteria to recommend or choose and use such tools.

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. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model. Objective This study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio. Methods Three groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required. Results The one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time. Conclusions We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale.

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 digital interventions could be evaluated, but there are few standards for this. And although there are many methods for evaluating apps and digital interventions, a more specific approach might be needed for assessing digital diabetes self-management interventions. Objective This review aims to identify which methods and criteria are used to evaluate apps and digital interventions for diabetes self-management, and to describe how patients were involved in these evaluations. Methods We searched CINAHL, EMBASE, MEDLINE, and Web of Science for articles published from 2015 that referred to the evaluation of apps and digital interventions for diabetes self-management and involved patients in the evaluation. We then conducted a narrative qualitative synthesis of the findings, structured around the included studies’ quality, methods of evaluation, and evaluation criteria. Results Of 1681 articles identified, 31 fulfilled the inclusion criteria. A total of 7 articles were considered of high confidence in the evidence. Apps were the most commonly used platform for diabetes self-management (18/31, 58%), and type 2 diabetes (T2D) was the targeted health condition most studies focused on (12/31, 38%). Questionnaires, interviews, and user-group meetings were the most common methods of evaluation. Furthermore, the most evaluated criteria for apps and digital diabetes self-management interventions were cognitive impact, clinical impact, and usability. Feasibility and security and privacy were not evaluated by studies considered of high confidence in the evidence. Conclusions There were few studies with high confidence in the evidence that involved patients in the evaluation of apps and digital interventions for diabetes self-management. Additional evaluation criteria, such as sustainability and interoperability, should be focused on more in future studies to provide a better understanding of the effects and potential of apps and digital interventions for diabetes self-management.

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 which mHealth interventions for patient self- management address psychological factors. By understanding users' motivations, facilitators, and mindsets, we can better tailor mHealth interventions to promote behavior change.

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 how to gather these reliable data. Objective This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

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 effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. Objective The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. Methods We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). Results Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

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 glucose. In this paper we present a system that automatically estimates and dispenses the amount of juice (or similar) according to the current patients' blood glucose values. The system is remotely accessible and customizable from a chatbot, exploits sensors and actuators to dispense the necessary amount of liquid carbohydrates. It relies on a cloud solution (Nightscout) to acquire the patient's blood glucose values, which are constantly updated thanks to a commercial wearable continuous glucose monitor (CGM).

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 engage their users the most. Objective The objective of this study was to analyze (1) the content and features of posts shared over a 3-year period on 3 diabetes social media channels (Facebook, Twitter, and Instagram) of a diabetes association, and (2) users’ engagement with these posts (likes, comments, and shares). Methods All social media posts published from the Norwegian Diabetes Association between January 1, 2017, and December 31, 2019, were extracted. Two independent reviewers classified the posts into 7 categories based on their content. The interrater reliability was calculated using Cohen kappa. Regression analyses were carried out to analyze the effects of content topic, social media channel, and post features on users’ engagement (likes, comments, and shares). Results A total of 1449 messages were posted. Posts of interviews and personal stories received 111% more likes, 106% more comments, and 112% more shares than miscellaneous posts (all P Conclusions Diabetes social media users seem to be least engaged in posts with content topics that a priori could be linked to greater empowerment: research and innovation on diabetes, and health education. Diabetes social media groups, public health authorities, and other stakeholders interested in sharing research and innovation content and promoting health education on social media should consider including videos and emoji in their posts, and publish on popular and visual-based social media channels, such as Facebook and Instagram, to increase user engagement. International Registered Report Identifier (IRRID) RR2-10.1186/s12913-018-3178-7

Accepted manuscript version, licensed CC BY-NC-ND 4.0. 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 central to the treatment. This includes actively tracking BG levels and managing physical activity, diet, and insulin intake. The recent advancements in diabetes technologies and self-management applications have made it easier for patients to have more access to relevant data. In this regard, the development of an artificial pancreas (a closed-loop system), personalized decision systems, and BG event alarms are becoming more apparent than ever. Techniques such as predicting BG (modeling of a personalized profile), and modeling BG dynamics are central to the development of these diabetes management technologies. The increased availability of sufficient patient historical data has paved the way for the introduction of machine learning and its application for intelligent and improved systems for diabetes management. The capability of machine learning to solve complex tasks with dynamic environment and knowledge has contributed to its success in diabetes research. Motivation: Recently, machine learning and data mining have become popular, with their expanding application in diabetes research and within BG prediction services in particular. Despite the increasing and expanding popularity of machine learning applications in BG prediction services, updated reviews that map and materialize the current trends in modeling options and strategies are lacking within the context of BG prediction (modeling of personalized profile) in type 1 diabetes. Objective: The objective of this review is to develop a compact guide regarding modeling options and strategies of machine learning and a hybrid system focusing on the prediction of BG dynamics in type 1 diabetes. The review covers machine learning approaches pertinent to the controller of an artificial pancreas (closed-loop systems), modeling of personalized profiles, personalized decision support systems, and BG alarm event applications. Generally, the review will identify, assess, analyze, and discuss the current trends of machine learning applications within these contexts. Method: A rigorous literature review was conducted between August 2017 and February 2018 through various online databases, including Google Scholar, PubMed, ScienceDirect, and others. Additionally, peer-reviewed journals and articles were considered. Relevant studies were first identified by reviewing the title, keywords, and abstracts as preliminary filters with our selection criteria, and then we reviewed the full texts of the articles that were found relevant. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming among the authors. Results: The initial search was done by analyzing the title, abstract, and keywords. A total of 624 papers were retrieved from DBLP Computer Science (25), Diabetes Technology and Therapeutics (31), Google Scholar (193), IEEE (267), Journal of Diabetes Science and Technology (31), PubMed/Medline (27), and ScienceDirect (50). After removing duplicates from the list, 417 records remained. Then, we independently assessed and screened the articles based on the inclusion and exclusion criteria, which eliminated another 204 papers, leaving 213 relevant papers. After a full-text assessment, 55 articles were left, which were critically analyzed. The inter-rater agreement was measured using a Cohen Kappa test, and disagreements were resolved through discussion. Conclusion: Due to the complexity of BG dynamics, it remains difficult to achieve a universal model that produces an accurate prediction in every circumstance (i.e., hypo/eu/hyperglycemia events). Recently, machine learning techniques have received wider attention and increased popularity in diabetes research in general and BG prediction in particular, coupled with the ever-growing availability of a self-collected health data. The stateof-the-art demonstrates that various machine learning techniques have been tested to predict BG, such as recurrent neural networks, feed-forward neural networks, support vector machines, self-organizing maps, the Gaussian process, genetic algorithm and programs, deep neural networks, and others, using various group of input parameters and training algorithms. The main limitation of the current approaches is the lack of a welldefined approach to estimate carbohydrate intake, which is mainly done manually by individual users and is prone to an error that can severely affect the predictive performance. Moreover, a universal approach has not been established to estimate and quantify the approximate effect of physical activities, stress, and infections on the BG level. No researchers have assessed model predictive performance during stress and infection incidences in a free-living condition, which should be considered in future studies. Furthermore, a little has been done regarding model portability that can capture inter- and intra-variability among patients. It seems that the effect of time lags between the CGM readings and the actual BG levels is not well covered. However, in general, we foresee that these developments might foster the advancement of next-generation BG prediction algorithms, which will make a great contribution in the effort to develop the long–awaited, so-called artificial pancreas (a closed-loop system).

