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.

Dataset of wearable sensors with possibilities for data exchange.

We performed a search to identify available wearable sensors systems that can collect patient health data and have data sharing capabilities. Findings available in "Wearable sensors with possibilities for data exchange: Analyzing status and needs of different actors in mobile health monitoring systems" [1]. We performed an initial search of the Vandrico wearable database, and supplemented the resulting device list with an internet search. In addition to relevant meta-data (i.e. name, description, manufacturer, web-link, etc.) for each device, we also collected data on 13 attributes related to data exchange. I.e. device type, communication interface, data transfer protocol, smartphone and/or PC integration, direct integration to open health platform, 3rd platform integration with open health platform, support for health care system/middleware connection, recorded health data types, integrated sensors, medical device certification, whether or not the use can access collected data, device developer access, and device availability on the market. In addition, we grouped each device into three groups of actors that these devices are relevant for: electronic health record providers, software developers, and patients. The collected data can be used as an overview of available devices for future researchers with interest in the mobile health (mHealth) area.

Wearable sensors with possibilities for data exchange: Analyzing status and needs of different actors in mobile health monitoring systems.

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: 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.

Users' acceptability of a mobile application for persons with type 2 diabetes: a qualitative study.

BACKGROUND: The use of mobile health apps is now common in diabetes self-management and acceptability of such tools could help predict further use. There is limited research on the acceptability of such apps: use over time, the factors and features that influence self-management, how to overcome barriers, and how to use an app in relation to health-care personnel. In this study, we aimed to obtain an in-depth understanding of users' acceptability of a mobile app for diabetes self-management, and to explore their communication with health-care personnel concerning the app. METHODS: The study had a qualitative descriptive design. Two researchers conducted 24 semi-structured in-depth interviews with adults with type 2 diabetes who had used a digital diabetes diary app for 1 year, during participation in the Norwegian Study in the EU project RENEWING HeALTH. We recruited the participants in a primary health-care setting. The transcripts of the interviews were analyzed using qualitative content analysis on developing themes, which we interpreted according to a theory of acceptability. We used NVivo 11 Pro during the process. RESULTS: The users' acceptability of the app diverged. Overall, the responses indicated that the use of a digital diabetes diary requires hard work, but could also ease the effort involved in following a healthy lifestyle and better-controlled levels of blood glucose. Crucial to the acceptability was that a routine use could give an overview of diabetes registration and give new insights into self-management. In addition, support from health-care personnel with diabetes knowledge was described as necessary, either to confirm the decisions made based on use of the app, or to get additional self-management support. There were gradual transitions between practical and social acceptability, where utility of the app seems to be necessary for both practical and social acceptability. Lack of acceptability could cause both digital and clinical distress. CONCLUSIONS: Both practical and social acceptability were important at different levels. If the users found the utility of the app to be acceptable, they could tolerate some lack of usability. We need to be aware of both digital and clinical distress when diabetes apps form a part of relevant health-care. TRIAL REGISTRATIONS: Self-management in Type 2 Diabetes Patients Using the Few Touch Application, NCT01315756, https://clinicaltrials.gov/show/NCT01315756 March 15, 2011.

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).

Design and Development of a Context-Aware Knowledge-Based Module for Identifying Relevant Information and Information Gaps in Patients With Type 1 Diabetes Self-Collected Health Data.

BACKGROUND: Patients with diabetes use an increasing number of self-management tools in their daily life. However, health institutions rarely use the data generated by these services mainly due to (1) the lack of data reliability, and (2) medical workers spending too much time extracting relevant information from the vast amount of data produced. This work is part of the FullFlow project, which focuses on self-collected health data sharing directly between patients' tools and EHRs. OBJECTIVE: The main objective is to design and implement a prototype for extracting relevant information and documenting information gaps from self-collected health data by patients with type 1 diabetes using a context-aware approach. The module should permit (1) clinicians to assess the reliability of the data and to identify issues to discuss with their patients, and (2) patients to understand the implication their lifestyle has on their disease. METHODS: The identification of context and the design of the system relied on (1) 2 workshops in which the main author participated, 1 patient with type 1 diabetes, and 1 clinician, and (2) a co-design session involving 5 patients with type 1 diabetes and 4 clinicians including 2 endocrinologists and 2 diabetes nurses. The software implementation followed a hybrid agile and waterfall approach. The testing relied on load, and black and white box methods. RESULTS: We created a context-aware knowledge-based module able to (1) detect potential errors, and information gaps from the self-collected health data, (2) pinpoint relevant data and potential causes of noticeable medical events, and (3) recommend actions to follow to improve the reliability of the data issues and medical issues to be discussed with clinicians. The module uses a reasoning engine following a hypothesize-and-test strategy built on a knowledge base and using contextual information. The knowledge base contains hypotheses, rules, and plans we defined with the input of medical experts. We identified a large set of contextual information: emotional state (eg, preferences, mood) of patients and medical workers, their relationship, their metadata (eg, age, medical specialty), the time and location of usage of the system, patient-collected data (eg, blood glucose, basal-bolus insulin), patients' goals and medical standards (eg, insulin sensitivity factor, in range values). Demonstrating the usage of the system revealed that (1) participants perceived the system as useful and relevant for consultation, and (2) the system uses less than 30 milliseconds to treat new cases. CONCLUSIONS: Using a knowledge-based system to identify anomalies concerning the reliability of patients' self-collected health data to provide information on potential information gaps and to propose relevant medical subjects to discuss or actions to follow could ease the introduction of self-collected health data into consultation. Combining this reasoning engine and the system of the FullFlow project could improve the diagnostic process in health care.

