Use of a Data-Sharing System During Diabetes Consultations.

Patient-gathered self-management data and shared decision-making are touted as the answer to improving an individual's health situation as well as collaboration between patients and their providers leading to more effective treatment plans. However, there is a gap between this ideal and reality - a lack of data-sharing technology. Here, we present the impact that the FullFlow System for sharing patient-gathered data during diabetes consultations, had on the patient-provider relationship and consultation discussion.

Diverse Recruitment Strategies Are Needed to Reduce Digital Divide: Results from a Workshop Addressing Digital Divide and Effects of Pandemic Restrictions.

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.

Criteria for Assessing and Recommending Digital Diabetes Tools: A Delphi Study.

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.

Exploring Real-World mHealth Use for Diabetes Consultations: Pros and Pitfalls of a Pragmatic Mixed-Methods Approach.

Intervention research is often highly controlled and does not reflect real-world situations. More pragmatic approaches, albeit less controllable and more challenging, offer the opportunity of identifying unexpected factors and connections. As the introduction of mHealth into formal diabetes care settings is relatively new and less often explored from the perspectives of patients and providers together, such an opportunity for exploration should be embraced. In this paper we demonstrate our experiences and results in designing and administering a pragmatic mixed-methods feasibility study to understand the impacts of a diabetes data-sharing system on patients and providers. In doing so, we aim to provide a realistic account of the pros and pitfalls of this approach to diabetes mHealth intervention research.

mHealth: Where Is the Potential for Aiding Informal Caregivers?

The health and well-being of informal caregivers often take a backseat to those that they care for. While systems, technologies, and services that provide care and support for those with chronic illnesses are established and continuously improved, those that support informal caregivers are less explored. An international survey about motivations to use mHealth technologies was posted to online platforms related to chronic illnesses. We focused on responses regarding the facilitators and challenges of achieving health goals, including the use of mHealth technologies, for the subgroup who identified as "Caregivers". Findings indicate that mHealth technology is not yet the most important motivational factor for achieving health goals in this group, but greater future potential is suggested.

How mHealth can facilitate collaboration in diabetes care: qualitative analysis of co-design workshops.

BACKGROUND: Individuals with diabetes are using mobile health (mHealth) to track their self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users' needs and expectations for a PGD-sharing system. METHODS: N = 15 participants provided feedback about their experiences and needs in diabetes care and expectations for sharing PGD. The first workshop (2017) included patients with Type 2 Diabetes (T2D) (n = 4) and general practitioners (GPs) (n = 3). The second workshop (2018) included patients with Type 1 Diabetes (T1D) (n = 5), diabetes specialists (n = 2) and a nurse. The workshops involved two sessions: separate morning sessions for patients and healthcare providers (HCPs), and afternoon session for all participants. Discussion guides included questions about end-users' perceptions of mHealth and expectations for a data-sharing system. Activities included brainstorming and designing paper-prototypes. Workshops were audio recorded, transcribed and translated from Norwegian to English. An abductive approach to thematic analysis was taken. RESULTS: Emergent themes were mHealth technologies' impacts on end-users, and functionalities of a data-sharing system. Within these themes, similarities and differences between those with T1D and T2D, and between HCPs, were revealed. Patients and providers agreed that HCPs could use PGD to provide more concrete self-management recommendations. Participants' paper-prototypes revealed which data types should be gathered and displayed during consultations, and how this could facilitate shared-decision making. CONCLUSION: The diverse and differentiated results suggests the need for flexible and tailorable systems that allow patients and providers to review summaries, with the option to explore details, and identify an individual's challenges, together. Participants' feedback revealed that both patients and HCPs acknowledge that for mHealth integration to be successful, not only must the technology be validated but feasible changes throughout the healthcare education and practice must be addressed. Only then can both sides be adequately prepared for mHealth data-sharing in diabetes consultations. Subsequently, the design and performance of the joint workshop sessions demonstrated that involving both participant groups together led to efficient and concrete discussions about realistic solutions and limitations of sharing mHealth data in consultations.

Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review.

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.

Qualitative Evaluations of mHealth Interventions: Current Gaps and Future Directions.

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.

Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review.

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.

Measuring the Effects of Sharing Mobile Health Data During Diabetes Consultations: Protocol for a Mixed Method Study.