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 be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. Objective - This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. Methods - A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results - The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions - Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

Source at https://doi.org/10.2196/13678. 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 patients’ decisions regarding doctor-seeking behavior and whether information acquired from the internet was discussed with a doctor. Methods: We used email survey data collected in 2018 from members of the Norwegian Diabetes Association (aged 18 to 89 years) diagnosed with diabetes. Using logistic regressions, we studied patients’ internet-triggered changes in decisions regarding doctor visits; whether they discussed information from the internet with a doctor; and whether these topics were associated with gender, age, education, self-rated health, and self-reported anxiety/depression. Results: Among the 895 informants, 75.4% (645/856) had never made an internet-triggered change of decision in any direction regarding visiting a doctor, whereas 16.4% (41/859) had decided to visit and 17.3% (148/856) had decided not to visit. The probability of changing decisions decreased with higher age and increased with the severity of self-reported anxiety/depression. Around half of the study participants (448/858, 52.2%) had never discussed information from the internet with a doctor. The probability of discussing internet information with a doctor was higher for those in bad/very bad self-rated health (odds ratio 2.12, CI 1.15-3.90) and for those with moderate self-reported anxiety/depression (odds ratio 2.30, CI 1.30-4.10). Conclusions: Our findings suggest that using eHealth has a significant impact on doctor-visiting decisions among people with diabetes, especially among people aged 18 to 39 years and among those reporting anxiety/depression. It is of great importance that the information posted is of high quality and that the large differences between internet-users regarding age as well as mental and somatic health status are taken into account. More research is needed to confirm and further explore the findings of this study.

Source at https://doi.org/10.2196/13615. 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 - The aim of this study was to investigate the use of 4 different eHealth platforms (apps, search engines, video services, and social media sites) and the association with socioeconomic status (SES) among people diagnosed with type 1 and type 2 diabetes mellitus (T1D and T2D, respectively). Methods - We used email survey data from 1250 members of the Norwegian Diabetes Association (aged 18-89 years), collected in 2018. Eligible for analyses were the 1063 respondents having T1D (n=523) and T2D (n=545). 5 respondents reported having both diabetes types and thus entered into both groups. Using descriptive statistics, we estimated the use of the different types of eHealth. By logistic regressions, we studied the associations between the use of these types of eHealth and SES (education and household income), adjusted for gender, age, and self-rated health. Results - We found that 87.0% (447/514) of people with T1D and 77.7% (421/542) of people with T2D had used 1 or more forms of eHealth sometimes or often during the previous year. The proportion of people using search engines was the largest in both diagnostic groups, followed by apps, social media, and video services. We found a strong association between a high level of education and the use of search engines, whereas there were no educational differences for the use of apps, social media, or video services. In both diagnostic groups, high income was associated with the use of apps. In people with T1D, lower income was associated with the use of video services. Conclusions - This paper indicates a digital divide among people with diabetes in Norway, with consequences that may contribute to sustaining and shaping inequalities in health outcomes. The strong relationship between higher education and the use of search engines, along with the finding that the use of apps, social media, and video services was not associated with education, indicates that adequate communication strategies for audiences with varying education levels should be a focus in future efforts to reduce inequalities in health outcomes.

Source at https://doi.org/10.2196/13465. 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. Objective - The objective of this study was to investigate which eHealth services are used among out-of-hours (OOH) visitors with type 1 diabetes (T1D), and whether the use of eHealth (eg, apps, search engines, video services, and social media) was associated with the use of OOH services. We also wanted to investigate associations between anxiety, reassurance, and change in doctor-seeking behavior because of health information acquired from the Internet, and the use of OOH services. Methods - We used data from a 2018 email survey of members of the Norwegian Diabetes Association (18-89 years old). Respondents with T1D were eligible for analyses. Using descriptive statistics, we estimated the use of OOH services and eHealth. Using logistic regressions, we studied the associations between the use of OOH services and the use of eHealth, as well as associations between the use of OOH services and reported consequences of using Internet-based health information. Results - In the sample of 523 people with T1D (mean age 47 years), 26.7% (129/484) visited OOH services once or more during the previous year. Among the OOH visitors, search engines were used for health purposes by 86.7% (111/128), apps (health apps in general) by 63.6% (82/129), social media by 45.3% (58/128), and video services by 28.4% (36/127). The use of OOH services was positively associated with self-reported anxiety/depression (odds ratio [OR] 4.53, 95% CI 1.43-14.32) and with the use of apps (OR 1.73, 95% CI 1.05-2.85), but not with other types of eHealth. Those who had felt anxious based on information from the Internet were more likely to visit OOH services compared with those who had not felt anxious (OR 2.38, 95% CI 1.50-3.78). People who had decided to consult a doctor based on information from the Internet were more likely to visit OOH services (OR 2.76, 95% CI 1.64-4.66), compared to those who had not made such an Internet-based decision. Conclusions - People with T1D were frequent users of OOH services, and the OOH visitors were frequent users of eHealth. The use of OOH services was positively associated with the use of health apps, with self-reported anxiety/depression, and with feeling anxious based on information from the Internet. Likewise, deciding to consult a doctor based on information from the Internet was positively associated with OOH visits. The use of eHealth seems to have a significant impact on people with T1D.