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.

ASSESSMENT OF MAST IN EUROPEAN PATIENT-CENTERED TELEMEDICINE PILOTS.

OBJECTIVES: Model for ASsessment of Telemedicine Applications (MAST) is a health technology assessment (HTA) inspired framework for assessing the effectiveness and contribution to quality of telemedicine applications based on rigorous, scientific data. This study reports from a study of how it was used and perceived in twenty-one pilots of the European project RENEWING HEALTH (RH). The objectives of RH were to implement large-scale, real-life test beds for the validation and subsequent evaluation of innovative patient-centered telemedicine services. The study is a contribution to the appraisal of HTA methods. METHODS: A questionnaire was administered for project leaders of the pilots. It included questions about use and usefulness of MAST for (i) preceding considerations, (ii) evaluation of outcomes within seven domains, and (iii) considerations of transferability. Free text spaces allowed for proposals of improvement. The responses covered all pilots. A quantitative summary of use and a qualitative analysis of usefulness were performed. RESULTS: MAST was used and considered useful for pilot evaluations. Challenges included problems to scientifically determine alternative service options and outcome within the seven domains. Proposals for improvement included process studies and adding domains of technological usability, responsible innovation, health literacy, behavior change, caregiver perspectives and motivational issues of professionals. CONCLUSIONS: MAST was used according to its structure. Its usefulness in patient centered pilots can be improved by adding new stakeholder groups. Interdependencies between scientific rigor, resources and timeliness should be addressed. Operational options for improvements include process studies, literature reviews and sequential mini-HTAs for identification of areas for more elaborate investigations.

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.

Mobile patient applications within diabetes - from few and easy to advanced functionalities.

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.

Low-intensity self-management intervention for persons with type 2 diabetes using a mobile phone-based diabetes diary, with and without health counseling and motivational interviewing: protocol for a randomized controlled trial.

BACKGROUND: The present study protocol is designed to cover the Norwegian part of the European Union Collaborative Project-REgioNs of Europe WorkINg together for HEALTH (RENEWING HEALTH). Self-management support is an important element of care for persons with type 2 diabetes (T2D) for achieving metabolic control and positive lifestyle changes. Telemedicine (TM) with or without health counseling may become an important technological aid for self-management and may provide a user-centered model of care. In spite of many earlier studies on TM, there remains a lack of consensus in research findings about the effect of TM interventions. OBJECTIVE: The aim of RENEWING HEALTH is to validate and evaluate innovative TM tools on a large scale through a common evaluation, making it easier for decision makers to choose the most efficient and cost-effective technological interventions. The Norwegian pilot study evaluates whether the introduction of a mobile phone with a diabetes diary application together with health counseling intervention produces benefits in terms of the desired outcomes, as reflected in the hemoglobin A1c level, health-related quality of life, behavior change, and cost-effectiveness. METHODS: The present study has a mixed-method design comprising a three-armed prospective randomized controlled trial and qualitative interviews with study data collected at three time points: baseline, after 4 months, and after 1 year. The patients' registrations on the application are recorded continuously and are sent securely to a server. RESULTS: The inclusion of patients started in March 2011, and 100% of the planned sample size is included (N=151). Of all the participants, 26/151 patients (17.2%) are lost to follow-up by now, and 11/151 patients (7.3%) are still in the trial. Results of the study protocol will be presented in 2014. CONCLUSIONS: The key goals of this trial are to investigate the effect of an electronic diabetes diary app with and without health counseling, and to determine whether health counseling is important to the continued use of the application and the patients' health competence and acceptability. Research within this area is needed because few studies have investigated the effectiveness of apps used in long-term interventions with this degree of self-management. TRIAL REGISTRATION: Clinicaltrials.gov NCT01315756; http://clinicaltrials.gov/ct2/show/NCT01315756 (Archived by WebCite at http://www.webcitation/6BTyuRMpH).