BACKGROUND: There is rising demand for health care's limited resources. Mobile health (mHealth) could be a solution, especially for those with chronic illnesses such as diabetes. mHealth can increases patients' options to self-manage their health, improving their health knowledge, engagement, and capacity to contribute to their own care decisions. However, there are few solutions for sharing and presenting patients' mHealth data with health care providers (HCPs) in a mutually understandable way, which limits the potential of shared decision making. OBJECTIVE: Through a six-month mixed method feasibility study in Norway, we aim to explore the impacts that a system for sharing patient-gathered data from mHealth devices has on patients and HCPs during diabetes consultations. METHODS: Patients with diabetes will be recruited through their HCPs. Participants will use the Diabetes Diary mobile phone app to register and review diabetes self-management data and share these data during diabetes consultations using the FullFlow data-sharing system. The primary outcome is the feasibility of the system, which includes HCP impressions and expectations (prestudy survey), usability (System Usability Scale), functionalities used and data shared during consultations, and study-end focus group meetings. Secondary outcomes include a change in the therapeutic relationship, patient empowerment and wellness, health parameters (HbA1c and blood pressure), and the patients' own app-registered health measures (blood glucose, medication, physical activity, diet, and weight). We will compare measures taken at baseline and at six months, as well as data continuously gathered from the app. Analysis will aim to explain which measures have changed and how and why they have changed during the intervention. RESULTS: The Full Flow project is funded for 2016 to 2020 by the Research Council of Norway (number 247974/O70). We approached 14 general practitioner clinics (expecting to recruit 1-2 general practitioners per clinic) and two hospitals (expecting to recruit 2-3 nurses per hospital). By recruiting through the HCPs, we expect to recruit 74 patients with type 2 and 33 patients with type 1 diabetes. Between November 2018 and July 2019, we recruited eight patients and 15 HCPs. During 2020, we aim to analyze and publish the results of the collected data from our patient and HCP participants. CONCLUSIONS: We expect to better understand what is needed to be able to share data. This includes potential benefits that sharing patient-gathered data during consultations will have on patients and HCPs, both individually and together. By measuring these impacts, we will be able to present the possibilities and challenges related to a system for sharing mHealth data for future interventions and practice. Results will also demonstrate what needs to be done to make this collaboration between HCPs and patients successful and subsequently further improve patients' health and engagement in their care.

Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study.

BACKGROUND: Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health data in a standard representation of medical data such as Fast Healthcare Interoperability Resources (FHIR), from patients systems like mobile health apps and display the data directly to their users-the clinicians. However, although Norwegian EHRs are working on implementing FHIR, no solution or graphical interface is available today to display self-collected health data. OBJECTIVE: The objective of this study was to design and assess a dashboard for displaying relevant self-collected health data from patients with diabetes to clinicians. METHODS: The design relied on an iterative participatory process involving workshops with patients, clinicians, and researchers to define which information should be available and how it should be displayed. The assessment is based on a case study, presenting an instance of the dashboard populated with data collected from one patient with diabetes type 1 (in-house researcher) face-to-face by 14 clinicians. We performed a qualitative analysis based on usability, functionality, and expectation by using responses to questionnaires that were distributed to the 14 clinicians at the end of the workshops and collected before the participants left. The qualitative assessment was guided by the Standards for Reporting Qualitative Research. RESULTS: We created a dashboard permitting clinicians to assess the reliability of self-collected health data, list all collected data including medical calculations, and highlight medical situations that need to be investigated to improve the situation of the patients. The dashboard uses a combination of tables, graphs, and other visual representations to display the relevant information. Clinicians think that this type of solution will be useful during consultations every day, especially for patients living in remote areas or those who are technologically interested. CONCLUSIONS: Displaying self-collected health data during consultations is not enough for clinicians; the data reliability has to be assured and the relevant information needs to be extracted and displayed along with the data to ease the introduction during a medical encounter. The prestudy assessment showed that the system provides relevant information to meet clinicians' need and that clinicians were eager to start using it during consultations. The system has been under testing in a medical trial since November 2018, and the first results of its assessment in a real-life situation are expected in the beginning of next year (2020).

Inequalities in the Use of eHealth Between Socioeconomic Groups Among Patients With Type 1 and Type 2 Diabetes: Cross-Sectional Study.

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.

What Are Diabetes Patients Versus Health Care Personnel Discussing on Social Media?

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.

Preferences and interests of diabetes social media users regarding a health-promotion intervention.