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 view. Social media have a potential for improved communication if each stakeholder group knows about, acknowledges, and accepts one another’s perspective. Method - We extracted and analyzed posts from three Norwegian Facebook groups representing patients, patients’ organization, and health care personnel. Qualitative content analysis was done to find the distribution of main categories, followed by a thematic analysis of subcategories that were posted and discussed. Results - The patient organization’s posts are the most equally distributed over the four main identified categories: scientific content, health care services, self-management, and diabetes awareness. The closed patient group’s posts were dominated by self-management; the open diabetes nurses’ group was dominated by diabetes awareness. The three social media groups differed substantially in what and how they posted and discussed within the main topics. The nurses’ open group had percentage-wise both the most liked and commented post, and the posts on self-management had the highest average number of comments. Conclusions - There is a big discrepancy in posted information and discussions on social media, between patient closed group, patient organization open group, and health care personnel open group. To reach the aim of using social media for better health, there is a need for more information of what is posted and discussed in the other groups, to harmonize and ensure safe and accurate dissemination of information.

The following article, Hansen, A.H., Brož, J., Claudi, T. & Årsand, E. (2018). Relations between the use of electronic health and the use of general practitioner and somatic specialist visits in patients with type 1 diabetes: Cross-sectional study. Journal of Medical Internet Research, 20(11), can be accessed at https://doi.org/10.2196/11322. 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 people with diabetes. Objective: The objective of this study was to investigate the use of 4 different eHealth platforms (apps, search engines, video services, and social media sites) and associations with the use of provider-based health care visits among people diagnosed with type 1 diabetes mellitus (T1DM). Methods: We used email survey data collected from 1250 members of the Norwegian Diabetes Association (aged 18 to 89 years) in 2018. Eligible for analyses were the 523 respondents with T1DM. Using descriptive statistics, we estimated the use of eHealth and the use of general practitioners (GPs) and somatic specialist outpatient services. By logistic regressions, we studied the associations between the use of these provider-based health services and the use of eHealth, adjusted for gender, age, education, and self-rated health. Results: Of the sample of 523 people with T1DM, 90.7% (441/486) had visited a GP once or more, and 61.0% (289/474) had visited specialist services during the previous year. Internet search engines (such as Google) were used for health purposes sometimes or often by 84.0% (431/513), apps by 55.4% (285/514), social media (such as Facebook) by 45.2% (232/513), and video services (such as YouTube) by 23.3% (118/506). Participants aged from 18 to 39 years used all forms of eHealth more than people aged 40 years and older, with the exception of social media. The use of search engines was positively associated with the use of somatic specialist services (odds ratio 2.43, 95% CI 1.33-4.45). GP visits were not associated with any kind of eHealth use. Conclusions: eHealth services are now widely used for health support and health information by people with T1DM, primarily in the form of search engines but often in the form of apps and social media as well. We found a positive association between the use of search engines and specialist visits and that people with T1DM are frequent users of eHealth, GPs, and specialist services. We found no evidence that eHealth reduces the use of provider-based health care; these services seem to be additional rather than alternative. Future research should focus on how health care services can meet and adapt to the high prevalence of eHealth use. Our results also indicate that many patients with T1DM do not visit specialist clinics once a year as recommended. This raises questions about collaboration in health care services and needs to be followed up in future research.

Accepted manuscript. Final version published in International Journal of Medical Informatics is available at https://doi.org/10.1016/j.ijmedinf.2019.104017. 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. This has a potential to reduce the cost of care due to prevention and early detection. Objective - The objective of this study was to provide an overview of available wearable sensor systems with data exchange possibilities. Due to the heterogeneous capabilities these systems possess today, we aimed to systematize this in terms of usage, where there is a need of, or users benefit from, transferring self‐ collected data to health care actors. Methods - We searched for and reviewed relevant sensor systems (i.e., devices) and mapped these into 13 selected attributes related to data‐exchange capabilities. We collected data from the Vandrico database of wearable devices, and complemented the information with an additional internet search. We classified the following attributes of devices: type, communication interfaces, data protocols, smartphone/PC integration, connection to smartphone health platforms, 3rd party integration with health platforms, connection to health care system/middleware, type of gathered health data, integrated sensors, medical device certification, access to user data, developer‐access to device, and market status. Devices from the same manufacturer with similar functionalities/characteristics were identified under the same device family. Furthermore, we classified the systems in three subgroups of relevance for different actors in mobile health monitoring systems: EHR providers, software developers, and patient users. Results - We identified 362 different mobile health monitoring devices belonging to 193 device families. Based on an analysis of these systems, we identified the following general challenges: Few systems have a Conformité Européene (CE) marking class II or above, or approval from the US Food and Drug Administration (FDA) Few systems use the standardized Bluetooth Low Energy GATT profile for wireless transfer of health data Few systems support health middleware Approximately 30% of the device families provide the user access to the source data. However, only 16% allow the transfer of data through direct communication with the device (i.e., without using a proprietary cloud‐based service) Conclusions - Few of the identified mobile health monitoring systems use standardized, open communication protocols, which would allow the user to directly acquire sensor data. Use of open protocols can provide mobile health (mHealth) application developers an alternative to proprietary cloud services and communication tools, which are often closely integrated with the devices. Emerging new types of sensors, often intended for everyday use, have a potential to supplement health records systems with data that can enrich patient care.

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 technologies can record participants’ interactions via usage-logs during research interventions. Objective: The objective of this paper is to demonstrate the potential of analysing mHealth usage-logs by presenting an in-depth analysis as a preliminary study for using behavioural theories to contextualize the user-recorded results of mHealth intervention studies. We use the logs collected by persons with type 2 diabetes during a randomized controlled trial (RCT) as a use-case. Methods: The Few Touch Application was tested in a year-long intervention, which allowed participants to register and review their blood glucose, diet and physical activity, goals, and access general disease information. Usage-logs, i.e. logged interactions with the mHealth devices, were collected from participants (n = 101) in the intervention groups. HbA1c was collected (baseline, 4- and 12-months). Usage logs were categorized into registrations or navigations. Results: There were n = 29 non-mHealth users, n = 11 short-term users and n = 61 long-term users. Non-mHealth users increased (+0.33%) while Long-term users reduced their HbA1c (-0.86%), which was significantly different (P = .021). Long-term users significantly decreased their usage over the year (P < .001). K-means clustering revealed two clusters: one dominated by diet/exercise interactions (n = 16), and one dominated by BG interactions and navigations in general (n = 40). The only significant difference between these two clusters was that the first cluster spent more time on the goals functionalities than the second (P < .001). Conclusion: By comparing participants based upon their usage-logs, we were able to discern differences in HbA1c as well as usage patterns. This approach demonstrates the potential of analysing usage-logs to better understand how participants engage during mHealth intervention studies. The EU’s ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme(http://ec.europa.eu/cip/) (No. 250487)and The Research Councilof Norway (norges forskningsråd, https://www. forskningsradet.no/no/Forsiden/1173185591033) (No. 196364)funded the study designand data collection and analysis through the REgioNs of Europe WorkINg toGether for HEALTH (RENEWING HEALTH) project (https://cordis. europa.eu/project/rcn/191719_en.html), led by Lis Ribu and EirikÅrsand.The Research Council of Norway (norges forskningsråd, https://www. forskningsradet.no/no/Forsiden/1173185591033) funded the preparation of the manuscript and decision to publish through the “Full Flow of Health Data Between Patientsand Health Care Systems” project (https://ehealthresearch.no/en/projects/ fullflow) (grant number 247974/O70), led by Eirik Årsand.The publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway (https://uit.no/ub/forskningsstotte/art?p_ document_id=449104)(No. 551011),led by Meghan Bradway.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Norges forskningsråd 247974

The following article ,Gabarron, E., Årsand, E. & Wynn, R. (2018). Social media use in interventions for diabetes: Rapid evidence-based review. Journal of Medical Internet Research, 20:e10303(8). https://doi.org/10.2196/10303, was first published in Journal of Medical Internet Research. Source at https://doi.org/10.2196/10303. 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 whether the use of these channels in such interventions can help improve the patients' outcomes. OBJECTIVE: The objective of our study was to review and describe the current existing evidence on the use of social media in interventions targeting people affected with diabetes. METHODS: A search was conducted across 4 databases (PubMed, Scopus, EMBASE, and Cochrane Library). The quality of the evidence of the included primary studies was graded according to the Grading of Recommendations Assessment, Development and Evaluation criteria, and the risk of bias of systematic reviews was assessed by drawing on the Assessment of Multiple Systematic Reviews guidelines. The outcomes reported by these studies were extracted and analyzed. RESULTS: We included 20 moderate- and high-quality studies in the review: 17 primary studies and 3 systematic reviews. Of the 16 publications evaluating the effect on glycated hemoglobin (HbA1c) of the interventions using social media, 13 reported significant reductions in HbA1c values. The 5 studies that measured satisfaction with the interventions using social media found positive effects. We found mixed evidence regarding the effect of interventions using social media on health-related quality of life (2 publications found positive effects and 3 found no differences) and on diabetes knowledge or empowerment (2 studies reported improvements and 2 reported no significant changes). CONCLUSIONS: There is very little good-quality evidence on the use of social media in interventions aimed at helping people with diabetes. However, the use of these channels is mostly linked to benefits on patients' outcomes. Public health institutions, clinicians, and other stakeholders who aim at improving the knowledge of diabetic patients could consider the use of social media in their interventions.

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-centered CW (UC-CW), to address identified deficiencies in the original technique and perform a beginning validation with think aloud protocol (TA) to assess its effectiveness, efficiency and user acceptance in a case study with diabetes patient users on a mHealth self-management application. Materials and methods A total of 12 diabetes patients at University of Utah Health, USA, were divided into UC-CW and think aloud (TA) groups. The UC-CW method included: making the user the main evaluator for detecting usability problems, having a dual domain facilitator, and using three other improved processes: validated task development, higher level tasks and a streamlined evaluation process. Users interacted with the same mHealth application for both methods. Post-evaluation assessments included the NASA RTLX instrument and a set of brief interview questions. Results Participants had similar demographic characteristics. A total of 26 usability problems were identified with the UC-CW and 20 with TA. Both methods produced similar ratings: severity across all views (UC-CW = 2.7 and TA = 2.6), numbers of problems in the same views (Main View [UC-CW = 11, TA = 10], Carbohydrate Entry View [UC-CW = 4, TA = 3] and List View [UC-CW = 3, TA = 3]) with similar heuristic violations (Match Between the System and Real World [UC-CW = 19, TA = 16], Consistency and Standards [UC-CW = 17, TA = 15], and Recognition Rather than Recall [UC-CW = 13, TA = 10]). Both methods converged on eight usability problems, but the UC-CW group detected five critical issues while the TA group identified two. The UC-CW group identified needed personalized features for patients’ disease needs not identified with TA. UC-CW was more efficient on average time per identified usability problem and on the total evaluation process with patients. NASA RTLX scores indicated that participants experienced the UC-CW half as cognitively demanding. Common themes from interviews indicated the UC-CW as enjoyable and easy to perform while TA was considered somewhat awkward and more cognitively challenging. Conclusions UC-CW was effective for finding severe, recurring usability problems and it highlighted the need for personalized user features. The method was also efficient and had high user acceptance. These results indicate UC-CW’s utility and user acceptance in evaluating a mHealth self-management application. It provides an additional usability evaluation technique for researchers.

People with diabetes experience elevated blood glucose (BG) levels at the time of an infection. We propose to utilize patient-gathered information in an Electronic Disease Surveillance Monitoring Network (EDMON), which may support the identification of a cluster of infected people with elevated BG levels on a spatiotemporal basis. The system incorporates data gathered from diabetes apps, continuous glucose monitoring (CGM) devices, and other appropriate physiological indicators from people with type 1 diabetes. This paper presents a novel approach towards modeling of the individual's BG dynamics, a mechanism to track and detect deviations of elevated BG readings. The models were developed and validated using self-recorded data in the non-infection status using Dexcom CGM devices, from two type 1 diabetes individuals over a 1-month period. The models were also tested using simulated datasets, which resemble the individual's BG evolution during infections. The models accurately simulated the individual's normal BG fluctuations and further detected statistically significant BG elevations.

Source at https://doi.org/10.1186/s12913-018-3178-7 BACKGROUND: Participatory health approaches are increasingly drawing attention among the scientific community, and could be used for health promotion programmes on diabetes through social media. The main aim of this project is to research how to best use social media to promote healthy lifestyles with and within the Norwegian population. METHODS: The design of the health promotion intervention (HPI) will be participatory, and will involve both a panel of healthcare experts and social media users following the Norwegian Diabetes Association. The panel of experts will agree on the contents by following the Delphi method, and social media users will participate in the definition of the HPI by expressing their opinions through an adhoc online questionnaire. The agreed contents between both parties to be used in the HPI will be posted on three social media channels (Facebook, Twitter and Instagram) along 24 months. The 3 months before starting the HPI, and the 3 months after the HPI will be used as control data. The effect of the HPI will be assessed by comparing formats, frequency, and reactions to the published HPI messages, as well as comparing potential changes in five support-intended communication behaviours expressed on social media, and variations in sentiment analysis before vs during and after the HPI. The HPI's effect on social media users' health-related lifestyles, online health behaviours, and satisfaction with the intervention will be assessed every 6 months through online questionnaires. A separate questionnaire will be used to assess the panel of experts' satisfaction and perceptions of the benefits for health professionals of a HPI as this one. DISCUSSION: The time constraints of today's medical practice combined with the piling demand of chronic conditions such as diabetes make any additional request of extra time used by health care professionals a challenge. Social media channels provide efficient, ubiquitous and user-friendly platforms that can encourage participation, engagement and action necessary from both those who receive and provide care to make health promotion interventions successful.