BACKGROUND: Nowadays, rapid and accessible participatory research on diabetes can be carried out using social media platforms. The objective of this study was to identify preferences and interests of diabetic social media users regarding a health-promotion intervention targeting them. METHODS: Social media followers of the Norwegian Diabetes Association were invited to participate in the creation of a health-promotion intervention on diabetes by expressing their opinions through an online questionnaire posted on Facebook, Twitter, and Instagram. The questionnaire asked participants about their demographics and preferences regarding type of health content: format, frequency, and channels to deliver content. Questions regarding the perceived quality of diabetes-related information and satisfaction with content on social media were also included. RESULTS: The questionnaire was answered by 346 participants: 332 (96%) of those were reached via Facebook, 66.5% of respondents (n=230) identified themselves as women, 54% (n=187) as individuals diagnosed with type 1 diabetes, and 71% (n=235) were aged 30-64 years. The preferred type of content was "research and innovation on diabetes", selected by 78.0% of the respondents. "Text format" was the choice for 93.4%, and 97.3% would prefer to find health-promotion content on Facebook. There was heterogeneity in the desired frequency of this content. In a scale ranging from 0 to 100, the perceived quality of diabetes-related information on social media was 62.0±1.2 and satisfaction with such content 61.9±1.3. CONCLUSION: The approach used in this study was successful in reaching and involving participants quickly, and could also potentially increase diabetes patients' engagement and satisfaction with health-promotion interventions, enhance their sense of community, and thus help people attain healthier lifestyles. It is a limitation that our sample might not have been fully representative, as the most interested social media users might have chosen to participate.

Analysing mHealth usage logs in RCTs: Explaining participants' interactions with type 2 diabetes self-management tools.

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.

mHealth Assessment: Conceptualization of a Global Framework.

BACKGROUND: The mass availability and use of mobile health (mHealth) technologies offers the potential for these technologies to support or substitute medical advice. However, it is worrisome that most assessment initiatives are still not able to successfully evaluate all aspects of mHealth solutions. As a result, multiple strategies to assess mHealth solutions are being proposed by medical regulatory bodies and similar organizations. OBJECTIVE: We aim to offer a collective description of a universally applicable description of mHealth assessment initiatives, given their current and, as we see it, potential impact. In doing so, we recommend a common foundation for the development or update of assessment initiatives by addressing the multistakeholder issues that mHealth technology adds to the traditional medical environment. METHODS: Organized by the Mobile World Capital Barcelona Foundation, we represent a workgroup consisting of patient associations, developers, and health authority representatives, including medical practitioners, within Europe. Contributions from each group's diverse competencies has allowed us to create an overview of the complex yet similar approaches to mHealth evaluation that are being developed today, including common gaps in concepts and perspectives. In response, we summarize commonalities of existing initiatives and exemplify additional characteristics that we believe will strengthen and unify these efforts. RESULTS: As opposed to a universal standard or protocol in evaluating mHealth solutions, assessment frameworks should respect the needs and capacity of each medical system or country. Therefore, we expect that the medical system will specify the content, resources, and workflow of assessment protocols in order to ensure a sustainable plan for mHealth solutions within their respective countries. CONCLUSIONS: A common framework for all mHealth initiatives around the world will be useful in order to assess whatever mHealth solution is desirable in different areas, adapting it to the specifics of each context, to bridge the gap between health authorities, patients, and mHealth developers. We aim to foster a more trusting and collaborative environment to safeguard the well-being of patients and citizens while encouraging innovation of technology and policy.

The Use of eHealth and Provider-Based Health Services by Patients with Diabetes Mellitus: Protocol for a Cross-Sectional Study.

BACKGROUND: The prevalence of diabetes and the use of electronic health (eHealth) resources are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be of great significance in this regard. However, little is known about the extent to which different kinds of eHealth tools are used, and how the use of eHealth is associated with the use of provider-based health care services among people with diabetes. OBJECTIVE: The primary objective of this study is to investigate the use of eHealth and its association with the use of provider-based health care services. The secondary objectives include investigating which eHealth services are used (apps, search engines, video services, social media), the relationship between socioeconomic status and the use of different eHealth tools, whether the use of eHealth is discussed in the clinical encounter, and whether such tools might lead to (or prevent) doctor visits and referrals. METHODS: We will conduct cross-sectional studies based on self-reported questionnaire data from the population-based seventh Tromsø Study. Participants will be diabetic patients aged 40 years and older. According to our estimates, approximately 1050 participants will be eligible for inclusion. Data will be analyzed using descriptive statistics, chi-square tests, and univariable and multivariable logistic regressions. RESULTS: The grant proposal for this study was approved by the Northern Norway Regional Health Authority on November 23, 2015 (HST 1306-16). Recruitment of participants for the Tromsø Study started in 2015 and will continue throughout 2016. This particular project started on July 1, 2016. CONCLUSIONS: This project may yield benefits for patients, health care providers, hospitals, and society as a whole. Benefits are related to improved prevention services, health, experience of care services, self-management tools and services, organizational structures, efficiency of specialist care use, allocation of resources, and understanding of how to meet the challenges from the increasing prevalence of diabetes. This project has potential for generalization to other groups with chronic disease.

Mobile Health: empowering patients and driving change.

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.

Performance of the first combined smartwatch and smartphone diabetes diary application study.

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.