The goal of modern diabetes treatment is to a large extent focused on self-management to achieve and maintain a healthy, low HbA1c. Despite all new technical diabetes tools and support, including advanced blood glucose meters and insulin delivery systems, diabetes patients still struggle to achieve international treatment goals, that is, HbA1c < 7.5 in children and adolescents. In this study we developed and tested a mobile-phone-based tool to capture and visualize adolescents’ food intake. Our aim was to affect understanding of carbohydrate counting and also to facilitate doctor–adolescent communication with regard to daily treatment. Furthermore, we wanted to evaluate the effect of the designed tool with regard to empowerment, self-efficacy, and self-treatment. The study concludes that implementing a visualization tool is an important contribution for young people to understand the basics of diabetes and to empower young people to define their treatment challenges. By capturing a picture of their own food, the person’s own feeling of being in charge can be affected and better self-treatment achieved.

Diabetes is a global epidemic, with insufficient medical management capacity. It is becoming increasingly relevant to develop sustainable methods of self-management and collaboration between clinical personnel and those living with diabetes. While there have been favorable advances in mobile self-management tools for the disease, few have been validated and acknowledged. Health policies are not being established as quickly as these tools are becoming available, and the public has taken action into their own hands.

Background: Wearable computing has long been described as the solution to many health challenges. However, the use of this technology as a diabetes patient self-management tool has not been fully explored. A promising platform for this use is the smartwatch—a wrist-worn device that not only tells time but also provides internet connection and ability to communicate information to and from a mobile phone. Method: Over 9 months, the design of a diabetes diary application for a smartwatch was completed using agile development methods. The system, including a two-way communication between the applications on the smartwatch and mobile phone, was tested with 6 people with type 1 diabetes. A small number of participants was deliberately chosen due to ensure an efficient use of resources on a novel system. Results: The designed smartwatch system displays the time, day, date, and remaining battery time. It also allows for the entry of carbohydrates, insulin, and blood glucose (BG), with the option to view previously recorded data. Users were able to record specific physical activities, program reminders, and automatically record and transfer data, including step counts, to the mobile phone version of the diabetes diary. The smartwatch system can also be used as a stand-alone tool. Users reported usefulness, responded positively toward its functionalities, and also provided specific suggestions for further development. Suggestions were implemented after the feasibility study. Conclusions: The presented system and study demonstrate that smartwatches have opened up new possibilities within the diabetes self-management field by providing easier ways of monitoring BG, insulin injections, physical activity and dietary information directly from the wrist.

Background: A mobile phone-based application can be useful for patients with type 1 diabetes in managing their disease. This results in large datasets accumulated on the patient's devices, which can be used for individualized feedback. The effect of such feedback is investigated in this article. Materials and Methods: We developed an application that included a data-driven feedback module known as Diastat for patients on self-measured blood glucose regimens. Using a stepped-wedge design, both groups initially received an application without Diastat. Group 1 activated Diastat after 4 weeks, whereas Group 2 activated Diastat 12 weeks after startup (T1). End points were glycated hemoglobin (HbA1c) level and number of out-of-range (OOR) measurements (i.e., outside the range 72–270 mg/dL). Results: Thirty patients were recruited to the study, and 15 were assigned to each group after the initial meeting. There were no significant differences between groups at T1 in HbA1c or OOR events. Overall, all patients had a decrease of 0.6 percentage points in mean HbA1c (P

Co-design or participatory design has emerged as a useful concept where stakeholders and end-users have a greater stake in designing the end product. To date, few accounts exist of the use of the concept in serious game design, especially for children with chronic diseases. We report initial steps in serious game co-design for children with type 1 diabetes. Participants included 14 children (mean age 8.6 years, range of 4-13) who were invited to sketch a diabetes game. The most prevalent themes that emerged from the sketches (N=17) include blood glucose monitoring (n=12), nutrition (n=8) and insulin (n=8); all of which are consistent with diabetes education guidelines. Co-design is a promising concept for understanding children's world-view when designing healthcare games.

While Internet communities have become thriving sources of support, little is yet known about their effectiveness. We retrospectively sampled morbidly obese (Body Mass Index, BMIgt; 40) women who were active for at least a year in an Internet community. We compared self-reported weight changes between women who had high online participation levels (n = 71) versus those with low participation levels as control (n = 69). Women who actively participated online lost on average 7.52%, while those who were passive lost 5.39% of their original body weight. For active women, there was positive, albeit weak, correlation (r = 0.22, plt; 0.05) between online participation levels and weight loss, while no significant correlation was noted for the control. Current results indicate modest evidence supporting active participation in Internet groups as an effective weight loss strategy for the target group.

Self-management is critical to achieving diabetes treatment goals. Mobile phones and Bluetooth® can supportself-management and lifestyle changes for chronic diseases such as diabetes. A mobile health (mHealth) research platform--the Few Touch Application (FTA)--is a tool designed to support the self-management of diabetes. The FTA consists of a mobile phone-based diabetes diary, which can be updated both manually from user input and automatically by wireless data transfer, and which provides personalized decision support for the achievement of personal health goals. Studies and applications (apps) based on FTAs have included: (1) automatic transfer of blood glucose (BG) data; (2) short message service (SMS)-based education for type 1diabetes (T1DM); (3) a diabetes diary for type 2 diabetes (T2DM); (4) integrating a patient diabetes diary with health care (HC) providers; (5) a diabetes diary for T1DM; (6) a food picture diary for T1DM; (7) physical activity monitoring for T2DM; (8) nutrition information for T2DM; (9) context sensitivity in mobile self-help tools; and (10) modeling of BG using mobile phones. We have analyzed the performance of these 10 FTA-based apps to identify lessons for designing the most effective mHealth apps. From each of the 10 apps of FTA, respectively, we conclude: (1) automatic BG data transfer is easy to use and provides reassurance; (2) SMS-based education facilitates parent-child communication in T1DM; (3) the T2DM mobile phone diary encourages reflection; (4) the mobile phone diary enhances discussion between patients and HC professionals; (5) the T1DM mobile phone diary is useful and motivational; (6) the T1DM mobile phone picture diary is useful in identifying treatment obstacles; (7) the step counter with automatic data transfer promotes motivation and increases physical activity in T2DM; (8) food information on a phone for T2DM should not be at a detailed level; (9) context sensitivity has good prospects and is possible to implement on today's phones; and (10) BG modeling on mobile phones is promising for motivated T1DM users. We expect that the following elements will be important in future FTA designs: (A) automatic data transfer when possible; (B) motivational and visual user interfaces; (C) apps with considerable health benefits in relation to the effort required; (D) dynamic usage, e.g., both personal and together with HC personnel, long-/short-term perspective; and (E) inclusion of context sensitivity in apps. We conclude that mHealth apps will empower patients to take a more active role in managing their own health.

We performed two cycles of laboratory-based usability testing of three food registration prototypes for people with diabetes. The design concepts were a commercial web application, various smartphones and a mobile phone photo blogging approach. Six adults with Type 1 diabetes and three adults with Type 2 diabetes participated in the usability tests. The results provided five distinct implications for devices for the future dietary management support of people with diabetes. Study participants valued many of the features offered by the three systems that were tested, although the usability tests also revealed several opportunities to enhance their design. Our findings suggest that further development is justified of mobile dietary and nutritional support for individuals living with diabetes. Applications that support healthy eating habits should be integrated with applications for managing blood glucose data and physical activity data, and potentially medication data as well.

The Online Diabetes Exercise System was developed to motivate people with Type 2 diabetes to do a 25 minutes low-volume high-intensity interval training program. In a previous multi-method evaluation of the system, several usability issues were identified and corrected. Despite the thorough testing, it was unclear whether all usability problems had been identified using the multi-method evaluation. Our hypothesis was that adding the eye-tracking triangulation to the multi-method evaluation would increase the accuracy and completeness when testing the usability of the system. The study design was an Eye-tracking Triangulation; conventional eye-tracking with predefined tasks followed by The Post-Experience Eye-Tracked Protocol (PEEP). Six Areas of Interests were the basis for the PEEP-session. The eye-tracking triangulation gave objective and subjective results, which are believed to be highly relevant for designing, implementing, evaluating and optimizing systems in the field of health informatics. Future work should include testing the method on a larger and more representative group of users and apply the method on different system types.

Patient diaries as apps on mobile phones are becoming increasingly common, and can be a good support tool for patients who need to organize information relevant for their disease. Self-management is important to achieving diabetes treatment goals and can be a tool for lifestyle changes for patients with Type 2 diabetes. The autoimmune disease Type 1 diabetes requires a more intensive management than Type 2 - thus more advanced functionalities is desirable for users. Both simple and easy-to-use and more advanced diaries have their respective benefits, depending on the target user group and intervention. In this poster we summarize main findings and experience from more than a decade of research and development in the diabetes area. Several versions of the mobile health research platform-the Few Touch Application (FTA) are presented to illustrate the different approaches and results.

Despite a growing number of clinical-intervention studies of mobile applications for diabetes self-management, details of participants' engagement with the intervention tools and of usability and feasibility issues are seldom reported. The Few Touch application is a mobile-phone-based self-management system for people with Type 2 diabetes mellitus (T2DM) developed by involving patient-users in design processes from an early phase to a long-term trial. An improved version was tested in a five-month trial by 11 individuals either with T2DM or at high risk of T2DM. Results showed clearer correlations between usage and perceived usefulness among these individuals compared with those involved in the design process. However, feedback on usability issues was mostly consistent between the two trials. This study therefore confirmed: 1) the value of including patient-users not only in design-concept development but also in a long-term trial to identify as many factors critical to usability and usage as possible, and 2) the importance of reflecting their feedback in design iterations to minimize the number of critical factors.

Although mobile applications and social media have emerged as important facets of the Internet, their role in healthcare is still not well-understood. We present design artefacts, inspired by persuasive technology concepts, from a study of social media as part of a diabetes mHealth application. We used the design science approach for mobile application design, and real-life user testing and focus group meetings to test the application over a 12-week period with 7 participants. Based on the System Usability Score (SUS), the mobile application scored an average of 84.6 (SD=13.2), which represents a fairly high usability score compared to the literature. Regression analysis on the daily blood glucose levels showed significant decreases for some patients, and although the study is not powered, the HbA1c showed a promising trend, and self-efficacy marginally increased. Incorporating persuasive elements such as blood glucose tracking and visualisation, and social media access directly from the mobile application produced promising results that warrant a larger study of behaviour change for people with diabetes.

Background: Only 17% of Norwegian children and adolescents with diabetes achieve international treatment goals measured by glycated hemoglobin (HbA 1c ). Classic patient–physician consultations seem to be poorly adapted to young children. New strategies that are better attuned to young people to improve support of adolescents’ self-management of diabetes need to be tested and evaluated. Objective: (1) To explore how applications for mobile phones can be used in follow-up of adolescents with type 1 diabetes, and (2) to use the findings to guide further development of the applications and as a basis for future studies. Method: We pilot tested two mobile phone applications: (1) an application that contained a picture-based diabetes diary to record physical activity and photos taken with the phone camera of food eaten, where the phone also communicated with the glucometer by Bluetooth technology to capture blood glucose values, and (2) a Web-based, password-secured and encrypted short message service (SMS), based on access using login passwords received via SMS to be used by participants to send messages to their providers when they faced obstacles in everyday life, and to send educational messages to the participants. At the end of the 3-month pilot study, 12 participants (7 girls and 5 boys ) aged 13–19 years completed semistructured interviews. The participants had a mean HbA 1c value of 8.3 (SD 0.3), mean age of 16.2 (SD 1.7) years, mean body mass index of 23.3 (SD 3.2) kg/m 2 , and mean diabetes duration of 7.5 (SD 4.6) years. We applied three additional measurements: change in metabolic control as measured by HbA 1c , the System Usability Scale, and diabetes knowledge. Results: From the interviews, three main categories emerged: visualization, access, and software changes. Participants appreciated the picture-based diary more than the SMS solution. Visualization of cornerstones in diabetes self-care (ie, diet, insulin dosage, physical activity, and pre- and postprandial glucose measurements all transformed into one picture) in the mobile diary was found to be an important educational tool through reflections in action. This led to a change in participants’ applied knowledge about the management of their disease. Additional measurements supplemented and supported the qualitative findings. However, changes in HbA 1c and participants’ theoretical knowledge as tested by a 27-item questionnaire, based on a national health informatics’ diabetes quiz, before and after the intervention were not statistically significant ( P = .38 and P = .82, respectively, paired-samples t test). Participants suggested additional functionality, and we will implement this in the design of the next software generation. Conclusion: Participants reported an increased understanding of applied knowledge, which seem to positively affect diabetes self-care. Visual impressions seem well adapted to the maturation of the adolescent brain, facilitating the link between theoretical knowledge and executive functions. SMS gave the adolescents a feeling of increased access and security. Participants gave valuable input for further development of these applications. [J Med Internet Res 2012;14(4):e111]

Background Increasing healthcare costs related to lifestyle-related chronic diseases require new solutions. Research on self-management tools is expanding and many new tools are emerging. Recording food intake is a key functionality in many of these tools. Nutrition monitoring is a relevant method to gain an overview of factors influencing health. However, keeping a food diary often constitutes a challenge for a patient, and developing a user-friendly and useful electronic food diary is not straightforward. Purpose To gain insight into the existing approaches to recording food intake, and to analyze current functionalities and input methods. Methods We searched digital libraries, vendor markets and social networks focusing on nutrition. Selection criteria were publications written in English, and patient-oriented tools that offered recording of food intake or nutrition. The system properties that we searched for were types of data, types of terminal, target population, and types of reports and sharing functionalities. We summarized the properties based on their frequency in the reviewed sample. Results 31 publications met the selection criteria. The majority of the identified food recording systems (67%) facilitated entry of food type and the consumed quantity of food; 16% of the systems were able to record more than one type of data. The three most frequent target populations were people with obesity, diabetes and overweight. Mobile phones were used as terminals in 35% of the cases, personal computers (PCs) in 29%, and personal digital assistants in 23%. Only 10% supported both PCs and mobile phones. Data sharing was provided by 71% and reports by 51% of the systems. We searched for apps in Google Play and the Apple Store and tested 45 mobile applications that stored food intake data, of which 62% supported recording of types of food, 24% recording of carbohydrate intake and 15% recording of calorie intake. The majority of the mobile applications offered some kind of reports and data sharing, mainly via All of the tested social-network-enabled applications supported access from a personal computer and a mobile phone, search in a food database, reports, graphical presentation, listing of favorite foods, overview of own meals, and entering of consumed food type and quantity. Conclusion The analyzed apps reflected a variety of approaches to recording food intake and nutrition using different terminals – mostly mobile phones (35%), followed by PCs (29%) and PDAs (23%) for older studies, designed mainly for users with obesity (45%), diabetes mellitus (42%) and overweight (32%), or people who want to stay healthy (10%). The majority of the reviewed applications (67%) offered only input of food type and quantity. All approaches ( n =31), except for two, relied on manual input of data, either by typing or by selecting a food type from a database. The exceptions ( n =2) used a barcode scanning function. Users of mobile phone applications were not limited to data recording, but could view their data on the screen and send it via email. The tested web applications offered similar functionalities for recording food intake. The systems studied provided some degree of personalization: users can access some systems via PCs or mobile phones and they can choose among various types of data input content for recording food intake. Many functions, such as search in a food database, reports, graphical presentation, listing of favorite foods, and overview of the user's own meals, are optimized to simplify the recording process and save time. Data sharing and reports are common features of the reviewed systems. However, none use the user's recorded food history to make suggestions on new nutritional intake, during the food recording process. This may be an area for future research.

SummaryBackground: Detecting community structures in complex networks is a problem interesting to several domains. In healthcare, discovering communities may enhance the quality of web offerings for people with chronic diseases. Understanding the social dynamics and community attachments is key to predicting and influencing interaction and information flow to the right patients.Objectives: The goal of the study is to empirically assess the extent to which we can infer meaningful community structures from implicit networks of peer interaction in online healthcare forums.Methods: We used datasets from five online diabetes forums to design networks based on peer-interactions. A quality function based on user interaction similarity was used to assess the quality of the discovered communities to complement existing homophily measures.Results: Results show that we can infer meaningful communities by observing forum interactions. Closely similar users tended to co-appear in the top communities, suggesting the discovered communities are intuitive. The number of years since diagnosis was a significant factor for cohesiveness in some diabetes communities.Conclusion: Network analysis is a tool that can be useful in studying implicit networks that form in healthcare forums. Current analysis informs further work on predicting and influencing interaction, information flow and user interests that could be useful for personalizing medical social media.

Persons with type 1 diabetes who use electronic self-help tools, most commonly blood glucose meters, record a large amount of data about their personal condition. Mobile phones are powerful and ubiquitous computers that have a potential for data analysis, and the purpose of this study is to explore how self-gathered data can help users improve their blood glucose management. Thirty patients with insulin-regulated type 1 diabetes were equipped with a mobile phone application for 3–6 months, recording blood glucose, insulin, dietary information, physical activity, and disease symptoms. The data were analyzed in terms of usage of the different modules and which data processing and visualization tools could be constructed to support the use of these data. Eighteen patients (denoted “adopters”) recorded complete data for over 80 consecutive days, up to 247 days. Among those who withdrew or did not use the application extensively, the most common reasons given were outdated or difficult-to-use phone. Data analysis using period finding and scale-space trends was found to yield significant patterns for most adopters. Pattern recognition methods to predict low or high blood glucose were found to be performing poorly. Minimally intrusive mobile applications enable users with type 1 diabetes to record data that can provide data-driven feedback to the user, potentially providing relevant insight into their disease.

Mobile phones and other mobile information and communication technology applications and technologies hold great potential as a basis for powerful patient-operated self-management tools within diabetes. The work presented shows how such tools can be designed for supporting lifestyle changes among people with type 2 diabetes and how these were perceived by a group of 12 patients during a 6-month period.The study used focus groups, interviews, feasibility testing, questionnaires, paper prototyping, and prototyping of both software and hardware components. The design process was iterative, addressing the various elements several times at an increasing level of detail. The final test of the application was done qualitatively in everyday settings in a cohort of 12 people with type 2 diabetes, aged 44-70 (four men and eight women).A mobile phone-based system called the Few Touch application was developed. The system includes an off-the-shelf blood glucose (BG) meter, a tailor-made step counter, and software for recording food habits and providing feedback on how users perform in relation to their own personal goals. User feedback from the 6-month user intervention demonstrated good usability of the tested system, and several of the participants adjusted their medication, food habits, and/or physical activity. Of the five different functionalities, the cohort considered the BG sensor system the best.It was shown that it is possible and feasible to design an application where several sensors and feedback applications are integrated in an overall system. The presented Few Touch application challenges people with type 2 diabetes to think about how they can improve their health, providing them with a way to capture and analyze relevant personal information about their disease. The half-year user intervention demonstrated that the system had a motivational effect on the users.

Involving end users in the design process can be challenging and in many cases fails to become a priority for system developers. This is also the case with numerous applications in the health care domain. This article focuses on the design process for applications intended for direct use by the patients themselves, often referred to as self-help tools. A framework for the user involvement in the design process is presented. This framework is inspired both from existing methods and standards within the field of human computer interaction, as well as documented experiences from relevant e-health projects. An analysis of three case studies highlights the importance of patient involvement in the design process and informs guidelines for patient-centric system design.

Living with diabetes makes great educational demands on a family. We have tested the feasibility of using the mobile phone short message service (SMS) for reaching people with diabetes information. We also assessed user satisfaction and perceived pros and cons of the medium through interviews. Eleven parents of children with type 1 diabetes received messages for 11 weeks. The parents were positive about the system and said that they would like to continue to use it. The pop-up reminding effect of SMS messages in busy everyday life was noted as positive. Some parents experienced the messages as somewhat intrusive, arriving too often and at inconvenient times. The parents also noted the potential of the messages to facilitate communication with their adolescent children. The inability to store all of the messages or to print them out were seen as major disadvantages. Overall, the SMS seems to hold promise as means of delivering diabetes information.

Background: People with type 1 diabetes who use electronic self-help tools register a large amount of information about their disease on their participating devices; however, this information is rarely utilized beyond the immediate investigation. We have developed a diabetes diary for mobile phones and a statistics-based feedback module, which we have named Diastat, to give data-driven feedback to the patient based on their own data. Method: In this study, up to 40 participants will be given a smartphone on which is loaded a diabetes self-help application (app), the Few Touch Application (FTA). Participants will be randomized into two groups to be given access to Diastat 4 or 12 weeks, respectively after receiving the smartphone, and will use the FTA with Diastat for 8 weeks after this point. The primary endpoint is the frequency of high and low blood-glucose measurements. Discussion: The study will investigate the effect of data-driven feedback to patients. Our hypothesis is that this will improve glycemic control and reduce variability. The endpoints are robust indicators that can be assembled with minimal effort by the patient beyond normal routine. Trial registration: Clinicaltrials.gov: NCT01774149

BackgroundInterest in mobile health (mHealth) applications for self-management of diabetes is growing. In July 2009, we found 60 diabetes applications on iTunes for iPhone; by February 2011 the number had increased by more than 400% to 260. Other mobile platforms reflect a similar trend. Despite the growth, research on both the design and the use of diabetes mHealth applications is scarce. Furthermore, the potential influence of social media on diabetes mHealth applications is largely unexplored. ObjectiveOur objective was to study the salient features of mobile applications for diabetes care, in contrast to clinical guideline recommendations for diabetes self-management. These clinical guidelines are published by health authorities or associations such as the National Institute for Health and Clinical Excellence in the United Kingdom and the American Diabetes Association. MethodsWe searched online vendor markets (online stores for Apple iPhone, Google Android, BlackBerry, and Nokia Symbian), journal databases, and gray literature related to diabetes mobile applications. We included applications that featured a component for self-monitoring of blood glucose and excluded applications without English-language user interfaces, as well as those intended exclusively for health care professionals. We surveyed the following features: (1) self-monitoring: (1.1) blood glucose, (1.2) weight, (1.3) physical activity, (1.4) diet, (1.5) insulin and medication, and (1.6) blood pressure, (2) education, (3) disease-related alerts and reminders, (4) integration of social media functions, (5) disease-related data export and communication, and (6) synchronization with personal health record (PHR) systems or patient portals. We then contrasted the prevalence of these features with guideline recommendations. ResultsThe search resulted in 973 matches, of which 137 met the selection criteria. The four most prevalent features of the applications available on the online markets (n = 101) were (1) insulin and medication recording, 63 (62%), (2) data export and communication, 61 (60%), (3) diet recording, 47 (47%), and (4) weight management, 43 (43%). From the literature search (n = 26), the most prevalent features were (1) PHR or Web server synchronization, 18 (69%), (2) insulin and medication recording, 17 (65%), (3) diet recording, 17 (65%), and (4) data export and communication, 16 (62%). Interestingly, although clinical guidelines widely refer to the importance of education, this is missing from the top functionalities in both cases. ConclusionsWhile a wide selection of mobile applications seems to be available for people with diabetes, this study shows there are obvious gaps between the evidence-based recommendations and the functionality used in study interventions or found in online markets. Current results confirm personalized education as an underrepresented feature in diabetes mobile applications. We found no studies evaluating social media concepts in diabetes self-management on mobile devices, and its potential remains largely unexplored.

BACKGROUND: Children with type 1 diabetes and their parents face rigorous procedures for blood glucose monitoring and regulation. Mobile telecommunication systems show potential as an aid for families’ self-management of diabetes. OBJECTIVE: A prototype designed to automatically transfer readings from a child’s blood glucose monitor to their parent’s mobile phone was tested. In this formative stage of development, we sought insights into the appropriateness of the concept, feasibility of use, and ideas for further development and research. METHODS: During four months, a self-selected sample of 15 children (aged 9 to 15 years) with type 1 diabetes and their parents (n = 30) used the prototype approximately three times daily. Parent and child experiences were collected through questionnaires and through interviews with 9 of the parents. RESULTS: System use was easily integrated into everyday life, and parents valued the sense of reassurance offered by the system. Parents’ ongoing struggle to balance control of their children with allowing independence was evident. For children who measured regularly, use appeared to reduce parental intrusions. For those who measured irregularly, however, parental reminders (eg, “nagging”) appeared to increase. Although increased reminders could be considered a positive outcome, they can potentially increase parent-child conflict and thus also undermine proper metabolic control. Parents felt that system appropriateness tapered off with the onset of adolescence, partly due to a potential sense of surveillance from the child’s perspective that could fuel oppositional behavior. Parental suggestions for further developments included similar alerts of irregular insulin dosages and automatically generated dietary and insulin dosage advice. CONCLUSIONS: User enthusiasm suggests that such systems might find a consumer market regardless of whether or not they ultimately improve health outcomes. Thus, more rigorous studies are warranted to inform guidelines for appropriate use. Potentially fruitful approaches include integrating such systems with theory-based parenting interventions and approaches that can aid in interpreting and responding to experiences of surveillance, virtual presence, and balances of power in e-mediated relationships. [J Med Internet Res 2005;7(5):e57]